• 한국방재학회:학술대회논문집
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• 한국방재학회 2008년도 정기총회 및 학술발표대회
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• pp.549-552
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• 2008

In this study, we deployed the cable tunnel inspection and monitoring system by wireless sensor network. It is shown that the wireless sensor network which is composed of sensor, wireless communication module, and gateway can be applied to cable tunnel monitoring system. Sensors considered herein are flame detection sensor, flood detection sensor, intruder detection sensor, and temperature sensor, etc. It is also found that the wireless sensor network can deliver sensing data reliably by wireless sensing technology. The gateway system that can transmit sensed data to server by CDMA is developed. Monitoring system is constructed by web service technology, and it is observed that this system can monitor the present state of tunnel without difficulties. The system provides an alternative to inspecting and monitoring the tunnel efficiently where the conventional wired system is infeasible.

• Structural Engineering and Mechanics
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• 제44권4호
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• pp.417-429
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• 2012

Taking the superficial temperature increment as the major fatigue damage indicator, the infrared thermography was used to predict fatigue parameters (fatigue strength and S-N curve) of welded joints subjected to fatigue loading with a high mean stress, showing good predictions. The fatigue damage status, related to safety evaluation, was tightly correlated with the temperature field evolution of the hot-spot zone on the specimen surface. An energetic damage model, based on the energy accumulation, was developed to evaluate the residual fatigue life of the welded specimens undergoing cyclic loading, and a good agreement was presented. It is concluded that the infrared thermography can not only well predict the fatigue behavior of welded joints, but also can play an important role in health detection of structures subjected to mechanical loading.

• Asian Pacific Journal of Cancer Prevention
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• 제13권7호
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• pp.3423-3426
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• 2012

Background: The objective of this study was to measure the antibody content of NuTu-19 ovarian cancer cells in serum samples using a quartz crystal microbalance (QCM) immunosensor. Materials and Methods: NuTu-19 cells were first cultured onto the electrode surfaces of crystals in Dulbecco's modified Eagle medium, and then specified amounts of immunized serum samples of immunized rabbit were also added. The change in mass caused by specific adsorbtion of antibodies of NuTu-19 to the surfaces of the crystals was detected. Results: The change in resonance frequency of crystals caused by immobilization of NuTu-19 cells was from 83 to 429Hz. The antibody content of NuTu-19 detected was 341ng/ul. The frequency shifts were linearly dependent on the amount of antibody mass in the range of 69 to 340ng. The positive detection rate and the negative detection rate were 80% and 100%, respectively. Conclusion: This immunoassay provides a viable alternative to other early ovarian cancer detection methods and is particularly suited for health screening of the general population.

• Smart Structures and Systems
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• 제24권5호
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• 한국직업건강간호학회지
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• 제28권1호
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• pp.21-35
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• 2019

Purpose: The purpose of this study is to propose directions for the development of Occupational Health Nursing Intervention by identifying the current status and quality of Occupational Health Nursing Intervention Research in domestic industries. Methods: Between 2000 and August of 2018, total of 1,181 Occupational Health Nursing related published references were searched using 4 domestic databases, and of the total, 29 final theses that suited the requirements were analysed In this research, the quality assessment of literature that were selected as suitable was conducted using a tool for assessing the biasing risk of non-randomized studies, RoBANS(Risk of Biasing Assessment Tool for Non-randomized Study). Results: For all research, nonequivalent control group pre-posttest design was the most used as quasi-experimental designs. The effectiveness of intervention was found both in terms of physical and psychological aspects, and the result of the risk of biasing assessment showed a high risk levels in both "confounding variables" and "detection bias". Conclusion: Occupational Health Nursing Intervention have been steadily making improvements in terms of both quality and quantity, and as for more effective intervention developments that improves the physical and mental health of the workers, supplementation in strict research design and in ethical aspects deems necessary.

• 한국응급구조학회지
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• 제27권1호
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• pp.79-90
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• 2023

Purpose: The study attempted to improve the health promotion behavior of university students by identifying the factors that affect health promotion behavior and by checking depression, anxiety, and stress levels of university students after the COVID-19 pandemic. Methods: We collected data using a structured questionnaire targeting 170 university students in C-province between December 1 and December 31, 2022. Results: Health promotion behavior had a significantly negative correlation with Depression (r=-.361, p<.001), Anxiety (r=-.191, p=.012), and Stress (r=-.301, p<.001), respectively. The influencing factors of health promotion behavior are gender (r=0.184, p<.001) and depression (r=-0.303, p<.001); the explanatory power is accounted for 15%. Conclusion: A practical method with counseling programs and mental health support services for early detection of risk groups by periodically monitoring the depression state of university students requires practicing health promotion behavior. Therefore, active support and attention should be provided to manage the mental health of university students.

• 분석과학
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• 제33권1호
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• pp.42-48
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• 2020

Bromate is a disinfection by-product generated mainly from the oxidation of bromide during the ozonation and disinfection process in order to remove pathogenic microorganism of drinking water, and classified as a possible human carcinogen by International Agency for Research of Cancer (IARC) and World Health Organization (WHO). For the purpose of determining the trace level concentration of bromate, several sensitive techniques are applied mostly based on suppressed conductivity detection and UV/Visible detection after postcolumn reaction (PCR). In this study, the suppressed conductivity detection method and the PCR-UV/Visible detection method through the triiodide reaction were compared to analyze the trace bromate in water samples and estimated for the availability of these analytical methods. In addtion, the state-of-the-art techniques was applied for the determination of trace level bromate in various water matrices, i.e., soft drinking water, hard drinking water, mineral water, swimming pool water, and raw water. In comparison of two analytical methods, it was found that the conductivity detection had the suitable advantage to simultaneously analyze bromate and inorganic anions, however, the bromate might not be precisely quantified due to the matrix effect especially by chloride ion. On the other hand, the trace bromate was analyzed effectively by the method of PCR-UV/Visible detection through triiodide reaction to satisfactorily minimize the matrix interference of chloride ion in various water samples, showing the good linearity and reproducibility. Furthermore, the method detection limit (MDL) and recovery were 0.161 ㎍/L and 101.0-108.1 %, respectively, with a better availability compared to conductivity detection.

• Journal of Preventive Medicine and Public Health
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• 제41권4호
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• pp.214-218
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• 2008

Bioterrorism events have worldwide impacts, not only in terms of security and public health policy, but also in other related sectors. Many countries, including Korea, have set up new administrative and operational structures and adapted their preparedness and response plans in order to deal with new kinds of threats. Korea has dual surveillance systems for the early detection of bioterrorism. The first is syndromic surveillance that typically monitors non-specific clinical information that may indicate possible bioterrorism-associated diseases before specific diagnoses are made. The other is infectious disease specialist network that diagnoses and responds to specific illnesses caused by intentional release of biologic agents. Infectious disease physicians, clinical microbiologists, and infection control professionals play critical and complementary roles in these networks. Infectious disease specialists should develop practical and realistic response plans for their institutions in partnership with local and state health departments, in preparation for a real or suspected bioterrorism attack.

• Smart Structures and Systems
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• 제31권4호
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• pp.351-363
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• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.