• Journal of Navigation and Port Research
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• v.38 no.5
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• pp.479-484
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• 2014

This paper presents the crew members fatigue survey in order to understand the current state of various fatigue causal factors and personnel fatigue subjective symptoms, and then analyzes the survey items. The results of this survey are as follows. Firstly, many crew members were struggling with the lack of sleep and rest hour. Secondly, environmental factors such as weather, ship motion and vibration, noise, accommodation condition etc. disturbed the sleep of crew members. In third, their duty hours were more than 10 hours per day in certain types of ship. In fourth, they felt fatigue a lot when they were on board because of the workload and stress. Lastly, in some measurement items of fatigue symptoms(physical, mental, emotional), many crew members were experiencing more than moderate fatigue symptoms.

• Journal of Astronomy and Space Sciences
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• v.36 no.1
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• pp.35-43
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• 2019

The Optical Wide-field patroL-Network (OWL-Net) is a global optical network for Space Situational Awareness in Korea. The primary operational goal of the OWL-Net is to track Low Earth Orbit (LEO) satellites operated by Korea and to monitor the Geostationary Earth Orbit (GEO) region near the Korean peninsula. To obtain dense measurements on LEO tracking, the chopper system was adopted in the OWL-Net's back-end system. Dozens of angle-only measurements can be obtained for a single shot with the observation mode for LEO tracking. In previous work, the reduction process of the LEO tracking data was presented, along with the mechanical specification of the back-end system of the OWL-Net. In this research, we describe an integrity assessment method of time-position matching and verification of results from real observations of LEO satellites. The change rate of the angle of each streak in the shot was checked to assess the results of the matching process. The time error due to the chopper rotation motion was corrected after re-matching of time and position. The corrected measurements were compared with the simulated observation data, which were taken from the Consolidated Prediction File from the International Laser Ranging Service. The comparison results are presented in the In-track and Cross-track frame.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.

• Smart Structures and Systems
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• v.33 no.4
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• pp.291-300
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• 2024

Uncertainty of the model, system delay and drive dynamics can be considered as normal uncertainties, and the main source of uncertainty in the seismic control system is related to the nature of the simulated seismic error. In this case, optimizing the management strategy for one particular seismic record will not yield the best results for another. In this article, we propose a framework for online management of active structural management systems with seismic uncertainty. For this purpose, the concept of reinforcement learning is used for online optimization of active crowd management software. The controller consists of a differential controller, an unplanned gain ratio, the gain of which is enhanced using an online reinforcement learning algorithm. In addition, the proposed controller includes a dynamic status forecaster to solve the delay problem. To evaluate the performance of the proposed controllers, thousands of ground motion data sets were processed and grouped according to their spectrum using fuzzy clustering techniques with spatial hazard estimation. Finally, the controller is implemented in a laboratory scale configuration and its operation is simulated on a vibration table using cluster location and some actual seismic data. The test results show that the proposed controller effectively withstands strong seismic interference with delay. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results is believed to achieved in the near future by the ongoing development of AI and control theory.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.673-683
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• 2008

This study improved the existing method of using the longitudinal strain and concept of influence line to develop Bridge Weigh-in-Motion system without axle detector using the dynamic strain of the bridge girders and concrete slab. This paper first describes the considered algorithms of extracting passing vehicle information from the dynamic strain signal measured at the bridge slab, girders, and cross beams. Two different analysis methods of 1) influence line method, and 2) neural network method are considered, and parameter study of measurement locations is also performed. Then the procedures and the results of field tests are described. The field tests are performed to acquire training sets and test sets for neural networks, and also to verify and compare performances of the considered algorithms. Finally, comparison between the results of different algorithms and discussions are followed. For a PSC I-girder bridge, vehicle weight can be calculated within a reasonable error range using the dynamic strain gauge installed on the girders. The passing lane and passing speed of the vehicle can be accurately estimated using the strain signal from the concrete slab. The passing speed and peak duration were added to the input variables to reflect the influence of the dynamic interaction between the bridge and vehicles, and impact of the distance between axles, respectively; thus improving the accuracy of the weight calculation.

• The Korean Journal of Nuclear Medicine Technology
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• v.22 no.1
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• pp.15-22
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• 2018

