• Earthquakes and Structures
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• v.25 no.5
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• pp.359-367
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• 2023

High-rise structures are considered as symbols of economic power and leadership. Developing countries like India are also emerging as centers for new high-rise buildings (HRB). As the land is expensive and scarce everywhere, construction of tall buildings becomes the best solution to resolve the problem. But, as building's height increases, its stiffness reduces making it more susceptible to vibrations due to wind and earthquake forces. Several systems are available to control vibrations or deflections; however, outrigger systems are considered to be the most effective systems in improving lateral stiffness and overall stability of HRB. In this paper, a 42-storey RCC HRB is analyzed to determine the optimum position of outriggers of different materials. The linear static analysis of the building is performed with and without the provision of virtual outriggers of reinforced cement concrete (RCC) and pre-stressed concrete (PSC) at different storey levels by response spectrum method using finite element based Extended3D Analysis of building System (ETABS) software for determining responses viz. storey displacement, base shear and storey drift for individual models. The maximum allowable limit and percentage variations in earthquake responses are verified using the guidelines of Indian seismic codes. Results indicate that the outriggers contribute in significantly reducing the storey displacement and storey drift up to 28% and 20% respectively. Also, it is observed that the PSC outriggers are found to be more efficient over RCC outriggers. The optimum location of both types of outriggers is found to be at the mid height of building.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.15-20
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• 2023

This paper is a study on the development of an artificial intelligence (AI) system algorithm that recommends indirect advertising products suitable for character web dramas. The goal of this study is to increase viewers' content immersion and help them understand the story of the drama more deeply by recommending indirect advertising products that are suitable for writing lines for web dramas. In this study, we analyze dialogue and plot using the natural language processing model GPT, and develop two types of indirect advertising product recommendation systems, including prop type and background type, based on the analysis results. Through this, products that fit the story of the web drama are appropriately placed, allowing indirect advertisements to be exposed naturally, thereby increasing viewer immersion and enhancing the effectiveness of product promotion. There are limitations of artificial intelligence models, such as the difficulty in fully understanding hidden meanings or cultural nuances, and the difficulty in securing sufficient data for learning. However, this study will provide new insights into how AI can contribute to the production of creative works, and will be an important stepping stone to expand the possibilities of using natural language processing models in the creative industry.

• Journal of Korean Medicine Rehabilitation
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• v.33 no.4
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• pp.79-93
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• 2023

Objectives The purpose of this study is to investigate therapeutic exercise and to provide the evidence of daoyin exercise for shoulder pain. Methods Electronic databases including PubMed, EMBASE, China National Knowledge Infrastructure, Science ON, Oriental Medicine Advanced Searching Integrated System were searched up to October 2022. We selected randomized controlled trials. The quality of studies was assessed by Cochrane risk of bias tool. Meta-analysis were perfomed by Review Manager software. Results Eighteen randomized controlled trials were collected in accordance with the selection and exclusion criteria. Among the 18 trials, 7 trials used strengthening exercise, 4 trials used stablilization exercise, 5 trials used both types of intervention, and 2 trials used daoyin exercise. The study characteristics, results and method of intervention were analyzed. Meta-analysis showed that therapeutic exercise appeared to more effective than no treatment group for shoulder pain (standardized mean difference=-1.18, 95% confidence interval=-1.44 to 0.91, Z=8.82, p<0.00001; chi²=2.71, p=0.61; I²=0%). Conclusions All of 18 selected studies reported the effectiveness of therapeutic exercise for shoulder pain. Combining strengthening and stablilization exercise is considered the most efficient way for shoulder pain. Based on this study, well-designed studies should be performed to be evidence of the use of daoyin exercise for shoulder pain.

