• Structural Engineering and Mechanics
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• v.43 no.2
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• pp.163-177
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• 2012

This paper presents a LMI(linear matrix inequality)-based fuzzy approach of modeling and active vibration control of geometrically nonlinear flexible plates with piezoelectric materials as actuators and sensors. The large-amplitude vibration characteristics and dynamic partial differential equation of a piezoelectric flexible rectangular thin plate structure are obtained by using generalized Fourier series and numerical integral. Takagi-Sugeno (T-S) fuzzy model is employed to approximate the nonlinear structural system, which combines the fuzzy inference rule with the local linear state space model. A robust fuzzy dynamic output feedback control law based on the T-S fuzzy model is designed by the parallel distributed compensation (PDC) technique, and stability analysis and disturbance rejection problems are guaranteed by LMI method. The simulation result shows that the fuzzy dynamic output feedback controller based on a two-rule T-S fuzzy model performs well, and the vibration of plate structure with geometrical nonlinearity is suppressed, which is less complex in computation and can be practically implemented.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1020-1024
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• 2005

In this paper, an approach to dynamic systems control is addressed based on exploiting the potential features of the new nonlinear neural oscillator. Neural oscillators have recently enabled robots to exhibit natural dynamics using their robustness and entrainment properties. To technically accomplish this objective, the neural oscillator should be connected to the robot joints under the sensory feedback. This also requires the neural oscillator to adapt to the non-periodic nature of arbitrary input patterns. However, even in the most widely-used Matsuoka oscillator, when an unknown quasi-periodic or non-periodic signal is applied, its output signal is not always closely entrained. Therefore, current neural oscillators may not be applied to the precise control of the dynamic systems response. We illustrate the enhanced entrainment properties of the new neural oscillator by numerical simulation and show the possibility for implementation to control a variety of dynamic systems. It is verified that the oscillator can produce rhythmic signals for generating actuator signals which can be naturally modified by incorporating sensory feedback to adapt to outer circumstances.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.1040-1044
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• 1992

This paper presents an algebraic approach for finding a dynamic state feedback controller when the linear multi-input system with delays in both state and input is controllable. In the time-delay case, controllability of the system does not always imply that system is cyclizable. Therefore, reduced order augmentation systems which is cyclizable as the time-varying case are considered. It is possible to construct feedback contorl systems by using single-input methods.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.330-336
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• 2000

This paper discusses the potential of system dynamics modelling to support balanced scorecard. The balanced scorecard is a conceptual framework for translating an organisation's strategy into a set of performance indicators. These performance indicators are distributed across the 'classic'model's four perspective: Customers, Internal Business Processes, Financial, and Learning and Growth. This balanced scorecard, whilst having significant strength, suffers from the limitation of all performance indicator systems, namely that the interrelationships between indicators are overlooked and there is no way of taking into account the impact of delayed feedback which flows from introduction of new policy and legislative changes. System Dynamics is a methodology for understanding complex problems where there is dynamic behaviour and where feedback impacts significantly on system outcomes. System dynamics provides a rigorous basis for qualitative testing of the effects of performance indicators in complex environments such as health or social security. This can be supplemented with quantitative system dynamics simulation tools that further test the validity of indicators and the business rules implicit in them. System dynamics modelling has an important role to play in extending feedback cycle in performance measurements to a full systems approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3867-3886
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• 2015

This article investigates the problem of opportunistic spectrum access in dynamic environment, in which the signal-to-noise ratio (SNR) is time-varying. Different from existing work on continuous feedback, we consider more practical scenarios in which the transmitter receives an Acknowledgment (ACK) if the received SNR is larger than the required threshold, and otherwise a Non-Acknowledgment (NACK). That is, the feedback is discrete. Several applications with different threshold values are also considered in this work. The channel selection problem is formulated as a non-cooperative game, and subsequently it is proved to be a potential game, which has at least one pure strategy Nash equilibrium. Following this, a multi-agent Q-learning algorithm is proposed to converge to Nash equilibria of the game. Furthermore, opportunistic spectrum access with multiple discrete feedbacks is also investigated. Finally, the simulation results verify that the proposed multi-agent Q-learning algorithm is applicable to both situations with binary feedback and multiple discrete feedbacks.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1400-1406
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• 2016

This paper presents a new adaptive converter control approach for electric motor systems whose voltage source is excited from photovoltaic (PV) power generators. First, an electric model is represented with dynamic states and output velocity of such DC motor systems. We propose a hybrid converter control law in which a state feedback control is applied as an auxiliary control framework. Moreover, control parameter estimation is derived to realize adaptive converter systems for effective control performance against stochastic PV power excitation in practice. We carry out stability analysis for such converter system by using a well-known eigenvalue theory. Lastly, numerical simulation is conducted to test reliability of the proposed converter control approach and prove its superiority in the control point of view.

• International Journal of Quality Innovation
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• v.9 no.3
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• pp.123-144
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• 2008

This paper describes a comprehensive approach to examine how technological innovation contributes to the renewal of a firm's competences through its dynamic and reciprocal relationship with R&D and product commercialization. Three theories of technology and innovation (the R&D and technological knowledge concept, product-process concept, technological interdependence concept) are used to relate technology and innovation to strategic management. Based on these theories, this paper attempts to identify the dynamic relationship between product innovation and process innovation using system dynamics by investigating that aspect of the dynamic changes in the closed feedback circulation structure in which R&D investments drive the accumulation of technological knowledge. Further, such knowledge accumulation actualizes product innovation and process innovation, subsequently resulting in an increase in productivity, customer satisfaction, profit generation, and.

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

• The Journal of Korean Physical Therapy
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• v.27 no.6
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• pp.407-412
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• 2015

Purpose: This study aimed to evaluate changes in the balance ability of patients whose head positions were altered due to stroke. Subjects were divided into three groups to determine the effects of the training on dynamic balance and gait. Methods: Forty-two stroke patients were enrolled. The Visual Feedback Training (VFT) group performed four sets of exercises per training session using a Sensoneck device, while the Active Range of Motion (ART) group performed eight sets per training session after receiving education from an experienced therapist. The Visual Feedback with Active Range of Motion (VAT) group performed four sets of active range of motion and two sets of visual-feedback training per session using a Sensoneck device. The training sessions were conducted three days a week for eight weeks. Results: The comparison of changes in dynamic balance ability showed that a significant difference in the total distance of the body center was found in the VFT group (p<0.05) and Significant differences were found according to the training period (p<0.05). The comparison of the 10 m walk test showed that the main effect test, treatment period and interactions between group had statistically significant differences between the three groups (p<0.05). Conclusion: Head-adjustment training using visual feedback can improve the balance ability and gait of stroke patients. These results show that coordination training between the eyes and head with visual feedback exercises can be used as a treatment approach to affect postural control through various activities involving the central nervous system.