• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.167-176
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• 2008

Vulnerability assessment of power systems is important so as to determine their ability to continue to provide service in case of any unforeseen catastrophic contingency such as power system component failures, communication system failures, human operator error, and natural calamity. An approach towards the development of on-line power system vulnerability assessment is by means of using an artificial neural network(ANN), which is being used successfully in many areas of power systems because of its ability to handle the fusion of multiple sources of data and information. An important consideration when applying ANN in power system vulnerability assessment is the proper selection and dimension reduction of training features. This paper aims to investigate the effect of using various feature extraction methods on the performance of ANN as well as to evaluate and compare the efficiency of the proposed feature extraction method named as neural network weight extraction. For assessing vulnerability of power systems, a vulnerability index based on power system loss is used and considered as the ANN output. To illustrate the effectiveness of ANN considering various feature extraction methods for vulnerability assessment on a large sized power system, it is verified on the IEEE 300-bus test system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.10a
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• pp.3-10
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• 2006

Recently it is coming to be hish reality on visual system in ship-handling simulator depending on the technical development of 3D computer graphics. Even with high reality, it is possible that visual information presented seafarers through screen or display is not equivalent to the real world. In docking maneuvering, visual targets or obstructs are sighted close to ship's operator or within few hundred meters, so it might be possible to affect visual information such as the difference between both eyes' and single eye's visual sight. Because it is not possible to perceive of very slow ship's movement by visual in case of very large vessels, so the Doppler Docking SONAR and/or Docking Speed and Distance Measurement Equipment were developed and applied for safety docking maneuvering. By the way, the simulator training includes the ship's maneuvering training in docking, but in Ship-handling Simulator and also onboard, there are some limitations of perception of ship's movement with visual information. In this paper, perception of ship's movement with visual system in Ship-handling Simulator and competition of performances of visual systems that are conventional screen type with Fixed Eye-point system and Mission Simulator. We got some conclusions not only on the effectiveness for visual system but also on the human behavior in docking maneuver.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1626-1628
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• 2003

The lightning arrester is a very important overvoltage protection device in the electric power system. Therefore, the inspection of lightning arrester whether it keeps its performance or not properly has close related to verifying the safety confidence of the electric power system. But the development of the deterioration measuring method and on-line detecting system, is necessary to monitor the deterioration of the lightening arrestor. In this paper, we developed the lightning arrester degradation monitoring system. This system detected leakage current of lightning arrester by using the ZCT, and analyze the third harmonics ingredient of leakage current using DFT method in the Data Acquisition Unit(DAU). The analyzed current signal is transmit to the Human-Machine Interface(HMI), and HMI alarmed when accident are occurred and informed with the amplitude of leakage current to the operator.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.157-164
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• 2010

The objective of this study is to determine the dominance effects of environmental factors such as Illuminance, humidity and Wet Bulb Globe Temperature (WBGT) on the operators' productivity at Malaysian electronic industry. A case study was carried out at an electronic components assembly factory. The environmental factors examined were the Illuminance (lux), humidity and WBGT of the surrounding workstation area. Two sets of representative data including the illuminance, humidity and WBGT level and production rate were collected during the study. The production rate data were collected through observations and survey questionnaires while the illuminance level was measured using photometer model RS 180-7133, the humidity and WBGT level were measured by using Quest Temp apparatus and humidity. Taguchi Method was utilized to find the sequence of dominant factors that contributed to the productivity of operator at that specified production workstation. The study reveals that the dominant factor contributed to the productivity was WBGT, followed by illuminance and humidity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.714-720
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• 2004

In detecting human faces in color images, additional geometric computation is often necessary for validating the face-candidate regions having various forms. In this paper, we propose a method that detects the facial features using chrominance components of color which do not affected by face occlusion and orientation. The proposed algorithm uses the property that the Cb and Cr components have consistent differences around the facial features, especially eye-area. We designed the Mean-Gray Morphology operator to emphasize the feature areas in the eye-map image which generated by basic chrominance differences. Experimental results show that this method can detect the facial features under various face candidate regions effectively.

