• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.1
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• pp.119-130
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• 2018

The VE/LCC study on the existing multi-utility tunnel examined the economical feasibility of the collective area through the cost-benefit analysis of the ten major routes of Seoul for the benefit (7 items) and the cost (5 items) through quantitative methods. In this paper, we analyzed the economic efficiency of 61 sectors by adding 3 items (reduced traffic accident, reduced vehicle noise, social-economic loss). The reduced traffic accident item is an improvement of the traffic accident cost that can be saved by the implementation of the project, and the reduced vehicle noise item is an indicator of the amount of noise change caused by the implement of the business. Finally, the social-economic loss items represent the impact on the local economy due to the construction of the multi-utility tunnel. The amounts of the additional items were compared and items added in the open type and tunnel type multi-utility tunnel were analyzed for each analysis. Result on the analysis, it analyzed the items applied to the basic and detail models in the economic evaluation of the multi-utility tunnel design, and makes it possible to evaluate the economic efficiency more efficiently. The economic evaluation of the basic and detailed models including the items presented in this study will be often used in the design of the multi-utility tunnel design.

• Korean Journal of Construction Engineering and Management
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• v.12 no.2
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• pp.89-100
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• 2011

As an impactive factor on industries and national economy, The Social Overhead Capital(SOC) is major factor to determine the national competitiveness and the investment of SOC is essential for its economic growth. Accordingly, introduction of the preliminary feasibility study and establishment of legal institutionalization and evaluation system has been carried out and reviewed since 1999. Nevertheless of these efforts, basic problems such as lack of scientific method for investment evaluation and loss of effectiveness on feasibility studies are continuously being brought up. Moreover, as the preliminary study to improve the mentioned problems is mainly focused on the economic and estimated demand analysis, the study of policy analysis, the most important phase during a feasibility study, is still insufficient. Therefore, in this paper, a quantitative decision-making model, to which the performance assessment method of Value Engineering(VE) is applied, is developed and proposed to improve the policy analysis of (preliminary) feasibility study that requires combining with relative studies, to induce quantitative analysis method, and to contribute the improvement of value on the political aspect for SOC investment goals and use as a strategic decision-making method by systematic analysis.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• Journal of Korea Water Resources Association
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• v.57 no.4
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• pp.289-300
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• 2024

The purpose of this study is to evaluate the applicability of the GRM (Grid based rainfall-Runoff Model) to the continuous simulation by simulating the dam inflow. The GRM was previously developed for the simulation of rainfall-runoff events but has recently been improved to enable continuous simulation. The target watersheds are Chungju dam, Andong dam, Yongdam dam, and Sumjingang dam basins, and runoff models were constructed with the spatial resolution of 500 m × 500 m. The simulation period is 21 years (2001 to 2021). The simulation results were evaluated over the 17 year period (2005 to 2021), and were divided into three data periods: total duration, wet season (June to September), and dry season (October to May), and compared with the observed daily inflow of each dam. Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), correlation coefficient (CC), and total volume error (VE) were used to evaluate the fitness of the simulation results. As a result of evaluating the simulated dam inflow, the observed data could be well reproduced in the total duration and wet season, and the dry season also showed good simulation results considering the uncertainty of low-flow data. As a result of the study, it was found that the continuous simulation technique of the GRM model was properly implemented and the model was sufficiently applicable to the simulation of dam inflow in this study.

• The Journal of Information Systems
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• v.30 no.3
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• pp.201-216
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• 2021

Purpose The purpose of this study is to analyze the trend of patent technology in textile materials using text mining methodology based on Dynamic Embedded Topic Model and Structural Topic Model. It is expected that this study will have positive impact on revitalizing and developing textile materials industry as finding out technology trends. Design/methodology/approach The data used in this study is 866 domestic patent text data in textile material from 1974 to 2020. In order to analyze technology trends from various aspect, Dynamic Embedded Topic Model and Structural Topic Model mechanism were used. The word embedding technique used in DETM is the GloVe technique. For Stable learning of topic modeling, amortized variational inference was performed based on the Recurrent Neural Network. Findings As a result of this analysis, it was found that 'manufacture' topics had the largest share among the six topics. Keyword trend analysis found the fact that natural and nanotechnology have recently been attracting attention. The metadata analysis results showed that manufacture technologies could have a high probability of patent registration in entire time series, but the analysis results in recent years showed that the trend of elasticity and safety technology is increasing.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.14 no.2
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• pp.225-235
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• 2001

