• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.911-917
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• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.719-729
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• 2011

This paper presents a research on the fast and accurate method of line detection in the image of a wireless mobile robot (WMR). For the improvement of the processing time to detect lines, the characteristics of the transmitted image from the WMR was analyzed, and the efficient preprocessing method among the existing preprocessing methods was selected. And for the improvement of the accuracy to detect lines, the selection method of local maximum value at the Hough array (HA) which has the result of Hough transform was improved by designing a mask and applying it to HA. The experiment was performed with acquired images from the WMR, and the proposed method outperformed the existing methods in terms of processing time and line detection.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.75-82
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• 2009

In this paper, we propose a robust preprocessing scheme for entropy coding in gray-level image. The issue of this paper is to reduce additional information needed when bit stream is transmitted. The proposed scheme uses the preprocessing method of co-occurrence count about gray-levels in neighboring pixels. That is, gray-levels are substituted by their ranked numbers without additional information. From the results of computer simulation, it is verified that the proposed scheme could be reduced the compression bit rate by up to 44.1%, 37.5% comparing to the entropy coding and conventional preprocessing scheme respectively. So our scheme can be successfully applied to the application areas that require of losslessness and data compaction.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.159-166
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• 2009

In this paper, we analyse the noise robustness of MLPs(Multilayer perceptrons) through deriving the probability density function(p.d.f.) of output nodes with additive input noises and the misclassification ratio with the integral form of the p.d.f. functions. Also, we propose linear preprocessing methods to improve the noise robustness. As a preprocessing stage of MLPs, we consider ICA(independent component analysis) and PCA(principle component analysis). After analyzing the noise reduction effect using PCA or ICA in the viewpoints of SNR(Singal-to-Noise Ratio), we verify the preprocessing effects through the simulations of handwritten-digit recognition problems.

• Journal of Advanced Navigation Technology
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• v.10 no.1
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• pp.48-56
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• 2006

This paper proposes an efficient preprocessing technique for a binary centroid tracker in correlated image sequences. It is known that the following factors determine the performance of the binary centroid target tracker: (1) an efficient real-time preprocessing technique, (2) an exact target segmentation from cluttered background images and (3) an intelligent tracking window sizing, and etc. The proposed centroid tracker consists of an adaptive segmentation method based on novel distance features and an efficient real-time preprocessing technique in order to enhance the distinction between the objects of interest and their local background. Various tracking experiments using synthetic images as well as real Forward-Looking InfraRed (FLIR) images are performed to show the usefulness of the proposed methods.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.61.3-61.3
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• 2018

To understand the assembly of the galaxy population in clusters today, it is important to first understand the impact of previous environments prior to cluster infall, namely preprocessing. We use 15 cluster samples from hydrodynamic zoom-in simulation YZiCS to determine the significance of preprocessing focusing primarily on the tidal mass loss of dark matter halos. We find ~48% of the cluster member halos were once satellites of another host. The preprocessed fraction is not a clear function of cluster mass. Instead, we find it is related to each individual cluster's recent mass growth history. We find that the total mass loss is a clear function of time spent in a host. However, two factors can considerably increase the mass loss rate. First, if the satellite mass is approaching the mass of its host. Second, when the halo suffers tidal mass loss at a higher redshift. The preprocessing provides an opportunity for halos to experience tidal mass loss for a more extended period of time than would be possible if they simply fell directly into the cluster, and at earlier epochs when hosts were more destructive to their satellites.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• Journal of KIISE:Software and Applications
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• v.34 no.5
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• pp.487-501
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• 2007

To achieve high resource utilization for multi-issue DSPs, production compiler commonly includes variants of iterative modulo scheduling algorithm. However, excessive cyclic data dependences, which exist in communication and media processing loops, unduly restrict modulo scheduling freedom. As a result, replicated functional units in multi-issue DSPs are often under-utilized. To address this resource under-utilization problem, our paper describes a novel compiler preprocessing strategy for effective modulo scheduling. The preprocessing strategy proposed capitalizes on two new transformations, which are referred to as cloning and dismantling. Our preprocessing strategy has been validated by an implementation for StarCore SC140 DSP compiler.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.4
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• pp.41-53
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• 2018

Recently, the development of machine learning technology has led to the application of deep learning technology to existing image based application systems. In this context, some researches have been made to apply CNN (Convolutional Neural Network) to the field of fire detection. To verify the effects of existing preprocessing and feature extraction methods on fire detection when combined with CNN, in this paper, the recognition performance and learning time are evaluated by changing the VGG19 CNN structure while gradually increasing the convolution layer. In general, the accuracy is better when the image is not preprocessed. Also it's shown that the preprocessing method and the feature extraction method have many benefits in terms of learning speed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.1
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• pp.83-96
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• 1991

The use of SPOT Imagery is a growing trend in the field of small and middle scale mapping, as well as in establishing topographic database. This study is about 3-D positioning using the SPOT Imagery, where the accuracy and the gemetric characteristics of SPOT photos are analysed according to the preprocessing level (level 1AP,1B). As a result of this study the following could be determined, i. e 1) the geometric characteristics of SPOT Imagery according to the preprocessing level, 2) the optimal polynomial type for exterior orientations of each preprocessing level, and 3) the type of significant additional parameters. It was found that both the geometric precision and accuracy of level 1AP is higher than those of level 1B, which implies that level 1AP is more suitable for precise 3-D positioning and map production.