• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.159-172
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• 2023

In general, social problem-solving research aims to create important social value by offering meaningful answers to various social pending issues using scientific technologies. Not surprisingly, however, although numerous and extensive research attempts have been made to alleviate the social problems and issues in nation-wide, we still have many important social challenges and works to be done. In order to facilitate the entire process of the social problem-solving research and maximize its efficacy, it is vital to clearly identify and grasp the important and pressing problems to be focused upon. It is understandable for the problem discovery step to be drastically improved if current social issues can be automatically identified from existing R&D resources such as technical reports and articles. This paper introduces a comprehensive dataset which is essential to build a machine learning model for automatically detecting the social problems and solutions in various national research reports. Initially, we collected a total of 700 research reports regarding social problems and issues. Through intensive annotation process, we built totally 24,022 sentences each of which possesses its own category or label closely related to social problem-solving such as problems, purposes, solutions, effects and so on. Furthermore, we implemented four sentence classification models based on various neural language models and conducted a series of performance experiments using our dataset. As a result of the experiment, the model fine-tuned to the KLUE-BERT pre-trained language model showed the best performance with an accuracy of 75.853% and an F1 score of 63.503%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.175-185
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• 2023

As drought risk increases due to climate change, various research works are underway around the world to respond to drought so as to minimize drought damage. In particular, in recent years, many studies are focused on analyzing regional patterns of drought in a comprehensive manner, however there is still insufficient to quantitatively identify drought-risk areas in a large river basin considering climate change in Korea. In this study, we calculated the Standardized Precipitation Index (SPI) and the Modified Standardized Precipitation Index (M_SPI) as representative meteorological drought index, and performed spatial autocorrelation analysis to identify the drought hotspot region under climate change scenarios of Representative Concentration Pathway (RCP) 4.5 and RCP 8.5. The SPI was calculated by estimating parameters for each observation station within the study area, whereas the M_SPI was calculated by estimating parameters for the entire study area. It is more reasonable to use the M_SPI for assessing meteorological drought from an overall perspective within the study area. When the M_SPI was used, long-term droughts showed drought hotspot areas clearly larger than short-term droughts. In addition, the drought hotspot area moved from the center of the Nakdong River basin to the Seomjin River basin over time. Especially, the moving patterns of the short-term/long-term drought were apparent under the RCP 4.5, whereas the moving patterns of the long-term drought were distinct under the RCP 8.5 scenarios.

• Nuclear Engineering and Technology
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• v.25 no.4
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• pp.539-547
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• 1993

A stochastic approach using continuous time Markov process is presented to model the one-dimensional nuclide transport in fractured rock media as a further extension for previous works［1-3］. Nuclide transport of decay chain of arbitrary length in the single planar fractured rock media in the vicinity of the radioactive waste repository is modeled using a continuous time Markov process. While most of analytical solutions for nuclide transport of decay chain deal with the limited length of decay chain, do not consider the case of having rock matrix diffusion, and have very complicated solution form, the present model offers rather a simplified solution in the form of expectance and its variance resulted from a stochastic modeling. As another deterministic way, even numerical models of decay chain transport, in most cases, show very complicated procedure to get the solution and large discrepancy for the exact solution as opposed to the stochastic model developed in this study. To demonstrate the use of the present model and to verify the model by comparing with the deterministic model, a specific illustration was made for the transport of a chain of three member in single fractured rock medium with constant groundwater flow rate in the fracture, which ignores the rock matrix diffusion and shows good capability to model the fractured media around the repository.

• Korea Science and Art Forum
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• v.35
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• pp.145-155
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• 2018

The goal of the study is to provide a new type of audio-visualization method through case analysis and work production based on Polysomnography(PSG) data that is difficult to interpret or not familiar to the public. Most art works are produced with conscious actions during waking hours. On the other hand, during sleep, we get into the world of unconsciousness. Therefore, through the experiment, want to discover if could get something new when we were in the subconscious state, and if so, wondered what kind of art could be made through it. The study method is to consider definition of sleep and sleep data first. The sleep data were classified into normal group and Narcolepsy, Insomnia, and sleep apnea by focusing on sleep disorder graphs that is measured by sleep polygraph. After that, I refined and converted the acquired biometric data into a text-based script. The degree of sleep in the text form of the script was rendered as a 3D animated image using Maya. In addition, the heart rate data script was transformed into a midi format, and the audition was implemented in the garage band. After Effects combines the image and sound to create four single channel images of 3 minutes and 20 seconds each. As a result of the research, I made an opportunity for anyone easy to understand the results, having difference with the normal data, through art instead of using difficult medical term. It also showed the possibility of artistic expression even when conscious actions did not occur. Through the results of this research, I expect the expansion and diversity of artistic audiovisual expression of biometric data.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.669-676
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• 2023

