• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.509-515
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• 2023

Despite the continued development of alternative energies, fuel consumption is increasing. In particular, the price of gasoline fluctuates greatly according to fluctuations in international oil prices. Gas stations adjust their gasoline inventory to respond to gasoline price fluctuations. In this study, news datasets is used to analyze the gasoline consumption patterns through fluctuations of the gasoline inventory. First, collecting news datasets with web crawling. Second, summarizing news datasets using KoBART, which summarizes the Korean text datasets. Finally, preprocessing and deriving the fluctuations factors through N-Gram Language Model and TF-IDF. Through this study, it is possible to analyze and predict gasoline consumption patterns.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.679-690
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• 2023

Leakage is one of the representative abnormal conditions in Water distribution systems (WDSs). Leakage can potentially occur and cause immediate economic and hydraulic damage upon occurrence. Therefore, leakage detection is essential, but WDSs are located underground, it is difficult. Moreover, when multiple leakage occurs, it is required to prioritize restoration according to the scale and location of the leakage, applying for an optimal restoration framework can be advantageous in terms of system resilience. In this study, various leakage scenarios were generated based on the WDSs hydraulic model, and leakage detection was carried out containing location and scale using a Deep learning-based model. Finally, the leakage location and scale obtained from the detection results were used as a factor for the priority of leakage restoration, and the results of the priority of leakage restoration were derived. The priority of leakage restoration considered not only hydraulic factors but also socio-economic factors (e.g., leakage scale, important facilities).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.137-147
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• 2023

LiDAR is used in autonomous driving and various industrial fields to measure the size and distance of an object. In addition, the sensor also provides intensity images based on the amount of reflected light. This has a positive effect on sensor data processing by providing information on the shape of the object. LiDAR guarantees higher performance as the resolution increases but at an increased cost. These conditions also apply to LiDAR intensity images. Expensive equipment is essential to acquire high-resolution LiDAR intensity images. This study developed artificial intelligence to improve low-resolution LiDAR intensity images into high-resolution ones. Therefore, this study performed parameter analysis for the optimal super-resolution neural network model. The super-resolution algorithm was trained and verified using 2,500 LiDAR intensity images. As a result, the resolution of the intensity images were improved. These results can be applied to the autonomous driving field and help improve driving environment recognition and obstacle detection performance

• Advances in nano research
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• v.15 no.5
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• pp.451-466
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• 2023

Gastrointestinal cancer (GC) is a prevalent malignant tumor of the digestive system that poses a severe health risk to humans. Due to the specific organ structure of the gastrointestinal system, both endoscopic and MRI diagnoses of GIC have limited sensitivity. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high recurrence rates in surgical and pharmacological therapy. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for the detection and treatment of cancer. Because of its deep location and complex surgery, diagnosing and treating gastrointestinal cancer is very difficult. The early diagnosis and urgent treatment of gastrointestinal illness are enabled by nanotechnology. As diagnostic and therapeutic tools, nanoparticles directly target tumor cells, allowing their detection and removal. XGBoost was used as a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. The research sample included 300 GC patients, comprising 190 males (72.2% of the sample) and 110 women (27.8%). Using convolutional neural networks (CNN) and artificial neural networks (ANN)-EXtreme Gradient Boosting (XGBoost), the patients mean± SD age was 50.42 ± 13.06. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.037), distant metastasis (P = 0.004), and tumor stage (P = 0.015) were shown to have a statistically significant link with GC patient survival. AUC was 0.92, sensitivity was 81.5%, specificity was 90.5%, and accuracy was 84.7 when analyzing stomach picture.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.154-160
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• 2023

Recently, with the activation of eBook services, books are being published simultaneously as physical books and digitized eBooks. Paper books are more expensive than e-books due to printing and distribution costs, so demand for relatively inexpensive e-books is increasing. There are cases where previously published physical books cannot be digitized due to the circumstances of the publisher or author, so there is a movement among individual users to digitize books that have been published for a long time. However, existing research has only studied the advancement of the pre-processing process that can improve text recognition before applying OCR technology, and there are limitations to digitization depending on the condition of the book. Therefore, support for book digitization services depending on the condition of the physical book is needed. need. In this paper, we propose a method to support digitalization services according to the status of physical books held by book owners. Create images by scanning books and extract text information from the images through OCR. We propose a method to recover text that cannot be extracted depending on the state of the book using BERT, a natural language processing deep learning model. As a result, it was confirmed that the recovery method using BERT is superior when compared to RNN, which is widely used in recommendation technology.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.897-907
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• 2023

