2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)最新文献_第2页

Study on Science and Technology Policy trend using text network analysis in Korea 基于文本网络分析的韩国科技政策走向研究

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7802989

Yuntai Kim

{"title":"Study on Science and Technology Policy trend using text network analysis in Korea","authors":"Yuntai Kim","doi":"10.1109/ICKEA.2016.7802989","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7802989","url":null,"abstract":"As the social impact of science and technology is increasing day by day, it is increasing the importance of science and technology policy. The Third Science and Technology Basic Plan, which is to be the basis of Korea Science and Technology Policy, will be established in 2013. There is need to investigate the value-oriented and policy priorities of the Science and Technology Basic Plan. The purpose of this study is to find the policy priorities and strategies by utilizing social language network analysis techniques. The data used to demonstrate the social language network analysis were the keywords of the former Science and Technology Basic Plan. The results of this study are as folIows. First, the frequency of following words (S&T, reinforcement, enhancement, R&D) is high. It reflects the policy stance of the Republic of Korea. The word appears more related to the creative economy such as creative, jobs, startups, and commercialization. Secondly, research has shown a high correlation with the investment in a context where the Republic of Korea has continued to expand its research and development budget. This study has the following meanings. First, it is faithfully reflected in the science and technology policy to ensure that the needs of the researchers and policy experts to maximize policy efficiency. Second, the case study that survey research and statistical techniques can be applied to establish policies and practices, or to reflect the Science and Technology Basic Plan in earnest for the establishment of research results in the future to expand when the methodology can be applied.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"31 7 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":"124688261","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

Using thinking based on contradictions to solve the problem of storing hierarchical data 运用基于矛盾的思维解决分层数据的存储问题

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803022

S. Petrova, A. Kapitonova

引用次数: 0

An expert system for risk assessment of information system security based on ISO 27002 基于ISO 27002的信息系统安全风险评估专家系统

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7802992

S. W. Sihwi, Ferry Andriyanto, Rini Anggrainingsih

引用次数: 8

Credit Risk Analysis in Peer-to-Peer Lending System p2p借贷系统中的信用风险分析

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803017

K. Vinod, S. Natarajan, S. Keerthana, K. M. Chinmayi, N. Lakshmi

引用次数: 42

People counting base on head and shoulder information 人们根据头和肩的信息来计数

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7802991

J. Kuo, Guo Fan, T. Lai

引用次数: 9

Big Data as an enabler to prevent failures in the production area 大数据是防止生产区域故障的推动者

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803025

V. Stich, Kerem Oflazgil, M. Schroter, Felix Jordan, G. Fuhs

{"title":"Big Data as an enabler to prevent failures in the production area","authors":"V. Stich, Kerem Oflazgil, M. Schroter, Felix Jordan, G. Fuhs","doi":"10.1109/ICKEA.2016.7803025","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803025","url":null,"abstract":"Failure management in the production area has been intensely analyzed in the research community. Although several efficient methods have been developed and partially successfully implemented, producing companies still face a lot of challenges. The resulting main question is how manufacturers can be assisted by a sustainable approach enabling them to proactively detect and prevent failures before they occur. A high-resolution production system based on analyzed real-time data enables manufacturers to find an answer to the main question. In this context, Big Data technologies have gained importance since the critical success factor is not only to collect real-time data in the production but also to structure the data. Therefore, we present in this paper the implementation of Big Data technologies in the production area using the example of an actual research project. After the literature review, we describe a Big Data based approach to prevent failures in the production area. This approach mainly includes a real-time capable platform including complex event processing algorithms to define appropriate improvement measures.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"8 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":"126610399","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

Predicting the gross calorific value of coal based on support vector machine and partial least squares algorithm 基于支持向量机和偏最小二乘算法的煤总发热量预测

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803023

Li Jing

{"title":"Predicting the gross calorific value of coal based on support vector machine and partial least squares algorithm","authors":"Li Jing","doi":"10.1109/ICKEA.2016.7803023","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803023","url":null,"abstract":"In view of the problem of inaccurately measuring the gross calorific value of coal, this paper analyzed the relationship between industry analysis data and calorific value of fire coal into the furnace, and five kinds of industrial analysis components were selected as the original independent variables, which were moisture, ash, volatile matter, sulphur content and fixed carbon in fire coal. In the process of modelling, the latent variable factors were extracted by the partial least square regression method, and the latent variables were selected as the input vectors and coal-fired calorific value as the output vector, then the power plant coal calorific value forecasting model was built based on support vector machine regression algorithm. The predicted results showed that, the accuracy of the coupled model was higher than that of the single model and the forecasting deviations met engineering requirements. Therefore, the model proposed here has practical engineering application value. At the same time, in the process of modelling, the five-point method was used to determine the optimal combination of penalty coefficient and kernel coefficient in the process of modelling. The method could quickly and accurately determine the optimal combination and avoid the blindness of the traditional method in the process of determining the optimal combination of penalty coefficient and kernel coefficient.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"124 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":"121865705","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

Knowledge extraction through etymological networks: Synonym discovery in Sino-Korean words 基于词源网络的知识提取:汉朝词语的同义词发现

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803019

E. Pablo, Kyomin Jung

引用次数: 3

Reflection on the application of the DFSG method 对DFSG方法应用的思考

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803011

Mohanad Halaweh

引用次数: 1

The development of a Kinect-based online socio-meter for users with social and communication skill impairments: A computational sensing approach 为社交和沟通技能障碍的用户开发基于kinect的在线社会测量仪:一种计算传感方法

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803007

Pinata Winoto, C. Chen, T. Tang

{"title":"The development of a Kinect-based online socio-meter for users with social and communication skill impairments: A computational sensing approach","authors":"Pinata Winoto, C. Chen, T. Tang","doi":"10.1109/ICKEA.2016.7803007","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803007","url":null,"abstract":"Some people have impairments in social skills due to autism, phobia, or physical disabilities such as blindness. The proposed web-based system is intended to measure social interaction and communication among people situated around the target user based on their gestures and behaviors. Such multimodal portraits of behavioral data will be captured using the Kinect sensor (version 2) and provide rich sources to predict their social relationship, which will then be delivered to the target user in both qualitative and quantitative units, either in mobile or desktop applications. Currently, our system is capable of capturing and analyzing the raw data, sending the processed one to Microsoft Azure for data visualization and further behavioral analysis. The visualized data can then be ported to the therapists, doctors, parents, or caregivers for further actions. Unlike previous works which aim to accurately classify the dyadic social interactions between an adult-child pair, and thus characterizing the physiological synchrony between the pair, our study thereby adds richness to the understanding of computational sensing for the diagnosis, assessment and intervention of children with autism.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"142 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":"123410334","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