• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.15-23
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• 2021

In this paper, service life for RC(Reinforced Concrete) substructure subjective to carbonation was evaluated through deterministic and probabilistic method considering field investigation data and Design Code(KDS 14 20 40). Furthermore changes in service life with increasing COV(Coefficient of Variation) and equivalent safety index meeting the same service life were studied. From the investigation, the mean and its COV of cover depth were evaluated to 70.0 ~ 90.0 mm and 0.2, respectively. With intended failure probability of 10.0 % and 70 mm of cover depth, service life decreased to 137 years, 123 years, and 91 years with increasing COV of 0.05, 0.1, and 0.2, respectively. In the case of 80 mm of cover depth, it changes to 179 years, 161 years, and 120 years with increasing COV. The equivalent safety index meeting the same service life from deterministic method showed 1.66 ~ 3.43 for 70 mm of cover depth and 1.61 ~ 3.24 for 80 mm of cover depth, respectively. The various design parameters covering local environment and quality condition in deterministic method yields a considerable difference of service life, so that determination of design parameters are required for exposure conditions and parameter variation.

• Journal of Environmental Health Sciences
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• v.47 no.4
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• pp.366-377
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• 2021

Background: Polycyclic aromatic hydrocarbons (PAHs) are generated in petrochemical complexes, can spread to residential areas and affect the health of residents. Although harmful PAHs are mainly present in particle phase, gas phase PAHs can generate stronger toxic substances through photochemical reaction. Therefore, the risk assessment for PAHs around the petrochemical complex should consider both particle and gas phase concentrations. Objectives: This study aimed to investigate the concentration characteristics of particle and gas phase PAHs by season in residential areas around petrochemical complexes, and to assess the risk of PAHs. Methods: Samples were collected for 7 days by seasons in 2014~2015 using a high volume air sampler. Particle and gas phase PAHs were sampled using quartz filter and polyurethane foam, respectively, analyzed by GC-MS. Chronic toxicity and probabilistic risk assessment were performed on 14 PAHs. For chronic toxicity risk assessment, inhalation unit risk was used. Monte-Carlo simulation was performed for probabilistic risk assessment using the mean and standard deviation of measured PAHs. Results: The concentration of particle total PAHs was highest in autumn. The gas phase concentration was highest in autumn. The average gas phase distribution ratio of low molecular weight PAHs composed of 2~3 benzene rings was 85%. The average of the medium molecular weight composed of 4 benzene rings was 53%, and the average of the high molecular weight composed of 5 or more benzene rings was 9%. In the chronic toxicity risk assessment, 7 of the 14 PAHs exceeded the excess carcinogenic risk of 1.00×10^-6. In the Monte-Carlo simulation, Benzo[a]pyrene had the highest probability of exceeding 1.00×10^-6, which was 100%. Conclusions: The concentration of PAHs in the residential area around the petrochemical complex exceeded the standard, and the excess carcinogenic risk was evaluated to be high. Therefore, it is necessary to manage the air environment around the petrochemical complex.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.233-238
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• 2020

In the cloud environment, IoT devices using sensors and wearable devices are being applied in various environments, and technologies that accurately determine the information generated by IoT devices are being actively studied. However, due to limitations in the IoT environment such as power and security, information generated by IoT devices is very weak, so financial damage and human casualties are increasing. To accurately collect and analyze IoT information, this paper proposes a topographic information-based key management technique that considers IoT information errors. The proposed technique allows IoT layout errors and groups topographic information into groups of dogs in order to secure connectivity of IoT devices in the event of arbitrary deployment of IoT devices in the cloud environment. In particular, each grouped terrain information is assigned random selected keys from the entire key pool, and the key of the terrain information contained in the IoT information and the probability-high key values are secured with the connectivity of the IoT device. In particular, the proposed technique can reduce information errors about IoT devices because the key of IoT terrain information is extracted by seed using probabilistic deep learning.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.87-94
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• 2008

In this paper, we propose a new vision-based fire detection method for a real-life application. Most previous vision-based methods using color information and temporal variation of pixel produce frequent false alarms because they used a lot of heuristic features. Furthermore there is also computation delay for accurate fire detection. To overcome these problems, we first detected candidated fire regions by using background modeling and color model of fire. Then we made probabilistic models of fire by using a fact that fire pixel values of consecutive frames are changed constantly and applied them to a Bayesian Network. In this paper we used two level Bayesian network, which contains the intermediate nodes and uses four skewnesses for evidence at each node. Skewness of R normalized with intensity and skewnesses of three high frequency components obtained through wavelet transform. The proposed system has been successfully applied to many fire detection tasks in real world environment and distinguishes fire from moving objects having fire color.

