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Abstract: As an evolutionary computation (EC) technique, Genetic programming (GP) has been widely applied to image analysis in recent decades. However, there was no comprehensive and systematic literature review in this area. To provide guidelines for the state-of-the-art research, this paper presented a survey of the literature in recent years on GP for image analysis, including feature extraction, image classification, edge detection, and image segmentation. In addition, this paper summarised the current issues and challenges, such as computationally expensive, generalisation ability and transfer learning, on GP forimage analysis, and pointd out promising research directions for future work.

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Abstract: In view of the current situation that the traditional methane sensor technology is difficult to imple-ment the field detection and monitor on methane gas, a novel room-temperature SAW methane gas sensor coa-ted with cryptophane-A sensing interface is proposed by utilizing the supermolecular compound cryptophane-A’ s specific clathration to methane molecules. The sensor was composed of differential resonator-oscillators with excellent frequency stability, a supra-molecular CrypA coated along the acoustic propagation path, and a frequency acquisition module. The supramolecular CrypA was synthesized from vanillyl alcohol using a three-step method and deposited onto the surface of the sensing resonators via dropping method. Fast response and excellent repeatability were observed in gas sensing experiment, and the estimated detection limit and meas-ured sensitivity in gas dynamic range of 0 . 2% ~5% was evaluated as ~0 . 05 % and ~184 Hz/%, respec-tively. The measured results indicated the SAW sensor was promising for under-mine methane gas detection and monitor.

Abstract: By means of EDEM-FLUENT simulation and VOF(Volume of Fluid) method and Euler-Lagrangian model, a mixture model of discrete solid, continuous liquid and gas phase was constructed to simulate the three-phase flow with solid-liquid-gas in a stirring tank. The effect of the moving state of solid particles in stirring tank and free liquid level were explored. The gas-liquid continuous phase modeling based on VOF method using FLUENT software could capture gas-liquid interface well and the model was closer to the actual working condition. Based on the Discrete Element Method(DEM), the discrete element modeling of solid particles was established and its position information in the tank was simulated intuitively by the joint simulation of the two software. The dispersion of solid particles was consistent with the results obtained by Euler method.

Abstract: Urban capital structure was a complex?problem affected by multi-factors and multi-objective particle.This paper attempt ed to explore a scientific and appropriate d algorithm to construct the optimal capital structure model under the influence of multi-objective and multi-factors to analyze the situation of urban capital structure.First, the data in history could find the relationship among features of the data in history by using the regression characteristics of random forest. Then, the multi-objective particle swarm optimization algorithm was used to find values of the features that achieve the best results according to the existing relationship features. Then finding the most correlate data from the historical data based on the best eigenvalues of these effects. Therefore, the cities and the years with relatively better capital structure allocations are analyzed. We could play a good role in the reference and development of each city by continuously learning these superior structural configurations

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Abstract: In this work,graphene oxide (GO) was prepared by an improved Hummers method.Zirconia/graphene composites (ZrO2/rGO) were rapidly synthesized by hydrothermal method with Zr(OH)4/rGO as precursor prepared by ultrasound-stirred-coprecipitation.The adsorption capacity of Zr (OH) 4/rGO and ZrO2/rGO composites decreased with the increase of pH value and increased with the increase of phosphate concentration and the solution temperature.The maximum adsorption capacities of Zr (OH)4/rGO and ZrO2/rGO composites were 81.84 mg/g and 63.58 mg/g respectively at pH 2.0.The adsorption kinetics of these two adsorbents accorded with the pseudo-second-order model and isothermal adsorption complied with the Langmuir isotherm equation.The results of its recycling properties showed the adsorption capacity decreased for the Zr (OH) 4/rGO samples,while ZrO2/rGO samples were almost the same as the initial adsorption performance.

Abstract: For the modeling and control of proton exchange membrane fuel cells, the empirical model and mechanism model based on polarization curve and parameter dimension are summarized, the electrochemical steady-state model and dynamic model based on electrochemical reaction, temperature, pressure and other factors are analyzed, and the intelligent method model based on neural network identification, swarm intelligence algorithm and support vector machine is introduced.The existing intelligent control strategies of proton exchange membrane fuel cells are summarized. Finally, it is pointed out that it will be a development direction of modeling to optimize the model parameters and environmental parameters of proton exchange membrane fuel cells by using swarm intelligence algorithm. The generalized Hamilton theory can also be tried to be used in the modeling of proton exchange membrane fuel cells.At the same time, the intelligent control strategy combining the new algorithm will become the research trend of proton exchange membrane fuel cell control.

Abstract: The analysis was held with the 125MW axial flow steam turbine impulse stage blade.The three-dimensional numerical simulation and optimization were conducted by using the commercial software ANSYS CFX.The results showed that the pressure distribution of blade surface reduced,and the radial secondary flow loses was controlled effectively,with optimizing the structure geometric parameters such as ellipticity of the leading edge and trailing edge,relative pitch,inter-stage ratio,and so on.Isentropic efficiency increased by 0.43％,the total pressure loss coefficiency decreased about 0.005.After the optimization,the aerodynamic performance of the blade increased,and the energy loss in the blade decreased and the efficiency of steam turbine increased.

Abstract: A direct-contact method of zeolite adsorption liquid water was adopted to enhance heat and mass transfer rate within adsorption heat transformer.Hot water was recycled to generate superheated steam directly,and then saturated zeolite would be regenerated by drying gas.The reactor with was filled spherical zeolite with same mass and different diameters.The mass of steam generated by small particle packed bed was 64.89％ higher than that generated by big particle packed bed.The maximum steam temperature and gross temperature life had increased by about 37C.Experiments of two kinds of packed types in double layer reactor (finecoarse bed and coarse-fine bed) have shown that small particle played a more effective role for the heating of steam and packed bed;the mean maximum temperature of the steam at the top of fine-coarse bed is 37.23％ higher than that of coarse-fine bed and the lasting time of the maximum temperature is decreased by 14.25％.The steam generation rate of fine-coarse bed was 16.18％ higher than that of coarse-fine bed,which is more efficient in steam generation.In regeneration process,drying time of upper reactor was 25.03％ shorter than coarse-fine bed.It concluded that fine-coarse bed was more effective for zeolite regeneration.

Abstract: The language model based on neural network LSTM structure, the LSTM structure used in the hidden layer unit, the structure unit comprises a memory unit which can store the information for a long time, which has a good memory function for the historical information. But the LSTM in the current input information state9 does not affect the final output information of the output gate, get less historical information. To solve the above problems, this paper puts forward based on improved LSTM  (long short-term memory) modeling method of network model. The model increases the connection from the current input gate to the output gate, and simultaneously combines the oblivious gate and the input gate into a single update. The door keeper input and forgotten past and present memory consolidation, can choose to forget before the accumulation of information, the improved LSTM model can learn the long history of information, solve the drawback of the LSTM method is morerobust. This paper uses the neural network languag LSTM model based on the inproved model on TIMIT data sets show that the axxuracy of test. The results illustrate that the improved LSTM identification error rate is 5
% lower than the standard LSTM identification error rate. 

Abstract: To improve the adaptability of echo state network (ESN),an optimization method based on mutual information (MI) and Just-In-Time (JIT) learning was proposed in this paper to optimize the input scaling and the output layer of ESN.The method was named as MI-JIT optimization method and the obtained new network was MI-JIT-ESN.The optimization method mainly consists of two parts.Firstly,the scaling parameters of multiple inputs were adjusted on the basis of MI between the network inputs and outputs.Secondly,based on JIT learning,a partial model of output layer was established.The new partial model could make the regression results more accurate.Further,a multi-input multi-output MI-JIT-ESN model was developed for the fed-batch penicillin fermentation process.The experimental results showed that the obtained MI-JIT-ESN model performed well,and that it had better adaptability than ESN model without optimization and other neural network models.

Abstract: In order to improve the super-resolution reconstruction quality of single image, an improved learning based super-resolution approach was proposed in this paper. To tackle the problem of low details of semi-coupled dictionary learning super-resolution algorithm, the paper presented learning strategy where detail constraint factor and semi-coupled dictionary learning were performed in turn. In reconstruction stage, detail constraint factor was designed by the gradient in both horizontal and vertical direction. Combined with semi-coupled dictionary learning, detail constraint factor was used to further improve the super-resolution reconstruction quality. In order to improve the contribution of detail constraint factor on preserving boundary information, the adaptive regular parameter was explored via the approximate Laplacian distribution of edge difference. Compared with the semi coupled dictionary learning super-resolution algorithm, the peak signal-to-noise ratio of this method was increased by 1.5% on average. Experiments demonstrated that the proposed method could achieve better reconstruction effect in both subjective and objective evaluation and improve the quality of super-resolution.

