• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.4
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• pp.386-394
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• 2011

This paper is concerned with the application of the vision control scheme for robot's point placement task in discontinuous trajectory caused by obstacle. The proposed vision control scheme consists of four models, which are the robot's kinematic model, vision system model, 6-parameters estimation model, and robot's joint angles estimation model. For this study, the discontinuous trajectory by obstacle is divided into two obstacle regions. Each obstacle region consists of 3 cases, according to the variation of number of cameras that can not acquire the vision data. Then, the effects of number of cameras on the proposed robot's vision control scheme are investigated in each obstacle region. Finally, the practicality of the proposed robot's vision control scheme is demonstrated experimentally by performing the robot's point placement task in discontinuous trajectory by obstacle.

• Smart Media Journal
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• v.5 no.4
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• pp.26-40
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• 2016

Recent researches have been conducted for a user of the position acquisition and analysis since the mobile devices was developed. Trajectory data mining of location analysis method for a user is used to extract the meaningful information based on the user's trajectory. It should be preceded by a process of extracting Stay Point. In order to carry out trajectory data mining by analyzing the user of the GPS Trajectory. The conventional Stay Point extraction algorithm is low confidence because the user to arbitrarily set the threshold values. It does not distinguish between staying indoors and outdoors. Thus, the ambiguity of the position is increased. In this paper we proposed extraction method of Stay Point using a statistical analysis. We proposed algorithm improves position accuracy by extracting the points that are staying indoors and outdoors using Gaussian distribution. And we also improve reliability of the algorithm since that does not use arbitrarily set threshold.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.1
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• pp.95-104
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• 2016

To make a firing table of artillery with trajectory simulation, a precise trajectory model which corresponds with real firing test is required. Recent 4-DOF modified point mass trajectory model is considered accurate as a theoretical model, but fitting coefficients are used in calculation to match with real firing test results. In this paper, modified point mass trajectory model is presented and method of setting ballistic coefficient is introduced by applying optimization algorithms. After comparing two different algorithms, Particle Swarm Optimization and Covariance Matrix Adaptation - Evolutionary Strategy, we found that using CMA-ES algorithm gives fine optimization result. This fitting coefficient setting method can be used to make trajectory simulation which is required for development of new projectiles in the future.

• Journal of Ocean Engineering and Technology
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• v.37 no.3
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• pp.122-128
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• 2023

Autonomous berthing is a crucial technology for autonomous ships, requiring optimal trajectory planning to prevent collisions and minimize time and control efforts. This paper presents a two-phase, two-point boundary value problem (TPBVP) strategy for creating an optimal berthing trajectory for a twin-propeller, twin-rudder ship with autonomous berthing capabilities. The process is divided into two phases: the approach and the terminal. Tunnel thruster use is limited during the approach but fully employed during the terminal phase. This strategy permits concurrent optimization of the total trajectory duration, individual phase trajectories, and phase transition time. The efficacy of the proposed method is validated through two simulations. The first explores a scenario with phase transition, and the second generates a trajectory relying solely on the approach phase. The results affirm our algorithm's effectiveness in deciding transition necessity, identifying optimal transition timing, and optimizing the trajectory accordingly. The proposed two-phase TPBVP approach holds significant implications for advancements in autonomous ship navigation, enhancing safety and efficiency in berthing operations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.600-619
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• 2015

We propose a cost-efficient hybrid algorithm for online location updates that efficiently combines feature point detection with the online trajectory-based sampling algorithm. Our algorithm is designed to minimize the average trajectory error with the minimal number of sample points. The algorithm is composed of 3 steps. First, we choose corner points from the map as sample points because they will most likely cause fewer trajectory errors. By employing the online trajectory sampling algorithm as the second step, our algorithm detects several missing and important sample points to prevent unwanted trajectory errors. The final step improves cost efficiency by eliminating redundant sample points on straight paths. We evaluate the proposed algorithm with real GPS trajectory data for various bus routes and compare our algorithm with the existing one. Simulation results show that our algorithm decreases the average trajectory error 28% compared to the existing one. In terms of cost efficiency, simulation results show that our algorithm is 29% more cost efficient than the existing one with real GPS trajectory data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.574-577
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• 2001

This paper presents the comparison of FRI(Foot Rotation Indicator) point and ZMP(Zero Moment Point) in biped robot stability. We showed FRI may be employed as a useful tool in stability analysis in biped robot. Also, we proposed the balancing joint trajectory derived from FRI point equation for stable gait. The numerical calculation routines and walking algorithms for simulation are performed by MATLAB. The procedure is composed of the leg trajectory planning, the generation of balancing trajectory, and the verification of dynamic stability.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.263-266
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• 1996

A method to determine an optimal temperature trajectory that guarantees polymer products having controlled molecular weight distribution and desired values of molecular weight is presented. The coordinate transformation method and the optimal control theory are applied to a batch PMMA polymerization system to calculate the optimal temperature trajectory. Coordinate transformation method converts the original fixed-end-point, free-end-time problem to a free-end-point, fixed-end-time problem. The idea is that by making the reactor temperature track the optimal temperature trajectory one may be able to produce polymer products having the prespecified physical property in a minimum time. The on-line control experiments with the PID control algorithm have been conducted to establish the validity of the scheme proposed in this study. The experimental results show that prespecified polymer product could be obtained with tracking the calculated optimal temperature trajectory.

• Journal of the Korea Society for Simulation
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• v.27 no.4
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• pp.1-8
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• 2018

The most common way to control a target point using artificial intelligence is through reinforcement learning. However, it had to process complicated calculations that were difficult to implement in order to process reinforcement learning. In this paper, the enhanced Proximal Policy Optimization (PPO) algorithm was used to simulate finding the planned flight trajectory to reach the target point in the virtual environment. In this paper, we simulated how this problem was used to find the planned flight trajectory to reach the target point in the virtual environment using the enhanced Proximal Policy Optimization(PPO) algorithm. In addition, variables such as changes in trajectory, effects of rewards, and external winds are added to determine the zero conditions of external environmental factors on flight trajectory learning, and the effects on trajectory learning performance and learning speed are compared. From this result, the simulation results have shown that the agent can find the optimal trajectory in spite of changes in the various external environments, which will be applicable to the actual vehicle.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.311-312
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• 2011

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.750-759
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• 2020

The wide application of various integrated location-based services (LBS social) and tourism application (app) has generated a large amount of trajectory space data. The trajectory data are used to identify popular tourist attractions with high density of tourists, and they are of great significance to smart service and emergency management of scenic spots. A hot spot analysis method is proposed, based on spatial clustering of trajectory stop points. The DBSCAN algorithm is studied with fast clustering speed, noise processing and clustering of arbitrary shapes in space. The shortage of parameters is manually selected, and an improved method is proposed to adaptively determine parameters based on statistical distribution characteristics of data. DBSCAN clustering analysis and contrast experiments are carried out for three different datasets of artificial synthetic two-dimensional dataset, four-dimensional Iris real dataset and scenic track retention point. The experiment results show that the method can automatically generate reasonable clustering division, and it is superior to traditional algorithms such as DBSCAN and k-means. Finally, based on the spatial clustering results of the trajectory stay points, the Getis-Ord Gi* hotspot analysis and mapping are conducted in ArcGIS software. The hot spots of different tourist attractions are classified according to the analysis results, and the distribution of popular scenic spots is determined with the actual heat of the scenic spots.