bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Modified Online Intelligent Method to Calibrate Radar and Camera Sensors for Data Fusing
Authors: Xu Guobin , Li Dongdong , Chen Xiangyang , Li Qiankun , Yin Jun 
Published: September 2021

Abstract
This paper introduces a modified online intelligent calibration method to calibrate radar and camera sensors in traffic scenarios. Radar and camera sensor calibration is to construct a one-to-one mapping between the radar coordinate system and the image pixel coordinate system. Based on the currently widely used artificial intelligence method (deep neural networks), camera can accurately recognize the target type. For the research and application of radar sensor and camera sensor fusion, the most important thing is to synchronize the sensor sampling time of the radar and the camera and space calibration. This paper emphasizes on spatial calibration. At present, the common calibration methods are the calibration method based on the internal and external parameters of the camera, the four-point calibration method, and the neural network calibration method. However, these three calibration methods have significant advantages and disadvantages. In the field of transportation, the disadvantages are even more prominent. This paper proposes a modified online intelligent calibration method for the application field of radar and camera sensors in traffic scenarios. We have proved through simulation experiments that this method is less time-consuming and manual operation while ensuring high-precision calibration performance.
Fast Arbitrary Shaped Scene Text Detection via Text Discriminator
Authors: Zeng Chengbin , Song Chunli 
Published: September 2021

AbstractRobust scene text detection is one of the difficult and significant challenges in the computer vision community. Most previous methods detect arbitrary-shaped text using complicated post-processing steps. In this paper, we propose a trainable fast arbitrary-shaped text detection network by using the text discriminator, sharing visual information among the two complementary tasks. Specifically, we extend PSENet [1] by adding a text discriminator to fuse multiple predictions for each text instance, rather than using complicated post-processing steps which are time consuming. The text discriminator shares visual information with text detection network, and thus can achieve much faster detection speed compared with PSENet, while maintaining a similar accuracy reported in PSENet. Furthermore, our text discriminator can reduce the false alarms effectively. Experiments on ICDAR 2017 MLT, ICDAR 2015, and ICDAR 2019 ART datasets demonstrate that the proposed approach can achieve nearly realtime detection speed while keeping state-of-the-art detection accuracy.
Facial Expression Recognition Based on Transfer Learning and SVM
Authors: Yang Lei , Zhang Haiqing , Li Daiwei , Xiao Fei , Yang Shanglin 
Published: September 2021

Abstract
The facial expression datasets always have a problem: data with small amount or large amounts of data but also with large noisy. Both problems will affect the facial expression recognition accuracy of the model. A transfer learning method for facial expression recognition is proposed by combining the Convolutional Neural Network (CNN) and Support Vector Machine (SVM). SVM have good performance on small data sets and CNN based on transfer learning have better ability of feature extraction for large noisy data set. This method reduces the training time of model and increase the facial expression recognition accuracy. The experimental results show that the accuracy of the proposed method on the CK+ and FER2013 data sets has reached 99.6% and 68.1%.
An Interval Reliability Demand Prediction Method Combined with XGBoost and D-S Evidence Theory in Film Preparation Period
Authors: Tang Zhongjun , Ni Lang 
Published: September 2021

Abstract
The lack of historical sales data and word-of-mouth information in the film preparation period, the few available variables and the uncertainty in the prediction process lead to the difficulty in predicting the total box office demand of films. To solve this problem, this paper constructed and verified the prediction method of interval reliability demand in the film preparation period, which combined XGBoost algorithm and D-S evidence theory. Firstly, the total box office interval was effectively divided according to the sample data of the training set, and XGBoost was used to complete the calculation of the reliability function value of the evidence variables. Then, the D-S evidence theory was used for information fusion to obtain the results of box office interval reliability fusion. Finally, the box office attribution was judged by the interval reliability, so as to realize the interval reliability demand prediction in the preparatory period. The validity of the proposed method was verified by selecting the data of Chinese films from 2017 to 2019, and it was compared with the classical predictive classification algorithm. The results showed that the method has higher prediction accuracy and better generalization ability.
Ship Short-Term Trajectory Prediction Based on RNN
Authors: Zhu Feixiang 
Published: September 2021

Abstract
In the application of ship supervision, ship collision avoidance, maritime search and rescue, the trajectory prediction of the target ship is a key issue. Given the ship navigation trajectory is easily affected by wind and waves, in order to improve the accuracy and efficiency of prediction, a ship short-term trajectory prediction method combining with Automatic Identification System (AIS) data and deep learning is proposed. Based on the preprocessing of AIS data, a recurrent neural network (RNN) model is constructed to achieve the accurate prediction of ship position information is realized. Through the real ship AIS trajectory data experiment, the results show that the method is practical and effective. Compared with the traditional backpropagation (BP) neural network processing method, it has certain advantages in prediction accuracy.
Network Traffic Anomaly Detection Method Based on CAE and LSTM
Authors: Guo Shuai , Liu Yao , Su Yang 
Published: September 2021