Abstract:  In the field of engineering construction, foundations were often placed adjacent to slopes. In the present research work, the evaluation of the maximum bearing capacity of slope foundations lacked a sufficientrate method. A bilateral asymmetry slip failure model for ground foundation adjacent to slope was develthe strength of soil on the side of flat ground was reduced and this is characterized by a mobilization factor. Base on limit equilibrium method and superposition principle, three bearing capacity factors were ex-pressed. The upper bound bearing capacity for ground foundation adjacent to slope was deduced based on limitanalysis approach. Centrifugal model tests were used to verify the theoretical analysis results; and thetion and failure characteristics of these foundations were studied. In addition the influence of variousuch as the contact conditions of the foundation, the location of the foundation, and the height of slope on themaximum bearing capacity of these foundation

Abstract: Molybdenum disulfide ( MoS2 ) with a typical layered structure easily forms few-layered MoS2 nanosheets，and has a wealth of optical，electrical and catalytic performance with wide application potentials in areas such as photo-electrical and energy conversion． The preparation of few-layered MoS2 nanocrystals and MoS2-based nanocomposites using molybdenum-containing chemicals as starting materials by wet-chemical and vapor-deposition methods are the cutting-edge focuses of recent research． However，the synthesis of MoS2 nanocrystals from chemical reagents with a long route is not low-carbon and environment friendly． Molybdenite is a typical layered mineral and composed of layered MoS2 units． The amount of molybdenite in China is huge and it is a green and low-carbon way to prepare few-layered MoS2 nanomaterials via the intercalation-exfoliation strategy using the purified molybdenite as the direct raw materials．

Abstract: In order to prevent the serious safety problem caused by the dry pot burning and stove explosion and firing,an anti-overheating system was designed.The system of infrared temperature sensor MLX90614 on the bottom of the pot was used to realize the non-contact real-time temperature monitoring.The real-time temperature data was collected and processed by the STM32 microcontroller and SMBus.When the temperature of the bottom of the boiler was beyond the normal heating range,the temperature monitoring module could send a voice alarm.When the threshold value of the dry burning temperature was reached,the gas circuit could be cut off by the control circuit serially connected in the thermocouple temperature detection circuit.Experimental results showed that the proposed system could cut off the gas path once the preset temperature reached and prevent the dry pot burning effectively.

Abstract: In this paper,a new algorithm of license plate recognition in the hazy weather was designed.Firstly,defogging operation was introduced for license plate image in the environment of hazy by using improved dark channel prior.Then after the pretreatment,positioning,segmentation and extraction,coarse grid characteristic matrix is obtained.Finally,radial basis function (RBF) neural network,which was optimized by particle swarm algorithm in advance,was used to identify the character.The experiment results showed that the improved algorithm not only had a good effect on haze removal,but also reduced the duration of defogging,which effectively improve the license plate recognition speed and accuracy in fog and haze weather.

Abstract: In order to meet the practical requirements of automatic application and renewal of driver’s license,a high speed system for automatic recognition of driver’s licenser was designed and implemented.The hardware was designed to capture the image of the driver’s license that contained the smallest identifiable features.Because of the complex background such as the shadow line and so on in the driver’s license images,the existing recognition algorithms had the low recognition accuracy,universality and robustness problems.This paper first solved the segmentation difficulties for uneven illumination,noise,tilt and shadow line character by combined adaptive binarization and morphological processing.Then,the Blob analysis was used to extract the important local features of the driver’s license,and the recognition accuracy was further improved by using the prior information and the correlation matching algorithm.The experimental results showed that not only the false recognition rate was 0,but also the practical products was developed,and the better social effects were achieved.

Abstract: Interactive evolutionary algorithms with user preference implicitly extracted from interactions of user are more powerful in alleviating user fatigue and improving the exploration in personalized search or recommendation. However, the uncertainties existing in user interactions and preferences have not been considered in the previous research, which will greatly impact the reliability of the extracted preference model, as well as the effective exploration of the evolution with that model. Therefore, an interactive genetic algorithm with probabilistic conditional preference networks (PCP-nets)is proposed , in which, the uncertainties are further figured out according to the interactions, and a PCP-net is designed to depict user preference model with higher accuracy by involving those uncertainties. First, the interaction time is adopted to mathematically describe the relationship between the interactions and user preference, and the reliability of the interaction time is further defined to reflect the interactive uncertainty.The preference function with evaluation uncertainty is established with the reliability of interaction time. Second, the preference weights on each interacted object are assigned on the basis of preference function and reliability. With these weights, the PCP-nets are designed and updated by involving the uncertainties into the preference model to improve the approximation. Third, a more accurate fitness function is delivered to assign fitness for the individuals. Last, the proposed algorithm is applied to a personalized book search and its superiority in exploration and feasibility is experimentally demonstrated.

Abstract: With the increasing of highway extension projects and widely use of sheet piles in railway construction,the mechanical behavior of extension embankment was analyzed through simulating different kinds of pile and load of different positions.Then the optimal pile kind and the most unfavorable load position were proposed.Through continuous observing of settlement in sheet pile section and CFG pile section,the optimal adaptability of sheet pile was showed in extension projects.The analysis results showed that the effect on settlement of PTC pile,CFG pile and cement mixing pile was gradually decreased.The PTC pile and CFG pile should be firstly selected from the options of controlling settlement.The most unfavorable load position was in new embankment and its quality was the key control point in construction.The effect on decreasing differential settlement was appeared in process of semi-rigid base construction,and it would be even obvious in pavement construction.The sheet pile was an effective supplement to traditional soft soil treatment methods.It had better adaptability and foreground in highway extension projects.

Abstract: Based on the characterisities of large-scale problems, lager-scale optimization were grossly analyzed. This paper  introduced some methods for lager-scale problems.The methods included the initialization method, decomposition strategy, updating strategy and so on. This paper mainly focued on the search strategy, update strategy, mutation strategy and cooperative coevolution. Meanwhile, the characteristics of lager-scale optimization algorithm testing function set and evaluation method were listed. Finally, the future research directions were given.

Abstract: In order to improve the detection rate of the acoustic magnetic EAS system,and enhance the antiinterference performance,the paper studied a new label detection algorithm that was the combination of the improved artificial fish swarm algorithm (IAFSA) and the support vector machine (SVM).An improved scheme was proposed after analyzing the strengths and weaknesses of the traditional AFSA and SVM.The experimentalresults showed that the IASFA had the faster rate of convergence and the higher accuracy than AFSA,the genetic algorithm and the particle swarm algorithm;The IASFA-SVM had the higher detection rate,the longer detective distance and the lower rate of false than the traditional magnetic label detection algorithm,and the IASFA-SVM also could meet the requirements of real-time detection.

Abstract: In order for effective application of Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D) in engineering optimization，normalization of the range of objective values is needed. A self-a-daptive s constrained Differential Evolution ( gDE) algorithm is proposed to obtain the minimum and maximumvalues of each objective on the Pareto Front ( PF). After normalization，MOEA/D can then be effectively ap-plied. In addition ,the self-adaptive s constraint method is combined with MOEA/D for constraint handling. Abenchmark problem and a weld bean design problem are used to evaluate the performance of the algorithm a-gainst two other normalization methods. One main advantage of the proposed method is the selective concen-trated optimization on some regions on the Pareto front which allows handling of problems where regions of Pa-reto front are difficult to be optimized.

Abstract: BP neural network for effectively fusioning the information obtained by arc sensor and ultrasonic sensor and information of welding parameters such as welding current,welding speed,welding groove and so on was used to obtain the prediction model of weld penetration depth.Simulation results showed that:the prediction model of weld penetration depth could measure the weld penetration quickly,accurately and in real time.For the precise control of weld penetration,parameters self-tuning fuzzy PID controller was desing,which combined with the advantages of traditional PID controller and fuzzy controller.Smulation results showed that compared with traditional PID controller,parameters self-tuning fuzzy PID controller had a significant advantage in the performance of the system.

Abstract: The effects of processing variables,such as screw speed,initial moisture content and the length ofthe straw plug pipe of extrusion process on the dewatering rate,handling capacity,output per kW h etc.were experimentally studied using a low CR screw straw extruder. And the response surface optimization exper-imental results showed the extruder can run efficiently,stably and continuously with considerate dewateringrate,handling capacity and output per kW ·h under the conditions that moisture content is 85% ,screw speed50.8 r/min,length of the straw plug pipe is 26.91 mm.

