• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.54-63
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• 2007

The purpose of this study is to present a new energy minimization method for image completion with hierarchical approach. The goal of image completion is to fill in missing part in a possibly large region of an image so that a visually plausible outcome is obtained. An exemplar-based Markov Random Field Modeling(MRF) is proposed in this paper. This model can deal with following problems; detection of global features, flexibility on environmental changes, reduction of computational cost, and generic extension to other related domains such as image inpainting. We use the Priority Belief Propagation(Priority-BP) which is a kind of Belief propagation(BP) algorithms for the optimization of MRF. We propose the hierarchical Priority-BP that reduces the number of nodes in MRF and to apply hierarchical propagation of messages for image completion. We show that our approach which uses hierarchical Priority-BP algorithm in image completion works well on a number of examples.

• Korean Journal of Community Nutrition
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• v.16 no.1
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• pp.98-112
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• 2011

This study aimed to suggest a 1300 scale of a middle school foodservice facility floor plan which was compliant to the principle of HACCP, as well as ensuring food and work safety, and the flow of personnel and food materials. which consisted of 46 nutrition teachers and 6 experts, responded with a questionnaire on the relationship of functional area and space. Using their opinions, key principles for the design of the facility were single direction movement of food materials, customers and workers; minimization of the cross-contamination through the separation of functional space; and securement of customer-focused efficiency; staff-centered convenience and efficiency; and work and food safety. After the completion of an adjacency diagram, bubble diagram and program statement, the functional areas of a 1300 scale middle school food-service facility were allocated as follows: $9.9\;m^2$ for the receiving area, $56.1\;m^2$ for the pre-preparation area, $10.5\;m^2$ for the food storage area, $6.0\;m^2$ for the supplies storage area, $97.8\;m^2$ for the cooking area, $33.6\;m^2$ for the service area, $52.5\;m^2$ for dish washing area, cafeteria $410.5\;m^2$, $4.5\;m^2$ for the front room, for a total of $725.8\;m^2$. Expert groups have pointed to limitations within this model as there are no windows in the office for the influx of fresh outside air and a need for the straight line installation of steam-jacket and frying kettles on the sides of windows. This study can be useful as the guidelines for estimating the investment cost of the facility and placing the placement of functional areas and equipment in the renovation of the facility. It can be also useful data for a methodology of foodservice facility design.

• Journal of the Korean Institute of Gas
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• v.20 no.1
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• pp.7-12
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• 2016

Natural gas liquefaction process is essential to transport natural gas for long distances. Lots of compressors in this process are needed and the energy for these compressors can be supplied by drivers. Total driver cost can be changed by selecting various drivers. This study focused on the minimization of the driver cost to provide the energy to the compressors. Moreover, scenarios, extracting velocity is changed during whole operating period, are set with considering gas well capacity. The mathematical model was established by considering trade off relationship between the capital cost and the operating cost of the turbines. The model also considers the life time of the driver equipments. As the result, the driver cost of the optimized case was reduced by 6.4% than the base case.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3764-3773
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• 2012

In this study, modeling and optimization works were completed for the separation of 99.9 mol% of methyl ethyl ketone from water through a pressure-swing distillation process since the azeotropic composition varies very sensitively with the change of system pressure. PRO/II with PROVISION release 9.1 was used for the computer simulation and Wilson activity coefficient model was chosen as a modeling equation. A pressure-swing distillation process can be classified into a low-high pressure columns configuration and a high-low pressure columns configuration. In this work, each configurations were optimized for the minimization of steam consumptions, respectively and were compared.

• Journal of Korean Society on Water Environment
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• v.37 no.2
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• pp.103-113
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• 2021

In this study, an Excel-based model (ROADMOD) was developed to estimate pollutant loading from the road and evaluate BMPs. ROADMOD employs the Chezy-Manning equation and empirical expression for estimating surface runoff, and power function for pollutant buildup, and exponential function for pollutant washoff in SWMM. The results of model calibration for buildup and washoff using observed data revealed a good match between the simulation results and the observed data. The long-term surface runoff and sediment simulated by ROADMOD demonstrated a good match with those by SWMM with 2 ~ 14% of relative error. The shorter sweeping interval (within 8 days) remarkably decreased sediment loads from the road. It was found that the effect of reducing sediment loads from the road was greatly affected not only by the sweeping interval but also by sweeping on the day before a rainfall event. The 48% of removal efficiency of sediment loads from the road was achieved with 26 times of road sweeping per year when sweeping was performed on the day before the rainfall event. A 4-day sweeping interval showed similar removal efficiency (48%) with 96 times of sweeping per year. It is considered that the road sweeping on the day before a rainfall event could maximize the effect of reducing the non-point source pollution from the road with minimization of the number of road sweeping. So, the road sweeping on the day before a rainfall event can be considered as one of the useful and best management practices (BMPs) on road.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.12
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• pp.953-960
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• 2020

