• Journal of Korean Public Health Nursing
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• v.16 no.2
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• pp.436-444
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• 2002

This study surveyed 146 obese elementary school children(94 male and 52 female) in Seoul, and measured height, body weight, degree of obesity, blood pressure, fasting blood sugar, serum lipid profiles and liver function. The hypertension was above 95 percentile depending on age and sex. and the hyperglycemia was defined as above 110mg/dl. The hypercholesterolemia and hypertriglyceridemia were defined as above 200mg/dl, 160mg/dl, repectively. The abnormal liver function was considered as elevated alanine aminotransferase(ALT>35IU/L)or aspartate aminotransferase(AST>35IU/L). The results were as follows: 1. The mean degree of obesity was $29.56\pm12.56\%$, and the percentage of overweight was $18.5\%$, mild obesity $41.1\%$, moderate obesity $35.6\%$, severe obesity $4.8\%$ respectively in surveyed children. 2. The systolic blood pressure was $108.34\pm13.73mmHg$, diastolic blood pressure was $67.46\pm8.27mmHg$. 3. FBS was $93.79\pm6.51mg/dl$. 4. Total cholesterol, triglyceride, ALT and AST were $183.34\pm31.38mg/dl$. $115.55\pm56.43mg/dl,\;24.08\pm18.42IU/L,\; 28.73\pm10.45IU/L,$ respectively. 5. The prevalence of complications was $47.2\%$ : hypertension$(13\%)$, hyperglycemia$(0.7\%)$, hypercholesterolemia$(23.3\%)$, hypertriglyceridemia$(17.1\%)$, and liver dysfunction$(21.9\%)$. In conclusion. childhood obesity is associated with various risk factors. Therefore, the aggressive approaches to successful prevention, early detection, and effective treatment of obesity in children are urgently required.

• Korean Journal of Environmental Agriculture
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• v.26 no.3
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• pp.254-258
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• 2007

Several physicochemical properties including water solubility, vapor pressure, and hydrolysis of bistrifluron benzoylphenylurea insecticide developed in Korea, were measured based on OECD guidelines for testing of chemicals and Toxic Substances Control Act Test guidelines. Water solubility was low showing 30 ${\mu}g/l$ at $25^{\circ}C$ and in hydrolysis study, half-life at $20^{\circ}C$ was 10.9 days (pH 9.0) and was stable in pH 7.0 solution. At $40^{\circ}C$ hydrolysis rate was 20.6 days (pH 7.0) and 1.5 days (pH 9.0). The vapor pressure of bistrifluron was not obtained from vapor pressure experiments at $25^{\circ}C$ because the bistrifluron was not detected. Therefore, the vapor pressure was determined to be ${\leq}\;2.05{\times}10^{-8}$ torr which was calculated to apply the detection limit (50 ng) of bistrifluron. And this value is suggests it would not give environmental contamination by volatilization.

• Journal of Hydrogen and New Energy
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• v.31 no.2
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• pp.194-201
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• 2020

Recently, Type 2 high-pressure hydrogen storage tank is studied due to fast defect detection, easy manufacturing, and cost efficiency. Moreover, the dry winding a high-strength metal wire will make increased economic efficiency compare with the wet winding method and the carbon/glass fiber winding method. In this study, a theoretical study on the dry winding of a Type 2 high pressure hydrogen tank using a metal wire was done, and the equations of the total stress on the aligned and the staggered winding for the hoop winding were derived, and the following results were obtained by using these equations. As the diameter of the metal wire, the number of winding layers, and the outer diameter of the liner increase, the maximum stress decreases, but the difference between the maximum stress occurring in the aligned winding and the staggered winding increases. As the pressure increases, the thickness of the winding layer increases, but as the strength of the metal wire increases, the thickness of the winding layer decreases. In addition, regardless of the strength of the metal wire, the thickness of the winding layer of the staggered winding was about 13.4% thinner than that of the aligned winding.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.49-53
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• 2024

Maritime safety is a critical factor in protecting lives from accidents at sea and fostering a safer marine environment. In this study, we developed a TPMS device aimed at enhancing maritime safety, providing technological solutions for detecting accidents at sea and enabling swift responses. The device utilizes a Tube Pressure Monitoring System (TPMS) to detect tube expansion and is designed to collect real-time data and communicate with surrounding devices for rapid responses. Experimental results confirm the effective detection of pressure by TPMS (Tube Pressure Monitoring System) and stable data transmission and reception with the main IoT device. Additionally, a mobile app capable of receiving emergency alert messages and accessing information for rapid responses in emergency situations was developed. The developed device and mobile app encompass technology applicable not only in the maritime safety field but also in various other application areas, with potential for expanded application in real-world scenarios in the future. These results are expected to contribute to enhancing safety in the marine environment.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.3
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• pp.181-187
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• 2016

