• The KIPS Transactions:PartD
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• v.19D no.3
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• pp.257-262
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• 2012

The aim of this study is to present the importance of Risk Management and Risk Classification System in Supply Chain to cope with the rapidly changing distribution environment flexibly through the cooperation between a shipper and a distribution company. First of all, we considered existing studies related to the risks of Supply Chain Risk and analyzed 47 different risk factors by 18 kinds of risk causing factors. Second, we collected opinions of corporation specialist group based on the analyzed risk factors and then classified the risk factors into three categories and ten sub-categories. Third, we conducted a survey targeting shipping companies and distribution companies about classified risk and then verified the validity of Supply Chain Risk Classification using verification techniques such as Confirmatory Factor Analysis, Concentration Validity and Distinction Validity. Finally, we suggest some implications based on the verification results.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.171-178
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• 2022

In this paper, we propose a classification method according to the type and characteristics of stablecoins for risk analysis, and analyze the risk factors of each stablecoin based on this classification. First, this paper explains the technologies and ecosystem of blockchain and decentralized finance(DeFi) to understand stablecoins. In addition, the operation principle of the major stablecoins currently released and used is explained for each proposed classification type. Based on this, the risk type and risk factors of each stablecoin are derived. The risk types proposed in this paper are classified as defegging, liquidation, and exploit, and the risk factors are classified as depegging due to reliability of operator, depegging due to reliability of algorithm, depegging due to failure of algorithm, liquidation due to high volatilty and oracle attack. Based on the proposed classification, we analyze the risk factors of major stablecoins currently circulating in the crypto market.

• Physical Therapy Korea
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• v.30 no.2
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• pp.102-109
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• 2023

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model's performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.

• Geomechanics and Engineering
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• v.8 no.5
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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 the Korean Society of Radiology
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• v.13 no.2
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• pp.201-207
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• 2019

This study has evaluated whether the method of using the combination of different risk group, according to K-TIRADS classification and K-TIRADS classification in thyroid ultrasonography is useful in a differential diagnosis of benign and malignant nodules. The subject was patients underwent thyroid ultrasonography and retrospective analysis were performed based on the results of fine needle aspiration cytology. A chi-square test was performed for the difference analysis of the score system in K-TIRADS and different risk group according to the benign and malignant of thyroid nodule. The optimized cut off value was determined by the K-TIRADS score and different risk group to predict malignant nodule through ROC curve analysis. In the differential verification result of K-TIRADS and different risk group, according to the classification of benign and malignant nodule group each showed significant difference statistically(p=.001). In the point classification according to K-TIRADS for the prediction of benign and malignant in ROC curve analysis showed AUC 0.786, Cut-off value>2(p=.001), and in the different risk group, it was decided as AUC 0.640, Cut-off value>2(p=.001). When discovering the nodule in thyroid ultrasound, it is considered that the K-TIRADAS which helps in identifying benign and malignant thyroid nodules, it is considered to be helpful in the differential diagnosis of thyroid nodules, than the classification system according to Different risk group, and when applying the classification system according to K-TIRADS, it is considered that it can reduce unnecessary fine needle aspiration cytology and could be helpful in finding the malignant nodules early.

• Journal of Environmental Impact Assessment
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• v.26 no.6
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• pp.385-403
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• 2017

This study showed the national ecosystem classification for the spatial standards of ecosystems-based approaches to the risk assessments and adaptation plan. The characteristics of climate change risk assessment, implement national adaptation plans, and ecosystem/habitat classification status was evaluated. Focusing on the land cover classification widely utilized as spatial data for the assessments of biodiversity and ecosystem services in the UK and other countries in Europe, the applicability of the national land cover classification for climate change risk assessments was reviewed. Considering the ecosystem classification for climate change risk assessment and establishing adaptation measures, it is difficult to apply rough classification method to the land cover system because of lack of information on habitat trend by categorization. The results indicated that forest ecosystems and agro-ecosystem occupied 62.3% and 25.0% of land cover, respectively, of the entire country. Although the area is small compared with the land area, wetland ecosystem (2.9%), marine ecosystem (0.4%), coastal ecosystem (0.6%), and urban ecosystem (6.1%) can be included in the risk assessments. Therefore, it is necessary to subdivide below the medium classification for the forest and agricultural land, as well as Inland wetland, which has a higher proportion of habitat preference of taxa than land area, marine/coastal habitat, and transition areas such as urban and natural ecosystem.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.195-197
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• 2011

