• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.339-346
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• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).

• Journal of the Korean GEO-environmental Society
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• v.24 no.3
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• pp.23-27
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• 2023

Graphitic carbon nitride (g-C₃N₄) has been used as effective photocatalyst for degradation of antibiotics under visible light irradiation. However, the fast recombination of hole-electron pair may limit their photocatalytic efficiency. In our study, Ag was grafted on g-C₃N₄/g-C₃N₄ isotype heterojunction by a microwave-assisted decomposition method. The structure and physical properties of heterojunction photocatalyst were characterized through X-ray diffraction, UV-DRS, FT-IR, and Photoluminescence analyses. Ag decorated g-C₃N₄/g-C₃N₄ isotype heterojunction exhibited excellent photocatalytic activity for degradation of sulfamethoxazole under irradiation under visible light irradiation within 210 min, which is higher than g-C₃N₄/g-C₃N₄ isotype heterojunction and bulk g-C₃N₄. The addition of Ag may broaden the visible light absorption and restrict the recombination of hole-electron pair because of the surface plasmons resonance, resulting in the improving the photocatalytic activity.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.883-890
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• 2022

Purpose: Currently, in the case of domestic fire fighting facility design, it is difficult to secure highquality manpower due to low design costs and overheated competition between companies, so there is a limit to improving the fire safety performance of buildings. Accordingly, AI-based firefighting design solutions were studied to solve these problems and secure leading fire engineering technologies. Method: Through AutoCAD, which is widely used in existing fire fighting design, the procedures required for basic design and implementation design were processed, and AI technology was utilized through the YOLO v4 object recognition deep learning model. Result: Through the design process for fire fighting facilities, the facility was determined and the drawing design automation was carried out. In addition, by learning images of doors and pillars, artificial intelligence recognized the part and implemented the function of selecting boundary areas and installing piping and fire fighting facilities. Conclusion: Based on artificial intelligence technology, it was confirmed that human and material resources could be reduced when creating basic and implementation design drawings for building fire protection facilities, and technology was secured in artificial intelligence-based fire fighting design through prior technology development.

• Korean Journal of Culture and Social Issue
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• v.28 no.4
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• pp.661-690
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• 2022

Adolescent delinquency is a substantial social problem that occurs in both offline and online domains. The current study utilized random forest algorithms to identify predictors of adolescents' online and offline delinquency. Further, we explored the applicability of classic delinquency theories (social learning, strain, social control, routine activities, and labeling theory). We used the first-grade and fourth-grade elementary school panels as well as the first-grade middle school panel (N=4,137) among the sixth wave of the nationally-representative Korean Children and Youth Panel Survey 2010 for analysis. Random forest algorithms were used instead of the conventional regression analysis to improve the predictive performance of the model and possibly consider many predictors in the model. Random forest algorithm results showed that classic delinquency theories designed to explain offline delinquency were also applicable to online delinquency. Specifically, salient predictors of online delinquency were closely related to individual factors(routine activities and labeling theory). Social factors(social control and social learning theory) were particularly important for understanding offline delinquency. General strain theory was the commonly important theoretical framework that predicted both offline and online delinquency. Findings may provide evidence for more tailored prevention and intervention strategies against offline and online adolescent delinquency.

• Membrane Journal
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• v.32 no.6
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• pp.442-455
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• 2022

High-temperature polymer electrolyte membrane fuel cell (HT-PEMFC) has been studied as an alternative to low-temperature PEMFC due to its fast activation of electrodes and high resistance to electrode poisoning by carbon monoxide. It is highly required to develop stable PEMs operating at high temperatures even doped by ion-conducting materials for the development of high-performance and durable HT-PEMFC systems. A number of studies have been conducted to develop polybenzimidazole (PBI)-based PEMs for applications in HT-PEMFC due to their high interaction with doped ion-conducting materials and outstanding thermomechanical stability under high-temperature operation. This review focused on the development of PBI-based PEMs showing high performance and durability. Firstly, the characteristic behavior of PBI-based PEMs doped with various ion-conducting materials including phosphoric acid was systematically investigated. And then, a comparison of the physicochemical properties of the PEMs according to the different membrane manufacturing processes was conducted. Secondly, the incorporation of porous polytetrafluoroethylene substrate and/or inorganic composites to PBI matrix to improve the membrane performances was studied. Finally, the construction of cross-linked structures into PBI-based PEM systems by polymer blending method was introduced to improve the PEM properties.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.37-51
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• 2023

