• Safety and Health at Work
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• 제2권4호
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• pp.375-379
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• 2011

Control Banding (CB) strategies to prevent work-related illness and injury for 2.5 billion workers without access to health and safety professionals has grown exponentially this last decade. CB originates from the pharmaceutical industry to control active pharmaceutical ingredients without a complete toxicological basis and therefore no occupational exposure limits. CB applications have broadened into chemicals in general - including new emerging risks like nanomaterials and recently into ergonomics and injury prevention. CB is an action-oriented qualitative risk assessment strategy offering solutions and control measures to users through "toolkits". Chemical CB toolkits are user-friendly approaches used to achieve workplace controls in the absence of firm toxicological and quantitative exposure information. The model (technical) validation of these toolkits is well described, however firm operational analyses (implementation aspects) are lacking. Consequentially, it is often not known if toolkit use leads to successful interventions at individual workplaces. This might lead to virtual safe workplaces without knowing if workers are truly protected. Upcoming international strategies from the World Health Organization Collaborating Centers request assistance in developing and evaluating action-oriented procedures for workplace risk assessment and control. It is expected that to fulfill this strategy's goals, CB approaches will continue its important growth in protecting workers.

• Journal of the Korea Society of Computer and Information
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• 제27권12호
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• pp.267-275
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• 2022

This paper proposed a Deep Neural Network Model system utilizing Keras for predicting air pollution risk of the cardiovascular disease through the effect of each component of air on the harmful virus using past air information, with analyzing 18,000 data sets of the Seoul Open Data Plaza. By experiments, the model performed tasks with higher accuracy when using methods of sigmoid, binary_crossentropy, adam, and accuracy through 3 hidden layers with each 8 nodes, resulting in 88.92% accuracy. It is meaningful in that any respiratory disease can utilize the risk prediction system if there are data on the effects of each component of air pollution and fine dust on oil-borne diseases. It can be further developed to provide useful information to companies that produce masks and air purification products.

• International Journal of Advanced Culture Technology
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• 제7권1호
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• pp.209-215
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• 2019

In order to provide a safe free-weight-training environment to people without workout trainers, we suggest a smart bench press machine with an automatic weight control system sensitive to user tiredness. Physical weight plates on the machine are replaced with a hydraulic cylinder as a press load and the cylinder knob is coupled with a step motor to change its tensile force automatically in-between lifting exercises. Three subjects participated to verify the usability of the smart bench press machine. They were asked to lift a 6-RM press load 10 times with 3 different lifting conditions: 1) no assistance, 2) a human assistance, and 3) the automatic weight control. All subjects were not able to complete the 10 sets without assistance due to tiredness, but they finished the full sets under the two assistive conditions. Average lifting speeds under the automatic weight control condition showed the most consistent level. Normalized quasi-tension data based on surface electromyogram signals of both Pectoralis Majors revealed that the subjects maintained the target muscle activation level above 50% but not more than 80% throughout the 10 sets. Therefore, the smart bench press machine is expected to both keep pace with the lifting exercise and reduce risk of injuries due to excessive muscle tensions.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2023년도 학술발표회
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• pp.180-180
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• 2023

To improve global risk management, understanding the characteristics and distribution of precipitation is crucial. However, obtaining spatially and temporally resolved climatic data remains challenging due to sparse gauge observations and limited data availability, despite the use of satellite and reanalysis products. To address this challenge, merging available precipitation products has been introduced to generate spatially and temporally reliable data by taking advantage of the strength of the individual products. However, most of the existing studies utilize all the available products without considering the varying performances of each dataset in different regions. Comprehensively considering the relative contributions of each parent dataset is necessary since their contributions may vary significantly and utilizing all the available datasets for data merging may lead to significant data redundancy issues. Hence, for this study, we introduce a site-specific precipitation merging method that utilizes the Quadruple Collocation (QC) approach, which acknowledges the existence of error-cross correlation between the parent datasets, to create a high-resolution global daily precipitation data from 2001-2020. The performance of multiple gridded precipitation products are first evaluated per region to determine the best combination of quadruplets to be utilized in estimating the error variances through the QC approach and computation of merging weights. The merged precipitation is then computed by adding the precipitation from each dataset in the quadruplet multiplied by each respective merging weight. Our results show that our approach holds promise for generating reliable global precipitation data for data-scarce regions lacking spatially and temporally resolved precipitation data.

