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무단변속기(CVT) 벨트-풀리의 변속비 및 마찰계수에 관한 실험적 연구
고정한(Jeonghan Ko),오종선(Jongsun Oh) 한국자동차공학회 2004 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
CVT(Continuously Variable Transmission) has been widely accepted in a compact-car and HEV since it can improve both fuel economy and driving performance simultaneously. CVT with metal belt transmits the engine power through its belt-pulley, so belt should not slip and operate in optimal way. Consequently the accuracy of the friction coefficient between belt and pulley is very important. In this paper, the relation between the speed ratio and pulley thrust ratio is investigated by simulation and experiment. Moreover, the friction coefficient between belt and pulley is measured by the belt-pulley test rig. With this data, optimum belt clamping force can be calculated to improve efficiency and fuel economy.
Jeonghan Ko(고정한) 한국산학기술학회 2012 한국산학기술학회논문지 Vol.13 No.6
산업현장에서는 품질검사 과정 중의 잘못된 불량판정을 감소시키기 위하여, 각 제품단위에 대하여 불량판정 이 나왔을 경우 복수의 검사를 반복하여 수행하는 경우가 있다. 본 논문은 총 두 번까지의 연속된 검사를 허용하는 품질검사 프로세스에 대한 분석을 다루고 있다. 본 논문에서는 제품검사의 단계와 품질판정 상태를 모델링하는 수학 적 도구로서 마르코프 체인이 사용되었다. 그리고 산업체의 생산관리 시스템에서 수집된 불량 판정률을 마르코프 체 인 모델의 데이터로 사용하였다. 또한 본 논문은 제품의 품질 특성 별 최종 비율과 제품의 폐기율에 대한 이러한 연 속적인 이중검사 허용 프로세스의 영향에 대하여 분석을 수행하였다. 분석 결과, 연속이중검사 허용이 정상 제품에 대한 불량판정을 줄이며, 그 결과 재료, 노동 및 기타 비용을 줄이는 데 기여할 수 있다는 것을 확인하였다. When a quality inspection process rejects a product unit, consecutive repeated inspections are sometimes conducted for the rejected unit to reduce a false reject possibility. This paper analyzes a special inspection process that allows up to two times of consecutive testing for each product to decrease type I inspection errors. This study uses a Markov chain to model the steps of the inspection process and a product unit’s quality states during inspection. Historical inspection results from a company are used as the data for the Markov chain model. Using the Markov chain model and data, this study analyzes the effect of this special inspection rule on the proportion of the final quality levels and scrap rate. The results demonstrate that this inspection process of possible double testing could help reduce unnecessary rejects and consequently decrease material and production costs.
An Efficient One Class Classifier Using Gaussian-based Hyper-Rectangle Generation
Do Gyun Kim(김도균),Jin Young Choi(최진영),Jeonghan Ko(고정한) 한국산업경영시스템학회 2018 한국산업경영시스템학회지 Vol.41 No.2
In recent years, imbalanced data is one of the most important and frequent issue for quality control in industrial field. As an example, defect rate has been drastically reduced thanks to highly developed technology and quality management, so that only few defective data can be obtained from production process. Therefore, quality classification should be performed under the condition that one class (defective dataset) is even smaller than the other class (good dataset). However, traditional multi-class classification methods are not appropriate to deal with such an imbalanced dataset, since they classify data from the difference between one class and the others that can hardly be found in imbalanced datasets. Thus, one-class classification that thoroughly learns patterns of target class is more suitable for imbalanced dataset since it only focuses on data in a target class. So far, several one-class classification methods such as one-class support vector machine, neural network and decision tree there have been suggested. One-class support vector machine and neural network can guarantee good classification rate, and decision tree can provide a set of rules that can be clearly interpreted. However, the classifiers obtained from the former two methods consist of complex mathematical functions and cannot be easily understood by users. In case of decision tree, the criterion for rule generation is ambiguous. Therefore, as an alternative, a new one-class classifier using hyper-rectangles was proposed, which performs precise classification compared to other methods and generates rules clearly understood by users as well. In this paper, we suggest an approach for improving the limitations of those previous one-class classification algorithms. Specifically, the suggested approach produces more improved one-class classifier using hyper-rectangles generated by using Gaussian function. The performance of the suggested algorithm is verified by a numerical experiment, which uses several datasets in UCI machine learning repository.