This paper presents an optimization of gear web design used in a main gear train of an engine reduction gearbox for a rotorcraft. The optimization involves the minimization of a total weight, transmission error, misalignment, and face load distribution factor. In particular, three design variables such as a gear web thickness, location of rim-web connection, and location of shaft-web connection were set as design parameters. In the optimization process, web, rim and shaft of gears were converted from the 3D CAD geometry model to the finite element model, and then provided as input to the gear simulation program, MASTA. Lastly, NSGA-II optimization method was used to find the best combination of design parameters. As a result of the optimization, the total weight, transmission error, misalignment, face load distribution factor were all reduced, and the maximum stress was also shown to be a safe level, confirming that the overall gear performance was improved.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2215-2221
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• 2023

In order to further meet the requirements of weight, volume, and dose minimization for new nuclear energy devices, the bare-bones multi-objective particle swarm optimization algorithm is used to automatically and iteratively optimize the design parameters of radiation shielding system material, thickness, and structure. The radiation shielding optimization program based on the bare-bones particle swarm optimization algorithm is developed and coupled into the reactor radiation shielding multi-objective intelligent optimization platform, and the code is verified by using the Savannah benchmark model. The material type and thickness of Savannah model were optimized by using the BBMOPSO algorithm to call the dose calculation code, the integrated optimized data showed that the weight decreased by 78.77%, the volume decreased by 23.10% and the dose rate decreased by 72.41% compared with the initial solution. The results show that the method can get the best radiation shielding solution that meets a lot of different goals. This shows that the method is both effective and feasible, and it makes up for the lack of manual optimization.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.245-253
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• 2021

Purpose: This study develops a risk prediction model that predicts the risk of a fire site by using initial information such as building information and reporter acquisition information, and supports effective mobilization of fire fighting resources and the establishment of damage minimization strategies for appropriate responses in the early stages of a disaster. Method: In order to identify the variables related to the fire damage scale on the fire statistics data, a correlation analysis between variables was performed using a machine learning algorithm to examine predictability, and a learning data set was constructed through preprocessing such as data standardization and discretization. Using this, we tested a plurality of machine learning algorithms, which are evaluated as having high prediction accuracy, and developed a risk prediction model applying the algorithm with the highest accuracy. Result: As a result of the machine learning algorithm performance test, the accuracy of the random forest algorithm was the highest, and it was confirmed that the accuracy of the intermediate value was relatively high for the risk class. Conclusion: The accuracy of the prediction model was limited due to the bias of the damage scale data in the fire statistics, and data refinement by matching data and supplementing the missing values was necessary to improve the predictive model performance.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.4
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• pp.191-201
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• 2016

A stochastic process has been used to develop a condition-based model for preventive maintenance of armor units of rubble-mound breakwaters that can make a decision the optimal interval at which some repair actions should be performed under the perfect maintenance. The proposed cost model in this paper based on renewal reward process can take account of the interest rate, also consider the unplanned maintenance cost which has been treated like a constant in the previous studies to be a time-dependent random variable. A function for the unplanned maintenance cost has been mathematically proposed so that the cumulative damage, serviceability limit and importance of structure can be taken into account, by which a age-based maintenance can be extended to a condition-based maintenance straightforwardly. The coefficients involved in the function can also be properly estimated using a method expressed in this paper. Two stochastic processes, Wiener process and gamma process have been applied to armor stones of rubble-mound breakwaters. By evaluating the expected total cost rate as a function of time for various serviceability limits, interest rates and importances of structure, the optimal period of preventive maintenance can easily determined through the minimization of the expected total cost rate. For a fixed serviceability limit, it shows that the optimal period has been delayed while the interest rate increases, so that the expected total cost rate has become lower. In addition, the gamma process tends to estimate the optimal period more conservatively than the Wiener process. Finally, it is found that the more crucial the level of importance of structure becomes, the more often preventive maintenances should be carried out.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.111-122
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• 2015

This study purposed on introducing a new automated solution for detecting railway tracks and reconstructing track models from the mobile laser scanning data. The proposed solution completes following procedures; the study initiated with detecting a potential railway region, called Region Of Interest (ROI), and approximating the orientation of railway track trajectory with the raw data. At next, the knowledge-based detection of railway tracks was performed for localizing track candidates in the first strip. In here, a strip -referring the local track search region- is generated in the orthogonal direction to the orientation of track trajectory. Lastly, an initial track model generated over the candidate points, which were detected by GMM-EM (Gaussian Mixture Model-Expectation & Maximization) -based clustering strip- wisely grows to capture all track points of interest and thus converted into geometric track model in the tracking by detection framework. Therefore, the proposed railway track tracking process includes following key features; it is able to reduce the complexity in detecting track points by using a hypothetical track model. Also, it enhances the efficiency of track modeling process by simultaneously capturing track points and modeling tracks that resulted in the minimization of data processing time and cost. The proposed method was developed using the C++ program language and was evaluated by the LiDAR data, which was acquired from MMS over an urban railway track area with a complex railway scene as well.