• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 가을 학술논문 발표대회
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• pp.99-100
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• 2022

As the danger of exposure to the asbestos has been revealed, the importance of demolition asbestos in existing buildings has been raised. Extensive body of study has been conducted to evaluate the risk of demolition asbestos, but there were confined types of variables caused by not reflecting categorical information and limitations in collecting quantitative information. Thus, this study aims to derive a model that predicts the risk in workplace of demolition asbestos by collecting categorical and continuous variables. For this purpose, categorical and continuous variables were collected from asbestos demolition reports, and the risk assessment score was set as the dependent variable. In this study, the influence of each variable was identified using logistic regression, and the risk prediction model methodologies were compared through decision tree regression and artificial neural network. As a result, a conditional risk prediction model was derived to evaluate the risk of demolition asbestos, and this model is expected to be used to ensure the safety of asbestos demolition workers.

• 수산해양기술연구
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• 제60권2호
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• pp.170-178
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• 2024

This study aimed to quantitatively analyze the risk using data from 329 safety accidents that occurred in aquaculture fisheries management vessels over the recent five years (2018-2022). For quantitative risk analysis, the Bayesian network proposed by the International Maritime Organization (IMO) was used to analyze the risk level according to the fishing process and cause of safety accidents. Among the work processes, the fishing process was analyzed to have the highest risk, being 12.5 times that of the navigation, 2.7 times that of the maintenance, and 8.8 times that of the loading and unloading. Among the causes of accidents, the hull and working environment showed the highest risk, being 1.7 times that of fishing gear and equipment, 4.7 times that of machinery and equipment, and 9.4 times that of external environment. By quantitatively analyzing the safety accident risks for 64 combinations of these four work processes and four accident causes, this study provided fundamental data to reduce safety accidents occurring in aquaculture fisheries management vessels.

• 대한의용생체공학회:의공학회지
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• 제42권6호
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• pp.287-294
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• 2021

Cardiotoxicity assessment of all drugs has been performed according to the ICH guidelines since 2005. Non-clinical evaluation S7B has focused on the hERG assay, which has a low specificity problem. The comprehensive in vitro proarrhythmia assay (CiPA) project was initiated to correct this problem, which presented a model for classifying the Torsade de pointes (TdP)-induced risk of drugs as biomarkers calculated through an in silico ventricular model. In this study, we propose a TdP-induced risk group classifier of artificial neural network (ANN)-based. The model was trained with 12 drugs and tested with 16 drugs. The ANN model was performed according to nine features, seven features, five features as an individual ANN model input, and the model with the highest performance was selected and compared with the classification performance of the qNet input logistic regression model. When the five features model was used, the results were AUC 0.93 in the high-risk group, AUC 0.73 in the intermediate-risk group, and 0.92 in the low-risk group. The model's performance using qNet was lower than the ANN model in the high-risk group by 17.6% and in the low-risk group by 29.5%. This study was able to express performance in the three risk groups, and it is a model that solved the problem of low specificity, which is the problem of hERG assay.

• 상하수도학회지
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• 제20권6호
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• pp.799-811
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• 2006

As an alternative measure to replace reactive stance with proactive one, a risk based management scheme has been commonly applied to enhance public satisfaction on water service by providing a higher creditable solution to handle a rehabilitation problem of pipe having high potential risk of leaks. This study intended to examine the feasibility of a simulation model to predict a recurrence probability of pipe leaks. As a branch of the data mining technique, probabilistic neural network (PNN) algorithm was applied to infer the extent of leaking recurrence probability of water network. PNN model could classify the leaking level of each unit segment of the pipe network. Pipe material, diameter, C value, road width, pressure, installation age as input variable and 5 classes by pipe leaking probability as output variable were built in PNN model. The study results indicated that it is important to pay higher attention to the pipe segment with the leak record. By increase the hydraulic pipe pressure to meet the required water demand from each node, simulation results indicated that about 6.9% of total number of pipe would additionally be classified into higher class of recurrence risk than present as the reference year. Consequently, it was convinced that the application of PNN model incorporated with a data base management system of pipe network to manage municipal water distribution network could make a promise to enhance the management efficiency by providing the essential knowledge for decision making rehabilitation of network.

