• 반도체디스플레이기술학회지
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• 제17권4호
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• pp.105-108
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• 2018

Face Detection is an essential first step of the face recognition, and this is significant effects on face feature extraction and the effects of face recognition. Face detection has extensive research value and significance. In this paper, we present and analysis the principle, merits and demerits of the classic AdaBoost face detection and ASM algorithm based on point distribution model, which ASM solves the problems of face detection based on AdaBoost. First, the implemented scheme uses AdaBoost algorithm to detect original face from input images or video stream. Then, it uses ASM algorithm converges, which fit face region detected by AdaBoost to detect faces more accurately. Finally, it cuts out the specified size of the facial region on the basis of the positioning coordinates of eyes. The experimental result shows that the method can detect face rapidly and precisely, with a strong robustness.

• 스마트미디어저널
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• 제9권3호
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• pp.80-89
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• 2020

Out - of - distribution (OOD) samples are frequently encountered when deploying a classification model in plenty of real-world machine learning-based applications. Those samples are normally sampling far away from the training distribution, but many classifiers still assign them high reliability to belong to one of the training categories. In this study, we address the problem of removing OOD examples by estimating marginal density estimation using variational autoencoder (VAE). We also investigate other proper methods, such as temperature scaling, Gaussian discrimination analysis, and label smoothing. We use Chonnam National University (CNU) weeds dataset as the in - distribution dataset and CIFAR-10, CalTeach as the OOD datasets. Quantitative results show that the proposed framework can reject the OOD test samples with a suitable threshold.

• International Journal of Advanced Culture Technology
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• 제8권4호
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• pp.229-234
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• 2020

Dehazing significantly improves image quality by restoring the loss of contrast and color saturation for images taken in the presence. However, when applied to images not taken according to the prior information, dehazing can cause unintended degradation of image quality. To avoid unintended degradations, we present a hazy scene detection algorithm using a single image based on the distributions of hue, saturation, and dark channel. Through a heuristic approach, we find out statistical characteristics of the distribution of hue, saturation, and dark channels in the hazy scene and make a detection model using them. The proposed method can precede the dehazing to prevent unintended degradation. The detection performance evaluated with a set of test images shows a high hit rate with a low false alarm ratio. Ultimately the proposed method can be used to control the effect of dehazing so that the dehazing can be applied to wide variety of images without unintended degradation of image quality.

• 한국항행학회논문지
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• 제13권6호
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• pp.1011-1016
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• 2009

본 논문에서는 공공장소에 광범위하게 설치되어있는 CCTV의 감시 기능을 활용하여 화재 발생 감지 방법을 제안하였다. 제안한 방법은 고정된 카메라로부터 칼라 정보를 이용하여 비디오 시퀀스의 화재 프레임 후보를 찾아내고, 공간 기법을 기반으로 감지된 화재 정보의 전경 색상을 분석하였다. 실험 결과, 비디오 시컨스에서 시 공간적 화재 후보 정보들이 급격히 변화할 때, 화재 감지의 성능이 우수함을 확인할 수 있었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.654-655
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• 2003

Korea has experienced 10 a Cochlodinium polykrikoides red tide outbreaks during the last 10 years (1993-2002). The monitoring activities at National Fisheries Research and Development Institute (NFRDI) in Korea have been extended to all the coastal waters after the worst of fish killing by C. polykrikoides blooms in 1995. NFRDI is looking forward to finding out the feasibility of red tide detection around Korean waters using satellite remote sensing of NOAA/AVHRR, Orbview-2/SeaWiFS, IRS-P4/OCM and Terra/MODIS on real time base. In this study, we used several alternative methods including climatological analysis, spectral and optical methods which may offer a potential detection of the major species of red tide in Korean waters. The relationship between the distribution of SST and C. polykrikoides bloom areas was studied. In climatological analysis, NOAA, SeaWiFS, OCM satellite data in 20th and 26th August 2001 were chosen using the known C. polykrikoides red tide bloom area mapped by helicopter reconnaissance and ground observation. The 26th August, 2001 SeaWiFS chlorophyll a anomaly imageries against the imageries of non-occurring red tide for August 20, 2001 showed the areas C. polykrikoides occurred. The anomalies of chlorophyll a concentration from satellite data between before and after red tide outbreaks showed the similar distribution of C. polykrikoides red tide in 26th August, 2001. The distribution of the difference in SST between daytime and nighttime also showed the possibility of red tide detection. We used corrected vegetation index (CVI) to detect floating vegetation and submerged vegetation containing algal blooms. The simple result of optical absorption from C. polykrikoides showed that if we use the optical characteristics of each red tide we will be able to get the feasibility of the red tide detection.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 심포지엄 논문집 정보 및 제어부문
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• pp.228-229
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 춘계학술대회 논문집 전기설비전문위원
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• pp.171-173
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1503-1504
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

• 한국초전도ㆍ저온공학회논문지
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• 제19권1호
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• pp.26-29
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• 2017

We studied the distribution of the current density and its magnetic-field dependence in GdBCO coated conductors with AC bias currents using low temperature scanning Hall probe microscopy. We selectively measured magnetic field profiles from AC signal obtained by Lock-in technique and calculated current distributions by inversion calculation. In order to confirm the AC measurement results, we applied DC current corresponding to RMS value of AC current and compared distribution of AC and DC transport current. We carried out the same measurements at various external DC magnetic fields, and investigated field dependence of AC current distribution. We notice that the AC current distribution unaffected by external magnetic fields and preserved their own path on the contrary to DC current.

• 한국수자원학회논문집
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• 제56권10호
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• pp.679-690
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• 2023

누수는 용수공급시스템 내에서 발생할 수 있는 대표적인 비정상상황 중 하나이다. 누수는 관로가 매설된 이후부터 잠재적으로 발생할 수 있으며 발생 직후부터 즉시 경제적 및 수리학적 피해를 입을 수 있기 때문에 이를 적시에 감지하고 탐지하는 것이 중요하다. 하지만 시스템이 지하에 매설되어 있어 이를 빠르게 인지하는 것은 쉽지 않으며 인지한다 하여도 복구하기 위해서는 상대적으로 많은 가용자산이 요구된다. 따라서 다중 누수가 발생할 시 누수규모 및 위치에 따라 복구 우선순위에 대한 우선순위를 선정해야 할 필요성이 있으며 최적의 복구전략이 도출되어 이를 수행할 시 시스템의 탄력성 측면에 있어 유리함을 가질 수 있다. 본 연구에서는 프로그램 기반 모의 누수를 발생시켜 비정상상황 시나리오를 구축하였으며 이에 따라 딥러닝 기반 모델로 누수탐사를 수행하였다. 탐사 결과로 얻어지는 누수위치와 누수량은 이 후 누수복구 우선순위를 위한 요소로써 활용되며 타 요소와 함께 최적의 누수복구 시나리오를 도출하였다.