• 한국멀티미디어학회논문지
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• 제25권11호
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• pp.1653-1671
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• 2022

Crop diseases affect crop production, more than 30 billion USD globally. We proposed a classification study of crop species and diseases using deep learning algorithms for corn, cucumber, pepper, and strawberry. Our study has three steps of species classification, disease detection, and disease classification, which is noteworthy for using captured images without additional processes. We designed deep learning approach of deep learning convolutional neural networks based on Mask R-CNN model to classify crop species. Inception and Resnet models were presented for disease detection and classification sequentially. For classification, we trained Mask R-CNN network and achieved loss value of 0.72 for crop species classification and segmentation. For disease detection, InceptionV3 and ResNet101-V2 models were trained for nodes of crop species on 1,500 images of normal and diseased labels, resulting in the accuracies of 0.984, 0.969, 0.956, and 0.962 for corn, cucumber, pepper, and strawberry by InceptionV3 model with higher accuracy and AUC. For disease classification, InceptionV3 and ResNet 101-V2 models were trained for nodes of crop species on 1,500 images of diseased label, resulting in the accuracies of 0.995 and 0.992 for corn and cucumber by ResNet101 with higher accuracy and AUC whereas 0.940 and 0.988 for pepper and strawberry by Inception.

• 한국문헌정보학회지
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• 제40권1호
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• pp.39-57
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• 2006

한국학술진흥재단의 연구분야분류표에 의하면 지적학은 지역개발의 중분류아래 소분류항목으로 배정되어 있어 독립된 학문분야로 인정받지 못하고 있다. 따라서 이 연구의 목적은 현행 지적학 학문분류체계의 문제점을 찾아내고 개선방안을 제시하는데 있다 이를 위해 지적학의 학문적 정의와 연구대상 지적학 관련의 학문적 성과, 지적학 교육제도와 교과목의 현황과 특징을 분석하였으며, 학문분류체계와 문헌분류에서의 분류현황을 살펴보았다. 그 결과 지적학을 복합학의 대분류영역에 배정하여 중분류로 상향 이동시키고 소분류와 세분류 항목을 설정하여 지적학의 학문분류체계를 개선할 수 있는 것으로 나타났다.

• Geomechanics and Engineering
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• 제8권5호
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• pp.631-647
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• 2015

Engineers may encounter unpredictable cavities, sinkholes and karst conduits while tunneling in karst area, and water inrush disaster frequently occurs and endanger the construction safety, resulting in huge casualties and economic loss. Therefore, an optimal classification method based on grey system theory (GST) is established and applied to accurately predict the occurrence probability of water inrush. Considering the weights of evaluation indices, an improved formula is applied to calculate the grey relational grade. Two evaluation indices systems are proposed for risk assessment of water inrush in design stage and construction stage, respectively, and the evaluation indices are quantitatively graded according to four risk grades. To verify the accuracy and feasibility of optimal classification method, comparisons of the evaluation results derived from the aforementioned method and attribute synthetic evaluation system are made. Furthermore, evaluation of engineering practice is carried through with the Xiakou Tunnel as a case study, and the evaluation result is generally in good agreement with the field-observed result. This risk assessment methodology provides a powerful tool with which engineers can systematically evaluate the risk of water inrush in karst tunnels.

• Journal of Korean Neurosurgical Society
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• 제57권3호
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• pp.174-177
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• 2015

Objective : For improving the drawbacks of previous thoracolumbar spine trauma classification, the Spine Trauma Study Group was developed new classification, Thoracolumbar Injury Classification and Severity Score (TLICS). The simplicity of this scoring system makes it useful clinical application. However, considering criteria of Korean Health Insurance Review and Assessment Service (HIRA), the usefulness of TLICS system is still controversial in the treatment decision of thoracolumbar spine injury. Methods : Total 100 patients, who admitted to our hospital due to acute traumatic thoracolumbar injury, were enrolled. In 45, surgical treatment was performed and surgical treatment was decided following the criteria of HIRA in all patients. With assessing of TLICS score and Denis's classification, the treatment guidelines of TLICS and Denis's classification were applied to the criteria of Korean HIRA. Results : According to the Denis's three-column spine system, numbers of patients with 2 or 3 column injuries were 94. Only 45 of 94 patients (47.9%) with middle column injury fulfilled the criteria of HIRA. According to TLICS system, operation required fractures (score>4) were 31 and all patients except one fulfilled the criteria of HIRA. Conservative treatment required fractures (score<4) were 52 and borderline fracture (score=4) were 17. Conclusion : The TLICS system is very useful system for decision of surgical indication in acute traumatic thoracolumbar injury. However, the decision of treatment in TLICS score 4 should be carefully considered. Furthermore, definite criteria of posterior ligamentous complex (PLC) injury may be necessary because the differentiation of PLC injury between TLICS score 2 and 3 is very difficult.

