• Title/Summary/Keyword: Outlier Behaviour

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Expression Analysis System of Game Player based on Multi-modal Interface (멀티 모달 인터페이스 기반 플레이어 얼굴 표정 분석 시스템 개발)

  • Jung, Jang-Young;Kim, Young-Bin;Lee, Sang-Hyeok;Kang, Shin-Jin
    • Journal of Korea Game Society
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    • v.16 no.2
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    • pp.7-16
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    • 2016
  • In this paper, we propose a method for effectively detecting specific behavior. The proposed method detects outlying behavior based on the game players' characteristics. These characteristics are captured non-invasively in a general game environment and add keystroke based on repeated pattern. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players was used to analyze high-dimensional game-player data for a detection effect of repeated behaviour pattern. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. In addition, Repeated behaviour pattern can be analysed possible. The proposed method can also be used for feedback and quantification about analysis of various interactive content provided in PC environments.

Development of a Observational Settlement Analysis Method Using Outliers (이상치를 이용한 관측적 침하예측기법의 개발)

  • 우철웅;장병욱
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.45 no.5
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    • pp.140-150
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    • 2003
  • Observational methods such as the Asaoka's method and the hyperbolic method are widely applied on the settlement analysis using observed settlement. The most unreliable aspects in those methods is arose from the subjective discretion of initial non-linearity on linear regression. The initial non-linearity is inevitable due to the settlement behaviour itself. Therefore an objective method is essential to achieve more reliable results on settlement analysis. It was found that the initial non-linear data are statistical outliers. New automation algorithms of the hyperbolic and the Asaoka's method were developed based on outlier detection method. The methods are a successive detection of outliers and a searching method of suitable hyperbolic range for the Asaoka's and the hyperbolic method respectively. Applicability of the algorithms was verified through case studies.