Abstract
This paper constructs a deep learning method for detecting network traffic anomalies to enhance the secure transmission of data in networks due to the complex, diverse and numerous types of anomalous traffic in current networks. The method combines multiple convolutional auto-encoders (Multi-CAE) with a long short-term memory network. The convolutional auto-encoders are obtained by combining stacked auto-encoders with convolutional layers, which can not only reduce feature loss but also effectively extract the spatial structure of samples. The use of Multi-CAE greatly improves the feature extraction capability, and combined with the long short-term memory network to extract temporal features, the effective features extracted in this paper are more comprehensive and less losses compared to the models used in other researches. A comparison of the loss values in the training of CAE (Convolutional Auto-Encoders) and SAE (Stacked Auto-Encoders) in the experiments shows that the loss values of CAE are about one-tenth lower than those of SAE, and the method consisting of Multi-CAE and LSTM for the USTC- TFC2016 dataset was trained with accuracy values up to 99.98%, and the precision, recall and f1-score parameters were also above 99%, outperforming other studies.
Study on Preprocessing Method of TCM Prescription Data in Data Mining
Authors: Zhang S Z , Yu Y R , Shen M Z 
Published: September 2021

Abstract
Traditional Chinese medicine (TCM) prescriptions have been developed for thousands of years. Data forms are diverse, content is discrete and missing, and there are many uncertainties due to cultural and regional differences. Therefore, it has brought some difficulties to the mining of TCM prescriptions. Data based on the 3108 prescriptions for the treatment of typhoid fever, for example, is given priority to with data cleaning and data transformation of data preprocessing, prescriptions combined with multiple functions, expounds the unqualified prescriptions data cleansing, drug name normalization, dose for solving the problems of the unification, the data structured method, make the processed data can be effectively mining, It provides a strong support for exploring the compatibility law of prescription and the development of new drugs.
A Novel Multi-objective Lion Swarm Intelligent Optimization Based on Cloud Model for Manufacturing Scheduling Problem
Authors: Dong Shengwei , Jiang Mingyan 
Published: September 2021

Abstract
With the continuous upgrading of industrial manufacturing, various artificial intelligence technologies have gradually been applied to the field of industrial production, including swarm intelligence optimization algorithms. Aiming at the flow shop scheduling problem (FSP) and job shop scheduling problem (JSP) in industrial production, which are NP-hard problems, we use a multi-objective optimization method to solve them. We proposed a novel multi-objective optimization named multi-objective lion swarm optimization based on cloud model mutation (CMOLSO). This new optimization algorithm, which is based on the Lion Swarm Optimization (LSO), introduces the concept of cloud model and cloud generator algorithm. The introduction of the cloud model mechanism can expand the search range of CMOLSO in high-dimensional multi-objective problems, make it avoid falling into local extremes, and improve its optimization accuracy. Compared with the traditional multi-objective optimization, the new algorithm CMOLSO achieves better performance, and it can effectively solve the scheduling problems in practice.
Image Super Resolution Reconstruction Algorithm Based on Convolution Neural Network
Authors: Zhao Feng , Hu Zhifeng 
Published: September 2021

Abstract
Aiming at the characteristics of large amount of remote sensing image data, large terrain fluctuations and wide coverage, this paper proposes a method for super-resolution reconstruction of remote sensing image. This method combines dense network and deep back projection network to form dense projection. The unit forms a deep dense projection network, which solves the problems of insufficient texture, loss of image details and training difficulty in the super-resolution reconstruction of remote sensing image. The experimental results show that on multiple remote sensing image datasets, compared other traditional methods, the PSNR and SSIM are significantly improved, and reconstructed remote sensing image texture signs and details are more abundant.
Incremental Attribute Reduction Based on Knowledge Granularity under Incomplete Data
Authors: Zhong Qiangqiang , Wang Lei , Yang Wen , Liu Chao 
Published: September 2021

Abstract
Traditional attribute reductions can’t be directly applied to miss attribute values and dynamic changes data. Therefore, how to dynamically update attribute reductions efficiently when incomplete data changes dynamically is great research value. For the change of the object, using matrix’s simple and clear characteristics, the paper focus on the incremental attribute reduction algorithm in matrix form of knowledge granularity. Firstly, the knowledge granularity is obtained by matrix which uses to measure the uncertainty of the system, then calculating the attribute importance, and using the attribute importance to devise a static algorithm, and then analyzing incremental matrix form when the object increases. The comparative experiments show that the incremental algorithm can effectively and efficiently update the result of attribute reduction as the object changes.
Heuristic Attribute Reduction Based on Neighborhood Knowledge Granularity
Authors: Yang Wen , Wang Lei , Liu Chao , Zhong Qiangqiang 
Published: September 2021

