• 한국철도학회:학술대회논문집
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• 한국철도학회 2003년도 추계학술대회 논문집(III)
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• pp.204-209
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• 2003

Recently, The TCN(Train Communication Network} as the distributed control systems for electric vehicles, which is the international standard of the intra vehicle communication, actively recognizes variations and supports reconfiguration of the train network when a vehicle is separated or recombined. The technique of reconfiguration to take variety and interoperability of a vehicle constitution is used when the vehicle constitution is changed. At the time, each node making up vehicle network shares the information about the variation of vehicle constitutions and the state of nodes. In the hierarchical TCN structure, an exchange of data becomes available as a work to transmit information between components is performed at the node playing a role of gateway. This paper proposes a protocol to transmit the information of the train reconfiguration. The protocol gives an application to renew a list for transmitting information and to perform the transmission that can guarantee periodic and non-periodic data transmission between nodes when the network nodes changed by a variation of the network state are reconfigured. If use this protocol, can use functions that are offered in the electric railcar at the same time that composition of vehicles is completed without delay. And when driver of the electric railcar inspect before running of vehicles, can confirm state of vehicles visually through monitor in driver's room.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2006년도 제26회 춘계학술대회논문집
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• pp.209-212
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• 2006

본 논문에서는 항공기용 터보 축 엔진의 결함 진단 알고리즘을 개발하지 위해 Support Vector Machine(SVM)과 인공신경망(ANN)을 이용하였다. SVM을 이용하여 결함 위치를 판별한 후 인공신경망이 선택적으로 학습하는 분할 학습 알고리즘(SLA)을 제안하였으며 이를 고도 변화에 따른 가스 터빈 엔진의 결함 진단에 적용하여 분류 속도 및 예측 정확률 개선 가능성을 확인하였다.

• 상하수도학회지
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• 제20권3호
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• pp.357-366
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• 2006

In order to compute pressure variation for a water distribution system, an expression for the friction factor as a function of Reynolds number and the relative roughness needs to be properly incorporated in computational algorithm. Considering Moody s friction variation, Developed Unsteady Network Analyzer (UNA) has been modified to match computational results with EPANET 2.0. Substantial improvement can be found in the application of Improved UNA to both an hypothetical pipeline network and a real system located in Ulsan City. Random number generator is employed to represent the uncertainty of water use in real pipeline network. Comparisons of application between EPANET 2.0 and improved UNA 2.0 indicate advantages and potentials of this approach.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.281-286
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• 2005

The main objective of the research work presented in this article is to present a systematic approach for designing a multilayer feed-forward artificial neural network based self-tuning power system stabilizer (ST-ANNPSS). In order to suggest an approach for selecting the number of neurons in the hidden layer, the dynamic performance of the system with ST-ANNPSS is studied and hence compared with that of conventional PSS. Finally the effect of variation of loading condition and equivalent reactance, Xe is investigated on dynamic performance of the system with ST-ANNPSS. Investigations reveal that ANN with one hidden layer comprising nine neurons is adequate and sufficient for ST-ANNPSS. Studies show that the dynamic performance of STANNPSS is quite superior to that of conventional PSS for the loading condition different from the nominal. Also it is revealed that the performance of ST-ANNPSS is quite robust to a wide variation in loading condition.

• 한국해양공학회지
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• 제16권5호
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• pp.61-65
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• 2002

The fault detection and diagnosis of rotating machinery widely used in plants including the ship are important for maintaining the performance of Plants. Recently, the wavelet transform has been recognized an efficient method to detect a little variation of physical quantities by the synchronous localization of time and frequency domains using the translation and dilation of signals. In this Paper, In order to develop efficient and reliable fault detection and diagnosis system rotating machines, the performance of wavelet transformation to detect a little variation of machine status and neural network to diagnose the cause of machine faults are investigated and experimented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2747-2749
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• 2005

