• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1582-1587
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• 2005

In coagulant control of water treatment plants, rule extraction, one of datamining categories, was performed for coagulant control of a water treatment plant. Clustering methods were applied to extract control rules from data. These control rules can be used for fully automation of water treatment plants instead of operator's knowledge for plant control. To perform fuzzy clustering, there are some coefficients to be determined and these kinds of studies have been performed over decades such as clustering indices. In this study, statistical indices were taken to calculate the number of clusters. Simultaneously, seed points were found out based on hierarchical clustering. These statistical approaches give information about features of clusters, so it can reduce computing cost and increase accuracy of clustering. The proposed algorithm can play an important role in datamining and knowledge discovery.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.510-520
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• 2000

For content-based video indexing and retrieval, it is necessary to segment video data into video shots and then select key frames or representative frames for each shot. However, it is very difficult to select key frames automatically because the task of selecting meaningful frames is quite subjective. In this paper, we propose a new approach in selecting key frames based on visual contents such as region information and their temporal variations in the shot. First of all, we classify video shots into panning shots, zooming shots, tilting shots or no camera motion shots by detecting camera motion information in video shots. Then, in each category, we apply appropriate fuzzy rules to select key frames based on meaningful content in frame. Finally, we control the number of key frames in the selection process by adjusting the degree of detail in representing video shots.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.589-598
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• 2002

The color measuring/matching expert system is implemented with a new color measuring method that combines intelligent algorithms with image processing techniques. Color measuring part of the proposed system preprocesses the scanned original color input images to eliminate their distorted components by means of the image histogram technique of image pixels, and then extracts RGB(Red, Green, Blue)data among color information from preprocessed color input images. If the extracted RGB color data does not exist on the matching recipe databases, we can measure the colors for the user who want to implement the model that can search the rules for the color mixing information, using the intelligent modeling techniques such as fuzzy inference system and adaptive neuro-fuzzy inference system. Color matching part can easily choose images close to the original color for the user by comparing information of preprocessed color real input images with data-based measuring recipe information of the expert, from the viewpoint of the delta Eformula used in practical process.

• International Journal of Safety
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• v.4 no.2
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• pp.12-17
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• 2005

This paper aims at the development of an knowledge base for an electrical fire cause diagnosis system using the entity relation database. The relation database which provides a very simple but powerful way of representing data is widely used. The system focused on database construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. In order to store and access to the information concerned with electrical fires, the key index items which identify electrical fires uniquely are derived out. The knowledge base consists of a case base which contains information from the past fires and a rule base with rules from expertise. To implement the knowledge base, Access 2000, one of DB development tools under windows environment and Visual Basic 6.0 are used as a DB building tool. For the reasoning technique, a mixed reasoning approach of a case based inference and a rule based inference has been adopted. Knowledge-based reasoning could present the cause of a newly occurred fire to be diagnosed by searching the knowledge base for reasonable matching. The knowledge-based database has not only searching functions with multiple attributes by using the collected various information(such as fire evidence, structure, and weather of a fire scene), but also more improved diagnosis functions which can be easily wed for the electrical fire cause diagnosis system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7277-7282
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• 2014

In this study, the gradient descent algorithm was used for FLC analysis and the algorithm was used to represent the effects of nonlinear parameters, which alter the antecedent and consequence fuzzy variables of FLC. The controller parameters choose the control variable by iteration for gradient descent algorithm. The FLC consists of 7 membership functions, 49 rules and a two inputs - one output system. The system adopted the Min-Max inference method and triangle type membership function with a 13 quantization level.