Abstract: Moth-flame optimization algorithm (MFO) has some drawbacks in solving optimization problems, such as low precision and high possibility of being trapped in local optimum. A modified MFO algorithm based on chaotic initialization and Gaussian mutation is proposed. Firstly, the cube chaotic map is used to initialize the moth population, which makes the moth more evenly distributed in the search space. Then, Gaussian mutation is adopted to disturb a few poor individuals to enhance the ability of escaping the local optimum. Finally, Archimedes curve is introduced to expand the search scope and strength the exploration ability in the unknown field. A series of experiments are carried out on CEC14 test function set and 21 extensible Benchmark functions. Compared with standard moth-flame optimization algorithm, genetic algorithm, artificial bee colony algorithm, particle swarm algorithm, differential evolution algorithm, flower pollination algorithm, and butterfly optimization algorithm, the results demonstrate that the proposed algorithm is strengthened in obtaining solutions with better quality and convergence.

Abstract: Commercial automayic guided vehicles (AGV) usually used chain transmission mechanism power transmission, and the fixed structure of the wheel could be considered as cantilever structure. Therefore, the problem of wheels "tilting" and start-stop "shocking" easily occurs, which limited the accurate movement of the AGV during frequent and rapid acceleration or deceleration. In this paper, AGV designed by a company was taken as an example. Though repeated tests and numerical simulations, the structure and force analysis were used to find out the reasons for this phenomeno. The larger stress was caused by the "L"-shaped suspension mechanism, which magnified the contact gaps of each component; the uses of the chain transmission mechanism could make it easy for the AGV to form gaps between the sprocket and the chain when the AGV started, stopped, moved forward, backward frequently. Then a new drive unit structure was put forward from the engineering point of view, which could solves the above problems, at the same time-greatly could reduced the stress in the mechanism, could improve the transmission precision, and could provide a more practical and optimized driving structure for the design of AGV.

Abstract: To further improve the accuracy of power load forecasting，on the basis of the analysis of affectingfactors of power load, a combination prediction model based on HHT is proposed. This model uses EMD algo-rithm to decompose the original load sequence. Thus， a stationary sequence of different frequencies，which ismore predictable than the original load sequence，can be obtained. Based on the components of different fre-quencies，according to the characteristics of the different frequency of subsequence ，the RBF neural network ,BP neural network and time series model are selected to forecast while considering the influence of temperatureon the load. Then，a new combined model can be achieved. The experiment shows that the proposed modelcan effectively improve the accuracy of load forecasting.

Abstract: In order to solve the problem that electrospinning process is hard to control，FEA tool softwareCOMSOL Multiphysics was used to simulate the the electric field orientation within the electrospinning. Basedon the vector maps and contour lines, the electric fields distribution was analyzed. Which includes single-nee-dle electrospinning device，electrospinning device with circle and orparallel auxiliary electrodes. Experimentwith parallel auxiliary electrodes was conducted，and the deposition area with the ellipse shape matched thesimulation result.

Abstract: In view of the problem of object deviation when occlusions occur during the target tracking, a new algorithm using Mean Shift with ELM is proposed. According to the formal information of the object’ s loca-tion, current possible location was predicted by ELM, the iteration was started from the possible location in-stead of formal location, and the object’ s real center is calculated by mean shift algorithm. The simulation re-sults show that proposed algorithm can track precisely target occluded, operation time and number of iteration are reduced so that efficiency and robustness are improved.

Abstract: The energy supply of power system is very important to modern society, and the scientific and effective solution to the problem of environmental economic dispatch of power system is the guarantee of energy supply. The multi-objective evolutionary algorithm has unique advantages in solving the problem of environmental economic dispatch of power system. This paper presses In chronological order, the multi-objective evolutionary algorithm is first introduced, and then the application of the multi-objective evolutionary algorithm in the power system environmental economic dispatching problem is discussed. The direction of development is prospected.

Abstract: Impact effects on carbon emissions intensity by population, per capita GDP, and main types of energy in China were evaluated with the fixed effect model based on LSDV estimation with reasons of the results of Likelihood Ratio Test and Hausman Test. The traditional model of STIRPAT was improved by adding Carbon Emission Intensity and Energy Consumption Variables, which included consumptions of coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, and natural gas, except population and per capita GDP. The results show that consumptions of different types of energy have different impacts on carbon emissions intensity from 2004 to 2016 in China. Five variables of energy consumption, which were corresponding to coal, coke, gasoline, diesel oil, and natural gas, had played positive effects on carbon emission intensity from the data of China Statistical Yearbook and China Energy Statistical Yearbook of 200 5 to 201 7. Other variables of crude oil consumption, fuel oil consumption, and kerosene consumption took opposite impact on carbon emission intensity. Moreover, change of population had the most significant favorable influence on carbon emission intensity in all studied variables. Unfortunately, per capita GDP and coal consumption contributed to the increasing of carbon emission intensity in China in the studied period.

Abstract: Cliff and steep slope are important landscape elements of topographic map, and these elements play a very important role in the construction of the ecological environment and prevention of geological disasters, etc. However, it is unfavorable to observe and process data because of vegetation occlusion on cliff. In this paper, we present a cliff vegetation filtration method based on the principle of surface orthographic projection. Firstly, transform the original three dimensional point cloud of cliff to the spatial cartesian coordinate system, whose xy plane is the cliff face and z-axis is perpendicular to the direction of the cliff surface. Then the grid on the xy plane is divided to establish local grid Digital Terrain Model ( DTM) by fitting surface, and the vegeta-tion points can be extracted through setting a reasonable distance threshold. Finally, after inverse projection transformation, cliff rocky points preserved are mapped to the original spatial coordinate system. The experi-mental analysis using actual cliff point cloud data shows that the cliff point cloud vegetation filtering method based on the surface orthographic projection is feasible and effective.

Abstract: During heating process of sintering furnace,the model parameters were easy to change,and traditional PID control was difficult to achieve the desired control effect.This paper used particle swarm optimization algorithm to identify the mathematical model of sintering furnace,for sintering furnace with high inertia,time-variation and strong time delay etc,a method of supervision and control based on RBF neural network,which combined PID control with neural network control.When temperature or parameters changed greatly,PID control played a major role.neural network played a regulatory role and compensated the shortage of PID control.The simulation results of MATLAB software showed that this method could improve the control precision of sintering furnace,which had a certain practicality.

Abstract: Micro/Nano-particles of CuO were prepared with hexamethylenetetramine template. The composi-tion and morphology of the product were characterized by SEM and X-ray diffraction. The synthetic powder was prepared as sensitive membrane, and its gas sensitivity was studied with a static gas distribution method. The results indicated that the uniform copper oxide powders was synthesized at the 110℃, and the molar ratio be-tween copper nitrate and hexamethylenetetramine was 1∶45. The spindle structure was around 1~2 μm, and was composed of 100 nm nanoplates. The sensor had better selectivity with CH3 COCH3 and H2 S. Copper ox-ide showed good selectivity to hydrogen sulfide and its sensitivity had a certain degree of improvement after fur-ther doping 0. 25% ~1. 25% noble metal catalyst Pt.

Abstract: Principal Component Analysis (PCA) is a well known model for dimensionality reduction in data mining,it transforms the original variables into a few comprehensive indices.In this paper,we study the principle of PCA,the distributed architecture of Spark and PCA algorithm of distributed matrix from spark’s ML-lib,then improved the design and present a new algorithm named SNPCA (Spark’s Normalized Principal Component Analysis),this SNPCA algorithm computes principal components together with data normalization process.We carried out benchmarking on multicore CPUs and the results demonstrate the effectiveness of SNPCA.

Abstract: In view of the speed control problem of the linear permanent magnet synchronous motor ( L.PMSM) ,which is viewed as an energy-transformation device,from the viewpoint of energy shaping,applying port-con-trolled Hamultonian with dissipation and passivty-based control theory,the port-controlled Hamltonan modelof LPMSM is deduced. Based on the Hamiltonian structure,the desired Hamiltonian function of the closed-loop system is given,and the speed controller is designed by using the method of interconnection and dampingassignment. In the design,the Hamiltonian function is used directly as the storage function,and the systemcan achieve the required performance and bring more definite physical meaning on the condition of satisfyingpassivity. The simulation results show that the closed-loop control system can respond quickly to changes inload resistance and has good robustness.

Abstract: In the rapid development of integrated energy systems and energy network, power load forecasting played an important role in the economic and safe operation of energy and power systems. The traditional load forecasting modelling methods have been widely used in power systems. However, the simple computational model structure limited by traditional methods could not guarantee the dynamic load prediction accuracy under high randomness and big data background. In recent years, in the context of the continuous upgrading of computing tools and the increasing large-scale of training data volume, the application of deep learning methods in the field of power system load forecasting atrracted extensive attentions. This paper analyzed the applications of various deep learning methods in the field of load forecasting, and revieed the Recurrent Neural Network (RNN) , Long- and Short-Term Memory Network ( LSTM) , Deep Belief Network ( DBN) , and Convolutional Neural Network ( CNN). Compared with the traditional load forecasting method, the deep learning method showed higher prediction accuracy and better robustness to various external influences.

