• Journal of the Korean Geotechnical Society
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• v.39 no.4
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• pp.5-17
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• 2023

The advancement in large-scale underground excavation in urban areas necessitates monitoring and predicting technologies that can pre-emptively mitigate risk factors at construction sites. Traditionally, two methods predict the deformation of retaining walls induced by excavation: empirical and numerical analysis. Recent progress in artificial intelligence technology has led to the development of a predictive model using machine learning techniques. This study developed a model for predicting the deformation of a retaining wall under construction using a boosting-based algorithm and an ensemble model with outstanding predictive power and efficiency. A database was established using the data from the design-construction-maintenance process of the underground retaining wall project in a manifold manner. Based on these data, a learning model was created, and the performance was evaluated. The boosting and ensemble models demonstrated that wall deformation could be accurately predicted. In addition, it was confirmed that prediction results with the characteristics of the actual construction process can be presented using data collected from ground measurements. The predictive model developed in this study is expected to be used to evaluate and monitor the stability of retaining walls under construction.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.216-221
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• 2022

The 4th industrial revolution is accelerating the transition to digital innovation in various aspects of our daily lives, and efforts for manufacturing innovation are continuing in the manufacturing industry, such as smart factories. The 4th industrial revolution technology in manufacturing can be used based on AI, big data, IoT, cloud, and robots. Through this, it is required to develop a technology to establish a production facility data collection and analysis system that has evolved from the existing automation and to find the cause of defects and minimize the defect rate. In this paper, we implemented a system that collects power, environment, and status data from production facility sites through IoT devices, quantifies them in real-time in a cloud computing environment, and displays them in the form of MQTT-based real-time infographics using widgets. The real-time sensor data transmitted from the IoT device is stored to the cloud server through a Rest API method. In addition, the administrator could remotely monitor the data on the dashboard and analyze it hourly and daily.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.50.3-51
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• 2021

The progenitor of Type Ia supernovae is largely expected as a close binary system of a carbon/oxygen white dwarf (WD) primary and its secondary non-degenerate (single degenerate; SD) or degenerate companion (double degenerate; DD). Here we present a high-cadence monitoring observation of SN 2021hpr in a spiral galaxy, NGC 3147. SN 2021hpr shows typical characteristics as a normal type Ia supernova from its photometric (Δm₁₅(B)=1.01±0.03, dust free M_B,max=-19.45±0.02) and spectroscopic data. To investigate its progenitor system, we fit the early part of BVRI-band light curve simultaneously with a combined version of ejecta-companion and simple power-law model. As a result, we found a significant feature of an early excess possibly from a 7.63±0.52R_⊙-sized companion at the optimal viewing angle while the fit is not successful at the common viewing angle. No possible red sources brighter than F555W=-7.01 AB mag is detected at the SN location in Hubble Space Telescope (HST) pre-explosion images, excluding massive stars with initial mass of >16M_⊙ as companions. We suggest the progenitor system of SN 2021hpr can be a fairly large companion such as a main sequence, a low mass subgiant, and a helium giant star. In addition, a possibility of the ejecta-Disk Originated Matter (DOM) interaction for the DD scenario considering linearly-rising early flux still remains.

• Convergence Security Journal
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• v.22 no.1
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• pp.135-140
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• 2022

This study aims to analyze exercise cases and issues using smart devices and technologies, and to present the development direction of a smart exercise environment suitable for the wellness life of active seniors with high activity and economic power unlike the existing silver generation. In the fitness industry, the subscription economy that regularly receives or uses necessary exercise tools, services, and digital content is expanding, and business models based on hardware sales and content subscription continue to emerge. In order to have value competitiveness as a platform that provides active seniors with integrated exercise services for health care, not only fitness centers, but also home training exercise equipment, fitness-related applications, and smart wearable device markets should be organically connected to form an expanded total platform. In order to have value competitiveness as a platform that provides active seniors with integrated exercise services for health care, not only fitness centers, but also home training exercise equipment, fitness-related applications, and smart wearable device markets should be organically connected to form an expanded total platform. The linkage of the digital healthcare function, which provides real-time changes to exercise programs based on continuous monitoring and feed back through wearable devices before, after, and during exercise by receiving and selecting exercise programs suitable for individual health status, is the differentiating factor in the smart fitness model.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2388-2394
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• 2023

