• Title/Summary/Keyword: TSMM

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The Effects of Team Learning Behavior, Individual Creativity, Team Shared Mental Model, Mutual Performance Monitoring on Team Creativity in the College Classroom (팀 학습행동, 개인 창의성, 팀 공유정신모형, 상호 수행 모니터링이 대학 수업에서 팀 창의성에 미치는 영향)

  • Jun, Myongnam
    • Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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    • v.5 no.6
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    • pp.317-325
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    • 2015
  • The aim of this research was to investigate the relationship among team learning behavior, individual creativity, team shared mental model(TSMM), mutual performance monitoring on team creativity and then providing the fundamental data on the education. Also it intended to acknowledge relative predictive power on team creativity of independent variables. The total of 257 college students participated the team learning for 6 weeks in a semester. Pearson's product moment correlation and regression analysis were used for data analysis and testing of significance of verification, The main research results are summarized as follows; team learning behavior, TSMM, mutual performance monitoring had no significant effects on three subfactors of team creativity such as novelty, resolution, elaboration & synthesis. Therefore followed researches are needed about inter and intra processing of team creativity.

Kinematic Control of Double Pantograph Type Manipulator Using Neural Network (신경회로망을 이용한 더블 팬터그래프형 매니퓰레이터의 기구학적 제어)

  • 김성철;정원지;신중호
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1997.04a
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    • pp.20-24
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    • 1997
  • In general, pantograph type manipulators are used for carrying heavy payloads with positional accuracy. In this paper, a double pantograph type manipulator, activated by two slider joints, is studied for applying to file handing machine in atomic power plant. In order to realize the stable horizontal movement of a heavy fuel rod whit good positional accuracy, methods for allocating slider and finding constant joint rates are proposed. In addition, the static deflection of the proposed mechanism was studied using transfer-stiffness matrix method. A neural network control algorithm which compensates static deflections is explored with computer simulations.

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