• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.3
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• pp.191-208
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• 2024

In this study, a hydro-mechanical-damage coupled analysis model was developed to evaluate the structural safety of radioactive waste disposal structures. The Mazars damage model, widely used to model the fracture behavior of brittle materials such as rocks or concrete, was coupled with conventional hydro-mechanical analysis and the developed model was verified via theoretical solutions from literature. To derive the numerical input values for damage-coupled analysis, uniaxial compressive strength and Brazilian tensile strength tests were performed on concrete samples made using the mix ratio of the disposal concrete silo cured under dry and saturated conditions. The input factors derived from the laboratory-scale experiments were applied to a two-dimensional finite element model of the concrete silos at the Wolseong Nuclear Environmental Management Center in Gyeongju and numerical analysis was conducted to analyze the effects of damage consideration, analysis technique, and waste loading conditions. The hydro-mechanical-damage coupled model developed in this study will be applied to the long-term behavior and stability analysis of deep geological repositories for high-level radioactive waste disposal.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.693-700
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• 2024

As of the end of March 2022, the total area of domestic industrial complexes is 606 km2, which is only about 0.6% of the total land area. However, as of 2018, the annual energy consumption of domestic industrial complexes is 110,866.1 thousand TOE, accounting for 53.5% of the country's total energy consumption and 83.1% of the entire industrial sector energy consumption. In addition, industrial complexes have a significant impact on the environment, accounting for 45.1% of the country's total greenhouse gas emissions and 76.8% of industrial sector greenhouse gas emissions. Under this background, in this study, in order to contribute to the energy efficiency of industrial complexes, a prediction study on energy demand and supply for an industrial complex in Korea using machine learning was conducted. In addition, a simulator UI screen was designed to more efficiently convey information on energy demand/supply prediction results and energy consumption status. Among the machine learning algorithms, Multi-Layer Perceptron (MLP) was used, and Bayesian Optimization was applied as an optimization technique for the prediction model. The energy prediction model for the industrial complex built in this study showed a prediction accuracy of 87.90% for compressed air demand and 99.54% for the flow rate available for the public air compressor.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.4
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• pp.17-34
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• 2023

Camera trapping has been used as a non-invasive survey method that minimizes anthropogenic disturbance to ecosystems. Nevertheless, it is labor-intensive and time-consuming, requiring researchers to quantify species and populations. In this study, we aimed to improve the preprocessing of camera trapping data by utilizing an object detection algorithm. Wildlife monitoring using unmanned sensor cameras was conducted in a forested urban forest and a green space on a university campus in Cheonan City, Chungcheongnam-do, Korea. The collected camera trapping data were classified by a researcher to identify the occurrence of species. The data was then used to test the performance of the YOLO-X object detection algorithm for wildlife detection. The camera trapping resulted in 10,500 images of the urban forest and 51,974 images of green spaces on campus. Out of the total 62,474 images, 52,993 images (84.82%) were found to be false positives, while 9,481 images (15.18%) were found to contain wildlife. As a result of wildlife monitoring, 19 species of birds, 5 species of mammals, and 1 species of reptile were observed within the study area. In addition, there were statistically significant differences in the frequency of occurrence of the following species according to the type of urban greenery: Parus varius(t = -3.035, p < 0.01), Parus major(t = 2.112, p < 0.05), Passer montanus(t = 2.112, p < 0.05), Paradoxornis webbianus(t = 2.112, p < 0.05), Turdus hortulorum(t = -4.026, p < 0.001), and Sitta europaea(t = -2.189, p < 0.05). The detection performance of the YOLO-X model for wildlife occurrence was analyzed, and it successfully classified 94.2% of the camera trapping data. In particular, the number of true positive predictions was 7,809 images and the number of false negative predictions was 51,044 images. In this study, the object detection algorithm YOLO-X model was used to detect the presence of wildlife in the camera trapping data. In this study, the YOLO-X model was used with a filter activated to detect 10 specific animal taxa out of the 80 classes trained on the COCO dataset, without any additional training. In future studies, it is necessary to create and apply training data for key occurrence species to make the model suitable for wildlife monitoring.

• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.19-24
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• 2024

Recent advances in large-scale data processing technologies such as big data, cloud computing, and artificial intelligence have increased the demand for high-performance storage devices in data centers and enterprise environments. In particular, the fast data response speed of storage devices is a key factor that determines the overall system performance. Solid state drives (SSDs) based on the Non-Volatile Memory Express (NVMe) interface are gaining traction, but new bottlenecks are emerging in the process of handling large data input and output requests from multiple hosts simultaneously. SSDs typically process host requests by sequentially stacking them in an internal queue. When long transfer length requests are processed first, shorter requests wait longer, increasing the average response time. To solve this problem, data transfer timeout and data partitioning methods have been proposed, but they do not provide a fundamental solution. In this paper, we propose a dual queue based scheduling scheme (DQBS), which manages the data transfer order based on the request order in one queue and the transfer length in the other queue. Then, the request time and transmission length are comprehensively considered to determine the efficient data transmission order. This enables the balanced processing of long and short requests, thus reducing the overall average response time. The simulation results show that the proposed method outperforms the existing sequential processing method. This study presents a scheduling technique that maximizes data transfer efficiency in a high-performance SSD environment, which is expected to contribute to the development of next-generation high-performance storage systems

• Journal of Navigation and Port Research
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• v.48 no.4
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• pp.249-260
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• 2024

