• Journal of the Korea Institute of Building Construction
• /
• v.23 no.5
• /
• pp.629-638
• /
• 2023

This study introduces an innovative approach to monitor the whereabouts of workers within indoor construction settings. While traditional modalities such as GPS and NTRIP have demonstrated efficacy for outdoor localizations, their precision dwindles in indoor environments. In response, this research advocates for the adoption of Aruco markers. Leveraging computer vision technology, these markers facilitate the quantification of the distance between a worker and the marker, subsequently pinpointing the worker's instantaneous location with heightened accuracy. The methodology's efficacy was rigorously evaluated in a real-world construction scenario. Parameters including system stability, the influence of lighting conditions, the extremity of measurable distances, and the breadth of recognition angles were methodically appraised. System stability was ascertained by maneuvering the camera at a uniform velocity, gauging its marker recognition prowess. The impact of varying luminosity on marker discernibility was scrutinized by modulating the ambient lighting. Furthermore, the camera's spatial movement ascertained both the upper threshold of distance until marker recognition waned and the maximal angle at which markers remained discernible.

• Journal of Chest Surgery
• /
• v.57 no.2
• /
• pp.205-212
• /
• 2024

Background: Postoperative atrial fibrillation (A-fib) is a serious complication of cardiac surgery that is associated with increased mortality and morbidity. Traditional 24-hour Holter monitors have limitations, which have prompted the development of innovative wearable electrocardiogram (ECG) monitoring devices. This study assessed a patch-type wearable ECG device (MobiCARE-MC100) for monitoring A-fib in patients undergoing cardiac surgery and compared it with 24-hour Holter ECG monitoring. Methods: This was a single-center, prospective, investigator-initiated cohort study that included 39 patients who underwent cardiac surgery between July 2021 and June 2022. Patients underwent simultaneous monitoring with both conventional Holter and patchtype ECG devices for 24 hours. The Holter device was then removed, and patch-type monitoring continued for an additional 48 hours, to determine whether extended monitoring provided benefits in the detection of A-fib. Results: This 72-hour ECG monitoring study included 39 patients, with an average age of 62.2 years, comprising 29 men (74.4%) and 10 women (25.6%). In the initial 24 hours, both monitoring techniques identified the same number of paroxysmal A-fib in 7 out of 39 patients. After 24 hours of monitoring, during the additional 48-hour assessment using the patch-type ECG device, an increase in A-fib burden (9%→38%) was observed in 1 patient. Most patients reported no significant discomfort while using the MobiCARE device. Conclusion: In patients who underwent cardiac surgery, the mobiCARE device demonstrated diagnostic accuracy comparable to that of the conventional Holter monitoring system.

• Convergence Security Journal
• /
• v.24 no.2
• /
• pp.9-17
• /
• 2024

This paper examines the security threats to enterprise Generative Artificial Intelligence systems and proposes countermeasures. As AI systems handle vast amounts of data to gain a competitive edge, security threats targeting AI systems are rapidly increasing. Since AI security threats have distinct characteristics compared to traditional human-oriented cybersecurity threats, establishing an AI-specific response system is urgent. This study analyzes the importance of AI system security, identifies key threat factors, and suggests technical and managerial countermeasures. Firstly, it proposes strengthening the security of IT infrastructure where AI systems operate and enhancing AI model robustness by utilizing defensive techniques such as adversarial learning and model quantization. Additionally, it presents an AI security system design that detects anomalies in AI query-response processes to identify insider threats. Furthermore, it emphasizes the establishment of change control and audit frameworks to prevent AI model leakage by adopting the cyber kill chain concept. As AI technology evolves rapidly, by focusing on AI model and data security, insider threat detection, and professional workforce development, companies can improve their digital competitiveness through secure and reliable AI utilization.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.1
• /
• pp.93-104
• /
• 2024

Demand response is a strategy that encourages customers to adjust their consumption patterns at times of peak demand with the aim to improve the reliability of the power grid and minimize expenses. The integration of renewable energy sources into smart grids poses significant challenges due to their intermittent and unpredictable nature. Demand response strategies, coupled with reinforcement learning techniques, have emerged as promising approaches to address these challenges and optimize grid operations where traditional methods fail to meet such kind of complex requirements. This research focuses on investigating the application of reinforcement learning algorithms in demand response for renewable energy integration. The objectives include optimizing demand-side flexibility, improving renewable energy utilization, and enhancing grid stability. The results emphasize the effectiveness of demand response strategies based on reinforcement learning in enhancing grid flexibility and facilitating the integration of renewable energy.

• Advances in nano research
• /
• v.16 no.6
• /
• pp.623-638
• /
• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• Herbal Formula Science
• /
• v.31 no.4
• /
• pp.389-413
• /
• 2023

Objectives : This study aimed to review the current trends in experimental studies on the use of natural products for treatment of gastroesophageal reflux disease (GERD). Methods : Experimental studies assessing the efficacy of natural products against GERD were searched on PubMed. Articles were selected based on predefined inclusion and exclusion criteria and then analyzed for experimental methods, interventions, and result analysis techniques. Results : A total 37 studies were included in this review. Predominantly, in vivo experiments were conducted to induce GERD through surgery, involving the ligation of the pylorus and the transitional junction between the corpus and the forestomach using 7-week-old male Sprague-Dawley rats. The acute induction model, sacrificing animals after a single administration following GERD induction, was mainly used.The utilization of cell experiments was relatively infrequent, with a focus on assessing antioxidant and anti-inflammatory effects via the treatment of the RAW 264.7 cell line with lipopolysaccharides treatment. Glycyrrhizae Radix et Rhizoma, Pinelliae Tuber, Ginseng Radix and Zingiberis Rhizoma were used as single ingredients, and herbal formula, STW-5 (iberogast), Rikkunshito (六君子湯), Banhasasim-tang (半夏瀉心湯), and Hewei Jiangni granule (和胃降逆湯) were used. Outcome analysis methods encompassed Macroscopic evaluation, esophageal function assessment, blood biomarker analysis, histological examination, protein analysis, gene expression analysis, and gastric juice analysis. Proton pump inhibitors were predominantly employed as positive controls. Conclusions : This study revealed the current trends in non-clinical research evaluating natural products for GERD. Based on the results of this study, we expect that non-clinical research on clinically effective natural products will be revitalized.

