• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1177-1185
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• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

• Korean Journal of Health Education and Promotion
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• v.12 no.2
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• pp.48-61
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• 1995

This study attempted to analyze the health habits affecting health status of industrial workers. Data was collected from 57 industries of 900 workers at Inchon. The research has been carried out through self-administered questionnaire and from the analysis of the health examination records and the results were as follows: 1. Among 861 respondents, men were 81.2%, women were 18.8%, 42, 6% were of the 30-40 age group, the married were 65.7%, the single were 32.5%, the high school graduates were 62.4% and 37.3% were of people who worked between 1 and 5 years. 2. As far as the seven health habits, current smokers were 54.9%, people who regularly exercise was 31.7%, 7-8 hour of sleep, on the average were 74.0%, people eating breakfast nearly every day were 8.0%, and people eating between meals almost every day were 54.2%. Heavy drinkers who drink 3-4 times or more per week were 13.2%, 1-2 times per week were 26.8% and the obese were 4.6%, 3. Health status of A and B, estimated by doctors in the health examination were 81.7% and C, D1, D2, the unhealthy were 18.3%. Men were reported more than women in unhealthy groups and the results regarding health status reflect gender, educational level and age, That is to say that, lower educational level group and over 30 years of age group perceive their health to be worse than the higher educational level and under 30 age group, And these differences were statistically significant. 4. The relationship between health habits and health status, expressed in terms of the odds ratio. Current smokers had a consistently worse health status than a nonsmokers with a 1.36 odds ratio. The workers who reported eating breakfast rarely or never were more associated with the unhealthy group than the regular breakfast eating group with a 2.48 odds ratio. One or more drink per week had a worse health status than a never or a little drinker with a 1.42 odds ratio. 5, The Health habits score and duration of work were selected as significant factors influencing health status based on logistic analysis. According to the results of this model, the odds ratio of good health status was 2.06 for good health habit score, 1.55 for population who worked seven years or more duration of work. As we have seen, good practices were found to be associated with better health and the effect of the health habits was cummulative;those who followed 6-7 health habits were in better health, even though they were older than those who failed to do so. Therefore, in order to provide the health promotion of workers it is necessary to establish a health management plan for an effective health education and health service. If we use this type of study as a prospective study design, we can get a precise basic data for health promotion and a management plan for industrial workers.

• The Journal of Korean Society for Radiation Therapy
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• v.14 no.1
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• pp.175-186
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• 2002

The purpose of this study is directly measure and evaluate about absorbed dose change according to nominal energy and electron cone or medical accelerator on isodose curve, percentage depth dose, contaminated X-ray, inhomogeneous tissue, oblique surface and irradiation on intracavitary that electron beam with high energy distributed in tissue, and it settled standard data of hish energy electron beam treatment, and offer to exactly data for new dote distribution modeling study based on experimental resuls and theory. Electron beam with hish energy of $6{\sim}20$ MeV is used that generated from medical linear accelerator (Clinac 2100C/D, Varian) for the experiment, andwater phantom and Farmer chamber md Markus chamber und for absorbe d dose measurement of electron beam, and standard absorbed dose is calculated by standard measurements of International Atomic Energy Agency(IAEA) TRS 277. Dose analyzer (700i dose distribution analyzer, Wellhofer), film (X-OmatV, Kodak), external cone, intracavitary cone, cork, animal compact bone and air were used for don distribution measurement. As the results of absorbed dose ratio increased while irradiation field was increased, it appeared maximum at some irradiation field size and decreased though irradiation field size was more increased, and it decreased greatly while energy of electron beam was increased, and scattered dose on wall of electron cone was the cause. In percentage depth dose curve of electron beam, Effective depth dose(R80) for nominal energy of 6, 9, 12, 16 and 20 MeV are 1.85, 2.93, 4.07, 5.37 and 6.53 cm respectively, which seems to be one third of electron beam energy (MeV). Contaminated X-ray was generated from interaction between electron beam with high energy and material, and it was about $0.3{\sim}2.3\%$ of maximum dose and increased with increasing energy. Change of depth dose ratio of electron beam was compared with theory by Monte Carlo simulation, and calculation and measured value by Pencil beam model reciprocally, and percentage depth dose and measured value by Pencil beam were agreed almost, however, there were a little lack on build up area and error increased in pendulum and multi treatment since there was no contaminated X-ray part. Percentage depth dose calculated by Monte Carlo simulation appeared to be less from all part except maximum dose area from the curve. The change of percentage depth dose by inhomogeneous tissue, maximum range after penetration the 1 cm bone was moved 1 cm toward to surface then polystyrene phantom. In case of 1 cm and 2 cm cork, it was moved 0.5 cm and 1 cm toward to depth, respectively. In case of air, practical range was extended toward depth without energy loss. Irradiation on intracavitary is using straight and beveled type cones of 2.5, 3.0, 3.5 $cm{\phi}$, and maximum and effective $80\%$ dose depth increases while electron beam energy and size of electron cone increase. In case of contaminated X-ray, as the energy increase, straight type cones were more highly appeared then beveled type. The output factor of intracavitary small field electron cone was $15{\sim}86\%$ of standard external electron cone($15{\times}15cm^2$) and straight type was slightly higher then beveled type.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.156-165
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• 2006

