bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hidden continuous quantum phase transition without gap closing in non-Hermitian transverse Ising model
Authors: Yang Fei , Wang Heng , Yang Meng-Lei , Guo Cui-Xian , Wang Xiao-Ran , Sun Gao-Yong , Kou Su-Peng 
Published: April 2022

Abstract
Continuous phase transition in quantum matters is a significant issue in condensed matter physics. In general, the continuous quantum phase transitions in many-body systems occur with gap closing. On the other hand, non-Hermitian systems could display quite different properties as their Hermitian counterparts. In this paper, we show that a hidden, continuous quantum phase transition occurs without gap closing in non-Hermitian transverse Ising model. By using a projected Jordan–Wigner transformation, the one-dimensional (1D) non-Hermitian transverse Ising model with ferromagnetic order is mapped on to 1D non-Hermitian Kitaev model with topological superconducting order and becomes exactly solvable. A hidden, continuous quantum phase transition is really normal–abnormal transition for fermionic correlation in the 1D non-Hermitian Kitaev model. In addition, similar hidden, continuous quantum phase transition is discovered in two-dimensional non-Hermitian transverse Ising model and thus becomes a universal feature in certain non-Hermitian many-body systems.
Method of Differential-Based Remote Upgrade for ECUs on Vehicles*
Authors: Wu Xingchen , Gao Tianyu , Lee Guodong 
Published: April 2022

Abstract
In view of the problem that the current update flashing of the traditional automotive electronic control unit (ECU) software highly relies on professional maintenance personnel and external equipment, a method for ECU remote data flashing is proposed to achieve remote and efficient upgrade of ECU software. Furthermore, through the introduction of backup partitions and differential upgrade methods, the problems of lack of fault-tolerant processing, high bandwidth requirements and low efficiency in traditional ECU software update flashing are also solved. In this article, a remote update system is built, including the server-side and the vehicle, and the interface and communication protocols are defined based on HTTPS. The above methods have been verified on NXP IMX 6 platform. Associated testing of the simulation of the ECU by the STM32 and ROCKCHIP demo board has been made based on the CAN and Ethernet simulating the vehicle network architecture. By the real data catching from Ethernet and the CAN, the details of the log file of the programs’ event tracking information are analyzed to obtain the experimental results. The results indicated that the incremental remote data updating system built in this article can remotely update the CAN software accurately and efficiently in all simulation tests, which proved the practicability, accuracy, and efficiency of this method.
A flexible under-driven bionic climbing robot design
Authors: Guo Ai , Zeng Chao , Li Guo , Fang Ranran , Liu Yang , Li Rui 
Published: April 2022

Abstract
The study of a reliable bionic climbing robot to replace humans in dangerous work at height has been a hot and difficult research topic in the academic and engineering communities. Researchers have developed climbing robots such as magnetic adsorption, vacuum adsorption, claw-stick adhesion, bionic adhesion and wet adhesion, depending on the mode of adhesion. However, due to their single adhesion method and limited means of regulation, most of them can only climb under specific experimental conditions and cannot meet complex engineering environments such as variable diameter, roughness and curvature. In order to solve the above technical problems to a certain extent, this paper draws on the excellent grasping ability of primates and the excellent movement form of the inchworm to design a flexible under-driven bionic climbing robot, which includes under-driven fingers made of coupled rigid and soft materials, and a lightweight torsional and telescopic system with good climbing adaptability and variable diameter wall transition capability. The flexible under-driven fingers are made of coupled bones, joints, muscles and skin of different modulus. The telescopic system consists of an open round thin shell rod, a two-axis motor and a flexible torso. Based on the mechanical design, the flexible under-driven finger is fabricated and subjected to finite element simulations and experiments, which show that the flexible gripper has good gripping adaptability for different shapes of objects.
Longitudinal Driving Force Distribution of Four In-wheel Motors Drive Electric Vehicle Based on Fuzzy Logic
Authors: Zhao Yongqiang , Li Jun , Li Chang 
Published: April 2022