This research deals with an issue of preventive medicine in bioinformatics. We can diagnose liver conditions reasonably well to prevent Liver Cirrhosis by classifying liver disorder patients into fatty liver and high risk groups. The classification proceeds in two steps. Classification rules are first built by clustering five attributes (MCV, ALP, ALT, ASP, and GGT) of blood test dataset provided by the UCI Repository. The clusters can be formed by the K-mean method that analyzes multi dimensional attributes. We analyze the properties of each cluster divided into fatty liver, high risk and normal classes. The classification rules are generated by the analysis. In this paper, we suggest a method to diagnosis and predict liver condition to alcoholic patient according to risk levels using the classification rule from the new results of blood test. The K-mean classifier has been found to be more accurate for the result of blood test and provides the risk of fatty liver to normal liver conditions.

• Journal of the Korea Safety Management & Science
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• v.24 no.4
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• pp.61-70
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• 2022

In spite of lab safety act for over 10 years, over 100 safety accidents in the laboratory have been constantly occurring. The ideal safety management system is to prevent accidents by differential classifying and managing laboratory regulatory materials according to the risk level. In order to approach this system, in-depth interviews with safety managers were first conducted to identify the current status of safety management in domestic university laboratories. And then through comparative analysis of safety management systems in domestic and foreign laboratories, a new regulatory substance classification standard based on the analysis of the hazards and the classification of risk grades, and a safety management system are proposed. From this study, it will contribute to the creation of a safe laboratory environment by differential classification and management laboratory regulatory materials based on the risk level.

• Korean Journal of Clinical Laboratory Science
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• v.43 no.2
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• pp.48-56
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• 2011

Gastrointestinal stromal tumor (GIST) is a mesenchymal tumor and is associated with a specific immunophenotype index. It is very important to identify the specific immunophenotype and the diagnosis for the treatment GIST patients. Ninety two cases of GIST analyzed in this study were immuno-stained for c-kit, DOG1, CD34, PKC-${\theta}$, PDGFR-${\alpha}$. The rate of positive staining and statistical significance were then compared. In addition, the GISTs were analyzed as followings: very low risk, low risk, intermediate risk and high risk according to tumor size and nuclear division, and later correlated with clinical parameters. The results of the GIST positive stainings were: DOG1 (95.7%), PKC-${\theta}$ (90.2%), PDGFR-${\alpha}$ (88.0%), c-kit (87.0%) and CD34 (71.7%). Only DOG1 staining showed a statistical significance of p<0.05. It was identified in the classification system of histologic risk that staining expression of DOG1, PKC-${\theta}$, PDGFR-${\alpha}$ were significantly increased as histologic risk increases (p<0.05). However, clinical parameters such as age and sex of patients have no correlations with the classification system of histologic risk (p>0.05). Therefore, in this study, the expression of DOG1 showed statistical significance and DOG1, PKC-${\theta}$, PDGFR-${\alpha}$ staining increased significantly as the histologic risk increases in histologic classification system. Taken together, the DOG1 staining should be very effective for the diagnosis of GIST patients.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.14-20
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• 2021

Most accidents caused by road icing in winter lead to major accidents. Because it is difficult for the driver to detect the road icing in advance. In this work, we study how to accurately detect road traffic emerging risk using AutoML and CNN's ensemble model that use both structured and unstructured data. We train CNN-based road traffic emerging risk classification model using images that are unstructured data and AutoML-based road traffic emerging risk classification model using weather data that is structured data, respectively. After that the ensemble model is designed to complement the CNN-based classification model by inputting probability values derived from of each models. Through this, improves road traffic emerging risk classification performance and alerts drivers more accurately and quickly to enable safe driving.