• 전력전자학회논문지
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• 제12권3호
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• pp.213-220
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• 2007

고속 부하특성을 갖는 장비들이 일반화되면서 탑재형 발전기는 빠른 응답특성을 만족시켜야 한다. 본 논문은 고속 부하용으로 개발된 단극 발전기의 모델링, 시뮬레이션 및 실험 등을 기술한다. 입력 제한기가 있는 단극 발전기의 시뮬레이션 모델이 만들어졌고, 응답특성을 개선하는 최적 이득이 시뮬레이션 및 분석을 통해 결정된다. 최적 이득값을 적용하여 개발된 발전기의 향상된 대역폭 및 안정성이 시뮬레이션과 실험으로 입증되어졌다.

• 한국시뮬레이션학회논문지
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• 제11권1호
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• pp.71-82
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• 2002

One of the most important things in the research for MIN-based switch operation the management scheme of network cycle. In the traditional MIN, when the receving buffer module is empty, the sell has to move forward the front-most buffer position by the characteristic of the conventional FIFO queue. However, most of buffer modules are almost always full for practical amount of input loads. The long network cycle of the traditional scheme is thus a substantial waste of bandwidth. In this paper, we propose the modeling method for the input and multi-buffered MIN with unit step buffering scheme, In spite of simplicity, simulation results show that the proposed model is very accurate comparing to previous modeling approaches in terms of throughput and the trend of delay.

• 한국안전학회지
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• 제38권1호
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• pp.1-8
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• 2023

It is extremely important to design safety-guaranteed industrial processes because such process determine the ultimate outcomes of industrial activities, including worker safety. Application of artificial intelligence (AI) in industrial safety involves modeling industrial safety systems by using vast amounts of safety-related data, accident prediction, and accident prevention based on predictions. As a preliminary step toward realizing AI-based industrial safety in Korea, this study discusses neural network-based modeling of industrial safety systems. The input variables that are the most discriminatory relative to the output variables of industrial safety processes are selected using two information-theoretic measures, namely entropy and cross entropy. Normalized frequency and severity of industrial accidents are selected as the output variables. Our simulation results confirm the effectiveness of the proposed neural network model and, therefore, the feasibility of extending the model to include more input and output variables.

• 한국정밀공학회지
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• 제13권12호
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• pp.22-29
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• 1996

In this paper the nodes of the multilayer hidden layers have been modified for modeling the nonlinear systems. The structure of nodes in the hidden layers is built with the feedforward, the cross talk and the recurrent connections. The feedforward links are mapping the nonlinear function and the cross talks and the recurent links memorize the dynamics of the system. The cross talks are connected between the modes in the same hidden layers and the recurrent connection has self feedback, and these two connections receive one time delayed input signals. The simplified steam boiler and the analytic multi input multi output nonlinear system which contains process noise have been modeled using this neural networks.

• 드라이브 ㆍ 컨트롤
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• 제17권2호
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• pp.1-8
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• 2020

Recently, industrial systems are becoming quicker and lighter to enable the reduction of energy consumption and increase productivity. So the latest systems are more flexible and rapid than the previous systems. But, with this improvement, another problem has emerged, such as the increase in residual vibration when a system is started or stopped. The input shaper is a command generation method that can remove residual vibration. It can provide a solution to the problem of residual vibration in industrial systems. However, it is difficult to generate the input shaper in high order systems, such as a typical industrial system because the input shaper is induced from the system's vibration characteristics. This study focused on the extra insensitivity shaper that can compensate for the system's modeling error such as input dynamics, and the high order's system affection. A genetic algorithm was deployed to adjust a vibration limitation for the extra insensitivity of the input shaper. A plant is a low damping system that includes one zero and a pole. The fitness functions are an error signal of the system's response with normalized frequency variations. Verification of the suggested system is satisfied by comparison between the zero vibration derivative input shaper's response and the suggested one.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 A
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• pp.179-182
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• 2003

A simulation on bootstrapping circuit has been performed by modelling a coaxial cable as a transmission line. It is shown that the bootstrapping circuit could be unstable due to the transmission line effect though an ideal amplifier is used. While the conventional bootstrapping circuit does not cancel the input capacitance of the input buffer, a new bootstrapping circuit that cancels input capacitance of the input buffer has been proposed. The proposed bootstrapping circuit consists of the input buffer of which gam is larger than 1 and a feedback resistor to control the loop gain. The proposed bootstrapping circuit has higher input impedance than that of the conventional circuit.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2008년도 추계학술대회 논문집
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• pp.171-172
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• 2008

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 II
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• pp.943-946
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• 2003

This paper proposes new SPICE Macro-Model of MTJ(Magnetic Tunnel Junction). This Macro-Model has five I/O terminals, reproduces MR characteristics including hysteresis and behaves correctly to time varying input signals. Furthermore, this Model can be easily modified to various MTJs with different characteristics by simply varying internal parameters.

• 전기의세계
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• 제22권2호
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• pp.70-74
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• 1973

This paper dealt with circuit modeling of SCR and gate turn-off SCR by using complementary symmetrical tansistor circuit, which is modified circuit of input current dependent, current stable negative resitance circuit. Operation of this circuit is estimated and analyzed, with which compared with conventional SCR modeling circuit. Also operation and the design procedures are checked by experiments.

• 제어로봇시스템학회논문지
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• 제10권3호
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• pp.280-285
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• 2004

This paper is concerned with the modeling and identification of sequencing batch reactor (SBR) via genetic algorithm based polynomial neural network (GA-based PNN). The model describes a biological SBR used in the wastewater treatment process fur nitrogen removal. A conventional polynomial neural network (PNN) is applied to construct a predictive model of SBR process fur nitrogen removal before. But the performances of PNN depend strongly on the number of input variables available to the model, the number of input variables and type (order) of the polynomials to each node. They must be fixed by the designer in advance before the architecture is constructed. So the trial and error method must go with heavy computation burden and low efficiency. To alleviate these problems, we propose GA-based PNN. The order of the polynomial, the number of input variables, and the optimum input variables are encoded as a chromosome and fitness of each chromosome is computed. Simulation results have shown that the complex SBR process can be modeled reasonably well by the present scheme with a much simpler structure compared with the conventional PNN model.