• Journal of IKEEE
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• v.14 no.3
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• pp.224-231
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• 2010

Automatic detection of objects in images has been one of core challenges in the areas such as computer vision and pattern analysis. Especially, with the recent deployment of personal mobile devices such as smart phone, such technology is required to be transported to them. Usually, these smart phone users are equipped with devices such as camera, GPS, and gyroscope and provide various services through user-friendly interface. However, the smart phones fail to give excellent performance due to limited system resources. In this paper, we propose a new scheme to improve object recognition performance based on pre-computation and simple local features. In the pre-processing, we first find several representative parts from similar type objects and classify them. In addition, we extract features from each classified part and train them using regression functions. For a given query image, we first find candidate representative parts and compare them with trained information to recognize objects. Through experiments, we have shown that our proposed scheme can achieve resonable performance.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.142-144
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• 2008

한국전력공사(이하 한전)에서는 배전계통의 최적운영을 위하여 계통운영에 필요한 기술을 발굴하여 축적하는데 많은 노력을 기울여 왔으며, 계통운영의 핵심 툴인 배전자동화 시스템을 통하여 관련기술을 축적해오고 있다. 우리나라처럼 배전자동화 시스템을 전력회사에서 자체적으로 개발하여 활용하는 사례는 전 세계적으로 유일하며, 이를 기반으로 해외 전력시장에 배전자동화 시스템을 수출하기 위하여 활발한 활동을 지속적으로 추진되고 있다. 배전자동화 시스템을 이용하면 원격으로 현장에 있는 배전자동화기기를 감시, 제어, 조작, 정정이 가능한 기본기능이 구현가능하고, 각종 계통운영에 필요한 기술계산, 즉 전압강하계산, 보호협조검토, 표준조작지시서(SOP)의 생성, 상시개방점 위치조정으로 손실최소화, 분산전원 연계계통 기술검토의 자동처리가 가능해진다. 기술계산프로그램은 최초 개발 당시인 2002년에는 배전자동화시스템 주장치가 설치된 장소인 온라인 상태에서만 사용이 가능하여 사용상 제약을 가지고 있었으나, 이를 개선하고자 2006년 10월 배전자동화기반 오프라인형 기술계산프로그램을 개발하였고 이를 지속적으로 업그레이드를 추진하여 활용해오고 있다. 배전자동화기반 오프라인형 기술계산프로그램은 배전자동화주장치에서 데이터를 추출하여 배전자동화주장치가 아닌 일반PC나 노트북에서도 배전자동화시스템에 내장된 배전계통 정보 즉, 선로길이 및 전력선 제원 등을 직접 이용하므로 각종 기술계산을 신속하고 정확하게 수행하는 것이 가능하게 되었다. 따라서 이를 지속적으로 활용하고 성능개선을 추진하여 배전계통 운영시 신뢰도 제고에 크게 기여할 것으로 기대되고 있다.

• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.304-311
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• 2014

In this paper, we propose a ultra sound inspection technique for automatic defect detection and classification in aircraft composite materials. Using local maximum values of ultra sound wave, we choose peak values for defect detection. Distance data among peak values are used to construct histogram and to determine surface and back-wall echo from the floor of composite materials. C-scan image is then composed through this method. A threshold value is determined by average and variance of the peak values, and defects are detected by the values. PCA(principal component analysis) and QDA(quadratic discriminant analysis) are carried out to classify the types of defects. In PCA, 512 dimensional data are converted into 30 PCs(Principal Components), which is 99% of total variances. Computational cost and misclassification rate are reduced by limiting the number of PCs. A decision boundary equation is obtained by QDA, and defects are classified by the equation. Experimental result shows that our proposed method is able to detect and classify the defects automatically.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.541-547
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• 2019

Recent developments in hacking technology are continuing to increase the number of new security vulnerabilities. Approximately 80,000 new vulnerabilities have been registered in the Common Vulnerability Enumeration (CVE) database, which is a representative vulnerability database, from 2010 to 2015, and the trend is gradually increasing in recent years. While security vulnerabilities are growing at a rapid pace, responses to security vulnerabilities are slow to respond because they rely on manual analysis. To solve this problem, there is a need for a technology that can automatically detect and patch security vulnerabilities and respond to security vulnerabilities in advance. In this paper, we propose the technology to extract the features of the vulnerability-discovery target binary through complexity analysis, and select a vulnerability-discovery strategy suitable for the feature and automatically explore the vulnerability. The proposed technology was compared to the AFL, ANGR, and Driller tools, with about 6% improvement in code coverage, about 2.4 times increase in crash count, and about 11% improvement in crash incidence.

