• Journal of the Korean Institute of Gas
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• v.16 no.5
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• pp.21-27
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• 2012

The loss of life and property due to accidents in the research facilities or the laboratories of the university occurs steadily and the necessity of laboratory accident prevention is proposed. Above all, the main work to laboratory accident prevention is a systematic analysis of laboratories accidents. Analyzing reports or researches on industrial accidents in Korea had been carried out but these researches or reports did not based on laboratory accidents analysis. To the establishment of the accident prevention countermeasure in laboratory, a questionnaire sheet has been developed in this study. The questionnaires to survey the accident cases were gathered by electronic mail and visit survey from the laboratories and universities. The data of accident cases from the questionnaires was analyzed and discussed on accident distribution by season, the type of accident classification, the type of occurrence, the objects that caused the accident and laboratory accident by the damage incurred etc.. These results of this study can be used as basic data to the safety security and laboratory accident prevention of the laboratory worker.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.57 no.2
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• pp.149-161
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• 2021

The concern on the greenhouse gas emission is strongly increasing globally. In fishery industry section, the greenhouse gas emissions are an important issue according to The Paris Climate Change Accord in 2015. The Korean government has a plan to reduce the GHG emissions as 4.8% compared to the BAU in fisheries until 2020. Furthermore, the Korean government has also declared to achieve the carbon neutrality in 2050 at the Climate Adaptation Summit 2021. However, the investigation on the GHG emissions from Korean fisheries did not carry out extensively. Most studies on GHG emissions from Korean fishery have dealt with the GHG emissions by fishery classification so far. However, follow-up studies related to GHG emissions from fisheries need to evaluate the GHG emission level by species to prepare the adoption of environmental labels and declarations (ISO 14020). The purpose of this research is to investigate which degree of GHG emitted to produce the species (hairtail and small yellow croaker) from various fisheries. Here, we calculated the GHG emission to produce the species from the fisheries using the Life Cycle Assessment method. The system boundary and input parameters for each process level are defined for the LCA analysis. The fuel use coefficients of the fisheries for the species are also calculated according to the fuel type. The GHG emissions from sea activities by the fisheries will be dealt with. Furthermore, the GHG emissions for producing the unit weight species and annual production are calculated by fishery classification. The results will be helpful to understand the circumstances of GHG emissions from Korean fisheries.

• Journal of the Korean Institute of Gas
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• v.26 no.6
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• pp.65-75
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• 2022

Recently, the possibility of fire and explosion due to hydrogen leakage and the resulting risk are increasing since the operating pressure of the electrolysis increases. This study performed the hazardous area classification in accordance with KS C IEC 60079-10-1 and KGS GC101 in consideration of the general operating conditions of the electrolysis. In addition, in order to achieve a To Non-hazardous, an appropriate ventilation rate was estimated to maintain a concentration of less than 25 % of the lower explosive limit. As a result, it was reviewed that the electrolysis is classified as an hazardous area when only natural ventilation is applied, and a huge amount of ventilation is required to classify it as a non-hazardous area.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.59 no.1
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• pp.19-27
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• 2023

The interest in greenhouse gases (GHG) emitted from all industries is emerging as a very important issue worldwide. This is affecting not only the global warming, but also the environmentally friendly competitiveness of the industry. The fisheries sector is increasingly interested in greenhouse gas emissions also due to the Paris Climate Agreement in 2015. Korean industry and government are also making a number of effort to reduce greenhouse gas emissions so far, but the effort to reduce GHG in the fishery sector is insufficient compared to other fields. Especially, the investigation on the GHG emissions from Korean fisheries did not carry out extensively. The studies on GHG emissions from Korean fishery are most likely dealt with the GHG emissions by fishery classification so far. However, the forthcoming research related to GHG emissions from fisheries is needed to evaluate the GHG emission level by species to prepare the adoption of Environmental labels and declarations (ISO 14020). The purpose of this research is to investigate which degree of GHG emitted to produce the species (swimming crab and snow crab) from various fisheries. Here, we calculated the GHG emission to produce the species from the fisheries using the life cycle assessment (LCA) method. The system boundary and input parameters for each process level are defined for LCA analysis. The fuel use coefficients of the fisheries for the species are also calculated according to the fuel type. The GHG emissions from sea activities by the fisheries will be dealt with. Furthermore, the GHG emissions for producing the unit weight species and annual production are calculated by fishery classification. The results will be helpful to establish the carbon footprint of seafood in Korea.

