• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2649-2666
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• 2015

In the dynamic cloud computing environment, to ensure, under the terms of service-level agreements, the maximum efficiency of resource utilization, it is necessary to investigate the online dynamic management of virtual machine resources and their operational application systems/components. In this study, the feasibility and properties of the division of virtual machine resources on the cloud platform, using the virtual machine cluster as the management unit, are investigated. First, the definitions of virtual machine clusters are compared, and our own definitions are presented. Then, the feasibility of division using the virtual machine cluster as the management unit is described, and the isomorphism and reconfigurability of the clusters are proven. Lastly, from the perspectives of clustering and cluster segmentation, the dynamics of virtual machines are described and experimentally compared. This study aims to provide novel methods and approaches to the optimization management of virtual machine resources and the optimization configuration of the parameters of virtual machine resources and their application systems/components in large-scale cloud computing environments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.6 s.249
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• pp.713-721
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• 2006

Micro-drill wear deteriorates accuracy and productivity of the micro components. In order to improve productivity and qualify of micro components, it is required to investigate micro-drill wear exactly. In this study, a machine vision system is proposed to measure the wear of micro-drills using a precision servo stage. Calibration experiments are conducted to compensate for the machine vision system. In this paper, worn volume, area and length are defined as wear amounts. Micro-drill wear is reconstructed as the 3D topography and the quantized wear amount by using the shape from focus (SFF) method and wear parameters. Experiments have been conducted with HSS twist micro-drills and SM45C carbon steel workpieces. Validity of the proposed machine vision system is confirmed through experiments.

• Journal of Applied Reliability
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• v.3 no.1
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• pp.41-58
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• 2003

Recently, the reliability evaluation and analysis are applied for many industrial products, and many products are required to guarantee in quality and in efficiency. The purpose of this paper is to present some of reliability prediction methodologies that are applicable to machine tools. Especially ATC (Automatic Tool Changer) and Interface Card of PC-NC, which are core components of the machine tools, were chosen as the target of the reliability evaluation and analysis. The results of this research has shown the failure rate, MTBF(Mean Time Between Failure), and reliability for those components. It is expected that proposed methodologies will be applicable to evaluation of reliability for other industrial products.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1070-1073
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• 2004

This paper is on the development of a system for removing membranes which is designed exclusively for aircraft components. Membrane removal solution is a most critical issue in aerospace industries since a method of manufacturing the components tends to be changed from fabrication of many parts to cutting into one body. The cutting method inevitably produces a huge amount of chips and then membranes remain in the body. The membrane removal process, as a result, becomes an important issue since it is directly related to productivity. We tried to develop a new machine which will replace the conventional method that uses a handy tool. The machine has been designed for a cutting tool set to follow the unique shape of the slot in the body by a cam follower and cut the membrane automatically. The design has been checked by structural analysis: stress and vibration analysis. A prototype test has been finished. This paper summarizes a series of development process of the deburring machine and some design issues are discussed.

• Asia pacific journal of information systems
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• v.5 no.2
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• pp.143-176
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• 1995

We present an overview of the Extensional Object Model (ExOM) and describe in detail the learning and classification components which integrate concepts from machine learning and object-oriented databases. The ExOM emphasizes flexibility in information acquisition, learning, and classification which are useful to support tasks such as diagnosis, planning, design, and database mining. As a vehicle to integrate machine learning and databases, the ExOM supports a broad range of learning and classification methods and integrates the learning and classification components with traditional database functions. To ensure the integrity of ExOM databases, a subsumption testing rule is developed that encompasses categories defined by type expressions as well as concept definitions generated by machine learning algorithms. A prototype of the learning and classification components of the ExOM is implemented in Smalltalk/V Windows.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.2
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• pp.67-74
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• 2003

This paper presents the development of the Inspection machine for SMD mounted on cream solder of PCB. There are mounting errors of SMD such as misalignment, missing part, wrong orientation, wrong polarity and so on. The main hardware of the system consists of a machine vision part and a motion control part. Operating software has been developed in GUI environment to help user convenience. The Inspection Jobs consist of two procedures, that is, creation of the inspection reference data and automatic inspection. The Inspection reference data has a tree structure of linked list including PCB information, blocks, components, windows, and inspection methods. This paper presents versatile inspection methods which include a section length method, a projection method and histogram method. Therefore, user can choose the suitable procedure for various components. Finally, the automatic Inspection procedure using the reference data checks the mounting errors of components.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.235-241
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• 2020

Artificial intelligence and machine learning are used in many parts of our daily lives, but the basic processes and concepts are barely exposed to most people. Understanding these basic concepts is becoming increasingly important as kids don't have the opportunity to explore AI processes and improve their understanding of basic machine learning concepts and their essential components. Machine learning educational tools can help children easily understand artificial intelligence and machine learning. In this paper, we examine machine learning education tools and compare their features.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.610-614
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• 1996

In machining muliple components fixed on the same pallet of a NC machine, it is very important to minimize the total time required to finish the machining by carefully determining the component layout on the pallet, and the tool path of NC machine, and the sequence of tools to be used. In this paper, a linear integer programming model is presented to obtain an efficient layout; and a two-phased heuristic algorithm is proposed to minimize the total time for machining multiple components. Numerical examples are given for the case of identical components and nonidentical components, respectively. The result of either case shows significant reduction of 7.2~15.0% in the total time required to finish the machining.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.4
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• pp.747-754
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• 2013

This study introduces a recently designed desktop-sized NC turning system and its components. This machine is designed for the ultra-precise turning of parts with a diameter of 0.5-20 mm with minimum space usage for the machine. This study aims to achieve submicron-level accuracy of movements and good rigidity of the machine for precision machining using the desktop-sized machine. The components such as the main machine structure, air bearing servo spindle, and XZ stage with needle roller guides are designed, and the designed machine is built with a PC-based CNC controller. Its static and dynamic stiffness performances and positioning resolutions are tested. Through machining tests with single-crystal diamond tools, a form error less than $0.8{\mu}m$ and surface roughness (Ra) of $0.03{\mu}m$ for workpieces are obtained.