In this paper, a new intelligent moving target tracking and surveillance system basing on the pan/tilt-embedded stereo camera system is suggested and implemented. In the proposed system, once the face area of a target is detected from the input stereo image by using a YCbCr color model and phase-type correlation scheme and then, using this data as well as the geometric information of the tracking system, the distance and 3D information of the target are effectively extracted in real-time. Basing on these extracted data the pan/tilted-imbedded stereo camera system is adaptively controlled and as a result, the proposed system can track the target adaptively under the various circumstance of the target. From some experiments using 480 frames of the test input stereo image, it is analyzed that a standard variation between the measured and computed the estimated target's height and an error ratio between the measured and computed 3D coordinate values of the target is also kept to be very low value of 1.03 and 1.18$\%$ on average, respectively. From these good experimental results a possibility of implementing a new real-time intelligent stereo target tracking and surveillance system using the proposed scheme is finally suggested.

• The Journal of Engineering Geology
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• v.24 no.4
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• pp.643-648
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• 2014

Understanding of fracture networks and rock mass properties during tunnel construction is extremely important for the prediction of dangers during excavation, and for deciding on appropriate excavation techniques and support. However, rapid construction process do not allow sufficient time for surveys and interpretations for spatial distributions of fractures and rock mass properties. This study introduces a new statistical approach for predicting joint distributions at foreside of current excavation face during the excavation process. The proposed methodology is based on a cumulative space diagram for joint sets. The diagram displays the cumulative spacing between adjacent joints on the vertical axis and the sequential position of each joint plotted at equally spaced intervals on the horizontal axis. According to the diagram, the degree of linearity of points representing the regularity of joint spacing; a linear trend of the points indicates that the joints are evenly spaced, with the slope of the line being directly related to the spacing. The linear points which are stepped indicates that the fracture set show clustered distribution. A clustered pattern within the linear group of points indicates a clustered joint distribution. Fractures surveyed from an excavated space can be plotted on this diagram, and the diagram can then be extended further according to the plotted diagram pattern. The extension of the diagram allows predictions about joint spacing in areas that have not yet been excavated. To test the model, we collected and analyzed data during excavation of a 10-m-long tunnel. Fractures in a 3-m zone behind the excavation face were predicted during the excavation, and the predictions were compared with observations. The methodology yielded reasonably good predictions of joint locations.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.3
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• pp.374-380
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• 2017

