• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.17-22
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• 2001

Because the quantitative relationships between welding parameters and welding result are not yet blown, optimal values of welding parameters for $CO_2$ robotic arc welding is a difficult task. Using the various artificial data processing methods may solve this difficulty. This research aims to develop an expert system for $CO_2$ robotic arc welding to recommend the optimal values of welding parameters. This system has three main functions. First is the recommendation of reasonable values of welding parameters. For such work, the relationships in between the welding parameters are investigated by the use of regression analysis and fuzzy system. The second is the estimation of bead shape by a neural network system. In this study the welding current voltage, speed, weaving width, and root gap are considered as the main parameters influencing a bead shape. The neural network system uses the 3-layer back-propagation model and a generalized delta rule as teaming algorithm. The last is the optimization of the parameters for the correction of undesirable weld bead. The causalities of undesirable weld bead are represented in the form of rules. The inference engine derives conclusions from these rules. The conclusions give the corrected values of the welding parameters. This expert system was developed as a PC-based system of which can be used for the automatic or semi-automatic $CO_2$ fillet welding with 1.2, 1.4, and 1.6mm diameter the solid wires or flux-cored wires.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.261-294
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• 1993

Most of expert systems,as a rule-based system,should be convenient to modify a rule and to insert a new rule, which is called modularity of rules. When we think correlated evidences in expert systems. conventional systems are too local to recognize the common origin of the information, and they would update the belief of the hypothesis as if it were supposed by independence soureces. In this paper to overcome such drawbacks we propose Fuzzy Belief Network which is based on the Beysian Network which provide the modulartiy between rules. To build Fuzzy Belief Network, we define nodes and links and propose algorithms for data fusion in individual node and for propagation belief value obtained as a result of data fusion.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.160-163
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• 1995

An optimal EEG and artifact classifier is proposed using neural network operating characteristics. The neural network operating characteristics are two dimensional parametric representations of the right and false identification probabilities of the network classifier. Since the EEG and EP signals acquired from multi -channel electrodes placed on the head surface are often interfered by other relatively large physiological signals such as electromyogram (EMG) or electroculogram (EOG), the removal of the artifact-affected EEGs is one of the key elements in neuro-functional mapping. Conventionally this task has been carried out by human experts spending lots of examination time. Using the neural-network based classification, human expert's efforts and time can be substantially reduced. From experiments, the neural-network based classification performs as good as human experts: variation of decisions between the neural network and human expert appears even smaller than that between human experts.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.257-260
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• 1996

This study is about the construction of algorithm for selecting Yongshin of the Four Pillars. To emulate the method the expert uses when he select the Yongshin, we introduce the Hopfield Network. The result of the simulation classified with Yongshin is presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.691-699
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• 2011

Recently, diverse approaches retrieval services based on social network are suggested. Although existing recommendation systems can retrieve experts of specific fields, profiles and evaluations about experts that users want to be recommended are in a system. The proposed expert recommendation system can automatize collection of evaluation to evaluate experts and experts' profiles in separate systems by using the Knowledge Base and XMDR. We also attempt to construct system which can recommend a number of experts by dynamically constructing Social Network by using diverse resources distributed 로컬ly and composed of heterogeneous data sources. To resolve these problems efficiently, there is a need to provide constructed resources between heterogeneous systems with transparency and independence and provide users with a singular interface. Therefore, the proposed system in this paper uses Knowledge Base and XMDR for extracting distributed experts' profiles and designs expert recommendation system connecting Knowledge Base with Social Network.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• Journal of Korean Society on Water Environment
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• v.23 no.1
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• pp.103-110
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• 2007

An efficient operational strategy using expert system for metro water supply systems in case of emergency situations is developed in this study. The emergency situations of the water supply systems are classified into three categories : pipeline system accident, machinery and electric facility accident and water quality accident. A PC-based expert system is developed using CLIPS for Seoul metro water supply system, Phase 1 & 2 system and Phase 3 & 4 system. Broad professional knowledges and experiences from the experts in the water supply systems have been collected systematically to construct the knowledge base. Decision-making in case of an emergency is based upon the professional knowledge so that a rational and efficient operational management can be available even in the absence of experienced expert. Especially the expert model developed in this study also provides a guide for pumping operation in case of pipeline accident to confirm that the proper pressure to all nodes in the system is supplied. The pipe network simulator KYPIPE has been consecutively executed by trial and error fashion for each pipeline in the system. The results from KYPIPE were included in the knowledge base to supplement the knowledge of the field engineers.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.484-487
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• 1992

To resolve the knowledge acquisition bottleneck in the expert systems, the connectionist expert systems have been proposed, which facilitate learning capability of neural networks. This paper is to modify Gallant's connectionist expert network so that it can be applied to more general problems : 1) The hidden nodes are added between the input nodes and an output node, so that the back propagation learning algorithm is used instead of perception based Pocket algorithm. 2) Inference engine is thus modified by modeling that a node may have uncertainties due to unknown inputs.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.217-224
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• 1999

Trouble shooting of modern machine tools equipped with sophisticated electronic as well as mechanical parts is so difficult that it is usually depends upon the experience and accumulated knowledge of the diagnosing persons. On the other hand, tool users are scattered in wide area, which makes it expensive for a machine tool maker to run a vast service network. An unmanned diagnosis system to which users can have access at all times could be an efficient alternative. For this purpose, a rule-based expert system for diagnosing machine tools is developed. This paper describes the structure of diagnostic knowledge, the rule firing mechanism, the diagnosis flow, and user query process. An example shows the feasibility of problem solving on site without help of a service expert from machine tool maker.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.8
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• pp.437-443
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• 2003

In this paper an expert system is developed to decide the resetting area of protective devices in power transmission systems. A configuration change in power transmission networks from a substation extension such as new line or bus addition need resetting of protective devices around the point of configuration changes. To find the resetting area in complex power system is very difficult, especially when the distance protective relays are considered to be reset. The proposed expert system, in this paper to find the resetting area has many rules based on the changes of fault currents and apparent factors from the power system alteration. It solves the problem to find relay resetting area using the network information in the database and the rule-base. The case study shows a result of the problem to find relay resetting area in KEPCO system when there is any configuration change.