• 한국정보통신학회논문지
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• 제16권7호
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• pp.1447-1454
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• 2012

최근 P2P는 인터넷에서 매우 대중화된 서비스로 다양한 분야에 응용되고 있으나, P2P 네트워크 특성에 따른 취약점으로 인해 여러 가지 보안 위협이 등장하고 있다. P2P 네트워크는 인터넷을 기반으로 하는 오버레이 네트워크 형태이기 때문에 기존의 인터넷 환경에서 발생하는 보안 문제뿐만 아니라 P2P 네트워크 자체만의 보안 문제도 가지고 있다. 본 논문에서는 국내 상용 P2P 서비스의 취약점 및 위협 분석을 다양한 실험을 통하여 수행한 후 위험분석을 수행하고 대응방안을 제시하였다.

• 한국해양공학회지
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• 제26권1호
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• pp.60-63
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• 2012

The accident risks in the marine environment are increasing because of the tendency to build faster and larger ships. To secure ship safety, risk-based ship design (RBSD) was recently suggested based on a formal safety assessment (FSA). In the process of RBSD, a ship designer decides which risk reduction option is most cost-effective in the design stage using a cost-benefit analysis (CBA). There are three dimensions of risk in this CBA: fatality, environment, and asset. In this paper, we present an approach to estimate the environmental costs based on the size of an oil tanker involved in an accident using a neural network. An appropriate neural network model is suggested for the estimation,and the neural network is trained using IOPCF data. Finally,the learned neural network is compared with the cost regression equation by IMO MEPC 62/WP.13 (2011).

• 대한의용생체공학회:의공학회지
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• 제40권2호
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

• Advances in Computational Design
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• 제2권2호
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• pp.121-131
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• 2017

Collapse was one of the typical common geological hazards during the construction of tunnels. The risk assessment of collapse was an effective way to ensure the safety of tunnels. We established a prediction model of collapse based on Bayesian Network. 76 large or medium collapses in China were analyzed. The variable set and range of the model were determined according to the statistics. A collapse prediction software was developed and its veracity was also evaluated. At last the software was used to predict tunnel collapses. It effectively evaded the disaster. Establishing the platform can be subsequent perfect. The platform can also be applied to the risk assessment of other tunnel engineering.

• 전기학회논문지P
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• 제60권4호
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• pp.214-219
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• 2011

The substation grounding design,"IEEE Guide for Safety in AC Substation Grounding (ANSI / IEEE Std 80)"has been widely used. Substation grounding design and substation grounding resistance of grounding network site to predict the voltage at the risk of a very important task, which is a ground fault current due to the influx of the ground network and due to rise in the Earth's potential can be applied to human dangerous Voltage within safe tolerances be configured to be the ground because the network. IEEE Std. 80 for the substation construction safety equipment on the ground securing the ground electrode and the mesh around the boundary potential distribution in terms of risk analysis by the touch voltage and the reference was to clean up the definition and the basic steps of the voltage of the voltage limits the risk of peripheral grounding electrode Suppression by the simulator through a new secure from dangerous voltage design techniques were presented.

• International Journal of Internet, Broadcasting and Communication
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• 제11권3호
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• pp.59-63
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• 2019

VoIP technology is a technology for exchanging voice or video data through IP network. Various protocols are used for this technique, in particular, RTP(Real-time Transport Protocol) protocol is used to exchange voice data. In recent years, with the development of communication technology, there has been an increasing tendency of services such as "Kakao Voice Talk" to exchange voice and video data through IP network. Most of these services provide a service with security guarantee by a user authentication process and an encryption process. However, RTP protocol does not require encryption when transmitting data. Therefore, there is an exposition risk in the voice data using RTP protocol. We will present the risk of the situation where packets are sniffed in VoIP(Voice over IP) communication using RTP protocol. To this end, we configured a VoIP telephone network, applied our own sniffing tool, and analyzed the sniffed packets to show the risk that users' data could be exposed unprotected.