• Korean Journal of Remote Sensing
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• 제38권5_2호
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• pp.781-791
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• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• Journal of Korea Multimedia Society
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• 제21권9호
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• pp.1099-1109
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• 2018

Most of the automotive electronic systems are equipped with control software. ISO 26262 standard has been published to prevent unreasonable risk due to E/E system malfunction. And many automotive companies apply ISO 26262 for safe series product. In ISO 26262 standard, the product quality improves through deductive and inductive safety analysis in all processes including system and software development phase. However, there are few studies on software safety analysis than systems. In the paper, we study the software FMEA(Failure Mode Effect Analysis) technique for product quality of vehicular embedded software. And we propose an effective guideline of software FMEA as EPB industrial practice.

• Journal of the Korean Society of Hazard Mitigation
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• 제9권2호
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• pp.45-51
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• 2009

A probabilistic approach may be adopted to predict freeze and thaw depths to account for the variability of (1) material properties, and (2) contemporary and future surface energy input parameters(e.g. air temperatures, cloud cover, snow cover) predicted with global climate models. To illustrate the probabilistic approach, an example of the predicted of thaw depths in cold regions is considered. More specifically, the Stefan equation is used together with the Monte Carlo simulation technique to make a probabilistic prediction of thaw penetration. The simulation results indicate that the variability in material properties, surface energy input parameters and temperature data can lead to significant uncertainty in predicting thaw penetration.

• Proceedings of the Korean Information Science Society Conference
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• 한국정보과학회 2006년도 가을 학술발표논문집 Vol.33 No.2 (A)
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• pp.39-42
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• 2006

뉴럴네트워크에서 학습은 에러를 줄이는 방법으로 구현 된다. 이 때 parameter 공간에서 Risk function은 multi-minima potential로 표현 될 수 있으며 우리의 목적은 global minimum weight 좌표를 얻는 것이다. 이전의 연구로는 Attouch et al.의 damped oscillator 방정식을 이용한 방법이 있고, Qian의 critically damped oscillator를 통한 steepest descent의 momentum과 learning parameter 유도가 있다. 우리는 이 두 연구를 참고로 manifold 상에서 최단 경로인 geodesic을 Newton 역학의 Lagrangian에 적용함으로써 adaptive steepest descent 학습법을 얻었다. 우리는 이 새로운 방법을 Rosenbrock 과 Griewank 포텐셜들에 적용하여 그 성능을 알아 본다.

• Proceedings of the PSK Conference
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• 대한약학회 2003년도 Proceedings of the Convention of the Pharmaceutical Society of Korea Vol.2-1
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• pp.38-39
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• 2003

There have been remarkable medical advances, largely as the result of improved understanding of disease mechanisms and of advances in human genetics at the individual level. Subsequently, remarkable effective medicines continue to be discovered at an increasing rate. Development of these medicines become increasingly complex, accompanied by globalization and global standards, requiring ever higher standards of efficacy, safety and quality. (omitted)

• Korean Journal of Construction Engineering and Management
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• 제15권3호
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• pp.66-73
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• 2014

The Korean construction industry has been severely impacted by the 2008 global financial crisis, which resulted in a significant reduction in the overall contract amount. For survival, many construction management (CM) companies had to adapt a strategy of lowering bid prices to maintain their competitiveness. As a result of the strategy, companies faced a number of issues including their decreased capability in risk management. However, most risk management-related studies focused on construction risk management, yet these studies lacked consideration of enterprise-level risk management practices. To fill the gap, the objectives of the present study are (1) to investigate, the risk management practices of Korean CM companies, (2) to identify factors that determine efficient enterprise-level risk management practices, and (3) to propose a module for the development of enterprise-level risk management. Lastly, the efficiency of the proposed development module was validated by using a survey.