• Journal of information and communication convergence engineering
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• v.7 no.1
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• pp.7-12
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• 2009

The advanced computer network and Internet technology enables connectivity of computers through an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and can not detect new hacking patterns, making it vulnerable to previously unidentified attack patterns and variations in attack and increasing false negatives. Intrusion detection and prevention technologies are thus required. We proposed a network based hybrid Probe Intrusion Detection model using Fuzzy cognitive maps (PIDuF) that detects intrusion by DoS (DDoS and PDoS) attack detection using packet analysis. A DoS attack typically appears as a probe and SYN flooding attack. SYN flooding using FCM model captures and analyzes packet information to detect SYN flooding attacks. Using the result of decision module analysis, which used FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. For the performance evaluation, the "IDS Evaluation Data Set" created by MIT was used. From the simulation we obtained the max-average true positive rate of 97.064% and the max-average false negative rate of 2.936%. The true positive error rate of the PIDuF is similar to that of Bernhard's true positive error rate.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.537-540
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• 2000

The Distribution System consist of many tie-line switches and sectionalizing switches, operated a radial type. When an outage occurs in Distribution System, outage areas are isolated by system switches, has to restored as soon as possible. At this time, system operator have to get a information about network topology for service restoration of outage areas. Therefore, the searching result of restorative topology has to fast computation time and reliable result topology for to restore a electric service to outage areas, equal to optimal switching operation problem. So, the problem can be defined as combinatorial optimization problem. The service restoration problem is so important problem which have outage area minimization, outage loss minimization. Many researcher is applying to the service restoration problem with various techniques. In this paper, advanced Dijkstra algorithm is applied to searching a restoration topology, is so efficient to searching a shortest path in graph type network. Additionally, fuzzy rules and operator are applied to overcome a fuzziness of correlation with input data. The present technique has superior results which are fast computation time and searching results than previous researches, demonstrated by example distribution model system which has 3 feeders, 26 buses. For a application capability to real distribution system, additionally demonstrated by real distribution system of KEPCO(Korea Electric Power Corporation) which has 8 feeders and 140 buses.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.398-403
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• 1993

A rule based fuzzy expert system to self-tune PID controllers is proposed in this paper. The proposed expert system contains two rule bases, where one is responsible for "Long term tuning" and the other for "Incremental tuning". The rule for "Long term tuning" are extracted from the Wills'map and the knowledge about the implicit relations between PID gains and important long term features of the output response such as overshoot, damping and rise time, etc., while 'Incremental tuning" rules are obtained from the relations between PID gains and short term features, error and change in error. In the PID control environment, the proposed expert system operates in two phases sequentially. In the first phase, the long term tuning is performed until long term features meet their desired values approximately. Then the incremental tuning tarts with PID gains provided by the long term tuning procedure. It is noticeable that the final PID gains obtained in the incremental tuning phase are only the temporal ones. Simulation results show that the proposed rule base for "Long term tuning" provides superior control performance to that of Litt and that further improvement of control performance is obtained by the "Incremental tuning'.ance is obtained by the "Incremental tuning'.ing'.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1314-1317
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• 1999

In this paper, we design an Adaptive Neuro-Fuzzy Inference System(ANFIS) Precompensator for the performance improvement of conventional proportional integral derivative (PID) controller that the governor system of power plant constantly maintains the load frequency of two-area power system. The ANFIS Precompensator is expressed as the membership functions of premise parameters and the linear combination of consequent parameters by Sugeno's fuzzy if-then rules using nonlinear input-output relation for the set point automatic modification maintaining conventional PID controller. The proposed compensation design technique is hoped to be satisfactory method overcome difficulty of exact modelling and arising problems by the complex nonlinearities of power system, and our design shows merit that is easily implemented by adding an ANFIS precompenastor to an existing PID controller without replacement.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.189-193
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• 2013

In this paper, in order to predict and prevent electrical fires that occur in the power system, we measured the informations of electric power, and then proposed a system to predict the electrical fire using these informations. To this end, we analyzed the correlations for over-current, overload and overheating. These states are caused by the grounding current and the leakage current, and are the main causes of an electrical fire. Use these correlations to derive the derivative of the fuzzy rules for membership function. The designed algorithm was simulated by utilizing the informations of the actual power of the switchgear-panel.