• 전기학회논문지P
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• 제57권4호
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• pp.377-382
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• 2008

For stator winding protection of AC generator, KCL(Kirchhoff's Current Law) is widely applied. Actually a CRDR(Current Ratio Differential Relay) based on DFT(Discrete Fourier Transform) has been used for protecting generator. It has been pointed out that defects can occur during the process of transforming a time domain signal into a frequency domain one which can lead to loss of time domain information. Wavelets techniques are proposed for the analysis of power system transients. This paper introduces an algorithm to choose a suitable Mother Wave1et for generator stator fault detection. For optimal selection, we analyzed db(Daubechies), sym(Symlets), and coif(Coiflects) of Mother Wavelet. And we compared with performance of the choice algorithm using detail coefficients energy and RMS(root mean square) error. It can be improved the reliability of the conventional DFT based CRDR. The feasibility and effectiveness of the proposed scheme is proved with simulation using collected data obtained from ATP (Alternative Transient Program) package.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2172-2174
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses the motor's condition. The diagnostic system is constructed by using LabVIEW of National Instruments.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.645-648
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• 2003

Induction motors are a critical component of industrial processes. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyses the motor's supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.65.1-65
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system motors, the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis MCSA method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyzes the motor´s supply current, since this diagnoses the motor´s condition. The diagnostic system is constructed by using LabVIEW of National Instruments.

• International Journal of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.115-127
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• 2023

In this paper, we have undertaken a comprehensive investigation into the current state of anti-drone technology due to the increasing concerns and risks associated with the widespread use of drones. We carefully analyze anti-drone technology, dividing it into three crucial domains: detection, identification, and neutralization methods. This categorization enables us to delve into intricate technical details, highlighting the diverse techniques used to counter evolving drone threats. Additionally, we explore the legal and regulatory aspects of implementing anti-drone technology. Our research also envisions potential directions for advancing and evolving anti-drone tech to ensure its effectiveness in an ever-changing threat environment.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제52권12호
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• pp.562-567
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• 2003

Lightning arresters are deteriorated by repetition of protective operation against overvoltages or impulse currents in environments of its use. If a deteriorated arrester is left in power lines, it can lead to an accident such as a line to ground fault even in a normal system. Therefore, it is necessary to eliminate the deteriorated arrester in advance by checking the soundness of arresters on a regular basis, and to ensure the reliability of power systems by preventing accidents. Various deterioration diagnostic techniques and devices are suggested, and most of which measure leakage current components as an indicator of arrester ageing. However, the techniques based on the magnitude of leakage current measure simply RMS or peak value of leakage current components and do not provide detailed information needed in the diagnosis. In this study, we found that the wave height distributions of the total leakage currents are remarkably changed or a new wave height are produced with the progress of arrester deterioration. To propose a new technique for the diagnosis, we designed a leakage current detection unit and an analysis program which can measure leakage current magnitudes and analyze wave height distributions. From the experimental results, we confirmed that the proposed technique by analyzing the wave height distribution can simply diagnose the mode of defects such as a partial damage and an existence of punctures in arresters as well as deterioration of arresters.

• 한국정보통신학회논문지
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• 제16권7호
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• pp.1470-1476
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• 2012

콘텐츠 관리 시스템의 주요한 데이터인 비디오 콘텐츠의 효율적인 저장과 검색을 위한 비디오 브라우저의 성능은 장면 전환 검출의 성능에 따라 크게 의존한다. 본 논문에서는 구간의 평균 차분 히스토그램을 이용하여 장면전환 검출의 문제점인 점진적인 장면 전환 검출에 대한 오검출과 미검출의 빈도를 줄이며, 기존의 히스토그램을 이용한 장면 전환 검출의 정확도를 향상시키는 방법을 제안한다.

• Smart Structures and Systems
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• 제5권4호
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• pp.317-328
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• 2009

Current maintenance operations and integrity checks on a wide array of structures require personnel entry into normally-inaccessible or hazardous areas to perform necessary nondestructive inspections. To gain access for these inspections, structure must be disassembled and removed or personnel must be transported to remote locations. The use of in-situ sensors, coupled with remote interrogation, can be employed to overcome a myriad of inspection impediments stemming from accessibility limitations, complex geometries, the location and depth of hidden damage, and the isolated location of the structure. Furthermore, prevention of unexpected flaw growth and structural failure could be improved if on-board health monitoring systems were used to more regularly assess structural integrity. A research program has been completed to develop and validate Comparative Vacuum Monitoring (CVM) Sensors for surface crack detection. Statistical methods using one-sided tolerance intervals were employed to derive Probability of Detection (POD) levels for a wide array of application scenarios. Multi-year field tests were also conducted to study the deployment and long-term operation of CVM sensors on aircraft. This paper presents the quantitative crack detection capabilities of the CVM sensor, its performance in actual flight environments, and the prospects for structural health monitoring applications on aircraft and other civil structures.

• ETRI Journal
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• 제37권6호
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• pp.1199-1210
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• 2015

Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer-based automated two-phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU-to-emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two-phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components - multiple AU detection, AU detection fusion, and AU-to-emotion mapping. The experimental results on two real-world face databases demonstrate an improved performance over the previous two-phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.771-789
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• 2019

Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.