• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.4
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• pp.333-341
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• 2008

A remote sensing technique to identify trajectories of breaking waves in the surf zone using oblique digital nearshore videos is proposed. The noise arising from white foam induced by wave breaking has hindered accurate remote sensing of wave properties in the surf zone. For this reason, this paper focuses on image processing to remove the noise and wave trajectory identification essential for wave property estimation. The nearshore video imagery sampled at 3 Hz are used, covering length scale(100 m). Original image sequences are processed through image frame differencing and directional low-pass image filtering to remove the noise characterized by high frequencies in the video imagery. The extraction of individual wave crest features is conducted using a Radon transform-based line detection algorithm in the processed cross-shore image timestacks having a two-dimensional space-time domain. The number of valid wave crest trajectories identified corresponds to about 2/3 of waves recorded by the in-situ sensors.

• Journal of the Korean Society for Marine Environment & Energy
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• v.1 no.1
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• pp.56-65
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• 1998

An oil spill model, Green Sea Ranger(GSR) based on trajectory and fate modeling of spilt oil behavior is introduced. The various physical models on weathering processes are reviewed and those adopted by GSR are described. A database for currents, which is necessary for the real-time simulation of oil spill, is generated on the south sea of Korea. The real-time prediction of tidal currents in the South Sea of Korea is carried out. Four major constituents (M₂, S₂, K₁, O₁ tide) are employed in the prediction, and those angular speeds and phases are determined from the astronomical arguments. The harmonic constants of the constituents are computed by solving shallow-water tide equations. The GSR has user-freiendly GUI and flexible framework which makes it easy to expand the database for sea environments in Korean coastal waters. The GSR is validated by the simulation of O-Sung oil spill caused by a grounded oil tanker in coastal sea near Maemol-do. The simulated trajectory is compared with observed one and it is shown that the GSR gives reasonable estimation on spilt oil bahavior.

• Journal of Biomedical Engineering Research
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• v.10 no.2
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• pp.117-124
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• 1989

The intelligent trajectory control method that controls moving direction and average velocity for a prosthetic arm is proposed by pattern recognition and force estimations using EMG signals. Also, we propose the real time trajectory planning method which generates continuous accelleration paths using 3 stage linear filters to minimize the impact to human body induced by arm motions and to reduce the muscle fatigue. We use combination of MLP and fuzzy filter for pattern recognition to estimate the direction of a muscle and Hogan`s method for the force estimation. EMG signals are acquired by using a amputation simulator and 2 dimensional joystick motion. The simulation results of proposed prosthetic arm control system using the EMf signals show that the arm is effectively followed the desired trajectory depended on estimated force and direction of muscle movements.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.1
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• pp.67-74
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• 2022

To analyze the motion of aircraft through computing the dynamics equations, true airspeed is essential for obtaining aerodynamic loads. Although the airspeed is measured by on-board instruments such as pitot tubes, measurement data are difficult to obtain for commercial flights because they include sensitive data about the airline operations. One of the commonly available trajectory data, Automatic Dependent Surveillance-Broadcast data, provide aircraft's speed in the form of ground speed. The ground speed is a vector sum of the local wind velocity and the true airspeed. This paper present a method to estimate true airspeed by combining the trajectory, meteorological, and topology data available to the public. To integrate each data, we first matched the coordinate system and then unified the altitude reference to the mean sea level. We calculated the wind vector for all trajectory points by interpolating from the lower resolution grid of the meteorological data. Finally, we calculate the true airspeed from the ground speed and the wind vector. These processes were applied to several sample trajectories with corresponding meteorological data and the topology data, and the estimated true airspeeds are presented.

