bluuebunny/crossref_metadata_2025_split huggingface dataset:
Car Plate Detection Based on Yolov3
Authors: Shen Siyuan , Wang Lidan , Duan Shukai , He Xin 
Published: June 2020

Abstract
An intelligent car plate detection method can make the travel more convenient and efficient. However, traditional methods are reasonably effective under the specific circumstances or strong assumptions only, and there are few databases for car plate detection. Therefore, a novel real-time car plate detection method based on improved Yolov3 has been proposed. In order to select the more precise number of candidate anchor boxed and aspect ratio dimensions, the K-Means algorithm is utilized. To solve the short of the available car plate database, a car plate database which has 6668 pictures has been established. As shown in the experimental results, the method which is proposed by this paper is better than original Yolov3. Thanks to the car plate database, the proposed method obtained better results even in the situation of inclination, too bright or too dark, different weather and so on.
The general formula of entropy and similarity measures for hesitant fuzzy linguistic term sets
Authors: Yuan Xiujiu , Huang Lin , Zhao Xuejun , Zheng Mingfa 
Published: June 2020

Abstract
In order to quantitatively characterize the uncertainty of hesitant fuzzy linguistic information, the entropy and similarity measures of hesitant fuzzy linguistic term sets are studied. First, the axiomatic definitions of entropy and similarity measures of hesitant fuzzy linguistic term sets are given, and general formulas of entropy and similarity measures for hesitant fuzzy linguistic term sets are proposed. Moreover, the algorithms for generating entropy and similarity of hesitant fuzzy linguistic term set are given. Then the relationship between entropy and similarity of hesitant fuzzy linguistic term set is studied, and the general formula of entropy based on similarity is proposed. These lay the theoretical foundation for flexible selection of entropy and similarity in multi-attribute decision making.
Legs Components Micro-Doppler Estimation of Human Target Based on Doppler Radar
Authors: Lei Chengxi , Ding Yipeng 
Published: June 2020

Abstract
The radar based human micro-motion research is a new technology rapidly developed in recent years. Radar detection of human target has advantages that other sensors do not have. The radar system is not affected by light or weather condition, which makes it possible to achieve detection in various environments. Besides, targets hidden behind obstacles can be detected, because electromagnetic waves can penetrate barriers of a certain thickness. Doppler frequency shift caused by the modulation of electromagnetic waves by the swing of hands and legs during human motion can be obtained by various signal processing methods. Separation and estimation of multi-components signals is a prerequisite for studying micro-motion characteristics of human target. Because there are a lot of aliasing in the time-frequency diagram of component with each other, separation of multi-components signals is a difficult but urgent problem at present. In addition, kinematic parameters of human legs play a key role in distinguishing human and animal targets. Therefore, in order to extract human leg signal and estimate its frequency accurately, a signal separation method based on Bezier curve fitting is proposed in this paper. Because human radar echo signals are often non-stationary time-varying signals, we need to process the collected signals with time-frequency transforms. The accuracy of this method for extracting components of legs is precise, and it has met the accuracy requirement of Micro-Doppler research. The simulation and experimental results prove the effectiveness of this method.
Research on Calibration Method of Multi-camera System without Overlapping Fields of View Based on SLAM
Authors: Feng Mingchi , Jiang Panpan , Liu Yibo , Wang Jingshu 
Published: June 2020

Abstract
The basis of multi-camera system measurement is the calibration of the multi-camera system. Although multi-camera system calibration has achieved certain results, there are still problems such as tedious calibration process and high calibration cost. In this paper, we propose a practical calibration method for multi-camera systems without overlapping fields of view. Firstly, a global 3D model of the scene is reconstructed using Simultaneous Localization and Mapping (SLAM). Then, each camera is calibrated using the 2D-3D correspondence between the images acquired by the multi-camera system and the 3D model. Finally, the external parameters of the multi-camera system are calibrated. In the process of using SLAM to build the model, the graph optimization is used to improve the accuracy of the modeling, and the accuracy of the calibration depends on the accuracy of the modeling. After several experiments, the experimental results show that the method is feasible.
Single-station Passive Reconnaissance Target Location Based on Intelligent Technology
Authors: He Kun , Liu XuBo , Guo KaiFeng , Lan ChunYan , Liu Yi 
Published: June 2020