Abstract: A high-order non-singular terminal sliding mode control strategy is proposed to address the issue of achieving maximum wind energy capture in permanent magnet direct drive wind power generation systems. Based on the nonlinear model of the permanent magnet direct drive wind power generation system, a maximum power point tracking method based on optimal torque tracking is proposed, Applying high-order non-singular terminal sliding mode control to the design of torque controller and current controller for permanent magnet synchronous generator (PMSG), achieving fast tracking and stable control of the maximum power point of the permanent magnet direct drive wind power generation system without wind speed sensors. Simulation results verify the effectiveness of the proposed control scheme

Abstract: Chitosan ( CS)/poly ( vinyl alcohol) ( PVA) composite fibers were fabricated by electrospinning in this study. The influences of material formulation and formed time on the viscosity,electrical conductivity and the morphology, average diameter, diameter distribution of CS/PVA composite fiber were investigated. The re-sults showed that, the introduction of CS could increase the viscosity,electrical conductivity of CS/PVA blend solution. And the viscosity of blend solution decreased with the increase of formed time. In addition, the more CS content was, the smaller diameter of CS/PVA composite fiber would be. The fiber-forming capacity of CS/PVA blend solution decreased dramatically as the solution formed time increased.

Abstract: A numerical simulation of trisection-ellipse heat exchangers with helical baffles is carried out， andthe helix angles are 15° and 20° respectively ， and we studied the impact of triangle leakage between continu-ously overlapped and adjacent baffles on heat transfer and resistance performance of heat exchangers.Throughthe comparative analysis about the simulation results of existing triangle leakage and that of blocking trianglearea without leakage , the results show :triangle leakage makes a more serious short circuit flow for the shell-si-ded fluid;Triangle leakage makes heat transfer coefficient，shell-sided pressure drop and comprehensive per-formance of heat exchanger reduce. When triangle leakage is blocked，heat transfer coefficient increases by8.5% ~ 11% , shell-sided pressure drop increases marginally , comprehensive performance increases by 8.1 %~11 . 1 % .

Abstract: Focusing on the target tracking problem in resource-constrained wireless sensor networks,a novelenergy-balanced optimal distributed clustering mechanism is adopted by introducing an energy-balanced indexbased on the standard deviation of residual energv of nodes. Then,it is transformed into a multi-obijective con-strained optimization problem,and a binary particle swarm optimization algorithm is employed to solve thisproblem. Simulation results in Matlab environment show that the energy-balanced optimal distributed clustering mechanism guarantees energy balance and tracking accuracy comparing with the clustering mechanisms respec-tively based on the energy consumption and the extended Kalman filter,and that it improves the network life-time of nearly 2-fold,effectively prolonging the network lifetime.

Abstract: Low-resource few-shot speech recognition is an urgent technical demand faced by the speech recognition industry. The framework technology for few-shot speech recognition is first briefly discussed in this article. The research progress of several important low resource speech technologies, including feature extraction, acoustic model, and resource expansion, is then highlighted. The latest advancements in deep learning technologies, such as generative adversarial networks, self-supervised representation learning, deep reinforcement learning, and meta-learning, are then focused on in order to address few-shot speech recognition on the basis of the development of continuous speech recognition framework technology. On that basis, the problems of limited complementarity, unbalanced task and model deployment faced by this technology are analyzed for the subsequent development. Finally, a summary and prospect of few-shot continuous speech recognition are given.

Abstract: A way of tool axial dispersion was presented,and then each discrete unit of the variable helix cutterwas approximately simulated to be variable pitch cutter. Thus variable delay differential equations were trans-ferred to multi-delay differential equation. And the stability prediction model of variable helix milling was builtbased on the original ZOA method. Through comparisons with prior works,the prediction results are in good a-greement about 100% whether normal or variable helix cutter. Two methods were used to simulate. The calcu-lation time of the original method is more than 92 s,but for the proposed method is below 20 s.The resultsshow the proposed method can save computational time comparing with the original method. And the resultscould provide reference for the selection of reasonable processing parameters and chatter prediction in actualprocessing.

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Abstract: Knobs tuning is a key technology that affects the performance and adaptability of databases. However, traditional tuning methods have difficulty in finding the optimal configuration in high-dimensional continuous parameter spaces. The development of machine learning could bring new opportunities to solve this problem. By summarizing and analyzing relevant work, existing work was classified according to development time and characteristics, including expert decision-making, static rules, heuristic algorithms, traditional machine learning methods, and deep reinforcement learning methods. The database tuning problem was defined, and the limitations of heuristic algorithms in tuning problems were discussed. Traditional machine learning-based tuning methods were introduced, including random forest, support vector machine, decision tree, etc. The general process of using machine learning methods to solve tuning problems was described, and specific implementations were provided. The shortcomings of traditional machine learning models in adaptability and tuning capabilities were also discussed. The principles of deep reinforcement learning models were emphasized, and the mapping relationship between tuning problems and deep reinforcement learning models was defined. Recent relevant work on improving database performance, time consumption and model characteristics was introduced, and the process of building and training agents based on deep neural networks was described. Finally, the characteristics of existing work were summarized, and the research hotspots and development directions of machine learning in database tuning were outlined. Distributed scenarios, multi-granularity tuning, adaptive algorithms and self-maintenance capabilities were identified as future research trends

Abstract: The calculation of dynamic response and seismic safety evaluation of a high CFRD on alluvium de-posit subjected to seismic excitation of different transcendental probabilities were carried out,with emphasis onthe seismic response characteristics of dynamic displacement,acceleration,dynamic stresses of face slab andliquefaction of alluvium deposit under strong excitation. The results show that dynamic displacement,accelera-tion,dynamic stresses of face slab and liquefaction degree of alluvium deposit will increase gradually with theincreasing of seismic wave peak ,but the acceleration magnification will decrease.The seismic safety of dam isstill within a normal range even for transcendental probability 2% in 100 years.

Abstract: As an important part of autonomous driving, trajectory prediction aimed to forcast the vehicle′s driving path, so that the vehicle could make path planning according to the driving estimation, so as to make safe and accurate decisions. Firstly, in order to improve the accuracy of vehicle trajectory prediction, the directed graph method was used to construct a high-definition driving scene map, and the directed graph method vectorized the map information to effectively extract the map topology. Secondly, GAIL was used to learn the driving strategy of the dataset through the confrontation game between the generator and the discriminator, so as to adopt the corresponding driving behavior according to the current state. Finally, the multimodal prediction trajectory scheme was obtained by sampling traversal. Simulation was carried out on the nuScenes motion prediction dataset. The quantitative results showed that compared with other methods, when K = 5, the minimum final displacement error MinFDE5 was increased by 10. 8%; when K = 10, the minimum fianl displacement error MinFDE10 increased by 17. 53%, the minimum average displacement error MinADE10 increased by 9. 52%, and the error rate MissRate10 decreased by 28. 26%. The evaluation showed that the generated trajectories were multimodal, could conform to the basic structure of the scene, with improved accuracy.

Abstract: Molybdenum disulfide ( MoS2 ) with a typical layered structure easily forms few-layered MoS2 nanosheets，and has a wealth of optical，electrical and catalytic performance with wide application potentials in areas such as photo-electrical and energy conversion． The preparation of few-layered MoS2 nanocrystals and MoS2-based nanocomposites using molybdenum-containing chemicals as starting materials by wet-chemical and vapor-deposition methods are the cutting-edge focuses of recent research． However，the synthesis of MoS2 nanocrystals from chemical reagents with a long route is not low-carbon and environment friendly． Molybdenite is a typical layered mineral and composed of layered MoS2 units． The amount of molybdenite in China is huge and it is a green and low-carbon way to prepare few-layered MoS2 nanomaterials via the intercalation-exfoliation strategy using the purified molybdenite as the direct raw materials．

Abstract: In order to solve the problems in studies of multimodal named entity recognition, such as the lack of text feature semantics, the lack of visual feature semantics, and the difficulty of graphic feature fusion, a series of multimodal named entity recognition methods were proposed. Firstly, the overall framework of multi modal named entity recognition methods and common technologies in each part were examined, and classified into BilSTM-based MNER method and Transformer based MNER method. Furthermore, according to the model structure, it was further divided into four model structures, including pre-fusion model, post-fusion model, Transformer single-task model and Transformer multi-task model. Then, experiments were carried out on two data sets of Twitter-2015 and Twitter2017 for these two types of methods respectively. The experimental results showed that multi-feature cooperative representation could enhance the semantics of each modal feature. In addition, multi-task learning could promote modal feature fusion or result fusion, so as to improve the accuracy of MNER. Finally, in the future research of MNER, it was suggested to focus on enhancing modal semantics through multi-feature cooperative representation, and promoting model feature fusion or result fusion by multi-task learning.

