Abstract:To explore the effect of thoracic impact timing related with the cardiac cycle on blunt cardiac injury，a biomechanics model of the heart was developed and validated. Simulations of thoracic impact with 40 hearts in different phases of the cardiac cycle were conducted. The geometry of heart model was obtained from medical imaging，including the structures of four heart chambers，heart valves and vessels etc. The fluid-structure interaction between heart and blood was considered by the method of surface-based fluid cavities in Abaqus. The different phrases of hearts were presented by the intracardiac pressure and valve activation. The simulation results show that: （1）the established heart model can present the correct impact response of the heart under thoracic blunt from the curves of intracardiac blood pressure in the testing ranges. （2）When the passenger thoracic was under the blunt，the peak of intracardiac blood pressure in the left atrium was （164.91±17.33） kPa，which was obviously higher than that of the right heart，causing that the mitral valve is more vulnerable than the tricuspid valve; the stress on the right heart was（1887.07±168.74） kPa，which was higher than the left heart，so the heart rupture is more likely to occur on the right heart. （3）When the heart was impacted during ventricular filling period, the stress on the myocardium was（1901.3±150.7） kPa, which was higher than that during other impact periods. （4）Initial intracardiac pressure provided little effect on peak pressure, and the Pearson correlation coefficient was less than 0.2; while the state of the atrioventricular valves, open or closed, affects the myocardial injury a lot. This study is helpful to further understand the mechanism of blunt heart injury and provide a basis for vehicle safety design.

Abstract:In the aircraft mobile assembly line，the skill level of human resources can be improved through a certain period of learning by doing. For this phenomenon，a multi-project portfolio scheduling problem considering the skill evolution of flexible resource under the known expected human resource structure is proposed，and a mixed integer programming model is established to minimize the number of projects and the total input cost required to achieve the target human resource structure. To solve this model，a hybrid multi-objective teaching optimization algorithm with multi-layer linked list structure encoding is designed，and the neighborhood search is combined to improve the local search ability of the algorithm. Finally，the results of experimental data verify the effectiveness of this model and algorithm.

Abstract:In order to describe the change of the failure rate of computerized numerical control（CNC） machine tool motion components with time，this paper starts from the meta-action unit and proposes a new meta-action unit failure probability model. First，according to the cause of the meta-action unit failure，the failure types are divided into two categories：random failure and aging failure. Then，according to the different characteristics of the failure data of these two failure types，two different probability distribution functions are used to describe separately，where random failures are described by Poisson distribution，and aging failures are described by Weibull distribution. Next，the physical meaning and estimation method of each parameter in this failure probability model are given. Furthermore，the working load and working environment respectively affect the failure rate of the aging failure and random failure of the meta-action unit. In order to compare the magnitude of their influence on the failure rate，the working load parameter Rl and the working environment parameter Re are proposed，and the estimation method of the two parameters is also given. Finally，according to the collected failure data of the moving components，a frequency distribution histogram is made. At the same time，the probability density function is obtained by parameter estimation of the failure data，and these two are drawn on the same graph. It is found that both have a better simulation effect. The simulation effect shows that the proposed failure probability model of the meta-action unit is suitable for describing the change of the failure rate of moving components with time，and thus the model is effective.

Abstract:Functionally graded foam is introduced in this paper to replace the uniform-density energy-absorbing liner of a conventional motorcycle helmet for improving its protection performance and further reducing head injuries during impact scenarios. The helmet finite element model coupled with biomechanical head model was developed，the acceleration transmitted to the gravity center of head，biomechanical responses of the head and crushing behaviors of the helmet under the impacts are obtained to comprehensively investigate the effects of functionally graded foam on helmet impact protection. According to the results，functionally graded foam has more advantages under medium/high speed impact than uniform liner foam. In comparison with the uniform-density design for the conventional helmet liner and the positive/negative functionally graded foam design for the novel helmet liner，the negative functionally graded foam with a maximum density of 80 kg/m3 is of the best crushing responses and the severities of head injuries can be reduced more effectively. With the increase of density difference，the impact protection of novel helmet with negative functionally graded foam design can be further improved.