• 한국재난정보학회 논문집
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• 제14권1호
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• pp.28-35
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• 2018

본 연구에서는 재난현장 또는 일상에서 발생할 수 있는 다수사상자의 중증도 분류를 신속하고 정확하게 수행하기 위한 시스템을 설계하여 구현하였으며, 중증도 분류 알고리즘의 정확도뿐만 아니라 사용자 편의성 등 현장의 요구사항을 적극 반영하였다. 개발된 e-Triage System은 IoT개념을 활용하여 다양한 중증도 분류 알고리즘을 적용하였으며, 기존의 중증도 분류표의 단점을 극복하기 위하여 NFC 모듈 등 전자적 요소를 반영한 e-Triage Tag를 구현하였다. 앱으로 구현된 중증도 분류 알고리즘을 사용하여 신속하고 정확한 환자의 평가가 가능함을 입증하였고, 시인성을 위해 전자 중증도 분류 결과를 4가지 LED램프로 표출하였으며, 2차 분류를 통해 RTS 점수를 FND(Flexible Numeric Display)로 표출하였다.

• 한국산업보건학회지
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• 제23권2호
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• pp.156-163
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• 2013

Objectives: Numerous poisonous substances are used in paint manufacture, but there are differences in the results of GHS classification between the Ministry of Labor(MOL) and the Ministry of Environment(MOE). Therefore, paint manufacturers suffer confusion as to how to classify a given chemical's risk and hazard level. This paper was designed to compare the classification results of chemicals by the MOL and the MOE and suggest a harmonization measure. Methods: After selecting 25 poisonous substances from among the organic solvents, pigments, and additives used in paint manufacturer, the GHS classification results by MOL and MOE were compared. Further the logic and classification of the GHS proposed by each Ministry was analyzed. Based on the derived results, a harmonization plan was proposed. Results: Based on the GHS classification of the poisonous substances, the concordance is 10.0-66.6 %, excluded flammable liquid. The GHS classifications differed based on the suggested building blocks, the sub-classification method used, the references(data sources), and subjective judgment of the experts from each Ministry. In order to pursue the harmonization plan, cooperation is demanded from the MOL and MOE.

• 한국경영과학회지
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• 제38권1호
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• pp.139-152
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• 2013

The purpose of this study is to claim the validity of tuning the architecture of neural network models for multi-class classification. A neural network model for multi-class classification is basically constructed by building a series of neural network models for binary classification. Building a neural network model, we are required to set the values of parameters such as number of hidden nodes and weight decay parameter in advance, which draws special attention as the performance of the model can be quite different by the values of the parameters. For better performance of the model, it is absolutely necessary to have a prior process of tuning the parameters every time the neural network model is built. Nonetheless, previous studies have not mentioned the necessity of the tuning process or proved its validity. In this study, we claim that we should tune the parameters every time we build the neural network model for multi-class classification. Through empirical analysis using wine data, we show that the performance of the model with the tuned parameters is superior to those of untuned models.

• 정보관리학회지
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• 제24권4호
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• pp.153-171
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• 2007

본 연구는 이용이 매우 활발한 국내 학위논문의 원문 검색 서비스를 개선하기 위한 하나의 방법으로 한국직업능력개발원이 개발한 커리어넷의 학과정보 분류체계를 한국교육학술정보원이 운영하는 RISS에 포함된 학위논문 정보에 적용한 것이다. 연구 결과 커리어넷의 학과정보 분류체계는 최근 3년간 국내에서 생산되거나 이용된 학위논문을 분류하는데 비교적 적합한 것으로 나타났다. 최근 3개년 동안의 학과분류별 논문생산량과 논문이용량을 분석하였으며, 이를 바탕으로 학과분류 적용 가능성을 검토하고 활용방안을 모색하였다.

• 한국관개배수논문집
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• 제17권2호
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• pp.17-27
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• 2010

In order to manage the water quality in reservoir, it is necessary to understand the temporal and spatial variation of reservoirs and to classify the reservoirs. In this research, agricultural reservoirs are classified according to physical characteristics (depth, residence time, shape of the reservoir etc) and water quality using multivatriate analysis (PCA and CA). CA (Cluster Analysis) method classify reservoirs into several groups as a similarity of the reservoirs, but it is difficult to indicate a full list to the one table. In case of PCA (Principle Component Analysis) method, it has the advantage for the classification on the reservoirs depending on the water quality similarity and also it is useful to analyze the relationship between related factors through correlation analysis. However PCA is limited to classify into several groups based on the characteristics of the reservoirs and each user should be classified as randomly subjective according to the relative position of the reservoir in the figure. In conclusions, compared to conventional reservoirs classification methods, both CA and PCA methods are considered to be a classification method that describes the nature of the reservoir well, but classification results has a restriction on use, so further research will be needed to complement.

• 한국수자원학회논문집
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• 제48권11호
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• pp.945-955
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• 2015

The technology of the SWG (Smart Water Grid) as one of most important national projects results in significant assignment that is closely associated with systematic management and effective operation. The individual component technics are required to establish directory and classification for the purpose of effectively managing their information related to research and development (R&D). The national science technology (S&T) standard classification tree which results in the representative example has been established with an intention to manage R&D information, human resource, and budget. It has been also revised every five years and then used in the various fields related to the evaluation, administration, and prediction of the national R&D projects. In addition, the standard classification system for R&D projects has been widely used in the UNESCO (United Nations Educational, Scientific and Cultural Organization) and EU (European Union) since the Frascati Manual was established in the Organization for Economic Cooperation and Development (OECD). Therefore, it is necessary for SWG techniques to develop the standard S&T classification tree for research management and evaluation. For this, it is essential to draw the core techniques for the SWG, which are incorporated with IT (Information Technology), NT (Nano Technology), and BT (Biology Technology).