Abstract: In order for effective application of Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D) in engineering optimization，normalization of the range of objective values is needed. A self-a-daptive s constrained Differential Evolution ( gDE) algorithm is proposed to obtain the minimum and maximumvalues of each objective on the Pareto Front ( PF). After normalization，MOEA/D can then be effectively ap-plied. In addition ,the self-adaptive s constraint method is combined with MOEA/D for constraint handling. Abenchmark problem and a weld bean design problem are used to evaluate the performance of the algorithm a-gainst two other normalization methods. One main advantage of the proposed method is the selective concen-trated optimization on some regions on the Pareto front which allows handling of problems where regions of Pa-reto front are difficult to be optimized.

Abstract: Based on the quasi-static test study of seismic performance of HRB500 reinforced concrete piers ,influence law about steel strength ，the spacing，the axial compression ratio on seismic behavior was obtainedaccording to the analysis of its failure characteristics, hysteresis curves，skeleton curves，stiffness degradationunder low eyclic loads. The results show that increasing steel strength can improve components’ bearing ca-pacity and deformation capacity obviously , stirrup ratio can not influence members’ bearing capacity and de-formation capacity ,axial compression ratio can improve components’bearing capacity , but on the other hand,it is useless to improve components’deformation capacity.

Abstract: To address the difficulties of slow convergence and difficulty in finding feasible solutions when solving large-scale constrained multi-objective optimization problems, an adaptive two-stage large-scale constrained multiobjective evolutionary algorithm was proposed. In the first stage, the algorithm adaptively selected some variables for optimization according to the nature of the decision variables, without considering any constraint to make the population quickly cross the infeasible region and approach the unconstrained Pareto front. In the second stage, the algorithm considered all the constraints and optimizes the variables as a whole using the ε constraint-handling technique. At the same time, the feasible and non-dominated solutions obtained in the evolutionary process were saved and updated using archive to continuously improve the convergence and diversity of the population. Finally, the proposed algorithm was experimentally compared with the other six algorithms on 37 test functions, and the results showed that the proposed algorithm could achieved the best results on 25 functions and outperforms the comparison algorithm on at least 31 functions, respectively; meanwhile, the feasibility rate of the proposed algorithm in more than 90% of the functions could reach 100%, which could effectively solve large-scale constrained multi-objective optimization problems.

Abstract: The dissolved gas content in transformer oil is an important index to measure the operation status of transformers. The differential autoregressive moving average model (ARIMA) is used to predict the gas content in transformer oil. This method uses the time corresponding to the gas content value as an index to input the prediction model through python programming. The original non-stationary time series is converted into a stationary time series by means of difference processing, and then several sets of models are obtained by using the autocorrelation function and partial autocorrelation function parameter selection principles, and are used in the process of optimizing several sets of models. A set of optimal models were obtained by Chichi information, Bayesian information, and Hannan-Quine criteria. Finally, the residuals of the optimal models were tested by correlation testing methods, and the gas content was predicted using the models that met the residual requirements. Experiments show that the proposed prediction method has high prediction accuracy, which can provide a valuable reference for rationally arranging the condition-based maintenance of transformers.

Abstract: Fluid topology optimization is a breakthrough technology, which has broad application prospects in aerospace, automotive, electronic chips and other fields, however, the design of complex structure is difficult to process through the traditional manufacturing technology. With the development of additive manufacturing (3D printing) technology, it could provide an effective way to further expand the application and research of fluid topology optimization, which would of great significance for realizing the structural lightweight, dynamic optimization, safety optimization and performance improvement of related industrial equipment, and implementing the national strategy of “energy conservation and consumption reduction, carbon peak and carbon neutralization”. With the help of the literature metrology tool VOSviewer, were classified and summarized the literature related to fluid topology optimization in the Web of Science database were classified, comprehensively and the theoretical system, solution methods, optimization methods, and engineering applications of fluid topology optimization were expounded systematically, and the related problems were discussed. First of all, compared with solid topology optimization, fluid topology optimization involved more fields, more diverse flow regime characteristics, and more complex mathematical models, so it was more difficult to solve, took longer to calculate, and required more computing resources, which was the main factor restricting the engineering application of fluid topology optimization. Secondly, the three links and key technologies of fluid topology optimization were systematically described: representation method of design variable, CFD model and solution method, topology optimization model and solution method, and the characteristics and application scenarios of existing technologies were analyzed. At the same time, several application scenarios of fluid topology optimization, such as electronic chip heat sink, aircraft, automobile and heat exchanger, were briefly described. Finally, the development trend of fluid topology optimization was predicted and summarized. It was suggested that the multidisciplinary topology optimization research on turbulence, conjugate heat transfer, fluid-solid-heat coupling, fluid-solid-heat-mass coupling should be further strengthened; the research of topology optimization based on multi-objective function should be expanded; the deep combination with artificial intelligence should be further strengthened, more robust and mature intelligent CFD solver and intelligent optimization solver, and even intelligent software of fluid topology optimization should be developed.

Abstract: As the key equipment of tunneling a roadway, controlling the anchor-hole drills mainly depends on the operator’s experience. Improper rotary speed of an anchor-hole drill generally results in sticking or breaking pipes, which reduces the drilling efficiency. Especially, the nonlinearities and time-varying parameters, as well as the disturbances resulted from various factors in the anchor-hole drill rotary system shall be taken into consideration. A novel optimal active-disturbance-rejection controller is proposed in the paper. The set value of the rotary speed is dynamically estimated in terms of the geological condition of surrounding rocks. Brain storm optimization algorithm is employed to find the optimal parameters of the controller, which have the best dynamic and steady control performances. Based on the simulation platform composed of AMESim and Matlab, the experimental results for a single surrounding rock with or without the external disturbance show that the proposed ADRC controller has better dynamic and steady performances and stronger robustness than the optimal PID controller.

Abstract: The effects of processing variables,such as screw speed,initial moisture content and the length ofthe straw plug pipe of extrusion process on the dewatering rate,handling capacity,output per kW h etc.were experimentally studied using a low CR screw straw extruder. And the response surface optimization exper-imental results showed the extruder can run efficiently,stably and continuously with considerate dewateringrate,handling capacity and output per kW ·h under the conditions that moisture content is 85% ,screw speed50.8 r/min,length of the straw plug pipe is 26.91 mm.

Abstract: With the fact that plasticizers were used successfully in plastic products to improve the low-temperature flexibility of asphalt binder,two kinds of plasticizer are selected in this paper to study the impact of two plasticizers on asphalt.In this paper,4 different dosages of the two plasticizer totally 8 dosages were put into asphalt to study the performance of asphalt binders by several routine tests including the penetration,softening point,ductility,viscosity,measuring-stress ductility and elasticity resuming.And the modification effect was evaluated in the aspect of temperature sensitivity,high temperature and low temperature,elastic recovery and aging.The test results showed that the plasticizers did help significantly in the low-temperature performance of the modified asphalt binders,also in the facts of temperature sensitivity,anti-aging ability and elasticity resuming,but not in high-temperature performance.In general,the plasticizer DOM was better than DOP in improving the properties of asphalt binders.

Abstract: Chitosan ( CS)/poly ( vinyl alcohol) ( PVA) composite fibers were fabricated by electrospinning in this study. The influences of material formulation and formed time on the viscosity,electrical conductivity and the morphology, average diameter, diameter distribution of CS/PVA composite fiber were investigated. The re-sults showed that, the introduction of CS could increase the viscosity,electrical conductivity of CS/PVA blend solution. And the viscosity of blend solution decreased with the increase of formed time. In addition, the more CS content was, the smaller diameter of CS/PVA composite fiber would be. The fiber-forming capacity of CS/PVA blend solution decreased dramatically as the solution formed time increased.

Abstract: In multimodal multi-objective optimization problem, the same position of Pareto front often corresponded to multiple Pareto optimal solutions in decision space. However, the existing multi-objective optimization algorithms could only obtain one of the Pareto optimal solutions. Therefore, in this paper, a two-stage search multimodal multi-objective differential evolution algorithm was proposed, which divided the optimization process into two stages: elite search and partition search. In the elite search stage, elite mutation strategy was used to generate high-quality individuals to ensure the search accuracy and efficiency of the population. In the stage of partition search, the decision space was divided into several subspaces, and the detected population was used to explore each subspace in depth, so as to reduce the complexity of the problem and to improve the expansion and uniformity of the population in the decision space. The performance of the algorithm was compared with five classical algorithms NSGAII、MO_Ring_PSO_SCD、DN-NSGAII、Omni-Optimizer、MMODE on 18 multimodal and multi-objective optimization test functions, such as MMF1. Experimental results showed that there were 16 test functions in the performance index of Pareto approximation (PSP) of the proposed algorithm, which were better than the other five comparison algorithms.

