• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.27
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• pp.105-111
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• 1993

Human works shift its roll from physical condition to the system supervisory control task In this paper safety-presentation configuration is discussed instead of well-known fault-warning configuration. Of paticular interest was the personal factor which include the cognitive process. Through a performance between each person information processing(d') and decision process($\beta$) was pointed out to explain the sensitivity of personal cognitive process. Impact of uncertainty effect the supervisor having doubt situations. These facts are released by the use of flat fuzzy number of $\beta$ and its learning rate R.

• Proceedings of the Korean Nuclear Society Conference
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• 2003.10a
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• pp.220-220
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• 2003

• Journal of the Korean Society for Precision Engineering
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• v.14 no.6
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• pp.64-73
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• 1997

솔리드 모델러, 자동요소분할 기법, 4면체 특이요소, 응력확대계수의 해석 기능을 통합하여, 3차원 균열의 응력확대계수를 효율적으로 해석할 수 있는 시스템을 개발하였다. 균열을 포함하는 기하모델을 CAD 시스템을 이용하여 정의하고, 경계조건과 재료 물성치 및 절점밀도 분포를 기하모델에 직접 지정함으로써, 퍼지이론 에 의한 절점발생과 데로우니 삼각화법에 의한 요소가 자동으로 생성된다. 특히, 균열 근방에는 4면체 2차 특이요소를 생성시켰으며, 유한요소 해석을 위한 입력 데이터가 자동으로 작성되어 해석코드에 의한 응력 해석이 수행된다. 해석 후, 출력되는 변위를 이용하여 변위외삽법에 의한 응력확대계수가 자동적으로 계산되어 진다. 본 시스템의 효용성을 확인하기 위해, 인장력을 받는 평판내의 표면균열에 대해 해석하여 보았다.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.198-207
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• 2023

Responding to changes in artificial intelligence models and the data environment is crucial for increasing data-learning accuracy and inference stability of industrial applications. A learning model that is overfitted to specific training data leads to poor learning performance and a deterioration in flexibility. Therefore, an early stopping technique is used to stop learning at an appropriate time. However, this technique does not consider the homogeneity and independence of the data collected by heterogeneous nodes in a differential network environment, thus resulting in low learning accuracy and degradation of system performance. In this study, the generalization performance of neural networks is maximized, whereas the effect of the homogeneity of datasets is minimized by achieving an accuracy of 99.7%. This corresponds to a decrease in delay time by a factor of 2.33 and improvement in performance by a factor of 2.5 compared with the conventional method.

• Safety and Health at Work
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• v.8 no.3
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• pp.267-275
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• 2017

Background: The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent. Methods: The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc. Results: The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed. Conclusion: Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

• Journal of Digital Contents Society
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• v.2 no.2
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• pp.103-115
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• 2001

This paper proposes the two algorithms which generate a minimal decision rule and approximate inference operation, adapted the rough set and the factor space theory in fuzzy knowledge base. The generation of the minimal decision rule is executed by the data classification technique and reduct applying the correlation analysis and the Bayesian theorem related attribute factors. To retrieve the specific object, this paper proposes the approximate inference method defining the membership function and the combination operation of t-norm in the minimal knowledge base composed of decision rule. We compare the suggested algorithms with the other retrieval theories such as possibility theory, factor space theory, Max-Min, Max-product and Max-average composition operations through the simulation generating the object numbers and the attribute values randomly as the memory size grows. With the result of the comparison, we prove that the suggested algorithm technique is faster than the previous ones to retrieve the object in access time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.7
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• pp.3244-3251
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• 2012

