• The KIPS Transactions:PartA
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• v.14A no.2
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• pp.107-116
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• 2007

In this paper, we present a method on the construction of multiple-valued circuits using Reed-Muller Expansions(RME). First, we discussed the input output interconnection of multiple valued function using Perfect Shuffle techniques and Kronecker product and designed the basic cells of performing the transform matrix and the reverse transform matrix of multiple valued RME using addition circuit and multiplication circuit of GF(4). Using these basic cells and the input-output interconnection technique based on Perfect Shuffle and Kronecker product, we implemented the multiple valued logic circuit based on RME. The proposed design method of multiple valued RME is simple and very efficient to reduce addition circuits and multiplication circuits as compared with other methods for same function because of using matrix transform based on modular structures. The proposed design method of multiple valued logic circuits is simple and regular for wire routing and possess the properties of concurrency and modularity of array.

• The KIPS Transactions:PartA
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• v.9A no.3
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• pp.271-280
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• 2002

In this paper, the input-output interconnection method of the multiple-valued signal processing circuit using Perfect Shuffle technique and Kronecker product is discussed. Using this method, the circuit design method of the multiple-valued Reed-Muller Expansions (MRME) which can process the multiple-valued signal easily on finite fields GF$(p^m)$ is presented. The proposed input-output interconnection methods show that the matrix transform is an efficient and the structures are modular. The circuits of multiple-valued signal processing of MRME on GF$(p^m)$ design the basic cells to implement the transform and inverse transform matrix of MRME by using two basic gates on GF(3) and interconnect these cells by the input-output interconnection technique of the multiple-valued signal processing circuits. The proposed multiple-valued signal processing circuits that are simple and regular for wire routing and possess the properties of concurrency and modularity are suitable for VLSI.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.2
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• pp.130-137
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• 2008

This paper presents high resolution techniques of three-dimensional(3D) scattering center extraction for a radar backscattered signal in radar target recognition. We propose a 3D pairing procedure, a new approach to estimate 3D scattering centers. This pairing procedure is more accurate and robust than the general criterion. 3D MEMP(Matrix Enhancement and Matrix Pencil) with the 3D pairing procedure first creates an autocorrelation matrix from radar backscattered field data samples. A matrix pencil method is then used to extract 3D scattering centers from the principal eigenvectors of the autocorrelation matrix. An autocorrelation matrix is constructed by the MSSP(modified spatial smoothing preprocessing) method. The observation matrix required for estimation of 3D scattering center locations is built using the sparse scanning order conception. In order to demonstrate the performance of the proposed technique, we use backscattered field data generated by ideal point scatterers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.613-623
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• 2011

In this paper, we present a method on the construction of quinternary logic circuits using Perfect shuffle. First, we discussed the input-output interconnection of quinternary logic function using Perfect Shuffle techniques and Kronecker product, and designed the basic cells of performing the transform matrix and the reverse transform matrix of quinternary Reed-Muller expansions(QRME) using addition circuit and multiplication circuit of GF(5). Using these basic cells and the input-output interconnection technique based on Perfect Shuffle and Kronecker product, we implemented the quinternary logic circuit based on QRME. The proposed design method of QRME is simple and very efficient to reduce addition circuits and multiplication circuits as compared with other methods for same logic function because of using matrix transform based on modular structures. The proposed design method of quinternary logic circuits is simple and regular for wire routing and possess the properties of concurrency and modularity of array.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.8 no.1
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• pp.37-45
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• 1994

A method of optimal active and reactive power control for economic operation in electrical power system is presented in this paper. The major features and techniques of this paper are as follows: 1) The method presented for obtaining the equivalent active power balance equation applying the sparse Jacobian matrix of power flow equation instead of using B constant as active power Balance equation considering transmission loss, and for determining directly optimal active power allocation without repeating calculations. 2) More reasonable and economic profit by minimizing total fuel cost of thermal power plants instead of using transmission loss as objective function of reactive Power control can be achieved. 3) Particularly in reactive power control, computing time can be considerably reduced by using Fuzzy Linear Programming instead of using conventional Linear Programming.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.237-246
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• 2019

