• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 1999.04a
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• pp.149-156
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• 1999

In the United States (U.S.), the monitored natural attenuation (MNA) approach has been used as an alternative remedial option for organic and inorganic compounds retained in soil and dissolved in groundwater. The U.S. Environmental Protection Agency (EPA) defines the MNA as“in-situ naturally-occurring processes include biodegradation, diffusion, dilution, sorption, volatilization, and/or chemical and biochemical stabilization of contaminants and reduce contaminant toxicity, mobility or volume to the levels that are protective of human health and the environment”. The Department of Soil Environment. National Institute Environmental Research (NIER) is in the process for demonstrating the MNA approach as a potential remedial option for the BTEX contaminated site in Uiwang City. The project is charactering the research site in terms of the nature and extend of contamination, biological degradation rate, and geochemical and hydrological properties. The microbial-degradation rate and effectiveness of nutrient and redox supplements will be determined through laboratory batch and column tests. The geochemical process will be monitored for determining the concentration changes of chemical species involved in the electron transfer processes that include methanogenesis, sulfate and iron reduction, denitrification, and aerobic respiration. Through field works, critical soil and hydrogeologic parameters will be acquired to simulate the effects of dispersion, advection, sorption, and biodegradation on the fate and transport of the dissolved-phase BTEX plume using Bioplume III model. The objectives of this multi-years research project are (1) to evaluate the MNA approach using the BTEX contaminated site in Uiwang City, (2) to establish a standard protocol for future application of the approach, (3) to investigate applicability of the passive approach as a secondary treatment remedy after active treatments. In this presentation, the overall picture and philosophy behind the MNA approach will be reviewed. Detailed discussions of the site characterization/monitoring plans and risk-based decision-making processes for the demonstration site will be included.

• Smart Structures and Systems
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• v.19 no.1
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• pp.1-10
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• 2017

The operation of subway trains induces secondary structure-borne vibrations in the nearby underground spaces. The vibration, along with the associated noise, can cause annoyance and adverse physical, physiological, and psychological effects on humans in dense urban environments. Traditional tethered instruments restrict the rapid measurement and assessment on such vibration effect. This paper presents a novel approach for Wireless Smart Sensor (WSS)-based autonomous evaluation system for the subway train-induced vibrations. The system was implemented on a MEMSIC's Imote2 platform, using a SHM-H high-sensitivity accelerometer board stacked on top. A new embedded application VibrationLevelCalculation, which determines the International Organization for Standardization defined weighted acceleration level, was added into the Illinois Structural Health Monitoring Project Service Toolsuite. The system was verified in a large underground space, where a nearby subway station is a good source of ground excitation caused by the running subway trains. Using an on-board processor, each sensor calculated the distribution of vibration levels within the testing zone, and sent the distribution of vibration level by radio to display it on the central server. Also, the raw time-histories and frequency spectrum were retrieved from the WSS leaf nodes. Subsequently, spectral vibration levels in the one-third octave band, characterizing the vibrating influence of different frequency components on human bodies, was also calculated from each sensor node. Experimental validation demonstrates that the proposed system is efficient for autonomously evaluating the subway train-induced ambient vibration of underground spaces, and the system holds the potential of greatly reducing the laboring of dynamic field testing.

• Nuclear Engineering and Technology
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• v.51 no.5
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• pp.1417-1427
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• 2019

In this study, we evaluated the performance of a commercial pixelated cadmium zinc telluride (CZT) detector for spectroscopy and identified its feasibility as a Compton camera for radiation monitoring in a nuclear power plant. The detection system consisted of a $20mm{\times}20mm{\times}5mm$ CZT crystal with $8{\times}8$ pixelated anodes and a common cathode, in addition to an application specific integrated circuit. The performance of the various radioisotopes $^{57}Co$, $^{133}Ba$, $^{22}Na$, and $^{137}Cs$ was evaluated. In general, the amplitude of the induced signal in a CZT crystal depends on the interaction position and material non-uniformity. To minimize this dependency, a drift time correction was applied. The depth of each interaction was calculated by the drift time and the positional dependency of the signal amplitude was corrected based on the depth information. After the correction, the Compton regions of each spectrum were reduced, and energy resolutions of 122 keV, 356 keV, 511 keV, and 662 keV peaks were improved from 13.59%, 9.56%, 6.08%, and 5%-4.61%, 2.94%, 2.08%, and 2.2%, respectively. For the Compton imaging, simulations and experiments using one $^{137}Cs$ source with various angular positions and two $^{137}Cs$ sources were performed. Individual and multiple sources of $^{133}Ba$, $^{22}Na$, and $^{137}Cs$ were also measured. The images were successfully reconstructed by weighted list-mode maximum likelihood expectation maximization method. The angular resolutions and intrinsic efficiency of the $^{137}Cs$ experiments were approximately $7^{\circ}-9^{\circ}$ and $5{\times}10^{-4}-7{\times}10^{-4}$, respectively. The distortions of the source distribution were proportional to the offset angle.

