• Journal of Veterinary Clinics
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• v.18 no.4
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• pp.402-410
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• 2001

This study was performed to investigate the hemodynamic changes which occur after occlusion of coronary artery and relation to reperfusion arrhythmias(RA) which occur when occlusion materials were removed form coronary artery in dogs. The occlusion of coronary artery was designed by temporary ligation of left circumflex branch of coronary artery during 30 minutes in 16 dogs. During occlusion of coronary artery, cardiac output(CO), mean aortic pressure (mAP), aortic systolic pressure(ASP), aortic diastolic pressure(ADP). left ventricular systolic pressure(LVSP), left ventricular maximum dp/dt (LV max. dp/dt) and left ventricular end-diastolic pressure (LVEDP) were measured. The occurrence of RA were observed for 5 minute after reperfusion by explained of ligation. As a results, cardiac arrest occurred in 4 dogs during occlusion of coronary artery, and RA was not observed in 5 dogs while it was seen in 5 dogs when explained ligation(reperfusion) after 30 minutes, the rest 2 dogs occurred temporary tachycardia. In hemodynamics changes, LVSP decreased by 10.9% and LV max. dp/dt by 5.4 % in comparison to control value which not ligated coronary artery, and LVEDP increased by 73.3%. The CO/min and mAP also decreased by 10.7% and by 11.3% expectedly. In the relationship to occurrence RA and hemodynamics changes, the LVSP and LV max. dp/dt at the time of occlusion in the RA group decreased by 11.9% and 0.8% in comarison to the control value while the decrease was 7.7% and 10% in the non-RA group. But the LVEDP in creased by 109.1% in the RA group while the decreased was 44.6% in the non-RA group. Referring CO/min, the drop was 8.8% in the RA occurrence group and 12.9% in the non-occurence group. These parameters of LVEDP, LV max. dp/dt, and CO were significant difference(p<0.05). The mAP also decreased by 11.9 in the RA group and by 9.8% in the non-RA group, but these defference were not the significant difference.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.1-13
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• 2002

In this paper, we propose an object tracking system which can be convinced of moving area shaped on objects through color sequential images, decided moving directions of foot messengers or vehicles of image sequences. In static camera, we suggests a new evaluating method extracting co-occurrence matrix with feature vectors of RGB after analyzing and blocking difference images, which is accessed to field of camera view for motion. They are energy, entropy, contrast, maximum probability, inverse difference moment, and correlation of RGB color vectors. we describe how to analyze and compute corresponding relations of objects between adjacent frames. In the clustering, we apply an algorithm of FCM(fuzzy c means) to analyze matching and clustering problems of adjacent frames of the featured vectors, energy and entropy, gotten from previous phase. In the matching phase, we also propose a method to know correspondence relation that can track motion each objects by clustering with similar area, compute object centers and cluster around them in case of same objects based on membership function of motion area of adjacent frames.

• Proceedings of the Korean Society of Veterinary Pathology Conference
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• 2005.11a
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• pp.55-56
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• 2005

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.809-816
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• 2002

Since a natural language has inherently structural ambiguities, one of the difficulties of parsing is resolving the structural ambiguities. Recently, a probabilistic approach to tackle this disambiguation problem has received considerable attention because it has some attractions such as automatic learning, wide-coverage, and robustness. In this paper, we focus on Korean probabilistic parsing model using head co-occurrence. We are apt to meet the data sparseness problem when we're using head co-occurrence because it is lexical. Therefore, how to handle this problem is more important than others. To lighten the problem, we have used the restricted and simplified phrase-structure grammar and back-off model as smoothing. The proposed model has showed that the accuracy is about 84%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.13-16
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• 2015

본 논문에서는 어휘의 동시 발생 (co-occurrence) 빈도와 분포를 이용한 회의록 요약방법을 제안한다. 회의록은 일반 문서와 달리 문서에 여러 세부적인 주제들이 나타나며, 잘못된 형식의 문장, 불필요한 잡담들을 포함하고 있기 때문에 이러한 특징들이 문서요약 과정에서 고려되어야 한다. 기존의 일반적인 문서요약 방법은 하나의 주제를 기반으로 문서 전체에서 가장 중요한 문장으로 요약하기 때문에 다중 주제 회의록 요약에는 적합하지 않다. 제안한 방법은 먼저 어휘의 동시 발생 (co-occurrence) 빈도를 이용하여 회의록 분할 (segmentation) 과정을 수행한다. 다음으로 주제의 구분에 따라 분할된 각 영역 (block)의 중요 단어 집합 생성, 중요 문장 추출 과정을 통해 회의록의 중요 문장들을 선별한다. 마지막으로 추출된 중요 문장들의 위치, 종속 관계를 고려하여 최종적으로 회의록을 요약한다. AMI meeting corpus를 대상으로 실험한 결과, 제안한 방법이 baseline 요약 방법들보다 요약 비율에 따른 평가 및 요약문의 세부 주제별 평가에서 우수한 요약 성능을 보임을 확인하였다.

