• Journal of the Korean Society of Marine Environment & Safety
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• v.16 no.1
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• pp.135-140
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• 2010

Vibration on the ship was generated mainly by main engine and propeller. The vibration which is generated from the ship has an effect on durability of ship's machinery and it also has an evil influence on the working conditions for crew. In this research, vibration measurement system to measure ship's vibration was built and vibration signals using acceleration sensors were measured from an accomodation space of training ship. An evaluation of vibration with regard to habitability was also discussed and the evaluation process followed the guidelines ISO6954:2000E.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.85-99
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• 2015

It is urgent to redevelop the superannuated reservoir levee through the levee raising for countermeasure to climate change and improvement of storage capacity of reservoir. However, low compaction degree of the raised reservoir levee owing to poor construction condition leads to degradation of the stability of the reservoir levee on seepage and earthquake. In this study, seepage and seismic behavior of raised reservoir levee with low compaction degree was evaluated through numerical simulation. From the simulated results, water level raising possibly induces crack and/or sinkhole on the surface of the poorly-compacted raised reservoir levee owing to the increase of the subsidences at the crown and the front side of that. In addition, relatively larger displacement and acceleration response at the front side of raised reservoir levee in seismic condition may degrade overall stability of reservoir levee. Therefore, reasonable construction management for the compaction of the raised reservoir levee is required for ensuring long-term stability on seepage and earthquake.

• Journal of Navigation and Port Research
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• v.32 no.5
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• pp.333-339
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• 2008

When a vessel is underway in a heavy weather, passengers and crew suffer from seasickness caused by ship motions such as pitch, heave or roll, or all combined Sickness induces drowsiness, dizziness, headache, stomachache etc, in extreme conditions, they are met with a serious trouble which is physiologically unrecoverable. It results in weakening of spiritual activities or making errors from decrease of motivation, dropping off skills, poor recognition and poor judgement. In this paper, it was examined the international standards concerning the occurrence of sickness and the execution of works, also evaluated the boarding comfort by conducting several times of questionnaire on cadets boarding on the training ship HANBADA As a result, it was confirmed that the main factor of occurring the sickness was the vertical acceleration and the level was more than 0.2g. Also, it was presented the way how to reduce the sickness by changing the speed and/or course in relation to the encounter period.

• Progress in Medical Physics
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• v.24 no.1
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• pp.61-67
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• 2013

In this study, we developed and evaluated the patient respiration training method which can help to avoid the problems for the limitation of RGRT applicable patient cases. By using the MEMS (micro-electro-mechanical-system) acceleration sensor, we measured movement of motion phantom. We had compared the response of MEMS with commercially introduced real time patient monitoring (RPM) system. We measured the response of the MEMS with 1 dimensional motion phantom movement for 2.5, 3.0, 3.5 second of period and the 2.0, 3.0, 4.0 cm of the amplitudes. The measured period error of the MEMS system was 0.6~6.0% compared with measured period using RPM system. We found that the shape of MEMS signals were similar with RPM system. From this study, we found the possibility of MEMS as patient training system.

• Journal of Korean Society of Steel Construction
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• v.11 no.1 s.38
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• pp.79-88
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• 1999

Damages of a steel box girder bridge are detected using neural networks. Damage detection using neural networks has increasing momentum in structural engineering. It is a new effort to overcome the limitations of the conventional analytical approaches and applied to the damage detection of a steel box-girder bridge. Data sets for training neural networks are obtained from the acceleration response of the bridge under moving load. Finite element model is first defined and damages of 5, 10, 15 and 20% are assumed in the model. Not only the trained damages but untrained damages are detected in the assessment stage. The untrained damages can be detected with acceptable errors. Because the number of damaged locations are limited to a few parts, more researches are needed to put this technique into practice.

• Korean Journal of Applied Biomechanics
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• v.19 no.3
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• pp.611-617
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• 2009

The primary purpose of this study was to analysis quantitatively the degree of injury and/or the progress of treatment for physical impairment. This study provided a more detailed study that evaluates all test parameters including maximum grip strength, duration of operation, average grip strength, acceleration work, dynamic endurance time and percent change in static endurance. Also, a complete database management system is developed and used to store related training, evaluation, and personal information. In addition, the system is developed a grip sensor using load.cell transducer (${\leq}60kg$). The system will be efficient to operate and convenient to use, furthermore, it can be helped for understanding and analysis the progress of a patient during a doctor's rehabilitation program.

• Smart Structures and Systems
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• v.29 no.1
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• pp.63-75
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• 2022

The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns, scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.

• Corrosion Science and Technology
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• v.22 no.1
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• pp.55-63
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• 2023

The aim of this study was to propose a multiple linear regression (MLR) equation to predict ultimate tensile strength (UTS) of 316L stainless steel with unspecified pit corrosion. Tensile specimens with pit corrosion were prepared using a potentiostatic acceleration test method. Pit corrosion was characterized by measuring ten factors using a confocal laser microscope. Data were collected from 22 tensile tests. At 85% confidence level, total pit volume, maximum pit depth, mean ratio of surface area, and mean area were significant factors showing linear relationships with UTS. The MLR equation using these three significant factors at a 85% confidence level showed considerable prediction performance for UTS. Determination coefficient (R²) was 0.903 with training and test data sets. The yield strength ratio of 316L stainless steel was found to be around 0.85. All specimens with a pit corrosion presented a yield ratio of approximately 0.85 with R² of 0.998. Therefore, pit corrosion did not affect the yield ratio.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.23-37
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• 2024

Digital twin is an M&S (Modeling and Simulation) technology designed to solve or optimize problems in the real world by replicating physical objects in the real world as virtual objects in the digital world and predicting phenomena that may occur in the future through simulation. Digital twins have been elaborately designed and utilized based on data collected to achieve specific purposes in large-scale environments such as cities and industrial facilities. In order to apply this digital twin technology to real life and expand it into user-customized service technology, practical but sensitive issues such as personal information protection and personalization of simulations must be resolved. To solve this problem, this paper proposes a federated learning-based accelerated client training method (FACTS) for personalized digital twins. The basic approach is to use a cluster-driven federated learning training procedure to protect personal information while simultaneously selecting a training model similar to the user and training it adaptively. As a result of experiments under various statistically heterogeneous conditions, FACTS was found to be superior to the existing FL method in terms of training speed and resource efficiency.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.7
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• pp.762-768
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• 2013

This study investigated the effect of whole-body vibration on muscle function and muscular reaction in the knee joint. We recruited thirty healthy subjects and divided them into a training group, who experienced whole-body vibration, and a control group, who did not. The training group performed whole-body vibration exercises for 30 min per day, 3 days a week, for 8 weeks. We measured knee joint torque to estimate muscle strength and reaction, using BIODEX System 3. Knee joint peak torque and total work performed increased significantly in the training group, and muscle acceleration time decreased. These results suggest that stimulation by whole-body vibration can improve muscle strength and reaction by improving muscle tone and increasing blood temperature and flow speed in muscular fiber. Our results also indicate that 4 weeks of exercise with whole-body vibration is required to improve the reaction response, and six weeks to improve strength.