• Structural Engineering and Mechanics
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• v.71 no.1
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• pp.57-63
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• 2019

During the last two decades, much joint research regarding vibration based methods has been done, leading to developing various algorithms and techniques. These algorithms and techniques can be divided into modal methods and signal methods. Although modal methods have been widely used for health monitoring and damage detection, signal methods due to higher efficiency have received considerable attention in various fields, including aerospace, mechanical and civil engineering. Signal-based methods are derived directly from the recorded responses through signal processing algorithms to detect damage. According to different signal processing techniques, signal-based methods can be divided into three categories including time domain methods, frequency domain methods, and time-frequency domain methods. The frequency domain methods are well-known and interest in using them has increased in recent years. To determine dynamic behaviours, to identify systems and to detect damages of bridges, different methods and algorithms have been proposed by researchers. In this study, a new algorithm to detect seismic damage in the bridge's piers is suggested. To evaluate the algorithm, an analytical model of a bridge with simple spans is used. Based on the algorithm, before and after damage, the bridge is excited by a sine force, and the piers' responses are measured. The dynamic specifications of the bridge are extracted by Power Spectral Density function. In addition, the Least Square Method is used to detect damage in the bridge's piers. The results indicate that the proposed algorithm can identify the seismic damage effectively. The algorithm is output-only method and measuring the excitation force is not needed. Moreover, the proposed approach does not need numerical models.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.624-637
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• 2019

A semi-submersible offshore platform always operates under complex weather conditions, especially wind and waves. It is vital to analyze the structural dynamic responses of the platform in short-term sea states under the combined wind and wave loads, which touches upon three following work. Firstly, a derived relationship between wind and waves reveals a correlation of wind velocity and significant wave height. Then, an Improved Mixture Simulation (IMS) method is proposed to simulate the time series of wind/waves accurately and efficiently. Thus, a wind-wave scatter diagram is expanded from the traditional wave scatter diagram. Finally, the time series of wind/wave pressures on the platform in the short-term sea states are converted by Workbench-AQWA. The numerical results demonstrate that the proposed numerical methods are validated to be applicable for wind and wave simulations in structural analyses. The structural dynamic responses of the platform members increase with the wind and wave strength. In the up-wind and wave state, the stresses on the deck, the connections between deck and columns, and the connection between columns and pontoons are relatively larger under the vertical bending moment. These numerical methods and results are wished to provide some references for structural design and health monitoring of several offshore platforms.

• Korean Journal of Clinical Pharmacy
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• v.30 no.4
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• pp.243-249
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• 2020

Background: Despite the increased use of direct-acting oral anticoagulants, warfarin is still recommended as first-line therapy in patients with mechanical valves or moderate to severe mitral stenosis. Anticoagulation management services (AMSs) are warranted for patients receiving warfarin therapy due to the complexity of warfarin dosing and large interpatient variability. To overcome limited health care resources, we developed a messenger app-based chatbot that provides information to patients taking warfarin. Methods: We developed "WafarinTalk" as an add-on to the open-source messenger app KakaoTalk. We developed the prototype chatbot after building a database containing seven categories: 1) dosage and indications, 2) drug-drug interactions, 3) drug-food interactions, 4) drug-diet supplement interactions, 5) monitoring, 6) adverse events, and 7) precautions. We then surveyed 30 pharmacists and 10 patients on chatbot reliability and on participant satisfaction. Results: We found that 80% of the pharmacists agreed on the consistency of chatbot responses and 44% agreed on the appropriateness of chatbot. Furthermore, 47% of pharmacists said that they were willing to recommend the chatbot to patients. Of the seven categories, information on drug-food interaction was the most useful; 90% of patients said they were satisfied with the chatbot and 100% of patients said they were willing to use it when they were unable to see a pharmacist. We updated the prototype chatbot with feedback from the survey. Conclusion: This study showed that warfarin-related information could be provided to patients through a messenger application-based chatbot.

• Smart Structures and Systems
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• v.29 no.1
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• pp.237-250
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• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.

• Smart Structures and Systems
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• v.29 no.1
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.102-110
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• 2023

Wastewater Based Epidemiology (WBE) provides useful information not only on the use of illegal drugs in the community, but also on the presence of hygiene and health products and infectious pathogens in sewage facilities. As a consequence of the SARS-CoV-19 virus epidemic in 2019, monitoring the status of the infection is of utmost importance. SARS-CoV-19 was also detected in sewage, and the number and trend of infections in the community suggest that the application of the WBE system would be useful and appropriate. This study introduces a pre-treatment concentration method including viruses in sewage samples. A total of seven methods which were subdivided into methods for adsorption-extraction, ultra-filtration, PEG precipitation, and ultra-centrifugation, and the results for analyzing the recovery rates were included. Meanwhile, it is necessary to pay attention to rapid detection technologies which analyze infectious pathogens at the site of sewage facilities. These can include ELISA, FTIR, SERS, and biosensor based on the detection principle, and the characteristics, advantages, and disadvantages of each were summarized herein. If rapid detection technologies and accurate quantitative analyses are further developed, the use of sewage mechanics in response to pandemic viruses is expected to expand further.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.145-153
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• 2023

This study emphasizes the essential need in the military for effective measurement and monitoring of soldiers' physical fitness, health, and exercise capabilities to enhance both their overall fitness and combat effectiveness. The effective assessment of physical fitness is considered a core element of management, aligning with principles of modern management. Particularly, preparing soldiers with robust physical fitness is deemed crucial for adapting to dynamic changes on the battlefield. In this research, the RFM (Recency, Frequency, Monetary) customer analysis and clustering methods, validated in e-commerce, are introduced as a basis for applying an AI-driven customer analysis approach to assess military personnel fitness. To achieve this, the study explores the incorporation of the RSC (Reveal, Sustainable, Control) analysis model. This model aims to effectively categorize and monitor military personnel fitness. The application of the RFM technique in the RSC analysis model quantifies and models military fitness, fostering continuous improvement and seeking strategies to enhance the effectiveness of fitness management. Through these methods, the study develops an AI customer analysis technique applied to the RSC clustering analysis method for improving and sustaining military personnel fitness.