Abstract: When dealing with massive terrain data ，the advantage of hardware performance can’t be fully uti-lized. This has become a bottleneck，which restricts the speed of massive terrain tiles rendering. This paperanalyzes the key factors that affect large-scale terrain rendering speed，and proposes a parallel algorithm formassive terrain data processing. The algorithm adopts double buffer queues and divides large scale terrain ren-dering into two parallel processing which includes data processing and rendering. The two buffer queues areresponsible for data reading and writing operations in turn. The loading priority of terrain tiles is consideredand tasks are allocated based on the priority. The experimental results show that this approach improves thespeed of rendering massive terrain tiles greatly.

Abstract: Aiming at the problems of unbalanced data types and incomplete feature learning in deep learning intrusion detection, a neural network intrusion detection model based on the fusion of convolutional neural networks(CNN)and bidirectional gated recurrent unit(BiGRU)was proposed.The SMOTE-Tomek algorithm was used to balance the data set, the feature importance algorithm based on mean decrease impurity was used to realize feature selection; the CNN and BiGRU models used for feature fusion and attention mechanism was introduced for feature extraction, so as to improve the overall detection performance of the model.The intrusion detection data set CSE-CIC-IDS2018 was used for multi classification experiments, the model was compared with the classical single deep learning models.The experimental results showed that, firstly, in terms of data set balance, after being processed by SMOTE-Tomek algorithm, the recognition accuracy of DoS attacks-Slow HTTP Test class was improved from 0 to 34.66%, that of SQL Injection class was improved from 0 to 100%, and DDoS attack-LOIC-UDP, Brute Force-Web and Brute Force-XSS classes were improved by 5.22 percentage points, 6.55 percentage points and 35.71 percentage points respectively.It was proved that the balanced data set improved the recognition accuracy of a few classes significantly compared with the unprocessed data set.Secondly, in terms of the overall detection performance of the model, in the comparison of multi classification experiments, the overall classification accuracy, recall and F1 value of the model in this study were higher than those of several other single neural network models.The overall evaluation accuracy of each attack traffic category was about 2.10 percentage points higher than that of the highest LSTM model.The recall rate of the overall evaluation was about 1.50 percentage points higher than that of the highest LSTM model.Compared with the highest GRU model, the overall F1 value increased by about 1.97 percentage points.It was proved that the model had better detection effect.

Abstract: Few-shot spoken language understanding ( SLU) is one of the urgent problems in dialogue artificial intelligence (DAI) . The relevant literature on SLU task, combining the latest research trends both domestic and foreign was systematically reviewed. The classic methods for SLU task modeling in non-few-shot scenarios were briefly introduced, including single modeling, implicit joint modeling, explicit joint modeling, and pre-trained paradigms. The latest studies in few-shot SLU were introduced, which included three kinds of few-shot learning methods based on model fine-tuning, data augmentation and metric learning. Representative models such as ULMFiT, prototypical network, and induction network were discussed. On this basis, the semantic understanding ability, interpretability, generalization ability and other performances of different methods were analyzed and compared. Finally, the challenges and future development directions of SLU tasks were discussed, it was pointed out that zero-shot SLU, Chinese SLU, open-domain SLU, and cross-lingual SLU would be the research difficulties in this field

Abstract: The concrete pipe with the bell-and-spigot joints is the most common urban drainage pipe structure, but the coupling of the fluid and the overlying load in the pipe may cause damage to the joint and lead to pipe leakage. Based on Abaqus and Fluent finite element software, this paper establishes a three-dimensional refined model of the drainage pipe with gasketed bell-and-spigot joints and the flow field model inside the pipe. With the interaction of pipe and soil, the contact between the bell-and-spigot joint and the rubber as well as the fluid in the pipe being considered, the structure and fluid model are solved jointly by using MpCCI (Mesh-based parallel Code Coupling Interface) platform. The influence of different flow rates, different traffic load amplitude and different load position on the dynamic response of the socket is mainly studied. The results show that under the multi-field load, the maximum principal stress and vertical deformation of the central pipe joint are the largest, and the stress distribution of the pipe bottom and the pipe top is the same, both are tension stresses, but the stress value at the bottom of the pipe is slightly larger The change of flow rate has a little effect on the mechanical response of the bell-and-spigot joint The magnitude of traffic load amplitude has a significant effect on the maximum principal stress and vertical deformation of the bell-and-spigot joint, and the influence is concentrated on the central pipe joint The movement of the load position has obvious influence on the vertical deformation of the bell-and-spigot joint and the mechanical response of the top and bottom of the pipe.

Abstract: In view of the current cumbersome preparation process and the low sensitivity to formaldehyde of gas sensing materials, this paper mainly prepares synthetic porous SnO2 hollow sphere materials by using the ratio of ethanol to water and use it to detect the low concentration formaldehyde. The structure and morphology of the materials were characterized by XRD, SEM and TEM. When the volume ratio of ethanol to water is 3.0:5.0, the prepared porous SnO2 hollow spheres grow uniformly and have a diameter of about 400 nm. The gas sensitivity test results show that the optimum operating temperature of SnO2 hollow sphere material is 210℃, the response value to 50 mg/L formaldehyde can reach 52.5, the response and recovery time are 14 s and 33 s, and the response value to other gases is lower. The material was also tested continuously in the range of formaldehyde concentration range of 1-50 mg/L, the lowest detection limit was calculated to be as low as 20 ug/L, indicating that it can be used for the detection of low concentration formaldehyde.

Abstract: In response to the issues of limited annotated data, insufficient fusion between modalities, and information redundancy in multimodal sentiment analysis, a multimodal sentiment analysis model called CLIP-CA-MSA based on contrastive language-image pretraining(CLIP) and cross-attention mechanism was proposed in this study. This model employed models such as BERT which was pre-trained by CLIP, and PIFT to extract feature vectors from videos and textual content. Subsequently, a cross-attention mechanism was applied to facilitate interaction between image feature vectors and text feature vectors, enhancing information exchange across different modalities. Finally, the uncertainty loss was utilized to compute the fused features, and the ultimate sentiment classification results were generated from the outputs. The experimental results showed that the model could increase accuracyrate by 5 percentage points to 14 percentage points and the F1 value by 3 percentage point to 12 percentage point over other multimodal models, which verifieed the superiority of the model in this study. And uses of ablation experiments to verified the validity of each module of the model. This model could effectively utilize the complementarity and correlation of multimodal data, and utilize uncertainty loss to improve the robustness and generalization ability of the model.

Abstract: In order to prevent the serious safety problem caused by the dry pot burning and stove explosion and firing,an anti-overheating system was designed.The system of infrared temperature sensor MLX90614 on the bottom of the pot was used to realize the non-contact real-time temperature monitoring.The real-time temperature data was collected and processed by the STM32 microcontroller and SMBus.When the temperature of the bottom of the boiler was beyond the normal heating range,the temperature monitoring module could send a voice alarm.When the threshold value of the dry burning temperature was reached,the gas circuit could be cut off by the control circuit serially connected in the thermocouple temperature detection circuit.Experimental results showed that the proposed system could cut off the gas path once the preset temperature reached and prevent the dry pot burning effectively.

Abstract: Recently, the global climate has sharply changed, which led to frequent floods. The specific high-intensity and extreme rainfall, and the consequent flood events occurred in the urban areas have seriously damaged the safety and property of residents.There was a torrential rainfall event happened in Zhengzhou on July 20th, 2021,which caused the most serious urban pluvial flood disaster since 1949 in China. Many studies have been done to explore the cause and mechanism of formation as well as the characteristics of the serious rainstorm，in order to improve the urban flood prevention and control. This paper analyzed the relevant studies of urban waterlogging systematically, especially focused on the Zhengzhou “7·20” Torrential Rain waterlogging disaster. Three contents were discussed, including: 1) the return period, spatial-temporal distribution and formation mechanism of the storm event 2) the shortcomings of drainage and waterlogging prevention infrastructure, as well as the weakest li＜x＞nk effect of emergency facilities 3) the main problems existing in urban flood emergency management 4) risk management and urban planning considering flood situation. The results show that the rainstorm in Zhengzhou has the characteristics of extreme and difficult to predict, and the single-day and cumulative precipitation both exceed the historical extreme values. Due to the coupling and comprehensive influence of typhoon, topography and "rain island effect", the heavy precipitation weather process was caused. Zhengzhou "7·20" pluvial disaster exposed the obvious shortcomings of drainage and waterlogging prevention infrastructure and construction in Zhengzhou city. There are bottlenecks in the river defense system. And inadequate emergency facilities and management capacity. ba＜x＞sed on the above problems, it is necessary to appropriately adjust the waterlogging prevention and control standards, strengthen the flood risk management and planning and construction control measures, build the engineering system of external flood waterlogging and prevention and control, and strengthen the intelligent dispatching and emergency command and decision-making ability of urban flood.

