2020 International Conference on Pervasive Artificial Intelligence (ICPAI)最新文献_第2页

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2020 International Conference on Pervasive Artificial Intelligence (ICPAI) Pub Date : 2020-12-01 DOI: 10.1109/icpai51961.2020.00003

引用次数: 0

Ridge-furrow Detection in Glycine Max Farm Using Deep Learning 基于深度学习的Glycine Max农场垄沟检测

2020 International Conference on Pervasive Artificial Intelligence (ICPAI) Pub Date : 2020-12-01 DOI: 10.1109/ICPAI51961.2020.00041

Shiow-Jyu Lin, Qi Wun Chen, Jian-Jun Chen

引用次数: 1

Sensor-based Badminton Stroke Classification by Machine Learning Methods 基于传感器的羽毛球击球分类机器学习方法

2020 International Conference on Pervasive Artificial Intelligence (ICPAI) Pub Date : 2020-12-01 DOI: 10.1109/ICPAI51961.2020.00025

Ju-Yi Lin, Chia-Wei Chang, Tsì-Uí İk, Y. Tseng

引用次数: 4

Analysis of the Eye-hand Coordination with Reaction Time in Different Exercise Stimulates 不同运动刺激下手眼协调与反应时间的关系分析

2020 International Conference on Pervasive Artificial Intelligence (ICPAI) Pub Date : 2020-12-01 DOI: 10.1109/ICPAI51961.2020.00024

Po-Hua Chen, H. Shih

引用次数: 0

The Risk Classification of Ergonomic Musculoskeletal Disorders in Work-related Repetitive Manual Handling Operations with Deep Learning Approaches 基于深度学习方法的重复性人工操作中人体工学肌肉骨骼疾病的风险分类

2020 International Conference on Pervasive Artificial Intelligence (ICPAI) Pub Date : 2020-12-01 DOI: 10.1109/ICPAI51961.2020.00057

Yu-Wei Chan, Tzu-Hsuan Huang, Y. Tsan, Wei-Chen Chan, Chih-Hung Chang, Yin-Te Tsai

{"title":"The Risk Classification of Ergonomic Musculoskeletal Disorders in Work-related Repetitive Manual Handling Operations with Deep Learning Approaches","authors":"Yu-Wei Chan, Tzu-Hsuan Huang, Y. Tsan, Wei-Chen Chan, Chih-Hung Chang, Yin-Te Tsai","doi":"10.1109/ICPAI51961.2020.00057","DOIUrl":"https://doi.org/10.1109/ICPAI51961.2020.00057","url":null,"abstract":"The injury resulted from the repetitive and load-bearing works is the most frequent work-related musculoskeletal disorders (WMSD) or cumulative trauma disorders (CTD). It comes from the overload of repetitive load-bearing actions, which resulting in fatigue, inflammation, even injuries of musculoskeletal system. According to the annular report of Labor Insurance Bureau in Taiwan, WMSD is up to 85-88% payment. Thus, the aim of this study is to evaluate the risk of WMSD during work by using the simple, quick, and correct methods by using the deep learning algorithms. In the proposed research method, after collection the videos of hand repeated movements, the ergonomic injuries are evaluated by using the 2D human pose estimation method, which is based on the Key Indicator Method - Manual Handling Operations (KIM-MHO). Then, a model of predefined classifications through deep learning approaches for manual handling operating tasks is built. The analysis results show that the classification accuracy is more than 80%, compared with the doctor's judgment. The goal of this study is to get the accuracy up to 90%, so as to achieve fast and accurate assistance for deciding the risk of ergonomics, and immediately give proper feedback.","PeriodicalId":330198,"journal":{"name":"2020 International Conference on Pervasive Artificial Intelligence (ICPAI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128116809","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: 1

A Light Weight Multi-Head SSD Model For ADAS Applications 用于ADAS应用的轻量级多磁头固态硬盘模型

2020 International Conference on Pervasive Artificial Intelligence (ICPAI) Pub Date : 2020-12-01 DOI: 10.1109/ICPAI51961.2020.00042

Chun-Yu Lai, Bo-Xun Wu, Tsung-Han Lee, V. M. Shivanna, Jiun-In Guo

引用次数: 4

Auto Curation on FaceNet Embeddings with Gamma and Gaussian Distribution to Predict Model Performance in Actual Industrial Deployment 基于Gamma和Gaussian分布的FaceNet嵌入的自动管理以预测实际工业部署中的模型性能

2020 International Conference on Pervasive Artificial Intelligence (ICPAI) Pub Date : 2020-12-01 DOI: 10.1109/ICPAI51961.2020.00016

Michael Mu-Chien Hsu, Richard Jui-Chun Shyur

引用次数: 0

Improving Pair Trading Performances with Structural Change Detections and Revised Trading Strategies 利用结构变化检测和修正交易策略改善配对交易绩效

2020 International Conference on Pervasive Artificial Intelligence (ICPAI) Pub Date : 2020-12-01 DOI: 10.1109/ICPAI51961.2020.00027

