• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.5
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• pp.125-131
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• 2021

The scientific understanding of Korean medicine theory remains largely unknown, since there is a lack of proper methods to investigate its complex and unique characteristics. Here, we introduce a drug-centered approach, a novel method to investigate Korean medicine theory by analyzing the mechanisms of herbal medicines. This method can be effectively conducted by employing network pharmacology that can analyze the systems-level mechanisms of herbal medicines on a large scale. Firstly, we introduce the method of network pharmacology that are applied to analyze the mechanisms of herbal medicines in a step-by-step manner. Then, we show how the drug-centered approach employing network pharmacology can be applied to investigate Korean medicine theory by describing studies that identify the biological correlates of the cold-hot nature of herbs, spleen qi deficiency syndrome, or Sasang constitution. Finally, we discuss the limitations and future directions of the proposed approach in two aspects: The methods of network pharmacology for a drug-centered approach and the process of inferring Korean medicine theory through it. We believe that a drug-centered approach employing network pharmacology will provide an advanced scientific understanding of Korean medicine theory and contribute to its development by generating biologically plausible hypothesis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1800-1817
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• 2022

With the development of the economy and the improvement of living standards, the hot issues in the subject area have become the main research direction, and the mining of the hot issues in the subject currently has problems such as a large amount of data and a complex algorithm structure. Therefore, in response to this problem, this study proposes a method for extracting hot keywords in scientific journals based on the improved BERT model.It can also provide reference for researchers,and the research method improves the overall similarity measure of the ensemble,introducing compound keyword word density, combining word segmentation, word sense set distance, and density clustering to construct an improved BERT framework, establish a composite keyword heat analysis model based on I-BERT framework.Taking the 14420 articles published in 21 kinds of social science management periodicals collected by CNKI(China National Knowledge Infrastructure) in 2017-2019 as the experimental data, the superiority of the proposed method is verified by the data of word spacing, class spacing, extraction accuracy and recall of hot keywords. In the experimental process of this research, it can be found that the method proposed in this paper has a higher accuracy than other methods in extracting hot keywords, which can ensure the timeliness and accuracy of scientific journals in capturing hot topics in the discipline, and finally pass Use information technology to master popular key words.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.8
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• pp.66-71
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• 2022

Metal additive manufacturing (AM) has revolutionized several manufacturing industries. AM can generate large-scale metal components and produce complex geometries close to net-shapes. WAAM is an AM technology that has garnered considerable interest among industries owing to its economics and relatively high deposition rates. However, the heat accumulation in the weld bead during deposition triggers distortion and residual stress. To address these problems, various methods of interpass pressure rolling systems have been suggested in recent research. In addition, combining the rolling and WAAM processes can mitigate residual stresses. The constant-pressure rolling of the interlayer also affect the microstructure. The coarse microstructure of the as-deposited sample was altered to finer equiaxed grains via these methods. However, the bead-shape accuracy of the interlayer constant-pressure method does not consider the heat accumulation in each layer. Therefore, this study develops an interpass variable pressure rolling system that considers the heat accumulation of each layer. The interpass variable pressure rolling system comprises deposition, detection, pressure, and transport units. Finally, verification tests are performed on the interpass variable-pressure rolling system (at 500 kg) with the WAAM process, and the obtained results are discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3172-3193
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• 2022

Cognitive radio-simultaneous wireless information and power transfer (CR-SWIPT) has attracted much interest since it can improve both the spectrum and energy efficiency of wireless networks. This paper focuses on the resource sharing between a point-to-point primary system (PRS) and a multiuser multi-antenna cellular cognitive radio system (CRS) containing a large number of cognitive users (CUs). The resource sharing optimization problem is formulated by jointly scheduling CUs and adjusting the transmit power at the cognitive base station (CBS). The effect of accessing CUs' spatial channel correlation on the possible transmit power of the CBS is investigated. Accordingly, we provide a low-complexity suboptimal approach termed the semi-correlated semi-orthogonal user selection (SC-SOUS) algorithm to enhance the spectrum efficiency. In the proposed algorithm, CUs that are highly correlated to the information decoding primary receiver (IPR) and mutually near orthogonal are selected for simultaneous transmission to reduce the interference to the IPR and increase the sum rate of the CRS. We further develop a spatial correlation-based resource sharing (SC-RS) strategy to improve energy sharing performance. CUs nearly orthogonal to the energy harvesting primary receiver (EPR) are chosen as candidates for user selection. Therefore, the EPR can harvest more energy from the CBS so that the energy utilization of the network can improve. Besides, zero-forcing precoding and power control are adopted to eliminate interference within the CRS and meet the transmit power constraints. Simulation results and analysis show that, compared with the existing CU selection methods, the proposed low-complex strategy can enhance both the achievable sum rate of the CRS and the energy sharing capability of the network.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.188-197
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• 2022

We develop a coastal wave forecasting system by using the unstructured grid based on sea wind data of Global Data Assimilation and Prediction System. The verification is performed to examine the performance and accuracy of the wave model. Since the conventional grid has limited wave forecasting on complex coastlines and bathymetry, the unstructured grid system is applied for precise numerical simulation, and applicability for operational support is evaluated. Both grid systems show similar prediction trends in offshore and coastal areas, and the difference in prediction errors according to the grid system is not large. In addition, the applicability of the operational wave forecasting system is confirmed by dramatically reducing the model execution time of the unstructured grid under the same conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.881-895
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• 2023

