• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.110-112
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• 1999

Recently the substation automation is actively researched at each country. Since the substation automation is an integrated system that consist of electrical, electronic and computer technologies, performance evaluation is very important to inspect the developed system's applicable possibility for real power system. Most studies are verified using computer simulation, because it is hard to apply them to real power systems. Therefore development of a substation simulator is necessary for the performance evaluation of many application systems such as operator aid system. This paper introduces a substation simulator. An intelligent fault diagnosis and restorative control expert system is also introduced UDP/IP is applied as a protocol for data transport between expert system and SC(station computer). As to the Graphic User Interface, C++ Language and Visual Basic is used in the Windows NT operating system together with four Pentium II systems.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.4
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• pp.152-157
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• 2002

This paper describes an effect of a single- and a quadruple-lightning impulse currents on electrical characteristics of ZnO blocks, and an expert system to diagnose arrestor deterioration. To deduce the parameters needed for diagnosing arrester deterioration, an accelerate deterioration teat is carried out. In the experiment, leakage current components are measured. Also, wave height distribution of the leakage current according to the progress of arrester deterioration is analyzed. From the experimental results, the wave height distribution of the leakage current showed conspicuous difference even in an immaterial leakage current increase. Therefore, the use of wave height distribution of the leakage current in deterioration diagnostic technique makes more accurate diagnosis than the conventional method by using only a leakage currant value. Finally, the expert system based on the experimental results is developed and the system can diagnose arrestor deterioration by measuring the leakage current and its wade height distribution.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.11
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• pp.1417-1423
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• 1999

Distribution system can experience the diverse events instantly and permanently. Also, it can experience high impedance fault or line drop under unbalanced situation, Accordingly, it is difficulty to identify the fault location because that data collected from distribution SCADA system may include uncertainty. This paper proposes an expert system, which can infer the faulted location the quickly and exactly for the diverse events in the distribution system. The expert system utilizes distribution SCADA function and collected data, especially, the monitoring mechanism for the normal open position switches is adopted newly in order to recognize the fault type exactly. Also, automated fault location diagnosis strategy is developed in order to minimize the spreading effect of fault obtained from the error of the system operator. The proposed strategy is implemented in C language. Especially, in order to prove the effectiveness of proposed expert system, the several scenario is simulated for the given model system. The real feeders are selected as model system for the simulation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.39-45
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• 2019

The aim of this research is to find out how convergent operation of the learning coaching and compensation system affects the academic self-efficiency and learning performance of university students. In the second semester, a compensation system was prepared based on learning coaching and learning outcomes, made a notice in advance, and conducted a survey before and after operation to measure the academic self-efficacy. In addition, the MLST-II Learning Strategy Diagnosis Examination was conducted on G university students to diagnose the learning tendency. As a result, although G University students felt a reluctance by coaching the learning community and expected negative results during the course of participation in the learning community due to low motivation and low expectation of results, they showed a significant improvement in academic self-efficiency and learning outcomes. Therefore, even students with negative learning tendency will need to consider how to operate these programs in the educational field, as the expert's learning coaching and compensation systems produce positive results for students' academic self-efficiency and learning outcomes rather than leaving them to autonomy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6A
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• pp.765-778
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• 2008

Performance Profiles are essential to predict the performance variation over time for the bridge management system (BMS) based on risk management. In general, condition profiles based on experts opinion and/or visual inspection records have been used widely because obtaining profiles based on real performance is not easy. However, those condition profiles usually don't give a good consistency to the safety of bridges, causing practical problems for the effective bridge management. The accuracy of performance evaluation is directly related to the accuracy of BMS. The reliability of the evaluation is important to produce the optimal solution for distributing maintenance budget reasonably. However, conventional methods of bridge assessment are not suitable for a more sophisticated decision making procedure. In this study, a method to compute quantitative performance profiles has been proposed to overcome the limitations of those conventional models. In Bridge Management Systems, the main role of performance profiles is to compute and predict the performance of bridges subject to lifetime activities with uncertainty. Therefore, the computation time for obtaining an optimal maintenance scenario is closely related to the efficiency of the performance profile. In this study, the Response Surface Method (RSM) based on independent and important design variables is developed for the rapid computation. Steel box bridges have been investigated because the number of independent design variables can be reduced significantly due to the high dependency between design variables.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.2
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• pp.131-140
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• 2004

It really needs fuzzy decision making of integrity assessment considering about both durability and load carrying capacity for maintenance and administration, such as repairing and reinforcing. This thesis shows efficient models about reinforced concrete structure using CART-ANFIS. It compares and analyzes decision trees parts of expert system, using the theory of fuzzy, and applying damage & diagnosis at reinforced concrete structure and decision trees of integrity assessment using established artificial neural. Decided the theory of reinforcement design for recovery of durability at damaged concrete ＆ the theory of reinforcement design for increasing load carrying capacity keep stability of damage and detection. It is more efficient maintenance and administration at reinforced concrete for using integrity assessment model of this study and can carry out predicting cost of life cycle.

• Korean Journal of Construction Engineering and Management
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• v.22 no.4
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• pp.41-49
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• 2021

Domestic maintenance strategies are shifting from safety assessment to performance assessment, and related systems and laws are being restructured to meet those criteria. In order to introduce asset management based on performance evaluation, related evaluation methods such as performance measures and level of service should be made to evaluate performance according to the characteristics of the structure, but these are not well-prepared in Korea. In this study, we present a computerized model and system for implementing asset management that introduces techniques such as performance evaluation, life cycle cost analysis, performance measures, and level of service in conjunction with existing maintenance and safety diagnosis procedures. The features of this system consist of three modules to enable separate operations of existing maintenance, safety management and asset management. The system is designed to be used as a reference for public institutions to introduce asset management in the future.

• Journal of Platform Technology
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• v.9 no.3
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• pp.36-43
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• 2021

Stroke disease is one of the leading causes of death worldwide, and in particular, it is the most important causative disease that causes disability in the elderly. Since stroke disease often causes death or serious disability, active primary prevention and early detection of prognostic symptoms are very important. In particular, it is necessary to detect and accurately predict stroke prognostic symptoms in daily life and prompt diagnosis and treatment by medical staff. In recent studies, image analysis such as computed tomography (CT) or magnetic resonance imaging (MRI) is mostly used as a methodology for predicting prognostic symptoms in stroke patients. However, this approach has limitations in terms of long test time and high cost. In this paper, we experimented with clinical data on how stroke disease affects foot pressure in elderly in walking. Experiments have shown that there is a significant difference in * p < .05 in 12 cells between the stroke elderly and the normal elderly during walking. As a result, it is significant that we found a significant difference in the gait patterns in daily life of the stroke elderly and the normal elderly.

• The Journal of the Korea Contents Association
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• v.6 no.11
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• pp.266-275
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• 2006

Mostly mechanical products are connected by several components instead of single accessory in product process. Although majority of assembly process is automated, the fault analysis is not automated because it needs expert knowledge in various fields to support inclusive decision-marking. This paper proposes an assembly fault analysis support system that uses image regions which can be easily accessed and understood by experts of various fields. An assembly fault analysis support system helps effective fault analysis from assembly by integrating image regions, product design information, and fault detection information. The proposed method enables fault information access from multimedia information by segmenting product images. After product images are segmented by labeling, design information and fault information are integrated in extended Attributed Relational Graph.