• Composites Research
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• v.28 no.6
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• pp.383-388
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• 2015

Ionic Polymer-Metal Composite (IPMC) consisting of soft membrane plated by platinum electrode layers on both surfaces generates electric energy when subjected to various mechanical stimuli. The paper proposes a circuit model that describes the physical composition of IPMC to predict the voltage generation characteristic corresponding to bending motion. The parameter values in the model are identified to minimize the RMS error between the real and simulated outputs. Following the design of IPMC circuit model, the state observer of the model is designed by using pole placement technique which improves the model accuracy. State observer design technique is also applied to find the inverse model which estimates the input bending angles from the output voltage data. The results show that the inverse model estimates input bending angles fairly well enough for the further applications of IPMC not only as an energy harvester but also as a bending sensor.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.1-4
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• 2001

The classification performance of Artificial Neural Network (ANN) and RBF-NN was compared for Landsat TM image. The RBF-NN was validated for three unique landuse types (e.g. Mixed landuse area, Cultivated area, Urban area), different input band combinations and classification class. The bootstrap resampling technique was employed to estimate the confidence intervals and distribution for unit load, The pollutant generation was varied significantly according to the classification accuracy and percentile unit load applied. Especially in urban area, where mixed landuse is dominant, the difference of estimated pollutant load is largely varied.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.39-48
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• 2005

This paper proposes novel texture segmentation method using Bayesian estimation method and neural networks. We use multi-scale wavelet coefficients and the context information of neighboring wavelets coefficients as the input of networks. The output of neural networks is modeled as a posterior probability. The context information is obtained by HMT(Hidden Markov Tree) model. This proposed segmentation method shows better performance than ML(Maximum Likelihood) segmentation using HMT model. And post-processed texture segmentation results as using multi-scale Bayesian image segmentation technique called HMTseg in each segmentation by HMT and the proposed method also show that the proposed method is superior to the method using HMT.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.879-885
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• 2004

This paper describes an adaptive signal processing of digital receiver with digital down convertor(DDC). DDC is composed of numerically controlled oscillator(NCO) and digital low pass filler and the received signal is processed by numerical algorithm. The simulation results of digital receiver using the passband sampling technique are presented and we confirmed that the received low IF signal is converted to zero IF by numerically processed DDC. Direction of arrival(DOA) estimation technique using multiple signal classification(MUSIC) algorithm with high resolution is also discussed. We knew that an accurate resolution of DOA depends on the input sampling numbers and antenna element numbers.

• Journal of the Korea Computer Graphics Society
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• v.18 no.1
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• pp.1-9
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• 2012

Video-based modeling / rendering developed to produce photo-realistic video contents have been one of the important research topics in computer graphics and computer visions. To smoothly combine original input video clips and 3D graphic models, geometrical information of light sources and cameras used to capture a scene in the real world is essentially required. In this paper, we present a simple technique to estimate the position and orientation of an optimal light source from the topology of objects and the silhouettes of shadows appeared in the original video clips. The technique supports functions to generate well matched shadows as well as to render the inserted models by applying the estimated light sources. Shadows are known as an important visual cue that empirically indicates the relative location of objects in the 3D space. Thus our method can enhance realism in the final composed videos through the proposed shadow generation and rendering algorithms in real-time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1772-1781
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• 2017

