• Asian Pacific Journal of Cancer Prevention
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• v.13 no.1
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• pp.269-273
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• 2012

We aimed to estimate the prevalence of high-risk human papillomavirus (HPV) infections in women of high socioeconomic status (SES) in Seoul, Republic of Korea and to identify risk factors. This study included 13,386 women visiting a prestigious healthcare center located in Seoul between 2003 and 2008. High-risk HPV infections were detected in 994 (7%) and the age-standardized prevalence was 8%. Abnormal Pap smear results ${\geq}$ atypical squamous cells of unknown significance (ASCUS) were observed in 280 of 12,080 women (2%). Based on univariate analysis, age, level of education and number of children were associated with high-risk HPV infections. Based on multivariate analysis, age and high-risk HPV infections had an inverse relationship. In women with high SES in Seoul, the prevalence of high-risk HPV infection was 7% and the age-standardized prevalence was 8%. Age was a strong determinant of high-risk HPV infection.

• Phonetics and Speech Sciences
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• v.8 no.4
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• pp.89-95
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• 2016

The purpose of this work is to analyze and synthesize the pseudo-periodicity of voice using a source model. A speech signal has periodic characteristics; however, it is not completely periodic. While periodicity contributes significantly to the production of prosody, emotional status, etc., pseudo-periodicity contributes to the distinctions between normal and abnormal status, the naturalness of normal speech, etc. Measurement of pseudo-periodicity is typically performed through parameters such as jitter and shimmer. For studying the pseudo-periodic nature of voice in a controlled environment, through collected natural voice, we can only observe the distributions of the parameters, which are limited by the size of collected data. If we can generate voice samples in a controlled manner, experiments that are more diverse can be conducted. In this study, the probability distributions of vowel pitch variation are obtained from the speech signal. Based on the probability distribution of vocal folds, pulses with a designated jitter value are synthesized. Then, the target and re-analyzed jitter values are compared to check the validity of the method. It was found that the jitter synthesis method is useful for normal voice synthesis.

• Nutrition Research and Practice
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• v.8 no.5
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• pp.533-538
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• 2014

BACKGROUND/OBJECTIVES: We investigated the effect of Pycnogenol (Pyc) on survival and immune dysfunction of C57BL/6 mice induced by low micronutrient supplementation. MATERIALS/METHODS: Female C57/BL/6 mice were fed a diet containing 7.5% of the recommended amount of micronutrients for a period of 12 wks (immunological assay) and 18 wks (survival test). For immunological assay, lymphocyte proliferation, cytokine regulation, and hepatic oxidative status were determined. RESLUTS: Pyc supplementation with 50 and $100mg{\cdot}kg^{-1}{\cdot}bw{\cdot}d^{-1}$ resulted in partial extension of the median survival time. Pyc supplementation led to increased T and B cell response against mitogens and recovery of an abnormal shift of cytokine pattern designated by the decreased secretion of Th1 cytokine and increased secretion of Th2 cytokine. Hepatic vitamin E level was significantly decreased by micronutrient deficiency, in accordance with increased hepatic lipid peroxidation level. However, Pyc supplementation resulted in a dose-dependent reduction of hepatic lipid peroxidation, which may result from restoration of hepatic vitamin E level. CONCLUSION: Findings of this study suggest that Pyc supplementation ameliorates premature death by restoring immune dysfunction, such as increasing lymphocyte proliferation and regulation of cytokine release from helper T cells, which may result from the antioxidative ability of Pyc.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.4
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• pp.230-242
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• 2010

In this paper, an environmental monitoring and diagnosis system based on ubiquitous computing technology, shortly u-Eco Monitoring System, is proposed. u-Eco Monitoring System is designed to: 1) Collect information from the manufacturing processes via ubiquitous computing technology, 2) Analyze the current status, 3) Identify the cause of problem if detected by rule-based and case-based reasoning, and 4) Provide the results to the operator for proper decision making. Based on functional modeling, a generic architecture is derived, followed by application to a manufacturing system in iron and steel making industry. Finally, to show the validity of the proposed method, a prototype is developed and tested. The developed methods can be used as a conceptual framework for designing environmental monitoring and diagnosis system for industrial practices by which monitoring accuracy and response time for abnormal status can be significantly enhanced, and relieving operator pressure from manual monitoring and error-prone decision making.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.7
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• pp.3489-3493
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• 2012

