2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE)最新文献

Intelligent Light Design Based on Arduino 基于Arduino的智能照明设计

2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) Pub Date : 2020-09-27 DOI: 10.1109/ICISCAE51034.2020.9236885

Z. Wu, T. Zhou

引用次数: 0

Face Gender Recognition based on Face Recognition Feature Vectors 基于人脸识别特征向量的人脸性别识别

2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) Pub Date : 2020-09-27 DOI: 10.1109/ICISCAE51034.2020.9236905

Yongjing Lin, Huosheng Xie

{"title":"Face Gender Recognition based on Face Recognition Feature Vectors","authors":"Yongjing Lin, Huosheng Xie","doi":"10.1109/ICISCAE51034.2020.9236905","DOIUrl":"https://doi.org/10.1109/ICISCAE51034.2020.9236905","url":null,"abstract":"Automatic facial gender recognition is a widely used task in the field of computer vision, which is very easy for a human, but very challenging for computers. In this paper, a face gender classification algorithm based on face recognition feature vectors is proposed. Firstly, face detection and preprocessing are performed on the input images, and the faces are adjusted to a unified format. Secondly, the face recognition model is used to extract feature vectors as the representation of the face in the feature space. Finally, machine learning methods are used to classify the extracted feature vector. Meanwhile, this study uses t-distributed Stochastic Neighbor Embedding (T-SNE) to visualize the face recognition feature vectors to verify the effectiveness of the face recognition feature vectors on the issue of gender classification. The proposed method has achieved a recognition rate of 99.2% and 98.7% on the FEI dataset and the SCIEN dataset, respectively. Besides, it also achieves a recognition rate of 97.4% on the Asian star face dataset, outperforming existing methods, which shows that the proposed method is helpful for the research of facial gender.","PeriodicalId":355473,"journal":{"name":"2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"338 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115529546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

A Multi-Strategy Elite Ant System Algorithm for Vehicle Routing Problem with Time Window 带时间窗车辆路径问题的多策略精英蚁群算法

2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) Pub Date : 2020-09-27 DOI: 10.1109/ICISCAE51034.2020.9236790

Xu Yan, Licong Tan, Jinyong Chen, Bi Li

引用次数: 0

The Method of Functional Safety Validation Test of AEBS Based on Fault Injection 基于故障注入的AEBS功能安全验证试验方法

2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) Pub Date : 2020-09-27 DOI: 10.1109/ICISCAE51034.2020.9236817

Q. Kongjian, Zhang Tong, Guo Kuiyuan, Z. Hongwei, Wang Yu, Chen Haoxin

引用次数: 0

2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) Pub Date : 2020-09-27 DOI: 10.1109/ICISCAE51034.2020.9236843

Peng Jiang, W. Zhou, Yu-lian Li

{"title":"A Similarity Calculation Model of Weak Link Web Pages Based on Keyword Location Influence","authors":"Peng Jiang, W. Zhou, Yu-lian Li","doi":"10.1109/ICISCAE51034.2020.9236843","DOIUrl":"https://doi.org/10.1109/ICISCAE51034.2020.9236843","url":null,"abstract":"Weakly linked web pages are a group of related pages with weak connections. With the advent of the self-media era, the related information links between various platforms have become weaker, the calculation of the similarity of weakly linked web pages is becoming more and more important. The current method for calculating the similarity of weakly linked webpages is to combine the similarity of webpage content and the relevance of links, and then simply add the results. There is a large deviation in the calculation results of this method. To this end, this paper proposes a similarity calculation model for weak link webpages based on the influence of keyword position (WIWKLI). First, we start with the division of web pages, through URL matching deduplication, keyword similarity calculation to form a keyword sequence of web pages, then through the improvement of the RNN model to calculate the positional influence of keywords, so as to realize the precise definition of the webpage and form a link pseudo-network between related webpages accordingly, Finally, the similarity between web pages is calculated through the pseudo-network. By applying this calculation model to the focused crawler experiment, Experimental results show that the method proposed in this paper can calculate the similarity of weakly linked web pages, and the accuracy and page relevance have been improved to a certain extent.","PeriodicalId":355473,"journal":{"name":"2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129096040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Generation Method of Basketball Continuous Pitching Action Based on Multi-Objective Machine Vision 基于多目标机器视觉的篮球连续投球动作自动生成方法

2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) Pub Date : 2020-09-27 DOI: 10.1109/ICISCAE51034.2020.9236810