• Journal of Astronomy and Space Sciences
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• v.31 no.2
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• pp.149-157
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• 2014

As the prediction of geomagnetic storms is becoming an important and practical problem, conditions in the Earth's magnetosphere have been studied rigorously in terms of those in the interplanetary space. Another approach to space weather forecast is to deal with it as a probabilistic geomagnetic storm forecasting problem. In this study, we carry out detailed statistical analysis of solar wind parameters and geomagnetic indices examining the dependence of the distribution on the solar cycle and annual variations. Our main findings are as follows: (1) The distribution of parameters obtained via the superimposed epoch method follows the Gaussian distribution. (2) When solar activity is at its maximum the mean value of the distribution is shifted to the direction indicating the intense environment. Furthermore, the width of the distribution becomes wider at its maximum than at its minimum so that more extreme case can be expected. (3) The distribution of some certain heliospheric parameters is less sensitive to the phase of the solar cycle and annual variations. (4) The distribution of the eastward component of the interplanetary electric field BV and the solar wind driving function BV2, however, appears to be all dependent on the solar maximum/minimum, the descending/ascending phases of the solar cycle and the equinoxes/solstices. (5) The distribution of the AE index and the Dst index shares statistical features closely with BV and $BV^2$ compared with other heliospheric parameters. In this sense, BV and $BV^2$ are more robust proxies of the geomagnetic storm. We conclude by pointing out that our results allow us to step forward in providing the occurrence probability of geomagnetic storms for space weather and physical modeling.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.194-202
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• 2019

Reliability analysis of the components frequently starts with the data that manufacturer provides. If enough failure data are collected from the field operations, the reliability should be recomputed and updated on the basis of the field failure data. However, when the failure time record for a component contains only a few observations, all statistical methodologies are limited. In this case, where the failure records for multiple number of identical components are available, a valid alternative is combining all the data from each component into one data set with enough sample size and utilizing the useful information in the censored data. The ROK Navy has been operating multiple Patrol Killer Guided missiles (PKGs) for several years. The Korea Multi-Function Control Console (KMFCC) is one of key components in PKG combat system. The maintenance record for the KMFCC contains less than ten failure observations and a censored datum. This paper proposes a Bayesian approach with a Dirichlet mixture model to estimate failure time density for KMFCC. Trends test for each component record indicated that null hypothesis, that failure occurrence is renewal process, is not rejected. Since the KMFCCs have been functioning under different operating environment, the failure time distribution may be a composition of a number of unknown distributions, i.e. a mixture distribution, rather than a single distribution. The Dirichlet mixture model was coded as probabilistic programming in Python using PyMC3. Then Markov Chain Monte Carlo (MCMC) sampling technique employed in PyMC3 probabilistically estimated the parameters' posterior distribution through the Dirichlet mixture model. The simulation results revealed that the mixture models provide superior fits to the combined data set over single models.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.36-54
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• 2017

The purpose of this paper is to develop a multi-sensor fusion-based traffic information acquisition system with robust to environmental changes. it combines the characteristics of each sensor and is more robust to the environmental changes than the video detector. Moreover, it is not affected by the time of day and night, and has less maintenance cost than the inductive-loop traffic detector. This is accomplished by synthesizing object tracking informations based on a radar, vehicle classification informations based on a video detector and reliable object detections of a infrared range finder. To prove the effectiveness of the proposed system, I conducted experiments for 6 hours over 5 days of the daytime and early evening on the pedestrian - accessible road. According to the experimental results, it has 88.7% classification accuracy and 95.5% vehicle detection rate. If the parameters of this system is optimized to adapt to the experimental environment changes, it is expected that it will contribute to the advancement of ITS.

