• Earthquakes and Structures
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• v.6 no.2
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• pp.141-161
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• 2014

This article presents a new generation of empirical ground motion models for the prediction of response spectral accelerations in soil conditions, specifically developed for the Vrancea intermediate-depth seismic source. The strong ground motion database from which the ground motion prediction model is derived consists of over 800 horizontal components of acceleration recorded from nine Vrancea intermediate-depth seismic events as well as from other seventeen intermediate-depth earthquakes produced in other seismically active regions in the world. Among the main features of the new ground motion model are the prediction of spectral ordinates values (besides the prediction of the peak ground acceleration), the extension of the magnitudes range applicability, the use of consistent metrics (epicentral distance) for this type of seismic source, the extension of the distance range applicability to 300 km, the partition of total standard deviation in intra- and inter-event standard deviations and the use of a national strong ground motion database more than two times larger than in the previous studies. The results suggest that this model is an improvement of the previous generation of ground motion prediction models and can be properly employed in the analysis of the seismic hazard of Romania.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.5
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• pp.84-90
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• 2004

A prediction of SEE rates for a candidate microprocessor is made based on the ground experiment results with a proton accelerator. Populations of charged particles in space are estimated with numerical models such as AP8, JPL91 and CREME. The cross section curves that are previously obtained with the accelerator are then employed for SEE prediction. Both the high and low inclinations are considered for low-earth orbits with nominal altitudes of about 685km. The results show that the occurrence rate of SEEs for the candidate device is acceptable for low-inclinations, but can be considerable under worst conditions for high inclinations.

• Journal of the Korea Society for Simulation
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• v.28 no.3
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• pp.1-9
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• 2019

An agent-based simulation is a popular simulation tool to solve various problems, such as stock market, population prediction, disease prediction, and development of a traffic system. As the agents are developed and researched in different application fields, the agent has a rigid structure and may not acceptable in different domains. As a result, it is a challenging problem to define a structure for an agent structure to reflect the researcher's simulation objective. This research proposes an extendable form for an agent and its modeling environment. In order to propose a standard structure, this study adopts system entity structure and discrete event system specification formalism. Also, this research introduces the SESManager which supports the proposed specification method. The proposed environment can hierarchically define the agent structure and synthesize the agent so that it can perform the agent simulation according to the user's simulation purpose.

• 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 Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Journal of the Korean Society of Safety
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• v.20 no.2 s.70
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• pp.61-66
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• 2005

This paper presents an human reliability assessment(HRA) for a installation task of the temporary power cable in construction fields. HRA is evolved to ensure that the workers could reliably perform critical tasks such as a process of the temporary power cable. Human errors are extremely commonplace, with almost everyone committing at least some errors every day. The considerable parts of electric shock accidents in the construction field are caused by a series of human errors. Therefore it is required to analyze the human errors contained in the task causing electric shock event, the event tree analysis(ETA) is adopted in this paper, and particularly human reliability was estimated for a installation task of the temporary power cables. It was assumed that the error probabilities of the human actions may be obtained using the technique for human error rate prediction(THERP). The results show that the predominant task on reliability in the cable installation tasks is check-out tasks and the probability causing electric shock by human errors was calculated as $1.0\times10^{-9}$.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.1
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• pp.30-36
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• 2005

This study is to investigate a damage estimation of single edge notched tensile specimens as a function of acoustic emission(AE) according to the uni-directionally oriented carbon fiber/epoxy composites, CFRP In fiber reinforced composite materials, AE signals due to several types of failure mechanisms are typically observed. These are due to fiber breakage, fiber pull-out matrix cracking, delamination, and splitting or fiber bundle breaking. And these are usually discriminated on the basis of amplitude distribution, event counts, and energy related parameters. In this case, AE signals were analyzed and classified 3 regions by AE event counts, energy and amplitude for corresponding applied load. Bath-tub curve shows 3 distinct periods during the lifetime of a single-edge-notch(SEN) specimen. The characterization of AE generated from CFRP during SEN tensile test is becoming an useful tool f3r the prediction of damage failure or/and failure mode analysis.

• The Korean Journal of Nuclear Medicine
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• v.39 no.2
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• pp.114-117
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• 2005

As the indication of percutaneous coronary intervention (PCI) has expanded to the more difficult and complicated cases, frequent restenosis is still expected after PCI. According to AHA/ACC guideline of the present time, routine use of myocardial perfusion single photon emission tomography (SPECT) is not recommended after coronary intervention, but symptom itself or exercise EKG is not enough for the detection of restensis or for the prediction of event-free survival. In high risk and/or symptomatic subjects, direct coronary angiography is required myocardial perfusion SPECT could detect restenosis in 79% of the patients if performed 2 to 9 months after PCI. Reversible perfusion decrease in the myocardial perfusion SPECT is known to be the major prognostic indicator of major adrerse cardiac event in PCI patients and also the prognosis is benign in the patients without reversible perfusion decrease. Though the cumulated specificity is 79% in the literature and optimal timing of myocardial perfusion SPECT is in controversy, SPECT is recommended even in asymptomatic patients at 3 to 9 months after PCI. Considering the evidences recently reported in the literature, myocardial perfusion SPECT is useful for risk stratification and detection of coronary artery restenosis requiring re-intervention in the asymptomatic patients after PCI.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.15-32
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• 2016

Online news on the Internet, as published open data, contain facts or opinions about a specific affair and hence influences considerably on the decisions of the general publics who are interested in a particular issue. Therefore, we can predict the people's choices related with the issue by analyzing a large number of related internet news. This study aims to propose a text analysis methodto predict the outcomes of events that take place in a specific place. We used topics of the news articles because the topics contains more essential text than the news articles. Moreover, when it comes to mobile environment, people tend to rely more on the news topics before clicking into the news articles. We collected the titles of news articles and divided them into the learning and evaluation data set. Morphemes are extracted and their polarity values are identified with the learning data. Then we analyzed the sensitivity of the entire articles. As a result, the prediction success rate was 70.6% and it showed a clear difference with other analytical methods to compare. Derived prediction information will be helpful in determining the expected demand of goods when preparing the event.

• Journal of the Korean Geotechnical Society
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• v.32 no.4
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• pp.15-27
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• 2016

This study was conducted to suggest a landslide triggering rainfall threshold (ID curve) for landslide prediction by considering the effect of antecedent rainfall. 202 rainfall data including domestic landslide and rainfall records were used in this study. In order to consider the effect of antecedent rainfall, rainfall data were analyzed by changing Inter Event Time Definition (IETD) and IETD based ID curve were presented by regression analysis. Compared to the findings of the previous studies, the presented ID curve has a tendency to predict the landslides occurring at a relatively low rainfall intensity. It is shown that the proposed ID curve is appropriate and realistic for predicting landslides through the validation of proposed ID curve using records of landslides in 2014. Based on this analysis, it is found that the longer IETD, the greater the effect of antecedent rainfall, and the steeper the gradient of ID curve. It is also found that the rainfall threshold (intensity) is higher for the short period rainfall and lower for the long period rainfall.