This paper presents an intelligent control system for an ac servo motor dirve to track periodic commands using a neural network. AC servo motor drive system is rather similar to a linear system. However, the uncertainties, such as machanical parametric variation, external disturbance, uncertainty due to nonideal in transient state. therefore an intelligent control system that isan on-line trained neural network controller with adaptive learning rates.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅲ
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• pp.1549-1552
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• 2003

Semiconductor gas sensor using tin oxide as sensing material has been used to detect gases based on the fact that impedance of the sensing material varies when the gas sensor is exposed to the gases. This variation comprises of two parts. The first one is variation in resistance of the sensing material and the other is expressed in terms of the sensor capacitance variation. Normally, only variation of the sensor resistance is considered. In this paper, using AC measurement with a capacitor-coupled inverting amplifier circuit, both changes in the sensor resistance and variations in the sensor capacitance were investigated. These characteristics were represented as magnitude gain and phase shift of AC signal at a specific frequency after passing it through the sensor and the designed circuit. A two-stage artificial neural network, which utilized the information above, was employed to identify and quantify three combustible gases: methane, propane and butane. The network outputs were approximately proportional to concentrations of test gases with reasonable level of error.

• 한국멀티미디어학회논문지
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• 제20권10호
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• pp.1646-1654
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• 2017

Pupil size variation can not be controlled intentionally by the user and includes various features such as the blinking frequency and the duration of a blink, so it is suitable for understanding the user's emotional state. In addition, an ocular feature based emotion classification method should be studied for virtual and augmented reality, which is expected to be applied to various fields. In this paper, we propose a novel emotion classification based on CNN with pupil size variation signals which include not only various ocular feature information but also time information. As a result, compared to previous studies using the same database, the proposed method showed improved results of 5.99% and 12.98% respectively from arousal and valence emotion classification.

• 정보처리학회논문지C
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• 제12C권3호
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• pp.437-442
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• 2005

인터넷 망의 종단간 혼잡상태를 유추할 수 있는 가장 일반적인 방법은 RTT (Round Trip Time)값을 측정하는 것이며, 이 방법으로 망의 흔잡 정도를 측정할 수 있다. 그러나 RTT는 패킷의 왕복 시간만을 측정하기 때문에 패킷의 송수신시 순방향과 역방향에서 어느 정도의 혼잡과 전송지연이 발생하였는가는 알 수가 없다. 본 논문에서는 패킷이 전송될 때 순방향/역방향 전송 지연을 계산하여, 망의 흔잡 상태를 정확하게 유추할 수 있는 새로운 알고리즘을 제시한다. 본 알고리즘에서는 여러 RTT 값들 중에서 가장 작은 RTT 값을 기준으로 하여 기준이 되는 순방향/역방향 전송 시간을 결정하고, 이 값과 각 패킷이 전송될 때 측정된 전송 시간을 비교하여 순방향/역방향 전송 지연 시간을 계산한다. 본 연구에서는 NS-2에서의 시뮬레이션과 실제 네트워크 상에의 측정을 통하여 제안된 방법의 올바른 동작을 확인하였다.

• 한국군사과학기술학회지
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• 제13권5호
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• pp.894-902
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• 2010

This paper presents a vision-based robust off-road terrain classification method against environmental variation. As a supervised classification algorithm, we applied a neural network classifier using wavelet features extracted from wavelet transform of an image. In order to get over an effect of overall image feature variation, we adopted environment sensors and gathered the training parameters database according to environmental conditions. The robust terrain classification algorithm against environmental variation was implemented by choosing an optimal parameter using environmental information. The proposed algorithm was embedded on a processor board under the VxWorks real-time operating system. The processor board is containing four 1GHz 7448 PowerPC CPUs. In order to implement an optimal software architecture on which a distributed parallel processing is possible, we measured and analyzed the data delivery time between the CPUs. And the performance of the present algorithm was verified, comparing classification results using the real off-road images acquired under various environmental conditions in conformity with applied classifiers and features. Experiments show the robustness of the classification results on any environmental condition.