In order to improve the accuracy of the rating prediction of the recommendation model, not only user-item rating data are used but also consider auxiliary information of item such as comments, tags, or descriptions. The traditional approaches use a word-level model of the bag-of-words for the auxiliary information. This model, however, cannot utilize the auxiliary information effectively, which leads to shallow understanding of auxiliary information. Convolution neural network (CNN) can capture and extract feature vector from auxiliary information effectively. Thus, this paper proposes character-level deep-Convolution Neural Network based matrix factorization (Char-DCNN-MF) that integrates deep CNN into matrix factorization for a novel recommendation model. Char-DCNN-MF can deeper understand auxiliary information and further enhance recommendation performance. Experiments are performed on three different real data sets, and the results show that Char-DCNN-MF performs significantly better than other comparative models.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4
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• pp.210-216
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• 2005

We Propose a new feature normalization scheme based on eigenspace for achieving robust speech recognition. In general, mean and variance normalization (MVN) is Performed in cepstral domain. However, another MVN approach using eigenspace was recently introduced. in that the eigenspace normalization Procedure Performs normalization in a single eigenspace. This Procedure consists of linear PCA matrix feature transformation followed by mean and variance normalization of the transformed cepstral feature. In this method. 39 dimensional feature distribution is represented using only a single eigenspace. However it is observed to be insufficient to represent all data distribution using only a sin91e eigenvector. For more specific representation. we apply unique na independent eigenspaces to cepstra, delta and delta-delta cepstra respectively in this Paper. We also normalize training data in eigenspace and get the model from the normalized training data. Finally. a feature space rotation procedure is introduced to reduce the mismatch of training and test data distribution in noisy condition. As a result, we obtained a substantial recognition improvement over the basic eigenspace normalization.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.889-898
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• 2013

Due to the popularization of high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy to make high-quality counterfeit money. However, the probability of detecting counterfeit money to the general public is extremely low and the detection device is expensive. In this paper, a counterfeit money detection algorithm using a general purpose scanner and computer system is proposed. First, the printing features of color printers are calculated using morphological operations and gray-level co-occurrence matrix. Then, these features are used to train a support vector machine classifier. This trained classifier is applied for identifying either original or counterfeit money. In the experiment, we measured the detection rate between the original and counterfeit money. Also, the printing source was identified. The proposed algorithm was compared with the algorithm using wiener filter to identify color printing source. The accuracy for identifying counterfeit money was 91.92%. The accuracy for identifying the printing source was over 94.5%. The results support that the proposed algorithm performs better than previous researches.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.315-320
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• 2017

Estimating the correct pose of augmented objects on the real camera view efficiently is one of the most important questions in image tracking area. In computer vision, Homography is used for camera pose estimation in augmented reality system with markerless. To estimating Homography, several algorithm like SURF features which extracted from images are used. Based on extracted features, Homography is estimated. For this purpose, RANSAC algorithm is well used to estimate homography and DCS-RANSAC algorithm is researched which apply constraints dynamically based on Constraint Satisfaction Problem to improve performance. In DCS-RANSAC, however, the dataset is based on pattern of feature distribution of images manually, so this algorithm cannot classify the input image, pattern of feature distribution is not recognized in DCS-RANSAC algorithm, which lead to reduce it's performance. To improve this problem, we suggest the KCS-RANSAC algorithm using K-means clustering in CS-RANSAC to cluster the images automatically based on pattern of feature distribution and apply constraints to each image groups. The suggested algorithm cluster the images automatically and apply the constraints to each clustered image groups. The experiment result shows that our KCS-RANSAC algorithm outperformed the DCS-RANSAC algorithm in terms of speed, accuracy, and inlier rate.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.519-527
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• 2003

Speech is much influenced by the existence of outliers which are introduced by such an unexpected happenings as additive background noise, change of speaker's utterance pattern and voice detection errors. These kinds of outliers may result in severe degradation of speaker recognition performance. In this paper, we proposed the GMM based on robust principal component analysis (RPCA-GMM) using M-estimation to solve the problems of both ouliers and high dimensionality of training feature vectors in speaker identification. Firstly, a new feature vector with reduced dimension is obtained by robust PCA obtained from M-estimation. The robust PCA transforms the original dimensional feature vector onto the reduced dimensional linear subspace that is spanned by the leading eigenvectors of the covariance matrix of feature vector. Secondly, the GMM with diagonal covariance matrix is obtained from these transformed feature vectors. We peformed speaker identification experiments to show the effectiveness of the proposed method. We compared the proposed method (RPCA-GMM) with transformed feature vectors to the PCA and the conventional GMM with diagonal matrix. Whenever the portion of outliers increases by every 2%, the proposed method maintains almost same speaker identification rate with 0.03% of little degradation, while the conventional GMM and the PCA shows much degradation of that by 0.65% and 0.55%, respectively This means that our method is more robust to the existence of outlier.