• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.515-525
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• 2007

In this paper, we develop a co-design methodology of stochastic optimal controllers and network parameters that optimizes the overall quality of control (QoC) in networked control systems (NCSs). A new dynamic model for NCSs is provided. The relationship between the system stability and performance and the sampling frequency is investigated, and the analysis of co-design of control and network parameters is presented to determine the working range of the sampling frequency in an NCS. This optimal sampling frequency range is derived based on the system dynamics and the network characteristics such as data rate, time-delay upper bound, data-packet size, and device processing time. With the optimal sampling frequency, stochastic optimal controllers are designed to improve the overall QoC in an NCS. This co-design methodology is a useful rule of thumb to choose the network and control parameters for NCS implementation. The feasibility and effectiveness of this co-design methodology is verified experimentally by our NCS test bed, a ball magnetic-levitation (maglev) system.

• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.17-22
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• 2001

Because the quantitative relationships between welding parameters and welding result are not yet blown, optimal values of welding parameters for $CO_2$ robotic arc welding is a difficult task. Using the various artificial data processing methods may solve this difficulty. This research aims to develop an expert system for $CO_2$ robotic arc welding to recommend the optimal values of welding parameters. This system has three main functions. First is the recommendation of reasonable values of welding parameters. For such work, the relationships in between the welding parameters are investigated by the use of regression analysis and fuzzy system. The second is the estimation of bead shape by a neural network system. In this study the welding current voltage, speed, weaving width, and root gap are considered as the main parameters influencing a bead shape. The neural network system uses the 3-layer back-propagation model and a generalized delta rule as teaming algorithm. The last is the optimization of the parameters for the correction of undesirable weld bead. The causalities of undesirable weld bead are represented in the form of rules. The inference engine derives conclusions from these rules. The conclusions give the corrected values of the welding parameters. This expert system was developed as a PC-based system of which can be used for the automatic or semi-automatic $CO_2$ fillet welding with 1.2, 1.4, and 1.6mm diameter the solid wires or flux-cored wires.

• ETRI Journal
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• v.15 no.2
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

• Journal of Magnetics
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• v.19 no.4
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• pp.315-322
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• 2014

In this work, Artificial Neural Network (ANN) was used to model the dynamic behavior of ferromagnetic hysteresis derived from performing the mean-field analysis on the Ising model. The effect of field parameters and system structure (via coordination number) on dynamic critical points was elucidated. The Ising magnetization equation was drawn from mean-field picture where the steady hysteresis loops were extracted, and series of the dynamic critical points for constructing dynamic phase-diagram were depicted. From the dynamic critical points, the field parameters and the coordination number were treated as inputs whereas the dynamic critical temperature was considered as the output of the ANN. The input-output datasets were divided into training, validating and testing datasets. The number of neurons in hidden layer was varied in structuring ANN network with highest accuracy. The network was then used to predict dynamic critical points of the untrained input. The predicted and the targeted outputs were found to match well over an extensive range even for systems with different structures and field parameters. This therefore confirms the ANN capabilities and indicates the ANN ability in modeling the ferromagnetic dynamic hysteresis behavior for establishing the dynamic-phase-diagram.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.10
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• pp.65-77
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• 2004

In this paper, we propose an adaptive mechanism based on immune algorithm and neural network identifier technique. It is also applied fur an autonomous guided vehicle (AGV) system. When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged due to the abrupt change of PID parameters since the parameters are almost adjusted randomly. To solve this problem, we use the neural network identifier (NNI) technique fur modeling the plant and humoral immune algorithm (HIA) which performs the parameter tuning of the considered model, respectively. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using an immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. Finally, the simulation and experimental result fur the control of steering and speed of AGV system illustrate the validity of the proposed control scheme. These results for the proposed method also show that it has better performance than other conventional controller design methods.

