• Journal of Korea Planning Association
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• v.54 no.4
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• pp.26-37
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• 2019

It is necessary to implement a wide range of housing welfare policies that citizens can experience in order to improve residents' the quality of life, as it emphasizes the balance of supply and management of public housing. The purpose of this study is to analyze the factors affecting residential satisfaction considering the three hierarchical levels of individual, household, and area. In the background of the study, the individuals' quality-of-life satisfaction determined not only by the individual but also by the various influencing environmental factors. This study targets 1,736 households, 3,239 persons in 464 areas in Seoul. The main research results are as follows. At the level one, there were influencing factors such as age(-), education level and income, and housing area per person, recipient of basic living(-), period(-) and RIR (at the level two). At the level three, west-south region(-) and social mix affect the complex of public housing. In consideration of living infrastructure, the closer to public transportation, public facilities, and medical facilities, the higher the satisfaction of public housing. The results of this analysis suggest that public support needs to focus on individual household members, but there is a need for ways to link it with the complex and the region.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.10
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• pp.67-76
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• 1993

This paper presents the design and implementation of SVSIM (Sogang VHDL SIMulator), a multi-level VHDL simulator, designed for the construction of an integrated VGDL design environment. The internal model of SVSIM is the hierarchical C/DFG which is extended from C/DFG to include the network hierarchy and local/glabal control informations. Hierarchical network is not flattened for simulation, resulting in the reduction of space complexity. The predufined/user-defined types except for the record type and the predefined/user-defined attributes are supported in SVSIM. Algorithmic-level descriptions can be siumlated by the support of recursive procedure/function calls. Input stimuli can be generated by command script in stimuli file or in VHDL source code. Experimential results show SVSIM can be efficiently used for the simulation of the pure behavioral descriptions, structural descriptions or mixture of these.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.3
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• pp.25-43
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• 2010

We consider a multi-objective balancing and sequencing problem in mixed model assembly lines, which is important for an efficient use of the assembly lines. In this paper, we present a neighborhood symbiotic evolutionary algorithm to simultaneously solve the two problems of balancing and model sequencing under multiple objectives. We aim to find a set of well-distributed solutions close to the true Pareto optimal solutions for decision makers. The proposed algorithm has a two-leveled structure. At Level 1, two populations are operated : One consists of individuals each of which represents a partial solution to the balancing problem and the other consists of individuals for the sequencing problem. Level 2, which is an upper level, works one population whose individuals represent the combined entire solutions to the two problems. The process of Level 1 imitates a neighborhood symbiotic evolution and that of Level 2 simulates an endosymbiotic evolution together with an elitist strategy to promote the capability of solution search. The performance of the proposed algorithm is compared with those of the existing algorithms in convergence, diversity and computation time of nondominated solutions. The experimental results show that the proposed algorithm is superior to the compared algorithms in all the three performance measures.

• Journal of the Korean Geographical Society
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• v.48 no.2
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• pp.272-287
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• 2013

This paper describes the importance of selecting explanatory variables(e.g. land price determinants) in hedonic pricing models employed in predicting real estate price, and explores dynamics of the land price determinants over time. The City of Junju was chosen as the study area, and repeated measured price data of standard lots over 17 years were analyzed. We applied a three-level modeling approach to this data in consideration of its nested data structure and longitudinal characteristics. Main land price determinants we focused on are primarily based on items included in the standard comparison table of land price, which is an official hedonic pricing model used by Government to estimate land price for tax levy. Our result shows that the land price fluctuation over 17 years was not uniform over the whole study area with each neighborhood revealing different price trend, and as such warrants longitudinal model components. In addition, some of determinants previously recognized as important were proved insignificant. It was also found that significant determinants at a particular time point lost its power gradually over time and vice versa. It is expected that more accurate prediction of price would be possible when taken account for this dynamics of price determinants over time in applying hedonic pricing model method.

