• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.143-146
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• 1996

An adaptive digital filtering of the electroencophalogram(EEG) is a successful way of suppressing mains interference, but it affects some of the frequency components of the signal, an artifact which not be acceptable in some cases of automatic EEG processing. The types of electrical artifact seen on EEG records is described. Those are the EOG and the PVC roller pump noise. And we study the method for cancelling these artifacts. The method does not need the reference channel, and are obtained by cascading the linear predictor and the noise canceller. The simulation results illustrate the performances of the presented method in terms of the capability of interferences suppression.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.437-442
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• 2000

An alternative inverse feedback structure for adaptive active control of periodic noise is introduced for systems with nonminimum phase cancellation path. To obtain the inverse model of the nonminimum phase cancellation path, the cancellation path model can be factorized into a minimum phase term and a maximum phase term. The maximum phase term containing unstable zeros makes the inverse model unstable. To avoid the instability, we alter the inverse model of the maximum phase system into an anti-causal FIR one. An LMS predictor estimates the future samples of the noise, which are necessary for causality of both anti-causal FIR approximation for the stable inverse of the maximum phase system and time-delay existing in the cancellation path. The proposed method has a faster convergence behavior and a better transient response than the conventional FX-LMS algorithms with the same internal model control structure since a filtered reference signal is not required. We compare the proposed methods with the conventional methods through simulation studies.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.4
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• pp.395-405
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• 2004

The filling pattern and an adaptive grid refinement based on the finite element method and Eulerian mesh advancement approach have been developed to analyze incompressible transient viscous flow with free surfaces. The governing equation for flow analysis is Navier-Stokes equation including inertia and gravity effects. The mixed FE formulation and predictor-corrector method are used effectively for unsteady numerical simulation. The flow front surface and the volume inflow rate are calculated using the filling pattern technique to select an adequate pattern among four filling patterns at each triangular control volume. By adaptive grid refinement, the new flow field that renders better prediction in flow surface shape is generated and the velocity field at the flow front part is calculated more exactly. In this domain the elements in the surface region are made finer than those in the remaining regions for more efficient computation. Using the proposed numerical technique, the collapse of a water dam has been analyzed to predict flow phenomenon of fluid and the predicted front positions with respect to time have been compared with the reported experimental results.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.891-895
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• 2001

An alternative inverse feedback structure for adaptive active control of periodic noise is introduced for systems with nonminimum phase cancellation path. To obtain the inverse model of the nonminimum phase cancellation path, the cancellation path model can be factorized into a minimum phase term and a maximum phase term. The maximum phase term containing unstable zeros makes the inverse model unstable. To avoid the instability, we alter the inverse model of the maximum phase system into an anti-causal FIR one. An LMS predictor estimates the future samples of the noise, which are necessary for causality of both anti-causal FIR approximation for the stable inverse of the maximum phase system and time-delay existing in the cancellation path. The proposed method has a faster convergence behavior and a better transient response than the conventional filtered-x LMS algorithms with the same internal model control structure since a filtered reference signal is not required. We compare the proposed methods with the conventional methods through simulation studies.

• Journal of the Korean Society of Safety
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• v.7 no.2
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• pp.71-79
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• 1992

This paper presents a least-square algorithms of lattice structures and their use for adaptive prediction of time series generated from the dynamic system. As the view point of adaptive prediction, a new method of Identification of dynamic characteristics by means of estimating the parameters of linear auto regressive model is proposed. The fast convergence of adaptive lattice algorithms is seen to be due to the orthogonalization and decoupling properties of the lattice. The superiority of the least-square lattice is verified by computer simulation, then predictor coefficients are computed from the linear sequential time data. For the application to the dynamic characteristic analysis of unknown system, the transfer function of ideal system represented in frquency domain and the estimated one obtained by predicted coefficients are compared. Using the proposed method, the damping ratio and the natural frequency of a dynamic structure subjected to random excitations can be estimated. It is expected that this method will be widely applicable to other technical dynamic problem in which estimation of damping ratio and fundamental vibration modes are required.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.10
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• pp.166-174
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• 1998

