• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.21-27
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• 2019

In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.

• 전기학회논문지P
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• 제55권4호
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• pp.202-206
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• 2006

In this paper, an auto detection method of stator winding fault of small induction motor is suggested. The Park's vector pattern which is obtained from 3-phase current signal by d-q transforming, is very good to detect winding fault. Comparing the Park's vector pattern of testing motor with its of healthy motor, the Park's vector pattern of fault motor is became an ellipse and the asymmetry is increased by the winding fault series. So for detecting the dis-symmetry, id-filtered function, Min-value, and Max-value are suggested for auto detecting. Using LabVIEW programing, 3-phase healthy motor and several kind of winding fault motors are tested and the test results are shown that the suggested method can gives us a possibility of an auto detecting winding fault.

• 한국컴퓨터정보학회논문지
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• 제23권1호
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• pp.25-32
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• 2018

In this paper, we propose an edge detection algorithm for auto focus of infrared camera. We designed and implemented the edge detection of infrared image by using a spatial filter on FPGA. The infrared camera should be designed to minimize the image processing time and usage of hardware resource because these days surveillance systems should have the fast response and be low size, weight and power. we applied the $3{\times}3$ mask filter which has an advantage of minimizing the usage of memory and the propagation delay to process filtering. When we applied Laplacian filter to extract contour data from an image, not only edge components but also noise components of the image were extracted by the filter. These noise components make it difficult to determine the focus state. Also a bad pixel of infrared detector causes a problem in detecting the edge components. So we propose an adaptive edge detection filter that is a method to extract only edge components except noise components of an image by analyzing a variance of pixel data in $3{\times}3$ memory area. And we can detect the bad pixel and replace it with neighboring normal pixel value when we store a pixel in $3{\times}3$ memory area for filtering calculation. The experimental result proves that the proposed method is effective to implement the edge detection for auto focus in infrared camera.

• IEIE Transactions on Smart Processing and Computing
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• 제5권2호
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• pp.94-99
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• 2016

Application layer attacks have for years posed an ever-serious threat to network security, since they always come after a technically legitimate connection has been established. In recent years, cyber criminals have turned to fully exploiting the web as a medium of communication to launch a variety of forbidden or illicit activities by spreading malicious automated software (auto-ware) such as adware, spyware, or bots. When this malicious auto-ware infects a network, it will act like a robot, mimic normal behavior of web access, and bypass the network firewall or intrusion detection system. Besides that, in a private and large network, with huge Hypertext Transfer Protocol (HTTP) traffic generated each day, communication behavior identification and classification of auto-ware is a challenge. In this paper, based on a previous study, analysis of auto-ware communication behavior, and with the addition of new features, a method for classification of HTTP auto-ware communication is proposed. For that, a Not Only Structured Query Language (NoSQL) database is applied to handle large volumes of unstructured HTTP requests captured every day. The method is tested with real HTTP traffic data collected through a proxy server of a private network, providing good results in the classification and detection of suspicious auto-ware web access.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1406-1416
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• 2017

In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.

• 한국음향학회지
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• 제36권4호
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• pp.229-237
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• 2017

본 논문에서는 표적이 특정 탐지 빔 공간에 위치하는 동안 신호가 정상성을 유지하는 단기 정상성 개념을 활용한 자동 토널 탐지 기법을 제안 하였으며, 제안 기법의 연산량 감축 기법을 추가 제안하였다. 제안 기법은 신호의 정상성이 유지 되는 시간 동안 단일 빔 신호에서 추정된 문턱값과 입력신호의 기댓값을 비교함으로써 신호에 가변적이면서도 다수 표적에 의한 간섭 소음에 강인한 장점이 있다. 제안 기법의 성능 평가를 위하여 모사 신호 및 실제 해양 신호를 사용하였으며, 실험 결과 제안 기법이 기존 CFAR(Constant False Alarm Rate) 기법에 비하여 성능이 우수함을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.588-591
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• 2004

In this paper, we propose a ground plane detection algorithm, using a new image processing method (IPD). To extract the ground plane from the color image acquired by monocular camera, we use a new identical pixel detection method (IPD) and an edge detection method. This IPD method decides whether the pixel is identical with the ground plane pixel or not. The IPD method needs the reference area and its performance depends on the reference area size. So we propose the reference area auto-expanding algorithm in accordance with situation. And we evaluated the proposed algorithm by the experiments in the various environments. From the experiments results, we know that the proposed algorithm is efficient in the real indoor environment.

• 한국컴퓨터정보학회논문지
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• 제28권2호
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• pp.9-17
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• 2023

본 논문에서는 베어링의 결함 진단을 위한 단일 클래스 분류의 진동 이상 탐지 시스템을 제안한다. 베어링 고장으로 인해 발생하는 경제적 및 시간적 손실을 줄이기 위해 정확한 결함 진단시스템은 필수적이며 문제 해결을 위해 딥러닝 기반의 결함 진단 시스템들이 널리 연구되고 있다. 그러나 딥러닝 학습을 위한 실제 데이터 채집 환경에서 비정상 데이터 확보에 어려움이 있으며 이는 데이터 편향을 초래한다. 이에 정상 데이터만 활용하는 단일 클래스 분류 방법을 활용한다. 일반적인 방법으로는 AutoEncoder를 통한 압축과 복원 과정을 학습하여 진동 데이터의 특성을 추출한다. 추출된 특성으로 단일 클래스 분류기를 학습하여 이상 탐지를 실시한다. 하지만 이와 같은 방법은 진동 데이터의 주파수 특성을 고려하지 않아서 진동 데이터의 특성을 효율적 추출할 수 없다. 이러한 문제를 해결하기 위해 진동 데이터의 주파수 특성을 고려한 AutoEncoder 모델을 제안한다. 분류 성능은 accuracy 0.910, precision 1.0, recall 0.820, f1-score 0.901이 나왔다. 주파수 특성을 고려한 네트워크 설계로 기존 방법들보다 우수한 성능을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.229-231
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• 2006

NLPCA(Nonlinear Principal Component Analysis) is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(Auto Associative Neural Network) which performs the identity mapping. In this work, a sensor fault detection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from sensor network is executed.

• 한국정보통신학회논문지
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• 제13권12호
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• pp.2547-2554
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• 2009

본 논문은 얼굴을 자동 초점(AF, Auto Focusing) 기능의 관심영역(ROI, Region of Interest)으로 이용하기 위한 얼굴 검출(Face Detection) 알고리즘 및 하드웨어 구현에 관한 것이다. 얼굴 검출을 위해 YCbCr 색 좌표계에서의 피부색 영역을 바탕으로 얼굴의 특징을 이용하였다. 얼굴에 해당하는 피부, 눈에 해당하는 에지, 그리고 입에 해당하는 음영의 픽셀수를 얼굴 특징으로 선택하였고, 얼굴 특징은 2,000개의 얼굴 샘플을 통하여 통계적으로 구하였다. 제안된 알고리즘은 하드웨어 설계 시, 하드웨어 자원의 효율성을 고려하여 영상의 중심에 가까운 두 명의 얼굴을 검출하게 하였다. 그리고 검출된 얼굴을 자동 초점의 관심 영역으로 이용하기 위하여 얼굴 영역을 사각형의 박스로 표시하였고, 영상에서 박스의 시작점과 끝점에 해당하는 위치를 출력하게 하였다. 하드웨어로 설계된 얼굴 검출 기능은 FPGA 보드와 모바일 폰 카메라 센서를 사용하여 검증하였다.