Abstract:In order to improve the output torque density of the rotary magnetorheological（MR） damper，a design method of the rotary MR damper with higher precision was proposed. The quasi-steady-state flow differential equation of the MR fluid in each channel of the damper was established. The expression of the velocity distribution of the MR fluid was obtained by using the Herschel-Bulkley constitutive model. The calculation method of damping torque and dynamic range of the damper under high-speed conditions were studied. A numerical simulation of the output torque for each channel of the damper was carried out. The results show that under high-speed conditions，as the current increases，the torque enhancement effect of the helical flow mode shows a trend of first rising and then falling，and finally degenerates into a pure shear mode. The prototype was designed and processed，and low-speed and high-speed performance tests were carried out. The test results are consistent with the theoretical calculations. The improved model under zero-field and high-speed conditions reduce the average error by 129.4%，compared with the traditional model，providing a theoretical basis for designing a rotary MR damper with high output torque density.

Abstract:Aiming at solving the thermal inconsistency problem caused by non-uniformity of temperature field after lithium-ion battery cells are grouped，and the thermal safety problem caused by the thermal interaction between the battery cells at high temperature. The combined method with simulation and test is adopted. Three progressive heat dissipation schemes are designed based on the heat generation-transfer mechanism of lithium-ion batteries. including individual battery cells in groups，foam cotton between the battery cells，and liquid cooling plate arranged at the bottom of the battery module respectively. Also the liquid cooling plate is optimized. The finite element software STAR-CCM+ is used to simulate the temperature distribution of the battery modules at different discharge rates for the three schemes. The results show that the increase of foam cotton can reduce the thermal interaction between the batteries，thereby improving the thermal uniformity between the battery cells. Under the heat dissipation condition of the combination with foam cotton，heat conducting plate and optimized（using liquid cooled pipeline series parallel combination） liquid cooling system，the maximum temperature of the battery module is 35.08 ℃ at 2C discharge rate，and the maximum temperature difference is only 4.85 ℃. The research results can provide a theoretical basis for the structure design of cooling system for battery thermal management.

Abstract:Magnetic suspension cold compressor is one of the crucial components of the large superfluid helium refrigeration system. For the rotor system of magnetic suspension cold compressor，by constructing a finite element rotor model based on Timoshenko beam theory and defining the role of electronic control hardware as equivalent stiffness and equivalent damping，this study builds a completed magnetic suspension rotor model in the closed-loop system and completes the simulation calculation of critical speed and unbalanced response. The rigid body modal frequencies of the cold compressor rotor with the same PID controller，obtained by simulation and experiment，are 27 Hz and 28 Hz，respectively；and the percentage error of the first two critical bending modal frequencies does not exceed 2%. The rotor dynamic characteristics obtained respectively through unbalanced response simulation and the speed-up experiment within the rated speed range of 50 000 rpm，respectively，are consistent. It shows the modeling and dynamic calculation methods proposed in this paper are reasonable and reliable，which have essential reference significance for the cold compressor rotor’s structural design and controller commissioning.

Abstract:Under the case of low adhesion road，an improved control algorithm is proposed to solve the problem of the lack of accuracy and stability in the path tracking of autonomous vehicles based on Linear Time-Varying Model Predictive Control （LTVMPC）. Based on vehicle dynamics，the slip angles and ratios of four tires are accurately expressed as the nonlinear function of vehicle state parameters. The Jacobian matrix is obtained by taking wheel speed as constant when linearizing the vehicle state equation in prediction horizon so as to reduce the dimension of the system，which aims to establish the improved 3-DOF vehicle model. The yaw rate tracking error is added into the performance index of quadratic programming to improve the path tracking performance and the influence of the slip angle of vehicle on the tracking accuracy and vehicle stability is considered to modify the reference yaw angle，which improves the overall performance of LTVMPC. The double lane change tracking simulation under the condition of low adhesion coefficient is performed on Carsim-Simulink co-simulation platform，and the results show that the improved control algorithm can increase the accuracy of path tracking and stability of vehicle while ensuring real-time performance.