• The Journal of Bigdata
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• v.3 no.2
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• pp.11-18
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• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3
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• pp.111-118
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• 2008

In this paper, we propose a new automatic taxonomy generation algorithm for the audio genre classification. The proposed algorithm automatically generates hierarchical taxonomy based on the estimated classification accuracy at all possible nodes. The estimation of classification accuracy in the proposed algorithm is conducted by applying the training data to classifier using k-fold cross validation. Subsequent classification accuracy is then to be tested at every node which consists of two clusters by applying one-versus-one support vector machine. In order to assess the performance of the proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigated classification performance using the proposed algorithm and previous flat classifiers. The classification accuracy reaches to 89 percent with proposed scheme, which is 5 to 25 percent higher than the previous flat classification methods. Using low-dimensional feature vectors, in particular, it is 10 to 25 percent higher than previous algorithms for classification experiments.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.1
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• pp.19-28
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• 2019

Images captured by drone have the advantage of quickly constructing spatial information in small areas and are applied to fields that require quick decision making. If an image registration technique that can automatically register the drone image on the ortho-image with the ground coordinate system is applied, it can be used for various analyses. In this study, a methodology for geo-referencing of a single image and sequential images using drones was proposed even if they differ in spatio-temporal resolution using linear features and distinct points. Through the method using linear features, projective transformation parameters for the initial geo-referencing between images were determined, and then finally the geo-referencing of the image was performed through the template matching for distinct points that can be extracted from the images. Experimental results showed that the accuracy of the geo-referencing was high in an area where relief displacement of the terrain was not large. On the other hand, there were some errors in the quantitative aspect of the area where the change of the terrain was large. However, it was considered that the results of geo-referencing of the sequential images could be fully utilized for the qualitative analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.681-688
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• 2020

The Under Space Integrated Map has been constructed consistently from '15 construction projects until the present time in an effort to implement the "ground sinking prevention method" for the purpose of strengthening underground safety management. The constructed Under Space Integrated Map is utilized to provide information to the person in charge at local government through application of the system of underground information based on the administrative network and to deliver this to specialized underground-safety-effects -evaluation organizations through map extraction based on a floor plan. It suffers from a limitation in its practical use, however, since information is only provided, without promoting a separate renewal project. Although in Section 1 of Article 42 in the Special Law Concerning Underground Safety Management the content pertaining to submission obligations of completion drawings related to underground information including change and renewal are stated explicitly in order to solve this problem, submission is not sufficient since a submission window based only on the administrative network is operated. Accordingly, the Ministry of Land, Infrastructure, and Transport constructed an online system for submitting completion drawings, in an attempt to change the method by which entities involved in underground development directly submitted completion drawings. In this study, a DB standard relating to submitting completion drawings was designed and applied in order to construct an auto-renewal system based on submitted completion drawings, which will be extended to cover the range to underground structures hereafter.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.137-148
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• 2005

The position information of individual vehicles on a road at every time instant can be used to analyze the microscopic behaviors of driving of each vehicle. The limited information obtained from previous imaging technology such as traffic volume and interval velocity cannot be used to explore such microscopic traffic conditions. Also, information gathering for the microscopic behaviors by manual analysis of captured video takes large amount of time and man-power. In the paper we develop the rule-based vehicle tracking technology from which the position information of individual vehicles on a road at every time instant can be automatically obtained. Also, we extract the position data of driving vehicles on a road, length of 130m for every 0.05 second, and calculate the velocity of each traced vehicles to compare with the real velocity for the verification of accuracy. In the future, this type of tracking techniques based on video analysis can be widely used to provide the practically important information of road traffic conditions and to analyze the academically important microscopic behaviors of driving patterns.