• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.10 no.1
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• pp.208-222
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• 2000

This study was conducted to supplement limit of previous study, The objectives of this study were to select optimal conditions of high performance liquid chromatography(HPLC) operation for detecting urinary 2-thiothiazolicline-4-carboxylic acid(TTCA) and thiocarbamide simultaneously, and to evaluate recovery rates for various liquid-liquid extration method of these metabolites, The results are as follows : 1. The urinary TTCA and thiocarbamide were separate sharply when flow rate is $0.7m{\ell}/min$, using a series $C_8$ and $C_{18}$ column, 50 mM $KH_2PO_4$ : acetonitrile (93.5 : 6.5) and pH 3.5 as a mobile phase. The retention time was TTCA, $12.07{\pm}0.11$(mean${\pm}$SD, n=06), thiocarbamide, $7.85{\pm}0.01$ (mean${\pm}$SD, n=6), respectively. The calibration curve for TTCA and thiocarbamide was linear within the range 0.05 to $30{\mu}g/m{\ell}$. 2. By the liquid-liquid extration, butanol extration with $(NH_4)_2$ as a salting-out reagent was used as a simultaneous extration method for these metabolites in acid state, and recovery rates of this method are urinary TTCA, $49.6{\pm}17.7$ (mean${\pm}$SD, n=16), thiocarbamide, $43,9{\pm}5.50$ (mean${\pm}$SD, n=16), respectively 3. The precision(pooled coefficients of variation for 4 concentration) of the urinary thiocarbamide analysis was 0.03754 by butanol liquid-liquid extraction with $(NH_4)_2$ as a salting-out reagent, and TTCA was 0.04082 by ethyl acetate liquid-liquid extration with $(NH_4)_2$ as a salting out reagent The above results show that the butanol liquid-liquid extraction with $(NH_4)_2$ as a salting-out reagent in acid state, and using a series $C_8$ and $C_{18}$ column, 50 mM $KH_2PO_4$ : acetonitrile (93.5 : 6.5) and pH 3.5 as a mobile phase are suitable for the analysis of urinary TTCA and thiocarbamide simultaneously. The detection limit of TTCA and thiocarbamide was about $0.17{\mu}g/m{\ell}$, $0.07{\mu}g/m{\ell}$.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• Computers and Concrete
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• v.33 no.4
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• pp.341-348
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• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.93-102
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• 2019

As interest in wellness grows, There is a lot of research about monitoring individual health using wearable devices. Accordingly, a variety of methods have been studied to distinguish exercise from daily activities using wearable devices. Most of these existing studies are machine learning methods. However, there are problems with over-fitting on individual person's learning, data discontinuously recognition by independent segmenting and fake activity. This paper suggests a detection method for exercise activity based on the physiological response principle of heart rate up and down during exercise. This proposed method calculates activity intensity and heart rate from triaxial and photoplethysmography sensor to determine a heart rate recovery, then detects exercise by estimating activity intensity or detecting a heart rate rising state. Experimental results show that our proposed algorithm has 98.64% of averaged accuracy, 98.05% of averaged precision and 98.62% of averaged recall.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.3
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• pp.329-335
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• 2019

Annually, millions of children die from respiratory virus infections. Human rhinovirus (HRV) is a causative agent of severe respiratory infections in young, elderly, and asthmatic patients with weak immunity. In this study, 9,010 respiratory virus specimens were collected from January 2012 to December 2018 at Dankook University Hospital, Cheonan and examined by real-time reverse transcription polymerase chain reaction. Twelve respiratory viruses were detected. The mean detection rate was 21.3% (N=1,920/9,010), and the mean age of HRV-positive patients was 6.5 years (median age: 1.6 years, range: 0.0~96.0). The detection rate was the highest in July (32.4%) and the lowest in February (8.3%). When the detection rate was analyzed by age group, the detection rate was the second highest in patients aged 10~19 years. The co-infection rate of HRV was 35.3%, and the most common combination was with Adenovirus. Respiratory virus infections are known to occur in children and elderly people with weak immunity. However, in this study, the detection rate was second highest in patients aged 10~19 years. Indeed, the detection rate in this age group was more than 15%, except in January and February. These results suggested that steady-state studies on the infection patterns of HRV are required.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3949-3953
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• 2012

Head and neck cancers are amongst the commonest malignancies, accounting for approximately 20% of the cancer burden in India. The major risk factors are tobacco chewing, smoking and alcohol consumption, which are all preventable. This retrospective study presents data from the histopathology register for a five year period from 2002-2006 at Patna Medical College and Hospital, a tertiary care hospital drawing patients from the entire Bihar state, the 3rd most populous state of India with the majority of the population residing in rural areas. Incidence rates based on sex, age, site of lesion, including age standardized incidence rates for males and females, with mean age of presentation, distribution of histological variants and year wise trend were calculated. Out of 455 head and neck neoplasias, 241 were benign while 214 were malignant. The most common age group for all malignant biopsies was 7th decade for males and the 5th decade for females. Malignant cases were commoner in males than females with the male:female ratio of 3.1:1, which was found to be statistically significant by the chi-square (${\chi}^2$) test. The crude rate and age standardized incidence rate was 0.05 and 0.06 per 100,000 population respectively. Squamous cell carcinoma (SCC) contributed about 96% of all cases, with grade I being the most common. Larynx was the most common site for malignancy, the supraglottic region being its most commonly affected sub-site. This observed incidence patterns in the region are a reminder of widespread unawareness, low healthcare utilization with virtually non-existent cancer programs. It also underlines the need to advocate for reliable cost-effective programs to create awareness, for early detection and plan appropriate management strategies. There is a compelling demand for a cancer registry in this region as well as proper implementation of preventive measures to combat this growing threat of cancer, many of whose risk factors are preventable.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.265-271
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• 2018

