• 한국철도학회논문집
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• 제13권2호
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• pp.159-165
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• 2010

본 논문에서는 RAM(Reliability, Availability, and Maintainability)과 RCM(Reliability Centered Maintenance) 개념을 기반으로 하는 틸팅 열차 유지보수 시스템(TRMS; Tilting Rolling-stock Maintenance System)의 S/W 구축방안을 제시하였다. 먼저 RCM 개념 및 절차에 대하여 소개하고 TRMS S/W가 어떻게 이러한 개념과 절차를 구현하는지에 관하여 논하였다. TRMS는 네 개의 모듈 즉 시스템 및 운영정보 모듈, FMECA 모듈, RAM 정보 모듈, RCM 분석 모듈로 구성되어 있다. 시스템 및 운영정보 모듈에서는 RCM 분석의 기초 작업인 데이터 수집을 위한 인터페이스가 제공되고, FMECA 모듈에서는 시스템 및 운영정보 모듈의 고장 데이터를 이용하여 고장영향 분석을 할 수 있는 워크 스페이스(workspace)를 제공한다. RAM 모듈에서는 Weibull 분포의 신뢰성과 고장률을 산출하기위한 알고리즘이, RCM 분석모듈에서는 작업주기와 작업비용을 계산하기위한 계산식이 제안되었다. 유지보수 관계자들이 RCM의 장점을 충분히 인식한다면 RCM을 다른 전동차 유지보수 시스템에도 쉽게 적용할 수 있을 것이다.

• Industrial Engineering and Management Systems
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• 제13권1호
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• pp.52-66
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• 2014

Promotion of a closed-loop supply chain requires disassembly systems that recycle end-of-life (EOL) assembled products. To operate the recycling disassembly system, parts selection is environmentally and economically carried out with non-destructive or destructive disassembly, and the recycling rate of the whole EOL product is determined. As the number of disassembled parts increases, the recycling rate basically increases. However, the labor cost also increases and brings lower profit, which is the difference between the recovered material prices and the disassembly costs. On the other hand, since the precedence relationships among disassembly tasks of the product also change with the parts selections, it is also required to optimize allocation of the tasks in designing a disassembly line. In addition, because information is required for such a design, the recycling rate, profit of each part and disassembly task times take precedence among the disassembly tasks. However, it is difficult to obtain that information in advance before collecting the actual EOL product. This study proposes and analyzes an optimal disassembly system design using integer programming with the environmental and economic parts selection (Igarashi et al., 2013), which harmonizes the recycling rate and profit using recyclability evaluation method (REM) developed by Hitachi, Ltd. The first stage involves optimization of environmental and economic parts selection with integer programming with ${\varepsilon}$ constraint, and the second stage involves optimization of the line balancing with integer programming in terms of minimizing the number of stations. The first and second stages are generally and mathematically formulized, and the relationships between them are analyzed in the cases of cell phones, computers and cleaners.

• Journal of Computing Science and Engineering
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• 제1권2호
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• 한국HCI학회논문지
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• 제5권2호
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• pp.15-24
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• 2010

디지로그 북(Digilog book)은 기존 서적과 디지털 콘텐츠을 융합함으로써, 아날로그적 감성과 디지털 오감을 함께 제공하는 증강현실기반 차세대 출판물이다. 디지로그 북을 저작할 수 있는 저작 소프트웨어인 아틀렛(ARtalet)은 증강현실 환경에서 3 차원 사용자 인터페이스를 이용한 직관적인 In-Situ 저작 환경을 제공한다. 본 논문은 디지로그 북에 증강된 3D 객체에 이동 경로를 생성하고 조작 할 수 있는, 아틀렛 저작 환경 기반의 이동 궤적 편집 기법을 제안한다. 구체적으로 이동 궤적의 조정점(Control point)을 적절히 할당하기 위해서 3차원 조작도구의 이동 좌표에 대하여 조정점 할당 검사를 한다. 그리고 부드러운 곡선 형태로 이동 궤적을 복원하기 위해서 스플라인을 이용한 보간 과정을 수행한다. 또한 작고 밀집된 이동 궤적의 조정점을 효과적으로 선택하기 위해서 동적 스코어(Score)를 기반으로 한 조정점 선택 방법을 적용한다. 실험 결과 제안한 방법은 기존 방법에 비해 오차와 완료시간은 유의한 차이가 없었지만, 조정점의 수를 약 90% 이상 감소시킬 수 있었다. 이것은 매우 적은 수의 조정점만으로도 이동궤적을 복원할 수 있으며 추후 이동 궤적 조작에 필요한 조정점의 조작 횟수를 대폭 줄일 수 있다는 것을 의미한다. 또한 제안한 방법은 기존의 조정점 조작 방법에 비해 상대적으로 적은 손과 팔의 움직임만으로도 빠르게 이동 궤적의 형태를 변경 할 수 있었다. 본 논문에서 제안한 3D 객체의 이동 궤적 편집 방법은 몰입형 In-Situ 증강현실 환경의 교육, 게임, 디자인, 애니메이션, 시뮬레이션 등의 분야에서 드로잉 또는 곡선 편집 방법으로 응용될 수 있다.