• International Journal of Safety
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• v.2 no.1
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• pp.34-38
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• 2003

The lower and upper flash points of the flammable binary system, butylacetate+2-propanol were measured by air blowing tester. The shape of the concentration-temperature region of flash depended on the components of the mixture in solution. The experimental data were compared with the values calculated by the reduced model under an ideal solution assumption and the flash point-prediction models based on Van Laar equation. Good qualitative agreement was obtained with these models. The prediction results of these models can thus be applied to incorporate inherently safer design for chemical process, such as the determination of the safe storage conditions for flammable solutions.

• Journal of the Korean Society of Safety
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• v.24 no.3
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• pp.88-95
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• 2009

Most of subway stations are located underground and the number of passengers is far more than that of designed value, therefore the risk of accident is growing bigger and serious damage is expected in case of disaster. In Korea the period of evacuation study is short and numerical and experimental data of evacuation phenomena in subway station is rare. Many egress evaluation depend on foreign commercial S/Ws which are not yet proven its availability in special case such as subway station. In this paper outflow coefficients which are essential in egress evaluation are calculated at train door, stairway and turnstile at 3 most crowed subway stations. This numerical data can be used in prediction of egress evaluation and the result of other prediction methods can be verified with these experimental data.

• Earthquakes and Structures
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• v.18 no.1
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• pp.73-82
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• 2020

With the coal mining depth increasing, both stress and gas pressure rapidly enhance, causing coal and gas outburst risk to become more complex and severe. The conventional method for prediction of coal and gas outburst adopts one prediction index and corresponding critical value to forecast and cannot reflect all the factors impacting coal and gas outburst, thus it is characteristic of false and missing forecasts and poor accuracy. For the reason, based on analyses of both the prediction indicators and the factors impacting coal and gas outburst at the test site, this work carefully selected 6 prediction indicators such as the index of gas desorption from drill cuttings Δh₂, the amount of drill cuttings S, gas content W, the gas initial diffusion velocity index ΔP, the intensity of electromagnetic radiation E and its number of pulse N, constructed the Bayes discriminant analysis (BDA) index system, studied the BDA-based multi-index comprehensive model for forecast of coal and gas outburst risk, and used the established discriminant model to conduct coal and gas outburst prediction. Results showed that the BDA - based multi-index comprehensive model for prediction of coal and gas outburst has an 100% of prediction accuracy, without wrong and omitted predictions, can also accurately forecast the outburst risk even for the low indicators outburst. The prediction method set up by this study has a broad application prospect in the prediction of coal and gas outburst risk.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11B
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• pp.744-749
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• 2005

This paper proposes the prediction based QoS routing algorithm, PSS(Prediction Safety-Shortest) algorithm that minimizes network state information overhead and presumes more accurate knowledge of the present state of all the links within the network. We apply time series model to the available bandwidth prediction to overcome inaccurate information of the existing QoS routing algorithms. We have evaluated the performance of the proposed model and the existing algorithms on MCI networks, it thus appears that we have verified the performance of this algorithm.

• Journal of Biosystems Engineering
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• v.40 no.1
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• pp.47-60
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• 2015

Purpose: The purpose of this study was to non-destructively and quickly predict the capsaicinoid content of domestic red pepper powders from various areas of Korea using a pungency measurement system in combination with visible and near-infrared (VNIR) spectroscopic techniques. Methods: The reflectance spectra of 149 red pepper powder samples from 14 areas of Korea were obtained in the wavelength range of 450-950 nm and partial least squares regression (PLSR) models for the prediction of capsaicinoid content were developed using area models. Results: The determination coefficient of validation (RV2), standard error of prediction (SEP), and residual prediction deviation (RPD) for the capsaicinoid content prediction model for the Namyoungyang area were 0.985, ${\pm}2.17mg/100g$, and 7.94, respectively. Conclusions: These results show the possibility of VNIR spectroscopy combined with PLSR models in the non-destructive and facile prediction of capsaicinoid content of red pepper powders from Korea.

• Journal of the Korean Society of Marine Environment & Safety
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• v.17 no.4
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• pp.375-382
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• 2011

There is no system to obtain the basic data and proceed data and user input interface is complex, thus there are some limitation to utilize the oil spill prediction model. It is difficult to build the scientific response strategy in order to respond oil spill accident rapidly because it is impossible to operate the oil spill prediction model any time. In this study, the optimum operational system for oil spil prediction model has been developed considering the present system. External real time data has been linked because of impossibility of building all basic data and minimum database has been build in this study. Through this data system, real time oil spill prediction model can be utilized. And the user interface has been designed to reduce the error of the interface between user and model and the output interface has been proposed to analyze the result of modeling at multidimensional aspect. While the system for oil spill prediction model as the result of this study has some uncertainties because of depending on external data, the thing that we can predict oil spill using operate the model rapidly as soon as the accident occurred can be meaning in the response field.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• Safety and Health at Work
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• v.8 no.3
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• pp.267-275
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• 2017

Background: The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent. Methods: The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc. Results: The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed. Conclusion: Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

• Journal of the Korean Society of Safety
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• v.25 no.3
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• pp.22-27
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• 2010

A prediction performance of Fire Dynamics Simulator(FDS) developed by NIST for the diffusion flame structure was validated with experimental results of a laminar slot jet diffusion flame. Two mixture fraction combustion models and two finite chemistry combustion models were used in the FDS simulation for the validation of the jet diffusion flame structure. In order to enhance the prediction performance of flame structure, DNS and radiation model was applied to the simulation. The reaction rates of the finite chemistry combustion models were appropriately adjusted to the diffusion flame. The mixture fraction combustion model predicted the diffusion flame structure reasonably. A 1-step finite chemistry combustion model cannot predict the flame structure well, but the simulation results of a 2-step model were in good agreement with those of experiment except $CO_2$ concentration. It was identified that the 2-step model can be used in the investigation of flame suppression limit with further adjustment of reaction rates

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.888-895
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• 2009

Compaction is a process of increasing soil density using physical energy. It is intended to improve the strength and stiffness of soil. In embankment, degree of compaction affects the construction time, money, also method of soil improvement. In large scale embankment project, difficulties of embankment should change due to uncertainty of settlement. So it is very important to predict the final settlement and factor of safety induced by embankment. In many construction site, there are primarily design of high embankment using in-situ soil. Therefore numerical analyses are necessary for valid evaluation of the settlement prediction. But due to the construction cost and schedule, there were lacking in properties of soil and also limited number of in-situ test were performed. So we proposed the method that can easily estimate the proper soil parameters and suggest the proper method of numerical analysis. From this, two-dimensional finite-difference numerical analysis was conducted to investigate the settlement and factor of safety induced by embankment with various case of compaction rate and embankment height.