• Current Optics and Photonics
• /
• v.8 no.2
• /
• pp.162-169
• /
• 2024

This study investigates the temperature sensitivities of fiber Bragg grating (FBG) across a broad temperature spectrum ranging from -196 ℃ to 900 ℃. We developed the FBG temperature measurement system using a high-temperature tubular furnace and liquid nitrogen to supply consistent high and low temperatures, respectively. Our research showed that the FBG temperature sensitivity changed from 1.55 to 10.61 pm/℃ in the range from -196 ℃ to 25 ℃ when the FBG was packaged with a quartz capillary. In the 25-900 ℃ range, the sensitivity varied from 11.26 to 16.62 pm/℃. Contrary to traditional knowledge, the FBG temperature sensitivity was not constant. This inconsistency primarily stems from the nonlinear shifts in the thermo-optic coefficient and thermal expansion coefficient across this temperature spectrum. The theoretically predicted and experimentally determined temperature sensitivities of FBGs encased in quartz capillary were remarkably consistent. The greatest discrepancy, observed at 25 ℃, was approximately 1.3 pm/℃. Furthermore, it was observed that at 900 ℃, the FBG was rapidly thermally erased, exhibiting variable reflected intensity over time. This study focuses on the advancement of precise temperature measurement techniques in environments that experience wide temperature fluctuations, and has considerable potential application value.

• FOCUS: LIFE SCIENCE
• /
• no.1
• /
• pp.3.1-3.7
• /
• 2024

The article discusses the critical role of chromatography in the analysis and purification of proteins in biopharmaceuticals, emphasizing the importance of comprehensive characterization for ensuring their safety and efficacy. It highlights the use of Avantor® ACE® HPLC columns for the separation and purification of proteins, focusing on the analysis of intact proteins using reversed-phase liquid chromatography (RPLC) with fully porous particles. This article also details the application of different mobile phase additives, such as TFA and formic acid, and emphasizes the advantages of using type B ultra-pure silica-based columns for efficiency and peak shape in biomolecule analysis. Additionally, it addresses the challenges of analyzing intact proteins due to slow molecular diffusion and introduces the concept of solid-core (or superficially porous) particles, emphasizing their benefits over traditional porous particles for the analysis of therapeutic proteins. Furthermore, it discusses the development of Avantor® ACE® UltraCore BIO columns, specifically designed for the high-efficiency separation of large biomolecules, such as proteins, and demonstrates their effectiveness in achieving high-resolution separations, even for higher molecular weight proteins like monoclonal antibodies (mAbs). In addition, it underscores the complexity of analyzing and characterizing intact protein biopharmaceuticals, requiring a range of analytical techniques and the use of wide-pore stationary phases, operated at elevated temperatures and with relatively shallow gradients. It highlights the comprehensive range of options offered by Avantor® ACE® wide pore columns, including both fully porous and solid-core particles, bonded with a variety of complementary stationary phase chemistries to optimize selectivity during method development. The use of ultrapure and highly inert base silica is emphasized for enabling the use of lower concentrations of mobile phase modifiers without compromising analyte peak shape, particularly beneficial for LC-MS applications. Then the article concludes by emphasizing the significance of reversed-phase liquid chromatography and its compatibility with mass spectrometry as a valuable tool for the separation and analysis of intact proteins and their closely related variants in biopharmaceuticals.

• Journal of the Korean Geosynthetics Society
• /
• v.23 no.1
• /
• pp.17-25
• /
• 2024

The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture's structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.

• Journal of Environmental Science International
• /
• v.33 no.7
• /
• pp.501-509
• /
• 2024

Prunus mume is a culturally significant fruit tree in East Asia that is widely used in traditional foods and medicines. The present study investigated the effects of sunlight exposure and leaf position on the photosynthetic efficiency of P. mume using SPAD values. The study was conducted at Cheongju National University of Education, Korea, under contrasting conditions between sunny (Site A) and shaded (Site B) areas on P. mume trees. Over three days, under varied weather, photosynthetic photon flux density (PPFD) and SPAD measurements were collected using a SPAD-502 plus chlorophyll meter and a smartphone PPFD meter application. The SPAD values of the 60 leaves were measured in triplicate for each tree. The results indicated that trees in sunny locations consistently exhibited higher SPAD values than those in shaded areas, implying greater photosynthetic efficiency. Moreover, leaves positioned higher in the canopy showed increased photosynthetic efficiency under different light conditions, underscoring the significance of leaf placement and light environment in photosynthetic optimization. Despite the daily sunlight variability, these factors maintained a consistent influence on SPAD values. This study concludes that optimal leaf positioning, influenced by direct sunlight exposure, significantly enhances photosynthetic efficiency in P. mume. These findings highlight the potential of integrating smart farming techniques, especially open-field smart farming technology, to improve photosynthesis and, consequently, crop yield and efficiency. The findings also highlight the need for further exploration of environmental factors affecting photosynthesis for agricultural advancement.