• 한국환경생태학회지
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• 제4권1호
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• pp.33-43
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• 1990

속리산국립공원 법주사지역의 삼림군집을 대상으로 TWINSPAN에 의한 classification 및 DCA의 ordination 기법을 이용하여 식물군집구조를 밝히고 천이계열을 추정하기 위하여 70개 조사구(1조사구당 500$m^2$)를 설치하였다. TWINSPAN에 의한 classification 분석에서 6개의 군집으로 분리되어 소나무 군집, 신갈나무-소나무 군집, 졸참나무-신갈나무 군집, 신갈나무 군집, 서어나무-졸참나무 군집, 졸참나무 군집으로 나뉘었고, 분리환경인자는 해발 고와 토양습도였다. 본 연구에서는 DCA기법이 TWINSPAN보다 효율성이 더 좋았다. 천이계열은 교목상층에서는 소나무, 팥배나무$\longrightarrow$졸참나무$\longrightarrow$서어나무와 소나무, 쇠물푸래나무$\longrightarrow$신갈나무이었고, 교목하층 및 관목층에서는 참싸리, 개옻나무. 산초나무$\longrightarrow$철쭉, 참개암나무, 생강나무, 함박꽃나무$\longrightarrow$참회나무로 추정되었다. 산화발생에 의해서 식물군집의 종다양성은 매우 감소하였고, 참나무류의 상대우점치는 증가하였다.

• 한국멀티미디어학회논문지
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• 제23권10호
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• pp.1250-1257
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• 2020

The early detection of diseases is important in agriculture because diseases are major threats of reducing crop yield for farmers. The shape and color of plant leaf are changed differently according to the disease. So we can detect and estimate the disease by inspecting the visual feature in leaf. This study presents a vision-based leaf classification method for detecting the diseases of tomato crop. ResNet-50 model was used to extract the visual feature in leaf and classify the disease of tomato crop, since the model showed the higher accuracy than the other ResNet models with different depths. We propose a new ensemble approach using several DCNN classifiers that have the same structure but have been trained at different ranges in the DCNN layers. Experimental result achieved accuracy of 97.19% for PlantVillage dataset. It validates that the proposed method effectively classify the disease of tomato crop.

• 전기학회논문지
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• 제58권12호
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• pp.2532-2537
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• 2009

For defect characterization in steam generator tubes in nuclear power plant, artificial neural network has been extensively used to classify defect types. In this paper, we study the effectiveness of Bagging for improving the performance of neural network for the classification of tube defects. Bagging is a method that combines outputs of many neural networks that were trained separately with different training data set. By varying the number of neurons in the hidden layer, we carry out computer simulations in order to compare the classification performance of bagging neural network and single neural network. From the experiments, we found that the performance of bagging neural network is superior to the average performance of single neural network in most cases.

• 한국작물학회지
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• 제46권1호
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• pp.53-56
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• 2001

Barley Yellow Dwarf Virus (BYDV), an aphid-borne luteovirus, is a major plant pathogenic disease causing a huge economic loss in the grain production of a wide range of Gramineae species throughout the world. It has been recently reported that BYDV also occurred frequently in wheat field of Korea. Here, we performed to develop the detection and classification methods of BYDV strains that were accomplished by reverse transcription-polymerase chain reaction (RT-PCR). Since there are high variations among BYDV strains, three pairs of primers were designed to detect BYDV strains such as PAV (Vic-PAV and CN-PAV) and MAV (primer A) simultaneously, specifically Vic-PAV(primer B), and MAV (primer C) based on the genomic RNA sequences of BYDV strains previously published. The validity of the primers was confirmed using several BYDV strains obtained from CIMMYT. Though three BYDV strains were able to be detected using primer A, PCR products were not distinguished between two PAV strains. It was possible to separate them with a restriction enzyme, EcoRI, whose restriction site was present in the amplified DNA fragment from Vic-PAV, but not from CN-PAV.

• 플랜트 저널
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• 제11권1호
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• pp.39-49
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• 2015