Abstract
In order to improve the vehicle economy, this paper establishes the drive system efficiency optimization objective function and vehicle longitudinal driving constraints, design the drive system efficiency optimization control algorithm based on tolerance. Then establish the objective function of longitudinal adhesion coefficient utilization optimization algorithm, complete the development of longitudinal adhesion coefficient utilization optimization control algorithm to improve the vehicle stability. In order to give consideration to the economy and stability of vehicle longitudinal driving, the switching logic of longitudinal driving force distribution algorithm is designed. Fuzzy control is used in the switching logic to realize the smooth switching of longitudinal driving force in the transition process, and realize the optimal distribution of longitudinal driving force. Finally, compare the simulation to the typical control algorithm under multiple working conditions to comfirm the actively effect of the control algorithm.
Path planning of mobile robot based on genetic-ant colony algorithm
Authors: Hou Chenfei , Li Chenxu , Yang Jing , Zhu Jianhui , Wang Wenxuan 
Published: April 2022

Abstract
Mobile robots are widely used in daily life, and ant colony algorithm is often adopted for its path planning. However, the classic ant colony algorithm has poor search ability and search speed due to the monotony of individuals within the population. In order to solve the problems of local optimal solution, slow convergence speed and low search efficiency of classic ant colony algorithm, a genetic-ant colony algorithm is proposed in this paper. The algorithm parameters are optimized by simulation experiment to determine the best parameter combination. The method is to give a certain range of random pheromone values to the primary ant individuals to enrich the population diversity. In each cycle, m ants are sent for foraging. After each foraging cycle, aiming at the growth of individual pheromone value, the fitness function of genetic algorithm is established to evaluate individual fitness value, and the parental individuals carry out genetic operations such as selection, crossover and variation to produce offspring individuals with the same number and higher pheromone level, the offspring individuals carry out the next cycle of foraging and keep cycling. The population diversity is maintained at a high level, while the increase in the level of individual pheromone speeds up the convergence and operation of the algorithm. The simulation results show that the running time of the improved algorithm is significantly shorter than that of the traditional ant colony algorithm in the 40 * 40 grid map, and the running time of the algorithm is reduced by 21.34%; in the complex grid environment, the frequency of obtaining the optimal path of the algorithm is significantly higher than that of ordinary ant colonies, and the frequency is increased; and the number of iterations when the optimal path is reached for the first time is significantly reduced,–which has verified the effectiveness, stability and superiority of genetic-ant colony algorithm.
Programmable Current Source Design for Multi-channel Magnetic Field Generator* Programmable current source
Authors: Ding Ning , Zhang Qi , Xu Han , Feng Junfu , Li Xiao 
Published: April 2022

Abstract
The three-dimensional motion of magnetic micro-robots requires the control of multiple magnetic field generators working simultaneously. The current program-controlled current sources in the market are generally expensive and it is difficult to control multiple program-controlled current sources by a single controller to change the output current at the same time. In order to compress the experimental cost, reduce the experimental threshold, realize the simultaneous control of multiple magnetic field generators and make them work stably, this paper proposes a special program-controlled current source design for multiple magnetic field generators, and verifies the feasibility of the design through experimental tests. The results show that the dedicated current source is able to output 0.3∼6.5A under ±12V switching power supply test when each current source is connected to a 1.5 ohm solenoid coil with internal resistance. The system meets the requirements of 3% or less output current error and fast response time while greatly reducing costs and achieving good interactivity.
Local to global Tracker: A Siamese Network for Long-term Tracking
Authors: Yang Ya , Zhou Xinyu , Jiang KaiXun , Hou XiaoNan , Wang ChunLei , Zhang WenQiang 
Published: April 2022

