• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.63-68
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• 2009

As the complexity of the power system becomes higher, tests of the new devices, such as exciter and PCS(Power Conversion System) of the distributed generation sources, in the real operating condition are more important. However tests of the unverified devices in the real power system may cause hazardous malfunction of the system. In order to avoid this problem, power devices may be tested with the real-time simulators instead of the real power system. This paper presents an real-time multi machine power system simulator using PCs(Personal Computer) and Fast Ethernet. Developed real-time simulator performs the electro-mechanical dynamic simulation of multi-machine power system by the network distributed computing technique. Because the simulator consists of usual PCs and Fast Ethernet, it is possible to make up a simulation system very cheaper than the conventional real-time simulator which consists of dedicated expensive hardware devices. The performance of the developed simulator is tested and verified with the scaled model excitation system. The test which adjust the control parameters of the exciter is performed with the well-known New England 10 generator 39 bus sample power system.

• Computers and Concrete
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• v.20 no.5
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.93-100
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• 2022

In this paper, we propose a machine learning-based method for supporting resource management of IoT software platforms in a multi-modal sensing scenario. We assume that an IoT device installed with a oneM2M-compatible software platform is connected with various sensors such as PIR, sound, dust, ambient light, ultrasonic, accelerometer, through different embedded system interfaces such as general purpose input output (GPIO), I2C, SPI, USB. Based on a collected dataset including CPU usage and user-defined priority, a machine learning model is trained to estimate the level of nice value required to adjust according to the resource usage patterns. The proposed method is validated by comparing with a rule-based control strategy, showing its practical capability in a multi-modal sensing scenario of IoT devices.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.5
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• pp.52-59
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• 2008

The accuracy of the machine tools is degraded because of thermal error of structure due to thermal variation. To improve the accuracy of a machine tools, measurement and prediction of thermal error is very important. The main part of thermal source is spindle due to high speed with friction. The thermal error of spindle is very important because it is over 10% in total thermals errors. In this paper, the suitable thermal error prediction technology for machine tools with open architecture controller is developed and implemented to machine tools. Two thermal error prediction technologies, neural network and multi-linear regression, are investigated in several methods. The multi-linear regression method is more effective for implementation to CNC. The developed thermal error prediction technology is implemented on the internal function of CNC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.5
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• pp.1039-1044
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• 2005

A Multi-channel serial device using the embedded Linux system is designed for a remote controlling and monitoring system. The proposed device consists of a FIFO, a state machine, and an interrupter. The device program written in embedded Linux enables the effective programming of device. While the conventional multi-channel serial devices accesses every individual serial devices, the proposed device accesses the multi-channel serial device as if it is a single serial device. The device efficiently performs the multi-channel serial input/output operation and has fast access time than the conventional multi-channel serial device.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.583-593
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• 2022

The harvesting system in South Korea faces the problems of aging workers and high wages, so it is necessary to improve the operation system and train workers to use high-performance forestry machines. This study compared the effectiveness and costs of yarding and processing operations between a multi-operation system using a tower yarder (HAM300) and a processor (KESLA 20SH) with those of a single-system using a forestry combi-machine. A whole-tree (cable) yarding operation was conducted in the clear-cutting area located at Compartment 15, Gwangneung Experimental Forest, National Institute of Forest Science, and the productivity and cost of multi- and single-system were analyzed. The productivity of the single-system was 1.5 m³/PMH and 1.6 m³/PMH higher than that of the multi- system because the single-system produced 1 log/cycle more than the multi-system in the yarding operation. The cost was approximately 12.1% lower for the single-system (￦36,113/m³) than for the multi-system (￦41,065/m³). The costs of the single-system and multi-system were decreased by maximums of 22.6% and 15.9%, respectively, by decreasing the idle time.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.27
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• pp.83-89
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• 1993

In order to model the Group Technology Problem three formulations are used, that is, generally the following formulations are used: (1) Matrix formulation, (2) Mathematical programming formulation, (3) Graph formulation. In the case of Matrix formulation, it is difficult to discribe the situation using the multi-job machine. But this paper proposed the model of Group Technology using the multi-job machin, taking the method of making practical application of principle of similarity coefficient.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.1
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• pp.15-28
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• 2018

Recently Machine Learning algorithms are widely used to process Big Data in various applications and a lot of these applications are executed in run time. Therefore the speed of Machine Learning algorithms is a critical issue in these applications. However the most of modern iteration Machine Learning algorithms use a successive iteration technique well-known in Numerical Linear Algebra. But this technique has a very low convergence, needs a lot of iterations to get solution of considering problems and therefore a lot of time for processing even on modern multi-core computers and clusters. Tchebychev iteration technique is well-known in Numerical Linear Algebra as an attractive candidate to decrease the number of iterations in Machine Learning iteration algorithms and also to decrease the running time of these algorithms those is very important especially in run time applications. In this paper we consider the usage of Tchebychev iterations for acceleration of well-known K-Means and SVM (Support Vector Machine) clustering algorithms in Machine Leaning. Some examples of usage of our approach on modern multi-core computers under Apache Spark framework will be considered and discussed.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.911-913
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• 1997

This paper presents a method for optima] placement of series capacitors in order to improve the power system transient stability, using genetic algorithms. For the formulation, this paper considers the objective function which is the energy margin as the difference between transient energy and critical energy. The most important factor in determining an accurate critical energy is the controlling unstable equilibrium point (UEP). This paper proposes the controlling UEP methods, concurrently with the DFP(Davidon-Fletcher-Powell) method, which enables the enhancement of multi-machine analysis. The proposed method is applied to 6-bus, 7-line, 4-machine model system to show its effectiveness in determining the locations to install series capacitors and the it's size to be installed in system, simultaneously.