Abstract
At present, passive reconnaissance target location usually adopts multi-station direction-finding cross or multi-station time difference method, which has the disadvantages of complex scenes, large resource consumption, long time consumption and low positioning accuracy. In this paper, intelligence technology is used to perform machine learning and predictive evaluation of target radiated power in battlefields, and combined with the electromagnetic propagation model to complete the estimation of target approximate position of single-station passive reconnaissance. The results show that after training with a certain sample, the method can quickly complete the estimation of the target position, and has high positioning accuracy.
Design of lightweight pedestrian detection network in railway scenes
Authors: Han Chuanyi , Zhu Jinlei , Li Feng , Wan Shengkai , Chen Houjin 
Published: June 2020

Abstract
The deployment of many advanced pedestrian detection applications is largely hindered by the high computational cost of deep convolutional neural networks (CNNs). In this paper, we propose a two-step pruning method to design a lightweight pedestrian detection network in railway scenes. The first step is feature pyramid network (FPN) pruning, which utilizes the characteristic of pedestrian in railway scenes and the FPN structure in YOLOv3. The second step is regular channel pruning, which utilizes network slimming knowledge and is an accelerator-friendly pruning strategy. Our two-step pruning method gives about 88% reduction in parameters and about 74% reduction in computing complexity with comparable detection accuracy.
CSDA: A novel attention-based LSTM approach for code search
Authors: Ren Leiming , Shan Shinmin , Wang Kai , Xue Kun 
Published: June 2020

Abstract
Previous studies have proposed semantic-based approaches for code search over large-scale codebases, which has bridged the gap in understanding the semantics between natural language and source-code language. However, these studies either failed to determine an effective method for semantic representations or lacked the distinction of semantic features. In this study, we propose a novel attention-based LSTM neural network known as CSDA (Code Search based on Description Attention), which can effectively improve the code search performance. The proposed model can focus on different parts of a semantic feature when numerous aspects of a source code snippet are used as input. As opposed to assigning the same weight to different parts of the semantic vector, CSDA takes the semantics of natural language descriptions into account, so that the subtle differences hidden in the code snippet can be discriminated and associated with the corresponding queries. We compare CSDA with the existing state-of-the-art approach CODEnn, which uses a jointly embedding technique for code search. Our experimental evaluation demonstrates that CSDA outperforms previous methods and achieves superior code search performance to CODEnn, with higher success rates and mean reciprocal ranks. This study provides significant insights into the use of semantic representation methods in deep learning-based code search approaches.
Research and Optimization of Electromagnetic Compatibility of a Hardened Computer
Authors: Li Chao , Wang Tao , Gao Tongguo 
Published: June 2020

Abstract
A hardened computer is an army vehicle equipment. During the testing of electromagnetic compatibility, the RE102 electromagnetic radiation test item appeared multiple points exceeding the standard. In view of this problem, the electromagnetic environment of a reinforced computer is analyzed, and it is found that there are adverse factors that cause electromagnetic leakage in the aspects of the case aperture, cable shielding, power protection, and grounding methods. After optimization, the device passed the RE102 test smoothly. The radiated emission level of the improved equipment has decreased significantly, and the radiated disturbance suppression measures adopted are easy to implement and universal, which provides a reference for subsequent research and the development of similar equipment.
Application of PLC in Automatic Tapping Machine Control System
Authors: Liu Hengjuan 
Published: June 2020

