bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Test Device of Three-channel Electric Direct Drive Control System Simulating Automobile Steering System Parts
Authors: Gao Zi-chen , Su Zhong-hua , Fan Li-chang 
Published: July 2021

Abstract
This paper briefly describes the detection status and existing problems of the current durability test of automobile parts, and designs a set of detection equipment for some parts with more accurate and complex stress structure, mainly through the combination of mechanical structure design driven by servo motor and electronic measurement and control part, so as to realize the mutual cooperative action of three channels including one linear load and two rotating loads. In the fatigue test of the steering system pull rod ball joint assembly and other parts, through the comparative test and uncertainty analysis, it is found that the uncertainty of the single actuating direction of the equipment is 82% lower than that of the traditional hydraulic cylinder actuator. At the same time, the working principle of the equipment determines that its detection energy consumption cost is far lower than that of the traditional hydraulic cylinder actuator.
Railway obstacle detection based on radar and image data fusion
Authors: Qi Shuai , Yu Dong 
Published: July 2021

Abstract
In order to improve the poor adaptability of a single sensor in detection and further improve the train’s ability to perceive the running environment ahead, a railway obstacle detection method based on the fusion of microwave radar and image data was proposed. The environment in front of the train collected by the radar is preprocessed to eliminate false and out-of-bounds target signals, and the target within the safety limit is retained. At the same time, the frame difference method is used to detect the obstacles in the image sequence captured by the vehicle camera. The detection results of radar and camera are fused at the decision level. The experimental results show that this method can effectively realize the joint detection of obstacles by microwave radar and machine vision, which makes up for the deficiency of single sensor in obstacle detection.
Study on insulation performance of environmental protection C5-PFK gas mixture based on time sharing and mutual fiber sensing detection technology
Authors: Shiling Zhang 
Published: July 2021

Abstract
With the proposal of China’s goal of carbon peak and carbon neutralization, it is urgent for C5-PFK environment-friendly gas insulating medium to replace SF6 in key power equipment, and the research on C5-PFK mixed gas on-line detection technology should be carried out in advance. In view of this, this project is the first time to apply the time-sharing mutual optical fiber sensing detection technology to the C5-PFK mixed gas insulation and decomposition performance research. Theoretical and software analysis: the insulation and decomposition characteristics of C5-PFK gas mixture were simulated from the molecular dynamics level. The variation of absorption spectrum with the temperature and pressure was simulated by self programming simulation software platform, and coupling field distribution of electric power equipment in C5-PFK insulation environment was obtained. The grey system and the NSGA-II hybrid algorithm are applied to inverse and predict the decomposed gas concentration high coupling and nonlinear data series. Hardware and device development: develop a time-sharing and interactive optical fiber live detection device to achieve quantitative online acquisition of decomposition components of mixed gas. Build a real test platform for testing the power frequency, lightning and switching impulse performance of gas mixture. The influence of high frequency vibration and large temperature difference on the experimental results is solved by optimizing the optical system through structure iteration. The time-sharing and interactive optical fiber sensing detection technology can realize the detection of C5-PFK mixed gas with high sensitivity and wide concentration range, which provides theoretical and experimental basis for China’s dual carbon target construction demonstration project, and has good economic and social benefits.
Research on Explosive Wave Impulse Measurement Method Based on Momentum Block
Authors: Li Yugang , Lin Yuliang , Zhang Yuwu 
Published: July 2021

Abstract
The shock wave impulse measurement method based on the blast contour method is quantitatively researched. Firstly, the measuring device is designed, and then the calculation model is established based on the AUTODYN software. The result is carried out under the working conditions of several equivalent TNT charge. The results show that the measurement result which is related to the momentum block density of this method is slightly larger than the scaled impulse of the shock wave free field. The result momentum block obtained with high density is greater than that of low density. When the proportional distance is large, the measurement result has a stronger correlation with momentum ball density. Also, the law of result is similar in Sadovskyi’s empirical formula. Under loading of shock wave, the movement process of momentum ball in the tube mainly is divided into four stages: instantaneous acceleration, deceleration, uniform speed, and secondary acceleration.
Detection and Analysis of Metal Induction Hardened Layer Based on Ultrasonic Technology
Authors: Huaishu HOU , Xiao Dong YU , Ding LU , Xinchong Fang , Jicai Shen 
Published: July 2021

