• Agricultural and Biosystems Engineering
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• v.5 no.2
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• pp.35-39
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• 2004

A review of accidents involving farm machinery, which occurred in Korea during 2002, shows that $90.7\%$ of farm-work accidents and $9.3\%$ of traffic accidents were linked to agricultural machinery. The frequency of accidents per annum amounts to 72.2 out of 10,000 vehicles, $94.4\%$ of which relates to tillers and tractors. An analysis of the causes of the accidents revealed that $49.1\%$ of farm-work accidents were due to the tiller driver's fault or error. In the case of traffic accidents, $83\%$ of farm machinery mishaps were found to be due to violation of traffic regulations while $60.4\%$ of the farm machinery damage resulted from faulty driving or lack of safe driving practices. The average casualty count of farm machinery-related accidents is 1.39 persons per case, which consist of 0.07 deaths, 0.74 serious injuries, and 0.58 slight injuries.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.84-89
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• 2014

Data-driven method for fault diagnostics system often use machine learning technique. To use such technique proper signal processing should be implemented such as time synchronous averaging (TSA) for ball bearing systems. However, for journal bearing diagnostics systems not much has been researched, and yet a proper signal processing method has not been studied. Therefore, in this research an optimal datum unit for a reliable journal bearing diagnostics system along with angular resampling process is being suggested. Before extracting time and frequency domain features, angular resampling is applied to each cycle of vibration data. As to preserve the characteristics of vibration signal, averaging method is replaced by finding the optimal datum unit which strengthens statistical characteristics of vibration signal. Then 20 features were extracted for various cases, and those features are being evaluated by two criteria, separability and classification accuracy.

• 전기의세계
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• v.29 no.3
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• pp.193-200
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• 1980

A new algorithm for power system stability calculations is developed which considers the nonlinear state equations of 8 state variables for each generator dynamics, expollential load models in respect to bus voltages for nonlinear loads, network equations expressed in terms of bus-injected current sources, various kinds of generator and transmission line outages, abrupt changes in loads, and operations of various kinds of portective relaying systems such as distance relaying, reclosing load shedding by under-frequency relays. In the algorithm are included efficient and reliable schemes for solving network equations by means of the Newton-Raphson iterative method and the Optimally-Ordered Triangular Factorization Technique, and simple procedures for determining fault-point negative and zero sequence impedances for unbalanced line faults. An application of the Optimally-Ordered Triangular Factorization Techniques results in remarkable savings in computing time and memory requirements.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.815-823
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• 1999

This study considers an implementation of artificial neural networks to the receding horizon optimal control and is applications to power systems. The Generalized Backpropagation-Through-Time (GBTT) algorithm is presented to deal with a quadratic cost function defined in a finite-time horizon. A decentralized approach is used to control the complex global system with simpler local controllers that need only local information. A Neural network based Receding horizon Optimal Control (NROC) 1aw is derived for the local nonlinear systems. The proposed NROC scheme is implemented with two artificial neural networks, Identification Neural Network (IDNN) and Optimal Control Neural Network (OCNN). The proposed NROC is applied to a power system to improve the damping of the low-frequency oscillation. The simulation results show that the NROC based power system stabilizer performs well with good damping for different loading conditions and fault types.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.41-43
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• 2001

This paper presents a Web-based educational tool for Power System Engineering, especially for power engineers. This tool has Web-oriented educational courses for power system education. The Web-based learning has become more popular year by year in the various fields[1-2]. The proposed educational tool provides some courses such as power system introduction Course, KEPCO's system introduction Course, Power system devices Course, Power Flow Course, OPF Course, ELD Course, Stability Analysis Course, Fault analysis Course, Voltage & Frequency control Course, and power system planning & operation Course. Finally, it will be scheduled to install for the education tool of the power system engineering for KEPS[3].

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.458-460
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• 2001

This paper described the unbalanced voltage on the tertiary bus of a single Phase auto transformer in the case of parallel operation with different manufacturer at each Phase. The unbalanced capacitances between primary to secondary winding, secondary to tertiary winding and primary to tertiary winding makes unbalanced bus voltage in the tertiary bus side. The unbalanced voltage let the surge arrester to operate in the power frequency range, and it causes the arrester to burn out. The failure of the arrester at one phase makes line to ground fault, which lead to the surge arrester failure of the other two phase on the tertiary bus.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.311-315
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• 2008

Ground systems set the reference voltage level of circuit and system, and suppress Ground Potential Rise (GPR) by flowing fault currents to ground safely. There are several parameters which evaluate the performance of ground systems as ground resistance, touch voltage and step voltage. The touch and step voltages are especially important to ensure safety of human body. In this paper, we measured the touch and step voltages by injection of power frequency and surge current. Also correlation between touch and step voltages is compared and analyzed for the same ground systems.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.471-472
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• 2013

When ESS(Energy Storage System) is under normal operation, island situation can be occur because of grid accidents. When island operation occur, the system mode switches in to UPS(Uninterruptible Power Supply) mode to supply stabled load power. To decide island operation, checking grid magnitude or frequency is general. But regulation for sag and swell is required. In this paper, it proposes grid connection error detection algorithm for UPS operation when sag and swell occurs in grid, and analyzed by simulation.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.120-124
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• 2002

A new methodology is presented to diagnose faults in equipment plasma. This is accomplished by using neural networks as a pattern recognizer of radio frequency (rf) impedance match data. Using a match monitor system, the match data were collected. The monitor system consisted mainly of a multifunction board and a signal flow diagram coded by Visual Designer. Plasma anomaly was effectively represented by electrical match positions. Twenty sets of fault-symptom patterns were experimentally simulated with variations in process factors, which include rf source power, pressure, Ar, and $O_$2 flow rates. As an input to neural networks, two means and standard deviations of positions were used as well as a reflected power. Diagnostic accuracy was measured as a function of training factors, which include the number of hidden neurons, the magnitude of initial weights, and two gradients of neuron activation functions. The accuracy was the most sensitive to the number of hidden neurons. Interaction effects on the accuracy were also examined by performing a 2$^$4 full factorial experiment. The experiments were performed on multipole inductively coupled plasma equipment.

• Journal of the Korea Safety Management & Science
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• v.9 no.2
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• pp.97-111
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• 2007

In risk assessment, there are several methods such as Safety Review, Checklist, FMEA(Failure Mode and Effect Analysis), FTA(Fault Tree Analysis), ETA (Event Tree Analysis) etc, however, the level of accident is indentified by the probability of accident and severity resulting from accident which used widely in assessing accidents and disasters. In this paper, the risk assessment method to decide the level of risk will be introduced by using severity, frequency and detection according to accident theory.