• Geotechnical Engineering
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• v.7 no.2
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• pp.51-66
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• 1991

In the stability analysis of hillside slopes, the roots of vegetation have been considered to act as a soil reinforcement. In order to predict the amount of increase in soil shear resistance, produced by tensile strength of roots that intersect a potential slip surface in hillside slopes, new soil -root interaction models are proposed in this paper. For this purpose, firstly, laboratary teats and in-situ tests wert performed on soil-root systems, and experimental results were compared with a couple of soil-root interaction models which had been proposed by Gray, Waldron, and Wu etc. Based on this comparison, a new soil-root interaction model is proposed. Secondly, a probabilistic soil-root model is proposed based on statistical analysis considering random nature of root distribution, root characteristics, and soil-root interactions. Finally, to examine the effect of this root reinforcement system on stability of hillside slopes, a simple three-dimensional stability analysis was performed, and it was shown that root reinforcement had a significant stabilizing influence on shallow slips rather than deep slips in hillside slopes.

• Journal of Environmental Science International
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• v.10 no.3
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• pp.195-200
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• 2001

The personal exposures of nitrogen dioxide(NO$_2$), microenvironmental levels and daily time activity patterns on Seoul subway station workers were measured from February 10 to March 12, 1999. Personal NO$_2$exposure for 24 hours were 29.40$\pm$9.75 ppb. NO$_2$level of occupational environment were 27.87$\pm$7.15 ppb in office, 33.60$\pm$8.64 ppb in platform and 50.13$\pm$13.04 ppb in outdoor. Personal exposure time of subway station workers was constituted as survey results with $7.94\pm$3.00 hours in office, $2.82\pm$1.63 hours in platform and 1 hours in outdoor. With above results, personal $NO_2$exposure distributions on subway station workers in Seoul were estimated with Monte Carlo simulation which uses statistical probabilistic theory on various exposure scenario testing. Some of distributions which did not have any formal patterns were assumed as custom distribution type. Estimated personal occupational $NO_2$exposure using time weighted average (TWA) model was 31.$29\pm$5.57 ppb, which were under Annual Ambient Standard (50ppb) of Korea. Though arithmetic means of measured personal $NO_2$exposure was lower than that of occupational $NO_2$exposure estimated by TWA model, considering probability distribution type simulated, probability distribution of measured personal $NO_2$exposures for 24 hours was over ambient standard with 3.23%, which was higher than those of occupational exposure(0.02%). Further research is needed for reducing these 24 hour $NO_2$personal excess exposures besides occupational exposure on subway station workers in Seoul.

• Journal of Radiation Protection and Research
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• v.41 no.2
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• pp.117-121
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• 2016

Background: Definition and grouping of initiating events (IEs) are important basics for probabilistic safety assessment (PSA). An IE in a spent fuel reprocessing plant (SFRP) is an event that probably leads to the release of dangerous material to jeopardize workers, public and environment. The main difference between SFRPs and nuclear power plants (NPPs) is that hazard materials spread diffusely in a SFRP and radioactive material is just one kind of hazard material. Materials and Methods: Since the research on IEs for NPPs is in-depth around the world, there are several general methods to identify IEs: reference of lists in existence, review of experience feedback, qualitative analysis method, and deductive analysis method. While failure mode and effect analysis (FMEA) is an important qualitative analysis method, master logic diagram (MLD) method is the deductive analysis method. IE identification in SFRPs should be consulted with the experience of NPPs, however the differences between SFRPs and NPPs should be considered seriously. Results and Discussion: The plutonium uranium reduction extraction (Purex) process is adopted by the technics in a model reprocessing plant. The first extraction cycle (FEC) is the pivotal process in the Purex process. Whether the FEC can function safely and steadily would directly influence the production process of the whole plant-production quality. Important facilities of the FEC are installed in the equipment cells (ECs). In this work, IEs in the FEC process were identified and categorized by FMEA and MLD two methods, based on the fact that ECs are containments in the plant. Conclusion: The results show that only two ECs in the FEC do not need to be concerned particularly with safety problems, and criticality, fire and red oil explosion are IEs which should be emphatically analyzed. The results are accordant with the references.