• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2006.04a
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• pp.391-393
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• 2006

In this study, the performance of artificial neural network (ANN) models for estimating spatial distribution of water quality was evaluated using electric conductivity (EC) values in landfill site. For the ANN model development, feedforward neural networks and backpropagation algorithm with gradient descent method were used. In Test 1, the interpolation ability of the ANN model was evaluated. Results of the ANN model were more precise than those of the Kriging model. In Test 2, spatial distributions of EC values were predicted using precipitation data. Results seemed to be reasonable, however, they showed a limitation of ANN models in extrapolations.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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• pp.133-140
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• 1999

In this study, a monitoring method to detect chip disposal state in drilling system based on neural network was proposed and its performance was evaluated. If chip flow is bad during drilling, not only the static component but also the fluctuation of dynamic component of drilling. Drilling torque is indirectly measured by sensing spindle motor power through a AC spindle motor drive system. Spindle motor power being measured drilling, four quantities such as variance/mean, mean absolute deviation, gradient, event count were calculated as feature vectors and then presented to the neural network to make a decision on chip disposal state. The selected features are sensitive to the change of chip disposal state but comparatively insensitive to the change of drilling condition. The 3 layerd neural network with error back propagation algorithm has been used. Experimental results show that the proposed monitoring system can successfully recognize the chip disposal state over a wide range of drilling condition even though it is trained under a certain drilling condition.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.439-443
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• 1996

A fuzzy teaming controller is experimentally designed to control the ball k beam system in this paper. Although most fuzzy controllers have been built just to emulate human decision-making behavior, it is necessary to construct the rule bases by using a learning method with self-improvement when it is difficult or impossible to get them only by expert's experience. The algorithm introduces a reference model to generate a desired output and minimizes a performance index function based on the error and error-rate using the gradient-decent method. In our balancing experiment of the ball ＆ beam system, this paper shows that the fuzzy control rules by learning are superior to the expert's experience.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.790-795
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• 2002

The object of this research is limited to the reduction of compression process noise only among the main sources of compressor noise such as motor noise, compression process noise, and valve port flow noise. Thus the research is focused on the wave motion rather than the particle motion of sound wave travels. A muffler is a commonly used device to reduce the compression process noise, generated by the pressure pulsations caused by the cyclic compression process. In this research, the acoustic characteristics of the muffler are analyzed by using the normal gradient integral equation proposed by Wu and Wan. Moreover, a commercial code SYSNOISE developed by indirect variational boundary integral equation is also used to validate the results. For the noise reduction, the topology optimization technique using a genetic algorithm is used. The number, size and position of the muffler holes are considered as design variables. Compared with original design, the optimized design has very improved acoustic characteristics. Both numerical and experimental analyses are used to evaluate new design.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.4
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• pp.360-364
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• 2011

Backlash is common in mechanical and hydraulic systems and severely limits overall system performance. In this paper, the development of an adaptive control scheme for systems with unknown backlash is presented. An adaptive backlash inverse based controller is applied to a plant that has an unknown backlash in its input. The harmful effects of backlash are presented. Compensation for backlash by adding a discrete adaptive backlash inverse structure and the gradient-type adaptive algorithm, which provides the estimated backlash parameters, are also presented. The supposed adaptive backlash control algorithms are applied to an aircraft with unknown backlash in the actuator of control surfaces. Simulation results show that the proposed compensation scheme improves the tracking performance of systems with backlash.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2284-2298
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• 2013

In this paper, a fast and efficient signal reconstruction algorithm for solving the basis pursuit (BP) problem in compressed sensing (CS) is proposed. This fast linearized Bregman method (FLBM), which is inspired by the fast method of Beck et al., is based on the fact that the linearized Bregman method (LBM) is equivalent to a gradient descent method when applied to a certain formulation. The LBM requires $O(1/{\varepsilon})$ iterations to obtain an ${\varepsilon}$-optimal solution while the FLBM reduces this iteration complexity to $O(1/\sqrt{\varepsilon})$ and requiring almost the same computational effort on each iteration. Our experimental results show that the FLBM can be faster than some other existing signal reconstruction methods.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.421-424
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• 1987

Recently, the problem associated with the achievement of desired trajectories for non-linear robotic manipulatory systems are researched. The control system which is designed for this robot manipulator, poses a number of severe problem. The methods proposed to deal with the problem fall loosely into three main classes : "direct" "adaptive", "anthropomorphic". Besides there is an approach which is described based upon the application of optimal control theory. In this paper, using the optimal theory, we choose error-coordinate, between the desired trajectories and the practical as the state values, and determine the control law U which minimize a corresponding performance criterion. Let's consider the robotic arm proposed by Freund and set up the deviations of it's trajectory as a measure of performance. To find the optimal control law $U^*$ and the next state value which need to obtain $U^*$ here, we should introduced the conjugate gradient algorithm and the Runge Kutta method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.249-252
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• 1996

Fuzzy logic has been successfully used for nonlinear control systems. However, when the plant is complex or expert knowledge is not available, it is difficult to construct the rule bases of fuzzy systems. In this paper, we propose a new method of how to construct automatically the rule bases using fuzzy neural network. Whereas the conventional methods need the training data representing input-output relationship, the proposed algorithm utilizes the gradient of the object function for the construction of fuzzy rules and the tuning of membership functions. Experimental results with the inverted pendulum show the superiority of the proposed method in comparison to the conventional fuzzy controller.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.526-533
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• 2008

We propose a wavelet neural network (WNN) control method for active queue management (AQM) in an end-to-end TCP network, which is trained by adaptive learning rates (ALRs). In the TCP network, AQM is important to regulate the queue length by passing or dropping the packets at the intermediate routers. RED, PI, and PID algorithms have been used for AQM. But these algorithms show weaknesses in the detection and control of congestion under dynamically changing network situations. In our method, the WNN controller using ALRs is designed to overcome these problems. It adaptively controls the dropping probability of the packets and is trained by gradient-descent algorithm. We apply Lyapunov theorem to verify the stability of the WNN controller using ALRs. Simulations are carried out to demonstrate the effectiveness of the proposed method.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.54-57
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• 2007

Recently, multidimensional joint inversion of geophysical data based on fundamentally different physical properties has been actively studied. Joint inversion can provide a way to much more accurately image the subsurface structure. Through the joint inversion, furthermore, it is possible to directly estimate non-geophysical material properties from geophysical measurements. In this study, I derive the objective functions and normal equations of three different joint inversion approaches: one approach based on the structural similarity using cross-gradient, and the other two using the a priori information on the model parameters and the correlation between material properties. Since all the equations derived in this study are based on the same inversion method (smoothness constrained least-squares), it is possible to mix the joint inversion methods so as to produce a new joint inversion algorithm.