Spatial information is essential for interpreting urban phenomena. Methodologies for spatializing urban information, especially when it lacks location details, have been consistently developed. Typical methods include Geocoding using structured address information or place names, spatial integration with existing geospatial data, and manual tasks utilizing reference data. However, a vast number of documents produced by administrative agencies have not been deeply dealt with due to their unstructured nature, even when there's demand for spatialization. This research utilizes the natural language processing model BERT to spatialize public documents related to urban planning. It focuses on extracting sentence elements containing addresses from documents and converting them into structured data. The study used 18 years of urban planning public announcement documents as training data to train the BERT model and enhanced its performance by manually adjusting its hyperparameters. After training, the test results showed accuracy rates of 96.6% for classifying urban planning facilities, 98.5% for address recognition, and 93.1% for address cleaning. When mapping the result data on GIS, it was possible to effectively display the change history related to specific urban planning facilities. This research provides a deep understanding of the spatial context of urban planning documents, and it is hoped that through this, stakeholders can make more effective decisions.

• Clean Technology
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• v.29 no.3
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• pp.178-184
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• 2023

Transition metal chalcogenides have garnered significant attention as anode materials for sodium-ion batteries due to their high theoretical capacity. Nevertheless, their practical application is impeded by their limited lifespan resulting from substantial volume expansion during cycling and their low electrical conductivity. To tackle these issues, this study devised a solution by synthesizing a nanostructured anode material composed of porous CNT (carbon nanotube) spheres and (Ni,Co)Se₂ nanocrystals. By employing spray pyrolysis and subsequent heat treatments, a porous-structured (Ni,Co)Se₂-CNT composite microsphere was successfully synthesized, and its electrochemical properties as an anode for sodium-ion batteries were evaluated. The synthesized (Ni,Co)Se₂-CNT microsphere possesses a porous structure due to the nanovoids that formed as a result of the decomposition of the polystyrene (PS) nanobeads during spray pyrolysis. This porous structure can effectively accommodate the volume expansion that occurs during repeated cycling, while the CNT scaffold enhances electronic conductivity. Consequently, the (Ni,Co)Se₂-CNT anode exhibited an initial discharge capacity of 698 mA h g^-1 and maintained a high discharge capacity of 400 mA h g^-1 after 100 cycles at a current density of 0.2 A g^-1.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.403-411
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• 2023

Functional ultrasound imaging, such as elasticity imaging and micro-blood flow Doppler imaging, enhances diagnostic capability by providing useful mechanical and functional information about tissues. However, the implementation of functional ultrasound imaging poses limitations such as the storage of vast amounts of data in Radio Frequency (RF) data acquisition and processing. In this paper, we propose a sub-Nyquist approach that reduces the amount of acquired axial samples for efficient shear-wave elasticity imaging. The proposed method acquires data at a sampling rate one-third lower than the conventional Nyquist sampling rate and tracks shear-wave signals through RF signals reconstructed using band-pass filtering-based interpolation. In this approach, the RF signal is assumed to have a fractional bandwidth of 67 %. To validate the approach, we reconstruct the shear-wave velocity images using shear-wave tracking data obtained by conventional and proposed approaches, and compare the group velocity, contrast-to-noise ratio, and structural similarity index measurement. We qualitatively and quantitatively demonstrate the potential of sub-Nyquist sampling-based shear-wave elasticity imaging, indicating that our approach could be practically useful in three-dimensional shear-wave elasticity imaging, where a massive amount of ultrasound data is required.

• Applied Chemistry for Engineering
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• v.34 no.4
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• pp.331-346
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• 2023

Recently, green processes that can be directly used in an energy-efficient and electrified society to achieve carbon neutrality are attracting attention. Existing heat and pressure-based desalination technologies that consume tremendous amounts of energy are no exception, and the growth of next-generation electrochemical-based desalination technologies is remarkable. One of the most representative electrochemical desalination technologies is electrochemical ion separation (EIONS) technology, which includes capacitive desalination (CDI) and battery desalination (BD) technology. In the research field of EIONS, various system applications have been developed to improve system performance, such as capacity and cyclability. However, it is very difficult to understand the meaning and novelty of these applications immediately because there are only a few papers that summarize the research background for domestic readers. Therefore, in this review paper, we aim to describe the technological advances and individual characteristics of each system in clear and specific detail about the latest EIONS research. The driving principle, research background, and strengths and weaknesses of each EIONS system are explained in order. In addition, this paper concluded by suggesting the future development and research direction of EIONS. Researchers who are just beginning out in EIONS research can also benefit from this study because it will help them understand the research trend.