Purpose Respiratory motion during PET/MRI acquisition may result in image blurring and error in measurement for volume and quantification of lesion. The aim of this study was to evaluate changes of quantitative accuracy, tumor size and image quality by applying MR based respiratory motion correction technique (MBRMCT) using integrated PET/MR scanner. Materials and Methods Data of 30 patients (aged $62.5{\pm}10.2y$) underwent $^{18}F-FDG$ liver PET/MR (Biograph mMR 3.0T, Siemens) study were collected. PET listmode data for 7 minutes was simultaneously acquired with maximum average gate (MAG), minimum time gate (MTG) and non gate (NG) T1 weighted MR images. Gated PET reconstruction was performed using mu-maps generated from MAG and MTG by setting 35% of efficiency window. Maximum SUV ($SUV_{max}$), peak SUV ($SUV_{peak}$), tumor size and full width at half maximum (FWHM) in the z-axis direction of MAG, MTG and NG PET images were evaluated. Results Compared to NG, mean $SUV_{max}$ and $SUV_{peak}$ were increased in MAG 13.15%(p<0.0001), 8.66%(p<0.0001), MTG 13.27%(p<0.0001), 8.80%(p<0.0001) and mean tumor size and FWHM were decreased in MAG 14.47%(p<0.0001), 15.49%(p=0.0004), MTG 14.89%(p<0.0001), 15.79%(p=0.0003) respectively. Mean $SUV_{max}$ and $SUV_{peak}$ of MTG were increased by 0.07%(p=0.8802), 0.13%(p=0.7766). Mean tumor size and FWHM of MTG were decreased by 0.49%(p=0.2786), 0.36%(p=0.2488) compared to MAG. There was no statistically significant difference between MAG and MTG which increase total scan time for about 7 and 2 minutes. Conclusion SUV, accuracy of tumor size and spatial resolution were improved in both of MAG and MTG by applying MBRMCT without installing additional hardware in liver PET/MR study. More accurate information can be provided with the increase of 2 minutes scan time if applying MTG of MBRMCT to various abdominal PET/MR studies affected by respiratory motion.

• Progress in Medical Physics
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• v.20 no.3
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• pp.132-138
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• 2009

Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.

• Journal of Radiation Protection and Research
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• v.34 no.3
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• pp.137-143
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• 2009

This study was conducted to evaluate the accuracy of CyberKnife $Synchrony^{TM}$ respiratory tracking system which was applied to Stereotactic Radiosurgery (SRS) for moving tumors in chest and abdomen with breathing motion. For accurate evaluation, gold fiducial marks were implanted into a moving phantom. The moving phantom was a cube imbedding an acryl ball as a target. The acryl ball was prescribed to 20 Gy at 70% of isodose curve in a virtual treatment and radiochromic films were inserted into the acryl ball for dose verification and tracking accuracy evaluation. The evaluation of position tracking consists of two parts: fiducial mark tracking in a stationary phantom and $Synchrony^{TM}$ respiratory tracking in a moving phantom. Each measurement was done in three directions and was repeated to 5 times. Range of position error was 0.1957 mm to 0.6520 mm in the stationary phantom and 0.4405 mm to 0.7665 mm in the moving phantom. Average position error was 0.3926 mm and 0.5673 mm in the stationary phantom and the moving phantom respectively. This study evaluates the accuracy of CyberKnife $Synchrony^{TM}$ Respiratory tracking system, and confirms the usefulness when it's used for Stereotactic Radiosurgery of body organs.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.247-258
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• 2003

In this paper, we present a pen-shaped input device equipped with accelerometers and gyroscopes that measure inertial movements when a user writes on 2 or 3 dimensional space with the pen. The measurements from gyroscope are integrated once to find the attitude of the system and are used to compensate gravitational effect in the accelerations. Further, the compensated accelerations are integrated twice to yield the position of the system, whose basic concept stems from the field of inertial navigation. However, the accuracy of the position measurement significantly deteriorates with time due to the integrations involved in recovering the handwriting trajectory This problem is common in the inertial navigation system and is usually solved by the periodic or aperiodic calibration of the system with external reference sources or other information in the filed of inertial navigation. In the presented paper, the calibration of the position or velocity is performed on-line and off-line. In the on-line calibration stage, the complementary filter technique is used, where a Kalman filter plays an important role. In the off-line calibration stage, the constant component of the resultant navigational error of the system is removed using the velocity information and motion detection algorithm. The effectiveness and feasibility of the presented system is shown through the experimental results.

• Journal of Biomedical Engineering Research
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• v.35 no.5
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• pp.125-131
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• 2014

The purpose of this study is to verify the utility of reflected photoplethysmography sensor using two green light emitting diodes that influenced by ambient light. Recently it has been studied that green light emitting diode is suitable for light source of reflected photoplethysmography sensor at low temperature and high temperature. Another study showed that, green light is better for monitoring heart rate during motion than led light. However, it has a bad characteristic about ambient light noise. To verify the utility of reflected photoplethysmography sensor using green light emitting diode, this study measures the photoplethysmography signal that is distorted by ambient light and will propose a solution. This study has two parts of research method. One is measurement system that composed sensor and board. The sensor is made up PE-foam and Non-woven fabric for flexible sensor. The photoplethysmography signal is measured by measurement board that composed high-pass filter, low-pass filter and amplifier. Ambient light source is light bulb and white light emitting diode that has three steps brightness. Photoplethysmography signal is measured with lead II electrocardiography signal at the same time and it is measured at the finger and radial artery for 1 minute, 1000 Hz sampling rate. The lead II electrocardiography signal is a standard signal for heart rate and photoplethysmography signal that measured at the finger is a standard signal for waveform. The test is repeated 3 times using three sensor. The data is processed by MATLAB to verify the utility by comparing the correlation coefficient score and heart rate. The photoplethysmography sensor using two green light emitting diodes is shown better utility than using one green light emitting diode and red light emitting diode at the ambient light. The waveform and heart rate that measured by two green light emitting diodes are more identical than others. The amount of electricity used is less than red light emitting diode and error peak detectability factor is the lowest.