• Journal of Biomedical Engineering Research
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• v.44 no.4
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• pp.255-263
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• 2023

Clinical ultrasound (US) is a widely used imaging modality with various clinical applications. However, capturing a large field of view often requires specialized transducers which have limitations for specific clinical scenarios. Panoramic imaging offers an alternative approach by sequentially aligning image sections acquired from freehand sweeps using a standard transducer. To reconstruct a 3D volume from these 2D sections, an external device can be employed to track the transducer's motion accurately. However, the presence of optical or electrical interferences in a clinical setting often leads to incorrect measurements from such sensors. In this paper, we propose a deep learning (DL) framework that enables the prediction of scan trajectories using only US data, eliminating the need for an external tracking device. Our approach incorporates diverse data types, including correlation volume, optical flow, B-mode images, and rawer data (IQ data). We develop a DL network capable of effectively handling these data types and introduce an attention technique to emphasize crucial local areas for precise trajectory prediction. Through extensive experimentation, we demonstrate the superiority of our proposed method over other DL-based approaches in terms of long trajectory prediction performance. Our findings highlight the potential of employing DL techniques for trajectory estimation in clinical ultrasound, offering a promising alternative for panoramic imaging.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.581-587
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• 2023

The purpose of this study was to analyze the change trajectory and latent class types of self-esteem in first to fourth grade elementary school students. For the purpose, the Korean Children's Panel data were analyzed using potential growth model and the growth mixture model. As the results, the linear change model was selected as the most appropriate model. The change trajectory was found to increase slightly as the grade increased. In addition, four latent class groups were derived through: 'high level-maintenance,' 'low level-increase,' 'high level-decrease,' and 'low level-maintenance.' Most children were in the 'high level-maintenance' group, followed by 'high level-decrease,' 'low level-increase,' and 'low level-maintenance' groups. Therefore, based on the results of the study, we suggest that educational institutions and local communities pay attention to trends in elementary school students' self-esteem and provide appropriate support for students in each class.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.27 no.1
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• pp.48-55
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• 2016

Objectives: Children with attention-deficit hyperactivity disorder (ADHD) may have deficits in time perception, as assessed by the time estimation task and the time reproduction task, however its age-related trajectory is not yet determined. Therefore we examined the correlation between accuracy of time perception tasks and age, and the association between accuracy of estimation tasks and reproduction tasks. Methods: Sixty-three patients with ADHD, aged 8 to 18 years tested the tasks for five time durations (2, 4, 12, 45, and 60 seconds). Accuracy of tasks was assumed differences (absolute values) between raw results of tasks and original time durations. Spearman's correlation analysis was performed to determine correlation between accuracy of time perception tasks and age. Multivariate regression was used to determine the association of accuracy of estimation tasks with accuracy of reproduction tasks. Results: Age showed correlation with accuracy of estimation tasks, but not with that of reproduction tasks. We observed that the higher the accuracy in 12, 45, and 60 seconds duration time reproduction, the higher the accuracy in longer seconds duration time estimation. Conclusion: Age was correlated with time estimation accuracy whereas there was no impact on time reproduction accuracy. Association of each of the two time perception tasks, particularly in longer time duration, suggested specific impairments in time perception.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.208-221
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• 2020

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1448-1454
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• 2018

In an optical observing system, three pairs of observations at equal time intervals are required for the orbit determination method to obtain orbital information of an unknown space objects. In this paper, we propose a method of estimating a streak for acquiring three pairs of observations using one streak image information. Satellite trajectory simulation data were generated for nine cases using the STK program in order to verify the characteristics of the orbit of space object and estimation performance. Simulation was performed by applying three approaches that can estimate the next streak position after a few seconds from one streak image information, and the estimation performance was evaluated. Linear vector method and Kalman Filter method based on the linear assumption tend to increase the estimation error in the region where the nonlinearity is large. However estimation method using the polynomial curve fitting based on the least square method showed smaller and uniform error result than the previous methods.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.6
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• pp.47-56
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• 2015

In the multi-sensor system, sensor registration errors such as a sensor bias must be corrected so that the individual sensor data are expressed in a common reference frame. If registration process is not properly executed, large tracking errors or formation of multiple track on the same target can be occured. Especially for launch vehicle tracking system, each multiple observation lies on the same reference frame and then fused trajectory can be the best track for slaving data. Hence, this paper describes an on-line bias estimation/correction and asynchronous sensor fusion for launch vehicle tracking. The bias estimation architecture is designed based on pseudo bias measurement which derived from error observation between GPS and radar measurements. Then, asynchronous sensor fusion is adapted to enhance tracking performance.