• Journal of Forest and Environmental Science
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• 제40권1호
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• pp.1-8
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• 2024

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.

• 대한한의학회지
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• 제27권2호
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• pp.134-144
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• 2006

Tongue diagnosis is an important diagnostic method in traditional Oriental medicine. It has been especially accepted that quantitative analysis of tongue images allows the accurate diagnosis of the exterior-interior and cold-heat patterns of a patient. However, to ensure stable and reliable results, the color reproduction of such images must first be error-tree. Moreover, tongue diagnosis is much influenced by the surrounding illumination and subjective color recognition, so it has to be performed objectively and quantitatively using a digital diagnostic machine. In this study, 457 tongue images of outpatients were collected using the Digital Tongue Inspection System. Through statistical analysis, the result shows that the heat and cold patterns can be distinguished clearly based on the hue value of the tongue images. The average hue value (1.00) of the tongue's image in the cold pattern is higher than that in the heat pattern (0.99).

• 조명전기설비학회논문지
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• 제20권2호
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• pp.75-80
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• 2006

본 논문은 전력 케이블의 절연체인 XLPE 시료에 상용주파수 교류전압을 인가하였을 때, 절연열화에 의한 보이드방전 펄스신호와 AE신호 및 트리진전 특성을 검출하고 관찰하였다. 또한 XLPE 절연체의 트리 채널내 인가전압에 따라 보이드방전 펄스신호와 AE신호를 각각 관측하였다. 실험 결과 전력 케이블의 절연체인 XLPE 시료의 보이드 존재 유 무에 따른 트리진전 형상은 보이드 존재시에는 수지형으로 성장하였으나, 무보이드시에는 수초형으로 성장하였다. XLPE 절연체내의 트리진전 특성은 보이드 존재시에는 열화시간이 경과함에 따라 급격히 증가하였으나, 무보이드시에는 열화 시간이 경과함에 따라 트리의 성장 형상은 감소되는 것으로 나타났다. 전력 케이블 절연체인 XLPE의 트리 채널내 보이드방전에 의한 AE신호의 주파수 스펙트럼으로 분석한 결과, AE신호의 크기에 따라 변화되었으며 그 주파수 영역은 대략 1.0[MHz] 이하의 분포인 것으로 관측되었다.

• 공학교육연구
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• 제13권4호
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• pp.51-59
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• 2010

대학에서 운영되는 강좌를 효과적으로 관리하고 교육성과를 향상시키기 위해서는 각 클래스의 현재의 교육성과를 진단하고 교육성과에 영향을 미치는 요인들을 파악하는 과정이 요구된다. 요인을 발견하는 연구에는 연관성 분석, 회귀분석 등의 통계기법들이 많이 사용되고 있으며 최근에는 데이터마이닝의 결정트리 분석도 사용되고 있다. 결정트리 분석은 결과 모델을 이해하기 쉽고 의사결정에 적용하기 쉽다는 장점이 있지만, 다중공선성 등의 입력 데이터의 특성에 견고하지 못한 문제점이 있다. 본 연구에서는 기존의 결정트리 분석의 문제점들을 정리하고, 이 문제점들을 보완하기 위한 하나의 실험적 해결책으로 다중 결정트리를 이용한 요인의 발견 방법을 제안한다. 실험을 통해 다중 결정트리를 수행이 다중 결정트리를 적용할 때보다 신뢰할 수 있는 요인을 발견하고 각 변수의 중요성을 발견할 수 있음을 보였다.

• Journal of Electrical Engineering and Technology
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• 제10권2호
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• pp.670-675
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• 2015

Medical imaging is one of the most powerful tools for gaining information about internal organs and tissues. It is a challenging task to develop sophisticated image analysis methods in order to improve the accuracy of diagnosis. The objective of this paper is to develop a Computer Aided Diagnostics (CAD) scheme for Brain Tumour detection from Magnetic Resonance Image (MRI) using active contour models and to investigate with several approaches for improving CAD performances. The problem in clinical medicine is the automatic detection of brain Tumours with maximum accuracy and in less time. This work involves the following steps: i) Segmentation performed by Fuzzy Clustering with Level Set Method (FCMLSM) and performance is compared with snake models based on Balloon force and Gradient Vector Force (GVF), Distance Regularized Level Set Method (DRLSE). ii) Feature extraction done by Shape and Texture based features. iii) Brain Tumour detection performed by various tree classifiers. Based on investigation FCMLSM is well suited segmentation method and Random Forest is the most optimum classifier for this problem. This method gives accuracy of 97% and with minimum classification error. The time taken to detect Tumour is approximately 2 mins for an examination (30 slices).