Purpose: In this study, We aim to estimate a untrained person's three postures using a 2D CNN model which is trained with minimal FFT data collected by a 24GHz FMCW radar. Method: In an indoor space, we collected FFT data for three distinct postures (standing, sitting, and lying) from three different individuals. To apply this data to a 2D CNN model, we first converted the collected data into 2D images. These images were then trained using the 2D CNN model to recognize the distinct features of each posture. Following the training, we evaluated the model's accuracy in differentiating the posture features across various individuals. Result: According to the experimental results, the average accuracy of the proposed scheme for the three postures was shown to be a 89.99% and it outperforms the conventional 1D CNN and the SVM schemes. Conclusion: In this study, we aim to estimate any person's three postures using a 2D CNN model and a 24GHz FMCW radar for disastrous situations in indoor. it is shown that the different posture of any persons can be accurately estimated even though his or her data is not used for training the AI model.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1763-1770
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• 2023

The availability of high-resolution satellite image time series data has led to an increase in change detection research. Various methods are being studied, such as satellite image pixel and object-level change detection algorithms, as well as algorithms that apply deep learning technology. In this paper, we propose a QGIS plugin-based system to enhance the utilization of these useful results and present an actual implementation case. The proposed system is a system for intensive change detection and monitoring of areas of interest, and we propose a convenient system expansion method for algorithms to be developed in the future. Furthermore, it is expected to contribute to the construction of satellite image utilization systems by presenting the basic structure of commercialization of change detection research.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.45-52
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• 2023

Recently, as the importance of walking in daily life has been emphasized, projects to guarantee walking rights and create a pedestrian environment are being promoted throughout the region. In previous studies, a pedestrian environment assessment was conducted using Jeonju-si road images, and an image comparison pair data set was constructed. However, data sets expressed in numbers have difficulty in generalizing the judgment criteria of pedestrian environment assessors or visually identifying the pedestrian environment preferred by pedestrians. Therefore, this study proposes a method to interpret the results of the pedestrian environment assessment through data visualization by building a web application. According to the semantic segmentation result of analyzing the walking environment components that affect pedestrian environment assessors, it was confirmed that pedestrians did not prefer environments with a lot of "earth" and "grass," and preferred environments with "signboards" and "sidewalks." The proposed study is expected to identify and analyze the results randomly selected by participants in the future pedestrian environment evaluation, and believed that more improved accuracy can be obtained by pre-processing the data purification process.

• Steel and Composite Structures
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• v.49 no.2
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• pp.161-180
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• 2023

In the previous research, the axial compressive capacity models for the glass fiber-reinforced polymer (GFRP)-reinforced circular concrete compression elements restrained with GFRP helix were put forward based on small and noisy datasets by considering a limited number of parameters portraying less accuracy. Consequently, it is important to recommend an accurate model based on a refined and large testing dataset that considers various parameters of such components. The core objective and novelty of the current research is to suggest a deep learning model for the axial compressive capacity of GFRP-reinforced circular concrete columns restrained with a GFRP helix utilizing various parameters of a large experimental dataset to give the maximum precision of the estimates. To achieve this aim, a test dataset of 61 GFRP-reinforced circular concrete columns restrained with a GFRP helix has been created from prior studies. An assessment of 15 diverse theoretical models is carried out utilizing different statistical coefficients over the created dataset. A novel model utilizing the group method of data handling (GMDH) has been put forward. The recommended model depicted good effectiveness over the created dataset by assuming the axial involvement of GFRP main bars and the confining effectiveness of transverse GFRP helix and depicted the maximum precision with MAE = 195.67, RMSE = 255.41, and R2 = 0.94 as associated with the previously recommended equations. The GMDH model also depicted good effectiveness for the normal distribution of estimates with only a 2.5% discrepancy from unity. The recommended model can accurately calculate the axial compressive capacity of FRP-reinforced concrete compression elements that can be considered for further analysis and design of such components in the field of structural engineering.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.885-890
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• 2023

As satellite technology progresses, a growing number of satellites-like CubeSat and radar satellites-are available with a higher spectral and spatial resolutions than previous. National initiatives used to be the main force behind satellite development, but current trendsindicate that private enterprises are also actively exploring and developing new satellite technologies. This special issue examines the recent research results and advanced technology in remote sensing approaches for Earth environment analysis. These results provide important information for the development of satellite sensors in the future and are of great interest to researchers working with artificial intelligence in thisfield. The special issue introduces the latest advances in remote sensing technology and highlights studies that make use of data to monitor and forecast Earth's environment. The objective is to provide direction for the future of remote sensing research.