2019 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献_第2页

Deconstruction of Perception Verbs: Structure and Value 感知动词的解构:结构与价值

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949202

Yan Liu, Zhengjun Lin, Ying Pan

引用次数: 0

Intelligent and Disaster Prevention Hard Hat Based on AIOT and Speeches Recognition 基于AIOT和语音识别的智能防灾安全帽

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949271

Feng-Long Huang, Zih-Zrong Liao, Tsun-Hong Wang, Qiming Chen, Ting-Hua Wu, Ching-Hsiang Chang

{"title":"Intelligent and Disaster Prevention Hard Hat Based on AIOT and Speeches Recognition","authors":"Feng-Long Huang, Zih-Zrong Liao, Tsun-Hong Wang, Qiming Chen, Ting-Hua Wu, Ching-Hsiang Chang","doi":"10.1109/ICMLC48188.2019.8949271","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949271","url":null,"abstract":"Technology always comes from human nature and life safety is more important than anything. In recent years natural disasters and work safety accidents happened frequently. So we put what have learned into practice. With the technology of IoT, our team has developed the Intelligent and Disaster Prevention Hard Hat, which improve the life safety more than tradition hard hat. Different from traditional Hard Hats, we combine Raspberry 3 and various sensors to transform into an Intelligent and Disaster Prevention Hard Hat, with Global Positioning System and MQ2 toxic gas detection, which is the best to apply in a variety of disaster situations. The main control terminal is built with a Responsive Web Design (RWD), which can change the webpage frame with various devices to provide the best visual effect. With 21 kinds of multi-hazard instant alarms, users can instantly know whether there will be a secondary disaster in near future, and combine voice and face recognition, etc. The Hakka's speech recognition is included for communication between client and backsite center.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117163966","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: 3

Person Re-Identification via Feature Representation Learning Based on Verification Sample Constrain 基于验证样本约束的特征表示学习的人物再识别

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949262

Ruifeng Zhao, Ajian Liu, Yanyan Liang, Haozhi Huang

{"title":"Person Re-Identification via Feature Representation Learning Based on Verification Sample Constrain","authors":"Ruifeng Zhao, Ajian Liu, Yanyan Liang, Haozhi Huang","doi":"10.1109/ICMLC48188.2019.8949262","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949262","url":null,"abstract":"In person re-identification (ReID) task, the variance between the samples are quite large, and there is no standard sample as a comparison. The current common method is to implement it as a classification task, which can get better results than the classical methods in verification task such as contrastive loss. However, the identification loss used in classification task only seeks the boundary of the classification, the intra-class distance between samples is still large so that it is insufficient for ReID task. In this paper, we consider to overcome these difficulties by proposing a joint loss with an identification loss and constrains of modeling the Euclidean distances between samples and their corresponding eigen data which is constructed by Principal Component Analysis method (PCA), we call it EigenPerson. The entire loss is formed in a linear combination. This work is mainly motivated by the center loss for face recognition problems, of which regularizations are restricted by a simultaneously learned center. We substitute the center with EigenPerson which we constructed offline as an auxiliary training sample. The learned model with our proposed method is evaluated on the benchmark of Market1501 and CUHK03 and achieve comparable results to those methods proposed in the same period.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129516160","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

Time Series Forecasting Using Optimized Rolling Grey Model 基于优化滚动灰色模型的时间序列预测

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949310

M. Yeh, Hung-Ching Lu, Ti-Hung Chen

引用次数: 0

Depth Image-Based Obstacle Avoidance for an In-Door Patrol Robot 基于深度图像的室内巡逻机器人避障方法

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949186

Zhenghan Jiang, Qiangfu Zhao, Yoichi Tomioka

{"title":"Depth Image-Based Obstacle Avoidance for an In-Door Patrol Robot","authors":"Zhenghan Jiang, Qiangfu Zhao, Yoichi Tomioka","doi":"10.1109/ICMLC48188.2019.8949186","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949186","url":null,"abstract":"Image-based obstacle avoidance has been studied for decades. One weak point of image-based approaches is that the performance usually depends on the lighting condition. That is, the performance can be very poor in dark environments. In this research, we investigate the possibility of the depth image-based approach for full-time indoor patrolling. As the first step, we consider a 3-class problem. Each depth image is classified as “danger” if some obstacle is too close, as “notice” if the obstacle is close, and as “normal” if there is no obstacle in the vicinity. The label of each depth image is defined based on the RGB image captured at the same time, and an AlexNet, which is a well-trained convolutional neural network, is retrained via transfer learning, and used for classification. In our primary experiment, we collected 102,776 image data in the Research Quadrangle of the University of Aizu. Test results show that the performance of the depth image-based approach is good during both day and night, and in most cases, it is better than the RGB image-based approach. This result can provide new insights when designing more practical full-time patrol robots.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128767417","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: 3

Prediction Stock Price Based on Different Index Factors Using LSTM 基于不同指标因子的LSTM股票价格预测

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949162

Chun Yuan Lai, R. Chen, R. Caraka

引用次数: 14

Depressive Symptoms and Functional Impairments Extraction From Electronic Health Records 从电子健康记录中提取抑郁症状和功能障碍

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949199

You-Chen Zhang, Chung-Hong Lee, Tyng-Yeu Liang, Wei-Che Chung, Kuei-Han Li, Cheng-Chieh Huang, Hong-Jie Dai, Chi-Shin Wu, C. Kuo, Chu-Hsien Su, Horng-Chang Yang

引用次数: 1

Hardware-Software Codesign of Histogram of Oriented Gradients on Heterogeneous Computing Platform 异构计算平台上定向梯度直方图的软硬件协同设计

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949276

Yuan-Kai Wang, Hung-Yu Chen, Kuan-Yu Chen, Shih-Yu Huang

引用次数: 0

Head Motion Recognition Using a Smart Helmet for Motorcycle Riders 摩托车手头部运动识别的智能头盔

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949319

K. Wong, Yi-Chung Chen, Tzu-Chang Lee, Shengmin Wang

引用次数: 10

A Concept Framework of Using Education Game With Artificial Neural Network Techniques to Identify Learning Styles 利用教育游戏与人工神经网络技术识别学习风格的概念框架

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949311

Chih-Hung Wu

引用次数: 3