Abstract
The classical attribute reduction algorithm is not suitable for the neighborhood decision information system of numerical attribute. Since any feature subset in real feature space can be approximated by neighborhood information particles, the concept of knowledge granularity is extended to neighborhood rough set from the perspective of the granularity computation. In this paper, knowledge granularity, attribute importance and heuristic algorithm based on the granularity of knowledge in neighborhood rough set are studied from the point of view of neighborhood relation matrix. The feasibility of heuristic algorithm is analyzed on UCI data sets. Experiments achieve better classification accuracy and lower attribute reduction results than the existing algorithms.
Computation of Differential in Strong Product Network of Paths via Recursive Iteration
Authors: Chen Yuanmei , Ren Haizhen , Zhang Lei , Zhao Yang 
Published: September 2021

Abstract
The set differential can be attributed to some influence maximization problems, which are widely used. For example, it is a basic algorithm problem of information diffusion in social networks. In 2004, Lewis et al. extended this concept to the graph, defined the differential of graph. The present article mainly studies the differential in strong product network of paths. Using the basic block recursive iterative method, we obtain closed form formulas for the differential on the strong product network of paths. The method used here is also effective for the differentials in square graphs.
Gated Fusion of Infrared and Visible Light Images Based on CNN
Authors: Liu Dong , Yang Huihua , Wang Lemeng , Shao Yuying , Peng Peng 
Published: September 2021

Abstract
As a new research direction, fusion image technology has attracted more and more attention in many fields. Among them, infrared image and visible image, the two kinds of multimodal data have strong complementarity, the fusion image of the two modes contains not only the radiation information of infrared image, but also the texture detail information of visible image. In this paper, a convolutional neural network-based encoding-fusing-decoding network model structure is used. In the encoding stage, Dense Block, which has the advantage of feature extraction, was adapted to extract the image features. In the fusion stage, four fusion methods were compared and analyzed, and the gated fusion was selected as the main method of fusion layer. In the decoding stage, RDB (Residual Dense Blocks) was used to restore the fused features to the fused image. The fusion image based on this method is sensitive to temperature characteristics and has a better performance in image quality. The fused image has a high contrast, a relatively smooth fusion effect, and the overall visual effect is more natural.
Sliding Mode Variable Structure Chaos Control for Propulsion Motor of Deep Sea Robot
Authors: Meng Junhong 
Published: September 2021

Abstract
As the deepwater robot’s dynamic thruster system, the brushless thruster system’s performance will influence the whole system reliability, stabilization, safety. The brushless thruster system will appear chaotic phenomena when its system structure and parameter match badly. The phenomena will lead the deepwater robot out of control, even involved by ocean wave. Aiming the chaos phenomenon of propulsion motor of deepwater robot, this paper proposes the application of sliding mode variable structure control theory to chaos control. The sliding mode variable structure chaotic control of brushless propulsion motor for deep-sea robot is designed, and the controller is designed. The feasibility and effectiveness of the chaotic control are simulated, analyzed and verified. The research result proves that, the gradation sliding mode variable structure using in control of the deepwater robot thruster motor chaotic motion are feasible and effective, i.e. the thruster motor can research the stabilization state from chaotic state and the procedure of control is related to the controller structure, parameter, and control time. And for the example system, the exponent gradation sliding mode variable stature control effect is the better.
Research on Optimization Control Strategy of Vehicle-Road-Grid Charging and Exchanging Based on User Demand Guidance
Authors: Zhang Yuanxing , Li Bin , Cao Boyuan , Li Taoyong , Huang Xingde , Yang Xingang 
Published: September 2021

Abstract
Considering the time-space uncertainty of electric vehicle charging and changing demand, the uneven distribution and random diversity of charging stations, the big data cloud network is used to realize the real-time information interaction between charging and changing power station and electric vehicle. Based on the user demand orientation, an intelligent charging and changing optimization control strategy of “Vehicle-Road-Grid” integrating cloud vehicle operation data is proposed. Under the discrete electric vehicle charging demand, this strategy quickly organizes the supply information around the discrete point. On the basis of the minimum energy consumption between the electric vehicle charging request point and the supply point, considering the multi-dimensional factors such as the optimal interests of the electric vehicle users, the shortest waiting time for charging and changing, and the shortest driving distance for charging and changing, the multi-objective optimal configuration strategy is realized. The simulation results show that the strategy can stably select the charging and swapping scheme that best meets the actual needs of users, effectively improve the charging and swapping service experience of users, and enhance the adhesion and participation of users.
Research on the Method of Reusing Injection Process Knowledge Based on CBR
Authors: Chen Haowei , Zhan Hongfei , Wang Rui , Yu Junhe 
Published: September 2021