Hao-Han Chang, Tian-Shyr Dai, Kuan-Lun Wang, Chao-Hsien Chu, Jun-Zhe Wang

{"title":"Improving Pair Trading Performances with Structural Change Detections and Revised Trading Strategies","authors":"Hao-Han Chang, Tian-Shyr Dai, Kuan-Lun Wang, Chao-Hsien Chu, Jun-Zhe Wang","doi":"10.1109/ICPAI51961.2020.00027","DOIUrl":"https://doi.org/10.1109/ICPAI51961.2020.00027","url":null,"abstract":"A pairs trading strategy (PTS) forms a market-neutral portfolio whose value moves back and forth around a certain price level. An investor can long (short) the portfolio when its price moves below (above) the price level and cash out when the portfolio value converges back to earn the price difference. The profit for each successful trading is relatively small, so transaction costs and structural changes (that invalidate market-neutral property) could significantly erode the profits. This paper proposes three improvement methods to reduce the costs and stabilize aggregated profits. First, we change the open and close thresholds to increase the number of transactions; this would increase and stabilize the aggregated profits due to the law of large numbers. Second, we derive the expected return for each trading before opening the portfolio and execute the trading only when the expected return exceed the transaction cost. Third, we detect the structural change with our revised (statistical) tests to close the position in advance to reduce losses. Empirical studies show that our three methods can be simultaneously adopted to improve trading performance significantly.","PeriodicalId":330198,"journal":{"name":"2020 International Conference on Pervasive Artificial Intelligence (ICPAI)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117129158","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: 2

Predicting Internal Energy Consumption of a Wind Turbine Using Semi-Supervised Deep Learning 利用半监督深度学习预测风力发电机内部能耗

2020 International Conference on Pervasive Artificial Intelligence (ICPAI) Pub Date : 2020-12-01 DOI: 10.1109/ICPAI51961.2020.00048

Shih-Sheng Hsu, Chun-Cheng Lin

{"title":"Predicting Internal Energy Consumption of a Wind Turbine Using Semi-Supervised Deep Learning","authors":"Shih-Sheng Hsu, Chun-Cheng Lin","doi":"10.1109/ICPAI51961.2020.00048","DOIUrl":"https://doi.org/10.1109/ICPAI51961.2020.00048","url":null,"abstract":"Most previous works on wind power generation focused on the impact of the external environment on the efficiency of energy generation, but ignored the energy consumption of internal parts of the wind turbine. Reducing internal energy consumption can not only improve the generation efficiency, but also reduce the maintenance cost of wind turbines. Therefore, this study uses deep learning to predict the energy consumption inside the wind turbine by installing dozens of sensors inside it, and finds the parts that have greater impact on energy consumption to reduce energy consumption and improve generating efficiency. Since most data on wind turbines is collected by humans currently, it is inevitable that the data will have missing or wrong. Due to the large number of parts inside the wind turbine, the collected data belongs to multi-dimension data. In order to use these data effectively, this study proposes a semi-supervised deep learning method which can correct the data to solve this problem. After all the data are corrected and the model is completely trained, this study uses the MCC method to judge the predicting results of the model. The results show that when the label data accounts for 15-20% of the total data the trained model has the best predictive ability. Therefore, this study suggests that when establishing a prediction model of internal energy consumption of wind turbine in the future, the label data should account for 15-20% of the total data. In this way, not only can train a model with considerable accuracy, but also the most economical ways to determine the amount of revision data.","PeriodicalId":330198,"journal":{"name":"2020 International Conference on Pervasive Artificial Intelligence (ICPAI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124876714","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

Improving UAV Personalized-Tracking Services by Fusing Visual and Radio Data 融合视觉和无线电数据改进无人机个性化跟踪服务

2020 International Conference on Pervasive Artificial Intelligence (ICPAI) Pub Date : 2020-12-01 DOI: 10.1109/ICPAI51961.2020.00010

Abebe Belay Adege, Yun-Ruei Li, Hong-Han Shuai, Hsin-Piao Lin, Li-Chun Wang

{"title":"Improving UAV Personalized-Tracking Services by Fusing Visual and Radio Data","authors":"Abebe Belay Adege, Yun-Ruei Li, Hong-Han Shuai, Hsin-Piao Lin, Li-Chun Wang","doi":"10.1109/ICPAI51961.2020.00010","DOIUrl":"https://doi.org/10.1109/ICPAI51961.2020.00010","url":null,"abstract":"This work presents a high precision unmanned aerial vehicle (UAV) communications to detect, track and locate people during moving using real-time dataset. We use a fusion of visual and radio data collected using a UAV, where the UAV is used to provide radio signals to mobile users and to collect visual and wireless datasets simultaneously. During data collection, the UAV is connected to smartphones and laptops, and the UAV is connected to a controller and an edge server through a wireless network. You only look once version4 (YOLOv4), Kalman filter, and long-short term memory (LSTM) algorithms are combined to evaluate the proposed system. YOLOv4-Kalman filter uses video inputs to detect and track people. The detail information of the bounding boxes of the detected persons is integrated with wireless data to locate people in motion. We use a variety of optimization methods, such as batch normalization, error smoothing and DROPOUT to optimize the performances of our proposed system. The proposed model improves the conventional approach by 10 % during localization.","PeriodicalId":330198,"journal":{"name":"2020 International Conference on Pervasive Artificial Intelligence (ICPAI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123523053","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