Abstract
Aiming at traditional equipment’s problems, such as low tapping efficiency, poor reliability, a set of special automatic tapping machine control system is designed. The system can tap five workpieces at the same time by using Siemens S7-200 series CPU226 AC/DC/Relay PLC technology. The system will carry out technical reform of traditional equipment. The paper describes the control system scheme, work flow chat, I/O wiring diagram and program design of PLC. The operation results meet the use requirements. The system has the characteristics of good maneuverability, high operation safety and high production efficiency, which can greatly reduce the production cost.
QoS Dynamic Perception Scheduling Strategy for Edge Intelligent Computing
Authors: Hou Wanwan , Sun Depeng , Sheng Mengxue 
Published: June 2020

Abstract
Edge intelligent computing devices are often deployed in some extreme environments, where the transmission network bandwidth is low or the network environment changes greatly. Therefore, the traditional queue scheduling algorithms cannot guarantee the QoS of edge intelligent computing. WF2Q+ allocates bandwidth according to a fixed weight, which causes real-time data flow delay to increase when the network is unstable. The dynamic perception scheduling strategy proposed in this paper is to dynamically change the weight of WF2Q+ by dynamically sensing the backlog length of the queue. At the same time, combined with the queue scheduling algorithm of PQ, this algorithm can prioritize the transmission of real-time data with certain fairness. In addition, the token bucket algorithm is used to limit the sending rate of the device and prevent network congestion caused by burst data injection into the network. After experimental simulation, the improved algorithm can achieve good results on the delay index.
Elderly care system based on cloud platform internet of things
Authors: Zhou Changhu , Xia Huiwen , Lv Xiaotian , Tan Meifang , Xiong Yuhang 
Published: June 2020

Abstract
Against the background of a gradual increase in the elderly population, the care system for the elderly appears extremely important. The nursing system based on the cloud platform Internet of Things mainly includes the design of the lower computer, the debugging of the upper computer and the design of wireless communication. The design of the lower computer uses STM32, pulse detector, accelerometer, gyroscope and other modules. And with the cooperation of the host computer and the communication module, it can realize health monitoring functions such as medicine warehouse switch detection, elderly heart rate monitoring and fall detection. In addition, family members can also use the mobile phone APP to control connected devices around the elderly, such as smart pill boxes, to remind them to take medicine. There is no doubt that the design will greatly reduce family stress.
Application of Multisim Simulation Software in Teaching of Analog Electronic Technology
Authors: Li Zhengdong , Li Xiuling , Jiang Decai , Bao Xingzong , He Yan 
Published: June 2020

Abstract
Analog electronic technology is a core basic course of electronic specialty in colleges and universities. It has the characteristics of complex theory, abstract content, difficult to explain by teachers and difficult to understand by students. This article mainly discusses some typical applications of Multisim virtual simulation software in the teaching of analog electronic technology courses, and analyzes the impact on classroom teaching methods. The practice shows that the application of simulation software in the teaching of simulation electronic technology can solve the problems of insufficient experimental teaching site and potential safety hazards. It can help students to understand abstract theory and master related knowledge. It can help to organize lively and efficient classroom teaching and promote the quality and efficiency of classroom teaching.
Needle-tissue interaction force study during simulated regional anaesthesia
Authors: Zhang Shengli , Graeme  , George  , Qiao Panpan , Huang Zhihong , Xia Chunming 
Published: June 2020

Abstract
Needle insertion usually performed with the aid of an Ultrasound (US) image in the regional anaesthesia. Getting the position of the needle tip from US requires the technical expertise and skilled anaesthetist. To detect where the needle tip is around the nerve during the needle insertion, a modified block needle attached with a force sensor is used to feedback the amount of counter force from the needle tip. This paper is to investigate whether needle force under ultrasound guidance can guide anaesthesiologists in identifying the relative position of the needle tip and nerve The goal of our research was to use the visual needle tip force to guide the anaesthetist to detect the relative positions between the needle tip and nerve.
A Traditional Chinese Medicine Prescription Recommendation method based on Mutual Information Clustering
Authors: Qin Yao , Ma Zherui 
Published: June 2020