Abstract
The quenched SAE 1552 steel has excellent mechanical properties. The depth of hardened layer is closely related to its mechanical properties, so it is of great significance to detect the depth of hardened layer. By using ultrasonic testing technology, the relationship between each ultrasonic parameter and quenchable material with different thickness is established. Through short-time Fourier transform, the ultrasonic signal is analyzed. It is observed that the time-domain waveform of steel with different hardened layer thickness is very different, and the ultrasonic frequency and signal amplitude are also different. This feature can be used as an important theoretical method of ultrasonic testing, which proves that the ultrasonic testing method can accurately detect the hardened metal layer.
Research on the Application of Analytic Hierarchy Process in the Multi-target Decision Making of Networked Ammunition
Authors: Hongzhi Zhao , Xu Zeng 
Published: July 2021

Abstract
In the process of networked operations, a large amount of target parameter information can be obtained. How to allocate targets to obtain the maximum attack effectiveness of the entire weapon system is of great significance. This paper proposes a multi-objective decision-making method based on analytic hierarchy process. Through the establishment of analytic hierarchy structure model, the construction of comparison judgment matrix, the weight calculation and consistency test of target factors, the calculation of composite weight and the total ranking of levels, etc., finally a reasonable judgment order can be obtained, and the best decision result can be obtained. It provides a reference for the solution of the decision-making problem of multi-point attack, and provides a simple and effective solution for the decision-making of networked ammunition multi-target attack.
Garbage image recognition and classification based on hog feature and SVM-Boosting
Authors: Weifeng Wang , Baobao Zhang , ZhiQiang Wang , FangZhi Zhang , Qiang Liu 
Published: July 2021

Abstract
Due to the diversity of garbage types in our daily life, we will encounter many difficulties in the process of classification. In this regard, I combine hog features and boosting algorithm to develop a SVM classification method. Firstly, the input image is preprocessed to make the image more recognizable. Secondly, the hog algorithm is used to extract the features of the image. Finally, the classification device is trained, and the relevant information is sent to the image set. On this basis, the classification situation is detected. The final results show that the classification efficiency of the algorithm is as high as 95% or even more, which is about 10% higher than that of single SVM classification method. It can accurately classify garbage and has certain feasibility.
Gait Recognition of Amur Tiger Based on Deep Learning
Authors: Lili Zhou , Tongjun Liu , Yinfu Du , Jinyu Wang 
Published: July 2021

Abstract
The gait of Amur tiger was studied through video images, and a more accurate individual recognition system of Wild Amur tiger was given on the premise of supplementing pattern recognition technology. Through further research on the common gait of Amur tiger, the basic information database was established to realize the physiological state research of Amur tiger. The characteristic structure and movement standard model of Amur tiger were constructed to complete the simulation and reconstruction of Amur tiger’s routine movement. Through the simulation corridor of tiger movement trajectory, the ecological protection area can be divided effectively.
Application of improved sparrow search algorithm in SVM optimization
Authors: Ouyang Chengtian , Zhu Donglin , Wang Fengqi 
Published: July 2021

Abstract
Sparrow search algorithm has good global search performance, but there is still a probability of falling into local optimum. In order to optimize the algorithm, K-means is proposed to make the population evenly distributed and improve the efficiency at the beginning. Then, the sine-cosine search and adaptive local search strategies are introduced to reduce the probability of falling into the local optimum in the middle and late stages, so that the optimal solution can be obtained easily. Finally, the two strategies are discussed and applied to SVM parameter optimization. The UCI dataset classification results show that the algorithm with the two strategies is suitable for SVM parameter optimization, and the algorithm with sine-cosine search has better optimization ability and stability.
Optimization of Meteorological Elements and Icing Prediction Based on Satellite-ground Data Fusion
Authors: Yang Zhi , Si Weiguo , Zhao Bin , Li Mengxuan , Liu Bin , Zhao Binbin , Ma Xiao , Luo Meng 
Published: July 2021

Abstract
Icing prediction and early warning depend on icing prediction model and weather element forecast. In order to improve the accuracy of weather forecast, we test and analyze the forecast improvement of the temperature, wind speed and precipitation using numerical model and data fusion and assimilation of satellite-ground data. Through comparative analysis, data fusion and assimilation can significantly improve the forecast level of temperature and precipitation. The accuracy of 72-hour forecast does not decrease significantly compared with that of 24-hour forecast. Therefore, data fusion and assimilation using satellite-ground data has a better guiding significance for the prediction and early warning of 24-72 hour ice cover.
Application Research on Combat Simulation Automation based on Intelligent Speech Interaction
Authors: Fu Xiang , Shen Hanji , Chen Tao , Liu Hongpeng 
Published: July 2021

