• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.3
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• pp.125-150
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• 2017

This study aims to propose requirements for developing an integrated classification system for disaster and safety information by analyzing classifications currently used in disaster and safety-related organizations in Korea. To do that, this study first analyzed existing disaster category classifications. Then, it collected classifications currently used in disaster and safety-related organizations, and through interviews with practitioners, analyzed considerations to manage disaster and safety information comprehensively. The analysis shows that to develop a disaster category classification, consistency, exhaustivity, systemicity, and disasters frequently occurring in Korea should be considered. In addition, this study suggests a facet classification system for disaster and safety information given the intricacy of disaster and safety management and the occurrence of compound disasters.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.4
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• pp.89-101
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• 2020

In order to enhance the safety of the international aviation industry, the International Civil Aviation Organization has recommended establishing an operational foundation for systematic and integrated collection, storage, analysis and sharing of aviation safety data. Accordingly, the Korea aviation industry also needs to comprehensively manage the safety data which generated and collected by various stakeholders related to aviation safety, and through this, it is necessary to previously identify and remove hazards that may cause accident. For more effective data management and utilization, a standard structure should be established to enable integrated management and sharing of safety data. Therefore, this study aims to propose the framework about how to manage and integrate the aviation safety data for big data-based aviation safety management and shared platform.

• Journal of the Korean Institute of Gas
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• v.13 no.3
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• pp.7-14
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• 2009

Many countries are managing the transportation of hazardous materials under the specific provisions especially, as well as use, storage and management, because of their high risks. For the purpose of the revision of rail safety law for the safe transportation of hazardous materials, amount and kind of hazardous materials transported by rail in Korea are analysed and the standards of classification of hazardous materials are compared in domestic and abroad. There are lots of benefits for national rail safety law to implement an international law because our country's geographic location is convenient to connect the continent and to across the border. It is suggested that implementing a classification and test methods of hazardous materials enable to use internationally for the preparation of rail transportation to be increased.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.52-60
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• 2021

As the prevention of fatal accidents is considered an essential part of social responsibilities, both government and individual have devoted efforts to mitigate the unsafe conditions and behaviors that facilitate accidents. Several studies have analyzed the factors that cause fatal accidents and compared them to those of non-fatal accidents. However, studies on mathematical and systematic analysis techniques for identifying the features of fatal accidents are rare. Recently, various industrial fields have employed machine learning algorithms. This study aimed to apply machine learning algorithms for the classification of fatal and non-fatal accidents based on the features of each accident. These features were obtained by text mining literature on accidents. The classification was performed using four machine learning algorithms, which are widely used in industrial fields, including logistic regression, decision tree, neural network, and support vector machine algorithms. The results revealed that the machine learning algorithms exhibited a high accuracy for the classification of accidents into the two categories. In addition, the importance of comparing similar cases between fatal and non-fatal accidents was discussed. This study presented a method for classifying accidents using machine learning algorithms based on the reports on previous studies on accidents.

• Journal of Food Hygiene and Safety
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• v.34 no.2
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• pp.212-218
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• 2019

The process of establishing domestic standards on hazardous substances in food safety regulations requires harmonization with standards from the Codex Alimentarius Commission (CAC). For this purpose, food classification by the CAC (Codex Classification of Foods and Animal Feeds) also needs to be clearly understood. Therefore, this paper aimed to introduce the Codex Classification on cereal grains, nuts/seeds and herbs/spices because revisions of the Codex were completed in 2017 for cereal grains and in 2018 for nuts/seeds and herbs/spices. The revised Codex Classification on those foods is briefly summarized as follows. Cereal grains in the domestic food classification by the Ministry of Food and Drug Safety, Korea (MFDS) corresponds to the Codex Group 020 cereal grains with six subgroups. The MFDS's nuts and seeds classification corresponds to three groups in the Codex, namely, Group 022 (tree nuts with no subgroups), Group 023 (oilseeds and oilfruits with 5 subgroups), and Group 024 (seeds for beverages and sweets with no subgroups). The food commodities of herbs and spices are included in two Codex groups, Group 027 (with 3 subgroups) and Group 028 (with 9 subgroups). The number of Codex commodity codes assigned to food commodities was 27 for Group 020, 32 for Group 022, 46 for Group 023, 4 for Group 024, 127 for Group 027 and 138 for Group 028. In between the Codex Classification and the MFDS's classification, some differences are shown. For example, the MFDS did not create a subgroup under groups of cereal grains and herbs. The MFDS classified peanuts into the nut group, though a separate group for oilseeds is present, while the Codex classified peanuts into the oilseed and oilfruit group. In addition, there is also a separate group of "plants, others" present in the MFDS's classification. Therefore, care is needed in using the Codex Classification.