Abstract
Traditional Chinese medicine (TCM) data is the main knowledge resource of TCM, which contains a wealth of clinical experience knowledge. Machine learning has made remarkable achievements in natural language processing. As the carrier of TCM knowledge and information stored in the form of text, using machine learning method to study these TCM data can save a lot of manpower cost, improve the objectivity of TCM, promote TCM related knowledge better, and have certain guiding significance for the research of TCM human engineering experiment. This paper proposes a recommendation algorithm based on mutual information clustering. Its core idea is calculating mutual information between two symptoms, and set symptom “relatives and friends group”, after getting the symptom clustering results of mutual information, then combine the clustering results and search algorithm to achieve the effect of recommendation and filtering. Experimental results show that the proposed method is effective.
Research on hotel online sales forecast model based on improved WaveNet
Authors: Duan Dongmei 
Published: June 2020

Abstract
In recent years, with the rapid development of the Internet in China, online transactions have grown greatly. For example, OTAs with a large number of hotels have accumulated a large amount of hotel data and user consumption data. And the online sales of hotels is the basis and core of revenue management. Time series prediction has always been one of the main application fields of machine learning algorithm. From the classical traditional time series prediction methods to long-term and short-term memory networks and closed-loop neural networks, the prediction ability is constantly improving. With the development of deep neural networks, convolution neural networks show superior performance in the prediction of time series. This paper proposes a new prediction model based on the improved WaveNet using not only the parameters of historical sales and hotel property, but also the parameters of holiday time and time position in the prediction range, which are processed by serialization. Simulation results are presented in details in this paper, where these results indicate the effectiveness of the proposed forecasting tool as an accurate technique.
Automatic shutdown system for unmanned air-conditioned room
Authors: Tan Meifang , Chen Zhenyu , Lv Xiaotian , Yu Maochang , Guo Jiabin , Fan Qin 
Published: June 2020

Abstract
With the development of society and economy, air conditioning has also become an integral part of our lives and it allows us to enjoy warmth in winter and cool in summer. However, there is also a phenomenon that people do not turn off the air conditioner due to personal savings awareness or anxious to go out. This paper intends to design a system where the air-conditioning room is automatically turned off if there is no one in half an hour. Pyroelectric sensor is used to detect the presence of people in the room, and wind speed sensor detects whether the air conditioner is on. If the room is empty and the air conditioner is not turned off for more than half an hour, the system is used to automatically turn off the air conditioner. It can reduce some waste of resources, it can also reduce certain environmental pollution, and realize economic and social benefits.
Application on Development of Modern Service Industry in China Based on Artificial Intelligent Model Optimized by TANSAFOA
Authors: Wang Tian , Lin Jianbang 
Published: June 2020

Abstract
Modern service industry is one of the industries that widely applies artificial intelligent (AI) in the era of big data. The development of modern service industry plays a very important role in industrial transformation. The emergence of AI technology improves the effectiveness of data mining, and more and more scholars suggest a series of optimization methods to lift the prediction ability. In this study, tangent fruit fly optimization algorithm with step adjust (TANSAFOA) is proposed and it is used to optimize multivariate adaptive regression splines (MARS) and back propagation neural network (BPNN) to construct a prediction model of business performance. The result shows TANSAFOA can effectively optimize the prediction model and the BPNN model optimized by TANSAFOA has higher prediction performance than MARS. TANSAFOA BPNN-model is the most appropriate prediction model for modern service industry in China.
Airborne Communication Computer Based on PowerPC+ SRIO Architecture
Authors: Gao Tongguo , Lyu Meimei , Yang Hongbin 
Published: June 2020