• Journal of radiological science and technology
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• v.47 no.1
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• pp.1-6
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• 2024

In Recent years, there has been a noticeable increase in the global incidence of breast cancer, with approximately 2.3 million cases of female breast cancer reported worldwide in 2020. Numerous studies are currently underway to enhance the accuracy of breast cancer diagnosis through the development of digital mammography detectors. This study aims to create Monte Carlo simulation-based mammographic anti-scatter grids and investigate their utility in evaluating the performance of digital mammography detector. Two types of mammographic anti-scatter grids, MAM-CP and Senographe 600T HF, were created using Monte Carlo simulation software (MCNPX 2.7.0), with grid ratios of 3.7 : 1 and 5 : 1, respectively. The grid physical characteristics (sensitivity, exposure factor, contrast improvement ratio) were calculated based on the KS C IEC60627 in the simulations using two X-ray qualities, RQA-M2 (28 kVp) and MW4 (35 kVp). As the X-ray tube voltage increased from 28 kVp to 35 kVp, sensitivity and exposure factor exhibited a decreasing trend, while contrast improvement ratio demonstrated an increasing trend. With an increase in grid ratio from 3.7 : 1 to 5 : 1, all physical characteristics showed an upward trend. Our results were consistent with a previous study that conducted measurements of physical properties using a real phantom. However, the pattern of change in the contrast improvement ratio with X-ray tube voltage differed from the previous study.

• Structural Engineering and Mechanics
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• v.90 no.4
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• pp.371-390
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• 2024

Investigating the impact of openings on the structural behavior of ferrocement I-beams with two distinct types of reinforcing metallic and non-metallic meshes is the primary goal of the current study. Up until failure, eight 250x200x2200 mm reinforced concrete I-beams were tested under flexural loadings. Depending on the kind of meshes used for reinforcement, the beams are split into two series. A control I-beam with no openings and three beams with one, two, and three openings, respectively, are found in each series. The two series are reinforced with three layers of welded steel meshes and two layers of tensar meshes, respectively, in order to maintain a constant reinforcement ratio. Structural parameters of investigated beams, including first crack, ultimate load, deflection, ductility index, energy absorption, strain characteristics, crack pattern, and failure mode were reported. The number of mesh layers, the volume fraction of reinforcement, and the kind of reinforcing materials are the primary factors that vary. This article presents the outcomes of a study that examined the experimental and numerical performance of ferrocement reinforced concrete I-beams with and without openings reinforced with welded steel mesh and tensar mesh separately. Utilizing ANSYS-16.0 software, nonlinear finite element analysis (NLFEA) was applied to illustrate how composite RC I-beams with openings behaved. In addition, a parametric study is conducted to explore the variables that can most significantly impact the mechanical behavior of the proposed model, such as the number of openings. The FE simulations produced an acceptable degree of experimental value estimation, as demonstrated by the obtained experimental and numerical results. It is also noteworthy to demonstrate that the strength gained by specimens without openings reinforced with tensar meshes was, on average, 22% less than that of specimens reinforced with welded steel meshes. For specimens with openings, this value is become on average 10%.

• Informatization Policy
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• v.31 no.2
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• pp.39-64
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• 2024

This study assesses the national AI competitiveness of 38 OECD countries with focus on AI human capital, AI infrastructure, and AI innovation capacity. Utilizing the fuzzy-set ideal type analysis method, these countries were categorized into eight distinct types based on their national AI competitiveness levels, leading to the derivation of pertinent implications. The analysis identified a category termed "AI Leading Country" consisting of North American, Western European, and Nordic countries, along with several Asian nations including South Korea. Remarkably, the United States demonstrated dominant global national AI competitiveness, achieving the highest fuzzy scores across all three evaluative factors. South Korea was classified as an "AI Leading Country" primarily due to its superior AI infrastructure, but its performance in AI human capital and AI innovation capacity was found to be moderate relative to other analyzed nations; thus highlighting the necessity of sustained focus on the accumulation of AI human capital and bolstering of AI innovation capacity.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.531-536
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• 2024

