Pub Date : 2016-01-16DOI: 10.14257/ASTL.2016.121.40
Xiang Chen, Y. Liu, Yuxia Liang, Xin Zhao
The unascertained clustering is a new clustering method, which combines unascertained theory and clustering theory to construct the unascertained measure, and uses the unascertained measure as set membership to indicate the membership relation between the samples with the different classes. It overcomes the disadvantage of means clustering algorithm, that a sample definitely belongs to a class, which made greater progress than -means clustering. There are complex nonlinear relationship between the coal industry competitiveness and various factors. The article established the evaluation influencing factors system of coal industry international competitiveness. 6 unascertained clustering method to cluster competitiveness. It found out each class center, and gave the membership degree of the samples belong to each class, which better resolved the problem of classifying the coal industry international competitiveness.
{"title":"Coal Industry International Competitiveness Research","authors":"Xiang Chen, Y. Liu, Yuxia Liang, Xin Zhao","doi":"10.14257/ASTL.2016.121.40","DOIUrl":"https://doi.org/10.14257/ASTL.2016.121.40","url":null,"abstract":"The unascertained clustering is a new clustering method, which combines unascertained theory and clustering theory to construct the unascertained measure, and uses the unascertained measure as set membership to indicate the membership relation between the samples with the different classes. It overcomes the disadvantage of means clustering algorithm, that a sample definitely belongs to a class, which made greater progress than -means clustering. There are complex nonlinear relationship between the coal industry competitiveness and various factors. The article established the evaluation influencing factors system of coal industry international competitiveness. 6 unascertained clustering method to cluster competitiveness. It found out each class center, and gave the membership degree of the samples belong to each class, which better resolved the problem of classifying the coal industry international competitiveness.","PeriodicalId":153703,"journal":{"name":"Advanced Science and Technology Letters","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132995066","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}
Pub Date : 2014-04-26DOI: 10.14257/ASTL.2014.48.01
Yuto Nakano, S. Kiyomoto, Yutaka Miyake
Oblivious RAM schemes were proposed to protect software on un- trusted systems. However their huge overhead encumbers their practical deploy- ment. Recently, we proposed a new protection scheme using history of accesses. However, only the concept of the scheme was presented, and several issues were unclear for its implementation. In this paper, we propose a new instance of the scheme, which can be implemented with only software with less overhead than the previous instance. We also consider three attack scenarios and propose coun- termeasures. We implement the scheme with countermeasure and show that our scheme only requires 0.125(s) to write 1MB data.
{"title":"Evaluation of Memory Access Pattern Protection in a Practical Setting","authors":"Yuto Nakano, S. Kiyomoto, Yutaka Miyake","doi":"10.14257/ASTL.2014.48.01","DOIUrl":"https://doi.org/10.14257/ASTL.2014.48.01","url":null,"abstract":"Oblivious RAM schemes were proposed to protect software on un- trusted systems. However their huge overhead encumbers their practical deploy- ment. Recently, we proposed a new protection scheme using history of accesses. However, only the concept of the scheme was presented, and several issues were unclear for its implementation. In this paper, we propose a new instance of the scheme, which can be implemented with only software with less overhead than the previous instance. We also consider three attack scenarios and propose coun- termeasures. We implement the scheme with countermeasure and show that our scheme only requires 0.125(s) to write 1MB data.","PeriodicalId":153703,"journal":{"name":"Advanced Science and Technology Letters","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117129473","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}
Pub Date : 2013-12-11DOI: 10.14257/ASTL.2013.39.27
Sang-yong Park, Hanmoi Sim, Won-Hyung Lee
Balance design taking game difficulty into account has an important role in game design. In recent years, a number of studies have tried to adjust difficulty by using various player dependent difficulty detection algorithms. But most of these methods need customizing its algorithm for each game. In this paper, we investigate the way to find adaptive game difficulty levels according to player’s emotion by analyzing electroencephalogram (EEG) signals for improving player’s emersion. A player’s EEG signals during playing a rhythm game which has three different difficulty levels were analyzed by using PAD model. We focus on the states of emotion from players EEG signals.
{"title":"EEG-based Emotion Recognition for Game Difficulty Control","authors":"Sang-yong Park, Hanmoi Sim, Won-Hyung Lee","doi":"10.14257/ASTL.2013.39.27","DOIUrl":"https://doi.org/10.14257/ASTL.2013.39.27","url":null,"abstract":"Balance design taking game difficulty into account has an important role in game design. In recent years, a number of studies have tried to adjust difficulty by using various player dependent difficulty detection algorithms. But most of these methods need customizing its algorithm for each game. In this paper, we investigate the way to find adaptive game difficulty levels according to player’s emotion by analyzing electroencephalogram (EEG) signals for improving player’s emersion. A player’s EEG signals during playing a rhythm game which has three different difficulty levels were analyzed by using PAD model. We focus on the states of emotion from players EEG signals.","PeriodicalId":153703,"journal":{"name":"Advanced Science and Technology Letters","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125363236","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}
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