Abstract
Combat simulation test is the main means of evaluating command information systems. In order to improve the intelligence and efficiency of simulation experiment, the method of applying intelligent speech interaction technology (ISIT) to the simulation experiment automation process is proposed. The application processes of speech instruction recognition, instruction element analysis, data instruction generation and speech synthesis feedback are designed based on human-machine ISIT. Meanwhile, taking the air combat command and guidance scene an example, the command and guidance voice instruction database is constructed, and the speech interaction simulation experiment is designed and verified. The results show that ISIT can effectively reduce the number of commanders and operators in air combat simulation test, and improve test efficiency.
Research on Improved Intelligent Generative Dialogue Algorithm Based on Knowledge Graph
Authors: Liu Caizhen , Li Yu , Chen Bangge 
Published: July 2021

AbstractThe rise of a new generation of artificial intelligence technology, represented by deep learning, has promoted the vigorous development of natural language processing technology. As a typical application of natural language processing technology, human-machine intelligent dialogue system, coupled with its commercial value in the fields of voice assistants and chat robots, has become a hot topic in the current academic and industrial circles. In the past, the responses generated by intelligent dialogue robots is single and universal, and even the content is inappropriate. Therefore, this study proposes to use the knowledge graph as the background knowledge when the dialogue model generates the responses[1]. In order to meet the needs of users more closely, it also proposes to introduce the user information participating in the dialogue and the dialogue scene into the model. The model is trained and evaluated on the DuRecDial public dataset, and the optimized model is compared with the original model. The experimental results show that the model with these two modules has better effect than the original model, especially in the generation index, the F1, BLEU2 and DIST-2 indexes have been improved by 0.91%, 0.5% and 0.9% respectively.
Research on Current Situation of 3D face reconstruction based on 3D Morphable Models
Authors: Hu Weirong , Li Yu , Wang Xuesong 
Published: July 2021

Abstract
3D face reconstruction based on 3D morphable models (3DMM) uses single or multiple 2D face RGB images to reconstruct the 3D information of the target and restore the spatial structure of the face, which has important research significance for face recognition, film industry, medical field and so on. This paper introduces the main technical methods and research status of 3D face reconstruction based on 3dmm in recent 20 years, summarizes the advantages and disadvantages and applicability of various researches on 3D face reconstruction using 3dmm, and analyzes the current research hotspot and future development trend of 3D face reconstruction.
Prediction method of landslide geological hazard based on AdaBoost model
Authors: Luo XiaoXia , Wang ChenYang 
Published: July 2021

Abstract
Aiming at the problem of landslide geological disaster prediction, based on the analysis of geological structure and historical disaster data of a county in southwest Shaanxi, this paper puts forward a landslide geological disaster prediction model based on AdaBoost. Firstly, the data of landslide geological disasters are analyzed, and the main factors of landslide geological disasters are determined by principal component analysis and Spearman grade correlation coefficient method. Then, the main factors are used as feature data to train the model, and grid search is used to optimize the super parameters in the model. Finally, experiments on real data sets show that the prediction accuracy of AdaBoost model is 1.5% higher than the best prediction results of decision tree, logistic regression, support vector machine and k-nearest neighbor method, and AUC is 0.24% higher. It further verifies the effectiveness and feasibility of the model, which can provide a scientific basis for the prediction of landslide geological disasters.
Research on Classification Method of 3D Point Cloud Model Based on KE-PointVNet
Authors: Cheng Haixia , Zhao Bo , Zhang Zhiyu , Liu Xunjun 
Published: July 2021

Abstract
With the popularization and use of three-dimensional data acquisition equipment, the acquisition of three-dimensional data is more convenient. Three-dimensional data contains rich shape and scale information. How to effectively and accurately classify, segment and identify three-dimensional data is a research hotspot in the field of computer vision. Aiming at the particularity of 3D point cloud data and the neglect of local correlation between points, this paper proposes a new end-to-end depth network framework, namely KE-PointVNet. KE-PointVNet can directly deal with point cloud and construct deep convolutional network. KE-PointVNet extracts local geometric features of each point based on the EdgeConv module. By using the KNN proximity algorithm to construct local neighborhood of the point cloud, it can avoid its non-local diffusion, and finally obtain the high-level semantics of the point cloud through the local aggregation vector VLAD layer. The experimental results show that this method has a higher classification accuracy than most of the existing point cloud classification methods.
Person re-identification based on frequency channel attention networks under the surveillance scenario
Authors: Chen Shengbo , Zhang Hongchang 
Published: July 2021

Abstract
Under the surveillance scenario, due to the influence of occlusion, deformation and illumination, the accuracy of person re-identification (ReID) task will be greatly affected. The extraction of effective pedestrian features has become the key of person ReID task. In view of this problem, this paper creatively uses frequency channel attention (FCA) network to carry out person ReID task. FCA network solves the problem of insufficient information representation caused by global average pooling (GAP) in traditional channel attention network. In addition, this paper conducts experiments on two datasets. The ablation experimental results show the effectiveness of FCA network in person ReID task, and the results of contrastive experiment show the superiority of FCA network in person ReID task.
Improved differential evolution LQG control of active suspension
Authors: Dafeng Jin , Feifei Liu 
Published: July 2021

