• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.676-681
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• 2014

In order to discern and classify high-quality slides, our research proposes a classification method that utilizes a knowledge structure containing information on the presentation slides. After analyzing whether our knowledge structure captures the content's quality information, we developed a classification method based on the knowledge structure produced from the analysis results. With the proposed method, we compared results classified by quality of presentation slides. Through this comparison, we verified that the slides in the high quality group could be classified and were able to retrieve high quality slides. The results show that, by utilizing the cognitive model of a knowledge structure, our method can increase the effectiveness of classification when search or recommendation is conducted mainly with high-quality slides.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.349-352
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• 2007

[ $\ulcorner$ ]국토의 계획 및 이용에 관한 법률$\lrcorner$ 개정은 국토 개발에 있어서 '선 계획 후 개발'이라는 원칙하에 국토 및 도시를 삶의 질 향상, 도시공간의 효율적 개발, 관리, 보전하는데 환경친화적으로 관리하고자 하였다. 선 계획 후 개발이라는 기조 하에 도시공간의 사회적, 지형적, 지리적, 문화적 등 다양한 측면으로 검토 및 분석을 통해 보전과 개발의 적합한 토지를 분류하는 방안이 필수적일 것이며, 이에 대한 의사결정을 함에 있어서 과학적 효율적인 분석방법이 필요하다. 본 연구는 증평군을 대상으로 충남 연기군의 행정중심복합도시의 입지, 진천 음성군의 혁신도시 선정, 증평지방산업단지조성 등 도시 내 외의 여건변화 및 도시의 확장, 주변 도시인구의 지속적 증가 등으로 인한 도시공간구조 재검토를 위하여 GSIS 기법을 이용하여 공간분석을 수행하여 도시개발 적지분석을 수행하였다.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2011.10a
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• pp.17-17
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• 2011

본 연구는 지역이 가지고 있는 중요한 자원을 자연 생태적 요소, 역사 문화적 요소, 시각 환경적 요소 등으로 분류한 후 충남지역 16개 시 군을 대상으로 군의 상징적 요소로서 가치 있는 자원의 발굴과 동시에 개발에 밀려 점점 사라져가는 농산어촌 대표경관의 추출을 통해 자원의 중요성을 AHP(계층 분석적 의사 결정기법)모델을 통해 분석하여 상호 중요도를 파악하고자 한다. 이러한 연구를 통해 지역의 대표적인 상징적 요소와 경관적 요소의 상관성을 파악함과 동시에 중요도에 따라 순위(ranking ordering)를 결정함으로서 지역의 정체성(identity)을 확보하고, 향후 보전할 자원의 특성을 파악함으로써 지역의 문화적, 환경적, 생태적 자원가치의 패러다임을 구축하고자 한다. 연구방법은 1차적으로 문헌조사를 통한 지역의 자원특성에 대한 분류체계를 설정한 후 전문가 집단을 선정하여 설문조사를 통한 통계적 분석방법을 사용하고자 한다. 전문가 집단은 지역에 대한 비교적 잘 파악하고 있는 지역전문가(관련대학 교수, 공무원, 연구원 등)로 구성하였으며, 1, 2차 설문을 통한 상징성 및 대표경관자원을 최종적으로 선정한 후, 3차 설문에서는 농촌 및 경관관련 전문가 집단을 재선정하여 AHP(계층 분석적 의사결정)기법을 통한 자원의 중요도를 파악하고자 한다. 분석방법은 자료의 계량적 분석을 위해 통계프로그램인 SPSS 12.0 for Windows와 도출된 상징요소의 가중치를 파악하기 위해 AHP 프로그램인 ExpertChoice 2006을 사용하여 도출된 상징요소의 가중치별 순위를 측정하여 분석의 과학성, 논리성, 타당성을 확보하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.30-40
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• 2022

Electrocardiogram (ECG) signal's shape and characteristic varies through each individual, so it is difficult to classify with one neural network. It is difficult to classify the given data directly, but if corresponding normal beat is given, it is relatively easy and accurate to classify the beat by comparing two beats. In this study, we classify the ECG signal by generating the reference normal beat through the template cluster, and combining with the input ECG signal. It is possible to detect abnormal beats of various individual's records with one neural network by learning and classifying with the imaged ECG beats which are combined with corresponding reference normal beat. Especially, various neural networks, such as GoogLeNet, ResNet, and DarkNet, showed excellent performance when using the comparative learning. Also, we can confirmed that GoogLeNet has 99.72% sensitivity, which is the highest performance of the three neural networks.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.100-118
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• 2015

This study established a land-cover classification method on objects using satellite images, and figured out distributional patterns of land cover according to categories through spatial statistics techniques. Object-based classification generated each land cover classification map by spectral information, texture information, and the combination of the two. Through assessment of accuracy, we selected optimum land cover classification map. Also, to figure out spatial distribution pattern of land cover according to categories, we analyzed hot spots and quantified them. Optimal weight for an object-based classification has been selected as the Scale 52, Shape 0.4, Color 0.6, Compactness 0.5, Smoothness 0.5. In case of using the combination of spectral information and texture information, the land cover classification map showed the best overall classification accuracy. Particularly in case of dry fields, protected cultivation, and bare lands, the accuracy has increased about 12 percent more than when we used only spectral information. Forest, paddy fields, transportation facilities, grasslands, dry fields, bare lands, buildings, water and protected cultivation in order of the higher area ratio of DMZ according to categories. Particularly, dry field sand transportation facilities in Yanggu occurred mainly in north areas of the civilian control line. dry fields in Cheorwon, forest and transportation facilities in Inje fulfilled actively in south areas of the civilian control line. In case of distributional patterns according to categories, hot spot of paddy fields, dry fields and protected cultivation, which is related to agriculture, was distributed intensively in plains of Yanggu and in basin areas of Cheorwon. Hot spot areas of bare lands, waters, buildings and roads have similar distribution patterns with hot spot areas related to agriculture, while hot spot areas of bare lands, water, buildings and roads have different distributional patterns with hot spot areas of forest and grasslands.

