홈 네트워크 시스템을 위한 지능적 사용자 프로파일 관리 기법
    1.
    发明公开
    홈 네트워크 시스템을 위한 지능적 사용자 프로파일 관리 기법 无效
    用于家庭网络系统的智能用户配置文件管理技术

    公开(公告)号:KR1020100115967A

    公开(公告)日:2010-10-29

    申请号:KR1020090034650

    申请日:2009-04-21

    CPC classification number: H04L12/2829

    Abstract: PURPOSE: An intelligent user profile management technique for a home network system is provided to manage a home service usage pattern by a user profile, thereby automatically providing a desired effective service. CONSTITUTION: A middleware layer of a component includes a basic data management unit, a basic rule data management unit, a data collecting manger, and a rule generation manger as a sub system. A user profile management component provides a window-based interface to a client. The user profile administration component provides inquiry/generation/deletion/modification functions of basic data.

    Abstract translation: 目的:提供一种用于家庭网络系统的智能用户简档管理技术,用于通过用户简档来管理家庭服务使用模式,从而自动提供期望的有效服务。 构成:组件的中间件层包括基本数据管理单元,基本规则数据管理单元,数据采集管理器和作为子系统的规则生成管理器。 用户配置文件管理组件为客户端提供基于窗口的界面。 用户简档管理组件提供基本数据的查询/生成/删除/修改功能。

    생체신호 기반의 감정인식 시스템
    4.
    发明公开
    생체신호 기반의 감정인식 시스템 无效
    基于生物信号的感应识别系统

    公开(公告)号:KR1020100128023A

    公开(公告)日:2010-12-07

    申请号:KR1020090046429

    申请日:2009-05-27

    CPC classification number: G06K9/00496

    Abstract: PURPOSE: A bio-signal based emotion recognition system is provided to recognize the emotion of a subject by applying a pattern recognition algorithm to the electrocardiogram and pulse wave of the subject. CONSTITUTION: A video clip selecting part prepares an emotion recognition experiment. A data management part normalizes the electrocardiogram data and pulse wave data of a subject. An emotion estimation part estimates the emotion of the subject by applying the electrocardiogram data and the pulse wave data to a SVM(Support Vector Machine) method and a KNN(Nearest Neighbor) method. An emotion analysis part analyzes an emotion estimation rate during an estimation process.

    Abstract translation: 目的:提供一种基于生物信号的情绪识别系统,通过对受试者的心电图和脉搏波应用模式识别算法来识别受试者的情感。 构成:视频剪辑选择部分准备情绪识别实验。 数据管理部件对被检体的心电图数据和脉波数据进行归一化。 情绪估计部通过将心电图数据和脉搏波数据应用于SVM(支持向量机)方法和KNN(最近邻)方法来估计被摄体的情感。 情绪分析部分在估计过程中分析情绪估计率。

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