Abstract
Visual object tracking is a basic task in computer vision, which can be applied to different applications. Former methods of single object tracking problem demands to perform target state estimation according to object position of previous frame, but these methods are poorly capable of handling the fast movement of the target. In this paper, we introduce a Siamese network for single object tracking task. The network consists of two branches, one of which is the classification branch used to predict positive or negative samples, and the other is the regression branch used to predict the specific location of the object in sequence. In addition, we propose a local to global algorithm for long-term tracking to expand the search region when the target is out-of-view or full occlusion. Experiments show that our tracker achieves very promising results on VOT2018LT datasets while running at over 89 FPS.
Single-channel speech separation method based on attention mechanism
Authors: Xiao Ting , Mo Jialing , Hu Weiping , Chen Dandan , Wu Qing 
Published: April 2022

Abstract
In order to improve the efficiency of speech interaction so that both humans and machines can hear it clearly, to solve the problem of single channel speech separation, a singlechannel speech separation method combined with attention mechanism is proposed. Based on the time-domain speech separation network, the method uses Encoder-separator-decoder framework and improves the Separator structure of the framework. First, the encoder transforms the input mixed speech signal into the latent space and extracts the feature of the speech signal to obtain the deep representation of the speech. Then, the deep attention features of speech were extracted by the splitter combined with the attention mechanism, and the mask was estimated for each independent sound source. Finally, the decoder inversely transforms the separated source signals back to the time domain. Experiments on WSJ0 speech separation data set show that the proposed method can effectively improve the SI-SNR of mixed speech separation, which is 0.31dB higher than the original time-domain network, and 0.94dB higher for the mixed case of boys and girls.
Torque Distribution Algorithm for Four In-wheel Motors Drive Electric Vehicle Based on Torque Vector Control
Authors: Zhao Yongqiang , Li Jun , Zhao Yang 
Published: April 2022

Abstract
Torque distribution algorithm, including the calculation of additional yaw moment and the distribution of four in-wheel motors torque, is studied. An additional yaw moment controller based on the MPC is designed. The predictive model is constructed based on 2-DOF vehicle model. The optimal additional yaw moment is obtained by solving the equivalent quadratic programming problem, which considers the constraints of target yaw rate, CG sideslip angle and minimum variation of additional yaw moment.Wheel torque distribution based on the optimal tire adhesion coefficient is established to realize additional yaw moment. Combined with the constraints of front and rear axle driving force, additional yaw moment and tire friction circle, the optimal wheel force distribution problem is transformed into a quadratic programming problem. In the special conditions such as lateral force saturation and driving anti-skid function trigger, the constraints are relaxed to avoid the equation without feasible solution.The simulation results of transient steering test, steady state steering test and extreme steering test showed that the application of the algorithm can shorten the response time of yaw rate, improve the cornering behavior of the vehicle under large lateral acceleration, and reduce the maximum passing speed under in slalom test to improve the handling and stability of the vehicle.
Kinematics modeling and trajectory planning of KUKA manipulator based on MATLAB
Authors: Deng Haofeng , Zhu Liucun , Wang Jiyue , Chen Mingyou , Wu Hongwei , He Congwei 
Published: April 2022

Abstract
For the purpose of engraving, KUKA KR60 mechanical arm was used in the laboratory for research and analysis.Firstly, the improved D-H parameter method was used to establish the D-H coordinate system of the manipulator, and the kinematic equation of the manipulator was obtained by calculating the homogeneous transformation matrix.Secondly, mathematical modeling was carried out with MATLAB Robbtics Toolbox, and the forward and inverse kinematics problems were analyzed and demonstrated to verify the correctness of the kinematic model of the mechanical arm. Thirdly, Monte Carlo method was used to calculate the movement Angle of the mechanical arm joint to reach the working area. Finally, trajectory planning was adopted.The variation curves of Angle, angular velocity and angular acceleration were obtained, and the rationality of its trajectory planning was verified by simulation analysis, which provided a research basis for the next step of carving trajectory path optimization.
WAVE communication based on improved NLMS adaptive filtering delay compensation method*
Authors: Wu Xingchen , Lee Guodong 
Published: April 2022

