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: 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: 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.

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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: 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.

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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: 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 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: 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: 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: 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: 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.

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: 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: In order to solve the problems，such as low efficiency，poor real-time performance and so on，in on－line detection of glass fiber fabric，a new method of fabric defect detection based on Blob analysis is proposed． Firstly，the image is smoothed by using mean filter，and the noises and the fabric textures are weakened． Then，the Otsu algorithm is used to find the best threshold to segment the image into Blob and background pixels． The shape of the Blob region is adjusted by using morphological processing． Finally，the connectivity analysis and feature extraction of the image are carried out． The number and size of the defects are obtained by using the least square fitting of the Blob region． Experimental results show that the method is simple，reliable and robust．

Abstract: Aiming at the application of epoxy asphalt in concrete bridge pavement, epoxy asphalt was prepared. The effects of different resin content on viscosity, high and low temperature properties of epoxy asphalt bond, mechanical properties, low temperature crack resistance and high temperature stability of epoxy asphalt mixture were studied.The results show that the addition of epoxy resin can improve the road performance and mechanical properties of asphalt mixture. With the increase of the addition amount, the curing reaction process of epoxy asphalt is accelerated, the high temperature performance and fatigue resistance are enhanced, the stiffness modulus is increased, the creep rate is decreased, the low temperature crack resistance is decreased, and the fatigue resistance and high temperature stability of asphalt mixture are improved.By comprehensive analysis,30% is the best dosage of epoxy resin.

Abstract: Impulse charactersitic of grounding devices was the important factor of lightning withstand level and lightning trip-out rate of transmission line.Based on HIFREQ program and FFTSES program in grounding power system analysis software CDEGS,this paper presented a grounding system impulse characteristic modeling considered soil frequency-dependence,especially,the Visacro-Alipio soil frequency-dependence formula has been introduced.The impact of the soil frequency-dependence on the effective length of the grounding device in different initial soil resistivity and different impulse current waveform was analyzed.The calculating results showed that when considering soil frequency-dependence,the impulse effective length would be shorter,especially for the grounding devices buried in high resistivity soil.

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: 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: 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: A novel approach to construct accurate and interpretable fuzzy classification system based on fire-works optimization algorithm(FOA) combined with differential evolution operators is proposed.It is the firsttime to apply FOA in fuzzy modeling.The fireworks optimization algorithm is a novel swarm intelligent algo-rithm based on simulating the explosion process of fireworks,which can optimize the construction and parame-ters of fuzzy system with good convergence speed and optimization accuracy.To improve the diversity of theswarm and avoid being trapped in local optima too early ,the differential evolution is performed to further opti-mize the model at each iteration.The proposed approach is applied to the lris benchmark classification prob-lem,and the results prove its validity.

Abstract: In order to study the influence of asphalt pavement temperature on pavement deflection, a finite element coupling model of asphalt pavement was established considering the temperature sensitivity of road material parameters.Based on the numerical model, the variation of pavement deflection under FWD dynamic loading under different temperature conditions and the influence of temperature on the maximum deflection of asphalt pavement with different thickness are analyzed.At the same time, the influence of asphalt pavement structure and material parameters on the dynamic bending temperature correction coefficient is analyzed. Finally, the dynamic bending temperature correction coefficient of asphalt pavement is studied based on the coupling model and compared with the test results.The results show that the pavement thickness and base modulus have great influence on the temperature correction coefficient. The temperature correction coefficient of asphalt pavement deflection established based on the finite element model is in good agreement with the temperature correction coefficient established through the experimental research, and can be used as an effective supplement to the experimental research.

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: 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: 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: 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: 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: In order to research the effects of coefficient of thermal expansion ( CTE) on the PCC pavement de-sign，some efforts have been done. According to the PCC pavement design standard in our country ，the influ-ence of CTE on the temperature stress is analyzed , and the M-E design method is used to analyses the influ-ence of CTE on transverse joint faulting. The conclusion is drawn that CTE has a great impact on the designingof pavement thickness especially on the joint load transfer and the warping of slab corner. So introducing theparameter of CTE to the PCC pavement design is of great significance.