Control drones, which are recently classified as smart agricultural machines in the agricultural field, are striving to build smart control and automatic control systems by combining hardware and software in order to shorten working hours and increase the effectiveness of control in the aging era of rural areas. In this paper, the characteristics of the nozzle dedicated to the control drone were analyzed as a basic study for the establishment of management control and automatic control systems. In order to consider various variables such as the type of various drone models, controller, wind, flight speed, flight altitude, weather conditions, and UAV pesticide types, related studies are needed to be able to present the drug spraying criteria in consideration of the characteristics and versatility of the nozzle. Therefore, to enable the consideration of various variables, flow analysis (CFD) simulation was conducted based on the self-designed nozzle, and the theoretical and experimental values of the droplet distribution were compared and analyzed through water reduction experiments. In the future, we intend to calculate accurate scattering in consideration of various variables according to drone operation and use it in management control and automatic control systems.

• Nutrition Research and Practice
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• v.18 no.2
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• pp.294-307
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• 2024

BACKGROUND/OBJECTIVES: Dietitians frequently use nutrition education tools to facilitate dietary counselling sessions. Nevertheless, these tools may require adaptation to keep pace with technological advancements. This study had a 2-fold purpose: first, to identify the types of nutrition education tools currently in use, identify their limitations, and explore dietitians' perspectives on the importance of these tools; second, to investigate the features that dietitians prefer in digital nutrition education tools. SUBJECTS/METHODS: A semi-structured face-to-face interview was conducted among 15 dietitians from selected public hospitals, primary care clinics, and teaching hospitals in Malaysia. Inductive thematic analysis of the responses was conducted using NVivo version 12 software. RESULTS: Most dietitians used physical education tools including the healthy plate model, pamphlets, food models, and flip charts. These tools were perceived as important as they facilitate the nutrition assessment process, deliver nutrition intervention, and are time efficient. However, dietitians described the current educational tools as impersonal, outdated, limited in availability due to financial constraints, unhandy, and difficult to visualise. Alternatively, they strongly favoured digital education tools that provided instant feedback, utilised an automated system, included a local food database, were user-friendly, developed by experts in the field, and seamlessly integrated into the healthcare system. CONCLUSION: Presently, although dietitians have a preference for digital educational tools, they heavily rely on physical nutrition education tools due to their availability despite the perception that these tools are outdated, impersonal, and inconvenient. Transitioning to digital dietary education tools could potentially address these issues.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of Radiation Industry
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• v.17 no.3
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• pp.265-273
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• 2023

To effectively and safely manage the radiation exposure to nuclear power plant (NPP) workers in accidents, major overseas NPP operators such as the United States, Germany, and France have developed and applied realistic 3D model radiation dose assessment software for workers. Continuous research and development have recently been conducted, such as performing NPP accident management using 3D-VR based on As Low As Reasonably Achievable (ALARA) planning tool. In line with this global trend, it is also required to secure technology to manage radiation exposure of workers in Korea efficiently. Therefore, in this paper, it is described the application method and assessment results of radiation exposure scenarios for workers in response to accidents assessment technology, which is one of the fundamental technologies for constructing a realistic platform to be utilized for radiation exposure prediction, diagnosis, management, and training simulations following accidents. First, the post-accident sampling after the Loss of Coolant Accident(LOCA) was selected as the accident and response scenario, and the assessment area related to this work was established. Subsequently, the structures within the assessment area were modeled using MCNP, and the radiation source of the equipment was inputted. Based on this, the radiation dose distribution in the assessment area was assessed. Afterward, considering the three principles of external radiation protection (time, distance, and shielding) detailed work scenarios were developed by varying the number of workers, the presence or absence of a shield, and the location of the shield. The radiation exposure doses received by workers were compared and analyzed for each scenario, and based on the results, the optimal accident response scenario was derived. The results of this study plan to be utilized as a fundamental technology to ensure the safety of workers through simulations targeting various reactor types and accident response scenarios in the future. Furthermore, it is expected to secure the possibility of developing a data-based ALARA decision support system for predicting radiation exposure dose at NPP sites.