• Title/Summary/Keyword: Blackbox Simulation

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Analyzing the Characteristics of Pre-service Elementary School Teachers' Modeling and Epistemic Criteria with the Blackbox Simulation Program (블랙박스 시뮬레이션에 참여한 초등예비교사의 모형 구성의 특징과 인식적 기준)

  • Park, Jeongwoo;Lee, Sun-Kyung;Shim, Han Su;Lee, Gyeong-Geon;Shin, Myeong-Kyeong
    • Journal of The Korean Association For Science Education
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    • v.38 no.3
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    • pp.305-317
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    • 2018
  • In this study, we investigated the characteristics of participant students' modeling with the blackbox simulation program and epistemic criteria. For this research, we developed a blackbox simulation program, which is an ill-structured problem situation reflecting the scientific practice. This simulation program is applied in the activities. 23 groups, 89 second year students of an education college participated in this activity. They visualized, modeled, modified, and evaluated their thoughts on internal structure in the blackbox. All of students' activities were recorded and analyzed. As a result, the students' models in blackbox activities were categorized into four types considering their form and function. Model evaluation occurred in group model selection. Epistemic criteria such as empirical coherence, comprehensiveness, analogy, simplicity, and implementation were adapted in model evaluation. The educational implications discussed above are as follows: First, the blackbox simulation activities in which the students participated in this study have educational implications in that they provide a context in which the nature of scientific practice can be experienced explicitly and implicitly by constructing and testing models. Second, from the beginning of the activity, epistemic criteria such as empirical coherence, comprehensiveness, analogy, simplicity, and implementation were not strictly adapted and dynamically flexibly adapted according to the context. Third, the study of epistemic criteria in various contexts as well as in the context of this study will broaden the horizon of understanding the nature of scientific practice. Simulation activity, which is the context of this study, can lead to research related to computational thinking that will be more important in future society. We expect to be able to lead more discussions by furthering this study by elaborating and systematizing its context and method.

A Security-Enhanced Storing Method for the Voice Data in the Aircraft (항공기에서 보안 강화된 음성 데이터 저장 방식)

  • Cho, Seung Hoon;Suh, Jeong Bae;Moon, Yong Ho
    • IEMEK Journal of Embedded Systems and Applications
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    • v.6 no.4
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    • pp.255-261
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    • 2011
  • In this paper, we propose a security-enhanced storing method for the voice data obtained during the flight. When an emergency occurs during flight, the flight data in the storage device such as DTS or Blackbox can be exposed to antagonist or enemy. Currently, zeroize function is embedded in these devices in order to prevent this situation. However, this could not be operated if the system is malfunctioned or the pilot is wounded in the emergency. In order to solve this problem, the voice data compressed by the ADPCM is encrypted in the proposed method composed of the AES algorithm and a reordering method. The simulation results show that the security for the voice date is further enhanced due to the proposed method.

Vehicle Dynamic Simulation Using the Neural Network Bushing Model (인공신경망 부싱모델을 사용한 전차량 동역학 시뮬레이션)

  • 손정현;강태호;백운경
    • Transactions of the Korean Society of Automotive Engineers
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    • v.12 no.4
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    • pp.110-118
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    • 2004
  • In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra's algorithm of ‘NARMAX’ form is employed in the neural network bushing module. A numerical example is carried out to verify the developed bushing model.

Empirical Bushing Model For Vehicle Dynamic Analysis (차량동역학해석을 위한 실험적 부싱모델 개발)

  • Sohn, Jeong-Hyun;Kang, Tae-Ho;Baek, Woon-Kyung;Park, Dong-Woon;Yoo, Wan-Suk
    • Proceedings of the KSME Conference
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    • 2004.04a
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    • pp.864-869
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    • 2004
  • In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra's algorithm of 'NARMAX' form is employed in the neural network bushing module. A numerical example is carried out to verify the developed bushing model.

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Empirical Bushing Model using Artificial Neural Network (인공신경망을 이용한 실험적 부싱모델링)

  • 손정현;유완석;박동운
    • Transactions of the Korean Society of Automotive Engineers
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    • v.11 no.4
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    • pp.151-157
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    • 2003
  • In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra algorithm of 'NARMAX' form is employed to consider these effects. A numerical example is carried out to verify the developed bushing model.

Forecasting of Runoff Hydrograph Using Neural Network Algorithms (신경망 알고리즘을 적용한 유출수문곡선의 예측)

  • An, Sang-Jin;Jeon, Gye-Won;Kim, Gwang-Il
    • Journal of Korea Water Resources Association
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    • v.33 no.4
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    • pp.505-515
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    • 2000
  • THe purpose of this study is to forecast of runoff hydrographs according to rainfall event in a stream. The neural network theory as a hydrologic blackbox model is used to solve hydrological problems. The Back-Propagation(BP) algorithm by the Levenberg-Marquardt(LM) techniques and Radial Basis Function(RBF) network in Neural Network(NN) models are used. Runoff hydrograph is forecasted in Bocheongstream basin which is a IHP the representative basin. The possibility of a simulation for runoff hydrographs about unlearned stations is considered. The results show that NN models are performed to effective learning for rainfall-runoff process of hydrologic system which involves a complexity and nonliner relationships. The RBF networks consist of 2 learning steps. The first step is an unsupervised learning in hidden layer and the next step is a supervised learning in output layer. Therefore, the RBF networks could provide rather time saved in the learning step than the BP algorithm. The peak discharge both BP algorithm and RBF network model in the estimation of an unlearned are a is trended to observed values.

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