• 전기전자학회논문지
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• 제20권3호
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• pp.251-259
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• 2016

본 논문에서는 순차바이어스와 스위칭 타임 튜닝기법을 통한 반도체 송수신모듈(TRM : Transmitter and Receiver Module)의 강건성 향상 방법에 대해 기술한다. 기존의 회로설계는 TRM의 소형화로 인한 송신출력신호가 수신기로 유기되어 최소수신감도(MDS : Minimum Detection Signal) 개선에 초점을 맞추어졌으나, 평균고장시간(MTBF : Mean Time Between Failure)을 만족하지 못하고 빈번히 고장이 발생하는 문제가 있었다. 본 연구는 이러한 현상을 개선하는 방법으로 순차바이어스 및 스위칭 타임 튜닝기법을 제안한다. 첫 번째로 주요 고장증상 수집 및 원인을 추론하였으며, 두 번째로 개선방법을 도출하고 시스템에 적용하여 효과를 검증하였다. 제안한 방법을 적용하여 격리도 부족에 따른 빈번한 고장증상이 해소되었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.250-255
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• 1993

In this paper, for detection and isolation of instrument and process faults related with steam generator(S/G) in nuclear power plant, two types of observers are designed based on the linearized dynamic model of S/G : a bank of Dedicated Observers (DOS) for instrument faults detection and a bank of Unknown Input Observers(UIO) for process faults detection. And then, they are combined to decide which one between the above two faults occurs. In principle, the failure in ith instrument(process) can be isolated by monitoring the error between the ith output and its estimation obtained from the ith DOS(UIO). It is shown via computer simulations that the present scheme is feasible in finding out the source of a fault.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 전기설비전문위원
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• pp.77-79
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• 2009

Nonlinear system fault detection and isolation of this paper is about the failure of unknown function approximation using neural network for fault detection and isolation techniques of induction motors were applied. observer-based fault signal residual value was used. Induction motor using the speed controller of the backstepping controller. Proposed fault detection and isolation to prove the performance of the simulation was applied to and the actual system.

• 전기학회논문지
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• 제59권2호
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• pp.417-422
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• 2010

When memory devices are exposed to space environments, they suffer various effects such as SEU(Single Event Upset). Memory systems for space applications are generally equipped with error detection and correction(EDAC) logics against SEUs. In this paper, several error detection and correction codes - RS(10,8) code, (7,4) Hamming code and (16,8) code - are analyzed and compared with each other. Each code is implemented using VHDL and its performances(encoding/decoding speed, required memory size) are compared. Also the failure probability equation of each EDAC code is derived, and the probability value is analyzed for various occurrence rates of SEUs which the STSAT-3 possibly suffers. Finally, the EDAC algorithm for STSAT-3 is determined based on the comparison results.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 추계학술대회논문집
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• pp.815-820
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• 2000

The early damage detection of large structures is very important to prevent the disaster due to its global failure. In this paper, a damage detection method of the beam-analogy structure based on the wavelet transformation of mode shape is presented. This can effectively detect the singularity of mode shape caused to the inconsistency of bending moment and shear force at the damaged part using the discrete wavelet and its inverse transforms. To investigate the validity and the applicability of the presented damage detection method, numerical simulation and experiment are carried out for the idealized beam and the real ship structures.

• 전력전자학회논문지
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• 제27권1호
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• pp.80-83
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• 2022

This paper proposes an initial rotor polarity detection algorithm of a single-phase permanent magnet synchronous motor (SP-PMSM) related to stable open-loop starting for sensorless operation. Generally, the SP-PMSM needs an asymmetric air-gap structure to can avoid the initial starting failure at zero torque point. Therefore, the rotor polarity information can be obtained by using the DC offset current direction of a stator current through a high frequency voltage injection into an SP-PMSM with an asymmetric air gap. In this paper, the proposed rotor initial polarity detection algorithm is verified through several experimental results.

• Smart Structures and Systems
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• 제29권3호
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• pp.421-431
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• 2022

Concrete slab cracks monitoring of modern high-speed railway is important for safety and reliability of train operation, to prevent catastrophic failure, and to reduce maintenance costs. This paper proposes a curvature filtering improved crack detection method in concrete slabs of high-speed railway via graph-based anomalies calculation. Firstly, large curvature information contained in the images is extracted for the crack identification based on an improved curvature filtering method. Secondly, a graph-based model is developed for the image sub-blocks anomalies calculation where the baseline of the sub-blocks is acquired by crack-free samples. Once the anomaly is large than the acquired baseline, the sub-block is considered as crack-contained block. The experimental results indicate that the proposed method performs better than convolutional neural network method even under different curvature structures and illumination conditions. This work therefore provides a useful tool for concrete slabs crack detection and is broadly applicable to variety of infrastructure systems.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 봄 학술논문 발표대회
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• pp.173-174
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• 2023

In this paper, we introduce a model for detecting Personal Protective Equipment (PPE) using YOLO (You Only Look Once), an object detection neural network. PPE is used to maintain a safe working environment, and proper use of PPE protects workers' safety and health. However, failure to wear PPE or wearing it improperly can cause serious safety issues. Therefore, a PPE detection system is crucial in industrial settings.

• Structural Engineering and Mechanics
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• 제91권2호
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• pp.163-175
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• 2024

The objective of this research is to improve public safety in civil engineering by recognizing fractures in concrete structures quickly and correctly. The study offers a new crack detection method based on advanced image processing and machine learning techniques, specifically transfer learning with convolutional neural networks (CNNs). Four pre-trained models (VGG16, AlexNet, ResNet18, and DenseNet161) were fine-tuned to detect fractures in concrete surfaces. These models constantly produced accuracy rates greater than 80%, showing their ability to automate fracture identification and potentially reduce structural failure costs. Furthermore, the study expands its scope beyond crack detection to identify concrete health, using a dataset with a wide range of surface defects and anomalies including cracks. Notably, using VGG16, which was chosen as the most effective network architecture from the first phase, the study achieves excellent accuracy in classifying concrete health, demonstrating the model's satisfactorily performance even in more complex scenarios.

• Journal of Electrical Engineering and Technology
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• 제7권3호
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• pp.411-419
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• 2012

This paper proposes a new FDI(Fault Detection and Isolation) method, which is called EPSA(Extended Parity Space Approach). This method is particularly suitable for fault detection and isolation of the system with one faulty sensor or two faulty sensors. In the system with two faulty sensors, the fault detection and isolation probability may be decreased when two faults are occurred between the sensors related to the large fault direction angle. Nonetheless, the previously suggested FDI methods to treat the two-faults problem do not consider the effect of the large fault direction angle. In order to solve this problem, this paper analyzes the effect of the large fault direction angle and proposes how to increase the fault detection and isolation probability. For the increase the detection probability, this paper additionally considers the fault type that is not detected because of the cancellation of the fault biases by the large fault direction angle. Also for the increase the isolation probability, this paper suggests the additional isolation procedure in case of two-faults. EPSA helps that the user can know the exact fault situation. The proposed FDI method is verified through Monte Carlo simulation.