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: 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 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: The surface morphology and the different mechanical properties between crystalline region and amorphous region of the stereocomplex crystal were studied in this paper. The same mass ratio of Poly (L-lactic acid) (PLLA) and poly (D-lactic acid) (PDLA) stereocomplex was prepared by solution blending. Differential Scanning Calorimetry, Polarizing Microscope, Atomic Force Microscopy, Confocal Laser Scanning Microscope and Nano Indentation Tester were used to list the surface morpholigy of PLA stereocomplex crystal and the diversification of mechanical properties. The result showed  that a high degree of stereo-tacticity of PLLA/PDLA blend could be achieved from the mass ratio of 1/1 for sample.The research also showed that obviously depression phenomenon on the surface of crystal was formed due to the contraction of the molecular chain. Furthermore, the hardness and modulus of crystalline region were improved compared to the amorphous region.

Abstract: Based on the characteristic of face orientation,position and the light background in face recognition,a new method of face orientation recognition based on development network is proposed.The characteristic of human’s eye was very prominent,so the position of eyes was chosen as the face orientation feature vector.And the deveiopment network model was used to recognize human’s face orientation in the different light background images.The result showed that this method could effectively solve the difficult problem of face orientation recognition under varying illumination conditions by comparing with the test results of other methods,which was fast,stable and effective.The recognition rate was as high as 100％.

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: 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: 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: In this paper, the system function analysis was carried out based on the capability demand of the joint operation for Cyberspace War Situation Visualization System(CWSVS). Then, a distributed system architecture based on HLA was constructed based on the capability perspective, service perspective and operational perspective, and the scenario generating architecture and real-time running architecture were designed. The component-based rapid and customizable situation driven technology and the map-based multi-layered dynamic fusion visualization technology were taken as the key technology examples. At last, the cyberspace Offensive and defensive operations against the C4ISR system was given as an typical example, and the utility of the system was demonstrated

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: 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: 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: In view of the disadvantages of the traditional Fuy C-means clustering algorithm， the author pro-poses an adaptive FCM algorithm. This algorithm is based on two clustering results evaluation index of withinthe class distance MIA and between the class distance MDC. The ratio of MDC and MIA，defined as l, is anadaptive function to determine the clustering number c of FCM algorithm. At the same time，according to thefuzy decision method，we use the objective function and partition entropy of FCM algorithm together to deter-mine the value of optimal fuzzy weighted m. ’This algorithm not only overcomes the FCM algorithm disadvan-tage of not being able to determine the clustering number automatically and fuzzy weighted index needs to begiven by experience,but also the clustering result is optimal. Finally,the correctness and effectiveness of thealgorithm were proved through example analysis.

Abstract: Based on FLUENT6.3 software，the simulation and analysis were applied to the optimization designof splitters in Selective Catalytic Reduction ( SCR ) denitrification system for a 600MW coal-fired boiler. Theresults show that because of variable cross-section and deflection in flue,flow field in the system appears non-uniform severely when there is no splitters. The velocity difference on two sides of the section at AlG lowerreaches is up to 15 m/s，the velocity deviations in the reactor inlet is 31.25% , and the flow field is poor intail flue，which affects the operation of downstream equipments.Reasonable splitters can improve flow field influe effectively. After equipping the transition pipe of the inlet with two group of longitudinal splitters, the ve-locity deviations in section at AIG lower reaches is reduced to 15% and in the reactor inlet is reduced to 13% .Splitters in elbow is equipped with an extension for guiding，which have a good effect on homogenization of thedownstream flow field.

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: 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: 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: 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: 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: To solve the problem of contract-based cooperative spectrum sharing between multiple primary usersand multiple secondary users,we put forward a contract design method based on statistical theory to maximizethe total utility of primary users. Firstly，the primary users make full use of the accepting contract statisticalinformation of secondary users to design a reasonable contract. Then，the primary users according to the statis-tical expected utility to obtain the optimization problem of maximizing theirs total expected utilities. Further-more，we simplify the problem in theory and exploit the genetic algorithm to derive the sub-optimal solution.The simulation results and analysis show the contract design method can solve the problem of contract-basedcooperative spectrum sharing successfully.Compared with the DMA-UI,the approach we formulated can makeprimary users obtain a higher utility， and further improve the primary users’energy efficiency and spectrumefficiency.