• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.920-926
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• 2005

Nearest neighbor algorithms classify an unseen input instance by selecting similar cases and use the discovered membership to make predictions about the unknown features of the input instance. The usefulness of the nearest neighbor algorithms have been demonstrated sufficiently in many real-world domains. In nearest neighbor algorithms, it is an important issue to assign proper weights to the attributes. Therefore, in this paper, we propose a new method which can automatically assigns to each attribute a weight of its importance with respect to the target attribute. The method has been implemented as a computer program and its effectiveness has been tested on a number of machine learning databases publicly available.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1686-1696
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• 2022

The impedance matching circuit design technique based on the phasor plot introduced in this paper is based on the impedance triangle of electric circuit. It is a technique that designs through the construction of a phasor figure using the values given to the matching circuit design. The design pattern is based on L-type, inverted L-type, T-type, and 𝜋-type, and unknown reactance elements are determined through phasor shapes. In this paper, using a design by phasor plot, we design several cases, such as the case where the input and output ports are pure resistance and have reactance. It was confirmed that the design value was verified by serial-parallel equivalent conversion to achieve matching. This design technique can immediately grasp the phase or size of input/output power, so it is expected to be applied mainly in a low frequency band due to rapid design change and application.

• The KIPS Transactions:PartA
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• v.9A no.1
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• pp.75-82
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• 2002

A robust signal transition density propagation method for a zero delay model is presented to obtain the signal transition density for estimating the power consumption. The power estimation for the zero delay model is a proper criteria for the lower boundary of power consumption. Since the input characteristics are generally unknown at design stage, robust estimation for wide range input characteristics is very important for the power consumption. In this paper, a conventional transition estimation method will be explored. And this exploration will be analyzed with the input/output signal transition behavior and used to propose the robust signal transition density propagation for the power estimation. In order to apply to practical circuits, the reconvergent path, which is crucial to affect the exactness of the power estimation, will be studied and an algorithm to take the reconvergent path into consideration will be presented. In experiment, the proposed methodology shows better robustness, comparable accuracy and elapsed time compared to the conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.1894-1899
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• 1994

In this paper, a new identification method is proposed for unknown digitally modulated input signals. The proposed identification method is implemented using a self-organized neural network which is based on the characteristic features of the symbol magnitude; the number of symbol magnitude levels, amplitude probability density and adjacent symbol magnitude ratio. The proposed method was performed for 5 QAM signals. The simulation results show that the self-organized neural network can accurately recognize all kinds of patterns even at SNR 8dB. The proposed method can be applied to the intelligent communication system on ISDN and multi-point polling networks.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.299-302
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• 1996

A control algorithm is proposed for nonlinear multi-input multi-output(MIMO) batch processes by combining quadratic iterative learning control(Q-ILC) with model predictive control(MPC). Both controls are designed based on output feedback and Kalman filter is incorporated for state estimation. Novelty of the proposed algorithm lies in the facts that, unlike feedback-only control, unknown sustained disturbances which are repeated over batches can be completely rejected and asymptotically perfect tracking is possible for zero random disturbance case even with uncertain process model.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1423-1427
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• 1997

This paper deals with an algebraic approach to FDI observer design procedure. In general, FDI observer can be designed a sLuenbrger-type and equations for unknown input and actuator fault estimation include derivation of system outputs which is not available from the measurement directly. At this point, this paper presents STWS approach which can convert the derivation procedure to the recursive algebraic form by using its orthogonality and disjointess to alleviate such problems.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.19-27
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• 2003

It is well known that one can easily design a high-gain adaptive output feedback control for a class of nonlinear systems which satisfy a certain condition called output feedback exponential passivity (OFEP). The designed high-gain adaptive controller has simple structure and high robustness with regard to bounded disturbances and unknown order of the controlled system. However, from the viewpoint of practical application, it is important to consider a robust control scheme for controlled systems for which some of the assumptions of output feedback stabilization are not valid. In this paper, we design a robust high-gain adaptive output feedback control for the OFEP nonlinear systems with uncertain nonlinearities and/or disturbances. The effectiveness of the proposed method is shown by numerical simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.107-112
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• 2003

In general, the dynamics of autonomous underwater vehicles(AUVs) are highly nonlinear and time-varying, and the hydrodynamic coefficients of vehicles are hard to estimate accurately because of the variations of these coefficients with different navigation conditions. For this reason, in this paper, the control gain function is assumed to be unknown and the exogenous input term is assumed to be unbounded, although it still satisfies certain restrict condition. And these two kinds of wild assumptions have been seldom handled simultaneously in one system because of the difficulty of stability analysis. Under the above two relaxed assumptions, a robust neural network control scheme is presented for autonomous diving control of an AUV, and can guarantee that all the signals in the closed-loop system are UUB (uniformly ultimately bounded). Some practical features of the proposed control law are also discussed.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.149-152
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• 2004

Speed and torque control of PMSM(Permanent Magnet Synchronous Motor) are usually achieved by using position and speed sensors which require additional mounting space, reduce the reliability in harsh environments and increase the cost of a motor. Therefore, many studies have been performed for the elimination of speed and position sensors. In this paper, a novel speed sensorless control of a permanent magnet synchronous motor based on SVMR(Support Vector Machine Regression) is presented. The SVM regression method is an algorithm that estimates an unknown mapping between a system's input and outputs, from the available data or training data. Two well-known different voltage model is necessary to estimate the speed of a PMSM. The validity and the usefulness of proposed algorithm are thoroughly verified through numerical simulation.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.678-680
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• 2004

In many cases the systems are so complex that it is not possible to obtain reasonable models using physical laws. Also a model based on physical laws contains a number of unknown parameters even if the structure is derived from physical laws. These problems can be solved by system identification. In this paper, plate activity vibration is selected as an example for system identification. The transfer functions of this system is derived by using ARMAX based on input/output data through experiment.