Modern image inpainting techniques based on deep learning have achieved remarkable performance, and more and more people are working on repairing more complex and larger missing areas, although this is still challenging, especially for facial image inpainting. For a face image with a huge missing area, there are very few valid pixels available; however, people have an ability to imagine the complete picture in their mind according to their subjective will. It is important to simulate this capability while maintaining the identity features of the face as much as possible. To achieve this goal, we propose a three-stage network model, which we refer to as the identity and structure feature refinement network (ISFRNet). ISFRNet is based on 1) a pre-trained pSp-styleGAN model that generates an extremely realistic face image with rich structural features; 2) a shallow structured network with a small receptive field; and 3) a modified U-net with two encoders and a decoder, which has a large receptive field. We choose structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), L1 Loss and learned perceptual image patch similarity (LPIPS) to evaluate our model. When the missing region is 20%-40%, the above four metric scores of our model are 28.12, 0.942, 0.015 and 0.090, respectively. When the lost area is between 40% and 60%, the metric scores are 23.31, 0.840, 0.053 and 0.177, respectively. Our inpainting network not only guarantees excellent face identity feature recovery but also exhibits state-of-the-art performance compared to other multi-stage refinement models.

• Smart Structures and Systems
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• v.32 no.6
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• pp.349-358
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• 2023

Real-Time Hybrid Testing (RTHT) requires the numerical substructure calculations to be completed within the defined integration time interval due to its real-time loading demands. For solving the problem, A Real-Time Hybrid Testing based on Restart-Loading Technology (RTHT-RLT) is proposed in this paper. In the proposed method, in case of the numerical substructure calculations cannot be completed within the defined integration time interval, the experimental substructure was returned back to the initial state statically. When the newest loading commands were calculated by the numerical substructure, the experimental substructure was restarted loading from the initial state to the newest loading commands so as to precisely disclosing the dynamic performance of the experimental substructure. Firstly, the methodology of the RTHT-RLT is proposed. Furthermore, the numerical simulations and experimental tests on one frame structure with a viscous damper are conducted for evaluating the feasibility and effectiveness of the proposed RTHT-RLT. It is shown that the proposed RTHT-RLT innovatively renders the nonreal-time refined calculation of the numerical substructure feasible for the RTHT. The numerical and experimental results show that the proposed RTHT-RLT exhibits excellent performance in terms of stability and accuracy. The proposed RTHT-RLT may have broad application prospects for precisely investigating the dynamic behavior of large and complex engineering structures with specific experimental substructure where a restarting procedure does not affect the relevant hysteretic response.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Journal of ILASS-Korea
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• v.29 no.2
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• pp.68-74
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• 2024

As awareness of environmental pollution problems increases worldwide, interest in air pollutants is increasing. In particular, NOx and PM, which are major pollutants in diesel vehicles, are contributing significantly to emissions. As a result, its importance is increasing. In this study, based on research results applied to large diesel vehicles, the problem of natural regeneration caused by low exhaust gas temperature during low speed and low load operation is solved by applying a complex regeneration DPF that is not affected by temperature conditions to small diesel vehicles. The feasibility of application to small diesel vehicles was reviewed by measuring the emission reduction efficiency. As a result of the engine test, the power reduction rate and fuel consumption rate before and after device installation under full load conditions were 2.9% decrease and 3.5% increase, respectively, satisfying the standard for a 5% reduction, and as a result of the regeneration equilibrium temperature (BPT) test, the regeneration temperature was 310℃. appeared at the level. The reduction efficiency test results for the actual vehicle durability test equipment showed 97.3% PM, 51.0% CO, and 31.1% HC, while the city commuter vehicle had PM 97.5%, CO 61.7%, HC 40.0%, and the school bus vehicle had PM 96.8%, CO 44.4%, HC 34.3%, and low-speed logistics vehicles showed a reduction efficiency of 98.2% for PM, 36.0% for CO, and 45.7% for HC. Based on the results of this study, in the future, it is necessary to secure DPF technology suitable for all vehicle types through actual vehicle application research on temperature condition-insensitive composite regenerative DPF for medium-sized vehicles.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.33-43
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• 2024

Automated text summarization (ATS) systems rely on language resources as datasets. However, creating these datasets is a complex and labor-intensive task requiring linguists to extensively annotate the data. Consequently, certain public datasets for ATS, particularly in languages such as Thai, are not as readily available as those for the more popular languages. The primary objective of the ATS approach is to condense large volumes of text into shorter summaries, thereby reducing the time required to extract information from extensive textual data. Owing to the challenges involved in preparing language resources, publicly accessible datasets for Thai ATS are relatively scarce compared to those for widely used languages. The goal is to produce concise summaries and accelerate the information extraction process using vast amounts of textual input. This study introduced ThEconSum, an ATS architecture specifically designed for Thai language, using economy-related data. An evaluation of this research revealed the significant remaining tasks and limitations of the Thai language.