Li Qiaomei, Xu Yi

{"title":"Automatic Generation Method of Basketball Continuous Pitching Action Based on Multi-Objective Machine Vision","authors":"Li Qiaomei, Xu Yi","doi":"10.1109/ICISCAE51034.2020.9236810","DOIUrl":"https://doi.org/10.1109/ICISCAE51034.2020.9236810","url":null,"abstract":"In order to improve the effectiveness of basketball continuous pitching motion guidance, the image characteristics of basketball continuous pitching motion are analyzed with image processing method, and an automatic generation method of basketball continuous pitching motion based on multi-objective machine vision is proposed. The method comprises the following steps of: establishing a shape feature point feature matching model of a basketball continuous pitching motion fuzzy image; extracting posture key action points of the basketball continuous pitching motion fuzzy image by combining a spectral feature detection method; carrying out adaptive enhancement processing on the basketball continuous pitching motion fuzzy image by adopting a shape feature segmentation method; and obtaining sub-band pixel feature points of the basketball continuous pitching motion fuzzy image by combining a point tracking matching method and a feature extraction method. The fuzzy image of basketball continuous pitching motion is enhanced to improve the expression ability of posture information of fuzzy noise points in the image. The significant contrast enhancement processing of the fuzzy image of basketball continuous pitching motion is carried out through the point tracking recognition method, and the multi-objective machine vision features of the fuzzy image of basketball continuous pitching motion are extracted to realize automatic generation and accurate recognition of the fuzzy image of basketball continuous pitching motion. The simulation results show that the output peak signal-to-noise ratio automatically generated by using this method for basketball continuous pitching motion is higher, and the image recognition ability is better. Therefore, it is necessary to improve the performance of the system.","PeriodicalId":355473,"journal":{"name":"2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120845453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Spatio-Temporal Context Tracking Method Blended with LBP Texture Feature 一种融合LBP纹理特征的时空背景跟踪方法

2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) Pub Date : 2020-09-27 DOI: 10.1109/ICISCAE51034.2020.9236874

Guangshuai Liu, Xurui Li, Si Sun

{"title":"A Spatio-Temporal Context Tracking Method Blended with LBP Texture Feature","authors":"Guangshuai Liu, Xurui Li, Si Sun","doi":"10.1109/ICISCAE51034.2020.9236874","DOIUrl":"https://doi.org/10.1109/ICISCAE51034.2020.9236874","url":null,"abstract":"A spatio-temporal context (STC) tracking method blended with LBP texture feature is proposed with regard to the problem that it's hard to track a target effectively by a conventional STC tracking method where target background mutation, shading or shape change takes place during tracking. First, the similarity in the texture histogram of the target area between the first image frame and each image frame behind it is calculated to solve the problem of central position shift as a result of the background mutation of the tracked target. The similarity in the texture histogram of the target area between two adjacent frames is then calculated to solve the problem of shading that happens to the tracked target. Finally, a judgment is made as to whether or not the STC update coefficient is changed and the central position coordinates of the tracked target offset, based on the relation between the worked-out similarities in texture histogram and the threshold. Shape change does not alter local texture information about the tracked target and the STC tracking method blended with LBP textural feature has thus good robustness to shape change. Experimental results show that the method in this paper sees a 73.06% increase in average rate of successful tracking and an 89.94-pixel decrease in average error in target's central position, compared with the original STC tracking method. The method in this paper allows more stable tracking of a target where target background mutation, shading or shape change takes place.","PeriodicalId":355473,"journal":{"name":"2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114616166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Method for Leaf Recognition Based on D-LLE and Polar Coordinate Feature Extraction 基于D-LLE和极坐标特征提取的叶片识别新方法

2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) Pub Date : 2020-09-27 DOI: 10.1109/ICISCAE51034.2020.9236850

Li Yang, J. Ding, Liheng Jiang, Renrui Han, Yingchun Bi, Shangzhi Zheng

引用次数: 1

Design of Component Fault Recovery of Integrated Control System Based on Log Information 基于日志信息的综合控制系统组件故障恢复设计

2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) Pub Date : 2020-09-27 DOI: 10.1109/ICISCAE51034.2020.9236830

Hongyan Zhan, Xinyu Han, Kai Zhang, Longli Tang, Lijin Wu

引用次数: 0

Research on Optimization of Customer Value Segmentation Based on Improved K-Means Clustering Algorithm 基于改进k均值聚类算法的客户价值细分优化研究

2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) Pub Date : 2020-09-27 DOI: 10.1109/ICISCAE51034.2020.9236867

Xiaochuan Pu, Chang-xin Song, Junli Huang

引用次数: 4