• Journal of Information Technology Services
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• v.8 no.1
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• pp.203-215
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• 2009

This paper illustrates the application of co-occurrence theory to generate lightweight ontologies semi-automatically. The proposed model includes three steps of a (Semi-) Automatic creation of Ontology; (they are conceptually named as) the Syntactic-based Ontology, the Semantic-based Ontology and the Ontology Refinement. Each of these three steps are designed to interactively work together, so as to generate Lightweight Ontologies. The Syntactic-based Ontology step includes generating Association words using co-occurrence in web documents. The Semantic-based Ontology step includes the Alignment large Association words with small Ontology, through the process of semantic relations by contextual terms. Finally, the Ontology Refinement step includes the domain expert to refine the lightweight Ontologies. We also conducted a case study to generate lightweight ontologies in specific domains(news domain). In this paper, we found two directions including (1) employment co-occurrence theory to generate Syntactic-based Ontology automatically and (2) Alignment large Association words with small Ontology to generate lightweight ontologies semi-automatically. So far as the design and the generation of big Ontology is concerned, the proposed research will offer useful implications to the researchers and practitioners so as to improve the research level to the commercial use.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.43-50
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• 2017

According to living environment, the number of deaths due to thyroid diseases increased. In this paper, we proposed an algorithm for recognizing a thyroid detection using texture analysis based on shape, gray level co-occurrence matrix and gray level run length matrix. First of all, we segmented the region of interest (ROI) using active contour model algorithm. Then, we applied a total of 18 features (5 first order descriptors, 10 Gray level co-occurrence matrix features(GLCM), 2 Gray level run length matrix features and shape feature) to each thyroid region of interest. The extracted features are used as statistical analysis. Our results show that first order statistics (Skewness, Entropy, Energy, Smoothness), GLCM (Correlation, Contrast, Energy, Entropy, Difference variance, Difference Entropy, Homogeneity, Maximum Probability, Sum average, Sum entropy), GLRLM features and shape feature helped to distinguish thyroid benign and malignant. This algorithm will be helpful to diagnose of thyroid nodule on ultrasound images.

• Child Health Nursing Research
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• v.27 no.3
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• pp.201-210
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• 2021

Purpose: This study analyzed research trends related to childhood and adolescent cancer survivors (CACS) using word co-occurrence network analysis on studies registered in the Korean Citation Index (KCI). Methods: This word co-occurrence network analysis study explored major research trends by constructing a network based on relationships between keywords (semantic morphemes) in the abstracts of published articles. Research articles published in the KCI over the past 10 years were collected using the Biblio Data Collector tool included in the NetMiner Program (version 4), using "cancer survivors", "adolescent", and "child" as the main search terms. After pre-processing, analyses were conducted on centrality (degree and eigenvector), cohesion (community), and topic modeling. Results: For centrality, the top 10 keywords included "treatment", "factor", "intervention", "group", "radiotherapy", "health", "risk", "measurement", "outcome", and "quality of life". In terms of cohesion and topic analysis, three categories were identified as the major research trends: "treatment and complications", "adaptation and support needs", and "management and quality of life". Conclusion: The keywords from the three main categories reflected interdisciplinary identification. Many studies on adaptation and support needs were identified in our analysis of nursing literature. Further research on managing and evaluating the quality of life among CACS must also be conducted.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.50-59
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• 2021

The main purpose of accident analysis is to identify the causal factors and the mechanisms of those factors leading to the accident. However, current accident analysis techniques focus only on finding the factors related to the accident without providing more insightful results, such as structures or mechanisms. For this reason, preventive actions for safety management are concentrated on the elimination of causal factors rather than blocking the connection or chain of accident processes. This greatly reduces the effectiveness of safety management in practice. In the present study, a technique to model the correlational structure of accident risk factors is proposed by using the co-occurrence keyword network analysis technique. To investigate the effectiveness of the proposed technique, a case study involving a portable ladder fall accident is conducted. The results indicate that the proposed technique can construct the correlational structure model of the risk factors of a portable ladder fall accident. This proves the effectiveness of the proposed technique in modeling the correlational structure of accident risk factors.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.403-410
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• 2004

Recently, studies of image analysis, as the preprocessing stage for medical image analysis or image retrieval, are actively carried out. This paper intends to propose a way of utilizing color components for image retrieval. For image retrieval, it is based on color components, and for analysis of color, CLCM (Color Level Co-occurrence Matrix) and statistical techniques are used. CLCM proposed in this paper is to project color components on 3D space through geometric rotate transform and then, to interpret distribution that is made from the spatial relationship. CLCM is 2D histogram that is made in color model, which is created through geometric rotate transform of a color model. In order to analyze it, a statistical technique is used. Like CLCM, GLCM (Gray Level Co-occurrence Matrix)[1] and Invariant Moment [2,3] use 2D distribution chart, which use basic statistical techniques in order to interpret 2D data. However, even though GLCM and Invariant Moment are optimized in each domain, it is impossible to perfectly interpret irregular data available on the spatial coordinates. That is, GLCM and Invariant Moment use only the basic statistical techniques so reliability of the extracted features is low. In order to interpret the spatial relationship and weight of data, this study has used Principal Component Analysis [4,5] that is used in multivariate statistics. In order to increase accuracy of data, it has proposed a way to project color components on 3D space, to rotate it and then, to extract features of data from all angles.