The radioactivity concentration of environmental radionuclides was analyzed for soil and sand at eight locations within a radius of 255 m centered on the Dongnam Institute of Radiological & Medical Science (DIRAMS), Korea. The average activity concentrations of ⁴⁰K, ¹³⁷Cs, ²²⁶Ra, and ²³²Th were 661.1 Bq/kg-dry, 0.9 Bq/kg-dry, 21.9 Bq/kg-dry, and 11.1 Bq/kg-dry, respectively. The activity of ⁴⁰K and ¹³⁷Cs was lower than the 3-year (2017-2019) average reported by the Korea Institute of Nuclear Safety, respectively. Due to the nature of granite-rich soil, the radioactivity of ⁴⁰K was 0.6-fold higher than in other countries, while ¹³⁷Cs was in the normal fluctuation range (15-30 Bq/kg-dry) of the concentration of radioactive fallout from nuclear tests. The activity of ²²⁶Ra and ²³²Th was lower than in Korean soils reported by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). The average activity concentrations of ²³²Th and ⁴⁰K for the soil and sand samples from DIRAMS were within the range specified by UNSCEAR in 2000. The radium equivalent activity and internal and external hazard index values were below the recommended limits (1 mSv/y). These radionuclide concentration (²²⁶Ra, ²³²Th, ⁴⁰K, and ¹³⁷Cs) data can be used for regional environmental monitoring and ecological impact assessments of nuclear power plant accidents.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.9-18
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• 2024

Small streams, despite their rich ecosystems, face challenges in vegetation assessment due to the limitations of traditional, time-consuming methods. This study presents a groundbreaking approach, combining unmanned aerial vehicles(UAVs), convolutional neural networks(CNNs), and the vegetation differential vegetation index (VDVI), to revolutionize both assessment and management of stream vegetation. Focusing on Idong Stream in South Korea (2.7 km long, 2.34 km² basin area)with eight diverse revetment methods, we leveraged high-resolution RGB images captured by UAVs across five dates (July-December). These images trained a ResNeXt101 CNN model, achieving an impressive 89% accuracy in classifying vegetation cover(soil,water, and vegetation). This enabled detailed spatial and temporal analysis of vegetation distribution. Further, VDVI calculations on classified vegetation areas allowed assessment of vegetation vitality. Our key findings showcase the power of this approach:(a) TheCNN model generated highly accurate cover maps, facilitating precise monitoring of vegetation changes overtime and space. (b) August displayed the highest average VDVI(0.24), indicating peak vegetation growth crucial for stabilizing streambanks and resisting flow. (c) Different revetment methods impacted vegetation vitality. Fieldstone sections exhibited initial high vitality followed by decline due to leaf browning. Block-type sections and the control group showed a gradual decline after peak growth. Interestingly, the "H environment block" exhibited minimal change, suggesting potential benefits for specific ecological functions.(d) Despite initial differences, all sections converged in vegetation distribution trends after 15 years due to the influence of surrounding vegetation. This study demonstrates the immense potential of UAV-based remote sensing and CNNs for revolutionizing small-stream vegetation assessment and management. By providing high-resolution, temporally detailed data, this approach offers distinct advantages over traditional methods, ultimately benefiting both the environment and surrounding communities through informed decision-making for improved stream health and ecological conservation.

• Journal of muscle and joint health
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• v.31 no.1
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• pp.42-52
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• 2024

Purpose: This study aimed to identify the factors affecting compliance with personal protective equipment (PPE) use among medical device reprocessing staff. Methods: This descriptive cross-sectional study included 163 cleaning staff members from ten general hospitals in Seoul and Gyeonggi. Data were collected using self-report questionnaires administered between July and September 2023. Analysis included t-tests, ANOVA, Pearson's correlation coefficient, Bonferroni correction, and multiple regression, conducted using SAS ver.9.4. Results: Statistically significant differences in compliance with PPE were found based on department and exposure to contamination within six months (t=-2.82, p=.007). Attitudes toward PPE (r=.22, p=.006) and awareness of the safety climate (r=.22, p=.006) showed a statistically significant positive correlation with PPE compliance. Factors influencing use of personal protective equipment by cleaning staff during medical device reprocessing were department, compliance with PPE, and awareness of the safety climate. The explanatory power of these factors was 58.0%. Conclusion: Improving PPE compliance and creating a safe cleaning environment entails fostering a supportive safety climate. Additionally, regular training that takes into consideration the characteristics of the cleaning staff, alongside continuous monitoring, is required.

