• Journal of Korea Multimedia Society
• /
• v.15 no.4
• /
• pp.417-424
• /
• 2012

In this paper, we propose an automatic system that recognizes dynamic human gestures activity, including Arabic numbers from 0 to 9. We assume the gesture trajectory is almost in a plane that called principal gesture plane, then the Least Squares Method is used to estimate the plane and project the 3-D trajectory model onto the principal. An improved FNN model combined with HMM is proposed for dynamic gesture recognition, which combines ability of HMM model for temporal data modeling with that of fuzzy neural network. The proposed algorithm shows that satisfactory performance and high recognition rate.

• The Journal of Korea Robotics Society
• /
• v.4 no.1
• /
• pp.17-24
• /
• 2009

This work deals with an intuitive method for a planar biped to walk, which is named Relative Trajectory Control (RTC) method. A key feature of the proposed RTC method is that feet of the robot are controlled to track a given trajectory, which is specially designed relative to the base body of the robot. The trajectory of feet is presumed from analysis of the walking motion of a human being. A simple method to maintain a stable posture while the robot is walking is also introduced in RTC method. In this work, the biped is modeled as a free-floating robot, of which dynamic model is obtained in the Cartesian space. Using the obtained dynamic model, the robot is controlled by a model-based feedback control scheme. The author shows a preliminary experimental result to verify that the biped robot with RTC method can walk on the even or uneven surfaces.

• Journal of the Korean Society for Precision Engineering
• /
• v.26 no.12
• /
• pp.69-77
• /
• 2009

In this study an ANFIS-based trajectory tracking motion control algorithm is proposed for autonomous garage and parallel parking of a model car. The ANFIS controller is trained off-line using data set which obtained by Mandani fuzzy inference system and thereby the processing time decreases almost in half. The controller with a steering delay compensator is tuned through simulations performed under MATLAB/Simulink environment. Experiments are carried out with the model car for garage and parallel parking. The experimental results show that the trajectory tracking performance is satisfactory under various initial and road conditions

• Journal of Advanced Navigation Technology
• /
• v.21 no.3
• /
• pp.264-271
• /
• 2017

This paper presents air traffic control simulation model for generating 4D trajectory, and aircraft dynamic model based on 4D trajectory information. With aircraft parameters from BADA and Total Energy Model, the trajectory is defined through modified Bezier curve and the simulation supports two aircraft control methods based on controlled time of arrival (CTA) or airspeed. The simulation results shown that flight time and path were almost identical to the defined trajectory, and derived the differences of each control methods according to wind conditions. Based on the simulation model developed in this study, it is expected to be applied to various air traffic management researches. Future studies will focus on applying optimization techniques in order to minimize the difference between generated trajectories and actual flight routes. This work will increase utilization of developed simulation futhermore.

• Proceedings of the KIEE Conference
• /
• 2006.07d
• /
• pp.1960-1961
• /
• 2006

In this paper, The trajectory control of robot manipulator is proposed. It divides by trajectory planning and tracking control. A trajectory planning and tracking control of robot manipulator is used to the neural network and evolutionary algorithm. The trajectory planning provides not only the optimal trajectory for a given cost function through evolutionary algorithm but also the configurations of the robot manipulator along the trajectory by considering the robot dynamics. The computed torque method (C.T.M) using the model of the robot manipulators is an effective means for trajectory tracking control. However, the tracking performance of this method is severely affected by the uncertainties of robot manipulators. The Radial Basis Function Networks(RBFN) is used not to learn the inverse dynamic model but to compensate the uncertainties of robot manipulator. The computer simulations show the effectiveness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.12
• /
• pp.5782-5799
• /
• 2018

With the development of GPS and the popularity of mobile devices with positioning capability, collecting massive amounts of trajectory data is feasible and easy. The daily trajectories of moving objects convey a concise overview of their behaviors. Different social roles have different trajectory patterns. Therefore, we can identify users or groups based on similar trajectory patterns by mining implicit life patterns. However, most existing daily trajectories mining studies mainly focus on the spatial and temporal analysis of raw trajectory data but missing the essential semantic information or behaviors. In this paper, we propose a novel trajectory semantics calculation method to identify groups that have similar behaviors. In our model, we first propose a fast and efficient approach for stay regions extraction from daily trajectories, then generate semantic trajectories by enriching the stay regions with semantic labels. To measure the similarity between semantic trajectories, we design a semantic similarity measure model based on spatial and temporal similarity factor. Furthermore, a pruning strategy is proposed to lighten tedious calculations and comparisons. We have conducted extensive experiments on real trajectory dataset of Geolife project, and the experimental results show our proposed method is both effective and efficient.

• Journal of information and communication convergence engineering
• /
• v.21 no.4
• /
• pp.346-350
• /
• 2023

Trajectory prediction is an essential element for driving autonomous vehicles, and various trajectory prediction models have emerged with the development of deep learning technology. Convolutional neural network (CNN) is the most commonly used neural network architecture for extracting the features of visual images, and the latest models exhibit high performances. This study was conducted to identify an efficient CNN backbone model among the components of deep learning models for trajectory prediction. We changed the existing CNN backbone network of multiple-trajectory prediction models used as feature extractors to various state-of-the-art CNN models. The experiment was conducted using nuScenes, which is a dataset used for the development of autonomous vehicles. The results of each model were compared using frequently used evaluation metrics for trajectory prediction. Analyzing the impact of the backbone can improve the performance of the trajectory prediction task. Investigating the influence of the backbone on multiple deep learning models can be a future challenge.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.4
• /
• pp.169-176
• /
• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

• Journal of Navigation and Port Research
• /
• v.47 no.2
• /
• pp.49-56
• /
• 2023

As interest in autonomous ships continues to grow, researchers around the world are dedicating themselves to the development of relevant technologies. However, these technologies are not yet perfect. Several technical problems remain unresolved. To address these problems, this study presents the implementation of a ship trajectory algorithm for group navigation, where followers can navigate by following the trajectory of a leader. The algorithm works by storing the leader's trajectory as a follow-point and by calculating the azimuth using the line-of-sight guidance law to reach it. A course-keeping controller based on PD control is implemented to follow the target course and a speed control algorithm is designed to prevent collisions. Sea experiments were conducted using 1 m class small RC model boats to verify the proposed algorithm. The follower successfully navigated by following the leader's trajectory and maintained the designated distance to the forward boat. This study is significant in that it implements an algorithm for the follower to follow the trajectory of the leader rather than directly following it as in conventional methods, and verifies it through sea experiments.

• ETRI Journal
• /
• v.40 no.5
• /
• pp.653-663
• /
• 2018

Patients with spinal cord injuries cannot move their limbs using their intact muscles. A suitable controller can be used to move their arms by employing the functional electrical stimulation method. In this article, a fuzzy exponential sliding-mode controller is designed to move a musculoskeletal human arm model to track an optimal trajectory in the sagittal plane. This optimal arm trajectory is obtained by developing a policy for the central nervous system. In order to specify the optimal trajectory between two points, two dynamic and static optimal criteria are applied simultaneously. The first dynamic objective function is defined to minimize the joint torques, and the second static optimization is offered to minimize the muscle forces at each moment. In addition, fuzzy logic is used to tune the sliding-surface parameter to enable an appropriate tracking performance. Simulation results are evaluated and compared with experimental data for upward and downward movements of the human arm.