Image analysis and k-mean clustering were conducted to develop a grading system of dried sea cucumber (SC) based on rehydration rate. The SC images were obtained by taking pictures in a box under controlled light conditions. The region of interest was extracted to depict the shape of the SC in a 2D graph, and those 2D shapes were rendered to build a 3D model. The results from the image analysis provided the morphological features of the SC, including length, width, surface area, and volume, to obtain the parameters of the k-mean clustering weight. The k-mean clustering classified the SC samples into three different grades. Each SC sample was rehydrated at $30^{\circ}C$ for 40 h. During rehydration, the flux of each grade was analyzed. Our study demonstrates that the mass transfer rate of SC increased as the surface area increased, and the grade of SC was classified based on rehydration rate. This study suggests that the optimal rehydration process for SC can be achieved by applying a suitable grading system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.5
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• pp.673-681
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• 2024

The purpose of this study was to estimate the flood flow and the range of inundation in the downstream area of the Aprokgang(Riv.) that occurred in 2024 using satellite images. The study area was the Aprokgang(Riv.) watershed, the longest river in North Korea. Satellite image Sentinel-1A(SAR-C) data was used for flood analysis before and after the flood. As a result of the analysis of the inundation range using satellite images, it was confirmed that there was a change in the water surface before and after the flood. However, the image after the flood was taken six days after the peak discharge of the flood, so there are limitations in extracting the accurate extent of the flooding. As a result of the analysis using satellite precipitation and ground observed rainfall, the rainfall exceeded 500-year rainfall amount, and the peak-flow was simulated as 85,102 m³/s. As a result of analyzing the inundation range by using the contour lines from AW3D30, it was determined that the inundation occurred around the 15 m contour line. In the future, it is need to conduct a study that can extract more scientifically the inundation range through the use of the inundation analysis model.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.59 no.1
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• pp.89-96
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• 2014

We aimed to evaluate the effects of climatic elements on potato yield and create a model with climatic elements for estimating the potato yield, using the results of the regional adjustment tests of potato. We used 86 data of the yield data of a potato variety, Sumi, from 17 regions over 11 years. According to the results, the climatic elements showed significant level of correlation coefficient with marketable yield appeared to be almost every climatic elements except wind velocity, which was daily average air temperature (Tave), daily minimum air temperature (Tmin), daily maximum air temperature(Tmax), daily range of air temperature (Tm-m), precipitation (Prec.), relative humidity (R.H.), sunshine hours (S.H.) and days of rain over 0.1 mm (D.R.) depending on the periods of days after planting or before harvest. The correlations between these climatic elements and marketable yield of potato were stepwised using SAS, statistical program, and we selected a model to predict the yield of marketable potato, which was $y=7.820{\times}Tmax_-1-6.315{\times}Prec_-4+128.214{\times}DR_-8+91.762{\times}DR_-3+643.965$. The correlation coefficient between the yield derived from the model and the real yield of marketable yield was 0.588 (DF 85).

• Geophysics and Geophysical Exploration
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• v.22 no.1
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• pp.21-28
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• 2019

We perform a 3D tomographic inversion using surface wave dispersion curves to obtain S-velocity model and radial anisotropy beneath Saudi Arabia. The Arabian Peninsula is geologically and topographically divided into a shield and a platform. We used event data with magnitudes larger than 5.5 and epicentral distances shorter than $40^{\circ}$ during 2008 ~ 2014 from the Saudi Geological Survey. We obtained dispersion curves by using the multiple filtering technique after preprocessing the event data. We constructed SH- and SV-velocity models and consequently radial anisotropy model at 10 ~ 60 km depths by inverting Love and Rayleigh group velocity dispersion curves with period ranges of 5 ~ 140 s, respectively. We observe high-velocity anomalies beneath the Arabian shield at 10 ~ 30 km depths and low-velocity anomalies beneath the Arabian platform at 10 km depth in the SV-velocity model. This discrepancy may be caused by the difference between the Arabian shield and the Arabian platform, that is, the Arabian shield was formed in Proterozoic thereby old and cold, while the Arabian platform is covered by predominant Paleozoic, Mesozoic, and Cenozoic sedimentary layers. Also we obtained radial anisotropy by estimating the differences between SH- and SV-velocity models. Positive anisotropy is observed, which may be generated by lateral tension due to the slab pull of subducting slabs along the Zagros belt.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.214-224
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• 2024

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R² score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R², total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R²: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.