Abstract
The injection molding process is the most critical factor affecting the quality and efficiency of injection molded products. The traditional process setting mainly relies on the operator’s limited experience and simple calculation formulas to make repeated “Try”, which has the problems of long cycle, high cost, and difficulty in guaranteeing product quality. To this end, this article takes the injection molding process as the research object, and proposes a CBR-based injection process knowledge reusing method. This method models the historical cases of injection molding from seven dimensions to form an injection case library. Through the weighted calculation of the similarity algorithm, the parameters, context and auxiliary knowledge of the case with the highest similarity are pushed to the staff to provide references for the staff to determine the corresponding process parameters and improve work efficiency.
Optimal Design of Remanufacturing Process for Injection Molding Machine Parts
Authors: Chen Chong , Zhan Hongfei , Wang Rui , Yu Junhe 
Published: September 2021

Abstract
The remanufacturing process of an injection molding machine is a key link that affects the remanufacturing process of an injection molding machine. Uncertainties such as damage form and degree of damage will increase the difficulty of remanufacturing repair process technology and process parameter selection. To this end, this article is based on the failure characteristics of injection molding machine parts, first establishes the failure feature fault tree extraction model of used parts, and uses damage to describe the failure characteristics such as wear, fracture and corrosion. This paper establishes a mathematical model of damage, and then uses Fuzzy comprehensive evaluation method is used to establish a quantitative scoring table for the failure characteristics of waste parts of injection molding machines. Afterwards, a directed graph is used to indicate the priority relationship between the failure feature processing elements, and finally the genetic-neural network algorithm is used to optimize the remanufacturing process route in combination with the remanufacturing time and cost to realize the optimized design of the remanufacturing process of the injection molding machine parts.
Research on the Evolutionary Development Method of Software Service Based on Trusted Agent
Authors: Gao Tilei , Yang Ming , Jia Xiaohui , Zhang Tao 
Published: September 2021

Abstract
The factors that affect the trustworthiness of complex software system include not only the trustworthiness of subsystem services, but also the selection and construction process of subsystem services. According to the selection and construction process of sub services, this paper has proposed the concept of trusted agent. The trusted agent contains the key modules such as trustworthiness measurement and selection of software services, which can filter external software services and send the screened high-quality results to the system, so as to optimize the construction process of complex system. The method of software system construction based on trusted agent not only ensures the trustworthiness of the construction process, but also reduces the degree of manual participation, and improves the efficiency and automation of the software system construction process.
Research on Test Method of Power Sensor Battery Considering the Influence of Air Flow
Authors: Zhou Wen , Jiang Yi , Zhang Jing , Yu Chunlai , Zhu Hao 
Published: September 2021

Abstract
The reliability of sensor is an important index to determine the outdoor operation efficiency of power sensor, and the reliability detection of power sensor is an important means to ensure the reliability of network equipment. Based on the analysis of the thermal characteristics and thermal behavior of the battery, this paper studies the current detection methods of the low-temperature battery of the power sensor, and proposes a reliability test method of the power sensor considering the influence of outdoor airflow. Through multi-physical field simulation and analysis, the key parameters of relevant experiments are obtained, and the key test parameters are given.
Research on the Control Method of Short-term Transmission and Distribution Price Level
Authors: Zhang Xuejun 
Published: September 2021

Abstract
In order to ensure the orderly competition in the power market, through a variety of measures to reduce the electricity cost of enterprises and users, and improve the service quality of power enterprises, this paper puts forward the research on the control method of the total level of short-term transmission and distribution price. Combined with the principle of “three sets and five big”, the power grid marketing system is established, the main responsibilities of customer service center are assigned, and the main factors affecting pricing are extracted by bird swarm algorithm. Combining the rate of return on investment with the price cap regulation mode, a growth type short-term total level regulation mode of transmission and distribution electricity price is constructed. Simulation results show that the proposed control method has high accuracy in forecasting electricity demand and can give full play to the role of market price regulation.
Simulation Design of Spot Welding of Body Structure and Transfer Robot Workstation
Authors: Gao Jun , Han Chang , Liu Yicai 
Published: September 2021

Abstract
Application of virtual simulation technology to manufacturing industry today can shorten the development cycle, reduce costs and improve the design accuracy. This paper takes the spot welding workstation of auto body structure as the research object. Firstly, the production demand of spot welding of auto body structure is analyzed, the structure and production characteristics of spot welding robot workstation are analyzed, and the appropriate equipment is selected for the layout of spot welding robot workstation. Secondly, the work path of the robot is planned reasonably, and the planning of the robot trajectory can facilitate the writing of program instructions later. In the design process, three-dimensional modelling software is used to model some components in the workstation. Finally, through the use of simulation software, the welding path optimization and off-line programming of the whole spot welding robot workstation are simulated. The results show that the designed robot workstation can meet the use requirements.