A thermal power plant has a heat exchanger tube to collect and convert the heat generated from the high temperature and pressure steam to energy, but the tubes are arranged in a complex manner. In the event that a leakage occurs in any of these tubes, the high-pressure steam leaks out and may cause the neighboring tubes to rupture. This leakage can finally stop power generation, and hence there is a dire need to establish a suitable technology capable of detecting tube leaks at an early stage even before it occurs. As shown in this paper, by applying acoustic emission (AE) technology in existing boiler tube leak detection equipment (BTLD), we developed a system that detects these leakages early enough and generates an alarm at an early stage to necessitate action; the developed system works better that the existing system used to detect fine leakages. We verified the usability of the system in a 560MW-class thermal power plant boiler by conducting leak tests by simulating leakages from a variety of hole sizes (ⵁ2, ⵁ5, ⵁ10 mm). Results show that while the existing fine leakage detection system does not detect fine leakages of ⵁ2 mm and ⵁ5 mm, the newly developed system could detect leakages early enough and generate an alarm at an early stage, and it is possible to increase the signal to more than 18 dB.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.703-711
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• 2018

Water distribution system (WDS) pipe bursts are caused from excessive pressure, pipe aging, and ground shift from temperature change and earthquake. Prompt detection of and response to the failure event help prevent large-scale service interruption and catastrophic sinkhole generation. To that end, this study proposes a improved Western Electric Company (WECO) method to improve the detection effectiveness and efficiency of the original WECO method. The original WECO method is an univariate Statistical Process Control (SPC) technique used for identifying any non-random patterns in system output data. The improved WECO method multiples a threshold modifier (w) to each threshold of WECO sub-rules in order to control the sensitivity of anomaly detection in a water distribution network of interest. The Austin network was used to demonstrated the proposed method in which normal random and abnormal pipe flow data were generated. The best w value was identified from a sensitivity analysis, and the impact of measurement frequency (dt = 5, 10, 15 min etc.) was also investigated. The proposed method was compared to the original WECO method with respect to detection probability, false alarm rate, and averaged detection time. Finally, this study provides a set of guidelines on the use of the WECO method for real-life WDS pipe burst detection.

• Journal of Korean Society on Water Environment
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• v.25 no.2
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• pp.205-211
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• 2009

In this study, the membrane filter method was compared to the MPN method for the analysis of total coliforms from raw water using raw test waters and controls including 6 standard strains of coliforms, and the various factors were analyzed on the detection of general and fecal origin coliforms. The range of error rate for the detection of 5 standard strains using the membrane filter and the MPN methods was 0 to 6% and 45 to 133%, respectively. The error rate of the membrane filter method was lower than that of the MPN method. The membrane filter method (m-Endo) showed 10% (11 out of 111) of difference for the detection sensitivity of coliforms isolated from raw water compared to the MPN method (BGLB). The membrane filter method was less affected by the factors including temperature, turbidity, charcoals of powder form, contamination, and reverse pressure. In conclusion, the membrane filter method is a better method for the analysis of total coliforms from raw water than the MPN method, considering the accuracy of detection and the tolerance to various experimental factors.

• Journal of Microbiology and Biotechnology
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• v.11 no.6
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• pp.979-985
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• 2001

A fluorometric detection technique for HDL (High Density Lipoprotein) and LDL (Low Density Lipoprotein) was developed for application in a fiber-optic immunosensor using a protein A Langmuir-Blodgget (LB) film. For the fluorescence immunoassay, antibodies specific to HDL or LDL were imobilied on the protein A LB film, and a fluorescence amplification method was developed to overcome their weak fluorescence. The deposition of protein A using the LB technique was monitored using a surface pressure-are $({\pi}-A)$ curve, and the antibody immobilization of the protein A LB film was experimentally verified. The immobilized antibody was used to separate only HDL and LDL from a sample, then the fluorescence of he separated HDL or LDL was amplified. The amount of LDL or HDL was measured using the developed fiber optic fluorescence detection system. The optical properties resulting from the reaction of HDL or LDL with o-phtaldialdehyde, detection range, response time, and stability of the immunoassay were all investigated. The respective detection ranges for HDL and LDL were sufficient to diagnose the risk of coronary heart disease. The amplification step increased the sensitivity, while selective separation using the immobilized antibody led to linearity in the sensor signal. The regeneration of the antibody-immobilized substrate could produce a stable and reproducible immunosensor.

• Smart Structures and Systems
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• v.5 no.2
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• pp.153-171
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• 2009

In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.199-208
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• 2023

The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field.