Abstract:Aiming at design loss and premature tooth wear on lubrication of the modified super-large modulus gear-rack in Three Gorges ship lift，the lubrication characteristics of the drive system was investigated under low-speed and overload. A transient thermal-elastohydrodynamic lubrication (TEHL) model was developed for the gear-rack drive system. The transient TEHL model under variable velocity among the line of action is solved by multi-grid method and FFT method. Then，the influence of speed and load， modification coefficient，modulus and pressure angle on the contact pressure，film thickness and tooth surface friction， modification coefficient，modulus and pressure angle during the process from start to normal operation is investigated. The results show that the film thickness becomes thinner and the friction force is larger during the gear engagement stage，which causes the rack top easy to wear. It is found that the harder the surface material，the worse the lubrication performance. When the modification coefficient，modulus，pressure angle and viscosity are increased，the lubricating property can be improved.

Abstract:In view of the poor robustness and real-time performance of lane detection under complex working conditions，this paper extracts the feature points of lane line by fusing the results of edge detection and multi-color space threshold segmentation. Combined with the location characteristics of lane line in aerial view，a feature point reclustering algorithm based on RC-DBSCAN （Reclustering based on Density-Based Spatial Clustering of Application with Noise） is proposed. Based on whether the cluster points are clustered twice or not and the average gray value of the cluster points sampled in Lab space，the lane line shape and color are identified. The lane line is fitted by the least square method，and the fitted lane line is tracked by the Kalman filter algorithm based on the trusted region. Finally，the experiment is carried out in the real road video and public data set. Experimental results show that the robustness of the proposed algorithm is better than the traditional clustering algorithm in complex road conditions，and the real-time performance can meet the actual needs；on the structured road，the recognition of lane type also has high accuracy.

Abstract:Storm cells are the basic units that form various types of severe convective weather. Their radar echoes have complex shapes，uneven internal distribution，and intertwined outer layers，which makes cell segmentation difficult. This paper proposes an automatic iterative segmentation method for convective cells based on their morphological structure characteristics. Taking the cell segmentation results based on the region-tree structure on the radar image as the initial input，in each iterative segmentation process，the three morphological structure features of each segmentation result are first calculated，and then a pre-trained SVM classifier is used to determine whether the segmentation result is a convective cell. The segmentation results that are not cells are segmented again. The method in this paper was tested through three storm cases with different types. The results show that the method can effectively identify aggregated cells and cells in a split/merged state，and can obtain the complete structure of cells. In the quantitative evaluation test，the algorithm presented in this paper obtained a Critical Success Index score of 0.84，which is higher than that of the traditional SCIT method （0.55） and the single threshold method （0.49）.

Abstract:There is strong dependence among the variables of multivariate time series，which makes the data trend unobvious and the prediction difficult. Traditionally，recurrent neural network with gating mechanisms and its variants are used for prediction. But the interdependence between sequences makes the prediction result of mutation data not accurate. Based on information entropy，a new modified gating weight unit is presented. The change degree of data is quantified by using information entropy to dynamically adjust the weight matrix and describe the trend of data. The experiment is conducted with four public data sets. The experimental results show that the proposed model has better prediction performance than the traditional recurrent neural network.

Abstract:The increasing drug resistance of bacteria，as well as the long cycle of current drug resistance testing methods，bring great challenges and difficulties to accurate drug use at the first time in clinic.Therefore，this paper will explore the application of deep learning technology in the prediction of antimicrobial resistance，and proposes a dual-channel convolution neural network model integrating attention mechanisms. Through the upper and lower channels，different granularity features are extracted from laboratory data after modeling. After convolution and pooling，an attention mechanism is introduced in each channel to focus on important feature information，and then the features of the two channels are fused to complete the classification output. The model is applied to the historical data set of bacterial drug sensitivity test in a tertiary hospital，and compared with other methods.The results show that the proposed method achieves an average improvement of 20.35% in F-value index of classification accuracy，and performs better in small sample classification.

