• Journal of Digital Convergence
• /
• v.5 no.2
• /
• pp.91-98
• /
• 2007

Destination marketing organizations (DMO) have been building up the cyber city in the WWW. Website for DMO is a core channel to promote regional attractions. This research suggests the issue of criteria for evaluating DMO's performance in the Internet. The method of evaluation focuses on the structure in perspective of linkage based on small world theory and direct network. Convention & Visitors & Bureau (CVB) in tourism and travel industry playa role to promote and held the international meeting and exhibitions. CVB's websites evaluated according to web link structure and performance.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1050-1052
• /
• 1996

This paper presents a neuro-control algorithm for the speed control of a direct drive motor without the knowledge of the dynamics of the motor and the characteristics of a nonlinear load. In the field of motor control, it is not possible to directly use the back-propagation method in order to train a network since the desired output of the network is not known. Hence, we propose an extended back-propagation algorithm to force the closed loop system to give desired results. Experimental results shown that the proposed neuro-controller can reduce the unknown load effects and have the good velocity tracking capabilities.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1730-1731
• /
• 2007

In this paper, we present a direct adaptive control method using neural networks for the control of nonlinear systems. The weights of neural networks are trained by an adaptive backpropagation algorithm based on Lyapunov stability theory. We develop the parameter update-laws using the neural network input and the error between the desired output and the output of nonlinear plant to update the weights of a neural network in the sense that Lyapunove stability theory. Beside the output tracking error is asymptotically converged to zero.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.212-218
• /
• 2021

The Proportional Integral Derivative (PID) controller is the most popular industrial controller and more than 90% process industries use this controller. During the past 50 years, numerous good tuning methods have been proposed for Single Input Single Output Systems. However, design of PI/PID controllers for multivariable processes is a challenge for the researchers. A comparative study of three PID controllers design methods has been carried-out. These methods include the DS (Direct Synthesis) method, IMC (Internal model Control) method and ETF (Effective Transfer Function) method. MIMO PID controllers are designed for a number of 2×2, 3×3 and 4×4 process models with multiple delays. The performance of the three methods has been evaluated through simulation studies in Matlab/Simulink environment. After extensive simulation studies, it is found that the Effective Transfer Function (ETF) Method produces better output responses among two methods. In this work, only decentralized methods of PID controllers have been studied and investigated.

• East Asian Economic Review
• /
• v.23 no.1
• /
• pp.3-26
• /
• 2019

This study investigates the effect of high-skilled inventor emigration rate on growth rate of the country of origin (COO). Inventor emigrants represent the human capital that can generate highly innovative work. The social network they form spurs knowledge diffusion and technology transfer back to their COOs, which in turn affects innovation and growth in their home countries. We run dynamic panel estimation for 154 countries during 1990-2011, and empirically show that a positive and statistically significant effect exists for the interaction of inventor emigration and trade. The result indicates that the direct negative impact of the brain drain can be mitigated by the positive feedback effect generated by the high-skilled inventor emigrants abroad. When coupled with an active trade policy that reinforces growth, countries can partially recoup the direct effect of the human capital loss. We stress the importance of international trade for successful technology transfer to occur, and offer insights for policies that can utilize the benefits of the rich social network of their high-skilled emigrants.

• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.11-18
• /
• 2021

The Proportional Integral Derivative (PID) controller is the most popular industrial controller and more than 90% process industries use this controller. During the past 50 years, numerous good tuning methods have been proposed for Single Input Single Output Systems. However, design of PI/PID controllers for multivariable processes is a challenge for the researchers. A comparative study of three PID controllers design methods has been carried-out. These methods include the DS (Direct Synthesis) method, IMC (Internal model Control) method and ETF (Effective Transfer Function) method. MIMO PID controllers are designed for a number of 2×2, 3×3 and 4×4 process models with multiple delays. The performance of the three methods has been evaluated through simulation studies in Matlab/Simulink environment. After extensive simulation studies, it is found that the Effective Transfer Function (ETF) Method produces better output responses among two methods. In this work, only decentralized methods of PID controllers have been studied and investigated.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.4
• /
• pp.209-215
• /
• 2022

This paper proposes a novel approach for training an unsupervised depth estimation algorithm. The objective of unsupervised depth estimation is to estimate pixel-wise distances from camera without external supervision. While most previous works focus on model architectures, loss functions, and masking methods for considering dynamic objects, this paper focuses on the training framework to effectively use depth cue. The main loss function of unsupervised depth estimation algorithms is known as the photometric error. In this paper, we claim that direct depth cue is more effective than the photometric error. To obtain the direct depth cue, we adopt the technique of knowledge distillation which is a teacher-student learning framework. We train a teacher network based on a previous unsupervised method, and its depth predictions are utilized as pseudo labels. The pseudo labels are employed to train a student network. In experiments, our proposed algorithm shows a comparable performance with the state-of-the-art algorithm, and we demonstrate that our teacher-student framework is effective in the problem of unsupervised depth estimation.

• Structural Engineering and Mechanics
• /
• v.18 no.3
• /
• pp.303-314
• /
• 2004

An artificial neural network (ANN) application is presented for flexural and axial vibration analysis of elastic beams with various support conditions. The first three natural frequencies of beams are obtained using multi layer neural network based back-propagation error learning algorithm. The natural frequencies of beams are calculated for six different boundary conditions via direct solution of governing differential equations of beams and Rayleigh's approximate method. The training of the network has been made using these data only flexural vibration case. The trained neural network, however, had been tested for cantilever beam (C-F), and both end free (F-F) in case the axial vibration, and clamped-clamped (C-C), and Guided-Pinned (G-P) support condition in case the flexural vibrations which were not included in the training set. The results found by using artificial neural network are sufficiently close to the theoretical results. It has been demonstrated that the artificial neural network approach applied in this study is highly successful for the purposes of free vibration analysis of elastic beams.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.4
• /
• pp.338-344
• /
• 2002

In this paper, neural network force tracking control is proposed. The conventional impedance function is reformulated to have direct farce tracking capability. Neural network is used to compensate for all the uncertainties such as unknown robot dynamics, unknown environment stiffness, and unknown environment position. On line training signal of farce error for neural network is formulated. A large x-y table is built as a test-bed and neural network loaming algorithm is implemented on a DSP board mounted in a PC. Experimental studies of farce tracking on unknown environment for x-y table robot are presented to confirm the performance of the proposed technique.

• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.325-327
• /
• 2005

In this paper, we propose the design of active queue management (AQM) control system using the self-recurrent wavelet neural network (SRWNN). By regulating the queue length close to reference value, AQM can control the congestions in TCP network. The SRWNN is designed to perform as a feedback controller for TCP dynamics. The parameters of network are tunes to minimize the difference between the queue length of TCP dynamic model and the output of SRWNN using gradient-descent method. We evaluate the performances of the proposed AQM approach through computer simulations.