• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.210-217
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• 2022

Passive source depth estimation has been studied for decades since the source depth can be used for target classification, target tracking, etc. The purpose of this paper is to solve the problem of ambiguity in the previous paper [S.-il. Cho et al. (in Korean), J. Acoust. Soc. Kr. 38, 120-127 (2019)] that source depth is estimated in two points. The patterns of phase shift of Channel Impulse Response(CIR) reflected in ocean surface and bottom is used for removing ambiguity of the source depth estimation, and after removing ambiguity, source depth is estimated at one point through the intersection of CIR. In order to extract CIR in case of unknown source signal and continuous signal or noise, Ray-based blind deconvolution is used. The proposed algorithm is demonstrated through numerical simulation in ocean waveguide.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.295-305
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• 2023

This study introduces a smoothed finite-element implementation into the phase-field framework. In recent years, the phase-field method has recieved considerable attention in crack initiation and propagation since the method needs no further treatment to express the crack growth path. In the phase-field method, high strain-energy accuracy is needed to capture the complex crack growth path; thus, it is obtained in the framework of the smoothed finite-element method. The salient feature of the smoothed finite-element method is that the finite element cells are divided into sub-cells and each sub-cell is rebuilt as a smoothing domain where smoothed strain energy is calculated. An adaptive quadtree refinement is also employed in the present framework to avoid the computational burden. Numerical experiments are performed to investigate the performance of the proposed approach, compared with that of the finite-element method and the reference solutions.

• Journal of Satellite, Information and Communications
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• v.7 no.3
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• pp.16-21
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• 2012

Recently, an importance of culture industry has been emphasized through an increased income level, spare time and changed values of modern people. And demands of the performance, arts, exhibit are steadily being increased. However the stage equipment depends on foreign manufactures on account of the inactive domestic technical skills. Especially in the lighting direction part, it is essential to control the lighting source and detect the moving line of actors but it generally uses the manual control type and realization of actor's moving line regardless of existing IT-based technologies. Also the system operation of existing sensor-based tracking and detecting technologies depends on the main lighting source of the stage. Therefore, this paper proposed LED lighting control system using the variable FOV and multi sensor-based tracking algorithm, which are possible to efficiently track the stage actors and direct the stage lights. Also we demonstrated the practicality and possibility of realization through the integrated experiment of the proposed system and implementation of the salient hardware, software. Additionally, the usefulness of proposed system was demonstrated using performance simulations and actual measurements of implemented sensor output.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.444-450
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• 2015

The accuracy of small and low cost CCD camera is insufficient to provide data for precisely tracking unmanned aerial vehicles(UAVs). This study shows how UAV can hover on a human targeted tracking object by using CCD camera rather than imprecise GPS data. To realize this, UAVs need to recognize their attitude and position in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for an UAV to estimate of his attitude by environment recognition for UAV hovering, as one of the best important problems. In this paper, we describe a method for the attitude of an UAV using image information of a maker on the floor. This method combines the observed position from GPS sensors and the estimated attitude from the images captured by a fixed camera to estimate an UAV. Using the a priori known path of an UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a marker on the floor and the estimated UAV's attitude. Since the equations are based on the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the UAV. The Kalman filter scheme is applied for this method. its performance is verified by the image processing results and the experiment.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.29-37
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• 2024

In this paper, we propose an efficient image processing system to detect and track the movement of specific objects such as patients. The proposed system extracts the outline area of an object from a binarized difference image by applying a thinning algorithm that enables more precise detection compared to previous algorithms and is advantageous for mixed-mode design. The binarization and thinning steps, which require a lot of computation, are designed based on RTL (Register Transfer Level) and replaced with optimized hardware blocks through logic circuit synthesis. The designed binarization and thinning block was synthesized into a logic circuit using the standard 180n CMOS library and its operation was verified through simulation. To compare software-based performance, performance analysis of binary and thinning operations was also performed by applying sample images with 640 × 360 resolution in a 32-bit FPGA embedded system environment. As a result of verification, it was confirmed that the mixed-mode design can improve the processing speed by 93.8% in the binary and thinning stages compared to the previous software-only processing speed. The proposed mixed-mode system for object recognition is expected to be able to efficiently monitor patient movements even in an edge computing environment where artificial intelligence networks are not applied.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.14-21
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• 2021

