• Journal of the Korean Crystal Growth and Crystal Technology
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• v.33 no.2
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• pp.54-60
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• 2023

In this study, microstructure and basic property analysis of DG (Desulfurization gypsum) and CCMs (Carbondioxide conversion capture materials) made by reacting CO₂ with DG were conducted to analyze applicability as a cement admixture. The main crystalline phases of DG were CaO and CaSO₄, and CCMs were CaSO₄, CaCO₃, Ca(OH)₂ and CaSO₄·H₂O. As a result of particle size analysis, the difference in average particle sizes between the two materials was about 7 ㎛. No major heavy metals were detected in the CCMs, and as a result o f TGA, the CO₂ decomposition of CCMs was more than twice as high as that of DG. Therefore, it was judged that CCMs could be used as a cement admixture through optimization of manufacturing conditions. As a results of measuring the strength behavior of DG and CCMs mixture ratios, the long-term strength of CCMs-mixed mortar was higher, and this is due to the filler effect of CaCO₃ in CCMs.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.107-117
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• 2008

Private financing is playing an increasing role in public infrastructure construction projects worldwide. However, private investors/operators are exposed to the financial risk of low profitability due to the inaccurate estimation of facility demand, operation income, maintenance costs, etc. From the operator's perspective, a sound and thorough financial feasibility study is required to establish the appropriate capital structure of a project. Operators tend to reduce the equity amount to minimize the level of risk exposure, while creditors persist to raise it, in an attempt to secure a sufficient level of financial involvement from the operators. Therefore, it is important for creditors and operators to reach an agreement for a balanced capital structure that synthetically considers both profitability and repayment capacity. This paper presents an optimal capital structure model for successful private infrastructure investment. This model finds the optimized point where the profitability is balanced with the repayment capacity, with the use of the concept of utility function and multi-objective GA (Generic Algorithm)-based optimization. A case study is presented to show the validity of the model and its verification. The research conclusions provide a proper capital structure for privately-financed infrastructure projects through a proposed multi-objective model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.409-416
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• 2006

Real-time traveler information disseminated through Variable Message Signs (VMS) is known to have effects on driver route choice decisions. In the past, many studies have attempted to optimize the system performance using VMS message content as the primary control variable of driver route choice. This research proposes a VMS information provision optimization model which searches the best combination of VMS message contents and display sequence to minimize the total travel time on a highway network considered. The driver route choice models under VMS information provision are developed using a stated preference (SP) survey data in order to realistically capture driver response behavior. The genetic algorithm (GA) is used to find the optimal VMS information provision strategies which consists of the VMS message contents and the sequence of message display. In the process of the GA module, the system performance is measured using micro traffic simulation. The experiment results highlight the capability of the proposed model to search the optimal solution in an efficient way. The results show that the traveler information conveyed via VMS can reduce the total travel time on a highway network. They also suggest that as the frequency of VMS message update gets shorter, a smaller number of VMS message contents performs better to reduce the total travel time, all other things being equal.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.9-18
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• 2009

This paper introduces the application of DE (Differential Evolutionary) method for the estimation of tensile force of the externally prestressed tendon. The proposed technique, a SI (System Identification) method using the DE algorithm, can make global solution search possible as opposed to classical gradient-based optimization techniques. The numerical tests show that the proposed technique employing DE algorithm is a useful method which can detect the effective nominal diameters as well as estimate the exact tensile forces of the externally prestressed tendon with an estimation error less than 1% although there is no a priori information about the identification variables. In addition, the validity of the proposed technique is experimentally proved using a scale-down model test considering the serviceability state condition without and with the loss of the prestressed force. The test results prove that the technique is a feasible and effective method that can not only estimate the exact tensile forces and detect the effective nominal diameters but also inspect the damping properties of test model irrespective of the loss of the prestressed force. The 2% error of the estimated effective nominal diameter is due to the difference between the real tendon diameter with a wired section and the FE model diameter with a full-section. Finally, The accuracy and superiority of the proposed technique using the DE algorithm are verified through the comparative study with the existing theories.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.63-72
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• 2009

This study aims at establishing the decision-making support system for the highway assets, long-term performance presumption and evaluation of asset value, which are appropriate for Korea, and proposing the methods of the optimal engineering method and the timing decision for the preventive maintenance through the project evaluation, the optimization method and life-cycle analysis related to the highways. In order to supplement the current problem of the near-sighted budget management system, which chooses the maintenance place of the highway, depending on the level of the budget with fixed amount, the long-term required budget prediction system and the economy principle were introduced, so that the pavement agency can predict the level of the required budget, and it was aimed to develop the pavement asset evaluation system to maintain the performance of the highway with the minimum of the cost. In the use of the highway pavement asset evaluation system, to maintain the appropriate level of the pavement evaluation index, when the budget was efficiently established in the reference of the required maintenance budget for the chosen section of the highway in the year concerned, it was possible to analyze the most rational pavement maintenance budget. With this result, it is estimated to prevent the unnecessary waste of budget in advance, and through the development of the decision-making system for the long-term performance presumption and the asset value estimation of the pavement, it is expected to able to analyze the previous evaluation of the project related to the highway and the feasibility of introduction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.399-401
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• 2022