This paper presents a method for detecting damage to a structure at low cost using its impedance. The impedance technique is a typical method to detect local damage for structural health monitoring. This is a common technique for estimating damage by monitoring the electro-mechanical admittance signal of the structure. To apply this technique, an expensive impedance analyzer is generally used. On the other hand, it is necessary to develop a low-cost variant to effectively disseminate the technique. In this study, a method based on the transfer impedance using a function generator and digital multimeter, which are generally used in the laboratory instead of an impedance analyzer, was developed. That is, this technique estimates the damage by comparing the damage index using the amplitude ratio of the output voltage measured in the healthy and damaged state. A transfer impedance test was carried out on a steel specimen. By comparing the damage index, the presence of damage could be assessed reasonably. This study is a basic investigation of an impedance-based low-cost damage detection method that can be used effectively for structural health monitoring if supplemented with future research to estimate the damage location and severity.

• Journal of Preventive Medicine and Public Health
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• v.27 no.4 s.48
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• pp.747-761
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• 1994

Workers' periodic health examination is the main tools used to manage the health problems of most workers in Korea. The most common health problem found in workers' periodic health examination is liver disorder. Liver disorder is also one of the most common health problems in general population and one of the leading causes of mortality in adult population. Regulation proposed by government (No. 207, Ministry of Labor, 1992) defines the criteria for selection of workers with the liver dysfunction for further evaluative examination and the examination items used for diagnosis of the workers with liver dysfunction. This study was designed to evaluate the proficiency of each examination items presently defined in Regulation and propose the optimal examination items for detection of the liver disorders found by workers' periodic health examination. Study subjects are 186 workers with abnormal liver function tests in screening examination of workers' periodic health examination. Questionnaire survey including past history of liver disorder, drinking history, height and weight was done. Physical examination by physician, routine test items defined by Regulation (SGOT, SGPT, $\gamma$-GTP, protein, albumin, total and direct bilirubin, alkaline phosphatase, $\alpha$-feto protein, HBsAg and anti-HBs), anti-HCV antibody test and liver ultrasonography were done. Results are as follows; 1. Result of evaluative examination utilizing only the items defined in Regulation was; There were 75 workers with suspected live. disorder(40.3%), 63 with no liver dysfunction (33.9%), 13 with suspected hepatitis B(7.0%), 10 workers with hepatitis B(5.4%), 10 workers with hepatitis B carrier state(5.4%), 10 with alcoholic liver disorders(5.4%), 5 with fatty liver(2.7%). When alternative diagnostic criteria applying additional examination items (drinking history, body mass index, anti-HCV antibody and ultrasonography) diagnosability of liver disorder was increased. When all four items were included, final results were; 23 workers (17.8%) with hepatitis B (10 carriers, 13 suspects and 10 hepatitis B), 10 (5.4%) with hepatitis C(4 carriers, 5 suspects and 1 hepatitis C), 13(7.0%) with alcoholic liver disorder, 45(24.2%) with fatty liver (40 suspects, 5 fatty liver), 410%) with suspected liver disorders and 44 (23.7%) with normal liver. 2. Of examination items defined by Regulation, only SGOT, SGPT, $\gamma$-GTP and HBsAg were significantly different in abnormal rate and mean value, and all other laboratory findings did not showed significant difference between two groups. Drinking history, body mass index and anti-HCV antibody test which are the items that authors included in this study, also showed significant difference between two groups. Utilization of body mass index (BMI) for abnormal liver function group in diagnosis of fatty liver had high specificity (97.6%) but sensitivity (22.3%) was low. Therefore we suggest that SGOT, SGPT, $\gamma$-GTP, HBsAg, alcohol drinking history, BMI and anti-HCV Ab were useful for diagnosis of liver disorders among worker's periodic health examination.

• Journal of Preventive Medicine and Public Health
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• v.28 no.2 s.50
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• pp.526-541
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• 1995

This study was conducted to estimate the validity of reverse transcriptase-polymerase chain reaction(RT-PCR) compared to enzyme immunoassay(EIA) for the detection of hepatitis C virus (HCV) infection. EIA for antibody to HCV(anti-HCV) and RT-PCR for HCV was executed on the subjects from Pusan and Kyungnam area with questionnaire survey to collect some relating factors of HCV infection. As the result from 617 cases, the prevalence of HCV infection was 1.5% by EIA and 3.7% by RT-PCR(p<0.05), and the age standardized rate was 1.7% and 3.4% by EIA and RT-PCR, respectively. The prevalence of hepatitis B surface antigen(HBsAg) was 6.8% by enzyme linked immunosorbent assay(ELISA) and the age standardized rate was 7.7%. It was the higher in male group comparing to female group(p<0.01). Both of the prevalence of HCV and HBsAg were higher in elevated asparate aminotransferase(AST) and alanine aminotransferase (ALT) group than in normal AST and ALT group(p<0.01). There was no specific risk factor of HCV infection. Though the degree of agreement of EIA and RT-PCR by gamma statistics was 97.2%, it showed a significant difference between the two methods(p<0.01). For the detection of HCV infection, positive predictive value of EIA was 66.7% and negative predictive value of EIA was 97.2%. This study suggests that negative result to anti-HCV by EIA didn't mean the free state of HCV infection, therefore it would be helpful that further monitoring for HCV infection by RT-PCR in the case of elevated AST and ALT and/or clinically suspected.