Abstract: In order to know the adhesion between natural asphalt and aggregate,two types of base asphalts andthree kinds of typical aggregates were selected.The adhesion between asphalt and aggregate were tested usingphotoelectric colorimetric method with dfferent doses of natural asphalt into base asphalt.The test results werecompared with that of boiling method. And the relation between adhesion rate and adhesion level was estab-lished. Meanwhile water stability of asphalt mixture through immersion Marshall test and freeze-thaw splittingtest were studied.Test results indicate that asphalt-aggregate adhesion can be analyzed quantitatively by photo-electric colorimetric method,and the optimal dosage of natural bitumen can be determined more accuratelyfrom the standpoint of adhesion.The adhesion may be improved significantly after base asphalt mixed with nat-ural asphalt.But the improving degree is different with different base asphalt and aggregate.The test results ofboiling method,immersion Marshall test and freeze-thaw splitting test verified the reliability of photoelectriccolorimetric method.

Abstract: The power-train of APU including the engine and generator for a range-extended electric vehicle iscompared to get the minimum curve of the fuel consumption. The forward vehicle model is built on the Matlab/Simulik. Two control strategies of the output of the APU including the constant power working point and pow-er-follow are analyzed based on the Chinese classic urban driving cycle. The results show that the reasonablemach of the engine and generator can improve the vehicle ’s fuel economy and the fuel consumption is grownwith the power-follow mode when the APU outputs a wider range of the power.

Abstract: In this paper, a new multi-focus image fusion algorithm is proposed based on convolution neural network in non-subsampled Shearlet (NSST) domain by using the advantages of time-frequency of NSST. Firstly, the source image is decomposed by NSST. Secondly, the fusion strategy based on the convolution neural network (CNN) is applied to the low frequency coefficients of the decomposition. Then, the improved weighted sum of Laplace energy based on the guided filtering are carried out to the high-frequency coefficients of the decomposition. Finally, the fused image can be gotten by inverse NSST transform. The algorithm fully preserves the information of the source image and improves the continuity of the image space. Experimental results show that the fusion algorithm can not only achieve better visual effects, but also improve its objective evaluation index.

Abstract: This study aimed to solve the scheduling problem of large-scale agile software project. It was decomposed into three strong-coupled subproblems: story selection, story allocation and task allocation. Dynamic events such as the addition and deletion of user stories, the change of employee′s working hours in each sprint, and other constraints such as team development speed, task duration and skills were introduced. To maximize the total value of user stories completed by the project, a large-scale agile software project scheduling mathematical model was established. According to the characteristics of the problem, the Markov decision process was designed. Ten state features were used to describe the agile scheduling environment at the beginning of each sprint; 12 composite scheduling rules were designed as candidate actions of the agent; and rewards were defined according to the objective function of the scheduling model. A priority experience replay double deep Q network algorithm based on composite scheduling rules was proposed to solve the built model. The double Q network strategy and priority experience replay strategy were introduced to avoid the over-estimation problem of deep Q network and improve the utilization efficiency of trajectory information in the experience replay pool. In order to verify the effectiveness of the proposed algorithm, experiments were carried out in six large-scale agile software project scheduling numerical examples, and the convergence of the proposed algorithm was analyzed. According to the performance measurement of the algorithm, it was compared with the existing representative algorithm DQN, double deep Q network and 12 single composite scheduling rules. The results showed that it had the highest average cumulative reward value in 6 different numerical examples.

Abstract: Warning technology of drilling engineering was the key technolog of drilling safety protection. Through the monitoring of real-time well site drilling process parameters, huge amounts of drilling data mining and intelligent learning, abnormal state modeling and optimization, abnormal state modeling and optimization, abnormal characteristics of the early warning model online judging process, achieved the goal of oil drilling abnormal state arly warning, and prevention of drilling engineering accidents. This paper reviewed the development course of early warning technology, introduced the drilling engieering warning technology architecture, and also introduced the early warning teachnology in detail and compared their characteristics, finally depicted the development of future early warning system for drilling engineering.

Abstract: A pure electric vehicle with a fixed speed ratio was changed into two gear transmission scheme.Thematching method of main parameters for powertrain components was analyzed based on specifications of vehicleperformance.In order to prove that the parameter matching is reasonable,the dynamic shift schedule and the e-conomy shift schedule were formulated.Through the vehicle performance simulation platform which was estab-lished under Matlab/Simulink,the vehicle dynamic performance and the driving range under the different shiftscneaue were simuLalea. Ine simuaion resuIs snow nat ne parameter maicnng is reasonane,ana tne powerperformance and the driving range can meet the design requirements.The driving range of the NEDC conditionunder economy shift schedule is 0.14% higher than under the dynamic shift schedule. The acceleration time in100km under the dynamic shift schedule decreased by 6.02% than under the economy shift schedule.

Abstract: The method of extracting degradation features was proposed based on MEMD and MMSE to solve the problem that non-stationarity of fault signals of roller bearing and degradation condition, which was characteristic of non-ststionarity and hard to recognize. The character of MEMD was adopted to catch different scales of signals effectively during the process of multiscalization,  which made complexity of different degradation condition distinguished better than other methods. Firstly, multichannel signals corresponding to various degradation condition of roller bearing were decomposed adaptively using MEMD, then, the reconstructed signals by multiscale IMF was dealt with MSE analysis. The results showed that the proposed method could efficiently evaluate the degradation trend of roller bearing by handing the experimental signals.

Abstract: Only the randomness of variables was considered in the traditional reliability analysis for crack resistance of earth-rock dam. By introducing fuzzy set theory, the randomness and fuzziness of soil strain parameters and the fuzziness of failure criterion were considered comprehensively to establish the risk assessment model of cracking failure of earth-rock dam. Furthermore, the Monte Carlo simulation method was used to solve the upper and lower limits of fuzzy risk probability based on the interval numbers which were transferred from the fuzzy parameters by the level cut set. The model was applied to the cracking risk analysis of Maojianshan reservoir dam. When the level cut set α=0.5, the fuzzy risk intervals of cracking failure for 5 and 39.5 years of dam operation were [5.23%, 7.91%] and [28.91%, 32.49%], respectively. Compared with the conclusions based on the traditional risk determination, the result showed that the conclusions based on the fuzzy risk interval were closed to the actual situation of dam cracking, which could provide reference and basis for dam structure safety assessment and management.

Abstract: This study aimed to explore a microfluidic chip that could generate a single droplet with a short cycle,consume a small amount of continuous phase reagents,and have low processing costs.The FLUENT simulation and VOF method were employed to simulate 16 microfluidic chips with different structure sizes in orthogonal experiments.Finally,the TOPSIS was used to comprehensively evaluate the numerical simulation results,and the order of superiority and inferiority of 16 structures was obtained.The evaluation results showed that a microfluidic chip with the optimal size structure could be obtained under the conditons of the continuous phase channel size was 40 μm,the discrete phase channel size was 30 μm,the cross exit channel size was 25 μm and the channel depth was 20 μm.The microfluidic chip could be produced the performance with smaller droplets,highest frequency and consumes less continuous phase reagent per unit time.

Abstract: Based on the meso-scale heterogeneity of recycled aggregate thermal insulation concrete (RATIC), MATLAB software was used to generate a 2-D polygonal random aggregate model of RATIC by Monte Carlo method, and then the finite element analysis software ABAQUS was used to simulate the uniaxial compression mechanical properties of meso-scale RATIC based on coupled thermo-mechanical modeling. Firstly, the heat conduction behavior of RATIC at different temperatures was simulated. According to the simulation results, the effects of meso-scale constituents at different temperatures (100, 200, 300, 400, 500, 600, 700 and 800 ℃), such as the thermal parameters (conductivity, specific heat and thermal expansion coefficient) and the mechanical parameters (strength, elastic model and Poisson′s ratio), on the meso-scale RATIC mechanical properties were explored. Furthermore, a comparative analysis was conducted to study the uniaxial compression failure modes at different fire temperatures of RATIC under simulated and experimental conditions. The results show that the temperature stress weakens the strength of RATIC when the temperature exceeds 400 ℃. And at 800 ℃, there is a maximum temperature stress of 3.309 MPa generated inside the specimen. The high temperature damage of RATIC specimens under uniaxial compression first appears in the interfacial transition zone, and then develops to the mortar. It is mainly concentrated on the free end of the specimen, and with the increase of the fire temperature and loading time, the damage shows a gradual increase trend. The results indicate that the meso-scale model can be well used to simulate the uniaxial compression mechanical properties and failure patterns of RATIC at high temperature.