• 한국생활과학회지
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• 제17권6호
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• pp.1065-1077
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• 2008

The purpose of this study was to verify whether BND test was an appropriate tool for diagnosis of attachment security and to investigate difference of responsive Characteristics to the Bird's Nest Drawings according to parents attachment degree. The subjects in the study were 525 students, selected from senior high schools in D-city. The instruments used were parents attachment scale and Bird's Nest Drawings, and Discriminant analyses and crosstab analyses were used. The results were as follows. First, attachment indicators in the Bird's Nest Drawings discriminated according to group of parents attachment. Second, for male student, there was a significant difference placement nest, eggs, entire birds family, quality of line and tree picture according to attachment to father. For female student, there was a significant difference eggs, entire birds family, quality of line and tree picture according to attachment to father. For male student, there was a significant difference nest contents, placement nest, eggs, entire birds family, quality of line and tree picture according to attachment to mother. For female student, placement nest, space, nest size, eggs, entire birds family and quality of line according to attachment to mother.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1901-1910
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• 2023

The safety-class (1E) digital control system (DCS) of nuclear power plant characterized structural multiple redundancies, therefore, it is important to quantitatively evaluate the reliability of DCS in different degree of backup loss. In this paper, a reliability evaluation model based on T-S fuzzy fault tree (FT) is proposed for 1E DCS of nuclear power plant, in which the connection relationship between components is described by T-S fuzzy gates. Specifically, an output rejection control system is chosen as an example, based on the T-S fuzzy FT model, the key indicators such as probabilistic importance are calculated, and for a further discussion, the T-S fuzzy FT model is transformed into Bayesian Network(BN) equivalently, and the fault diagnosis based on probabilistic analysis is accomplished. Combined with the analysis of actual objects, the effectiveness of proposed method is proved.

• 한국한의학연구원논문집
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• 제15권1호
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• pp.63-68
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• 2009

In Sasang Constitution Medicine (SCM), it is most important that a personal SCM type is determined accurately ahead of applying any Sasang treatments. Although SCM doctors have districted personal SCM types in many hospitals and universities via their own discriminant, it still lacks objective criteria on diagnosis of SCM type. Therefore, many researchers have been studied to diagnose the SCM type using constitutional clinical data. Previous work, we have developed decision tree program to analyze the clinical information. In this paper, we developed a clinical index program based on the web to analyze the correlation among clinical information. Finally, we identified useful factors(4 clinical indexes) which have significant influence on SCM types using clinical index program with previously developed decision tree program.

• 한국지능시스템학회논문지
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• 제23권5호
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• pp.400-405
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• 2013

The purpose of this study is to develop a composite platform for knowledge extractions, visualizations, and inference. Generally, the big data sets were frequently used in the healthcare and medical area. To help the knowledge managers/users working in the field, this study is focused on knowledge management (KM) based on Data Mining (DM), Knowledge Distribution Map (KDM), Decision Tree (DT), RDBMS, and SQL-inference. The proposed mechanism is composed of five key processes. Firstly, in Knowledge Parsing, it extracts logical rules from a big data set by using DM technology. Then it transforms the rules into RDB tables. Secondly, through Knowledge Maintenance, it refines and manages the knowledge to be ready for the computing of knowledge distributions. Thirdly, in Knowledge Distribution process, we can see the knowledge distributions by using the DT mechanism.Fourthly, in Knowledge Hierarchy, the platform shows the hierarchy of the knowledge. Finally, in Inference, it deduce the conclusions by using the given facts and data.This approach presents the advantages of diversity in knowledge representations and inference to improve the quality of computer-based medical diagnosis.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.107-112
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• 2024

Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques