• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.797-800
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• 1992

Much experience and knowledge is needed in welding because there are many working parameters and quantitative description is difficult. Therefore, introduction of expert system based on such data base has been required. In this study, welding heat and shape of bead was controlled by fuzzy inference with the welding condition, position error and voltage and current error of robot. For this, torch trajectory of robot was generated by modeling the working data with CAD and then welding was carried out through down loading to robot. And working error was controlled by on-line communication.

• 대한안전경영과학회지
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• 제10권1호
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• pp.11-22
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• 2008

Humans are well-known for being adept at using intuition and expertise in many situations. However, human experts are still susceptible to errors in judgment or execution, and failure to recognize the limits of knowledge. This would happen especially in semi-structured situations, in multi-disciplinary settings, under time or other stress, under uncertainty, or when knowledge is outdated Human errors are caused by cognitive biases, attentional slips/memory lapses, cultural motivations, and missing knowledge. The purpose of this research is to study errors of human experts committed in judgment and the general idea of critiquing systems as corresponding plan. Compared to expert systems, critiquing systems are narrowly focused programs useful in limited situations for collaborating with and supporting experts in their task activities. It supports an expert by detecting the human's errors by deploying various strategies that stimulate humans to improve their performance. A variety of types of critiquing systems has spread through numerous application areas.

• 한국정밀공학회지
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• 제20권4호
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• pp.29-38
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• 2003

This paper deals with an automated computer-aided process planning and die design system with which designer can determine operation sequences even after only a little experience in process planning and die design of multi former-bolt products by multi-stage former working. The approach is based on knowledge-based rules, and a process knowledge base consisting of design rules is built. Knowledge fur the system is formulated from plasticity theories, empirical results and the empirical knowledge of field experts. Programs for the system have been written in AutoLISP for AutoCAD with a personal computer. An attempt is made to link programs incorporating a number of expert design rules with the process variables obtained by commercial FEM softwares, DEFORM and ANSYS, to form a useful package. The system is composed of four main modules. The process planning and die design module considers several factors, such as the complexities of preform geometry, punch and die profiles, specifications of available multi former, and the availability of standard parts. It can provide a flexible process based on either the reduction in the number of forming sequences by combining the possible two processes in sequence, or the reduction of deviation of the distribution and the level of the required forming loads by controlling the forming ratios. The system uses 2D geometry recognition and is integrated with the technology of process planning, die design, and CAE analysis. The standardization of die parts for multi former-bolt products requiring a cold forging process is described. The system developed makes it possible to design and manufacture multi former-bolt products more efficiently.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.563-573
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• 2018

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts' knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.

• 대한기계학회논문집
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• 제18권8호
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• pp.2039-2052
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• 1994

In this study, an expert system for multi-stage cold forging process design of axisymmetric parts is developed. The available geometries are axisymmetric shape and cylinder with a hold in one end. The overall system is composed of knowledge-based system for process sequence design, output module interfaced with CAD system and material data-base. In the developed system, designed process can be modified in order to reduce the number of processes and make the distribution of forming load be almost equal at various deforming stages within the machine capacity. After process sequence design is completed, results can be stored as a text file or a commercial CAD system file. The capabilities of the developed system are illustrated through various examples of process design.

• 산업경영시스템학회지
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• 제40권4호
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• pp.112-119
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• 2017

Selecting suppliers in the global supply chain is the very difficult and complicated decision making problem particularly due to the various types of supply risk in addition to the uncertain performance of the potential suppliers. This paper proposes a multi-phase decision making model for supplier selection under supply risks in global supply chains. In the first phase, the model suggests supplier selection solutions suitable to a given condition of decision making using a rule-based expert system. The expert system consists of a knowledge base of supplier selection solutions and an "if-then" rule-based inference engine. The knowledge base contains information about options and their consistency for seven characteristics of 20 supplier selection solutions chosen from articles published in SCIE journals since 2010. In the second phase, the model computes the potential suppliers' general performance indices using a technique for order preference by similarity to ideal solution (TOPSIS) based on their scores obtained by applying the suggested solutions. In the third phase, the model computes their risk indices using a TOPSIS based on their historical and predicted scores obtained by applying a risk evaluation algorithm. The evaluation algorithm deals with seven types of supply risk that significantly affect supplier's performance and eventually influence buyer's production plan. In the fourth phase, the model selects Pareto optimal suppliers based on their general performance and risk indices. An example demonstrates the implementation of the proposed model. The proposed model provides supply chain managers with a practical tool to effectively select best suppliers while considering supply risks as well as the general performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1265-1270
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• 1990

