• Journal of the Korean Society of Safety
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• v.39 no.1
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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.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.10a
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• pp.377-377
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• 1998

• Journal of The Geomorphological Association of Korea
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• v.24 no.4
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• pp.57-73
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• 2017

The formation of the lava dam of the paleo lake blocked the entrance to the Chatancheon River on the Jeongok lava plateau and it suddenly transformed the terrestrial ecosystem into the aquatic one by the overflow. The spiders in the lava dam adapted in the wetland and evolved into water spiders that could survive by forming bubble houses. Since then, the lava dam was connected to the present Hantangang River due to the dissection and the lake became a terrestrial environment, a small area of marsh composed of primarily clay soil layer. Change in water level of the habitat and thus the extension of the terrestrial area made the species a endangered now. This study carried out frequency of occurrence, degree of wetness and plant habitats of the vascular plant in the water spider habitat. As a result of this study, total 180taxa are of 55 (30.6%) wetland plant groups and of 113 (62.8%) upland plant groups except facultative plant groups. Among the wetland plant groups, the Isachne globosa community occupied the largest area, where the water spiders were most observed. The result of this study, the classification and the types of vascular plant species, would provide useful information for the sustaining healthy wetland ecosystem and the restoration of the habitat for the water spiders.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.47 no.5
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• pp.335-340
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• 2002

To evaluate the efficiency of anther floating culture according to the maturing group, the varietal difference and classification of fifty varieties was conducted in N6 liquid medium containing 1mg $l^{-1}$ NAA, 0.25 mg $l^{-1}$ kinetin. The efficiency of callus induction was widely ranged from 0 to 113.4%, but the mean callus induction was not significantly different among maturing groups. The efficiency of anther floating culture showed the highest variation in early-maturing group among three maturing groups. The varieties with the best callus induction were Sambaegbyeo and Jinbuolbyeo, while the recalcitrant variety was Obongbyeo in early-maturing group. The efficiency of plant regeneration showed the highest trends in late-maturing group among three maturing groups. The fifty varieties were classified into three groups (distance=0.78) by cluster analysis based on the callus formation and plant regeneration. Group including only two varieties, Shinunbongbyeo and Sambaegbyeo had the excellent androgenic efficiency, and the medium efficiency of Group was included thirty-six varieties. Whereas twelve varieties, including three Tongil varieties were fell into the bad efficiency of Group. Especially, Tongil varieties containing Japonica rice, Obongbyeo were the recalcitrant genotypes for the anther floating culture.

• The Plant Pathology Journal
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• v.34 no.5
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• pp.435-444
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• 2018

Receptor-like proteins (RLPs) are involved in plant development and disease resistance. Only some of the RLPs in tomato (Solanum lycopersicum L.) have been functionally characterized though 176 genes encoding RLPs, which have been identified in the tomato genome. To further understand the role of RLPs in tomato, we performed genome-guided classification and transcriptome analysis of these genes. Phylogenic comparisons revealed that the tomato RLP members could be divided into eight subgroups and that the genes evolved independently compared to similar genes in Arabidopsis. Based on location and physical clustering analyses, we conclude that tomato RLPs likely expanded primarily through tandem duplication events. According to tissue specific RNA-seq data, 71 RLPs were expressed in at least one of the following tissues: root, leaf, bud, flower, or fruit. Several genes had expression patterns that were tissue specific. In addition, tomato RLP expression profiles after infection with different pathogens showed distinguish gene regulations according to disease induction and resistance response as well as infection by bacteria and virus. Notably, Some RLPs were highly and/or unique expressed in susceptible tomato to pathogen, suggesting that the RLP could be involved in disease response, possibly as a host-susceptibility factor. Our study could provide an important clues for further investigations into the function of tomato RLPs involved in developmental and response to pathogens.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.662-666
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• 2005