Abstract: For the modeling and control of proton exchange membrane fuel cells, the empirical model and mechanism model based on polarization curve and parameter dimension are summarized, the electrochemical steady-state model and dynamic model based on electrochemical reaction, temperature, pressure and other factors are analyzed, and the intelligent method model based on neural network identification, swarm intelligence algorithm and support vector machine is introduced.The existing intelligent control strategies of proton exchange membrane fuel cells are summarized. Finally, it is pointed out that it will be a development direction of modeling to optimize the model parameters and environmental parameters of proton exchange membrane fuel cells by using swarm intelligence algorithm. The generalized Hamilton theory can also be tried to be used in the modeling of proton exchange membrane fuel cells.At the same time, the intelligent control strategy combining the new algorithm will become the research trend of proton exchange membrane fuel cell control.

Abstract: A method of considering tolerance principle for three dimensional tolerance analysis was put forward. Based on small displacement torsor (SDT) theory and modal interval arithmetic, the tolerance models of size tolerance and geometrical tolerance of the feature of size apply independent principle, envelope requirement, maximum material requirement, least material requirement or reciprocity requirement, were established respectively. By using the space vector to represent 3D dimension chain, a mathematical model is built to calculate the closed loop tolerance based on space vector loop stack principle. The application of the proposed method is illustrated through presenting an example, the tolerance analysis steps are given, and the availability of the proposed method was proved successfully.

Abstract: The calibration method of the zoom camera is studied. The self-calibration method based on the two vanishing points is used to calibrate the general parameters of the zoom camera under two fixed focal lengths. By comparing with Zhang Zhengyou’s calibration method and the results of the machine vision software Halcon calibration, the results are verified. The feasibility and robustness of this method are verified. In order to better reflect the zoom characteristics of the zoom camera, a thick lens model that can more accurately describe the zoom camera is established. The author performs SIFT feature matching on the zoom image, and according to the matching point pair The linear equations are established, and the least square method is used to estimate the zoom center of the zoom image. In addition, the optical center displacement between different focal lengths is also calculated. The experimental results show that there is an obvious gap between the optical center displacement and the focal length, which shows that The thick lens model is more suitable for describing the zoom lens of the camera.

Abstract: The steady-state thermal analysis model of reactor core was established for a 900MW pressurizedwater reactor. The steady - state thermal-hydraulic of reactor core was calculated and analyzed with COBRA-IV. The temperature of fuel element,coolant flow distribution and temperature and the departure from nucleateboiling ratio ( DNBR ) of the reactor core were obtained. The results show that the coolant in the core exits lat-eral flow from the center to the around. The maximum temperature of coolant in the core outlet is up to 338.2℃. The maximum temperature of fuel in the core is up to 1 350 ℃. The maximum temperature of claddingsurface and fuel pellet appears above the center. The DNBR near the inlet is much higher than near the outlet,and the minimum DNBR appears near the center.

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Abstract: Based on the cooling characteristics of the electric vehicle drive motor, a novel cooling structure the circumferential multi spiral structure, was proposed. The three dimensional numerical model of fluid flow and heat transfer in the shell was established. The flow field and temperature field of different water cooling schemes were calculated based on CFD technology. The numerical results showed that the temperature uniformity and cooling performance of Circumferential "Z" structure is better than the circumferential multi spiral structure; and the circumferential "Z" structure was suitable for the cooling of 135KW electric vehicle drive motor under the condition of inlet water temperature was 65ºC, with the optimal water flow rate 9.8L/min. However, the circumferential multi spiral structure could be used for higher power density of the motor cooling for the better performance of pressure resistance. The research provided a theoretical basis for cooling design and optimization of the small size and high power density motor.

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Abstract: Cu-doped SnO was synthesized via a simple and facile oxalic acid water hydrothermal route by u-sing polyvinylpyrrolidone as surfactant and stannous chloride as well as cupric chloride dissolved in this solu-tion. The structure and morphology of the as-synthesized samples were characterized by XRD,SEM and TEMetc.And the influence of the doping ratio of copper ( 0 ~ 20% ) to gas-sensing properties were analyzed sys-tematically by using a computer-controlled measure system of WS-30A. The gas sensing results indicated thatthe appropriate proportion of Cu-doping can improve the gas-sensing properties,especially the 10%,in whichthe response time,recovery time,selectivity and durability of sensors towards to hydrogen sulfide gas improvedsignificantly,and the optimum response temperature decreased dramatically to as low as 120 ℃.Finally,themechanism of the SnO gas-sensing properties enhanced by Cu-doping was discussed.

Abstract: In the human-computer interaction system based on fingertip, the position of fingertip center is very important. By solving the multi-objective optimization model for the fingertip localization, several fingertip center positions can be obtained. The fingertip pixels distribute around the fingertip centers, so the optimal solution components of this optimization model have the above distribution law. Using the estimation of distribution algorithm with the distribution law to solve this optimization model, can obtain accurate results. This paper discusses the estimation of distribution algorithm for the fingertip localization. It proposes that the decision variable dimension, population size, maximum sampling variance, and minimum sampling variance are the main parameters of this estimation of distribution algorithm. The experimental results show that each main parameter has its best value; when their values are their best values, the fingertip center positions obtained by the proposed method excel the results of the existing methods.

Abstract: This article analyzes the working principles of SNIh to estimate compensation control and sampling PI control, and compares the two control algorithms through simulation.

Abstract: The core idea of swarmintelligence (swarmintelligence) is that several simple individuals form a group, through cooperation, competition, interaction and learning mechanisms to show advanced and complex functions, in the absence of local information and models, still able to complete the solution of complex problems.The solution process is to initialize the variable randomly, and calculate the output value of the objective function after iterative solution.Swarm intelligent optimization algorithm is not dependent on gradient information, and it is not continuous and derivable to solve problems, which makes it suitable for both continuous numerical optimization and discrete combinational optimization.At the same time, the potential parallelism and distributed characteristics of swarm intelligence optimization algorithm make it have significant advantages in dealing with big data.

Abstract: As an evolutionary computation (EC) technique, Genetic programming (GP) has been widely applied to image analysis in recent decades. However, there was no comprehensive and systematic literature review in this area. To provide guidelines for the state-of-the-art research, this paper presented a survey of the literature in recent years on GP for image analysis, including feature extraction, image classification, edge detection, and image segmentation. In addition, this paper summarised the current issues and challenges, such as computationally expensive, generalisation ability and transfer learning, on GP forimage analysis, and pointd out promising research directions for future work.

Abstract: In view of the defects of accuracy and robustness caused by the uncertainty of noise and model parameters in the process of generator dynamic state estimation, a robust dynamic state estimation method for generators—H-infinity unscented particle filter (HUPF) was proposed. Firstly, a fourth-order dynamic state space model of generator was established. Secondly, the uncertainty constraint criterion of model was constructed based on the H-infinity theory to define the uncertainty boundary range. By effectively combining robust control theory and particle filtering, and using unscented transformation to calculate the important density function, the particle swarm would be closer to the actual posterior probability distribution. Finally, a novel estimation error covariance update strategy was designed, which could be dynamically adjusted based on model uncertainty. In IEEE 39-bus system, the effectiveness of the proposed method was verified. The simulation results demonstrated that the minimum root mean square error (RMSE) of the proposed HUPF method was 0.006 and the maximum was 0.045 8. Compared with UKF, UPF, and AUKF methods, the HUPF method had the smallest RMSE and could significantly improve the state estimation accuracy of the generator with model uncertainty and stronger robustness.

Abstract: To promote the theoretical development and practical application of tendon /cable driven continuum robots，the relevant studies were analyzed and summarized from the aspect of modeling and control． Firstly，the structure design methods and development trend of tendon /cable driven continuum robots were introduced． On this basis，the kinematic and dynamic modeling methods of tendon /cable driven continuum robots were summarized． The modeling methods were compared from the view of modeling principles，model accuracy，complexity and computational efficiency，which would provide a guideline to select the modeling methods in different application scenarios． Then，the reason of model mismatch，parameter perturbation and dynamic response in the control of tendon / cable driven continuum robots was discussed and the existing control strategies were divided into three categories including model-based control，model-free control and hybrid control． The implementation methods，advantages and disadvantages and the development trends of control strategies in open loop and closed loop application scenarios were surveyed． Finally，the challenges in modeling and control of tendon /cable driven continuum robots were concluded and the future development direction was prospected