• Journal of the Korean Society for Railway
• /
• v.13 no.2
• /
• pp.159-165
• /
• 2010

In this paper the TRMS (Tilting Rolling-stock Maintenance System) that applies the concept of RAM (Reliability, Availability, and Maintainability) and RCM (Reliability Centered Maintenance) to Preventive and Corrective Maintenance Policy for TTX (Tilting Train Express) will be discussed. We will briefly introduce the RCM concepts and discus show these concepts and procedures are implemented in the TRMS S/W. In the TRMS S/W there are four modules, System and Operations Information Module, FMECA(Failure Modes, Effects, and Criticality Analysis)module, RAM Information Module, and RCM Analysis Module. The System and Operations Information Module provides the user interface for collection of systems and operations related data and the FMECA module provides a groundwork for the RCM analysis. The algorithms to calculate the reliability and failure rate for Weibull distribution and formulae to calculate the task intervals and task costs are proposed in the RAM and RCM Analysis Module respectively. There is a good possibility of applying RCM to other rolling stock maintenance systems if the benefit that RCM can brings to the maintenance world is fully recognized.

• Industrial Engineering and Management Systems
• /
• v.13 no.1
• /
• pp.52-66
• /
• 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
• /
• v.1 no.2
• /
• pp.177-194
• /
• 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)
• /
• v.17 no.7
• /
• pp.1773-1793
• /
• 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.

• Journal of the HCI Society of Korea
• /
• v.5 no.2
• /
• pp.15-24
• /
• 2010

A Digilog Book is an augmented reality (AR) based next generation publication supporting both sentimental analog emotions and digitized multi-sensory feedbacks by combining a conventional printed book and digital contents. As a Digilog Book authoring software, ARtalet provides an intuitive authoring environment through 3D user interface in AR environment. In this paper, we suggest ARtalet authoring environment based trajectory editing method to generate and manipulate a movement path of an augmented 3D object on the Digilog Book. Specifically, the translation points of the 3D manipulation prop is examined to determine that the point is a proper control point of a trajectory. Then the interpolation using splines is conducted to reconstruct the trajectory with smoothed form. The dynamic score based selection method is also exploited to effectively select small and dense control points of the trajectory. In an experimental evaluation our method took the same time and generated a similar amount of errors as the usual approach, but reduced the number of control points needed by over 90%. The reduced number of control points can properly reconstruct a movement path and drastically decrease the number of control point selections required for movement path modification. For control manipulation, the task completion time was reduced and there was less hand movement needed than with conventional method. Our method can be applicable to drawing or curve editing method in immersive In-Situ AR based education, game, design, animation, simulation application domains.