Using data gathered from expert opinion of plant and equipment professionals; this paper presents the key variables that may constitute a maintenance proficient plant operator. The Multivariate Discriminant Analysis (MDA) was applied to generate data and was tested for sensitivity analysis. Results showed that the MDA model was able to classify plant operators' proficiency at 94.10 percent accuracy and determined nine (9) key variables of a maintenance proficient plant operator. The key variables included: i) number of years of experience as equipment operator (PQ1); ii) eye-hand coordination (PQ9); iii) eye-hand-foot coordination (PQ10); iv) planning skills (TE16); v) pay/wage (MQ1); vi) work satisfaction (MQ4); vii) operator responsibilities as defined by management (MF1); viii) clear management policies (MF4); and ix) management pay scheme (MF5). The classification procedure of nine variables formed the general model with the equation viz: OMP (general) = 0.516PQ1 + 0.309PQ9 + 0.557PQ10 + 0.831TE16 + 0.8MQ1 + 0.0216MQ4 + 0.136MF1 + 0.28MF4 + 0.332MF5 - 4.387

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.709-714
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• 2002

This paper discussed and analyzed results of different classification algorithms for land use classification in arid and semiarid areas using CBERS-1 image, which in case of our study is Shihezi Municipality, Xinjiang Province. Three types of classifiers are included in our experiment, including the Maximum Likelihood classifier, BP neural network classifier and Fuzzy-ARTMAP neural network classifier. The classification results showed that the classification accuracy of Fuzzy-ARTMAP was the best among three classifiers, increased by 10.69% and 6.84% than Maximum likelihood and BP neural network, respectively. Meanwhile, the result also confirmed the practicability of CBERS-1 image in land use survey.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.300-311
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• 2014

According to fast change of the environment, the structured study of the ecosystem by analyzing the plant leaves are needed. Expecially, the methodology that searches and classifies the leaves from captured from the smart device have received numerous concerns in the field of computer science and ecology. In this paper, we propose a plant leaf classification technique using shape descriptor by combining Scale Invarinat Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG) from the image segmented from the background via Graphcut algorithm. The shape descriptor is coded in the field of Locality-constrained Linear Coding to optimize the meaningful features from a high degree of freedom. It is connected to Support Vector Machines (SVM) for efficient classification. The experimental results show that our proposed approach is very efficient to classify the leaves which have similar color, and shape.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.863-869
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• 2017

In this study, the information breakdown structure of water purification plant has been made by classifying various the water purification methods and facilities. this can be utilized as a code system of computer for integrating information and analyzing quantitative of environmental impact and calculating cost of maintenance and energy consumption which was used during life cycle of water purification plant. Since the construction information contains many heterogeneous information, it is very important to have a code system for managing the integrated information. In addition, since water purification plant facilities are mainly composed of installation of facilities including many processes, a more detailed classification code is required. Therefore, in this study, the water purification breakdown structure which is not yet attempted in Korea was constructed by using facet classification system.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.581-589
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• 2021

Using visible-near infrared(Vis-NIR) spectra combined with machine learning methods, the feasibility of quick and non-destructive classification of Convolvulaceae species was studied. The main aim of this study is to classify six Convolvulaceae species in the field in different geographical regions of South Korea using a handheld spectrometer. Spectra were taken at 1.5 nm intervals from the adaxial side of the leaves in the Vis-NIR spectral region between 400 and 1,075 nm. The obtained spectra were preprocessed with three different preprocessing methods to find the best preprocessing approach with the highest classification accuracy. Preprocessed spectra of the six Convolvulaceae sp. were provided as input for the machine learning analysis. After cross-validation, the classification accuracy of various combinations of preprocessing and modeling ranged between 43.4% and 98.6%. The combination of Savitzky-Golay and Support vector machine methods showed the highest classification accuracy of 98.6% for the discrimination of Convolvulaceae sp. The growth stage of the plants, different measuring locations, and the scanning position of leaves on the plant were some of the crucial factors that affected the outcomes in this investigation. We conclude that Vis-NIR spectroscopy, coupled with suitable preprocessing and machine learning approaches, can be used in the field to effectively discriminate Convolvulaceae sp. for effective weed monitoring and management.