• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.215-218
• /
• 1997

A dynamic back-propagation neural network is addressed for adaptive neural control system to approximate non-linear control system rather than static networks. It has the capability to represent the approximation of nonlinear system without mathematical analysis and to carry out the on-line learning algorithm for real time application. The simulated results show fast tracking capability and adaptive response by using dynamic back-propagation neurons.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.4
• /
• pp.394-400
• /
• 2018

A heterogenous network is the one of key technologies for 5G, where one cell is divided into small cells in order to extend coverage and support high data rates. Divided cells aggravates the intercell interference problem as the cell edge increases. In order to avoid the intercell interference, it is the best to allocate the different spectrum for each cells. However, it also decreases the spectral efficiency. Therefore, the trade-off between the spectral efficiency gain and the signal quality loss by the interference should be considered for an efficient spectrum sharing in the heterogenous network. The adaptive modulation is the method to change the transmitted bit according to the channel quality, which is adopted as the standard in the most practical communication systems. It should be considered to applied the performance analysis into the practical systems. In this paper, the overall cell data rates is analyzed for the heterogenous network under the adaptive modulation. The Monte Carlo simulation results verify the correctness of the analysis.

• Journal of the Korean Society of Industry Convergence
• /
• v.4 no.2
• /
• pp.177-183
• /
• 2001

This paper proposes an efficient method for recognizing the fingerprint using the extracted features by adaptive principal component analysis(PCA). The adaptive PCA is implemented by a single-layer neural network for extracting the linear features of fingerprint data. And, the extracted data are transformed into binary data for reducing storage space and transmission time. The proposed method has been applied to recognize the 100 fingerprint data. The simulation results show that the recognitions are all successful and capable of about ${\pm}8^{\circ}$ rotated data.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.8 no.5
• /
• pp.70-75
• /
• 1999

Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor the information about weld characteristics and process paramters as well as modification of those parameters to hold weld quality within the acceptable limits. Typical characteristics are the bead geometry composition micrrostructure appearance and process parameters which govern the quality of the final weld. The main objectives of this paper are to realize the mapping characteristicso f penetration through the learning. After learning the neural network can predict the pene-traition desired from the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) were chosen from an error analysis. partial-penetration single-pass bead-on-plate welds were fabricated in 12mm mild steel plates in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the penetration with reasonable accuracy and gurarantee the uniform weld quality.

• Journal of the Korea Society for Simulation
• /
• v.21 no.4
• /
• pp.47-56
• /
• 2012

In this study, we use a parallel simulator PASENS(Parallel SEnsor Network Simulator) to predict power consumption and data reception rate of the hierarchical routing protocols for sensor network - LEACH (Low-Energy Adaptive Clustering Hierarchy), TL-LEACH (Two Level Low-Energy Adaptive Clustering Hierarchy), M-LEACH (Multi hop Low-Energy Adaptive Clustering Hierarchy) and LEACH-C (LEACH-Centralized). According to simulation results, M-LEACH routing protocol shows the highest data reception rate for the wider area, since more sensor nodes are involved in the data transmission. And LEACH-C routing protocol, where the sink node considers the entire node's residual energy and location to determine the cluster head, results in the most efficient energy consumption and in the narrow area needed long life of sensor network.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.9
• /
• pp.781-788
• /
• 2005

This paper proposes an identification method using a self recurrent wavelet neural network (SRWNN) for dynamic systems. The architecture of the proposed SRWNN is a modified model of the wavelet neural network (WNN). But, 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. Thus, in the proposed identification architecture, the SRWNN is used for identifying nonlinear dynamic systems. The gradient descent method with adaptive teaming rates (ALRs) is applied to 1.am the parameters of the SRWNN identifier (SRWNNI). The ALRs are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of an SRWNNI. Finally, through computer simulations, we demonstrate the effectiveness of the proposed SRWNNI.

• Journal of Communications and Networks
• /
• v.8 no.4
• /
• pp.422-431
• /
• 2006

This paper proposes an adaptive routing protocol called ARCA for converged ad-hoc and cellular network (CACN). Due to the limitation of both bandwidth and transmission range in a cell, a mobile host (MH) may not be able to make a call during busy time. CACN offers a flexible traffic diversion mechanism that allows a MH to use the bandwidth in another cell to ease the congestion problem and increase the throughput in a cellular network. Based on the presentation of CACN's physical characteristics, the paper details the design issues and operation of the adaptive routing protocol for CACN (ARCA). Detailed numerical analysis is presented in terms of both route request rejection rate and routing overhead, which, along with the simulation results, have indicated the effectiveness and efficiency of the ARCA protocol.

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.45-51
• /
• 2009

In this paper, a transparency analysis and network-adaptive transport scheme are proposed in order to improve transparency of EBA-based force-reflecting teleoperation. EBA guarantees stability of force-reflecting teleoperation over network delay and loss but has limitation that it cannot overcome transparency deterioration of haptic interactions. The proposed transparency analysis quantifies the force feedback distortion caused by network delay and loss. Based on the analysis, the proposed haptic data synchronization and transmission rate control schemes adapt synchronization delay and transmission rate to current network state for more transparent haptic interaction. Through Matlab/Simulink simulations, it is confirmed that the proposed analysis provides an acceptable quantification method about haptic interaction quality and that the proposed haptic data transport scheme effectively improves haptic interaction quality with respect to network delays and losses.

• International Journal of Control, Automation, and Systems
• /
• v.3 no.4
• /
• pp.552-563
• /
• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.10
• /
• pp.4865-4885
• /
• 2019

We propose a FPGA based design that performs real-time power-efficient analysis of heterogeneous sensor data using adaptive ANN on edge gateway of smart military wearables. In this work, four independent ANN classifiers are developed with optimum topologies. Out of which human activity, BP and toxic gas classifier are multiclass and ECG classifier is binary. These classifiers are later integrated into a single adaptive ANN hardware with a select line(s) that switches the hardware architecture as per the sensor type. Five versions of adaptive ANN with different precisions have been synthesized into IP cores. These IP cores are implemented and tested on Xilinx Artix-7 FPGA using Microblaze test system and LabVIEW based sensor simulators. The hardware analysis shows that the adaptive ANN even with 8-bit precision is the most efficient IP core in terms of hardware resource utilization and power consumption without compromising much on classification accuracy. This IP core requires only 31 microseconds for classification by consuming only 12 milliwatts of power. The proposed adaptive ANN design saves 61% to 97% of different FPGA resources and 44% of power as compared with the independent implementations. In addition, 96.87% to 98.75% of data throughput reduction is achieved by this edge gateway.