• Proceedings of the KIEE Conference
• /
• 1993.07a
• /
• pp.51-53
• /
• 1993

This paper proposes a design of the tracking controller using artificial neural network and the compare the result with a result of optimal controller. In practical use, conventional Optimal controller has some limits. First, optimal controller can be designed only for linear system. Second, for many systems state observation is difficult or sometimes impossible. But the controller using artificial neural network does not need mathmatical model of the system including state observation, so it can be used for both linear and nonlinear system with no additional cost for nonlinearity. Designed multi layer neural network controller is composed of two parts, feedforward controller gives a steady state input & feedback controller gives transient input via minimizing the quadratic cost function. From the comparison of the results of the simulation of linear & nonlinear plant, the plant controlled by using neural network controller shows the trajectory similar to that of the plant controlled by an optimal controller.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.640-645
• /
• 2004

This paper proposes a direct adaptive control method using self-recurrent wavelet neural network (SRWNN) for stable path tracking of mobile robots. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). Unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. For this ability of the SRWNN, the SRWNN is used as a controller with simpler structure than the WNN in our on-line control process. The gradient-descent method with adaptive learning rates (ALR) is applied to train the parameters of the SRWNN. The ALR are derived from discrete Lyapunov stability theorem, which are used to guarantee the stable path tracking of mobile robots. Finally, through computer simulations, we demonstrate the effectiveness and stability of the proposed controller.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.4
• /
• pp.1359-1375
• /
• 2015

Energy efficiency is critical for Wireless Sensor Networks (WSNs) since sensor nodes usually have very limited energy supply from battery. Sleep scheduling and nodes cooperation are two of the most efficient methods to achieve energy conservation in WSNs. In this paper, we propose an adaptive energy optimization approach for target tracking applications, called Energy-Efficient Node Coordination (EENC), which is based on the grid structure. EENC provides an unambiguous calculation and analysis for optimal the nodes cooperation theoretically. In EENC, the sleep schedule of sensor nodes is locally synchronized and globally unsynchronized. Locally in each grid, the sleep schedule of all nodes is synchronized by the grid head, while globally the sleep schedule of each grid is independent and is determined by the proposed scheme. For dynamic sleep scheduling in tracking state we propose a multi-level coordination algorithm to find an optimal nodes cooperation of the network to maximize the energy conservation while preserving the tracking performance. Experimental results show that EENC can achieve energy saving of at least 38.2% compared to state-of-the-art approaches.

• The Journal of Society for e-Business Studies
• /
• v.13 no.4
• /
• pp.1-16
• /
• 2008

LBS (Location Based Service) cannot be realized unless we can obtain the user's current location. Therefore, in order to realize indoor LBS, many researchers have been working on WLAN (Wireless Local Area Network) based indoor positioning and tracking. Meanwhile, Kalman filter has been widely used in the field of GPS based outdoor user tracking. The main purpose of this paper is proposing an extended Kalman filter method for indoor tracking. Our experimental results show that Kalman filter can be used to improve the accuracy of the measured tracks and the track can be further improved by making use of the map information.

• Journal of Korea Spatial Information System Society
• /
• v.8 no.2 s.17
• /
• pp.1-11
• /
• 2006

This paper addresses a GIS (Geographic Information System) based system in order to reduce the rate of public transportation accidents occurring in Iranian roads network. Over the years, the road accidents are a major issue throughout the world. Today, particular consideration is given to those technologies which can lead to diminish on the number of critical incidents. One of the main factors resulting in accidents and fatalities rates growth is the speed violation of buses in Iranian road network. The conventional speed controlling approach in Iran based on the Tachograph which records vehicle's speed, time, and stoppage in the mechanical processing has many problems. Hence, this research is intended to design and implement a GIS-based system to manage road accident of Bus transportation system using offline tracking system. This was accomplished using a GIS-based technique that encompasses three steps. The first step is developing a GIS-based accident system. The second step includes designing and applying a tracking system inside 90 buses for recording Bus information for speed controlling. Lastly, by using mentioned system in police center, the illegal drivers' punishment would be considered properly. Overall, this system has been successfully applied in this work. Therefore, the police and transportation office are able to control and make policy to diminish the number of accident. It is anticipated that online tracking system through the Web GIS would be utilized In this system in the near future.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.12 no.1
• /
• pp.195-200
• /
• 2012

We present a new algorithm which can improve the tracking performance of the double-talk robust algorithm. A detection method of the echo path change and a modification method for the update equation of the conventional adaptive filter are proposed. A duration of the high error signal to scale parameter ratio varies according to the call status and this property is used to detect the echo path change. The proposed update equation of the adaptive filter improves the tracking performance by prohibiting wrong selection of the error signal. Simulations using real speech signals and echo paths of the ITU-T G.168 standard confirmed that as compared to the conventional algorithm, the proposed algorithm improved the tracking performance by more than 4 dB.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.17 no.1
• /
• pp.96-107
• /
• 2014

Tracking the tactical object is a fundamental affair in network-equipped modern warfare. Geodetic coordinate system based on longitude, latitude, and height is suitable to represent the location of tactical objects considering multi platform data fusion. The motion of tactical object described as a dynamic model requires an appropriate filtering to overcome the system and measurement noise in acquiring information from multiple sensors. This paper introduces the filter suitable for multi-sensor data fusion and tactical object tracking, particularly the unscented transform(UT) and its detail. The UT in Unscented Kalman Filter(UKF) uses a few samples to estimate nonlinear-propagated statistic parameters, and UT has better performance and complexity than the conventional linearization method. We show the effects of UT-based filtering via simulation considering practical tactical object tracking scenario.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.6
• /
• pp.710-715
• /
• 2006

With recent advances in device fabrication technology, economical deployment of large scale sensor networks, a design of pervasive monitoring and control system has been made possible. In this paper, we present a new algorithm for one of the most likely applications for sensor networks; tracking moving targets. The proposed algorithm uses a cooperations between the sensor nodes which detect moving objects. Therefore, the proposed scheme is robust against prediction failures which may result in temporary loss of the target. Using simulations we show that tile proposed moving object tracking algorithm is capable of accurately tracking targets with random movement patterns.

• Journal of Korea Multimedia Society
• /
• v.25 no.2
• /
• pp.145-156
• /
• 2022

Nowadays, the complexity of object tracking models among hardware applications has become a more in-demand duty to complete in various indeterminable environment tracking situations with multifunctional algorithm skills. In this paper, we propose a virtual city environment using AirSim (Aerial Informatics and Robotics Simulation - AirSim, CityEnvironment) and use the DQN (Deep Q-Learning) model of deep reinforcement learning model in the virtual environment. The proposed object tracking DQN network observes the environment using a deep reinforcement learning model that receives continuous images taken by a virtual environment simulation system as input to control the operation of a virtual drone. The deep reinforcement learning model is pre-trained using various existing continuous image sets. Since the existing various continuous image sets are image data of real environments and objects, it is implemented in 3D to track virtual environments and moving objects in them.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.548-551
• /
• 1997

Performance of force tracking impedance control of robot manipulators is degraded by the uncertainties in the robot and environment dynamic model. The purpose of this paper is to improve the controller robustness by applying neural network. Neural networks are designed to learn the uncertainties in robot and environment model for compensating the uncertainties. The proposed scheme is verified through the simulation of 20DOF robot manipulator.