• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.54-66
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• 2006

Korea Forest Service made the landslide hazard map for all mountainous districts over the country in May 2005. In this study, we selected landslide areas occurred in Jeonbuk from 02 August 2005 to 03 August 2005 as the study area. We extracted landslide areas using images taken by PKNU 3 System, which was developed by PE&RS Laboratory in Dept. of Satellite Information Sciences, Pukyong National University and verified the accuracy of landslide hazard map by overlaying landslide hazard areas extracted by PKNU 3 images. And we analyzed characteristics of an altitude, a gradient, an inclined direction, a flow length, a flow accumulation for landslide areas using mountainous terrain analysis and Stream Network analysis of ArvView 3.3. As a result of this study, it is necessary to adjust the unitage(%) by the class and to modify and improve the score table for prediction of landslide-susceptible area forming the foundation of making the landslide hazard maps.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.351-360
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• 2010

In 2010, the number of registered vehicles reached almost at 17.48 millions in Korea. This dramatic increase of vehicles influenced to increase the number of traffic accidents which is one of the serious social problems and also to soar the personal and economic losses in Korea. Through this research, an enhanced intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network will be developed in order to obtain the important data for developing the countermeasures of traffic accidents and eventually to reduce the traffic accidents in Korea. Firstly, this research has investigated the influencing factors of road geometric features on the traffic volume of each approaching for the intersections where traffic accidents and congestions frequently take place and, a linear regression model of traffic accidents and traffic conflicts were developed by examining the relationship between traffic accidents and traffic conflicts through the statistical significance tests. Secondly, this research also developed an intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network through applying the intersection traffic volume, the road geometric features and the specific variables of traffic conflicts. Lastly, this research found out that the developed model is better than the existed forecasting models in terms of the reliability and accuracy by comparing the actual number of traffic accidents and the predicted number of accidents from the developed model. In conclusion, it is expect that the cost/effectiveness of any traffic safety improvement projects can be maximized if this developed intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network use practically at field in the future.

• Journal of the Korean Society of Hazard Mitigation
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• v.1 no.2 s.2
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• pp.93-101
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• 2001

The objective of this study is to derive a shrinkage correction formula that considers temperature effect and to develop a methodology for the improved prediction of the long-term behavior of the FCM bridges by considering seasonal temperature variations in-situ. Thereby, current formulation were performed by using the actual experimental shrinkage data including seasonal temperature variation. The investigation of the long-term behavior of the FCM bridge was performed on the construction site in order to decide applicability of the shrinkage formula Numerical results by the general method indicates inaccurate values of total strain when considering real strain, whereas the applied method demonstrates a good agreement in the resultant strain. In consequently, the applied method will improve the prediction of the long-term deformation of the FCM Bridges.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.10 no.S_1
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• pp.23-28
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• 2001

A new numerical weather prediction and dispersion model, the Operational Multi-scale Environment model with Grid Adaptivity(OMEGA) including an embedded Atmospheric Dispersion Model(ADM), is introduced as a next generation atmospheric simulation system for real-time hazard predictions, such as severe weather or the transport of hazardous release. OMEGA is based on an unstructured grid that can facilitate a continuously varying horizontal grid resolution ranging from 100 km down to 1 km and a vertical resolution from 20 -30 meters in the boundary layer to 1 km in the free atmosphere. OMEGA is also naturally scale spanning and time. In particular, the unstructured grid cells in the horizontal dimension can increase the local resolution to better capture the topography or important physical features of the atmospheric circulation and cloud dynamics. This means the OMEGA can readily adapt its grid to a stationary surface, terrain features, or dynamic features in an evolving weather pattern. While adaptive numerical techniques have yet to be extensively applied in atmospheric models, the OMEGA model is the first to exploit the adaptive nature of an unstructured gridding technique for atmospheric simulation and real-time hazard prediction. The purpose of this paper is to provide a detailed description of the OMEGA model, the OMEGA system, and a detailed comparison of OMEGA forecast results with observed data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.2
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• pp.204-211
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• 2012

In this study, appropriate preventive measures are suggested as follows by analyzing industrial accident cases occurred for five years of company J and workers' survey. First, personnel management including meticulous shift work and rotation task for older workers should be made strictly in terms of safety and health management. Second, Industrial disasters occurred more in spring than in other seasons, so safety management will have to be enhanced in spring. That is, to reduce industrial disaster various occupational safety and health activities to prevent spring fatigue are needed. Third, to improve safety consciousness of workers, penalties for those who have not received safety training and for people without protective equipment are required. Also, hazard prediction training such as Tool Box Meeting shall be proceeded. Fourth, building of proactive safety culture that the workers themselves handle risk factors is urgent. Fifth, the company-wide safety and health programs including safety awards and cases presentation are needed to be planned.

• 한국지구과학회:학술대회논문집
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• 2005.02a
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• pp.182-187
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• 2005

The weather hazard by worldwide global warming rapidly increases year by year, and the damage becomes also enormous. especially, the damage by the random local severe rain in Korea is conspicuous. The forecast is difficult, because the random local severe rain arises by the complicated mechanism. However, local weather field in the Honam district where the weather hazard arises well is accurately grasped, and the systems that predict the local severe rain early are necessary. The purpose of this research is development of radar data assimilation observed at Jindo S-band radar. The accurate observational data assimilation system is required for meteorological numerical prediction of the region scale. Diagnostic analysis system LAPS(Local Analysis and Prediction System) developed by US FSL(Forecast Systems Laboratory) is adopted assimilation system of the Honam district forecasting system.

