Abstract:

Abstract: To ensure that the software products could work as designed,a series of tests were needed.Due to the different importance of different modules,testers could have special requirements for test projects.How to help testers improve efficiency was the main problem of test sequence generation.Through the analysis of the above problems,this paper designed and implemented:①combined with automatic code analysis and software measurement technology,the automatic calculation of the importance weight of function module was realized.②Based on the existing test sequence generation strategy,the dynamic test sequence optimization technology and regression test sequence generation technology were designed and implemented.Through the fine-grained modeling of function modules and the combination of a variety of automatic test sequence generation strategies,the author reduces the impact of faults on the test process and improved the efficiency of regression tests in the integration test.

Abstract: Arrhythmia is a common abnormality of cardiac electrical activity,which may seriously endanger human life.Therefore,in order to accurately diagnose arrhythmia,this paper presents a new method for the recognition and classification of heartbeat in the diagnosis of arrhythmia.This paper proposed a new method for the recognition and classification of heartbeat in the diagnosis of arrhythmia.Firstly,the original ECG signal was denoised and preprocessed,and the heartbeat segment was obtained according to the R peak position.Then 235 single heartbeat feature points,R wave amplitude,PR interval,QT interval,ST segment and RR interval as feature parameters,and the performance of classification under different feature combinations were comparatively analyzed to select the best feature combination.Finally,the KNN model was used to classify the heartbeat based on the best feature combination.In this paper,experiments on MIT-BIH arrhythmia database,and according to ANSI/AAMI classification,they were classified three types of heart beats:normal or bundle branch block (N),supraventricular ectopic beat (S),and ventricular ectopic beat (V).The results showed that the sensitivity and positive predictive value of S type heart beats were 87.8% and 95.1%,respectively.The sensitivity and positive predictive value of V type heart beats were 96.6% and 98.2%,respectively.The average accuracy of measurement was 99.2%.Compared with other cardiac classification methods,the proposed cardiac classification method based on multi-feature fusion and KNN model could improve the classification accuracy,with higher sensitivity and positive predictive value,and it was of great significance for clinical decision-making.

Abstract: An adaptive test method based on random forest was proposed to solve the problem of high test cost caused by increasing test time.For the chip of the training model,by calculating the Gini index,the importance of each test group to the model classification in the process of the chip parameter test was obtained,and the feature importance was used to quantify it.Then the importance of the test group was ranked to screen out the most important test groups for predicting the quality of the chip.At the same time,the number of defective chips that could be detected in each test group was counted.Part of the test was performed on the chips in the test set,the test time was reduced by deleting some test groups,and the random forest algorithm was used to predict the quality of the chips,so as to achieve a compromise between prediction accuracy and less time.Experimental results showed that compared with KNN and logistic regression algorithms,random forest could always maintains the best prediction accuracy,test escape level and running time.Compared with traditional test methods,the test time could be reduced by about 28% while maintaining a low test escape level.Compared with the other two representative adaptive testing methods,the proposed method performed better in reducing the test time.

Abstract: Corner detection is one of the fundamental topics in image processing and computer vision.The complexity of the construction of the corner response function or the multiple smoothing of the curve often restricts detection efficiency of the corner detection scheme.Thus,a novel method for image corner detection based on the diagonal of a parallelogram to was proposed estimate the curvature value in this paper.Firstly,the Canny edge detector was used to extract each edge contour from the input image.Secondly,curves were smoothed by using anisotropic Gaussian directional derivative filter,the discrete curvature of each pixel on the curve were estimated according to the corner response function proposed in this paper.And then,non-maximum suppression was applied to the candidate corner sets.Finally,the refined corner sets were retained with unstable and false corners removed.Compared with the existing five contour-based corner detection algorithms,the proposed algorithm did not require square root operation.The extensive experiments showed that the developed method could give the highest average repeatability and lowest localization error than the other five detectors,while the corner detection speed was about 3 times that of CTAR.The results showed that the corner detection algorithm using the ratio of parallelogram diagonals (FRPD)not only had excellent corner detection performance,but also greatly reduced the computational complexity,and has a good noise robustness.

