• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.5
• /
• pp.442-448
• /
• 2000

The conventional control of dynamic systems needs accurate mathematical modeling of control systems. But the modeling of dynamic systems require very complex computation process due to complex state equation and many control parameters. Accordingly this paper proposes a state space identification model of the dynamic system using neural networks. The Gauss-Newton method is used to train the proposed neural network and the effectiveness of proposed method is verified through the computer simulation of the Seesaw system identification problem.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.540-542
• /
• 1999

We develop jumping ring system with sensor and control system using dynamic neural networks. Jumping ring, sensor and control system are controlled by 586 PC using Turbo C program. Sensor system is composed of 20 optical sensors and encoder. The control circuits are consisted of thyristor, FET and phase controller. A/D converter and optical sensor acquire real time motion data of the jumping ring system. The information of acquired jumping ring Position is estimated by using dynamic neural networks. Estimated control signals are sent to control circuits and D/A converter to track desired position of the jumping ring system. Experiment results are given to verify that proposed dynamic controller is useful in real jumping ring system.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.7
• /
• pp.465-472
• /
• 2004

Analog CPPN-based optimal road boundary detection algorithm for autonomous vehicle is proposed. The CPPN is a massively connected analog parallel array processor. In the paper, the dynamic programming which is an efficient algorithm to find the optimal path is implemented with the CPPN algorithm. If the image of road-boundary information is utilized as an inter-cell distance, and goals and start lines are positioned at the top and the bottom of the image, respectively, the optimal path finding algorithm can be exploited for optimal road boundary detection. By virtue of the parallel and analog processing of the CPPN and the optimal solution of the dynamic programming, the proposed road boundary detection algorithm is expected to have very high speed and robust processing if it is implemented into circuits. The proposed road boundary algorithm is described and simulation results are reported.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.9
• /
• pp.540-546
• /
• 2003

A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.16 no.9
• /
• pp.838-844
• /
• 2004

In a long road tunnel, a tunnel ventilation system may be used in order to reduce the pollution below the required level. To develop control algorithms for a tunnel ventilation system, a dynamic simulation program may be used to predict the pollution level in a tunnel. Research was carried out to develop better pollution models for a tunnel ventilation control system. A neural network structure was adopted and compared by using actual poilution data. Simulation results showed that the dynamic model developed by a neural network may be effective for the development of tunnel ventilation control algorithms.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2011.04a
• /
• pp.57-60
• /
• 2011

Updating model parameters are required in order to simulate the actual behavior of the dynamic structure. A new strategy, mean-iterative neural networks (MINNs) has been proposed in this paper for model parameter updating of a steel box girder bridge. With new strategy for structural dynamic model updating, it offers many advantages such as potential savings of computational effort, more consistent in reaching convergence. The dynamic response obtained from the experimental test on a two span continuous bridge is used as the target for model updating. And the presented algorithm is applied to update the model parameters. These results show a good possible of using MINNs in practice for dynamic model updating.

• Journal of Electrical Engineering and Technology
• /
• v.3 no.2
• /
• pp.285-291
• /
• 2008

Active Queue Management(AQM) has been widely used for congestion avoidance in Transmission Control Protocol(TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the Back-Propagation(BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: Random Early Detection(RED) and Proportional-Integral(PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.

• Journal of information and communication convergence engineering
• /
• v.5 no.1
• /
• pp.29-34
• /
• 2007

This paper proposes a MAC protocol for real-time VBR (rt-VBR) services in wireless ATM networks. The proposed protocol is characterized by a contention-based mechanism of the reservation request, a contention-free polling scheme for transferring the dynamic parameters, and a priority scheme of the slot allocation. The design objective of the proposed protocol is to guarantee the real-time constraint of rt-VBR traffic. The scheduling algorithm uses a priority scheme based on the maximum cell transfer delay parameter. The wireless terminal establishes an rt-VBR connection to the base station with a contention-based scheme. The base station scheduler allocates a dynamic parameter minislot to the wireless terminal for transferring the residual lifetime and the number of requesting slots as the dynamic parameters. Based on the received dynamic parameters, the scheduler allocates the uplink slots to the wireless terminal with the most stringent delay requirement. The simulation results show that the proposed protocol can guarantee the delay constraint of rt-VBR services along with its cell loss rate significantly reduced.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.693-694
• /
• 2014

This paper proposes a new pipelined dynamic bandwidth allocation algorithm for XG-PON (10-Gbps-capable passive optical network) passive optical networks) system. The pipelined algorithm is used when a dynamic bandwidth allocation algorithm cannot finished in an unit time. In the proposed mechanism, the request is immediately transferred to each pipeline block to improve performance.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.9
• /
• pp.3563-3570
• /
• 2010

MAC protocol has been studied for reducing energy consumption in wireless sensor networks. The overhearing and idle In the existing asynchronous MAC Protocol will occur due to unnecessary energy consumption. In this paper, to solve these problems, the Preamble to change the structure of the destination address, the Preamble of the end times, the data including the length of the Preamble and Data Overhearing reduce the length of the Check Interval Data generated according to the presence of the Dynamic Value dynamically adjustable by changing the DPL (Dynamic Preamble Length)-MAC protocol was proposed. Moreover, the existing asynchronous MAC protocol of wireless sensor networks and DPL-MAC protocol proposed in this paper to simulate the energy consumption and latency were assessed in terms of comparative analysis.