Neural networks, having highly nonlinear dynamics by virtue of the distributed nonlinearities and the learing ability, have the potential for the adaptive prediction of nonstationary signals. This paper describes the nonlinear prediction of these signals in two ways; using a nonlinear module and the cascade combination of nonlinear and linear modules. Fully-connected recurrent neural networks (RNNs) and a conventional tapped-delay-line (TDL) filter are used as the nonlinear and linear modules respectively. The dynamic behavior of the proposed predictors is demonstrated for chaotic time series adn speech signals. For the relative comparison of prediction performance, the proposed predictors are compared with a conventional ARMA linear prediction model. Experimental results show that the neural networks based adaptive predictor ourperforms the traditional linear scheme significantly. We also find that the cascade combination predictor is well suitable for the prediction of the time series which contain large variations of signal amplitude.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.247-256
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• 2021

In this paper, we propose a packet loss concealment (PLC) algorithm using pitch harmonic motion prediction and adaptive signal amplitude prediction and. The spectral motion prediction method divides the spectral motion of the previous usable frame into predetermined sub-bands to predict and restore the motion of the lost signal. In the proposed algorithm, the speech signal is classified into voiced and unvoiced sounds. In the case of voiced sounds, it is further divided into pitch harmonics using the pitch frequency to predict and restore the pitch harmonic motion of the lost frame, and for the unvoiced sound, the lost frame is restored using the spectral motion prediction method. When the continuous loss of speech frames occurs, a method of adjusting the gain using the least mean square (LMS) predictor is proposed. The performance of the proposed algorithm was evaluated through the objective evaluation method, PESQ (Perceptual Evaluation of Speech Quality) and was showed MOS 0.1 improvement over the conventional method.

• Journal of The Korean Association For Science Education
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• v.19 no.3
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• pp.471-478
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• 1999

In this study, the relationships among learners' cognitive variables, motivational variables, and conceptual understandings in learning with analogy were investigated. The instruments regarding analogical reasoning ability, field dependence-independence, mental capacity, and logical thinking ability were administered. Some subtests (self-efficacy, expectancy, self-concept of ability, and value) of the Patterns of Adaptive Learning Survey were administered. After students learned with a worksheet that included analogy, a conception test regarding 'stoichiometry that included limiting reagent' was also administered. It was found that learners' conceptual understandings were significantly correlated with the logical thinking ability and the field dependence-independence among the cognitive variables, and the self-efficacy and the self-concept of ability among the motivational variables. The multiple regression analysis of the cognitive variables on conceptual understandings revealed that the logical thinking ability was the most significant predictor. The field dependence-independence also had predictive power. In the analysis of the motivational variables, the self concept of ability was the only significant predictor.

• Smart Structures and Systems
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• v.14 no.1
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• pp.1-16
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• 2014

In this work, we address the so-called sensor reachback problem for Wireless Sensor Networks, which consists in collecting the measurements acquired by a large number of sensor nodes into a sink node which has major computational and power capabilities. Focused on applications such as Structural Health Monitoring, we propose a cooperative communication protocol that exploits the spatio-temporal correlations of the sensor measurements in order to save energy when transmitting the information to the sink node in a non-stationary environment. In addition to cooperative communications, the protocol is based on two well-studied adaptive filtering techniques, Least Mean Squares and Recursive Least Squares, which trade off computational complexity and reduction in the number of transmissions to the sink node. Finally, experiments with real acceleration measurements, obtained from the Canton Tower in China, are included to show the effectiveness of the proposed method.

• Journal of Broadcast Engineering
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• v.16 no.2
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• pp.372-383
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• 2011

This paper proposes an improved design of an interleaved motion vector coding scheme with flexibility in predictive motion vector component by exploiting motion characteristics. It can use component-wise adaptive motion vector predictor based on the utility of spatial and temporal motion field without any signaling bit for indicating decoder of the selected predictive motion vector component. Experiment with test video data shows about 1.99% (max 8.71%) bit rate reduction compared to the conventional H.264/AVC method.