This study analyzed the relationship between major parameters and numerical analysis results according to various excavations conducted around the urban railway, application of machine learning techniques and verified the scope of influence of the adjacent excavation on the existing urban railway box structure and the appropriateness of the safety area. This study targeted the actual negotiated adjacent excavation works and box structures around the urban railway, and the analysis was conducted on the most representative two-line box structures. The analysis confirmed that the difference in depth of urban railway, excavation depth of adjacent excavation, and depth of underground water level are important parameters, and the difference in excavation depth of adjacent excavation is the parameter that affects the behavior of underground box structures and is an important requirement for setting safety areas. In particular, the deeper the depth of the adjacent excavation work, the greater the effect on the deflection of the underground box structure, and the horizontal separation distance, one of the important requirements for determining the management grade of the existing adjacent excavation work, is relatively small.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6A
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• pp.965-976
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• 2006

A realistic lifetime seismic-reliability based approach is unavoidable to perform Life-Cycle Cost (LCC)-effective optimum design, maintenance, and retrofitting of structures against seismic risk. So far, though a number of researchers have proposed the LCC-based seismic design and retrofitting methodologies, most researchers have only focused on the methodological point. Accordingly, in most works, they have not been quantitatively considered critical factors such as the effects of seismic retrofit, maintenance, and environmental stressors on lifetime seismic reliability assessment of deteriorating structures. Thus, in this study, a systemic lifetime seismic reliability analysis methodology is proposed and a program HPYER-DRAIN2DX-DS is developed to perform the desired lifetime seismic reliability analysis. To demonstrate the applicability of the program, it is applied to an example bridge with or without seismic retrofit and maintenance strategies. From the numerical investigation, it may be positively stated that HYPER-DRAIN2DX-DS can be utilized as a useful numerical tool for LCC-effective optimum seismic design, maintenance, and retrofitting of bridges.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.469-479
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• 2006

In order to investigate characteristics of the primary flow and the secondary currents in meandering channels, the laboratory experiments were conducted in S-curved channels with angle of bend, $150^{\circ}$, and sinuosity of 1.52. The experimental conditions was decided varying average depth and velocity. Under these experimental conditions, spatial variations of the secondary currents in multiple bends were observed. The experimental results revealed that the distribution of primary flow in straight section is symmetric without respect to the experimental condition and the maximum velocity line of the primary flow occurs along the shortest path in experimental channel, supporting the result of previous works. The secondary currents in second bend became more developed than those in first bend. Particularly, the outer bank cell developed distinctively and the secondary current intensity was low at the straight section and high at the bends, periodically. Also, the secondary current intensity at the bends was as twice to three times as that at the straight section, and has its maximum value at the second bend. The turbulent flow characteristics of meandering channel was investigated with turbulent intensity of the primary flow and Reynolds shear stress. It was observed that the turbulent intensity is increasing when the velocity deviation of the primary flow is large whereas Reynolds shear stress increases when both the velocity deviation of the primary flow and the secondary current are large.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1C
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• pp.33-42
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• 2006

Lots of river embankments or levees in Korea are quite so old and unknown the origin even. The river deposits, moreover, obtainable easily somewhere were used for materials of embankment without any technical considerations such as the influence soil properties and construction methods on embankment stability. It's natural that safety would be threatened if the water level rises due to flood or rainfall when it comes to abnormal weather conditions, especially. From this point of view, enlargement of embankment, irrigation works, etc. are in progress recently at the situation from a reinforcement work. However, taking influence of soil properties and construction methods on embankment stability into account against cracking or piping is still insufficient. Fragmentary design criteria or irrational construction methods are applied rather as the case may be. In this study, therefore, a way to estimate piping and cracking resistance (Sherard, 1953) has been introduced and reevaluated for practical use with an eye to material properties and its applicability to piping-experienced embankments was examined. Piping possibility was also examined in the present design criteria and compared. In view of the results achieved, it reflects that both yield piping possibility. But it's still necessary to complement how to judge and verify piping resistance of given soils with gradation curves by the representative curve, quantitatively and that piping resistance should consider compaction effects as well.

• 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 Navigation and Port Research Conference
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• 2023.05a
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• pp.57-58
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• 2023

The working time for loading and transporting containers in the container terminal is one of the factors directly related to port productivity, and minimizing working time for these operations can maximize port productivity. Among working time for container operations, the working time of yard tractors(Y/T) responsible for the transportation of containers between berth and yard is a significant portion. However, it is difficult to estimate the working time of yard tractors quantitatively, although it is possible to estimate it based on the practical experience of terminal operators. Recently, a technology based on IoT(Internet of Things), one of the core technologies of the 4th industrial revolution, is being studied to monitoring and tracking logistics resources within the port in real-time and calculate working time, but it is challenging to commercialize this technology at the actual port site. Therefore, this study aims to develop yard tractor working time prediction model to enhance the operational efficiency of the container terminal. To develop the prediction model, we analyze actual port operation data to identify factors that affect the yard tractor's works and predict its working time accordingly.