• Smart Structures and Systems
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• v.24 no.2
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• pp.209-221
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• 2019

The classical Kalman filter (KF) provides a practical and efficient way for state estimation. It is, however, not applicable when the external excitations applied to the structures are unknown. Moreover, it is known the classical KF is only suitable for linear systems and can't handle the nonlinear cases. The aim of this paper is to extend the classical KF approach to circumvent the aforementioned limitations for the joint estimation of structural states and the unknown inputs. On the basis of the scheme of the classical KF, analytical recursive solution of an improved KF approach is derived and presented. A revised form of observation equation is obtained basing on a projection matrix. The structural states and the unknown inputs are then simultaneously estimated with limited measurements in linear or nonlinear systems. The efficiency and accuracy of the proposed approach is verified via a five-story shear building, a simply supported beam, and three sorts of nonlinear hysteretic structures. The shaking table tests of a five-story building structure are also employed for the validation of the robustness of the proposed approach. Numerical and experimental results show that the proposed approach can not only satisfactorily estimate structural states, but also identify unknown loadings with acceptable accuracy for both linear and nonlinear systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.561-568
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• 2018

Passivity theory is significantly applied to analyze stability of nonlinear dynamic systems and construct its stable control systems. This paper presents a passivity based control design approach for exciters and governors which are employed to regulate the terminal voltage and the rotor velocity of synchronous generator systems in industry fields. We consider the IEEE type 1 exciter and the gas turbine (GT) governor models respectively in this paper. We first carry out a passivity analysis for exciter and governor control systems, which are numerically obtained from its mathematical models. And then its control parameters are selected to assure passivity conditions in a design procedure. Lastly, we investigate numerical simulations to demonstrate reliability of the proposed control approach against large-scale generators with parameter changes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.4
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• pp.777-783
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• 2007

In this paper, we present a novel control approach for linear motor-based transfer systems in which friction reduction and enhancement of control performance are considered. In general, in such systems friction effects from rails and wheels, and internal bearings complicate control scheme since in particularly its dynamics are arbitrarily changed due to mass variation, detent force of motor systems, and gaps among stators. Our control approach is achieved to reduce this undesired friction dynamics using fuzzy system. We construct hybrid control approach for this control system which Is composed of a nominal control and a vertical control against friction. Fuzzy parameter vector is optimally determined from iterative simulation experiments. We demonstrate its superiority via numerical simulations comparing with a traditional control method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.413-416
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• 1997

Recently, dynamic recurrent neural networks(DRNN) for identification of nonlinear dynamic systems have been researched extensively. In general, dynamic backpropagation was used to adjust the weights of neural networks. But, this method requires many complex calculations and has the possibility of falling into a local minimum. So, we propose a new approach to identify nonlinear dynamic systems using DRNN. In order to adjust the weights of neurons, we use evolution strategies, which is a method used to solve an optimal problem having many local minimums. DRNN trained by evolution strategies with mutation as the main operator can act as a plant emulator. And the fitness function of evolution strategies is based on the difference of the plant's outputs and DRNN's outputs. Thus, this new approach at identifying nonlinear dynamic system, when applied to the simulation of a two-link robot manipulator, demonstrates the performance and efficiency of this proposed approach.

• International Journal of Concrete Structures and Materials
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• v.7 no.2
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• pp.135-153
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• 2013

This study investigates the direct displacement based design (DDBD) approach for different types of reinforced concrete structural systems including single moment-resisting, dual wall-frame and dual steel-braced systems. In this methodology, the displacement profile is calculated and the equivalent single degree of freedom system is then modeled considering the damping characteristics of each member. Having calculated the effective period and secant stiffness of the structure, the base shear is obtained, based on which the design process can be carried out. For each system three frames are designed using DDBD approach. The frames are then analyzed using nonlinear time-history analysis with 7 earthquake accelerograms and the damage index is investigated through lateral drift profile of the models. Results of the analyses and comparison of the nonlinear time-history analysis results indicate efficiency of the DDBD approach for different reinforced concrete structural systems.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.4
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• pp.242-249
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• 2001

In this paper, the open-loop state response of the two-time-scale systems by unified approach using the $\delta$-operator is presented with an example of the aircraft longitudinal dynamics. First, the $\delta$-operator system unifies both the continuous system and the discrete system simultaneously, and the $\delta$-operator approach improves the finite word-length characteristics. This saves more computing time than that of the discrete system. Second, the singular perturbation method by block diagonalization reduces the sizes and orders of the systems. This also reduces the floating-point operations (flops). The advantage of those two approaches is shown by comparing our results with the earlier ones in the illustrative example of the longitudinal motion of F-8 aircraft.

• ETRI Journal
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• v.20 no.2
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• pp.192-213
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• 1998

The current approaches in Object-Oriented Analysis have limitations on modeling complex real world systems because they require priori knowledge about objects and their interactions before applying them. This may be practical in small systems and systems with clear domain knowledge, but not in large real world systems with unclear domain knowledge. Our approach uses a stepwise refinement technique in a top-down manner to the Object-Oriented Analysis stage with the application of use cases. This approach is especially good for new areas where we do not know all the information in advance. We present the approach with an example of its application to the B-ISDN service modeling and distributed systems.

• International Journal of Control, Automation, and Systems
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• v.5 no.2
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• pp.170-183
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• 2007

This paper presents a delay-dependent approach to robust filtering for linear parameter-varying (LPV) systems with discrete and distributed time-invariant delays in the states and outputs. It is assumed that the state-space matrices affinely depend on parameters that are measurable in real-time. Some new parameter-dependent delay-dependent stability conditions are established in terms of linear matrix inequalities (LMIs) such that the filtering process remains asymptotically stable and satisfies a prescribed $H_{\infty}$ performance level. Using polynomially parameter-dependent quadratic (PPDQ) functions and some Lagrange multiplier matrices, we establish the parameter-independent delay-dependent conditions with high precision under which the desired robust $H_{\infty}$ filters exist and derive the explicit expression of these filters. A numerical example is provided to demonstrate the validity of the proposed design approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.8
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• pp.1457-1471
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• 2011

A method to detect Trojan horses in messaging and Bluetooth in mobile phones by means of monitoring the events produced by the infections is presented in this paper. The structure of the detection approach is split into two modules: the first is the Monitoring module which controls connection requests and sent/received files, and the second is the Graphical User module which shows messages and, under suspicious situations, reports the user about a possible malware. Prototypes have been implemented on different mobile operating systems to test its feasibility on real cellphone malware. Experimental results are shown to be promising since this approach effectively detects various known malware.