Abstract
In order to break through the transmission capacity limitation of airborne computers, an airborne communication computer based on PowerPC+ SRIO Architecture is designed according to the wide application of the SRIO bus and the high performance of the PowerPC processor. In terms of optimization design, the computer adopts a redundant fault-tolerant system and improves the SRIO register initialization and address mapping method. The experimental results show that the transmission speed of the airborne communication computer based on PowerPC+ SRIO architecture reaches 81% of the theoretical value, and 16KBytes data can be transmitted in real time within 1μs.
Deformable Feature Pyramid Network for Aluminum Profile Surface Defect Detection
Authors: Wang Juan , Meng ZhaoHui 
Published: June 2020

AbstractSurface defect detection is an important part in the process of aluminum production. The goal of a complete defect detection task is to realize the specific category and precise location of each defect in the image. Due to the similarity between surface defects and background, as well as the large diversity and difference in the appearance of the defects, this task is still challenging for applying this task in practice. In order to solve these problems, a deep learning based aluminum profile defect detection network is proposed. In order to realize strong classification ability, the deep convolutional neural network is used to generate feature graphs at each stage. On this basis, the variable feature pyramid module (DFP) is proposed, and deformable convolution is added to the feature layer to make the feature layer have the ability to adapt to defect deformation. Based on these multi-layer features, the size of anchor box was customized by Kmeans clustering algorithm, and then the region extraction network (RPN) was used to generate the region of interest (ROIs). For each ROI, a detector, consisting of a classifier and a bounding box regressor, produces the final detection results. Finally, we used a defect detection dataset of aluminum profiles to train and evaluate our approach. Through two sets of ablation experiments, the effectiveness of the introduced module is proved. Finally, we reached 76.92 mAP in aluminum profile data set.
Research on object detection technology for human detection
Authors: Niu Yanling , Meng Zhaohui 
Published: June 2020

Abstract
There are many classical application scenarios in computer vision, and human detection in dense scenarios is a classical problem. Its goal is to find all the people in an image or each frame of an video, as well as their positions and sizes, and frame them in a rectangular box. In recent years, deep learning has been applied to the field of computer vision, laying a solid foundation for human detection based on deep learning. Aiming at the crowdhuman dataset, based on faster-RCNN and feature pyramid network (FPN), this paper studies the training effect. In order to detect small objects, a multi-scale feature fusion network (MFFN) was proposed for feature extraction. According to the serious occlusion of dataset, a double-branch structure was proposed to improve the detection accuracy. These two structures combine to form a new network, we call it MDN, namely multi-feature fusion and double-branch network. Experiments show that these methods are helpful to improve the detection accuracy.
A Study on the Influencing Factors of Conflicts Between Motor and Non-motor Vehicles in the Turning Lanes of Signalized Intersection
Authors: Wei Fulu , Ma Xueshi , Guo Yongqing , Li Qingyin , Cai Zhenggan 
Published: June 2020

Abstract
In order to reveal the influencing factors of the conflicts between the right-turning motor vehicles and the straight non-motor vehicles at the signalized intersection, the unmanned aerial vehicle (UAV) was used to collect the field data. And various methods such as manual statistics, video detection, deep learning were employed to analyze the data to obtain the traffic volume and the trajectory of pedestrians and vehicles. Pearson correlation analysis, 2-tailed significance test and other methods were used to examine the influences of traffic volume, speed, traffic control factors on traffic conflicts. The results show that (1) the correlation coefficient between the traffic volume of non-motor vehicles and the conflict frequency is 0.842 (close to 1), which indicates that the traffic flow of non-motor vehicle has a significant impact on the conflict frequency; (2) the speeds of right-turning vehicles at intersections have no significant impact on the conflict frequency (P = 0.071 > 0.05); (3) the setting of traffic markings is a key factor (P = 0.000 > 0.05), which can be seen in the traffic volume at intersections (not controlled by signals), and the improvement of traffic control elements can effectively reduce the frequency of conflict between motor and non-motor vehicles (MNV).