• MISULJARYO - National Museum of Korea Art Journal
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• v.98
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• pp.200-225
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• 2020

This paper, based on "Minhwa Chaekgeori" paintings from the collection of the National Museum of Korea, identifies and discusses fourteen similar works in domestic and international collections as products of the Seokdang workshop. First, the relevant paintings are identified as products of the workshop known as Seokdang (石堂, literally "stone hall") by the workshop's seal that is stamped on them. Second, analysis of the iconography indicates that the paintings were likely produced in the 1920s. Third, research on certain geographic names and addresses associated with this group of paintings suggests that they might not have been separately commissioned, but are rather examples of partially "ready-made" paintings. Fourth, the paper discusses how the designs of various cultural products in these paintings reflects contemporaneous changes in Korea's diplomatic and commercial relations, i.e., the decline of relations with China and rise of relations with the United States and Europe. Finally, a comparison of the Seokdang chaekgeori paintings with the popular chaekgeori paintings produced by Yi Hyeongrok and Yi Deokyeong in the early twentieth century provides important implications for the succession of tradition and innovation in visual culture.

• Journal of Nutrition and Health
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• v.54 no.4
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• pp.342-354
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• 2021

Purpose: Obesity is a serious public health issue for the modern society and is considered a chronic health hazard. There are many surgical and pharmacological approaches to treat obesity. However, various potentially hazardous side effects remain the biggest challenge. Therefore, diets based on foods derived from natural products have gained increasing attention compared to anti-obesity drugs. Recently, research on edible insects as a food source has been a topic of considerable interest in the scientific communities. This study examined the anti-obesity effects of ingesting an edible insect by feeding a high-fat diet (HFD)-induced obese mouse models with a diet containing Tenebrio molitor larvae powder (TMLP). Methods: Six-week-old female C57BL/6J mice were divided into 4 groups according to treatment: 100% normal diet (ND), 100% HFD (HFD), HFD 99% + TMLP 1% (TMLP), and HFD 97% + TMLP 3% (TMLP 3%). TMLP was added to the HFD for 6 weeks for the latter two groups. Results: Compared to the HFD group, mice in the TMLP group showed weight loss, and micro-computed tomographic imaging revealed that the volume of the adipose tissue in the abdominal area also showed significant reduction. After an autopsy, the fat weight was found to be significantly reduced in the TMLP group compared to the HFD group. In addition, the degree of fat cell deposition in the liver tissue and the size of the adipocytes significantly decreased in the TMLP group. Reverse transcription polymerase chain reaction analysis for the mRNA expression of adipogenesis-related genes namely CCAAT-enhancer-binding proteins (C/EBP-β, C/EBP-δ), and fatty acid-binding protein 4 (FABP4) showed that the expression levels of these genes were significantly reduced in the TMLP group compared to the HFD group. Serum leptin level also decreased significantly in the TMLP group in the comparison with the HFD group. In addition, total cholesterol, triglyceride, and glucose levels in mouse serum also decreased in the TMLP group. Conclusion: Taken together, our results showed that TMLP effectively inhibited adipocyte growth and reduced body weight in obese mice.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.341-347
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• 2022

This research is designed to help and offer hold up to complete the requirements of aged and disable in a home. The control approach and the tone approach are used to manage the house appliances. The major organize system implementation in technology of wireless to offer distant contact from a phone Internet Protocol connectivity for access and calculating strategy and appliance remotely. The planned system no need a committed server PC with value of parallel systems and offers a new communication-protocol to observe and control a house environment with more than just the switch functionality. To express the possibility and efficiency of this system, devices like as lights switches, power plugs, and motion-sensors have been included with the planned home control system and supply more security manage on the control with low electrical energy activate method. The rank of switches is corresponding in all this control system whereby all user interfaces indicate the real time existing status. This system planned to manage electrical-appliances and devices in house with reasonably low cost of design, user friendly interface, easily install and provide high security. Research community generally specified that the network "Reconnaissance Attacks" in IPv6 are usually impossible due to they will take huge challenge to carry out address scanning of 264 hosts in an IPv6 subnet."It being deployed of IPv6 shows that it definitely enhances security and undermines the probability". This research of the IPv6 addressing-strategies at present utilizes and planned a new strategy and move toward to "mitigate reconnaissance attacks".