Abstract: An unsupervised multi-level and multimodal fusion method was proposed for human brain magnetic resonance imaging data by multi-level feature calculation based on the improvement of MCCA+ jICA (multimodal canonical correlation analysis+joint independent component analysis).The method preprocessed the raw multimodal data,extracted the low-level features,calculated the high-level features,integrated the multi modal images with multi-level features,and performed fusion analysis with the spatial independent component technique.It was compared with MCCA+ jICA and MCCA+ jICA with reference.Results showed that for different signal-to-noise ratios,the proposed method (95%～99%)had the highest accuracy for detecting the target information,followed by MCCA+ jICA with reference (77%～82%)and MCCA+ jICA (74%～82%),the biggest correlation between the estimation target and the real target (0.890 6),followed by MCCA+ jICA (0.855 7)and MCCA+ jICA with reference (0.699 9),and the lowest standard deviation of the correlation between the mixed matrix from different modalities (0.105 5),followed by MCCA+ jICA with reference (0.138 4)and MCCA+ jICA (0.289 6).Therefore,the method proposed in this paper had a higher accuracy,stronger robustness and better stability in exploring the brain functional-structural co-variation and coupled relationship.This was of great significance to study the brain mechanism and pathophysiology of the brain-related diseases.

Abstract: To explore the impact of operating variables on the system optimal exhaust pressure,the influence of the system simulation model was built based on the MATLAB platform.It analyzed the specific conditions,in the presence of the heat exchanger in the middle of the case,the system would effect COP of the system,the variables to determine the main factors affecting the optimal exhaust pressure system,and fitting the corresponding correlations.At the same time,the influence of exhaust pressure on the system performance was studied under different evaporation temperature and air cooler outlet temperature by using the cross-critical CO₂ heat pump system test platform.And the corresponding optimal exhaust pressure value under different working conditions was obtained,which was verified by comparison with the simulation value of optimal exhaust pressure.The results show that there was an optimal exhaust pressure in the system,and COP of the system could reaches the maximum under this pressure.With the addition of intermediate heat exchanger,COP of the system increased somewhat,but the optimal exhaust pressure was almost unchanged.High dryness and superheat had little effect on COP of the system.COP of the system decreased sharply with the increase of the outlet temperature of the air cooler,and increased with the increase of the evaporation temperature.The optimal exhaust pressure was mainly related to the outlet temperature of the air cooler and the evaporation temperature.The maximum error between the optimal exhaust pressure obtained by experiment and that obtained by simulation was 3%,the correlation formula of optimal exhaust pressure fitted by simulation was well matched with the actual operation process.

Abstract: Since the uniformity of the temperature field distribution determines the therapeutic effect of magnetic fluid hyperthermia,the distribution characteristics of the temperature field are closely related to the magnetic field distribution and the magnetic fluid distribution.In this article,the non-uniform distribution of the magnetic field produced by the actual Helmholtz coil was considered,and the influence of the non-uniform distribution of the magnetic fluid on the temperature field were further explored based on the multi-point injection strategy,and a theoretical basis for the clinical application of magnetic fluid hyperthermia was provided.This paper took the temperature field distribution in biological tissues during magnetic fluid hyperthermia as the research object,and aimed to maximize the volume fraction in tumor tissues up to 42～46 ℃ to explore the influence of different center-border injection ratios on the temperature field,thereby increasing the uniformity of the temperature field distribution.The biological tissue was simplified into a concentric sphere model,and a physical model combining the magnetic field,the heat generation power of the magnetic fluid and the biological heat transfer was established,and the finite element method was used to numerically solve the model.In order to consider the actual situation of magnetic fluid hyperthermia,the physical model analyzed the difference in the distribution of blood perfusion rate and the concentration of magnetic fluid after the temperature of the biological tissue changes.The results indicated that with the increasing of the offset distance between the center of the biological tissue and magnetic field,the temperature difference increased;when the offset was 10 mm,it would cause a temperature difference of 1 ℃;therefore,when there was a big difference between the center of the biological tissue and the center of the magnetic field,the actual magnetic field distribution should be considered;non-equal dose multi-point injection could make more tumor tissues be in a reasonable treatment temperature range.When the injection ratio k was 1～2,the volume fraction would be more affected;reduced the maximum temperature in the tissue,so that more tumor tissues were in a reasonable treatment temperature range;when the injection volume remained the same,compared with the 5-point injection,the 7-point injection had a larger volume fraction at a reasonable temperature,and its temperature spatial distribution was more uniform.