Abstract
Aiming at the problem that the value of weighted matrix Q and R in linear quadratic optimal control (LQG) controller depends on the designer’s experience, adaptive differential evolution (JADE) algorithm with optional external archive is introduced to optimize the control parameters of LQG, so as to improve the performance of LQG controller. The dynamics model of 1/4 vehicle active suspension was established as the object, and the LQG objective function was constructed. The simulation experiments were carried out on B-class and C-class roads with the help of Matlab/Simulink.The experimental results show that the RMS values of the vertical acceleration of the vehicle body, the dynamic travel of the suspension and the dynamic displacement of the tire of the active suspension based on JADE optimized LQG controller under different road surfaces are reduced to a large extent compared with that of the passive suspension, which improves the stability of the vehicle and the comfort of the ride.
Design of Intelligent Somatosensory Deformation Skateboard Based on STM32
Authors: Fei Ruan , Li Songming , Li Xinxue 
Published: July 2021

Abstract
Based on the research of the existing intelligent scooter, this paper puts forward a more perfect scheme for the skateboard with STM32 as the core from the aspects of appearance, internal performance and whether the skateboard is safe in the process of running. Firstly, the performance and serial port of STM32 are analyzed, and then the corresponding gravity sensor is selected according to the module and connected to the host chip. Then the simulation debugging of gravity sensing was carried out. After normal operation, the assembly of the whole skateboard was started and the Bluetooth module and LED module were installed. At the same time, a detachable handle is also designed in appearance to ensure driving safety to the greatest extent. Finally, the wireless communication control system is used to achieve humanized design, and the intelligent direction and speed are adjusted through the gravity induction module.
Research on the Combination of Mobile Communication Technology and Computer Internet Technology
Authors: Feng Yuan 
Published: July 2021

Abstract
The integration of Internet tech and mobile communication tech has changed the form of traditional info and communication tech, which can better serve the real needs of people for real-time communication and high-speed sharing and interconnection of data, info and resources. Based on this, this paper first analyses the connotation and characteristics of mobile communication and computer Internet, then studies the integration mode of mobile communication tech and computer Internet tech, and finally gives the trend of the combination of mobile communication tech and computer Internet tech.
Random forest-based human pose detection system for through-the-wall radar
Authors: Xu Dongpo , Liu Yunqing , Wang Qian 
Published: July 2021

Abstract
Nowadays, the detection, identification and classification of targets behind buildings in the process of wall penetration sensing is one of the main solution directions for detection activities. In this paper, a random forest-based human pose detection system for through-wall radar is proposed, aiming at optimizing the traditional through-wall radar target detection and identification by machine learning methods. The actual data acquisition is performed by UWB-MIMO through-wall radar system to construct multidimensional data and identify the pose. The experimental results show that the random forest method has high recognition performance by identifying multiple poses and has a pose resolution that traditional target recognition does not have.
Adversarial Response Generation Against Topic Relevance
Authors: Zhang Peng , Wang Hongrong , Zhou Zhigang , Wang Yu 
Published: July 2021

Abstract
In recent years, generative adversarial networks have performed well in the field of dialogue generation to improve the information diversity of dialogue responses. Often overlooked, however, is that the query and response are not relevant on the topic. In order to improve the topic relevance of chat conversation, the paper proposed a topic-relevance adversarial response generation model, TR-ARG, which is composed of generator G, discriminator D and topic classifier T. The experiment was evaluated on OpenSubtitles, an open dialog dataset, and compared with the current baseline models SEQ2SEQ and GAN-AEL. The results show that our model can effectively improve the topic relevance of generated responses.
Study on the Influence of Automobile Intelligent Development on the Market Scale of Network Security
Authors: Hao Jingjing , Qin Yue , Wang Qipei , Pan Junjia , Liu Quanzhou , Han Guangsheng , Tang Fengmin , Rong Hui 
Published: July 2021

Abstract
As an important tool of intelligent transportation and intelligent travel, automobile is becoming more and more electronic and software-oriented. The intelligence and connection of automobile have brought a new and huge economic growth point to the automobile industry. But a number of new software and network technologies introduced have also posed a threat to automotive security. Fortunately, these technologies have also given birth to and promoted a new business of the automobile industry, namely the security business of intelligent connected vehicles (ICV). At the same time, under the huge volume base of the automobile industry, the development of the security market in the automobile industry will have an impact on the overall market scale of network security. Investigation, analysis and research were undertaken in this paper to demonstrate the relevance among the development of intelligent network vehicles, the automotive security, and the network security market.