• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2005년도 추계종합학술대회
• /
• pp.693-696
• /
• 2005

국내의 통신망은 효율적인 유지보수를 위하여 도로를 따라 기반시설이 구축되고 있기 때문에 전력망과 통신망의 교차와 병행이 가속화되고 있다. 전력망은 전력유도를 발생시킬 뿐만 아니라, 낙뢰와 같은 전기적 과도현상으로부터 그 자체가 전송매체로 되기 때문에 전력망 지중 접지체와 통신망 지중 접지체간을 통하여 낙뢰서지 및 지락선 과도 현상이 통신망에 유도되어 악영향을 초래한다. 본 연구에서는 통신망의 보호대책으로 지중접지체간의 이격거리를 통한 귀로전류의 차단과 양쪽 단자의 유휴심선을 접지하여 전력유도전압을 차폐하는 방법이 우수한 효과를 나타냄을 확인 할 수 있었다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2009년도 춘계학술대회 논문집 전기설비전문위원
• /
• pp.216-218
• /
• 2009

In this paper, a real time monitoring and management system being operated in the ubiquitous environment was developed to monitor leakage current, load current, and arc-fault, and an electrical safety network for reasonable management of electrical risk factor was proposed. For confirmation of usefulness and reliability of the proposed safety network and system, the developed intelligent panels were applied to 28 Korean traditional houses in Jeonjoo city, and the network including the panels was operated. If the National Electrical Safety Network is completely constructed in the houses of general electrical users, the network will have an effect on that a main manager transfers from general people to expert. As a result, the electrical fires caused by an over-load, an arc-fault, and an earth-fault will be prevented.

• 전기학회논문지
• /
• 제65권2호
• /
• pp.354-359
• /
• 2016

Power plants maintenance data is to be sent to management server system via a communication network. In this case, reliable communication network is required. Transmission of the power plants maintenance data is used in the wired communication network or wireless communication network. PLC communication network is a kind of wired communication network. However PLC communication network is easily affected by noise. On the vulnerable areas in power line system, such as a mountain or rural areas, it is difficult to form a power line communication network. For a wireless communication, environment are also influenced factors in wireless communication. Harsh environmental factors are bring the communication characteristic degradation. In such areas it can be used a combination of two networks and in this way the complementary function can be achieved. Power plants are distributed in various regions across the country. The appropriate communication network is needed to maintain the power plant.This study investigated the effect of environment on the wired communication and wireless communication. It would examine a variable factor which is affect to the communication characteristic. We used PLC communication for wired communication network and ZigBee communication for wireless communication network. We investigated the characteristics of a single communication network and it raised the need for a complex communication technology to complement a single communication network.

• Journal of Information Processing Systems
• /
• 제18권6호
• /
• pp.755-762
• /
• 2022

The existing electrical engineering design method and the dynamic objective function in the application process of automatic control system fail to meet the unbounded condition, which affects the control tracking accuracy. In order to improve the tracking control accuracy, this paper studies the electrical engineering design method based on neural network and the application of automatic control system. This paper analyzes the structure and working mechanism of electrical engineering automation control system by an automation control model with main control objectives. Following the analysis, an optimal solution of controllability design and fault-tolerant control is figured out. The automatic control power coefficient is distributed based on an ideal control effect of system. According to the distribution results, an automatic control algorithm is based on neural network for accurate control. The experimental results show that the electrical automation control method based on neural network can significantly reduce the control following error to 3.62%, improve the accuracy of the electrical automation tracking control, thus meeting the actual production needs of electrical engineering automation control system.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1997년도 하계학술대회 논문집 B
• /
• pp.397-399
• /
• 1997

This paper presents an Orthogonal Neural Network based on orthogonal functions and applies the network to nonlinear system control. The Orthogonal Neural Network doesn't have the problems of traditional feedforward neural networks such as the determination of initial weights and the numbers of layers and processing elements. In this paper, Orthogonal Neural Network is modified already introduced one by input transformation. The results show that the modified neural network has the better performance than existing one and performance of controller using this network is good.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.1558-1562
• /
• 2005

This paper describes the design and implementation of a wireless home network system using Home Network Control Protocol (HNCP) called the wireless HNCP home network system. For wireless interfaces of HNCP, IEEE 802.11b and IEEE 802.15.4 standard protocols are considered. With the implementation of the wireless HNCP home network system, a simple analysis about coexistence between IEEE 802.11b and IEEE 802.15.4 is achieved. Through the implemented wireless HNCP home network system and the analytical results about the coexistence between both two different wireless protocols, the feasibility of the wireless HNCP home network system is shown.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
• /
• pp.142-145
• /
• 1997

This paper presents an modified orthogonal neural network(MONN) based on orthogonal functions and applies the network to nonlinear system control. The accuracy of orthogonal neural network is essentially dependent on the choice of basic orthogonal functions. Modified orthogonal neural network is modified model of orthogonal neural network with input transformation to adapt its basic orthogonal functions. The results show that the modified orthogonal neural network has the excellent performance of approximating and controlling nonlinear systems and the input transformation make the ability of modified orthogoneural neural network better than one of orthogonal neural network.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.631-634
• /
• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2007년도 제38회 하계학술대회
• /
• pp.1674-1675
• /
• 2007

This paper presents a stability analysis of network systems with time delay. Time delay problem frequently occurs in network systems. Since it makes network systems unstable and unpredictable, an optimal controller is necessary to network systems. We prove the asymptotical stability of time delayed network systems using LMI optimization method and appropriate Lyapunov-Krasovskii functionals. Simulations show the effectiveness of the method.

• 한국정밀공학회지
• /
• 제15권7호
• /
• pp.44-51
• /
• 1998

Two-step neural network is designed for determining electrical discharge machining parameters in low erosion. The first neural network, which is used as a classification network, checks whether the current conditions are appropriate to electrical discharge machining in low tool erosion. If the conditions are appropriate to EDM in low erosion, suitable EDM parameters are generated by the second neural network. Theoretically known EDM conditions are produced and also utilized for training the second neural network. The trained neural network is tested how well suitable EDM machining conditions are generated under unknown machining situations Experimental result shows that the proposed two-step neural network approach could be effectively used for determining EDM parameters in low tool erosion. The results also have a practical contribution to EDM area in that it could be applied for maintaining low tool wear as well as obtaining maximum machining rates simultaneously.