• Journal of Korean Elementary Science Education
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• v.40 no.1
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• pp.66-80
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• 2021

This study is a case study that applies plant classification learning using machine learning to fourth graders in elementary school in online learning situations. In this study, a plant classification learning education program associated with 2015 revision science curriculum was developed by applying the Artificial Intelligence biological classification teaching Learning model. The study participants were 31 fourth graders who agreed to participate voluntarily. Plant classification learning using machine learning was applied six hours for three weeks. The results of this study are as follows. First, as a result of image analysis on artificial intelligence, participants were mainly aware of artificial intelligence as mechanical (27%), human (23%) and household goods (23%). Second, an artificial intelligence recognition survey by semantic discrimination found that artificial intelligence was recognized as smart, good, accurate, new, interesting, necessary, and diverse. Third, there was a difference between men and women in perception and emotion of artificial intelligence, and there was no difference in perception of the ability of artificial intelligence. Fourth, plant classification learning using machine learning in this study influenced changes in artificial intelligence perception. Fifth, plant classification learning using machine learning in this study had a positive effect on reasoning ability.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.625-626
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• 2015

The purpose of this study is to present a cost classification system that can be used in gas plant construction projects. Towards this end, it examined the detailed statements of the construction companies which had experience in carrying out plant construction projects. Based on the above, it also presented a life-cycle (i.e., EPCC) cost classification system for the gas plant construction projects by conducting the Delphi analysis through the expert opinions.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1336-1342
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• 2022

Risk-informed safety classification of structures, systems and components (SSCs) is very important for ensuring the safety and economic efficiency of nuclear power plants (NPPs). However, previous methods for safety classification of SSCs do not take the plant operating modes or the operational process of SSCs into consideration, thus cannot concentrate on the safety and economic efficiency accurately. In this contribution, a new method for safety classification of SSCs based on the categorization of plant operating modes is proposed, which considers the NPPs operating history to improve the economic efficiencies while maintaining the safety. According to the time duration of plant configurations in plant operating modes, average importances of SSCs are accessed for an NPP considering the operational process, and then safety classification of SSCs is performed for plant operating modes. The correctness and effectiveness of the proposed method is demonstrated by application in an NPP's safety classification of SSCs.

• Korean Journal of Computational Design and Engineering
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• v.12 no.3
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• pp.191-199
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• 2007

In order to construct a data warehouse of process plant equipment, a classification structure should be defined first, identifying not only the equipment categories but also attributes of an each equipment to represent the specifications of equipment. ISO 15926 Process Plants is an international standard dealing with the life-cycle data of process plant facilities. From the viewpoints of defining classification structure, Part 2 data model and Reference Data Library (RDL) of ISO 15926 are seen to respectively provide standard syntactic structure and semantic vocabulary, facilitating the exchange and sharing of plant equipment's life-cycle data. Therefore, the equipment data warehouse with an ISO 15926-based classification structure has the advantage of easy integration among different engineering systems. This paper introduces ISO 15926 and then discusses how to define a classification structure with ISO 15926 Part 2 data model and RDL. Finally, we describe the development result of an ISO 15926-based classification structure for a variety of equipment consisting in the reactor coolant system (RCS) of APR 1400 nuclear plant.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.571-582
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• 2018

Recently, development of deep learning has shown better image classification result than human. According to recent research, a hidden layer of deep learning is deeper, and a preservation of extracted features shows good results. However, in the case of general images, the extracted features are clear and easy to sort. This study aims to classify plant leaf images. This plant leaf image has high similarity in each image. Since plant leaf images have high similarity not only between images of different species but also within the same species, classification accuracy is not increased by simply extending the hidden layer or connecting the layers. Therefore, in this paper, we tried to improve the hidden layer of the algorithm called DenseNet which shows the recent excellent classification results, and compare the results of several different modified layers. The proposed method makes it possible to classify plant leaf images collected in a natural environment more easily and accurately than conventional methods. This results in good classification of plant leaf image data including unnecessary noise obtained in a natural environment.

