• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.335-337
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• 2024

This paper presents a new open-world object detection method emphasizing uncertainty representation in machine learning models. The focus is on adapting to real-world uncertainties, incrementally updating the model's knowledge repository for dynamic scenarios. Applications like autonomous vehicles benefit from improved multi-class classification accuracy. The paper reviews challenges in existing methodologies, stressing the need for universal detectors capable of handling unknown classes. Future directions propose collaboration, integration of language models, to improve the adaptability and applicability of open-world object detection.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.747-752
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• 2011

The position detection of overlapping area in the interframe for image stitching using auto and cross correlation function (ACCF) and compounding one image with the stitching algorithm is presented in this paper. ACCF is used by autocorrelation to the featured area to extract the filter mask in the reference (previous) image and the comparing (current) image is used by crosscorrelation. The stitching is detected by the position of high correlation, and aligns and stitches the image in shifting the current image based on the moving vector. The ACCF technique results in a few computations and simplicity because the filter mask is given by the featuring block, and the position is enabled to detect a bit movement. Input image captured from CMOS is used to be compared with the performance between the ACCF and the window correlation. The results of ACCF show that there is no seam and distortion at the joint parts in the stitched image, and the detection performance of the moving vector is improved to 12% in comparison with the window correlation method.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.353-354
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• 2016

본 논문은 IEC 60479 표준 "인체감전의 생리학적 현상"에 제시되어 있는 인체감전 현상에 대하여 교류환경과 직류환경에서 인체임피던스의 특성을 실험을 통하여 분석한다. 연구 결과, 적어도 상용주파수 이하에서는 IEC 60479 표준에서 제시하는 인체 임피던스 모델의 수정이 필요함을 발견하였다. 또한 도출된 실험 결과를 활용하여 직류 배전시스템의 TN 접지방식에서 인체 감전 및 설비 누전에 따른 사고에 대하여 구분동작이 가능한 직류전용 누설전류 검출(DC Residual Current Detection) 기법을 제안한다.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1608-1617
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• 2018

The rapid and correct isolation of faulty submodules (SMs) is of great importance for improving the reliability of modular multilevel converters (MMCs). Therefore, a fast diagnosis method containing fault detection and fault location determination was presented in this paper. An improved incremental predictive model of arm current was proposed to detect failures, and the multi-step prediction method was used to eliminate the negative impact of disturbances. Moreover, a control method was proposed to strengthen the fault characteristics to rapidly locate faulty arms and faulty SMs by detecting the variation rate of the SM capacitor voltage. The proposed method can rapidly and easily locate faulty SMs under different load conditions without the need for additional sensors. The experimental results have validated the effectiveness of the proposed method by using a single-phase MMC with four SMs per arm.

• Journal of Power System Engineering
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• v.14 no.2
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• pp.40-47
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• 2010

This paper proposes a fault detection system to detect the strip rupture in six-high stand Cold Rolling Mills based on transient current signal of an electrical motor. For this work, signal smoothing technique is used to highlight precise feature between normal and fault condition. Subtracting the smoothed signal from the original signal gives the residuals that contains the information related to the normal or faulty condition. Using residual signal, discrete wavelet transform is performed and acquire the signal presenting fault feature well. Also, feature extraction and classification are executed by using PCA, KPCA and SVM. The actual data is acquired from POSCO for validating the proposed method.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1930-1936
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• 2010

Because of the environmental matters the importance of the city railroad is as time goes by increasing. The case of obstacle of the power equipment which supplies electric power to city railroad will occur social and economical enormous loss. Thus, I studied on the preventing method in advance which makes it possible for us to maintain facilities efficiently. The main check points of the power facilities are voltage, current, humidity, partial discharge, move current. These points are gathered by sensor and transmitting to data acquisition device. These data are used to check equipment status in real time. In this paper I described in brief test process and results of the detection system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.259-262
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• 2012

In this paper, we propose a new fast algorithm for effective scene change detection. The proposed algorithm exploits Otsu threshold matching technique, which was proposed earlier. In this method, the current and the reference frames are divided into square blocks of particular size. After doing so, the pixel histogram of each block is generated. According to Otsu method, every histogram distribution is assumed to be bimodal, i.e. pixel distribution can be divided into two groups, based on within-group variance value. The pixel value that minimizes the within-group variance is said to be Otsu threshold. After Otsu threshold is found, the same procedure is performed at the reference frame. If the difference between Otsu threshold of a block in the current frame and co-located block in the reference frame is larger than predefined threshold, then a scene change between those two blocks is detected.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.04a
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• pp.185-188
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• 2008

An apparatus for serial arc in UL 1699 is implemented Current waveforms of arc and a nonlear load are investigated and a method of arc detection can be considered. Introduction of digital signal processing technologies is suggested to correctly detect arcs of electrical fire pyrognostics. This study is expected to be utilized as the basis of such arc detection researches.

• Journal of Biosystems Engineering
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• v.40 no.3
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• pp.277-283
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• 2015

Background: Foodborne disease outbreaks from various food sources are a major health concern worldwide. Current methods for detection of foodborne pathogens are both expensive and time-consuming. Purpose: This review aims to present the current information available on the use of lateral flow test strips to detect pathogens in food products to enhance food safety. Results: Frequent foodborne disease outbreaks from various food sources have increased the need for rapid and easy methods for routine analysis of foodborne pathogens. Present detection methods for foodborne pathogens require expensive instruments, experts, and long time for sample analysis. Lateral flow test strips have drawn attention in recent years because of their ability to detect analytes quickly and easily. This review focuses on the principle of the lateral flow test, the various formats of lateral flow test strips, recognition elements, labeling tags, and reading instruments. In addition, this review also discusses the future prospects for the lateral flow test strips.