In this study, the design methodology for alleviating the overfitting problem of Polynomial Neural Networks(PNN) is realized with the aid of two kinds techniques such as L2 regularization and Sum of Squared Coefficients (SSC). The PNN is widely used as a kind of mathematical modeling methods such as the identification of linear system by input/output data and the regression analysis modeling method for prediction problem. PNN is an algorithm that obtains preferred network structure by generating consecutive layers as well as nodes by using a multivariate polynomial subexpression. It has much fewer nodes and more flexible adaptability than existing neural network algorithms. However, such algorithms lead to overfitting problems due to noise sensitivity as well as excessive trainning while generation of successive network layers. To alleviate such overfitting problem and also effectively design its ensuing deep network structure, two techniques are introduced. That is we use the two techniques of both SSC(Sum of Squared Coefficients) and $L_2$ regularization for consecutive generation of each layer's nodes as well as each layer in order to construct the deep PNN structure. The technique of $L_2$ regularization is used for the minimum coefficient estimation by adding penalty term to cost function. $L_2$ regularization is a kind of representative methods of reducing the influence of noise by flattening the solution space and also lessening coefficient size. The technique for the SSC is implemented for the minimization of Sum of Squared Coefficients of polynomial instead of using the square of errors. In the sequel, the overfitting problem of the deep PNN structure is stabilized by the proposed method. This study leads to the possibility of deep network structure design as well as big data processing and also the superiority of the network performance through experiments is shown.

• ETRI Journal
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• v.26 no.6
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• pp.565-574
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• 2004

Differential unitary space-time modulation (DUSTM) has emerged as a promising technique to obtain spatial diversity without intractable channel estimation. This paper presents a study of the application of DUSTM on multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with frequency-selective fading channels. From the view of a correlation analysis between subcarriers of OFDM, we obtain the maximum achievable diversity of DUSTM on MIMO-OFDM systems. Moreover, an efficient implementation strategy based on subcarrier reconstruction is proposed, which transmits all the signals of one signal matrix in one OFDM transmission and performs differential processing between two adjacent OFDM blocks. The proposed method is capable of obtaining both spatial and multipath diversity while reducing the effect of time variation of channels to a minimum. The performance improvement is confirmed by simulation results.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.41-45
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• 2007

Multi-input multi-output (MIMO) system can increase data rate, capacity and bit error rate (BER) performance compare to traditional single antenna system. However MIMO technique is pointed out the problem that has high complexity to design receiver. So a recent trend of research on the MIMO system pays more attention to simplified implementation of receiver structure. In this paper, we propose differential space time block code (STBC) for MIMO system with cyclic delay diversity (CDD). This structure can provide a very close performance to that of the conventional diversity scheme with maximum likelihood (ML) detection without channel estimation block while the receiver structure is highly simplified. Bit error rate (BER) performance of the proposed system is simulated for an AWGN channel by theoretical and simulated approaches. The results of this paper can be applicable to the 4G mobile multimedia communication systems.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.54-66
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• 1997

The objectives of this study is to introduce and apply neural network theory to real hydrologic systems for stochastic nonlinear predicting of daily runoff discharge in the river catchment. Back propagation algorithm of neural network model is applied for the estimation of daily stochastic runoff discharge using historical daily rainfall and observed runoff discharge. For the fitness and efficiency analysis of models, the statistical analysis is carried out between observed discharge and predicted discharge in the chosen runoff periods. As the result of statistical analysis, method 3 which has much processing elements of input layer is more prominent model than other models(method 1, method 2) in this study.Therefore, on the basis of this study, further research activities are needed for the development of neural network algorithm for the flood prediction including real-time forecasting and for the optimal operation system of dams and so forth.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.91-106
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• 1996

Control Variate (CV) is very useful technique for variance reduction in a wide class of queueing network simulations. However, the loss in variance reduction caused by the estimation of the optimum control coefficients is an increasing function of the number of control variables. Therefore, in some situations, it is required to select an optimal set of control variables to maximize the variance reduction . In this paper, we develop the Adaptive Control Variates (ACV) method which selects an optimal set of control variates during the simulation adatively. ACV is useful to maximize the simulation efficiency when we need iterated simulations to find an optimal solution. One such an example is the Simulated Annealing (SA) because, in SA algorithm, we have to repeat in calculating the objective function values at each temperature, The ACV can also be applied to the queueing network optimization problems to find an optimal input parameters (such as service rates) to maximize the throughput rate with a certain cost constraint.