2019 11th International Conference on Knowledge and Systems Engineering (KSE)最新文献_第9页

General Semi-supervised Possibilistic Fuzzy c-Means clustering for Land-cover Classification 土地覆盖分类的一般半监督模糊c均值聚类

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919476

D. Mai, L. Ngo

{"title":"General Semi-supervised Possibilistic Fuzzy c-Means clustering for Land-cover Classification","authors":"D. Mai, L. Ngo","doi":"10.1109/KSE.2019.8919476","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919476","url":null,"abstract":"Satellite images with the advantage of wide coverage, short update times can help to establish land-cover maps quickly and efficiently. However, due to the influence of natural conditions, satellite images often contain noise, outliers, the boundary of the objects on the image is unclear and this makes it difficult for many clustering algorithms. The possibilistic fuzzy c-means clustering (PFCM) algorithm has advantages of both fuzzy c-means clustering (FCM) and possibilistic c-means clustering (PCM) algorithms due to the simultaneous use of both fuzzy and function functions, but it also has limitations such as sensitivity with noise and outliers. The paper proposes a general semi-supervised possibilistic fuzzy c-means clustering (GSPFCM) algorithm to improve the clustering quality of PFCM. Our proposed method can solve problems that labeled data has very little compared to unlabeled data. Results of land-cover classification using satellite images (Landsat-7 ETM+, Sentinel-2A) show that the proposed method can significantly improve the accuracy of classification results when compared to some previous methods.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129335295","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: 5

Reinforcement Learning Based Navigation with Semantic Knowledge of Indoor Environments 基于室内环境语义知识的强化学习导航

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919366

Tai-Long Nguyen, Do-Van Nguyen, T. Le

引用次数: 11

Recognizing hand gestures for controlling home appliances with mobile sensors 用移动传感器识别控制家用电器的手势

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919419

Khanh Nguyen Trong, Hai V. Bui, Cuong Pham

{"title":"Recognizing hand gestures for controlling home appliances with mobile sensors","authors":"Khanh Nguyen Trong, Hai V. Bui, Cuong Pham","doi":"10.1109/KSE.2019.8919419","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919419","url":null,"abstract":"Mobile and ambient sensors provide a scalable platform for the integration of computing devices and smart appliances for smart home. In which mobile devices, such as smart watches and smart-phone commonly embedded with actuators and sensors i.e., accelerometers and gyroscopes, have opened up chances for the user to easily control home appliances. This paper proposes an integrated method and system that utilize several deep models and mobile sensors for hand gestures applicable for smart homes. The system consists of three components of actual smart home configurations: (i) smart-watch worn on the user’s wrist for capturing gesture patterns (ii) a recognition application that runs on the smart mobile phone and sends correspond commands to the home automation platform; and (iii) home automation platform with connected smart devices instrumented with ambient sensors. In addition, we define a simple yet easy-to-learn hand-gesture vocabulary composing of 18 gestures to the user. With the F-score of over 75%, our experiment on our self-collected data-set consisting of 18 gestures from 20 subjects, demonstrates that the feasibility of the gesture recognition for controlling home appliances.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125274701","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: 12

Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors 基于注意力的多输入深度学习生物活性预测架构:在EGFR抑制剂中的应用

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-06-12 DOI: 10.1109/KSE.2019.8919265

Huy Pham, Trung Le

引用次数: 4