• Korean Journal of Bronchoesophagology
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• v.7 no.2
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• pp.161-167
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• 2001

Background and Objectives： Total laryngectomy is often required for advanced cases. But this operation induced the many inconvenience of basic daily life. Early diagnosis of laryngeal cancer is very important to prevent from this disastrous condition. In this point of view, mass screening test for early detection of laryngeal cancer is necessary. Screening test using voice has many advantages such as simple, less interventional. Voice collection by Automatic Response System(ARS) is comfortable and easy to got acoustic sample. Thus author tried to got the acoustic parameters which can differentiate normal, benign. and malignant laryngeal diseases and also checked the availability of parameters on neural network system. Materials and Methods: Author has evaluated the voice from 17 laryngeal cancer patients and 45 benign laryngeal disease patients who visited at Department of Otolaryngology, Pusan National University Hospital from May 1998 to April 2001, and 15 normal control. Author chose the sir Parameters (Jitt. vFo, Shim, vAm, NHR, SPI) that was thought to be related with voice collected by ARS among thirty-three parameters analysed by a Multi-Dimensional Voice Program (MDVP). Two-step neural network was used for the availability of six parameters. Results: The detection rate of normal voice by ARS voice analysis is 78.5% and detection rate of abnormal voice was 97.1 o/o. Among abnormal voice, the detection rate of benign laryngeal diseases and laryngeal cancers were 82.4 o/o, 70.6% respectively. Conclusion: Author concluded that six parameters and Matlab based neural network software may be effective in development of acoustic screening system for laryngeal cancer and further study should be necessary for development of new acoustic parameters.

• Journal of Communications and Networks
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• v.14 no.4
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• pp.434-442
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• 2012

Several gateway selection schemes have been proposed that select gateway nodes based on a single Quality of Service (QoS) path parameter, for instance path availability period, link capacity or end-to-end delay, etc. or on multiple non-QoS parameters, for instance the combination of gateway node speed, residual energy, and number of hops, for Mobile Ad hoc NETworks (MANETs). Each scheme just focuses on the ment of improve only a single network performance, i.e., network throughput, packet delivery ratio, end-to-end delay, or packet drop ratio. However, none of these schemes improves the overall network performance because they focus on a single QoS path parameter or on set of non-QoS parameters. To improve the overall network performance, it is necessary to select a gateway with stable path, a path with themaximum residual load capacity and the minimum latency. In this paper, we propose a gateway selection scheme that considers multiple QoS path parameters such as path availability period, available capacity and latency, to select a potential gateway node. We improve the path availability computation accuracy, we introduce a feedback system to updated path dynamics to the traffic source node and we propose an efficient method to propagate QoS parameters in our scheme. Computer simulations show that our gateway selection scheme improves throughput and packet delivery ratio with less per node energy consumption. It also improves the end-to-end delay compared to single QoS path parameter gateway selection schemes. In addition, we simulate the proposed scheme by considering weighting factors to gateway selection parameters and results show that the weighting factors improve the throughput and end-to-end delay compared to the conventional schemes.

• The Journal of Society for e-Business Studies
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• v.6 no.2
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• pp.25-38
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• 2001

ATM is the transport method for the broadband integrated services digital networks(B-ISDN). It may replace existing LAN, MAN and WAN technologies such as CSMA/CD, FDDI, Frame relay, X.25, etc. But it is more complicate than existing network technologies. One of the main difficulties in ATM network is performance management. Specifically, the problems are evaluating the performance and tuning the values of the performance parameters, The goal of this paper is to introduce a system architecture designed for ATM network performance management, The major ingredients of the system are generic performance parameters In be measured from ATM network, performance evaluation models and decision criteria concerning the network performance. In this paper, general requirements for performance management application in ATM network are discussed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.6
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• pp.57-63
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• 1998

An adaptive control in the robotic GMA welding is employed to monitor information about weld characteristics and process parameters as well as to modify those parameters to hold weld quality within acceptable limits. Typical characteristics are the bead geometry, composition, microstructure, appearance, and process parameters which govern the quality of the final weld. The main objectives of this thesis are to realize the mapping characteristics of bead width through learning. After learning, the neural estimation can estimate the bead width desired form 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) are chosen from an estimation error analysis. A series of bead of bead-on-plate GMA welding experiments was carried out in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the bead width with reasonable accuracy and guarantee the uniform weld quality.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.80-84
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• 2000

Network parameters in power systems are indispensable for all of power system engineering studies, including the power flow calculation and the state estimation. The network parameters required for the studios, in general, are estimated by using several estimation techniques, since it Is very difficult to measure. To improve the estimation accuracy of the network parameters, this paper adopt the synchronized phasor measurements which are acquired from the Phasor Measurement Unit with built-in GPS receiver. In this paper, the parameter estimation problem is formulated with over-determined nonlinear measurement equations and solved with Newton-Raphson method and pseudo-inverse. The effectiveness of the proposed parameter estimation with the synchronized phasor measurements is verified through some case studies with IEEE sample system. The results are very promising.