• Journal of the Korean housing association
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• v.22 no.1
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• pp.43-55
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• 2011

The purpose this study is to develop indicators that measure the healthy housing condition of multi-family housing. The major findings are as follow: first, healthy housing was defined by physical, mental, social, and management aspects and proposed the conceptual model of hierarchy structure of evaluation of healthy housing by literature reviews. Second, evaluating items were selected based on literature reviews of existing indicators and preceding studies about both domestic and overseas multi-family housing. The evaluating indicators were identified as a total of 87 evaluating items which were composed of four dimensions and 16 attributes on the basis of the conceptual model. They cover comprehensive scope of the multi-family housing such as unit, building, complex, and site. Third, as the measurement, the 5-point ordinal scale measure was suggested. The evaluating measurement including measure standards, measure methods, and measure contents were developed by each evaluating items. Lastly, the weighting of evaluating indicators was developed by AHP method conducted by survey of an expert group. Items were identified as high contributors or low contributors. The weighting of these items could suggest several evaluations according to the situation. The level of healthy housing condition may be evaluated by both total evaluation and a specific field of evaluation.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.250-251
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• 2021

A subcontract work order is the basis of the construction process and consists of the root and trunk of the construction industry. The construction process through a subcontract work order is an important element of project success, and it is the basic unit of creating profit in the construction industry. Therefore, correct analysis and forecasting of subcontract work orders allow correct estimation of construction cost and profit which is the foundation of corporate decision making. This study has started to provide predictions of subcontractor's bidding-ratio for decision-making. Since the actual project data has been used in this study, the contribution level of the model is highly expected in actual field. The statistical confidential level of adjusted decision coefficient is concluded low because of limited sample numbers. However, its accuracy and confidence level can be increased through increasing sample numbers, considering more variables, and studying of reducing error.

• Transactions of Materials Processing
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• v.29 no.5
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• pp.272-281
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• 2020

As the thickness of mobile communication devices is getting thinner, the size of the internal parts is also getting smaller. Among them, the battery case requires a high-level deep drawing technique because it has a rectangular shape with a large aspect ratio. In this study, the initial blank shape was optimized to minimize earing in a multi-stage deep drawing process using an artificial neural network(ANN). There has been no reported case of applying artificial neural network technology to the initial blank optimal design for a square case with large aspect ratio. The training data for ANN were obtained though simulation, and the model reliability was verified by performing comparative study with regression model using random sample test and goodness-of-fit test. Finally, the optimal design of the initial blank shape was performed through the verified ANN model.

• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.15-23
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• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

• Proceedings of the Technology Innovation Conference
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• 2003.02a
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• pp.173-190
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• 2003

Demand forecasting is essential in establishing national and corporate strategy as well as the management of their resource. We forecast demand for multi-generation product using discrete choice model combining diffusion model The discrete choice model generally captures consumers'valuation of the product's qualify in the framework of a cross-sectional analysis. We incorporate diffusion effects into a discrete choice model in order to capture the dynamics of demand for multi-generation products. As an empirical application, we forecast demand for worldwide DRAM (dynamic random access memory) and each of its generations from 1999 to 2005. In so doing, we use the method of 'Technological Forecasting'for DRAM Density and Price of the generations based on the Moore's law and learning by doing, respectively. Since we perform our analysis at the market level, we adopt the inversion routine in using the discrete choice model and find that our model performs well in explaining the current market situation, and also in forecasting new product diffusion in multi-generation product markets.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9A
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• pp.767-775
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• 2011

In this paper, we propose a new multi-input multi-output (MIMO) channel modeling method for mobile-to-mobile (M2M) communications based on IEEE 802.16n. To reflect the mobilities at the transmitter and the receiver, we propose a new channel model by invoking the geometrical two-ring scattering model and modifying the conventional IEEE 802.16m fixed-to-mobile (F2M) channel model considering M2M communication environments. Through computer simulations, we analyze the statistical properties of proposed channel model in terms of the time correlation and the spatial correlation. Finally, the performance of the system using the proposed M2M channel model is compared with that using the conventional 802.16m F2M channel model by link level simulations.