• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.1-9
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• 2010

In this paper, we consider a two-machine re-entrant permutation flowshop scheduling problem with the objective of minimizing total flowtime, and suggest branch and bound algorithms for the scheduling problem. In this scheduling problem, each job must be processed twice on each machine, that is, each job should be processed on the two machines in the order of machine 1, machine 2 and then machine 1 and machine 2. In this research, based on the results of existing researches for re-entrant permutation flowshop scheduling problems, various dominance properties, lower bound and heuristic algorithm are suggested for the problem, and those are used to develop branch and bound algorithms. In the computational experiments for evaluation of the performance of the algorithms, the suggested branch and bound algorithms are tested on randomly generated test problems and results are reported.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.450-453
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• 2001

Linear motor is characterized by its high velocity, high acceleration and good positioning accuracy. In recent years, linear motor is often used as a fast feed mechanism for high-speed machine tools. For the effective application of linear motors to machine tools, many demands on machine conceptions must be fulfilled. In this paper, some important construction concepts such as bending deformation of machine table, frictional force on the linear guidance and thermal behavior of linear motors are presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.4790-4795
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• 2014

The machine tool is used widely to manufacture and trial manufactured goods in many machinery industries. Blast-induced ground vibration may have an environmental impact, such as damage to the adjacent structures and facilities. This study examined the influence of blast vibration on the accuracy of machine tools. The blast vibration and vibration of machine tools was measured to evaluate the influence of blast vibration on machine tools. Based on the evaluation of the vibration limit of machine tools, the vibration criteria for machine tools in this study were SLIGHTLY ROUGH~ROUGH. By repeated blast vibration, machine tools are more likely show reduced accuracy.

• Journal of Power Electronics
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• v.15 no.5
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• pp.1244-1255
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• 2015

This paper presents a new multi-machine robust control based on an electric differential system for electric vehicle (EV) applications which is composed of four in-wheel permanent magnet synchronous motors. It is based on a new master-slave direct torque control (DTC) algorithm, which is used for the control of bi-machine traction systems based on a speed model reference adaptive system observer. The use of an electric differential in the design of a new EV constitutes a technological breakthrough. A classical system with a multi-inverter and a multi-machine comprises a three-phase inverter for each machine to be controlled. Another approach consists of only one three-phase inverter for several permanent magnet synchronous machines. The control of multi-machine single-inverter systems is the subject of this study. Several methods have been proposed for the control of multi-machine single-inverter systems. In this study, a new master-slave based DTC strategy is developed to generate an electric differential system. The entire system is simulated by Matlab/Simulink. The simulation results show the effectiveness of the new multi-machine robust control based on an electric differential system for use in EV applications.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.52-60
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• 2021

As the prevention of fatal accidents is considered an essential part of social responsibilities, both government and individual have devoted efforts to mitigate the unsafe conditions and behaviors that facilitate accidents. Several studies have analyzed the factors that cause fatal accidents and compared them to those of non-fatal accidents. However, studies on mathematical and systematic analysis techniques for identifying the features of fatal accidents are rare. Recently, various industrial fields have employed machine learning algorithms. This study aimed to apply machine learning algorithms for the classification of fatal and non-fatal accidents based on the features of each accident. These features were obtained by text mining literature on accidents. The classification was performed using four machine learning algorithms, which are widely used in industrial fields, including logistic regression, decision tree, neural network, and support vector machine algorithms. The results revealed that the machine learning algorithms exhibited a high accuracy for the classification of accidents into the two categories. In addition, the importance of comparing similar cases between fatal and non-fatal accidents was discussed. This study presented a method for classifying accidents using machine learning algorithms based on the reports on previous studies on accidents.

• Journal of Advanced Navigation Technology
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• v.18 no.1
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• pp.74-77
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• 2014

This paper suggested an error control for multi-channel running on machine to machine environment. This system is suitable for recovering software fault for multimedia CSCW(Computer Supported Cooperative Works) based on machine to machine environment. It is necessary for the system to be protected by reactivity of media service instance instead of breaking process of session. This paper explains a performance analysis of an error recovery system of M2M based computing collaboration environment using rule-based DEVS modeling and simulation techniques.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1576-1579
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• 1991

This paper discusses on the machine loading and tool allocation (MLTA) problem. Mathematical formulation of the problem is given first. Then a heuristic approach based on Group Technology (GT) is presented to deal with the MLTA problem effectively. By using this approach, part-tool group generation and their assignment to adequate machines can easily be obtained in consideration of the work load on each machine, the number of tool-set replacement, and the total number of cutting tools required through the interactive setting of the desired machine utilization rate.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• 전기의세계
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• v.22 no.1
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• pp.9-19
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• 1973

This paper is for the supplementary studies of the theoretical treaties on the machine dynamics of the amplidyne generator under the influencies of the armature reaction. The author has already shown the time-domain expression of the dynamic relations of the machine with balanced control winding, under this operating condition. In this paper, analytical and experimental studies of a test machine are intended to supplement the theories derived in the previous work, entitled "On the dynamics and the demagnetization effect of the amplidyne generator with auxiliary feedback compensating winding". FACOM 230 digital computer is incorporated for processing of a series of experimental data. The machine dynamics are then numerically analyzed with the aid of the computer. The virtual machine responses to stepwise inputs are compared with the computer output to confirm the influence of the armature reaction effect on to the machine dynamics. dynamics.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.3
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• pp.175-180
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• 2004

The machine-part group formation is to group the sets of parts having similar processing requirements into part families, and the sets of machines needed to process a particular part family into machine cells using grid computing. It forms machine cells from the machine-part incidence matrix by means of Self-Organizing Maps(SOM) whose output layer is one-dimension and the number of output nodes is the twice as many as the number of input nodes in order to spread out the machine vectors. It generates machine-part group which are assigned to machine cells by means of the number of bottleneck machine with processing part. The proposed algorithm was tested on well-known machine-part grouping problems. The results of this computational study demonstrate the superiority of the proposed algorithm.