2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献

Adaptive indirect inverse control for nonlinear systems actuated by smart-material actuator* 智能材料致动器非线性系统的自适应间接逆控制

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237877

Ruijing Jing, Y. Yao, Cheng Zhong, Yong Mu, Tao Wang, Xiuyu Zhang

引用次数: 0

Observer-based Adaptive Fuzzy Control for Uncertain Nonlinear time-delay systems 基于观测器的不确定非线性时滞系统自适应模糊控制

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237875

Jipeng Zhao, Shaocheng Tong, Yong-ming Li

引用次数: 0

Two-Stream Convolutional Network Extracting Effective Spatiotemporal Information for Gait Recognition 双流卷积网络提取有效的时空信息用于步态识别

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.244101

Yijun Huang, Yaling Liang, Zhisong Han, Minghui Du

引用次数: 4

Video-Based Traffic Flow Monitoring Algorithm for Single Phase Position at An Intersection 基于视频的交叉口单相位置交通流监控算法

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237873

Ke Yang, Ya-Xin Zhou, Shiyuan Han, Ya Fang, Xiao-Yue Ma, Jin Zhou, Kang Yao

引用次数: 1

Generative Method of Self-Organized Swarm with Designated Global Leader 具有指定全局领导者的自组织群体的生成方法

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237878

Dengxiu Yu, C. L. P. Chen, Gang Lu

引用次数: 1

Analysis of Customer Segmentation Based on Broad Learning System 基于广义学习系统的客户细分分析

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237870

Zhenyu Wang, Y. Zuo, Tie-shan Li, C. L. P. Chen, K. Yada

{"title":"Analysis of Customer Segmentation Based on Broad Learning System","authors":"Zhenyu Wang, Y. Zuo, Tie-shan Li, C. L. P. Chen, K. Yada","doi":"10.1109/SPAC49953.2019.237870","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237870","url":null,"abstract":"In the field of retail industry and marketing, identifying customer segments is one of the most important tasks. A meaningful segmentation is able to help the managers to enhance the quality of products and services for the targeting segments. Most of traditional methods used POS data to classify the customer loyalty as “heavy” segment while others are belonging to “light” segment. Based on the previous studies, this paper presents three improvements. Firstly, in addition to customer purchasing behavior, we also include RFID (Radio Frequency IDentification) data, which can accurately represent the consumers' in-store behavior. Secondly, this paper uses broad learning system (BLS) to analyze the consumer segmentation. BLS is one of the most state-of-the-art machine learning techniques, and quite efficient and effective for classification tasks. Thirdly, the customer behavior data used in this paper are collected from a real-world supermarket in Japan. We also consider the customer segmentation as a multi-label classification problem based on both of POS data and RFID data. In the experiment, the results were compared with other popular classification models, such as neural network and support vector machine, and it was found that BLS greatly reduced training time while guaranteeing accuracy.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126742252","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: 4

Prediction of Ship Fuel Consumption Based on Broad Learning System 基于广义学习系统的船舶油耗预测

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237871

Xinyu Li, Yongjie Zhu, Y. Zuo, Tie-shan Li, C. L. P. Chen

{"title":"Prediction of Ship Fuel Consumption Based on Broad Learning System","authors":"Xinyu Li, Yongjie Zhu, Y. Zuo, Tie-shan Li, C. L. P. Chen","doi":"10.1109/SPAC49953.2019.237871","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237871","url":null,"abstract":"With the increasing attention of IMO to green shipping, and the increasingly strict restrictions on fuel regulatory and operating costs of shipping enterprises, no matter from the perspective of energy conservation and environmental protection or operating economy, ships should be put into actual operations in the future with lower fuel consumption and less emissions. At present, the researches and applications of maritime big data are mostly concentrated in the field of shipping schedules and cargoes. However, there are few studies focusing on the ship energy management. This paper proposes a fuel consumption prediction model based on the Broad Learning System (BLS) and the Danish RO-RO ship Ms Smyril is taken as the case ship. With the measured operation data, the fuel consumption prediction model of the ship is constructed by using data analysis and machine learning. Finally, compared with the existing fuel consumption prediction methods, it is proved that the prediction effects of this method are better. The rapidity of BLS can be used for real-time prediction of fuel consumption. When there are some mechanical failures of the ship which may cause the abnormal fuel consumption of the ship, it can help the engineers and the deck officers response quickly and address problems in time. It can also provide decision-making basis for navigation optimization.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122114020","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: 6

A novel neural-network gradient optimization algorithm based on reinforcement learning 一种基于强化学习的神经网络梯度优化算法

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237884

Lei Lv, Ziming Chen, Zhenyu Lu

引用次数: 1

H-infinity Control for Nonlinear Systems Using Event-triggered Method 基于事件触发法的非线性系统h∞控制

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.243773

Wei Zhang, Yong-ming Li

引用次数: 0

Human Outline Reconstruction in Depth Prediction 深度预测中的人体轮廓重建

2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2019-12-01 DOI: 10.1109/SPAC49953.2019.237867

Xinyue Li, Samuel Cheng

{"title":"Human Outline Reconstruction in Depth Prediction","authors":"Xinyue Li, Samuel Cheng","doi":"10.1109/SPAC49953.2019.237867","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237867","url":null,"abstract":"Fully Convolutional Residual Network (FCRN) has already become one of the most significant models for depth map prediction. It has achieved high quality results but has problem in reconstructing the human outline. On this basis, we present our method, the purpose of which is to reinforce human reconstruction in depth prediction. Our main idea is to merge Mask R-CNN with FCRN, so we present our modified FCRN. Our modified FCRN, which can also be regarded as an improvement of FCRN through Mask R-CNN, is designed on the basis of attention mechanism and optimized on the basis of transfer learning. It needs to work with the original FCRN. For a single RGB image, first of all, Mask RCNN receives it as input and generates the mask images for the “person” instances. Then, the input image and the mask image are fed jointly to our modified FCRN which can give a new result in generating the depth map. After that, we present a depth filter to combine the raw result given by the original FCRN with the new result given by the modified FCRN. Our final result is generated through the depth filter. Both the image result and the metric result given by our experiment can illustrate that our method has the ability to improve the performance of FCRN in human outline reconstruction through Mask R-CNN.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124102097","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