• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.129-136
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• 1994

In this paper an adaptive moving method of the search region tracking the motion is proposed. The search region in BMA is determined by the capability of hardware implementation and the degree of motion. But once determined nothing can be changed during coding procedure. In this paper we predict the level of motion of the current block using motion vectors of previous frames without overhead information and change the location of the search region according to the level of the motion predicted. In short the proposed method can be archieved the dsirable effect such that the size of search region gets large when the motion is large. Results of experiments show that prediction efficiency has been improved by using adaptive moving method resulting in reduced prediction error in the blocks with large motion.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.15-16
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• 2006

In this paper, we propose an adaptive SLM scheme based on peak observation for PAPR reduction of OFDM signals. The proposed scheme is composed of three steps: peak scaling, sequence selection, and SLM procedures. In the first step, the peak signal samples in the IFFT outputs of the original input sequence are scaled down. In the second step, the sub-carrier positions where power difference between the original input sequence and the FFT outputs of the scaled signal is large, are identified. Then, the phase sequences which have the maximum number of phase-reversed sequence words only for these positions, are selected. Finally, only using the selected phase sequences, the generic SLM procedure is performed for the original input sequence. Simulation results reveal that the proposed adaptive SLM remarkably reduces the complexity in terms of IFFT and PAPR calculations than the conventional SLM, while maintaining the PAPR reduction performance.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.4
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• pp.92-103
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• 1993

In this paper, we propose an adaptive classifier based on uncertainty of features for 3D planar object recognition. First, we investigate the uncertainty of depth information and the feature values of 3D planar object by numerical method. And, we observed that the statistical behavior of feature is dependent on the position and orientation of objects. After that, the approximation of the statistical behavior is executed. Subsequently, the recognition procedure is executed by the adaptive classifier. By computer simulation, we confirmed that the proposed classifier is useful for 3D planar object recognition.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.537-539
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• 1998

In this paper, we construct the on-line model structure for the nonlinear process systems using the adaptive fuzzy-neural network. Adaptive fuzzy-neural network usually consists of two distinct modifiable structure, with both, the premise and the consequent part. These two parts can be adapted by different optimization methods, which are the hybrid learning procedure combining gradient descent method and least square method. To achieve the on-line model structure, we use the recursive least square method for the consequent parameter identification of nonlinear process. We design the interface between PLC and main computer, and construct the monitoring and control simulator for the nonlinear process. The proposed on-line modeling to real process is carried out to obtain the effective and accurate results.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.307-316
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• 2007

In real system application, the interacting multiple model (IMM) based algorithm operates with the following problems: it requires less computing resources as well as a good performance with respect to the various target maneuvering, it requires a robust performance with respect to the time-varying measurement noise, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an adaptive fuzzy interacting multiple model (AFIMM) algorithm, which is based on the basis sub-models defined by considering the maneuvering property and the time-varying mode transition probabilities designed by using the mode probabilities as the inputs of the fuzzy decision maker whose widths are adjusted, is proposed. To verify the performance of the proposed algorithm, a radar target tracking is performed. Simulation results show that the proposed AFIMM algorithm solves all problems in the real system application of the IMM based algorithm.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1155-1157
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• 1996

In this paper, the direct adaptive control using neural networks is presented for the control of chaotic nonlinear systems. The direct adaptive control method has an advantage that the additional system identification procedure is not necessary. Two direct adaptive control methods are applied to a Duffing's equation and the simulation results show the effectiveness of the controllers.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1186-1188
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• 1996

Neural networks and fuzzy systems have attracted the attention of many researehers recently. In general, neural networks are used to obtain information about systems from input/output observation and learning procedure. On the other hand, fuzzy systems use fuzzy rules to identify or control systems. In this paper we present a generalized FCMAC(Fuzzified Cerebellar Model Articulation Controller) networks, by integrating fuzzy systems with the CMAC(Cerebellar Model Articulation Controller) networks. We propose a direct adaptive controller design based on FCMAC(fuzzified CMAC) networks. Simulation results reveal that the proposed adaptive controller is practically feasible in nonlinear plant control.

• Smart Structures and Systems
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• v.5 no.6
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• pp.645-662
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• 2009

Adaptive wings are capable of properly modifying their shape depending on the current aerodynamic conditions, in order to improve the overall performance of a flying vehicle. In this paper is presented the concept design of a small-scale compliant wing rib whose outline may be distorted in order to switch from an aerodynamic profile to another. The distortion loads are induced by shape memory alloy actuators placed within the frame of a wing section whose elastic response is predicted by the matrix method with beam formulation. Genetic optimization is used to find a wing rib structure (corresponding to the first airfoil) able to properly deforms itself when loaded by the SMA-induced forces, becoming as close as possible to the desired target shape (second airfoil). An experimental validation of the design procedure is also carried out with reference to a simplified structure layout.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.281-294
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• 2021

In this paper, we propose an adaptive regression method based on the single-layer neural network structure. We adopt a symmetric activation function as units of the structure. The activation function has a flexibility of its form with a parametrization and has a localizing property that is useful to improve the quality of estimation. In order to provide a spatially adaptive estimator, we regularize coefficients of the activation functions via ℓ₁-penalization, through which the activation functions to be regarded as unnecessary are removed. In implementation, an efficient coordinate descent algorithm is applied for the proposed estimator. To obtain the stable results of estimation, we present an initialization scheme suited for our structure. Model selection procedure based on the Akaike information criterion is described. The simulation results show that the proposed estimator performs favorably in relation to existing methods and recovers the local structure of the underlying function based on the sample.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.53-64
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• 2022

High dimensional time series is gaining considerable attention in recent years. The sparse vector heterogeneous autoregressive (VHAR) model proposed by Baek and Park (2020) uses adaptive lasso and debiasing procedure in estimation, and showed superb forecasting performance in realized volatilities. This paper extends the sparse VHAR model by considering non-convex penalties such as SCAD and MCP for possible bias reduction from their penalty design. Finite sample performances of three estimation methods are compared through Monte Carlo simulation. Our study shows first that taking into cross-sectional correlations reduces bias. Second, nonconvex penalties performs better when the sample size is small. On the other hand, the adaptive lasso with debiasing performs well as sample size increases. Also, empirical analysis based on 20 multinational realized volatilities is provided.