• 대한전기학회논문지:전기기기및에너지변환시스템부문B
• /
• 제50권1호
• /
• pp.37-44
• /
• 2001

This paper describes the position control method in yard crane drive system using Lonworks network, which is a leading industrial control network. The network is composed of host computer and three motor drive systems for both gantry and trolley position controls of both gantry and trolley are controlled with the simulator of yard crane, the size of which is about 1/10 with the real yard crane.

• 대한전기학회논문지
• /
• 제36권2호
• /
• pp.140-147
• /
• 1987

In this paper, seven-order elliptic low-pass passive network is synthesized by using doublyterminated ladder network is directly transformed into the active network which has the advantage of low sensitivity and can be realized conveniently. The circuit simulation results of passive network and active network synthesized with FDNR and Leap-frog technique are compared, and it is proved that the two rest work has the same characteristics.

• 대한전기학회논문지
• /
• 제37권9호
• /
• pp.663-670
• /
• 1988

Advances in VLSI technology have made it possible to have a larger number of processing elements to be included in highly parallel processor system. A system with a large number of processing elements and memory requires a complex data path. Multistage Interconnection networks(MINS) are useful in providing programmable data path between processing elements and memory modules in multiprocessor system. In this thesis, the performance of MINS for the star network has been analyzed and compared with other networks, such as generalized shuffle network, delta network, and referenced crossbar network.

• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 2006년도 하계학술대회 논문집 Vol.7
• /
• pp.426-427
• /
• 2006

In this paper, the neural network model of the pretilt angle in the nematic liquid crystal on the homogeneous polyimide surface with different surface treatments is investigated. The pretilt angle is one of the main factors to determine the alignment of the liquid crystal display. The pretilt angle is measured to analyze the variation of the characteristics on the various process conditions. The rubbing strength and the hard baking temperature are considered as input factors. Latin hypercube sampling was used to generate initial weights and biases.

• Journal of Electrical Engineering and information Science
• /
• 제1권2호
• /
• pp.45-51
• /
• 1996

This paper presents a neural network approach for on-line estimation of partial discharge(PD) location using advanced correlation technique in power transformer. Ultrasonic sensors detect ultrasonic signals generated by a PD and the proposed method calculates time difference between the ultrasonic signals at each sensor pair using the cross-correlation technique applied by moving average and the Hamming window. The neural network takes distance difference as inputs converted from time difference, and estimates the PD location. Case studies showed that the proposed method using advanced correlation technique and a neural network estimated the PD location better than conventional methods.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.635-639
• /
• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.820-824
• /
• 2004

Personal Robot System in developing, have a module architecture, each module are connected through heterogeneors network systems like Ethernet, WLAN (802.11), IEEE1394 (Firewire), Bluetooth, USB, CAN, or RS-232C. In developing personal robot system we think that the key of robot performance is interoperability among modules. Each network protocol are well connected in the view of network system for the interoperability. So we make a bridging architecture that can routing, converting, transporting data packets with matcing each network's properties. Furthermore we suggest a advanced design scheme for realtime / non-realtime and control signal (short, requiring hard-realtime) / multimedia data (large, requiring soft-realtime). By some application systems, we could test performance, interoperability and stability. In this paper, we show our design concept, middleware architecture, and some applications systems using this middleware.

• Journal of Electrical Engineering and Technology
• /
• 제2권2호
• /
• pp.157-164
• /
• 2007

Diagnosis techniques based on the dissolved gas analysis(DGA) have been developed to detect incipient faults in power transformers. Various methods exist based on DGA such as IEC, Roger, Dornenburg, and etc. However, these methods have been applied to different problems with different standards. Furthermore, it is difficult to achieve an accurate diagnosis by DGA without experienced experts. In order to resolve these drawbacks, this paper proposes a novel diagnosis method using fuzzy clustering and a radial basis neural network(RBFNN). In the neural network, fuzzy clustering is effective for selecting the efficient training data and reducing learning process time. After fuzzy clustering, the RBF neural network is developed to analyze and diagnose the state of the transformer. The proposed method measures the possibility and degree of aging as well as the faults occurred in the transformer. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.

• 전기학회논문지
• /
• 제62권10호
• /
• pp.1368-1375
• /
• 2013

The Smart Grid, which can monitor or diagnose the power grid in real time to operate efficiently, has been pushed ahead systematically as one of alternatives to solve these issues by combining the advanced Information Communication Technology and the electrical network. Hence, the electric company which introduces smart grid technology can read remotely the electrical meter readings by means of two-way communication between the meter and the central system. This enabled the customer and the utility to take part in reasonable electrical energy utilization. AMI became one of the core foundations in realizing the Smart Grid. It is hard to test the entire process of AMI system before the full deployment because it covers the broad objects from the customer to the utility operation system and requires mass data handling and management. Therefore, we design an efficient AMI network model and a simulator for performance evaluation required to simulate the network model similar to the real environment. This tool supports to evaluate the efficiency of the AMI network equipments and deployment. Additionally, it estimates the appropriate number of deployments and the proper capabilities.

• 전기학회논문지
• /
• 제67권1호
• /
• pp.96-102
• /
• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.