2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)最新文献

Utilizing motion data retrieval techniques for person identification 利用运动数据检索技术进行人员识别

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-12-30 DOI: 10.1109/ICKEA.2016.7803016

Jing-Fu Juang, Wei-Guang Teng

{"title":"Utilizing motion data retrieval techniques for person identification","authors":"Jing-Fu Juang, Wei-Guang Teng","doi":"10.1109/ICKEA.2016.7803016","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803016","url":null,"abstract":"Identifying a specific user is an old but challenging problem, and its applications are ubiquitous in our daily lives. Conventional person identification methods are using an ID card or the combination of a username and password. Recently, new techniques based on biometrics have been introduced so that people do not need to worry if they forget their username and password. For example, fingerprint and iris recognition are becoming common methods of person identification; however, users are usually required to interact with a system to use these traits. In some non-critical situations, it may be more convenient to utilize soft biometrics for person identification, although these features are not as unique for a specific person. In this work, we propose to conduct gait analysis that can be performed from a distance without disturbing user activities. We utilize depth cameras to capture user movements and create motion sequences. Then, a motion sequence is transformed to a motion string with appropriate data preprocessing and clustering techniques. Representative motion strings representing the individual behaviour of a user are retrieved and utilized to identify people. Empirical studies based on real motion data show that our approach performs well in person identification.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128740152","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

Development of working environment comfort to improve productivity 开发舒适的工作环境，提高生产效率

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803014

H. Soewardi, Amarria Dila, Putrisari Rizkiningtias

引用次数: 3

Knowledge representation in education: Diagramming Carroll's syllogisms 教育中的知识表示:卡罗尔三段论的图解

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803009

S. Al-Fedaghi

引用次数: 2

Short-term joint optimization of cascade hydropower stations on daily power load curve 梯级水电站日负荷曲线短期联合优化

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803026

Liu Yuan, Jian-zhong Zhou, Chuyang Chang, Peng Lu, Chao Wang, Muhammad Tayyab

{"title":"Short-term joint optimization of cascade hydropower stations on daily power load curve","authors":"Liu Yuan, Jian-zhong Zhou, Chuyang Chang, Peng Lu, Chao Wang, Muhammad Tayyab","doi":"10.1109/ICKEA.2016.7803026","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803026","url":null,"abstract":"Short-term operation of hydropower stations is a vital part of power grid scheduling. In order to ensure security and stability of power grid, it is of great importance for stations to operate according to the power grid instructions. Yet as far as stations concerned, they'd like to run as an economical way so that the maximal benefit is reaped. To satisfy the demand of power system and optimize the operation of cascade hydropower stations at the same time, a daily joint optimization model of cascade hydropower stations is established, considering the daily power load curve and power generation of a single station. Then a heuristic hierarchic optimization (HHO) strategy is proposed to solve the model. The total hydroenergy consumption of single and joint operation of cascade hydropower stations of Qingjiang River is calculated. The results make clear that comparing with single operation, the hydroenergy consumption of joint operation is less, which is of great significance to cascade hydropower stations. The modeling and solution can be good references to cascade hydropower stations dispatched by the same power grid.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124156618","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

Support confidence and utility of action rules triggered by meta-actions 支持由元操作触发的操作规则的可信度和实用性

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803003

A. Tzacheva, Chandra C. Sankar, S. Ramachandran, R. Shankar

引用次数: 12

MR-Apriori count distribution algorithm for parallel Action Rules discovery 并行动作规则发现的MR-Apriori计数分布算法

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803005

A. Tzacheva, Midhun M. Sunny, Pranava Mummoju

{"title":"MR-Apriori count distribution algorithm for parallel Action Rules discovery","authors":"A. Tzacheva, Midhun M. Sunny, Pranava Mummoju","doi":"10.1109/ICKEA.2016.7803005","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803005","url":null,"abstract":"Data mining deals with the extraction of hidden predictive information from large databases. One of the central tasks associated with data mining is to discover profitable actions from the dataset for the decision maker. Discovering these actions can be accomplished through extracting Action Rules from the data, which has become an attractive research topic in data mining. Several methods have been developed for the discovery of Action Rules and variety of methods for association rules in the past few years. However, with the explosive recent growth of the amounts of data, there is a need for the development of scalable methods for Action Rules discovery to accommodate the massive datasets. We are not aware of any such methods existing at this time. In this paper, we propose a novel approach - a parallel Action Rule discovery algorithm based on MapReduce paradigm through count distribution. We use Hadoop, as a scalable and distributed framework for implementing this method. Experiment shows much faster computational time for Action Rules discovery in a distributed environment compared to the traditional single machine method.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132955647","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

Ontology-based indexing method for engineering documents retrieval 基于本体的工程文献检索索引方法

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803013

Weiguang Fang, Yu Guo, W. Liao

引用次数: 7

Strategic approach to the optimization of organization based on BSC-SWOT matrix 基于BSC-SWOT矩阵的组织优化战略途径

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803021

O. Korableva, O. Kalimullina

引用次数: 41

Prototyping a Cognitive-Centered Model to improve knowledge creation in geographically distributed teams 建立以认知为中心的模型原型，以改善地理分布团队的知识创造

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803015

S. Chujfi, C. Meinel

引用次数: 0

Predicting waiting time in customer queuing systems 预测顾客排队系统的等待时间

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803010

Andre Carvalho, O. Belo

{"title":"Predicting waiting time in customer queuing systems","authors":"Andre Carvalho, O. Belo","doi":"10.1109/ICKEA.2016.7803010","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803010","url":null,"abstract":"The implementation of a queuing management system is a critical task in every single store attendance service. This is very difficult to accomplish because we have to deal with customers, and in particular with its impatience. The goal must be always to have short queues and a very fast attendance service. However, rarely this is achieved. For many reasons, attendance service deteriorates as time pass or as the number of people waiting increases, reducing the quality of service of the store, and causing discomfort in the people that are in the queue. High waiting times are very risky to the store, because customers get impatient of waiting and leave the queues, provoking frequently customer dissatisfaction and leading to loss customers. In this paper, we analyzed and implemented several approaches for predicting the expected waiting time given an attendance time history of a store, and how it could evolve in the future. To do this, we used a real-world data set collected in a specific telecommunications company store, having important service management issues, which makes it a very good case study, and a strong application case.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122505430","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