• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.107-114
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• 2011

Location-Based Service(LBS) is a service that provides a variety of convenience in life using location information that can be obtained by mobile communication network or satellite signal. In order to provide LBS precisely and efficiently, we need the location determination technology, platform technology and server technology. In this study, we studied on how we can reduce the error on location determination of objects such people and things. Fingerprint location determination method was applied to this study because it can be used at current wireless communication infrastructure and less influenced by a variety of noisy environment than other location determination methods. We converted the probability value to logarithmic scale value because using the sum of k probability values is not suitable to be applied to weight determination. In order to confirm the performance of suggested method, we developed location determination test program with Visual Basic 6.0 and performed the test. According to indoor and outdoor test results, the suggested stochastic method reduced the distance error by 17%, 18% and 9% respectively at indoor environment and 25%, 11% and 4% at outdoor environment compared with deterministic NN, kNN and kWNN fingerprint methods.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.887-893
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• 2018

In this study, the optimum design technology is suggested by using reliability analysis method. Nowadays, urban flood inundation is easily occurred because of local heavy rain. Traditional deterministic design method for storm sewer may underestimate the size of pipe. Therefore, stochastic method for the storm sewer design is necessary to solve this problem. In the present study, reliability model using FORM (First Order Reliability Method) was developed for the storm sewer. Developed model was applied to the real storm sewers of 5 different areas. Probability of exceeding capacity has been calculated and construction costs according to diameter have been compared. Probability of exceeding capacity of storm sewers of 5 areas have been calculated after estimating the return period of rainfall intensity.

• Earthquakes and Structures
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• v.13 no.2
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• pp.151-164
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• 2017

This article aims to investigate the possible retrofitting of a deficient building with soft story failure mode by connecting it to an adjacent building which is designed based on current code with friction dampers at all floors. Low cost and high performance reliability along with significant energy dissipation pertaining to stable hysteretic loops may be considered in order to choose the proper damper for connecting adjacent buildings. After connecting two neighbouring floors by friction dampers, the sliding forces of dampers at various stories are set in two arrangements: uniform sliding force and then variable sliding force. In order to account for the stochastic nature of the seismic events, incremental dynamic analyses are employed prior and after the installation of the friction dampers at the various floors. Based on these results, fragility curves and mean annual rate of exceedance of serviceability and ultimate limit states are obtained. The results of this study show that the collapse mode of the deficient building can affect the optimum arrangement of sliding forces of friction dampers at Collapse Prevention (CP) performance level. In particular, the Immediate Occupancy (IO) performance level is not tangible to the sliding force arrangement and it depends solely on sliding force value. Generally it can be claimed that this rehabilitation scheme can turn the challenge of pounding two adjacent buildings into the opportunity of dissipating a large amount of the seismic input energy by the friction dampers, thus improving significantly the poor seismic performance of the deficient structure.

• Journal of the Korea Society for Simulation
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• v.33 no.1
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• pp.19-26
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• 2024

The phase-type, PH, distribution is defined as the time to absorption into a terminal state in a continuous-time Markov chain. As the PH distribution includes family of exponential distributions, it has been widely used in stochastic models. Since the PH distribution is represented and generated by an initial probability vector and a generator matrix which is called the Markovian representation, we need to find a vector and a matrix that are consistent with given set of moments if we want simulate a PH distribution. In this paper, we propose an approach to simulate a PH distribution based on distribution function which can be obtained directly from moments. For the simulation of PH distribution of order 2, closed-form formula and streamlined procedures are given based on the Jordan decomposition and the minimal Laplace transform which is computationally more efficient than the moment matching methods for the Markovian representation. Our approach can be used more effectively than the Markovian representation in generating higher order PH distribution in queueing network simulation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.6
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• pp.523-530
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• 2017

In this study, a method of probabilistic evaluation of the performance point of the structure obtained by capacity spectrum method (CSM) is presented. The performance point of the 4-story and 1-bay steel structure was determined by using CSM according to ATC-40. In order to analyze whether the demand spectrums exceed the performance limit of the structure, the limit displacements are derived for the performance limit of the structure defined from the plastic deformation angle of the structural member. In addition, by selecting a total of 30 artificial seismic wave having the response spectrum similar to the design response spectrum, the fragility curves were derived by examining whether the response spectrum obtained from the artificial seismic wave were exceeded each performance limit according to the spectral acceleration. The maximum likelihood method was used to derive the fragility curve using observed excess probabilities. It has been confirmed that there exists a probability that the response acceleration value of the design response spectrum corresponding to each performance limit exceeds the performance limit. This method has a merit that the stochastic evaluation can be performed considering the uncertainty of the seismic waves with respect to the performance point of the structure, and the analysis time can be shortened because the incremental dynamic analysis (IDA) is not necessary.

• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.292-299
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• 2017

Radiation protection aims to prevent a deterministic effect and minimize a stochastic effect. Overestimating a deterministic effect and a stochastic effect can result in an inaccurate assessment of the risks that will occur in the future, and thus accurate evaluation of the absorbed dose of these fundamental amounts is especially important. This study was intended to measure Kerma using PCXMC 2.0 based on Monte Carlo simulations and to assess the exact absorbed dose by comparing doses produced using multipurpose dosimeter and glass dosimeter. It has been decided to conduct experiments for skull, abdomen and pelvis, and Kerma measured PCXMC 2.0 based on Monte Carlo simulations. The absorbed dose was measured using muli purpose dosimeter and glass dosimeter. The results for the experiments conducted in skull, abdomen, pelvis show that the difference in dose appears great in the order of PCXMC 2.0, muli purpose dosimeter, and the glass dosimeter, and muli purpose dosimeter showed a value closer to that of Kerma. As a result, it has been found that the glass dosimeter was the most advantageous in measuring the actual absorbed dose.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1329-1341
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• 2022

Destroying the marine environment and marine ecosystem and causing marine accidents, marine debris is generated every year, and among them, submerged marine debris is difficult to identify and collect because it is on the seabed. Therefore, deep-learning-based semantic segmentation was experimented on waste fish nets and waste ropes using underwater images to identify efficient collection and distribution. For segmentation, a high-resolution network (HRNet), a state-of-the-art deep learning technique, was used, and the performance of each optimizer was compared. In the segmentation result fish net, F1 score=(86.46%, 86.20%, 85.29%), IoU=(76.15%, 75.74%, 74.36%), For the rope F1 score=(80.49%, 80.48%, 77.86%), IoU=(67.35%, 67.33%, 63.75%) in the order of adaptive moment estimation (Adam), Momentum, and stochastic gradient descent (SGD). Adam's results were the highest in both fish net and rope. Through the research results, the evaluation of segmentation performance for each optimizer and the possibility of segmentation of marine debris in the latest deep learning technique were confirmed. Accordingly, it is judged that by applying the latest deep learning technique to the identification of submerged marine debris through underwater images, it will be helpful in estimating the distribution of marine sedimentation debris through more accurate and efficient identification than identification through the naked eye.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.25-34
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• 2022

Brain computed tomography (CT) is useful for brain lesion diagnosis, such as brain hemorrhage, due to non-invasive methodology, 3-dimensional image provision, low radiation dose. However, there has been numerous misdiagnosis owing to a lack of radiologist and heavy workload. Recently, object detection technologies based on artificial intelligence have been developed in order to overcome the limitations of traditional diagnosis. In this study, the applicability of a deep learning-based YOLOv5s model was evaluated for brain hemorrhage detection using brain CT images. Also, the effect of hyperparameters in the trained YOLOv5s model was analyzed. The YOLOv5s model consisted of backbone, neck and output modules. The trained model was able to detect a region of brain hemorrhage and provide the information of the region. The YOLOv5s model was trained with various activation functions, optimizer functions, loss functions and epochs, and the performance of the trained model was evaluated in terms of brain hemorrhage detection accuracy and training time. The results showed that the trained YOLOv5s model is able to provide a bounding box for a region of brain hemorrhage and the accuracy of the corresponding box. The performance of the YOLOv5s model was improved by using the mish activation function, the stochastic gradient descent (SGD) optimizer function and the completed intersection over union (CIoU) loss function. Also, the accuracy and training time of the YOLOv5s model increased with the number of epochs. Therefore, the YOLOv5s model is suitable for brain hemorrhage detection using brain CT images, and the performance of the model can be maximized by using appropriate hyperparameters.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.61-69
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• 2023

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.396-407
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• 2020

As fossil fuels are depleted worldwide, alternative resources is required to replace fossil fuels, and biofuels are in the spotlight as alternative resources. Biofuels are produced from biomass, which is a renewable resource to produce biofuels or bio-chemicals. Especially, in order to substitute fossil fuels, the research focusing the biofuel (biodiesel) production based on CO₂ and biomass achieves more attention recently. To produce biomass-based biodiesel, the development of a supply chain network is required considering the amounts of feedstocks (ex, CO₂ and water) required producing biodiesel, potential locations and capacities of bio-refineries, and transportations of biodiesel produced at biorefineries to demand cities. Although many studies of the biomass-based biodiesel supply chain network are performed, there are few types of research handled the uncertainty in CO₂ supply which influences the optimal strategies of microalgae-based biodiesel production. Because CO₂, which is used in the production of microalgae-based biodiesel as one of important resources, is captured from the off-gases emitted in power plants, the uncertainty in CO₂ supply from power plants has big impacts on the optimal configuration of the biodiesel supply chain network. Therefore, in this study, to handle those issues, we develop the two-stage stochastic model to determine the optimal strategies of the biodiesel supply chain network considering the uncertainty in CO₂ supply. The goal of the proposed model is to minimize the expected total cost of the biodiesel supply chain network considering the uncertain CO₂ supply as well as satisfy diesel demands at each city. This model conducted a case study satisfying 10% diesel demand in the Republic of Korea. The overall cost of the stochastic model (US$ 12.9/gallon·y) is slightly higher (23%) than that of the deterministic model (US$ 10.5/gallon·y). Fluctuations in CO₂ supply (stochastic model) had a significant impact on the optimal strategies of the biodiesel supply network.