Abstract: To reduce the noise in a cabin and improve the comfort of drivers and passengers,study of reducing the interior noise of the cab was carried out by using dynamic vibration absorbers (DVA).A commercial vehicle cab was simplified.The structure-acoustic coupling model of the simplified cab and its acoustic cavity was established by the finite element method.At first,the sound pressure at the right ear of a driver was predicted,and the frequencies of the highest two pressure peaks were treated as the working frequencies of DVAs.Secondly,the structural panel acoustic pressure contribution at the field point was calculated by the acoustic transfer vector,and the panel with the highest acoustic contribution was treated as the object of the DVA.After then,the equivalent mass of the panel as the object was identified by the orthogonal polynomial method.Finally,the DVA was designed based on the equivalent mass.Compared with using the anti-vibration point as the controlled object of the DVA,using the panel with the highest acoustic contribution as the object of the DVA could reduced the peak sound pressure by 1.46 dB compared with the previous method.Compared with using the mass induction method to determine the equivalent mass of the controlled panel,using the orthogonal polynomial method to determine the equivalent mass could increase the noise reduction rate by 18.9%.According to the acoustic contribution to determine the controlled panel,and the equivalent mass computed by the orthogonal polynomials,this DVA design method could more effectively reduce the noise in the cab.

Abstract: In order to study the impact of motor pump temperature and cooling flow structure on the temperature rise,in this paper the axial piston motor pump was taken as the research object.On the basis of Newton′s law of cooling,the theoretical formula on the impact on the temperature rise of the motor pump flow channel structure was established.A simulation model of the three-dimensional temperature field simulation of motor pump temperature distribution and temperature rising under different flow channel structure was established.The theoretical derivation was consistent with the finite element simulation results.The rated working conditions,the stator region of the motor pump had the highest temperature,and the high temperature was mainly concentrated in the stator winding region,while the rotor region had a lower temperature.Reducing the width of a single channel to increase the circumferential number of channels could effectively enhance the heat dissipation performance of the motor pump.When the width of the channel was equal to the height of the channel,the heat dissipation performance of the motor pump was the worst.The stator temperature of motor pump decreased obviously compared with that before drilling,and the rotor temperature had no obvious change after the stator core was set with oil hole.The research results provided a theoretical basis for the design and optimization of the motor pump flow channel structure.

Abstract: In order to measure the hardness parameters of the surface-modified layer(SML)of key components after surface modification,in this paper a method to characterize the hardness of the SML was proposed.According to the relationship between hardness and depth,continuous indentation loading was used to characterize the hardness of the SML.The SML-substrate model was established through ANSYS finite element simulation,and continuous indentation was applied to verify that the calculation hardness was very close to the real hardness.Carburizing heat treatment was performed on 18CrNiMo7-6 alloy steel to produce a SML on the surface,and the hardness of the SML of the heat-treated 18CrNiMo7-6 alloy steel was characterized by this method.Compared with the micro hardness results characterized along the side of the SML,it was found that the hardness error of the SML obtained by the two methods was no more than 6%.This method could be used to characterize the real hardness of the SML on the surface of the material,and had certain engineering application value.