• Economic and Environmental Geology
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• v.36 no.4
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• pp.313-320
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• 2003

Slope disaster is one of the repeated occurring geological disasters in rainy season resulting in about 23 human losses in Korea every year. The slope disaster, however, mainly depends on the spatial and climate properties. such as geology, geomorphology, and heavy rainfall, and, hence, the prediction or hazard analysis of the slope disaster is a difficult task. Therefore, GIS and various statistical methods are implemented for slope disaster analysis. In particular, GIS technique is widely used for the analysis because it effectively handles large amount of spatial data. The GIS technique. however, only considers the statistics between slope disaster occurrence and related factors, not the mechanism. Accordingly. an infinite slope model that mechanically considers the balance of forces applied to the slope is suggested here with GIS for slope disaster analysis. According to the research results, the infinite slope model has a possibility that can be utilized for landslide prediction and hazard evaluation since 87.5％ of landslide occurrence areas have been predicted by this technique.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.2
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• pp.141-158
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• 2019

Objectives: An adverse outcome pathway is a biological pathway that disturbs homeostasis and causes toxicity. It is a conceptual framework for organizing existing biological knowledge and consists of the molecular initiating event, key event, and adverse output. The AOP concept provides intuitive risk identification that can be helpful in evaluating the carcinogenicity of chemicals and in the prevention of cancer through the assessment of chemical carcinogenicity predictions. Methods: We reviewed various papers and books related to the application of AOPs for the prevention of occupational cancer. We mainly used the internet to search for the necessary research data and information, such as via Google scholar(http://scholar.google.com), ScienceDirect(www.sciencedirect.com), Scopus(www.scopus. com), NDSL(http: //www.ndsl.kr/index.do) and PubMed(http://www.ncbi.nlm.nih.gov/pubmed). The key terms searched were "adverse outcome pathway," "toxicology," "risk assessment," "human exposure," "worker," "nanoparticle," "applications," and "occupational safety and health," among others. Results: Since it focused on the current state of AOP for the prediction of toxicity from chemical exposure at work and prospects for industrial health in the context of the AOP concept, respiratory and nanomaterial hazard assessments. AOP provides an intuitive understanding of the toxicity of chemicals as a conceptual means, and it works toward accurately predicting chemical toxicity. The AOP technique has emerged as a future-oriented alternative to the existing paradigm of chemical hazard and risk assessment. AOP can be applied to the assessment of chemical carcinogenicity along with efforts to understand the effects of chronic toxic chemicals in workplaces. Based on these predictive tools, it could be possible to bring about a breakthrough in the prevention of occupational and environmental cancer. Conclusions: The AOP tool has emerged as a future-oriented alternative to the existing paradigm of chemical hazard and risk assessment and has been widely used in the field of chemical risk assessment and the evaluation of carcinogenicity at work. It will be a useful tool for prediction, and it is possible that it can help bring about a breakthrough in the prevention of occupational and environmental cancer.

• Journal of the Korean Geotechnical Society
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• v.20 no.8
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• pp.67-75
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• 2004

The landslide is one of the most significant natural disasters, which cause a lot of loss of human lives and properties. The landslides in natural slopes generally occur by complicated problems such as soil properties, topography, and geology. Artificial Neural Network (ANN) model is efficient computing technique that is widely used to solve complicated problems in many research fields. In this paper, the ANN model with application of error back propagation method was proposed for estimation of landslide hazard in natural slope. This model can evaluate the possibility of landslide hazard with two different approaches: one considering only soil properties; the other considering soil properties, topography, and geology. In order to evaluate reasonably the landslide hazard, the SlideEval (Ver, 1.0) program was developed using the ANN model. The evaluation of slope stability using the ANN model shows a high accuracy. Especially, the prediction of landslides using the ANN model gives more stable and accurate results in the case of considering such factors as soil, topographic and geological properties together. As a result of comparison with the statistical analysis(Korea Institute of Geosciences and Mineral Resources, 2003), the analysis using the ANN model is approximately equal to the statistical analysis. Therefore, the SlideEval (Ver. 1.0) program using ANN model can predict landslides hazard and estimate the slope stability.

• Journal of Korean Society for Quality Management
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• v.25 no.3
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• pp.96-107
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• 1997

The recent stream to reliability prediction is that it is totally inclusive in depth to consider even the operating and environmental condition at the level of finished goods as well as component itselves. In this study, firstly we present the reliability prediction methods by entire failure rate model which failure rate at the system level is added to the failure rate model at the component level. Secondly we build up the improved bases of reliability demonstration through a, pp.ication of Kaplan-Meier, Cumulative hazard, Johnson's methods as non-parametric and Maximum Likelihood Estimator under exponential & Weibull distribution as parametric. And also present the methods of curve fitting to piecewise failure rate under Weibull distribution, PRST (Probability Ratio Sequential Test), curve fitting to S-shaped reliability growth curve, computer programs of each methods. Lastly we show the practical for determination of optimal burn-in time as a method of reliability enhancement, and also verify the practical usefulness of the above study through the a, pp.ication of failure and test data during 1 year.