Abstract:Aiming at the problems that the fetal electrocareliogram（ECG） signal in the mixed ECG signal of the mother's abdomen is weak，contains a lot of noise，and is difficult to be extracted clearly，this paper proposes a method based on singular value decomposition （SVD），smooth window （SW） technology and least square support vector machine （LSSVM） new method of fetal ECG extraction. Firstly，SVD is used to reconstruct the decomposition matrix from the single-channel maternal abdominal ECG signal in order to estimate the maternal ECG reference signal，and the SW method is used to smooth the estimated maternal ECG reference signal；then，LSSVM is used to establish a non-linear estimation model，the maternal ECG component in the abdominal signal is estimated through the model and the smoothed maternal ECG reference signal，and the cuckoo search algorithm（CS） is used to optimize the hyperparameters of LSSVM. Finally，the mixed abdominal signal is subtracted from the maternal ECG component estimated by the CS-LSSVM model so as to obtain the preliminary fetal ECG signal. To further eliminate the interference，the SW-SVD operation is performed on the initially obtained fetal ECG signal，thereby obtaining a clearer fetal ECG signal. Experiments with Daisy data set show that the method proposed in this paper is superior to the other three classic methods in visual comparative analysis and four statistical evaluation indicators，and can extract clearer fetal ECG signals from the mixed abdominal signals.

Abstract:In order to improve software quality，the combination of refactoring techniques，software metrics，and meta-heuristic search can effectively improve the structure of software without affecting its function. In this paper，a class diagram refactoring method based on Whale Optimization Algorithm is proposed，and the quality model constructed by index coupling，inheritance and abstraction is empolyed to guide the search for the optimal refactoring sequence. Cetacean optimization method is used to refactor the class diagram in six different open source programs. The results show that the class diagram refactor the based on cetacean optimization algorithm is superior to Simulated Annealing Algorithm and Hill Climbing Algorithm in terms of quality gain，and can effectively improve the quality refactoring.

Abstract:By acquiring knowledge from a graph，the recommendation algorithm based on the graph neural network improves the recommendation interpretability. However，with the continuous expansion of the network data scale of the recommended system，the user-item scoring matrix displays a sparsity problem，which makes the graph neural network difficult to learn high quality network node features，and finally leads to the decline of recommendation quality. In this paper，a recommendation algorithm based on heterogeneous graph neural network is proposed by combining graph neural network with heterogeneous information network. This algorithm uses heterogeneous information network to decode multi-source heterogeneous data. And the attention mechanism is introduced into the user and item aggregation process of user-item interaction network and user social network，in order to realize the effective fusion of Node and topology characteristics of user-item interaction and user social networks. The experiment on two continuous sparse datasets show that the recommendation error of the algorithm proposed in this paper is 40% less than that of the baseline method.

Abstract:A terahertz fundamental Voltage-Controlled Oscillator（VCO） with high output power in 55 nm CMOS process is proposed in this paper. The stacked structure is adopted to solve the problem of low output swing caused by the limited supply voltage of a single transistor，thereby effectively increasing the output power. Based on the unilateralization technique，a self-feeding line is embedded between the gate and drain of the core transistor to adjust the phase shift and gain in order to maximize the available gain of the transistor at the desired frequency，thereby increasing the power output potential of the transistor. The simulation results after extracting the parasitic parameters of the layout show that，under a supply voltage of 2.4 V，the output frequency of this VCO ranges from 200.5 GHz to 204.4 GHz，the peak output power of the circuit is 3.25 dBm，the minimum phase noise is -98.7 dBc/Hz at the frequency deviation of 1 MHz，and the maximum efficiency of the circuit is 8.1%. The layout area including the pad is only 0.18 mm2. This work achieves high output power with a compact area and provides a design mentality for the realization of high-power terahertz fundamental VCOs.