• Journal of Korean Elementary Science Education
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• v.26 no.1
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• pp.41-48
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• 2007

The purpose of this study is to examine how many plant names elementary school children how, and what kind of criteria they use for classifying these plants. The sample involved 926 students from the 2nd, the 4th, and the 6th grades dwelling in one urban, three suburban, and six rural areas. Their level of perception on the name of plants increased in correlation to the elevation of the grade level. However, different patterns of increases were shown depending on the local environments in which they live. The most well-known plant names for students were the rose of Sharon, the rose and the pine tree. The students mostly classified the plants using the following criteria such as 'with or without flower' and 'edible or inedible' regardless as to whether they had prior loaming experience of plant classification. 65.3% of the 6th graders correctly grouped 5 kinds of plants into the flowering and the non-flowering plant categories at the 1st level of classification. However, only 17.9% and 7.7% correctly divided the flowering and the non-flowering plants into two subgroups at the 2nd level of classification respectively. Therefore, their abilities in plant classification was shown overall to be poor. The students living in suburban areas appeared to be harmonized with both the natural and urbanized surroundings and classified the plants more scientifically than those from the urban or rural areas were able to. This suggests that the conception of plant classification by children is affected by the environment in which they live. If children have more opportunities to observe plants in surroundings such as their classrooms and school gardens, it will help them to form the relevant scientific concepts as well as to correct any alternative conceptions related to classification.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.3
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• pp.689-701
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• 2017

In recent offshore industries, various ambiguous terms have been used without clear definition or classification, causing difficulties in legal, technical, and educational understanding and usage. For an example, the commonly used term of 'Offshore Plant' in Korea is not an universal word technically. There has been no clear technical or legal definition about the 'Offshore Plant' and its classification is also very ambiguous; sometimes it is used to refer offshore oil and gas production platform or it is used to mean offshore renewable power generation plant in some cases. To build a conceptual framework, therefore, this paper suggests a classification of offshore units (1) using internationally agreed terms, (2) agreed with the technical classification used by the ship classification society and (3) being able to include not only the current but also future concepts of offshore units.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.663-676
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• 2020

Plant leaf classification is a significant application of image processing techniques in modern agriculture. In this paper, a multi-granular angle description method is proposed for plant leaf classification and retrieval. The proposed method can describe leaf information from coarse to fine using multi-granular angle features. In the proposed method, each leaf contour is partitioned first with equal arc length under different granularities. And then three kinds of angle features are derived under each granular partition of leaf contour: angle value, angle histogram, and angular ternary pattern. These multi-granular angle features can capture both local and globe information of the leaf contour, and make a comprehensive description. In leaf matching stage, the simple city block metric is used to compute the dissimilarity of each pair of leaf under different granularities. And the matching scores at different granularities are fused based on quotient space theory to obtain the final leaf similarity measurement. Plant leaf classification and retrieval experiments are conducted on two challenging leaf image databases: Swedish leaf database and Flavia leaf database. The experimental results and the comparison with state-of-the-art methods indicate that proposed method has promising classification and retrieval performance.

• Journal of Plant Biology
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• v.33 no.3
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• pp.173-182
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• 1990

To investigate the structure of the plant community structure of Mt. Yongmun in Kyonggi-do, fifty-four plots were set up by the clumped sampling method. The classification by TWINSPAN and DCA ordination were applied to the study area in order to classify them into several groups based on woody plant and environmental variables. By both techniques, the plant community were divided into two groups by the aspect. the dominant species of south aspect were Pinus densiflora, Quercus aliena, Q. mongolica, Carpinus laxiflora and of north aspect were Q. ongolica, Fraxinus rhynchophylla. The successional trends of tree species in south aspect seem to be from P. densiflora through Q. serrata, Q. aliena, A. mongolica to C. laxiflora. As a result of the analysis for the relationship between the stand scores of DCA and environmental variables, they had a tendency to increase significantly from the P. densiflora and Q. mongolica community to C. laxiflora and F. rhynchophylla community that was the soil moisture, the amount of soil humus and soil pH.

• The Plant Pathology Journal
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• v.39 no.4
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.