Abstract
In order to improve the signal quality of the Wireless Access Vehicle Environment (WAVE), in this paper, the dynamic step factor is introduced into the Normalized Least Mean Square (NLMS) algorithm based on multi-step step descent, so that the algorithm has a wide dynamic adaptive range. And an improved NLMS adaptive filtering method is proposed to reduce the noise of the communication signal of the WAVE system. At the same time, by introducing the unit delay operator z
−Δ of the adaptive FIR filter and the transmission delay t
p of the WAVE system, a delay compensation function is constructed to solve the delay problem in the signal transmission and processing process of the WAVE system and meet the real-time requirement of the system. In this paper, the above method is simulated and tested under typical working conditions of the Cooperative Adaptive Cruise Control (CACC) system. Experimental results show that the NLMS adaptive filtering method can effectively reduce the error between the acquired signal and the expected signal and make the signal waveform smoother by introducing the dynamic step factor. By introducing the filtering delay compensation function into the signal filtering results, it can effectively reduce the adverse effects of the delay problem caused by signal transmission and signal processing on the desired target signal.
Particle swarm optimization gray wolf fusion algorithm for solving the optimal solution of function
Authors: Jiang Lei , Liu kunkun , Wu LiJuan 
Published: April 2022

Abstract
In order to improve the local difference problem of wolf swarm algorithm, which is easy to fall into local optimization in solving function optimization, a particle swarm grey wolf fusion algorithm is proposed based on the characteristics of particle swarm algorithm, such as fast search speed, strong ability and easy implementation. Firstly, this paper compares particle swarm optimization and wolf swarm algorithm, and introduces particle swarm optimization gray wolf fusion algorithm by analyzing the root of the problem. The experimental results show that the particle swarm wolf swarm algorithm is feasible, and the search accuracy, stability and the optimal value of the objective function are significantly improved in the solution process.
A Novel NLOS Suppression Algorithm for Indoor Location based on FCM and REKF
Authors: Jin JiaWei , Cheng Long , Zhou JiaBao 
Published: April 2022

Abstract
Abstarct. The indoor positioning based on wireless sensor networks (WSN) has become one of the research hotpots. However, the NLOS propagation of the distance signals greatly challenges the accuracy and robustness of the algorithm. In this paper, we take the suppression of NLOS as the core goal and proposed the FCM-REKF-based positioning method. We firstly identify the signal states through the fuzzy c-means clustering (FCM), for the measurement distance judged to be NLOS, a refactoring method based on FCM is used. Then the corrected distance is smoothed by Kalman filter, and the Robust Extended Kalman Filter is used to calculate the final position. The simulation results show that our method has higher accuracy than EKF, REKF and IMM-EKF under NLOS environment.
Improved Image Super-Resolution Using Frequency Channel Attention and Residual Dense Networks
Authors: Sun Zhikuan , Li Zheng , Sun Mengchuan , Hu Ziwei 
Published: April 2022

Abstract
In our real life, due to the various influences, low-resolution images exist widely. The resolution of the image represents the amount of information carried by the image and the quality of the image. Image super-resolution means reconstructing low-resolution images, and it can help us improve the image quality to get more information. This paper attempts to improve the existing super-resolution reconstruction model based on deep learning. We use nested residual dense connection to prompt the model to focus on the recovery of detailed textures and accelerate convergence. Meanwhile, we use frequency channel attention mechanism to weight channels. By comparing the experimental results with other methods including FSRCNN, VDSR and MemNet, our proposed method has achieved better results and visual improvements. Especially on the Urban100 test dataset, the increases of PSNR and SSIM reach higher.
Error matching elimination based on a local affine algorithm
Authors: Hu Zhifeng , Xu Gang , Chen Ling , Fu Nana , Zhen Shuzhan 
Published: April 2022