Abstract:With the popularity of electric vehicles，the impact of large-scale electric vehicles on the distribution grid will become more apparent. In this context，a real-time rolling optimization strategy for charging stations considering the reactive response capability of charging piles is proposed. The spatial and temporal distribution characteristics of electric vehicles and the active and reactive interaction capability between charging stations and power grids are fully considered. A two-layer model is established to optimize the active and reactive power of the charging station in time and space. The quadratic programming and the second-order cone programming are used to solve the upper and lower models. Finally，the simulation is carried out with the improved IEEE33 node distribution network system，and the simulation results show that the strategy can effectively reduce the load peak and valley difference as well as the active power loss of the system and improve the voltage level of the network.

Abstract:An efficient production scheduling strategy is an important means for foundry companies to improve production efficiency and reduce production costs. At present，the related research on the optimization scheduling of casting production is usually carried out separately for the two stages of smelting casting processing and machining，which restricts the effect of the optimization scheduling of the whole process of the casting production line. Aiming at the collaborative scheduling problem of smelting，casting and machining in the production process of the foundry production line，an optimized scheduling model for the whole process of the foundry production line with the goal of minimizing the total completion time was established. In order to effectively solve the change scheduling model，a hybrid parallel chaos optimization algorithm（HPCOA） is proposed. In HPCOA，parallel chaotic search is designed for efficient global search，and variable neighborhood search based on critical path is introduced to enhance the local search capability of the algorithm. Through comparative experiments in actual cases，the effectiveness of the HPCOA algorithm is proved.

Abstract:Power swing is a balanced phenomenon in a three-phase power system， where accurate and fast parameter estimation of oscillation signal is important for the evaluation and elimination of power swing. This paper presents a method for parameter estimation of power system oscillation signal under power swing based on complex spectral interpolation DFT. The proposed method utilizes the symmetrical characteristics of a three-phase system. A complex exponential with the quadrature components is formed from three-phase real signals by using the Clarke transform. The DFT of the complex exponential is then performed. Moreover， various dynamic parameters are estimated by complex spectral interpolation using two DFT samples with the largest magnitude. The experimental simulation results show that the proposed method can accurately and effectively assess the dynamic parameters of power system under the power swing.

Abstract:The Autoencoder（AE） in the deep neural network is globally optimized through two neural network modules at the transmitter and receiver，and uses end-to-end training to improve the reliability of the communication system. However，the existing research on the AE does not have a special design for the channel，especially for the multipath effect of the time-varying underwater acoustic channel，and thus it is difficult to make flexible adjustments，which reduces the practicability of the method. This paper proposes an Attention-Autoencoder network model to improve the adaptability of the underwater acoustic communication system channel environment. Based on the Attention network's characteristic that it can efficiently filter out key information from a large amount of information，an Attention mechanism for the underwater acoustic channel is designed. The mechanism can increase the ability of the network to extract the characteristics of the underwater acoustic channel and greatly improve the adaptability of the system. Simulation verification and lake test results show that the communication system based on a comparision of. Attention-Autoencoder network model with the AE model in the literature and the underwater acoustic communication system without the introduction of neural networks，has a higher channel environment adaptability.

Abstract:The state of charge (SOC), state of health (SOH) and residual mission (RUL) of lithium-ion battery are important state parameters for the safe and stable operation of lithium-ion battery. In this paper, a coupled estimation method of lithium-ion battery state based on the rising segment of charging voltage is proposed to realize the coupled estimation of SOC, SOH and RUL in a long operation cycle from the starting point of battery prediction (SP) to the end of life (EOL). The framework estimates SOH and RUL in the charging phase and SOC in the discharge phase. Firstly, the rising time of constant current charging voltage curve segment is extracted as the health feature (HF), and the HF as the input and cycle capacity as the output are used to establish the least squares support vector machine (LSSVM) battery aging model for SOH estimation; The equivalent circuit model is used for nonlinear fitting of the voltage segment, and the state space model is established with the fitting parameters, which is combined with the unscented Kalman filter algorithm to estimate SOC; Gaussian process regression time series model is used to model the health feature series, and the change trend of HF is predicted by extrapolation of cycle times,which is combined with LSSVM model to predict RUL and the corresponding confidence interval. The experimental results show that the proposed method has high estimation accuracy and good stability.