본 연구는 석유화학 플랜트 설계, 구매 시공에 직간접적으로 많은 영향을 주는 배관자재의 관리 효율성을 높이기 위하여 자재 코드 및 자재관리 시스템의 근간이 되는 배관자재 코드 분류체계를 개선하였다. 기존 배관자재 코드 분류체계를 개선하기 위하여 내재된 문제점을 자세히 파악하고 국내외 대형 EPC 기업의 배관자재 코드 분류체계 특징을 조사하였으며, 최근 대형화, 전문화 되어가는 프로젝트의 특성을 고려하여 개선 방향을 설정하였다. 배관 자재별 특성에 맞는 코드분류체계를 정의하고, 표준 속성을 추가하고, 신규 자재 및 재질을 고려한 코드 자릿수 확장 및 계층적 분류 구조를 통하여 효율적 배관 자재관리를 위한 배관 자재 코드 분류체계의 개선 구조를 도출하였다. 개선된 배관자재 코드 분류체계를 수행중인 프로젝트에 적용한 결과, 자재 구매사양서의 재 작업률이 평균 4.98%에서 2.48%로 감소하였으며, 3차원설계에서 요구되는 배관 형상 구축 작업시간이 기존 평균 작업인원 2명이 6개월 소요 되었으나, 1명이 4개월로 67% 감소 효과를 가져왔다. 또한 피라미드 코드 구조를 통하여 전사 자재관리 시스템과 연동되어 구매, 견적 등 유관 부서에서 다양한 데이터를 축적하고 내부 경영관리 의사결정을 위한 프로젝트 분석에 활용할 수 있게 되었다.

• Plant Resources
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• 제2권2호
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• pp.75-80
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• 1999

Twenty two varieties of pecan including wild types were classified based on 6 characters measured by principal component analysis score distance. The results are summarized as fellow. Twenty two varieties were classified into 5 groups based in PCA score distance. Five groups were distinctly characterized by many morphological characters. Total variation could be explained by 51%, 95%, 99% with first, third and fifth principal components respectively. Varimax rotation of the factor loading of the first factors indicated that the first component was highly loaded with leaf characters, the second component with fruit characters, but fruit length was negative loaded. The second, the third and the fourths groups of cultivars had very close genetic parentage similarity.

• Korean Chemical Engineering Research
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• 제58권3호
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• pp.493-497
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• 2020

Hazardous area classification is designed to prevent chemical plant explosions in advance. Generally, the duration of the explosive atmosphere is used for zone type classification. Herein, IEC code, a quantitative zone type classification methodology, was used to achieve Zone 2 NE, which indicates a practical non-explosion condition. This study analyzed the operating pressure of a vessel handling propane to achieve Zone 2 NE by applying the IEC code via MATLAB. The resulting zone type and hazardous area grades were compared with the results from other design standards, namely API and EI codes. According to the IEC code, the operating pressure of vessels handling propane should be between 101325-116560.59 Pa. In contrast, the zone type classification criteria used by API and EI codes are abstract. Therefore, since these codes could interpret excessively explosive atmospheres, care is required while using them for hazardous area classification design.

• 전자통신동향분석
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• 제33권4호
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• pp.54-60
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• 2018

In this paper, we analyze the trends of deep-learning based plant data processing technologies. In recent years, the deep-learning technology has been widely applied to various AI tasks, such as vision (image classification, image segmentation, and so on) and natural language processing because it shows a higher performance on such tasks. The deep-leaning method is also applied to plant data processing tasks and shows a significant performance. We analyze and show how the deep-learning method is applied to plant data processing tasks and related industries.

• 한국자원식물학회지
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• 제11권
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• pp.80-88
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• 1998

Liliaceac is one of the largest families fo flowering plants, consisting of about 220-240 genra with 3500-4000 species. According to A. Englcr's classification(1964), it is divided into 13 subfamilies(Table I).

• 한국안전학회지
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• 제39권1호
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• pp.82-95
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• 2024

Analyzing the aftermath of events at domestic nuclear power plants brings in the question: "Why do workers not comply with the prescribed procedures?" The current investigation of nuclear power plant events identifies their reasons considering the factors affecting the workers' behaviors. However, there are some complications to it: in addition to confirming the action such as an error or a violation, there is a limit to identifying the intention of the actor. To overcome this limitation, the study analyzed and examined the reasons for non-compliance identified in nuclear power plant events by Reason's rule-related behavior classification. For behavior analysis, I selected unit behaviors for events that are related to human and organizational factors and occurred at domestic nuclear power plants since 2017, and then I applied the rule-related behavior classification introduced by Reason (2008). This allowed me to identify the intentions by classifying unit behaviors according to quality and compliance with the rules. I also identified the factors that influenced unit behaviors. The analysis showed that most often, non-compliance only pursued personal goals and was based on inadequate risk appraisal. On the other hand, the analysis identified cases where it was caused by such factors as poorly written procedures or human system interfaces. Therefore, the probability of non-compliance can be reduced if these factors are properly addressed. Unlike event investigation techniques that struggle to identify the reasons for employee behavior, this study provides a new interpretation of non-compliance in nuclear power plant events by examining workers' intentions based on the concept of rule-related behavior classification.