A definitive diagnosis to classify the breast malignancy status may be achieved by microscopic analysis using surgical open biopsy. However, this procedure requires experts in the specializing of histopathological image analysis directing to time-consuming and high cost. To overcome these issues, deep learning is considered practically efficient to categorize breast cancer into benign and malignant from histopathological images in order to assist pathologists. This study presents a pre-trained convolutional neural network model architecture with a 100% fine-tuning scheme and Adagrad optimizer to classify the breast cancer histopathological images into benign and malignant using a 40× magnification BreaKHis dataset. The pre-trained architecture was constructed using the InceptionResNetV2 model to generate a modified InceptionResNetV2 by substituting the last layer with dense and dropout layers. The results by demonstrating training loss of 0.25%, training accuracy of 99.96%, validation loss of 3.10%, validation accuracy of 99.41%, test loss of 8.46%, and test accuracy of 98.75% indicated that the modified InceptionResNetV2 model is reliable to predict the breast malignancy type from histopathological images. Future works are necessary to focus on k-fold cross-validation, optimizer, model, hyperparameter optimization, and classification on 100×, 200×, and 400× magnification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.171-173
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• 2022

The 4th industrial revolution technology is developing people's lives more efficiently. GIS provided on the Internet services such as traffic information and time information makes people getting more quickly to destination. National geographic information service(NGIS) and each local government are making basic data to investigate SOC accessibility for analyzing optimal point. To construct the shortest distance, the accessibility from the starting point to the arrival point is analyzed. Applying road network map, the starting point and the ending point, the shortest distance, the optimal accessibility is calculated by using Dijkstra algorithm. The analysis information from multiple starting points to multiple destinations was required more than 3 steps of manual analysis to decide the position for the optimal point, within about 0.1% error. It took more time to process the many-to-many (M×N) calculation, requiring at least 32G memory specification of the computer. If an optimal proximity analysis service is provided at a desired location more versatile, it is possible to efficiently analyze locations that are vulnerable to business start-up and living facilities access, and facility selection for the public.

• International Area Studies Review
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• v.15 no.1
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• pp.173-193
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• 2011

Most of the research regarding economic effects of policy loans has thus far been focused on whether policy loans can improve the financial status or the management performance of small and medium enterprises (SMEs). Unlike previous researches, this study implemented an empirical analysis focused on the contribution of policy loans to easing the liquidity restriction of investment. To analyze whether investment liquidity restriction can be alleviated or not, this study attempted an empirical analysis utilizing the nonlinear Euler equation induced through optimization of investment and GMM (generalized method of moments) as its analysis methodology. With the SMEs that received policy financing from the Small and medium Business Corporation (SBC) in 2004, this study analyzed three years of panel data before(2001~2003) and after(2004~2006) receipt of policy loans. According to the empirical results, it appears that policy loans had effects on resolving liquidity restriction of investment, implying that policy financing eases the liquidity restriction of SME investment and would contribute to the growth and development of SMEs. Further, I checked robustness of empirical results using Tobin's q model. The empirical results also support that policy loans help to resolve liquidity constraint. With these results, it is understood that the critical view to date, which has emphasized the ineffectiveness of policy financing due to it having no or insignificant economic effects, may be wrong.

• Journal of the Korean Recycled Construction Resources Institute
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• v.11 no.3
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• pp.226-233
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• 2023

In this study, two phases were conducted to investigate the direct injection of gaseous CO₂ into cement mortar. The aim was to advance carbon capture, utilization, and storage (CCUS) technology by harnessing industrial waste CO₂ from the domestic ready-mixed concrete industry. In the first phase, the factors influencing the physical properties of cement mortar when using gaseous CO₂ were identified. This included a review of materials to achieve physical properties comparable to a reference formulation. As a result of this phase, it was confirmed that traditional approaches, such as adjusting the water-to-cement ratio, had limitations in achieving the desired physical properties. Consequently, the second phase focused on the optimization of CO₂-injected mortar. This involved studying the CO₂ application and mixing method for cement mortar. Changes in properties were observed when gaseous CO₂ was injected into the mortar. The optimal injection quantity and time to enhance the compressive strength of mortar were determinded. As a result, this study indicated that an extra mixing time exceeding 120 seconds was necessary, compared to conventional mortar. The optimal CO₂ injection rate was identified as 0.1 to 0.2 % by weight of cement, taking both flowability and compressive strength performance into account. Increasing the CO₂ injection time did not further enhance strength. For this approach to be employed as a CCUS technology, additional studies are required, including a microstructural analysis evaluating the amount of immobilized CO₂.

• Phonetics and Speech Sciences
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• v.15 no.3
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• pp.75-82
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• 2023

Automatic speech recognition (ASR) has been revolutionized with deep learning-based approaches, among which self-supervised learning methods have proven to be particularly effective. In this study, we aim to enhance the performance of OpenAI's Whisper model, a multilingual ASR system on the Korean language. Whisper was pretrained on a large corpus (around 680,000 hours) of web speech data and has demonstrated strong recognition performance for major languages. However, it faces challenges in recognizing languages such as Korean, which is not major language while training. We address this issue by fine-tuning the Whisper model with an additional dataset comprising about 1,000 hours of Korean speech. We also compare its performance against a Transformer model that was trained from scratch using the same dataset. Our results indicate that fine-tuning the Whisper model significantly improved its Korean speech recognition capabilities in terms of character error rate (CER). Specifically, the performance improved with increasing model size. However, the Whisper model's performance on English deteriorated post fine-tuning, emphasizing the need for further research to develop robust multilingual models. Our study demonstrates the potential of utilizing a fine-tuned Whisper model for Korean ASR applications. Future work will focus on multilingual recognition and optimization for real-time inference.