• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.17 no.5
• /
• pp.567-571
• /
• 2004

Recently, GIS(Gas Insulated Switchgear) has been recognizing of importance on development of diagnosis technique which is happened problem on confidence for a long time use. Therefore, the measurement and analysis of PD with prior phenomenon of insulation breakdown is used many method of diagnosis for GIS. In this paper, we simulate trouble condition in DS and analysis trouble signal to use electrical and mechanical methods, interpretation of detected signal has analysed with to use ø-q-n pattern and neural network. For this analysis, we have used the induction and AE(acoustic emission) sensors. For the simulation experiment, we make DS for 170 KV GIS and analyze the classification and characteristics of detected signals with the application of neural network algorithm.

• Journal of the Institute of Convergence Signal Processing
• /
• v.2 no.2
• /
• pp.89-94
• /
• 2001

The position control accuracy of a robot manipulator is significantly deteriorated when a long arm robot is operated at a high speed. This paper presents a very simple sliding mode control which eliminates multiple mode residual vibration in a robot manipulator. The neural network is used to avoid that sliding mode condition is deviated due to the change of system parameter and disturbance. This paper is suggested control system which designed by sliding mode controller using neural network. The effectiveness of proposed scheme is demonstrated through computer simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.2 no.3
• /
• pp.215-220
• /
• 2002

This paper proposes a method to apply the Backpropagation(BP) algorithm of MVL(Multi-Valued Logic) Neural Network to pattern recognition. It extracts the property of an object density about an original pattern necessary for pattern processing and makes the property of the object density mapped to MVL. In addition, because it team the pattern by using multiple valued logic, it can reduce time f3r pattern and space fer memory to a minimum. There is, however, a demerit that existed MVL cannot adapt the change of circumstance. Through changing input into MVL function, not direct input of an existed Multiple pattern, and making it each variable loam by neural network after calculating each variable into liter function. Error has been reduced and convergence speed has become fast.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.361-361
• /
• 2000

In the 2-mass system with flexible shaft, a torsional vibration is often generated because of the elastic elements in torque transmission as the newly required speed response which is very close to the primary resonant frequency. This vibration makes it difficult to achieve quick responses of speed and disturbance rejection. In this paper, 2-mass system is designed by using pole placement based on optimal control theory fur fast speed response and torsional vibration elimination and using neural network for disturbance rejection in particular. The simulation results show that the proposed controller based on neural network and full state feedback controller has better performance than 려ll state feedback controller, especially fur disturbance rejection.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.142-142
• /
• 2000

During the operation of crane system in container yard, it is necessary to control the crane trolley position so that the swing of the hanging container is minimized. Recently an automatic control system with high speed and rapid transportation is required. Therefore, we designed a controller to control the crane system with disturbances. In this paper, Ive present the neural network two degree of freedom PID controller to control the swing motion and trolley position. Then we executed the computer simulation to verify the performance of the proposed controller and compared the performance of the neural network PID controller with our proposed controller in terms of the rope swing and the precision of position control . Computer simulation results show that the proposed controller has better performances than neural network PID with disturbances.

• Proceedings of the KOSOMBE Conference
• /
• v.1992 no.11
• /
• pp.47-50
• /
• 1992

This paper describes pattern classification algorithm of ECG using back-propagation neural network. We presents new feature extractor using second order approximating function as the input signals of neural network. We use 9 significant parameters which were extracted by feature extractor. 5 most characterized ECG signal pattern is classified accurately by neural network. We use AHA database to evaluate the performance ol the proposed pattern classification algorithm.

• Journal of Korean Society for Quality Management
• /
• v.30 no.2
• /
• pp.86-98
• /
• 2002

Defining the relationship between the quality of injection molded parts and the process condition is very complicate because of lots of factor are involved and each factor has a non-linearity. With the development of CAE(Computer Aided Engineering) technology, the estimation of volumetric shrinkage of injection mold parts is possible by computer simulation even though restricted application. In this research, Neural Network applied for finding optimal processing condition. The percent of volumetric shrinkage compared on each case and show neural network can be successfully applied selecting optimum condition not only within factor level but also between factor level.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 1998.11a
• /
• pp.189-194
• /
• 1998

Design factors are very important and deterministic in determining the first impression of products and environment. The final 30 number of channel button were chosen as a design factors at the Audio Unit. Then, we made the 8 types of prototype. with combining the design factors for experiment. Subjects rated the SD(Semantic Differential) evaluation sheets which have the 30 adjectives after watching each prototype. With the evaluated values, we simulated to identify the relation between the design factors and the adjectives using Neural Network. As a results, we could abstract the affective adjectives on each 8 types. Therefore, this research suggested the possibilities that we can infer the optimal design factors and types using Neural Network, if adjectives were given.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1995.04b
• /
• pp.451-456
• /
• 1995

This study investigates the feasibility of the real time detection of tool breadage in face milling operation. The proposed methodology using an ART2 neural network overcomes a cumbersome task in terms of the learning or determining a threshold value. The features taken in the researchare the AR parameters modelled from a RLS, and those are proven to be good features for tool breakage from experiments. From the results of the off line application, we can conclude that an ART2 neural network can be well applied to the clustering of tool states in real time regardless of the unsupervised learning.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.5
• /
• pp.33-39
• /
• 1995

This paper presents a method of detecting localized defects on tapered roller bearing in main spindle of machine tool system. The statistical parameters in time-domain processing technique have been calculated to extract useful features from bearing vibration signals. These features are used by the input feature of an artificial neural network to detect and diagnose bearing defects. As a results, the detection of bearing defect conditions could be successfully performed by using an artificial neural network with statistical parameters of acceleration signals.