Abstract: The combination of monocular vision and laser spot could measure the depth information.The laser spot was irradiated on the surface of the object to be measured,after imaging by CCD camera,it would be generally elliptical spot,the accuracy of the center coordinate value (CCV)of the speckle would effect of the depth information.Due to the influence of surface roughness,surface bumps,pits and other defects to be measured,the edge of the imaging speckle was scattered,resulting in noise in the contour after image processing.When this data with contour noise was used to fit the spot center,the CCV of the speckle would be affected by the contour noise.The principle of depth information measurement by monocular vision and laser point combination was expounded,the causes of speckle contour noise were analyzed,a method to eliminate contour noise based on statistical principle at introverted and extroverted ellipse boundaries was proposed,two-step method was adopted to verify the robustness of the CCV of the proposed algorithm.In the first step,ideal data was adopted,that was,the known speckle ellipse was added with various noise points to verify that the algorithm could converge to the known ellipse center,and the proposed algorithm was superior to other typical algorithms and had better robustness.In the second step,the actual images of different depths of the measured surface were collected by the image measuring system to obtain the CCV of the speckle contour.The relationship model between the depth information of the measured surface and the CCV of the speckle contour was obtained by the fitting method.The comparison between the measured data of different positions of the measured surface and the calculated data of the relationship model showed that the measurement accuracy of the algorithm was better than other algorithms,which indirectly verified that the speckle center of the algorithm after denoising was closer to the theoretical speckle center and has better robustness.

Abstract: Compared with a traditional battery-powered system,an energy harvesting system that could harvest energy from environment has the advantages of green economy,with no necessity of replacing or recharging betteries,etc.It had become a hot research topic in the field of Internet of Things.Targeting the "harvest-store-use" architecture of energy-harvesting system,a DC-DC converter,between the storage capacitor and the load,was essential to energy conversion efficiency,and further impact the performance and energy efficiency of the entire system.So it was a key issue to balance the relationship of the discharge voltage of the storage capacitor and the load in order to achieve high energy conversion efficiency.To address this issue,this paper proposed a reconfigurable DC-DC converter design for energy-harvesting computing-in-memory (CIM)system.Firstly,the conversion efficiency was fitted utilizing curve fitting.Then,according to the fitted data,the voltage interval of the best conversion efficiency for each DC-DC converter was calculated to guide the selection of the switch capacitor to the boost ratio.Finally,based on experiments on the accelerator load,which was on top of RRAM crossbar,the relationship between the discharge voltage of capacitor as well as the RRAM load and the DC-DC conversion efficiency were quantitatively analyzed.The experimental results showed that the maximum conversion efficiency and average conversion efficiency of the proposed reconfigurable DC-DC converter could reach 87.93% and 78.49% respectively,providing a wider range of input voltage,a higher conversion efficiency,and a theoretical basis for future optimizations on adaptive load schedule.

Abstract: The damage of natural disasters to transmission lines could affect the safety of power grid operation seriously.However,it would be difficult to evaluate the classification of transmission line towers accurately during emergency inspection by drone.Based on the emergency inspection of transmission lines by drone,this paper proposed a classification method of transmission line tower that integrated particle swarm optimization and extreme learning machine.Transmission line tower disaster state could be divided into three types:normal,half collapse and full collapse.Firstly,the disaster state image data set of the towers of the transmission line emergency inspection was established,and the tower contour and its 7 main characteristic parameters were extracted from the image data set by combining the linear segment detection and Harris corner detection method.Then,the grey relation analysis method was used to obtain the 4 key characteristic parameters associated with the tower image and the disaster state.Then,the classification accuracy of towers was taken as the fitness of particle swarm optimization algorithm,and the hidden weights and hidden deviation threshold were optimized by using particle swarm optimization algorithm.The weights were imported into the extreme learning machine to train the four key characteristic parameters of the tower disaster state images.Finally,it was applied to the emergency inspection to classify the disaster state images of transmission line towers.It was found that the four key characteristic parameters of transmission line tower image and disaster status classification were circularity,length-width ratio,rectangularity and relative position of gravity center.The experimental results showed that the classification accuracy of the fusion particle swarm optimization and extreme learning machine model was 88.33%,and the accuracy rate was 92.68%.Compared with the backpropagation neural network and support vector machine algorithm model,it had better detection and classification effect.At the same time,the feasibility and effectiveness of the classification method based on particle swarm optimization and extreme learning machine were verified.

