• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.108.2-108
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• 2001

This paper presents the application of adaptive phase-locked loop (adaptive PLL) technique to control the process variable of the process control system. The adaptive algorithm is related to the error. When the error of the system is changed, the adaptive gain will be directly changed according to the error. If the value of the adaptive gain is large, the value of the error will be large. In this experiment, the reference input is 50% step input. The experimental result in controlling the first order lag process by the adaptive PLL shows that the response of the controlled system has no overshoot, short rise time, and zero steady-state error. The experimental result also shows that when the output disturbance enters to the process control system, the adaptive PLL can maintain the stability of the system and the effect of the output disturbance can also be fast rejected. The adaptive PLL has better performance ...

• The Journal of the Acoustical Society of Korea
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• v.19 no.8
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• pp.26-34
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• 2000

This paper presents a new adaptive impulse noise cancelling technique based on the adaptive nonlinear suppressing function. The proposed "adaptive threshold algorithm (ATA)" is controlled by the normalized power prior input data term, and this adaptive threshold makes the cancelling system highly robust against additive impulse noise. For the performance evaluation, we have tested the proposed algorithm with the observed signals simulated in various impulsive noise environments and real EMG signals. As a result the proposed algorithm shows superior performance of 51.7% to the available techniques in the points of SNR and MSE.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.144-147
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• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.

• Structural Engineering and Mechanics
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• v.12 no.5
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• pp.475-490
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• 2001

Remeshing strategies are formulated for r-adaptive and h/r-adaptive analysis of crack propagation. The relocation of the nodes, which typifies r-adaptivity, is a very cheap method to optimise a given discretisation since the element connectivity remains unaltered. However, the applicability is limited. To further improve the finite element mesh, a combined h/r-adaptive method is proposed in which h-adaptivity is applied whenever r-adaptivity is not capable of further improving the discretisation. Two and three-dimensional examples are presented. It is shown that the r-adaptive approach can optimise a discretisation at minimal computational costs. Further, the combined h/r-adaptive approach improves the performance of a fully r-adaptive technique while the number of h-remeshings is reduced compared to a fully h-adaptive technique.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2005.10a
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• pp.17-22
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• 2005

This paper presents a new adaptive autopilot for ships based on the Adaptive Neural Networks. The proposed adaptive autopilot is designed with some modifications and improvements from the previous studies on Adaptive Neural Networks by Adaptive Interaction (ANNAI) theory to perform course-keeping, turning and track-keeping control. A strategy for automatic selection c! the neural network controller parameters is introduced to improve the adaptation ability and the robustness of new ANNAI autopilot. In Part II of the paper, to show the effectiveness and feasibility of the proposed ANNAI autopilot, computer simulations of course-keeping and track-keeping tasks with and without the effects of measurement noise and external disturbances are presented.

• Journal of Navigation and Port Research
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• v.29 no.9
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• pp.771-776
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• 2005

This paper presents a new adaptive autopilot for ships based on the Adaptive Neural Networks. The proposed adaptive autopilot is designed with some modifications and improvements from the previous studies on Adaptive Neural Networks by Adaptive Interaction (ANNAI) theory to perform course-keeping, turning and track-keeping control. A strategy for automatic selection of the neural network controller parameters is introduced to improve the adaptation ability and the robustness of new ANNAI autopilot. In Part II of the paper, to show the effectiveness and feasibility of the proposed ANNAI autopilot, computer simulations of course-keeping and track-keeping tasks with and without the effects of measurement noise and external disturbances will be presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.123-128
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• 2017

This paper relates with the Hybrid-SMMA adaptive equalization algorithm that is possible to improve the equalization performance based on adaptive modulus and adaptive stepsize which is propotional to the output power of equalizer in the current SMMA (Sliced Multi Modulus Algorithm). The fixed statistic modulus of transmitted signal is used in current SMMA algorithm in order to generate the error signal for updating the tap coefficient of equalizer. The proposed Hybrid-SMMA based on the adaptive modulus which is propotion to the equalizer output signal power and adaptive stepsize which is fuction of the nonlinearties of error signal. The computer simulation was performed in order to confirm the improved equalization performance of proposed algorithm. As a result of computer simulation, the proposed Hybrid-SMMA has fairly good in every performance than the current SMMA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1627-1648
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• 2012

We propose an adaptive method for detecting plagiarized pairs from a large set of source code. This method is adaptive in that it uses an adaptive algorithm and it provides an adaptive threshold for determining plagiarism. Conventional algorithms are based on greedy string tiling or on local alignments of two code strings. However, most of them are not adaptive; they do not consider the characteristics of the program set, thereby causing a problem for a program set in which all the programs are inherently similar. We propose adaptive local alignment-a variant of local alignment that uses an adaptive similarity matrix. Each entry of this matrix is the logarithm of the probabilities of the keywords based on their frequency in a given program set. We also propose an adaptive threshold based on the local outlier factor (LOF), which represents the likelihood of an entity being an outlier. Experimental results indicate that our method is more sensitive than JPlag, which uses greedy string tiling for detecting plagiarism-suspected code pairs. Further, the adaptive threshold based on the LOF is shown to be effective, and the detection performance shows high sensitivity with negligible loss of specificity, compared with that using a fixed threshold.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.8
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• pp.1-12
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• 2001

The adaptive learning circuit is designed on the basis of modeling of MFSFET (Metal-Ferroelectric-Semiconductor FET) and the numerical results are analyzed. The output frequency of the adaptive learning circuit is inversely proportional to the source-drain resistance of MFSFET and the capacitance of the circuit. The saturated drain current with input pulse number is analogous to the ferroelectric polarization reversal. It indicates that the ferroelectric polarization plays an important role in the drain current control of MFSFET. The output frequency modulation of the adaptive learning circuit is investigated by analyzing the source-drain resistance of MFSFET as functions of input pulse numbers in the adaptive learning circuit and the dimensionality factor of the ferroelectric thin film. From the results, the frequency modulation characteristic of the adaptive learning circuit are confirmed. In other words, adaptive learning characteristics which means a gradual frequency change of output pulse with the progress of input pulse are confirmed. Consequently it is shown that our circuit can be used effectively in the neuron synapses of nueral networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.501-506
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• 2010

The proposal set out in this paper, is the Adaptive-MCM(Modulation, Coding and MIMO) system, which results from the combination of adaptive modulation and coding (AMC) and multiple input multiple output (MIMO) schemes. The performance of this system is analyzed through computer simulation. By using the MIMO scheme adaptively as well, the proposed Adaptive-MCM system, presents a better improvement of data rate and error performance compared to the AMC system. The throughput performance of the Adaptive-MCM system is analyzed and compared with the throughput performance of Non-Adaptive-MCM Systems. As a result of the simulation, we can infer that, at a fixed MCM level, there is an improvement of the trade-off between secure Signal to Noise Ratio (SNR) and a high data rate. Consequently, this trade-off improvement results in a better average data rate.