In this paper, it is shown how the performance of the monopulse algorithm in the presence of an additive noise can be obtained analytically. In the previous study, analytic performance analysis based on the first-order Taylor series and the second-order Taylor series has been conducted. By adopting the third-order Taylor series, it is shown that the analytic performance based on the third-order Taylor series can be made closer to the performance of the original monopulse algorithm than the analytic performance based on the first-order Taylor series and the second-order Taylor series. The analytic MSE based on the third-order Taylor approximation reduces the analytic MSE error based on the second-order Taylor approximation by 89.5%. It also shows faster results in all cases than the Monte Carlo-based MSE. Through this study, it is possible to explicitly analyze the angle estimation ability of monopulse radar in an environment where noise jamming is applied.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.1
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• pp.119-125
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• 2018

The particle based computational fluid dynamics (CFD) method, which follow Lagrangian approach for fluid dynamics, fluid particle behavior by tracking all particle calculation physical quantities of each particle. According to basic concept of particle based CFD method, it is difficult to satisfy continuum theory and measure influences from neighboring particle. Article number density and weight function were used to solve aforementioned issue. Difficulties continuum mean simulate non-continuum particles such as solid including granular and sand. In this regard, the particle based CFD method modified solid particle problems by replacing viscous and surface tension forces friction and drag forces. In this paper, particle interaction model for solid particle friction model implemented to simulate solid particle problems. The broken dam problem, which is common to verify particle based CFD method, used fluid or solid particles. The angle of repose was observed in the simulation results the solid particle not fluid particle.

• Journal of the Korea Computer Graphics Society
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• v.24 no.3
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• pp.73-81
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• 2018

In this paper, we present a motion-driven rowing simulation framework that allows multiple players to row a boat together by their harmonized movements. In the actual rowing game, it is crucial for the players to synchronize their rowing with respect to time and pose so as to accelerate the boat. Inspired by this interesting feature, we measure the motion similarity among multiple players in real time while they are doing rowing motions and use it to control the velocity of the boat in a virtual environment. We also employ game components such as catching an item which can accelerate or decelerate the boat depending on its type for a moment once it has been obtained by synchronized catching behaviors of the players. By these components, the players can be encouraged to more actively participate in the training for a good teamwork to produce harmonized rowing movements Our methods for the motion recognition for rowing and item catch require the tracking data only for the head and the both hands and are fast enough to facilitate the real-time performance. In order to enhance immersiveness of the virtual environment, we project the rowing simulation result on a wide curved screen.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.2
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• pp.60-67
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• 2015

The finite element (FE) model updating is a commonly used approach in civil engineering, enabling damage detection, design verification, and load capacity identification. In the FE model updating, acceleration responses are generally employed to determine modal properties of a structure, which are subsequently used to update the initial FE model. While the acceleration-based model updating has been successful in finding better approximations of the physical systems including material and sectional properties, the boundary conditions have been considered yet to be difficult to accurately estimate as the acceleration responses only correspond to translational degree-of-freedoms (DOF). Recent advancement in the sensor technology has enabled low-cost, high-precision gyroscopes that can be adopted in the FE model updating to provide angular information of a structure. This study proposes a FE model updating strategy based on data fusion of acceleration and angular velocity. The usage of both acceleration and angular velocity gives richer information than the sole use of acceleration, allowing the enhanced performance particularly in determining the boundary conditions. A numerical simulation on a simply supported beam is presented to demonstrate the proposed FE model updating approach.

• Journal of the Korea Computer Graphics Society
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• v.27 no.1
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• pp.9-17
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• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.