• Journal of the Korean Institute of Gas
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• v.4 no.2 s.10
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• pp.52-57
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• 2000

This paper suggests the evaluation sheet to ensure the objective and detailed information based on a classification table of PIF (Performance Influencing Factor). And this paper shows the results of HEP(Human Error Probability), using a quantitative method with the evaluated data as a result of estimating the likelihood of . human errors in the gas industry facility together with the evaluation sheet. Finally, these results are programmed to be operated in personal computer so that field workers an apply it in easy and convenient manner. The results of this study offer two key benefits; sharing reliable information on human errors with the Data Base and establishing a strategy to reduce human errors as well as to improve working proficiency.

• ETRI Journal
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• v.37 no.2
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• pp.222-232
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• 2015

In this paper, we propose a dual-phase approach to improve the process of heart disease prediction in a mobile environment. Firstly, only the confident frequent rules are extracted from a patient's clinical information. These are then used to foretell the possibility of the presence of heart disease. However, in some cases, subjects cannot describe exactly what has happened to them or they may have a silent disease - in which case it won't be possible to detect any symptoms at this stage. To address these problems, data records collected over a long period of time of a patient's heart rate variability (HRV) are used to predict whether the patient is suffering from heart disease. By analyzing HRV patterns, doctors can determine whether a patient is suffering from heart disease. The task of collecting HRV patterns is done by an online artificial neural network, which as well as learning knew knowledge, is able to store and preserve all previously learned knowledge. An experiment is conducted to evaluate the performance of the proposed heart disease prediction process under different settings. The results show that the process's performance outperforms existing techniques such as that of the self-organizing map and gas neural growing in terms of classification and diagnostic accuracy, and network structure.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1122-1122
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• 2001

A compact and handhold near infrared (NIR) system using microspectrometer was developed. This system was suitable not only in the laboratory, but also in the field or in the process. This system was first applied for classification of geographical origin of herbal medicine such as ginseng and sesame. To identify the origin of ginseng on site, the portable NIR system is more suitable for real field application. For this study, using the compact NIR system, soft independent modeling of class analogies (SIMCA) with 1100-1750 nm NIR spectra was utilized for classification of geographical origin (Korea and China) of both ginseng and sesame. The accuracy of results is more than 90%. Quantitative analysis for petroleum such as toluene, benzene, tri-methyl benzene, and ethyl benzene was performed with partial least squares (PLS) regression with NIR 1100-1750 nm spectra. This study showed that the NIR method and gas chromatography (GC), which is a standard method, have good correlations. Furthermore, the ash content of Cornu Cervi Parvum was analyzed and the accuracy was confirmed by the developed compact NIR system.

• Spatial Information Research
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• v.5 no.1
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• pp.115-131
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• 1997

Effective management of underground facilities which include water line, sewer line, electric line, telephone line, gas line etc., is very important for people's safety as well as administrative efficiency. The purpose of this study is four-fold: first, investigate management status of utility information of each utility companies, second, develope classification system of underground facilities and use this classification system for guidelines of database construction and for the exchange of database among utility companies, third, construct database using existing utility maps in pilot study area and identify accuracy of the existing maps and suggest strategy of database construction, fourth, suggest strategy of database maintenance and its organizational plan in connection with national plan.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.102-109
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• 2006

In this Paper, Support Vector Machine(SVM) and Artificial Neural Network(ANN) are used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine. The system that uses the ANN falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the Separate Learning Algorithm(SLA) of ANN has been proposed by using SVM. This is the method that ANN learns selectively after discriminating the defect position by SVM, then more improved performance estimation can be obtained than using ANN only. The proposed SLA can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure.

• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.245-254
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• 2012

In this study, we introduce a prototype-based classifier with feature selection that dwells upon the usage of a biologically inspired optimization technique of Particle Swarm Optimization (PSO). The design comprises two main phases. In the first phase, PSO selects P % of patterns to be treated as prototypes of c classes. During the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative coordinates of the original feature space. The proposed scheme of feature selection is developed in the wrapper mode with the performance evaluated with the aid of the nearest prototype classifier. The study offers a complete algorithmic framework and demonstrates the effectiveness (quality of solution) and efficiency (computing cost) of the approach when applied to a collection of selected data sets. We also include a comparative study which involves the usage of genetic algorithms (GAs). Numerical experiments show that a suitable selection of prototypes and a substantial reduction of the feature space could be accomplished and the classifier formed in this manner becomes characterized by low classification error. In addition, the advantage of the PSO is quantified in detail by running a number of experiments using Machine Learning datasets.