• The Journal of the Acoustical Society of Korea
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• v.12 no.1E
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• pp.71-77
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• 1993

패턴매칭 기법에 근거한 음성 인식 시스템은 크게 clustering 과정과 labeling 과정으로 구성된다. 본 논문에서는 Kohonen의 featrue map 알고리즘과 LVQ2 알고리즘을 각각 clusterer와 labeler로 하는 음소인식 시스템을 구성한다. 구성된 인식시스템의 성능을 향상시키기 위해서 수정된 LVQ2알고리즘(MLVQ2)을 제안한다. MLVQ2는 selective learning, LVQ2, perturbed LVQ2 그리고 기존의 LVQ2의 4단계 학습과정으로 구성된다. 제안된 음소 인식 알고리즘의 성능을 평가하기 위하여 LVQ2와 MLVQ2를 각각 사용하여 6가지의 한국어 음소군에 대한 feature map을 만든다. 음소인식 실험결과, LVQ2와 MLVQ2를 사용하는 경우 각각 60.5%와 65.4%의 인식률을 얻을 수 있었다.

• Journal of IKEEE
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• v.22 no.1
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• pp.162-167
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• 2018

In this paper, we propose a method to classify radar signals according to the jamming technique by applying the machine learning to parameter data extracted from received radar signals. In the present army, the radar signal is classified according to the type of threat based on the library of the radar signal parameters mostly built by the preliminary investigation. However, since radar technology is continuously evolving and diversifying, it can not properly classify signals when applying this method to new threats or threat types that do not exist in existing libraries, thus limiting the choice of appropriate jamming techniques. Therefore, it is necessary to classify the signals so that the optimal jamming technique can be selected using only the parameter data of the radar signal that is different from the method using the existing threat library. In this study, we propose a method based on machine learning to cope with new threat signal form. The method classifies the signal corresponding the new jamming method for the new threat signal by learning the classifier composed of the hidden Markov model and the neural network using the existing library data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7C
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• pp.675-686
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• 2005

In this paper, we propose an efficient face region detection technique for the content-based video summarization. To segment video, shot changes are detected from a video sequence and key frames are selected from the shots. We select one frame that has the least difference between neighboring frames in each shot. The proposed face detection algorithm detects face region from selected key frames. And then, we provide user with summarized frames included face region that has an important meaning in dramas or movies. Using Bayes classification rule and statistical characteristic of the skin pixels, face regions are detected in the frames. After skin detection, we adopt the projection method to segment an image(frame) into face region and non-face region. The segmented regions are candidates of the face object and they include many false detected regions. So, we design a classifier to minimize false lesion using CART. From SGLD matrices, we extract the textual feature values such as Inertial, Inverse Difference, and Correlation. As a result of our experiment, proposed face detection algorithm shows a good performance for the key frames with a complex and variant background. And our system provides key frames included the face region for user as video summarized information.

• Journal of Conservation Science
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• v.34 no.2
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• pp.119-128
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• 2018

This study revealed the material composition and characteristics of 19 glass fragments excavated from stone chamber No. 1 of Jeongchon Tomb in Naju through chemical composition analyses and observations. These characteristics were compared with the characteristics of the glass fragments excavated from No. 3 tomb of Bogam-ri in Naju. The purpose of this study was to identify the characteristics of the ancient glass of the Mahan-Baekje period. The glass fragments excavated from the Jeongchon Tombs can be classified into purplish blue, light-purplish blue, greenish blue, green, and mixture of purple blue and purple, based on their color. These beads were made using a drawn and casting technique. In addition, blue glass fragments were primarily excavated form No. 3 tomb of Bogam-ri. However, red glass fragments were not excavated from either of the tombs. According to chemical composition analyses, soda glass group and potash glass group were common in both the tombs. Additionally, alkali mixed glass group and lead barium glass group were excavated from Jeongchon Tombs and No. 3 tomb of Bogam-ri, respectively. The glass fragments excavated from No. 3 tomb of Bogam-ri have more color variations than those excavated from Jeongchon Tombs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.301-311
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• 2013

This study aims to analyze characteristics, health behaviors and health management level related to private health examination recipients in one general hospital. To achieve this, we analyzed 150,501 cases of private health examination data for 11 years from 2001 to 2011 for 20,696 participants in 2011 in a Dae-Jeon general hospital health examination center. The cluster analysis for classify private health examination group is used z-score standardization of K-means clustering method. The logistic regression analysis, decision tree and neural network analysis are used to periodic/non-periodic private health examination classification model. 1,000 people were selected as a customer management business group that has high probability to be non-periodic private health examination patients in new private health examination. According to results of this study, private health examination group was categorized by new, periodic and non-periodic group. New participants in private health examination were more 30~39 years old person than other age groups and more patients suspected of having renal disease. Periodic participants in private health examination were more male participants and more patients suspected of having hyperlipidemia. Non-periodic participants in private health examination were more smoking and sitting person and more patients suspected of having anemia and diabetes mellitus. As a result of decision tree, variables related to non-periodic participants in private health examination were sex, age, residence, exercise, anemia, hyperlipidemia, diabetes mellitus, obesity and liver disease. In particular, 71.4% of non-periodic participants were female, non-anemic, non-exercise, and suspicious obesity person. To operation of customized customer management business for private health examination will contribute to efficiency in health examination center.