• Title/Summary/Keyword: Underwater equipment

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Development of hovering-type AUV test-bed 'OCTAGON' (호버링 타입 자율무인잠수정 'OCTAGON'의 테스트베드 개발)

  • Choi, Dong-Ho;Lee, Young-Jin;Hong, Sung-Min;Kim, Joon-Young
    • Journal of Advanced Marine Engineering and Technology
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    • v.40 no.6
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    • pp.516-526
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    • 2016
  • This paper introduces a hovering-type autonomous underwater vehicle (AUV) developed for research and its fundamental motion performance results obtained by simulation and field test. The AUV can control its motion in four degrees of freedom (DOF) by means of its horizontal and vertical thrusters, and it is designed to provide a test-bed that facilitates ease of operation and experimentation. Prior to the field tests, six DOF equations of motion are developed, and a simulation program is constructed using MATLAB and Simulink to verify the essential motion performance of the designed vehicle. Furthermore, a proportional-integral-derivative (PID) controller and fuzzy PID controller are designed, and their performances are verified through a simulation. Field tests are performed to verify the motion performance of the AUV; way-point tracking is executed by the PID and fuzzy PID controllers. The results confirmed appropriate control performance under current disturbances.

Radon Hazard Review of Spilled Groundwater and Tap Water in Incheon Metropolitan City Subway Station (인천광역시 지하철 역사 내 지하수 및 수돗물의 라돈 위해성 검토)

  • Lee, Yoo-Sang;Lee, Sang-Bok;Kang, Min-Seok;Jeong, Dong-Ha;Kim, Jin-Hong;Oh, Yoon-Sik;Choi, Se-Rin;Park, Jeong-Soo;Kim, Sungchul
    • Journal of radiological science and technology
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    • v.44 no.6
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    • pp.671-677
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    • 2021
  • Interest in the everyday hazards of radon has recently increased as such, this study attempted to examine the dangers of radon in spilled groundwater by comparing the radon concentrations of the drained groundwater and tap water used in recirculating systems in Incheon Subway restrooms. At five stations of Incheon Subway Line 1 and three stations of Line 2, drained groundwater is recirculated and used in restrooms for toilet flushing. Stations restroom tap water for hand washing that used as a control and the measured values of each were compared. With the cooperation of Incheon Transportation Corporation, samples of spilled groundwater and tap water were collected sealed to prevent contact with the air, and a DURRIDGE RAD7 was used as the experimental equipment. The collected samples were subjected to radial equilibration for approximately 3.5 h, at which the radon concentration reached its maximum, and then calculated as 10 measurements using the RAD7 underwater radon measurement mode. In all eight stations, the radon concentration in tap water was lower than the recommended amount. However, in an average of 7 out of the eight stations, the radon concentration in the effluent groundwater was 100 times higher than that in tap water. Since high radon concentrations in groundwater runoff can be harmful to humans, and there is no accurate standard for radon concentrations in domestic water, it is necessary to continuously monitor radon in water and prepare a guidance of recommended values.

Application of Multiple Linear Regression Analysis and Tree-Based Machine Learning Techniques for Cutter Life Index(CLI) Prediction (커터수명지수 예측을 위한 다중선형회귀분석과 트리 기반 머신러닝 기법 적용)

  • Ju-Pyo Hong;Tae Young Ko
    • Tunnel and Underground Space
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    • v.33 no.6
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    • pp.594-609
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    • 2023
  • TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.