• Nuclear Engineering and Technology
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• v.30 no.6
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• pp.531-540
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• 1998

The DYLAM-3 code which overcomes the limitation of event tree/fault tree was applied to LOOP (Loss of Off-site Power) in the mid-loop operation employing HEPs (Human Error Probabilities) supplied by the ASEP (Accident Sequence Evaluation Program) and the SEPLOT (Systematic Evaluation Procedure for Low power/shutdown Operation Task) procedure in this study. Thus the time history of core uncovery frequency during the mid-loop operation was obtained. The sensitivity calculations in the operator's actions to prevent core uncovery under LOOP in the mid-loop operation were carried out. The analysis using the time dependent HEP was performed on the primary feed & bleed which has the most significant effect on core uncovery frequency. As the result, the increment of frequency is shown after 200 minutes duration of simulation conditions. This signifies the possibility of increment in risk after 200 minutes. The primary feed & bleed showed the greatest impact on core uncovery frequency and the recovery of the SCS (Shutdown Cooling System) showed the least impact. Therefore the efforts should be taken on the primary feed & bleed to reduce the core uncovery frequency in the mid-loop operation. And the capability of DYLAM-3 in applying to the time dependent concerns could be demonstrated.

• Journal of Drive and Control
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• v.19 no.4
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• pp.62-69
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• 2022

Construction machinery is exposed to accidents such as collisions, narrowness, and overturns during operation. In particular, mobile crane is operated only with the driver's vision and limited information of the assistant worker. Thus, there is a high risk of an accident. Recently, some collision avoidance device using sensors such as cameras and LiDAR have been applied. However, they are still insufficient to prevent collisions in the omnidirectional 3D space. In this study, a rotating LiDAR device was developed and applied to a 250-ton crane to obtain a full-space point cloud. An algorithm that could provide distance information and safety status to the driver was developed. Also, deep-learning segmentation algorithm was used to classify human-worker. The developed device could recognize obstacles within 100m of a 360-degree range. In the experiment, a safety distance was calculated with an error of 10.3cm at 30m to give the operator an accurate distance and collision alarm.

• ETRI Journal
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• v.44 no.4
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• pp.641-653
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• 2022

In the process of autonomous vehicle motion planning and to create comfort for vehicle occupants, factors that must be considered are the vehicle's safety features and the road's slipperiness and smoothness. In this paper, we build a mathematical model based on the combination of a genetic algorithm and a neural network to offer lane-change solutions of autonomous vehicles, focusing on human vehicle control skills. Traditional moving planning methods often use vehicle kinematic and dynamic constraints when creating lane-change trajectories for autonomous vehicles. When comparing this generated trajectory with a man-generated moving trajectory, however, there is in fact a significant difference. Therefore, to draw the optimal factors from the actual driver's lane-change operations, the solution in this paper builds the training data set for the moving planning process with lane change operation by humans with optimal elements. The simulation results are performed in a MATLAB simulation environment to demonstrate that the proposed solution operates effectively with optimal points such as operator maneuvers and improved comfort for passengers as well as creating a smooth and slippery lane-change trajectory.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.30 no.4
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• pp.45-56
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• 2022

Recently, the airport operator provides information such as real-time airport processing status, which allows users to determine the time to arrive at the airport at their own judgment. It is provided through their website and mobile Apps in order to resolve the customer's failure to board the aircraft and the inconvenience caused by waiting for the airport, etc. However, there was a lack of research cases on the intention of users to continue using such real-time airport information. Therefore, research on the field was studied by applying flow and trust theory based on human emotions. The research method verified reliability, validity, correlation between variables, and hypotheses using structural equations. As a result of the study, it was found that flow and trust had a significant effect on the intention to continue using. In other words, passengers were intended to continue to use the behavior they were searching for when they felt wise, enjoyable, and reliable etc.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.509-519
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• 2007

Private financing is playing an increasing role in public infrastructure construction projects worldwide. However, private investors/operators are exposed to the financial risk of low profitability due to the inaccurate estimation of facility demand, operation income, maintenance costs, etc. From the operator's perspective, a sound and thorough financial feasibility study is required to establish the appropriate capital structure of a project. Operators tend to reduce the equity amount to minimize the level of risk exposure, while creditors persist to raise it, in an attempt to secure a sufficient level of financial involvement from the operators. Therefore, it is important for creditors and operators to reach an agreement for a balanced capital structure that synthetically considers both profitability and repayment capacity. This paper presents an optimal capital structure model for successful private infrastructure investment. This model finds the optimized point where the profitability is balanced with the repayment capacity, with the use of the concept of utility function and multi-objective GA (Generic Algorithm)-based optimization. A case study is presented to show the validity of the model and its verification. The research conclusions provide a proper capital structure for privately-financed infrastructure projects through a proposed multi-objective model.