• 한국초전도ㆍ저온공학회논문지
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• 제18권3호
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• pp.25-29
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• 2016

A quench detection system of KSTAR Poloidal Field (PF) coils is inevitable for stable operation because normal zone generates overheating during quench occurrence. Recently, new voltage quench detection method, combination of Central Difference Averaging (CDA) and Mutual Inductance Compensation (MIK) for compensating mutual inductive voltage more effectively than conventional voltage detection method, has been suggested and studied. For better performance of mutual induction cancellation by adjacent coils of CDA+MIK method for KSTAR coil system, balance coefficients of CDA must be estimated and adjusted preferentially. In this paper, the balance coefficients of CDA for KSTAR PF coils were numerically estimated. The estimated result was adopted and tested by using simulation. The CDA method adopting balance coefficients effectively eliminated mutual inductive voltage, and also it is expected to improve performance of CDA+MIK method for quench detection of KSTAR PF coils.

• The Plant Pathology Journal
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• 제22권4호
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• pp.314-317
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• 2006

A phage technique for detection of Xanthomonas axonopodis pv. citri, a causal bacterium of canker on Sastuma mandarin fruits was developed. Phage and ELISA techniques were compared for their sensitivity for detection of Xanthomonas axonopodis pv. citri on orange fruits. Both of techniques revealed a similar efficiency for the bacterial detection; the pathogenic bacteria were observed in pellet from the fruits with over one canker spot with below 2 mm in diameter. In field assays, the increase of phage population(120%) on surface of the fruits related to the disease development one month later indicated that the bacterial pathogens inhabit on the surface. The procedure will be effectively used for detection of only living bacterial pathogen on fruit surfaces of Satsuma mandarin and for the disease forecasting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.5159-5178
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• 2018

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 춘계학술대회
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• pp.404-406
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• 2013

영상 에지는 물체 검출, 물체 인식 등의 여러 분야에서 전처리 과정으로 활용되고 있다. 기존의 에지 검출 방법에는 Sobel, Prewitt, Roberts, Laplacian 연산자 등이 있다. 기존의 에지 검출 방법들은 구현이 간단하나 임펄스 잡음 영역에서 에지 검출 특성이 미흡하다. 따라서 기존의 에지 검출 방법들의 단점을 보완하기 위하여, 본 논문에서는 변형된 마스크를 이용한 에지 검출 알고리즘을 제안하였다.

• Journal of Multimedia Information System
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• 제8권4호
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• pp.211-220
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• 2021

The process of inspecting SMDs on the PCB boards improves the product quality, performance and reduces frequent issues in this field. However, undesirable scenarios such as assembly failure and device breakdown can occur sometime during the assembly process and result in costly losses and time-consuming. The detection of these components with a model based on deep learning may be effective to reduce some errors during the inspection in the manufacturing process. In this paper, YOLO models were used due to their high speed and good accuracy in classification and target detection. A SMD detection and classification method using YOLO networks based on robust data preprocessing and augmentation techniques to deal with various types of variation such as illumination and geometric changes is proposed. For 9 different components of data provided from a PCB manufacturer company, the experiment results show that YOLOv4 is better with fast detection and classification than YOLOv3.

• 전자통신동향분석
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• 제36권5호
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• pp.82-91
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• 2021

This study reviews application of data-driven anomaly detection techniques to the aviation domain. Recent advances in deep learning have inspired significant anomaly detection research, and numerous methods have been proposed. However, some of these advances have not yet been explored in aviation systems. After briefly introducing aviation safety issues, data-driven anomaly detection models are introduced. Along with traditional statistical and well-established machine learning models, the state-of-the-art deep learning models for anomaly detection are reviewed. In particular, the pros and cons of hybrid techniques that incorporate an existing model and a deep model are reviewed. The characteristics and applications of deep learning models are described, and the possibility of applying deep learning methods in the aviation field is discussed.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.461-463
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• 2003

The information on the cloud presence within a instantaneous field of view is the first step toward the derivation of many other geophysical parameters. Here, we first applied the current MODIS cloud detection algorithm developed by University of Wisconsin and compared the results to a visual interpretation of composite data, especially during the daytime. Most of cases, the detection algorithm performs very well, except a few cases with over-detection. One of the reasons for the false detection is due to the time independent use of land information which affects the threshold values of visible channel test. In the presentation, we show detailed analysis of the current cloud detection algorithm and suggest possible way to overcome the current shortfall.

• 한국도로학회논문집
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• 제15권5호
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• pp.177-185
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• 2013

PURPOSES : This study attempts to design and establish the road surface condition detection system by using the image processing that is expected to help implement the low-cost and high-efficiency road information detection system by examining technology trends in the field of road surface condition information detection and related case studies. METHODS : Adapted visual information collecting method(setting a stereo camera outside of the vehicle) and visual information algorithm(transform a Wavelet Transform, using the K-means clustering) Experiments and Analysis on Real-road, just as four states(Dry, Wet, Snow, Ice). RESULTS : Test results showed that detection rate of 95% or more was found under the wet road surface, and the detection rate of 85% or more in snowy road surface. However, the low detection rate of 30% was found under the icy road surface. CONCLUSIONS : As a method to improve the detection rate of the mobile road surface condition information detection system developed in this study, more accurate phase analysis in the image processing process was needed. If periodic synchronization through automatic settings of the camera according to weather or ambient light was not made at the time of image acquisition, a significant change in the values of polarization coefficients occurs.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.1441-1442
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• 2013

• Ocean and Polar Research
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• 제35권1호
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• pp.15-26
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• 2013

The toxic, Harmful Algal Blooms (HABs) caused by the Cochlodinium polykrikoides have a serious impact on the coastal waters of Korea. In this study, the acoustic detection system was developed for rapid HABs detection, based on the acoustic backscattering properties of the C. polykrikoides. The developed system was mainly composed of a pulser-receiver board, a signal processor board, a control board, a network board, a power board, ultrasonic sensors (3.5 and 5.0 MHz), an environmental sensor, GPS, and a land-based control unit. To evaluate the performance of the system, a trail was done at a laboratory, and two in situ trials were conducted: (1) when there was no red tide, and (2) when there was red tide. In the laboratory evaluation, the system performed well in accordance with the number of C. polykrikoides in the received level. Second, under the condition when there was no red tide in the field, there was a good correlation between the acoustic data and sampling data. Finally, under the condition when there was red tide in the field, the system successfully worked at various densities in accordance with the number of C. polykrikoides, and the results corresponded with the sampling data and monitoring result of NFRDI (National Fisheries Research & Development Institute). From the laboratory and field evaluations, the developed acoustic detection system for early detecting HABs has demonstrated that it could be a significant system to monitor the occurrence of HABs in coastal regions.