• Journal of the Korean Society of Safety
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• v.12 no.2
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• pp.3-16
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• 1997

In modern high performance engineering applications, the structural integrity of materials and structures are quite often evaluated using fracture mechanics. This evaluation in turn requires information on the flaw geometry (location, type, shape, size, and orientation). The ultrasonic nondestructive evaluation (NDE) method is one technique that is commonly used to provide such information. Flaw classification (determination of the flaw type ) and flaw sizing (prediction of the flaw shape, orientation and sizing parameters) are very important issues for quantitative ultrasonic NDE. In this paper new approaches to both classification and sizing of flaws are described together with extensive review of previous works on both topics. In the area of flaw classification, a methodology is developed which can solve classification problems using probabilistic neural networks, and in the area of flaw sizing, a time-of-flight equivalent (TOFE) sizing method is presented. The techniques proposed here are in a form that can be used directly in many practical applications to quantitative estimates of the flaw's significance.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.23-30
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• 2020

Recently, self-driving research has been actively studied in various institutions. Accurate recognition is important because information about surrounding objects is needed for safe autonomous driving. This study mainly deals with the signal processing of LiDAR among sensors for object recognition. LiDAR is a sensor that is widely used for high recognition accuracy. First, we clustered and tracked objects by predicting relative position and speed of objects. The characteristic points of all objects were extracted using point cloud data of each objects through proposed algorithm. The Classification between vehicle and pedestrians is estimated using number of characteristic points and distances among characteristic points. The algorithm for classifying cars and pedestrians was implemented and verified using test vehicle equipped with LiDAR sensors. The accuracy of proposed object classification algorithm was about 97%. The classification accuracy was improved by about 13.5% compared with deep learning based algorithm.

• Fire Science and Engineering
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• v.31 no.1
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• pp.108-115
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• 2017

This study evaluated a preventive information communication system for the storage, handing, and transportation of hazardous materials according to the hazardous materials safety management law of Korea. At present, the chemical management system has very little information on accident responses due to a problem in the initial response. Therefore, this study was designed to improve the hazardous materials safety management law as an advanced method for simultaneous accident prevention and response, such as GHS system. This can also cause confusion in industry, such as manufacturing and import-export companies, because safety management laws and the GHS system are very different from the hazard classification systems. This study suggests a harmonization plan between the hazardous materials safety management law and the GHS classification system through an analysis of the hazardous materials classification system of major advanced countries.

• Journal of the Korea Safety Management & Science
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• v.26 no.2
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• pp.23-31
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• 2024

In the military, ammunition and explosives stored and managed can cause serious damage if mishandled, thus securing safety through the utilization of ammunition reliability data is necessary. In this study, exploratory data analysis of ammunition inspection records data is conducted to extract reliability information of stored ammunition and to predict the ammunition condition code, which represents the lifespan information of the ammunition. This study consists of three stages: ammunition inspection record data collection and preprocessing, exploratory data analysis, and classification of ammunition condition codes. For the classification of ammunition condition codes, five models based on boosting algorithms are employed (AdaBoost, GBM, XGBoost, LightGBM, CatBoost). The most superior model is selected based on the performance metrics of the model, including Accuracy, Precision, Recall, and F1-score. The ammunition in this study was primarily produced from the 1980s to the 1990s, with a trend of increased inspection volume in the early stages of production and around 30 years after production. Pre-issue inspections (PII) were predominantly conducted, and there was a tendency for the grade of ammunition condition codes to decrease as the storage period increased. The classification of ammunition condition codes showed that the CatBoost model exhibited the most superior performance, with an Accuracy of 93% and an F1-score of 93%. This study emphasizes the safety and reliability of ammunition and proposes a model for classifying ammunition condition codes by analyzing ammunition inspection record data. This model can serve as a tool to assist ammunition inspectors and is expected to enhance not only the safety of ammunition but also the efficiency of ammunition storage management.

• The Korea Journal of Herbology
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• v.32 no.4
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• pp.33-38
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• 2017

Objectives : Termitomyces albuminosus (Berk.) Heim is one of the famous wild edible mushrooms in the southern part of China. It is known that Termitomyces albuminosus, like Aconitum koreanum used in Korean traditional medicine, contains a kind of cerebroside, termitomycesphin, causing a pharmacologic effect on the neuron system. The pharmacologic effect of Termitomyces albuminosus can be used to possibly replace Aconitum koreanum. However, It needs to be certified as safe before it can be used. Here, a single-oral toxicity test and safety classification was conducted to obtain acute information of the toxicity of dried-Termitomyces albuminosus powder and to secure its safety in clinical applications. Methods : In order to calculate approximate lethal dose(ALD), test substance was orally administered to male and female SD-rat at dose levels of 5,000 and 0 (vehicle control) mg/kg (body weight). Based on the result of this toxicity, also the estimation of safety classification was calculated using the HED-based (human equivalent dose) MOS (margin of safety). Results : There were no mortalities, test substances treatment-related clinical signs, no changes in the body or organ weights, and no gross or histopathological findings at 14 days after treatment with test substance. Thus, the approximate lethal dose of dried-Termitomyces albuminosus powder was considered over 5,000 mg/kg in both female and male mice. Conclusions : Based on the limit dose, 5000 mg/kg, it was estimated that dried-Termitomyces albuminosus powder is classified as "Specified class B" indicating that clinical dose is not limited to patients as safe as food.