Abstract
Aiming at the shortcomings of orb algorithm using the traditional RANSAC (random sampling consistency) algorithm to eliminate mismatches, such as the high number of iterations when the sample size is large, and the proportion of error points also affects the fitting accuracy of the traditional algorithm, a rough and fine method to eliminate mismatches is proposed; The threshold screening method is used to select the optimal threshold and preliminarily eliminate a large number of wrong matching pairs to achieve the effect of rough elimination; Then the sub-window method is used to calculate the local affine matching under all windows, reduce the number of error points, and increase the correctness of the fitting model of RANSAC algorithm. The experimental results are compared with the traditional algorithm, and the matching accuracy is improved by 6% and combined with orb-slam2.
Research on Situational Awareness Technology of Industrial Control Network Based on Big Data
Authors: Wu Yi , Guo Naiwang , Wang Binbin , Zhang Lei 
Published: April 2022

Abstract
With the rapid advancement of national strategies such as "Internet+" and "Made in China 2025", industrial control systems have been widely used in various industries such as energy, municipalities, transportation, water conservancy and aerospace, etc. The security of industrial control networks is always affecting the lifeline of the national economy. Therefore, the security of industrial control networks has not been given enough attention, resulting in frequent industrial control network security incidents, which makes us realize the importance of creating an industrial control network situational awareness system that integrates assessment and prediction. In this paper, we study the situational awareness technology of industrial control network based on big data, and integrate situational extraction as the premise, situational assessment as the core, and situational prediction as the goal to comprehensively sense the situational awareness of industrial control network system. Firstly, we adopt two ways of industrial control network data collection in the situational extraction, one is the use of traffic mirroring bypass access to sensor-aware terminals, without affecting the original production services on the premise of network traffic data collection. The second is the use of WireShark tools to achieve industrial control network traffic packet collection and statistics of traffic packets per second, analysis of the current network state, build Hadoop big data platform to achieve offline data pre-processing and feature extraction, and build Flink and TensorFlow model for graph neural network model training and complete prediction. Secondly, we use a combination of hierarchical analysis and correlation analysis to evaluate the situation, and use the evaluation graph to show the current network security state. Thirdly, the situation prediction is done by training the graph neural network model offline. We use the Flink real-time computation engine to read the data of the industrial control network into the graph neural network model in real time, which is used to enhance the feature representation of each node and to predict the probability of anomaly occurrence of the industrial control network in the future period. Finally, the graph neural network model of this paper is compared with traditional neural networks and machine learning models, and the accuracy and false alarm rate indexes are used to demonstrate the high accuracy and robustness of this model.
Research on Image Defogging Algorithms Based on Color Attenuation Prior
Authors: Tang Jialin , Zhang Zhongda , Niu Lihong , Feng Yi , Su Binghua , Lin Shounan 
Published: April 2022

Abstract
As for the defogging algorithm of color attenuation prior, an optimization algorithm based on color attenuation prior is proposed to solve the problem of image blurring in high frequency area of close-up view. Firstly, by improving the scene depth estimation information, the nonlinear model about scene depth is re-established to find the appropriate scene depth. Second, binary tree search algorithm is used to find the optimal atmospheric light value, and finally, a certain range of parameters are adjusted to achieve model optimization. The experimental results show that the algorithm solves the problem of blurring the close-up area and improves the speed of the algorithm.
Research and Implementation of Embedded Real-time Target Detection Algorithm Based on Deep Learning
Authors: Yang Dan , Yang Lichun , Liu Yiming 
Published: April 2022

Abstract
In view of the poor detection effect and low robustness of traditional target detection algorithms, this paper studies target detection algorithms based on deep learning, and designs an embedded real-time target detection evaluation board based on AI chip. It is realized that most of the common deep convolutional neural network models represented by YOLOv3 can be transplanted to the board. Although there is a certain accuracy loss within the acceptable range, this implementation achieves a faster speed to meet the needs of real-time detection. Meanwhile, a complete set of video component calling and network transmission schemes are proposed. By designing a unified standard interface accessed to the program framework, the implementation board can be flexibly extended to meet the needs of various artificial intelligence applications.