Abstract: The travel demand of residents in rural areas is low and scattered,which makes it difficult for conventional bus mode to sustain.According to the characteristics of rural residents′ travel demand,in order to reduce the operation cost and improve the transportation efficiency,a vehicle routing problem model considering the demand responsive transit (DRT)simultaneous pick-up and drop off mode in rural areas was constructed.And an improved two-stage adaptive large neighborhood search artificial bee colony algorithm was proposed to solve the model.The example results showed that in the rural areas with low demand density,the rural DRT simultaneous pick-up and drop off model was more economical and practical.Compared with the genetic algorithm and adaptive large neighborhood search algorithm,the average cost of the improved artificial bee colony algorithm was 9% and 3% lower than the above two algorithmsrespectively,and the convergence speed was faster,the performance was better in accuracy and stability,so it could effectively find the optimal solution with high quality.

Abstract: Underground structures seismic damage investigation indicated underground structures damage occurred more often in the non-uniform field.In order to study the influence of the location and thickness of weak interlayer on the seismic response of underground structure,a 2D soil-structure interaction model for a single-layer double span metro station structure was established based on the large-scale general finite element analysis software ABAQUS.The dynamic time history analysis method based on equivalent linearization was used to compare the seismic response of three seismic records with different spectral characteristics under normal incidence.The relative displacement of the top and bottom of the lower structure and the change of the internal force of the column in the key supporting components were analyzed.Analysis results showed that the existence of weak interlayers had a great influence on the seismic responses of the underground structures,especially when the weak interlayer was located in the middle of the structure and had a certain thickness,the seismic response of underground structures greatly increased than that of the conventional uniform site condition.As a result,in the practical engineering design，it should be seriously considered to cope with the existence of weak interlayer.

Abstract: This paper focused on the early construction risk warning issue in the construction of super high-rise building,the potential failure of the system was studied according to the general state and radical state.Taking the load-bearing components of integral steel platform equipment as the key risk link,a construction risk early warning index system and method for super high-rise building were proposed.Firstly,based on the analysis of accident evolution path and failure mode considering the coupling effect of risk factors,the core indexes were proposed,combined with the monitoring conditions.Then,the early-warning index system including human,environment,equipment and fuse indexes,as well as the classification and determination criteria,are proposed.The way of reducing index was adopt to reflect the impact of multi-factor coupling effects,and applicable early-warning method was established.Finally,a project was chosen to carry out case analysis.The results showed that the method of considering the coupling effects of human and environment risk factors as a reduction of the equipment indexes could be used as effective basis for judging the risk status of the project,to establish a risk early-warning method suitable for the construction of super high-rise buildings.This method fully considered the coupling effects of risk factors,and obtained values of early-warning indexes and reduction coefficient table through finite element model calculations.It was feasible.

Abstract: Shape stabilized phase change materials board (PCMB)with high performance was prepared.Based on the tested thermal physical parameters of PCMB,phase change wall model was set up.Simulation analysis on the wall heat transfer characteristics in summer in Zhengzhou region was conducted by ANSYS software.Variation of indoor wall temperature and heat load in PCMB were compared with those of traditional wall.The wall thermal resistance,heat storage coefficient,thermal inertia index were analyzed.The results showed that the phase-change plate could significantly improve the thermal insulation performance of the wall,reduce the fluctuation of indoor temperature.Besides,the energy consumption analysis showed that the wall could save electricity for air conditioning.