• Steel and Composite Structures
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• v.51 no.2
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• pp.185-202
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• 2024

In this research paper, and for the first time, wave propagations in sigmoidal imperfect functionally graded material plates are investigated using a simplified quasi-three-dimensionally higher shear deformation theory (Quasi-3D HSDTs). By employing an indeterminate integral for the transverse displacement in the shear components, the number of unknowns and governing equations in the current theory is reduced, thereby simplifying its application. Consequently, the present theories exhibit five fewer unknown variables compared to other Quasi-3D theories documented in the literature, eliminating the need for any correction coefficients as seen in the first shear deformation theory. The material properties of the functionally graded plates smoothly vary across the cross-section according to a sigmoid power law. The plates are considered imperfect, indicating a pore distribution throughout their thickness. The distribution of porosities is categorized into two types: even or uneven, with linear (L)-Type, exponential (E)-Type, logarithmic (Log)-Type, and Sinus (S)-Type distributions. The current quasi-3D shear deformation theories are applied to formulate governing equations for determining wave frequencies, and phase velocities are derived using Hamilton's principle. Dispersion relations are assumed as an analytical solution, and they are applied to obtain wave frequencies and phase velocities. A comprehensive parametric study is conducted to elucidate the influences of wavenumber, volume fraction, thickness ratio, and types of porosity distributions on wave propagation and phase velocities of the S-FGM plate. The findings of this investigation hold potential utility for studying and designing techniques for ultrasonic inspection and structural health monitoring.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.1-8
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• 2015

Wind power systems have gained much attention due to the relatively high reliability, good infrastructures and cost competitiveness to the fossil fuels. Advances have been made to increase the power efficiency of wind turbines while less attention has been focused on structural integrity assessment of structural sub-systems such as towers and foundations. Among many parameters for integrity assessment, the most perceptive parameter may be the induced horizontal displacement at the hub height although it is very difficult to measure particularly in large-scale and high-rise wind turbine structures. This study proposes an indirect displacement estimation scheme based on the combined use of inclinometers and accelerometers for more convenient and cost-effective measurements. To this end, (1) the formulation for data fusion of inclination and acceleration responses was presented and (2) the proposed method was numerically validated on an NREL 5 MW wind turbine model. The numerical analysis was carried out to investigate the performance of the propose method according to the number of sensors, the resolution and the available sampling rate of the inclinometers to be used.

• Journal of Distribution Science
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• v.12 no.4
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• pp.31-39
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• 2014

Purpose - This study investigates the following two issues. First, we attempt to find the important determinants of housing investment and to identify their significance rank using survey panel data. Recently, the expansion of global uncertainty in the real estate market has directly and indirectly influenced the Korean housing market; households demonstrate a sensitive reaction to changes in that market. Therefore, this study aims to draw conclusions from understanding how the impact of financial strength of the household is related to house investment. Second, we attempt to verify the effectiveness of diverse indices of financial strength such as DTI, LTV, and PIR as measures to monitor the housing market. In the continuous housing market recession after the global crisis, the government places top priority on residence stability. However, the government still imposes forceful restraints on indices of financial strength. We believe this study verifies the utility of these regulations when used in the housing market. Research design, data, and methodology - The data source for this study is the "National Survey of Tax and Benefit" from 2007 (1st) to 2011 (5th) by the Korea Institute of Public Finance. Based on this survey data, we use panel data of 3,838 households that have been surveyed continuously for 5 years. We sort the base variables according to relevance of house investment criteria using the decision tree model (DTM), which is the standard decision-making model for data-mining techniques. The DTM method is known as a powerful methodology to identify contributory variables for predictive power. In addition, we analyze how important explanatory variables and the financial strength index of households affect housing investment with the binary logistic multi-regressive model. Based on the analyses, we conclude that the financial strength index has a significant role in house investment demand. Results - The results of this research are as follows: 1) The determinants of housing investment are age, consumption expenditures, income, total assets, rent deposit, housing price, habits satisfaction, housing scale, number of household members, and debt related to housing. 2) The impact power of these determinants has changed more or less annually due to economic situations and housing market conditions. The level of consumption expenditure and income are the main determinants before 2009; however, the determinants of housing investment changed to indices of the financial strength of households, i.e., DTI, LTV, and PIR, after 2009. 3) Most of all, since 2009, housing loans has been a more important variable than the level of consumption in making housing market decisions. Conclusions - The results of this research show that sound financing of households has a stronger effect on housing investment than reduced consumption expenditures. At the same time, the key indices that must be monitored by the government under economic emergency conditions differ from those requiring monitoring under normal market conditions; therefore, political indices to encourage and promote the housing market must be divided based on market conditions.