• IE interfaces
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• v.17 no.1
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• pp.13-21
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• 2004

In this study, the prototype of a system for checking risk-factor of NC machine is proposed. The proposed system can get the risk-related information by monitoring the status of real-time processes of NC machines. Using the information, the accidents can be prevented by the background process of an expert system tool. This paper consists of 2 parts: (1) the method for gathering data and definition of the risk-related factors of NC machines and (2) the procedure for finding the proper actions by using the forward chaining of a rule-base. Proposed system is implemented with C++ language under MS-windows.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.9
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• pp.2297-2311
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• 1994

This paper presents the study on the development of a DNC, system IPIS(Interactive Plant Information System)/DNC, which can manage NC machines and robots as a distributed control method in the machine. processing factory. The IPIS/DNC system is composed of a host computer, satellites and NC machines. A set of software modules are developed on the host computer and the satellites separately. By modularizing each functions of the IPIS/DNC system and using multi-taking method, the functions such as NC program management, NC program distribution, and shop monitoring can be performed on the host computer, and the functions such as NC program transfer to the NC machines, and NC program editing can be performed on the satellites. A Relational database which is linked with job scheduling system is used for IPIS/DNC system.

• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.312-318
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• 2000

In this study, an exclusive CAD/CAM system is developed for enhancing the effectiveness and productivity of CNC universal cylindrical grinding machines on which the external/facing/internal grinding cycles and the wheel dressing cycles are integratively carried out. The CAD/CAM system can manage the various processes such as geometry design, NC code generation, NC code verification, DNC operation, and so on. Especially, the feature-based modeling concept is introduced to improve the geometry design efficiency. And the NC code verification is realized by virtual manufacturing technique based on the real-time analysis of NC codes and the boolean operation between workpiece and wheel.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.625-636
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• 2013

In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR), restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and support vector expectile regression (SVER). These kernel machines have obvious advantages such that they achieve nonlinear model but they do not require the explicit form of nonlinear mapping function. Moreover they need no assumption about the underlying probability distribution of errors. Through numerical studies on two artificial an two real data sets we show their effectiveness on the estimation performance at various confidence levels.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.1
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• pp.43-54
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• 1992

This paper discusses a real time production control system of a FMS (Flexible Manufacturing System). The control is organized in a hierarchical structure according to the various decisions at the different time scales. In the earlier work-set-up time was not considered. Because the capacity of the toll magazine is limited, this assumption may not be adequate for selecting times to change configurations so that new part family can be produced. The goal of the control system is to meet production requirements while the machines fail and are repaired at random times. In this thesis the machien failure and set-up are considered simultaneously. Here the hedging point of this system is calculated, and the formulation of the dynamic control for FMS that has unreliable machines with set-up suggested.

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.76-94
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• 2015

For different reasons, many viewers like to watch a summary of films without having to waste their time. Traditionally, video film was analyzed manually to provide a summary of it, but this costs an important amount of work time. Therefore, it has become urgent to propose a tool for the automatic video summarization job. The automatic video summarization aims at extracting all of the important moments in which viewers might be interested. All summarization criteria can differ from one video to another. This paper presents how the emotional dimensions issued from real viewers can be used as an important input for computing which part is the most interesting in the total time of a film. Our results, which are based on lab experiments that were carried out, are significant and promising.

• Journal of the Semiconductor & Display Technology
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• v.9 no.4
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• pp.93-97
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• 2010

Fast incipient fault diagnosis is becoming one of the key requirements for economical and optimal process operation management in high-tech industries. Artificial neural networks have been used to detect faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for fault detection and classification for module process of TFT-LCD manufacture using support vector machines (SVMs). In order to evaluate SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.2
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• pp.236-243
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• 2016

This paper presents a threat list acquisition method in an engagement area using the support vector machines (SVM). The proposed method consists of track creation, track estimation, track feature extraction, and threat list classification. To classify the threat track robustly, dynamic track estimation and pattern recognition algorithms are used. Dynamic tracks are estimated accurately by approximating a track movement using position, velocity and time. After track estimation, track features are extracted from the track information, and used to classify threat list. Experimental results showed that the threat list acquisition method in the engagement area achieved about 95 % accuracy rate for whole test tracks when using the SVM classifier. In case of improving the real-time process through further studies, it can be expected to apply the fire control systems.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.339-353
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• 2014

A straightforward method for optimal determining of a high frequency inductor's parasitic capacitance is presented. The proposed estimation method is based on measuring the inductor's impedance samples over a limited frequency range bordering on the resonance point considering k-dB deviation from the maximum impedance. An optimized solution to k could be obtained by minimizing the root mean squared error between the measured and the estimated impedance values. The model used to provide the estimations is a parallel RLC circuit valid at resonance frequency which will be transferred to the real model considering the mentioned interval of frequencies. A straightforward algorithm is suggested and programmed using MATLAB which does not require a wide knowledge of design parameters and could be implemented using a spectrum analyzer. The inputs are the measured impedance samples as a function of frequency along with the diameter of the conductors. The suggested algorithm practically provides the estimated parameters of a real inductance model at different frequencies, with or without design information. The suggested work is different from designing a high frequency inductor; it is rather concentration of determining the parameters of an available real inductor that could be easily done by a recipe provided to a technician.

• Management Science and Financial Engineering
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• v.13 no.2
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• pp.47-77
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• 2007

This paper considers a scheduling problem where a customer orders multiple products(jobs) from a production facility. The objective is to minimize the sum of the order(batch) completion times. While a machine can process only one job at a time, multiple machines can simultaneously process jobs in a batch. Although each job has a unique processing time, we consider the case where batch processing times are identical. This simplification allows us to develop heuristics with improved performance bounds. This problem was motivated by a real world problem encountered by foreign electronics manufacturers. We first establish the complexity of the problem. For the two parallel machine case, we introduce two simple but intuitive heuristics, and find their worst case relative error bounds. One bound is tight and the other bound goes to 1 as the number of orders goes to infinity. However, neither heuristic is superior for all instances. We extend one of the heuristics to an arbitrary number of parallel machines. For a fixed number of parallel machines, we find a worst case bound which goes to 1 as the number of orders goes to infinity. Then, a tighter bound is found for the three parallel machine case. Finally, the heuristics are empirically evaluated.