Hepatocellular carcinoma (HCC), the most common primary hepatic tumor, is highly prevalent in the Asia-Pacific region, including Thailand. Many genetic and epigenetic alterations in HCC have been elucidated. The aim of this study was to determine whether aberrant methylation of the suppressor of cytokine signaling 1 gene (SOCS1) occurs in HCCs. Methylation specific-PCR assays were performed to identify the methylation status of SOCS1 in 29 tumors and their corresponding normal liver tissues. An abnormal methylation status was detected in 17 (59%), with a higher prevalence of aberrant SOCS1 methylation significantly correlating with HCC treated without chemotherapy (OR=0.04, 95%CI=0.01-0.31; P=0.001). This study suggests that epigenetic aberrant SOCS1 methylation may be a predictive marker for HCC patients.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.35-42
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• 2016

Electrocardiogram (ECG) signal gives a clear indication whether the heart is at a healthy status or not as the early notification of a cardiac problem in the heart could save the patient's life. Several methods were launched to clarify how to diagnose the abnormality over the ECG signal waves. However, some of them face the problem of lack of accuracy at diagnosis phase of their work. In this research, we present an accurate and successive method for the diagnosis of abnormality through Discrete Wavelet Transform (DWT), QRS complex detection and Support Vector Machines (SVM) classification with overall accuracy rate 95.26%. DWT Refers to sampling any kind of discrete wavelet transform, while SVM is known as a model with related learning algorithm, which is based on supervised learning that perform regression analysis and classification over the data sample. We have tested the ECG signals for 10 patients from different file formats collected from PhysioNet database to observe accuracy level for each patient who needs ECG data to be processed. The results will be presented, in terms of accuracy that ranged from 92.1% to 97.6% and diagnosis status that is classified as either normal or abnormal factors.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.4
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• pp.137-147
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• 1998

The neural net application was tried to develop the technique for monitoring the health status of a steel truss bridge which was scaled down to 1/15 of the real bridge for the laboratory experiments. The damage scenarios were chosen as 7 cases. The dynamic behavior, which was changed due to the breakage of the members, of the bridge was investigated by finite element analysis. The bridge consists of single spam, and eight (8) main structural subsystems. The loading vehicle, which weighs as 100 kgf, was operated by the servo-motor controller. The accelerometers were bonded on the surface of 7 cross-beams to measure the dynamic behavior induced by the abnormal structural condition. Artificial neural network technique was used to determine the severity of the damage. At first, the neural net was learnt by the results of finite element analysis, and also, the maximum detection error was 3.65 percents. Another neural net was also learnt, and verified by the experimental results, and in this case, the maximum detection error was 1.05 percents. In future study, neural net is necessary to be learnt and verified by various data from the real bridge.

• Journal of Korean Medicine for Obesity Research
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• v.2 no.1
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• pp.1-12
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• 2002

Eating disorders are psychiatric disorders characterized by abnormal eating patterns and cognitive distortions related to food, weight and shape, which is in turn result in adverse effects on nutrition status, medical complications, and impaired health status and function. The American Psychiatric Association's DSMIVTR offers two diagnoses to describe disordered eating anorexia nervosa and bulimia nervosa. A third category, eating disorder not otherwise specified(EONOS) include binge eating disorder. The prevalence of eating disorder has greatly increased among adolescence and young adults since 1990's when rapid import of western culture took place. It is likely that patients who ask for weight loss are at high risk of having eating disorder. Severe dietary restriction for weight loss may cause eating disorder. Therefore it is recomendable for doctors to have appropriate understanding and guidelines of eating disorder to help their patients.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.500-502
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• 2014

Recently, a nursery and preschool are doing its best to protect children, but an unexpected accident happened. Generally, when children have been abnormal status by accident or disease, activity and body heat are changed. In this study, to prevent such accidents, we propose real-time monitoring system which take children's body heat and activity and manage children's status by smart-phone and PC.

• Journal of Drive and Control
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• v.18 no.1
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• pp.24-30
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• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.