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2444-2452
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• 2022

The field-programmable gate array (FPGA) is gaining popularity in industrial automation such as nuclear power plant instrumentation and control (I&C) systems due to the benefits of having non-existence of operating system, minimum software errors, and minimum common reason failures. Separate functions can be processed individually and in parallel on the same integrated circuit using FPGAs in comparison to the conventional microprocessor-based systems used in any plant operations. The use of FPGAs offers the potential to minimize complexity and the accompanying difficulty of securing regulatory approval, as well as provide superior protection against obsolescence. Wireless sensor networks (WSNs) are a new technology for acquiring and processing plant data wirelessly in which sensor nodes are configured for real-time signal processing, data acquisition, and monitoring. ZigBee (IEEE 802.15.4) is an open worldwide standard for minimum power, low-cost machine-to-machine (M2M), and internet of things (IoT) enabled wireless network communication. It is always a challenge to follow the specific topology when different Zigbee nodes are placed in a large network such as a plant. The research article focuses on the hardware chip design of different topological structures supported by ZigBee that can be used for monitoring and controlling the different operations of the plant and evaluates the performance in Vitex-5 FPGA hardware. The research work presents a strategy for configuring FPGA with ZigBee sensor nodes when communicating in a large area such as an industrial plant for real-time monitoring.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.49-60
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• 2023

The amount of data generated by electronic systems through e-commerce, social networks, and data computation has risen. However, the security of data has always been a challenge. The problem is not with the quantity of data but how to secure the data by ensuring its confidentiality and privacy. Though there are several research on cloud data security, this study proposes a security scheme with the lowest execution time. The approach employs a non-linear time complexity to achieve data confidentiality and privacy. A symmetric algorithm dubbed the Non-Deterministic Cryptographic Scheme (NCS) is proposed to address the increased execution time of existing cryptographic schemes. NCS has linear time complexity with a low and unpredicted trend of execution times. It achieves confidentiality and privacy of data on the cloud by converting the plaintext into Ciphertext with a small number of iterations thereby decreasing the execution time but with high security. The algorithm is based on Good Prime Numbers, Linear Congruential Generator (LGC), Sliding Window Algorithm (SWA), and XOR gate. For the implementation in C, thirty different execution times were performed and their average was taken. A comparative analysis of the NCS was performed against AES, DES, and RSA algorithms based on key sizes of 128kb, 256kb, and 512kb using the dataset from Kaggle. The results showed the proposed NCS execution times were lower in comparison to AES, which had better execution time than DES with RSA having the longest. Contrary, to existing knowledge that execution time is relative to data size, the results obtained from the experiment indicated otherwise for the proposed NCS algorithm. With data sizes of 128kb, 256kb, and 512kb, the execution times in milliseconds were 38, 711, and 378 respectively. This validates the NCS as a Non-Deterministic Cryptographic Algorithm. The study findings hence are in support of the argument that data size does not determine the execution.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.

• Genomics & Informatics
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• v.21 no.3
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• pp.42.1-42.23
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• 2023

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, one of the most deadly infections in humans. The emergence of multidrug-resistant and extensively drug-resistant Mtb strains presents a global challenge. Mtb has shown resistance to many frontline antibiotics, including rifampicin, kanamycin, isoniazid, and capreomycin. The only licensed vaccine, Bacille Calmette-Guerin, does not efficiently protect against adult pulmonary tuberculosis. Therefore, it is urgently necessary to develop new vaccines to prevent infections caused by these strains. We used a subtractive proteomics approach on 23 virulent Mtb strains and identified a conserved membrane protein (MmpL4, NP_214964.1) as both a potential drug target and vaccine candidate. MmpL4 is a non-homologous essential protein in the host and is involved in the pathogen-specific pathway. Furthermore, MmpL4 shows no homology with anti-targets and has limited homology to human gut microflora, potentially reducing the likelihood of adverse effects and cross-reactivity if therapeutics specific to this protein are developed. Subsequently, we constructed a highly soluble, safe, antigenic, and stable multi-subunit vaccine from the MmpL4 protein using immunoinformatics. Molecular dynamics simulations revealed the stability of the vaccine-bound Tolllike receptor-4 complex on a nanosecond scale, and immune simulations indicated strong primary and secondary immune responses in the host. Therefore, our study identifies a new target that could expedite the design of effective therapeutics, and the designed vaccine should be validated. Future directions include an extensive molecular interaction analysis, in silico cloning, wet-lab experiments, and evaluation and comparison of the designed candidate as both a DNA vaccine and protein vaccine.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.41-47
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• 2024

This paper utilizes computational asymptotic analysis to compute the boundary layer solution for composite beams and validates the findings through a comparison with ANSYS results. The boundary layer solution, presented as a sum of the interior solution and pure boundary layer effects, necessitates a mathematically rigorous formalization for both interior and boundary layer aspects. Computational asymptotic analysis emerges as a robust technique for addressing such problems. However, the challenge lies in connecting the boundary layer and interior solutions. In this study, we systematically separate the principles of virtual work and the principles of Saint-Venant to tackle internal and boundary layer issues. The boundary layer solution is articulated by calculating the Papkovich-Fadle eigenfunctions, representing them as linear combinations of real and imaginary vectors. To address warping functions in the interior solutions, we employed a least squares method. The computed solutions exhibit excellent agreement with 2D finite element analysis results, both quantitatively and qualitatively. This validates the effectiveness and accuracy of the proposed approach in capturing the behavior of composite beams.