In the construction and operation of large marine structure, hazard risk analysis is one of important factors. Therefore, this paper investigates the hazard risk indexes and evaluates the risk level in the construction and operation of SFT on the basis of expert survey and Fuzzy analytic hierarchy process. Hazard risk is divided into natural hazard risk (earthquake, typhoon, tsunami, and ice collision) and human factor hazard risk (fire, explosion, traffic accident, ship or submarine collision). Also, the influence of hazard risk indexes on SFT was evaluated in tunnel tube, supporting system, ventilation tower, foundation, and connection part. As the hazard risk level of SFT is compared with those of bridge, underwater tunnel, and immersed tunnel, the intrinsic risk level of SFT was evaluated. Tsunami and earthquake had higher risk level in natural hazard risk, and the risk levels of fire and explosion were higher in human factor hazard risk. Hazard risk level of SFT was 1.4 times higher than immersed tunnel, and 3.2 times higher than bridge.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.59-71
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• 2019

This research is aimed to identify the building factors and deriving the importance of each factor for establishing the smart distribution center in the fourth industrial revolution era. The research methodology used CFPR(Consistent Fuzzy Preference Relations) to effectively extract expert knowledge. Research has shown as principle factors that "Service" is the first factor to be considered as 0.271, followed by "Infra(Warehouse)"(0.254), "Information System"(0.247), and "Equipment"(0.228). And as detailed factors, "Reliability" showed the highest importance as 0.091, followed "Visibility of Information System"(0.076), "Space Advancement"(0.075), "Location"(0.074) and "Satisfaction of service"(0.073). This study has implications in that it has presented an establishment direction for the smart distribution center.

• The Korean Journal of Franchise Management
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• v.8 no.1
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• pp.31-41
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• 2017

Purpose - As the domestic coffee market is rapidly growing and competition is intensifying, coffee shops need to establish a marketing strategy that grasps the needs and desires of consumers in order to secure a competitive advantage in terms of survival. From this point of view, this study suggests what choice attributes consumers consider when visiting coffee shops, and analyzes the effect of customer choice attributes on franchise and private coffee shops using fsQCA. Research design, data, and methodology - In the present study, we tried to understand the effect of the combination of choice attribute on satisfaction by the type of coffee shop based on the complex system theory, while studying the existing coffee shop choice attribute focuses on the causal relationship. FsQCA is a complementary analytical method between quantitative and qualitative research, and is a method for effectively analyzing the complex combination of causal variables. Result - The results of the study are as follows. First, cleanliness was found to be the most important factor in determining coffee quality, which is the most important factor affecting customer satisfaction. Second, customers who prefer franchise coffee shops seem to be most concerned about atmosphere, menu, cleanliness and price. On the other hand, customers who prefer private coffee shops consider image the most important. Conclusions - The implications of this study are as follows. Overall, coffee shops should manage cleanliness basically regardless of the type of store, but they should manage the choice attributes differently depending on the type of coffee shop. Franchise coffee shops will be able to increase the level of store satisfaction by systematically managing the store atmosphere, menu, cleanliness, and price according to the manual using the advantages of the franchise system. On the other hand, unlike the franchise coffee shops, private coffee shops can operate autonomous stores, so customers can use various marketing mixes to enhance their store image.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.37-52
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• 2011

Supply chain management is a strategic thinking which enhances the value of supply chain and adapts more promptly for the changing environment. For the seamless partnership and value creation in supply chains, information and knowledge sharing and proper partner selection criteria must be applied. Thus, the partner selection criteria are critical to maintain product quality and reliability. Each part of a product is supplied by an appropriate supply partner. The criteria for selecting partners are technological capability, quality, price, consistency, etc. In reality, the criteria for partner selection may change according to the characteristics of the components. When the part is a core component, quality factor is the top priority compared to the price. For a standardized component, lower price has a higher priority. Sometimes, unexpected case occurs such as emergency order in which the preference may shift on the top. Thus, SCM partner selection criteria must be determined dynamically according to the characteristics of part and its context. The purpose of this research is to develop an OWL model for the supply chain partnership depending on its context and characteristics of the parts. The uncertainty of variable is tackled through fuzzy logic. The parts with preference of numerical value and context are represented using OWL. Part preference is converted into fuzzy membership function using fuzzy logic. For the ontology reasoning, SWRL (Semantic Web Rule Language) is applied. For the implementation of proposed model, starter motor of an automobile is adopted. After the fuzzy ontology is constructed, the process of selecting preference-based supply partner for each part is presented.