• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2009년도 한국우주과학회보 제18권1호
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• pp.56.1-56.1
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• 2009

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 추계학술대회 논문집 전력기술부문
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• pp.376-378
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• 2008

This paper covers that modem performance test is effected by traffic and analysis of the result in TETRA network. The SDS is a method of the between PEIs transmission. And only SDS message type-4 is used to test. The range of the SDS message data length is from 10 to 140 bytes with an increment of 10 bytes. For each length transmitted 1000 times for each delay of inter-transmissions 0.5 second, 1.0 second and 1.5 second. The modem used in the test is the Unimo MU-1000MD by UNIMO Corp.

• 대한전자공학회논문지SD
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• 제47권2호
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• pp.51-59
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• 2010

드모르간 및 재대입 논리변환은 unate gate network (UGN)을 보다 일반적인 balanced inversion parity (BIP) network으로 전환하는데 충분하다. 이러한 회로계층에 대해서도 자세히 논의하고 있다. 우리는 드모르간 및 재대입 논리변환이 경로지연고장 테스트집합을 유지한다는 것을 증명하였다. 본 논문의 결과를 이용하여 함수 z를 구현하는 모든 UGN에서 모든 경로지연고장을 검출하는 상위수준 테스트집합은 함수 z의 어떠한 BIP realization에서도 모든 경로지연고장을 검출한다는 것을 보일 수 있다.

• 한국인터넷방송통신학회논문지
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• 제15권3호
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• pp.125-130
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• 2015

본 논문에서는 네트워크에뮬레이터를 활용한 지휘통제시스템의 시험환경 구성, 결과 및 설계반영사항에 대해 다룬다. 정보통신기술의 발달에 의한 정보교환능력과 연산능력의 강화는 네트워크중심전이라는 개념을 가져오게 되었는데, 네트워크중심전에서 무기체계사용자가 빠른 상황판단을 내려 적합한 명령을 내릴 수 있게 지원하는 것이 지휘통제 체계이다. 본문에서는 소프트웨어 기반 네트워크 에뮬레이터중 하나인 dummynet을 활용한 시험환경 구축을 설명하고, 개발된 지휘통제시스템의 운용개념을 요약하였다. 더 나아가 시험 케이스 및 결과에 대해 설명하고, 목표 통신망에서의 제약사항을 극복할 운용개념을 제시하였다. 제안한 방식을 활용하면 항적메시지와 같이 여러 단말기가 같은 데이터를 빠른 주기로 공유할 때 유통 데이터를 크게 감소시킬 것으로 기대된다.

• 자동차안전학회지
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• 제13권4호
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• pp.33-38
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• 2021

This paper presents a comparative study between the parameter-optimized Pacejka model and artificial neural network model for the tire force estimation. The two different approaches are investigated and compared in this study. First, offline optimization is conducted based on Pacejka Magic Formula model to determine the proper parameter set for the minimization of tire force error between the model and test data set. Second, deep neural network model is used to fit the model to the tire test data set. The actual tire forces are measured using MTS Flat-Track test platform and the measurements are used as the reference tire data set. The focus of this study is on the applicability of machine learning technique to tire force estimation. It is shown via the regression results that the deep neural network model is more effective in describing the tire force than the parameter-optimized Pacejka model.

• 전기학회논문지P
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• 제60권3호
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• pp.161-166
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• 2011

In this paper, for analysing the major factors giving the effect to the quality of HD level video on the multicasting service, we tried this test experiment on the College school network similar to the real service network environment. We measure the video quality distortion on the multicasting HD level video according to generating and increasing the broadcasting traffic on the situation that apply the QoS technique to the test network and not apply the QoS technique and then find out the threshold value of network factors giving the effect to the video quality distortion on the multicasting HD level video service. This paper can be used for guaranteeing the constant video quality and reducing the video quality distortion on the multicasting HD level video service.

• 한국정보과학회논문지:정보통신
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• 제34권2호
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• pp.81-87
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• 2007

본 논문은 테스트 환경의 재구축에 의한 번거로움을 제거하고 손쉽게 테스트 환경을 재구성할 수 있는 리눅스 기반의 테스트 베드인 Network Traffic Control Test-bed(NTCT)를 구현하고 이를 활용한 실험 결과를 보인다. 본 논문에서 제시하는 NTCT는 원하는 사용자가 지정하는 네트워크 트래픽을 발생시키는 NS2 연동 트래픽 발생기와, 네트워크의 대역폭을 관리하여 실제 네트워크 상황과 유사한 환경을 유지시키는 트래픽 제어기, 그리고 성능 평가를 위한 실시간 네트워크 모니터로 구성되어 있다. 본 논문에서는 제시하는 NTCT를 이용하여 네트워크 성능을 평가하는 예제를 포함한다.

• Tribology and Lubricants
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• 제11권3호
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• pp.24-30
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• 1995

This paper was undertaken to recognize the pattern of the wear debris by neural network as a link for the development of diagnosis system for movable condition of the lubricated machine surface. The wear test was carried out under different experimental conditions using the wear test device was made in laboratory and wear testing specimen of the pin-on-disk type were rubbed in paraffine series base oil, by varying applied load, sliding distance and mating material. The neural network has been used to pattern recognition of four parameter (diameter, elongation, complex and contrast) of the wear debris and learned the friction condition of five values (material 3, applied load 1, sliding distance 1). The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by the neural network. The characteristic parameter of the large wear debris over a few micron size enlarged recognition ability.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권1호
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• pp.39-42
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• 2002

For the purpose of modeling EEG signal which has nonstationary and nonlinear dynamic characteristics, this paper propose a state feedback real time recurrent neural network model. The state feedback real time recurrent neural network is structured to have memory structure in the state of hidden layers so that it has arbitrary dynamics and ability to deal with time-varying input through its own temporal operation. For the model test, Mackey-Glass time series is used as a nonlinear dynamic system and the model is applied to the prediction of three types of EEG, alpha wave, beta wave and epileptic EEG. Experimental results show that the performance of the proposed model is better than that of other neural network models which are compared in this paper in some view points of the converging speed in learning stage and normalized mean square error for the test data set.

• 한국자동차공학회논문집
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• 제12권4호
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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.