A prototype integrated system and its theories for distributed SISO control structure synthesis of complete chemical plants is developed. The scope of this work includes control structure synthesis not only of simple units with unspecified control loops but also of the complex process at preliminary and basic design stage. Hierarchical approach and dual-decomposition strategy (that is multi-layer decomposition and multi-echelon decomposition) is applied to this system. Because automatic control structure synthesis of complex plants is a problem defined as a series of knowledge-intensive tasks within multiple spaces, the established methodology is complemented by not only techniques from knowledge-based expert systems but also shortcut and rigorous control theories. This system is used for education of control designers, process engineers, operators and students as well as for operability studying, in-line and on-line process control structure synthesis.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1050-1053
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• 2005

There is no agreed definition of intelligence. The ability to adapt to the environments is a kind of intelligence. Expert functionally recognize environment using their five senses, and acquire and memorize knowledge necessary for operating machines. Knowledge that they cannot acquire directly is acquired in indirect ways. The purpose of intelligence machines is applying to machines experts' knowledge acquisition process and their skills in operating machine. An agent is an autonomous process that recognizes external environment, exchanges knowledge with external machines and performs an autonomous decision-making function in order to achieve common goals. This paper describes agent application for intelligence machine.

• 대한공간정보학회지
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• 제16권3호
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• pp.87-94
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• 2008

이 연구에서는 복합형 환승센터의 돌발상황 대응을 위한 모니터링 대상의 선정과 돌발상황 모니터링, 검지, 확인, 전파, 처리, 종료까지의 진행 절차에서 관리되는 정보를 도출하고 데이터베이스로 통합관리 하도록 하여 상황진행 단계에 따라 상황처리 의사결정에 필요한 사전정보를 제공하도록 제시하였다. 시설물, 이용자, 교통류로 구성된 환승센터는 모니터링 정보의 한계성을 가지고 있다. 따라서 상황검지 및 상황대응 전략수립 방안에서는 상황대응 전문가의 경험 지식과 과거의 사례를 활용할 수 있는 전문가 시스템의 사례기반 추론을 활용하는 방안으로 접근하였다. 또한 돌발상황 발생 시 공간적 혼잡도 및 피해 최소화를 위해 환승센터에서 운영하고 있는 설비의 통제 방안을 제시하였으며, 대외기관의 서비스 지원 극대화를 위해 실시간으로 상황정보의 공유 서비스 체계가 유지되도록 하였다.

• Journal of Information Processing Systems
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• 제13권6호
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• pp.1496-1515
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• 2017

One of the most visible developments in Decision Support Systems (DSS) was the emergence of rule-based expert systems. Hence, despite their success in many sectors, developers of Medical Rule-Based Systems have met several critical problems. Firstly, the rules are related to a clearly stated subject. Secondly, a rule-based system can only learn by updating of its rule-base, since it requires explicit knowledge of the used domain. Solutions to these problems have been sought through improved techniques and tools, improved development paradigms, knowledge modeling languages and ontology, as well as advanced reasoning techniques such as case-based reasoning (CBR) which is well suited to provide decision support in the healthcare setting. However, using CBR reveals some drawbacks, mainly in its interrelated tasks: the retrieval and the adaptation. For the retrieval task, a major drawback raises when several similar cases are found and consequently several solutions. Hence, a choice for the best solution must be done. To overcome these limitations, numerous useful works related to the retrieval task were conducted with simple and convenient procedures or by combining CBR with other techniques. Through this paper, we provide a combining approach using the multi-criteria analysis (MCA) to help, the traditional retrieval task of CBR, in choosing the best solution. Afterwards, we integrate this approach in a decision model to support medical decision. We present, also, some preliminary results and suggestions to extend our approach.