This paper aimed to summarize research on technologies that could efficiently reduce wave-making resistance of container ships. Tto develop wave resistance reduction technology that could be applied to container ships and use it in real ship design, hull-form optimal design was performed by applying optimization algorithms, hull-form change algorithms, ship performance prediction algorithms, automation algorithms, and iterative optimal design techniques. A computer program was also developed. To properly set design variables known to be important elements in hull-form optimal design and to efficiently set lower and upper limits of design variables, a sensitivity analysis algorithm was developed and applied to hull-form optimal design. To predict the reliability and applicability of the developed computer program for real ships, hull-form optimal design was performed for a KRISO Container Ship (KCS), a container ship with various studies conducted worldwide. Hull-form optimal design was performed at Fn=0.26, the design speed of the KCS ship. Numerical analysis was performed on the hull-form of the target ship, the KCS ship, and the hull-form of the ship derived as a result of the hull-form optimal design to determine wave resistance, wave system, and wave height. The optimal ship's wave resistance was found to be reduced by 80.60% compared to the target ship. The displacement and wetted surface area were also found to be reduced by 1.54% and 1.21%, respectively.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.1075-1084
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• 2024

Efforts to expand the installation of devices that assist visually impaired individuals in their mobility are ongoing, but there are significantly fewer devices installed indoors compared to outdoors, causing considerable inconvenience for indoor navigation. Therefore, this paper aims to address these issues by applying the results of machine learning using YOLO(You Only Look Once) to a Raspberry Pi and by researching techniques to reduce errors through the trilateration method of UWB(Ultra-Wideband) sensors, applying it with a Kalman filter. The research results implemented an object recognition algorithm with a comprehensive accuracy of 91.7% using YOLO technology. Based on this object recognition, the direction (left, right, or front) was determined using the distance difference between two ultrasonic sensors set at an angle difference of 15 degrees. A distance of up to 1.5m was accepted through an infrared sensor to output a warning message according to the distance. The distance between the user's tag and the fixed three anchors was measured indoors through a UWB sensor, and the user's location was also measured indoors by linking the distance value with the three-side positioning technique.

• The Journal of the Acoustical Society of Korea
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• v.43 no.5
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• pp.570-583
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• 2024

The supply of inverter-type air conditioners for cooling in summer and heating in winter is increasing. In addition, since the operating speed of the compressor has been continuously increased for higher efficiency and higher performance, the flow speed of the refrigerant has also increased. As a result, it results in the increase of the relative contribution of flow-born noise to total noise generated from outdoor unit, and this highlights the importance of designing for the noise reduction to addressing flow-borne noise and requires necessary to analyze noise generation mechanisms by flow borne noise. Therefore, in this paper, the noise generation mechanisms by flow borne noise from air conditioner outdoor unit was numerically investigated. The wall pressure field was predicted using Large Eddy Simulation(LES) for the refrigerant flow inside the pipe, and the vibration and radiated noise were predicted using structure and acoustic coupled scheme based Finite Element Method (FEM). In this step, the compressible/in-compressible pressure field were separated using Wavenumber-Frequency Analysis(WFA) for inner pipe wall, and this results were used in analyzing the noise source due to refrigerant flow.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.5
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• pp.683-694
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• 2024

Flood control capacity enhancement measures in watersheds can be broadly categorized into structural and non-structural approaches. In this study, we propose the improvement of the flood control capacity in the Seomjin River basin through non-structural measures by optimizing the operation of the Seomjin River Dam, specifically by introducing a flexible flood season restricted water level (FSRWL). The flexible operation of FSRWL involves setting lower restricted water levels at the beginning of the flood season to increase flood control capacity and gradually raising them as the season progresses to manage flood control more effectively. As a structural measure, we examined the installation of riverside storage areas, a representative technique of nature-based solutions (NbS). Using the 2020 flood event as a case study, we analyzed the flood level reduction effects of implementing structural and non-structural measures both separately and simultaneously to identify the most effective and economical approach. The results indicate that the optimal flood prevention strategy for the main stream of the Seomjin River during the 2020 flood event involves operating the Seomjin River Dam FSRWL at EL. 190 m during the mid-flood season as a non-structural measure and installing a riverside storage area downstream of Godalgyo Bridge in Daepyeong-ri, Gokseong-gun as a structural measure.

• The Journal of Korean Medicine
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• v.45 no.3
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• pp.193-210
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• 2024

Objectives: We analyzed Sasang constitution case reports using text mining to derive network analysis results and designed a classification algorithm using machine learning to select a model suitable for classifying Sasang constitution based on text data. Methods: Case reports on Sasang constitution published from January 1, 2000, to December 31, 2022, were searched. As a result, 343 papers were selected, yielding 454 cases. Extracted texts were pretreated and tokenized with the Python-based KoNLPy package. Each morpheme was vectorized using TF-IDF values. Word cloud visualization and centrality analysis identified keywords mainly used for classifying Sasang constitution in clinical practice. To select the most suitable classification model for diagnosing Sasang constitution, the performance of five models-XGBoost, LightGBM, SVC, Logistic Regression, and Random Forest Classifier-was evaluated using accuracy and F1-Score. Results: Through word cloud visualization and centrality analysis, specific keywords for each constitution were identified. Logistic regression showed the highest accuracy (0.839416), while random forest classifier showed the lowest (0.773723). Based on F1-Score, XGBoost scored the highest (0.739811), and random forest classifier scored the lowest (0.643421). Conclusions: This is the first study to analyze constitution classification by applying text mining and machine learning to case reports, providing a concrete research model for follow-up research. The keywords selected through text mining were confirmed to effectively reflect the characteristics of each Sasang constitution type. Based on text data from case reports, the most suitable machine learning models for diagnosing Sasang constitution are logistic regression and XGBoost.