본 연구는 물리·역학적 특성을 고려한 점탄성 감쇠기의 수치모델에 의한 강뼈대구조물의 지진응답개선에 관해서 조사하고자 한다. 온도변화에 의한 감쇠기 이력거동에 미치는 영향을 고려하기 위하여, 점탄성 감쇠기의 모델은 온도-주기 등가원리와 더불어 개선된 분수도함수법에 기초하여 정식화하였다. 본 감쇠기 모델의 알고리즘을 일반화된 강뼈대구조물의 비선형 동적 해석 프로그램에 추가하였다. 강뼈대구조물에 대한 해석 예를 통하여, 제시된 모델에 의한 점탄성 감쇠기의 지진응답개선에 관한 효과를 확인할 수 있었다.

• Nutritional Sciences
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• v.9 no.3
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• pp.201-211
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• 2006

The purpose of this study was to provide dietitians with the guidance in forecasting meal counts for a university/college foodservice facility. The forecasting methods to be analyzed were the following: naive model 1, 2, and 3; moving average, double moving average, simple exponential smoothing, double exponential smoothing, Holt's, and Winters' methods, and simple linear regression. The accuracy of the forecasting methods was measured using mean squared error and Theil's U-statistic. This study showed how to project meal counts using 10 forecasting methods for dietitians. The results of this study showed that WES was the most accurate forecasting method, followed by $na\ddot{i}ve$ 2 and naive 3 models. However, naive model 2 and 3 were recommended for using by dietitians in university/college dining facilities because of the accuracy and ease of use. In addition, the 2000 spring semester data were better than the 2000 fall semester data to forecast 2001spring semester data.

• Journal of the Korea Convergence Society
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• v.7 no.5
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• pp.7-13
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• 2016

Since a smart phone became popular that leads mobile era, we've faced diverse life style changes. Particularly, a mobile messenger which changes communication method from voice and video call to text based multi media messenger, leads new gift-giving culture called mobile voucher gift-giving. Although many researches have validated diverse models about digital voucher, this research utilize the model which divide motivation of gift-giving from three theories: gift-giving motivation theory, technology acceptance model, and consumer value theory. The purpose of this research is not only validate the model but also grouping by giving frequency of samples and validate disparity among groups. In conclusion, there are three groups founded and shows difference motivations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2589-2604
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• 2024

Brain tumors, characterized by uncontrollable cellular growths, are a significant global health challenge. Navigating the complexities of tumor identification due to their varied dimensions and positions, our research introduces enhanced methods for precise detection. Utilizing advanced learning techniques, we've improved early identification by preprocessing clinical dataset-derived images, augmenting them via a Generative Adversarial Network, and applying an Improved Privacy-Preserving Collaborative Convolutional Neural Network (IPC-CNN) for segmentation. Recognizing the critical importance of data security in today's digital era, our framework emphasizes the preservation of patient privacy. We evaluated the performance of our proposed model on the Figshare and BRATS 2018 datasets. By facilitating a collaborative model training environment across multiple healthcare institutions, we harness the power of distributed computing to securely aggregate model updates, ensuring individual data protection while leveraging collective expertise. Our IPC-CNN model achieved an accuracy of 99.40%, marking a notable advancement in brain tumor classification and offering invaluable insights for both the medical imaging and machine learning communities.

• Journal of the Korean Geotechnical Society
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• v.35 no.12
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• pp.35-44
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• 2019

In this study, the axial stiffness of reinforcing piles (K_vr) for the vertical extension remodeling structures was estimated through 3D finite element analysis. In the computation of the minimum required axial stiffness of reinforcing piles, proposed maximum axial stiffness of old and deteriorated existing piles (K_ve) based on theoretical and experimental approaches will be applied. Through this, the required increase rate of axial stiffness of reinforcing piles in order to support the increased structural loading was proposed for end-bearing and friction piles by different slenderness ratio (L/D). The numerical model was validated by comparing the computed results with actual field measurements. Based on the computed results, it was concluded that the end-bearing reinforcing pile needs 44% - 67% increase in axial stiffness to deal with the deterioration of existing piles and support the additional structural load due to vertical extension remodeling.