• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.768-771
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• 2004

In this paper, we propose a content-based audio genre classification algorithm that automatically classifies the query audio into five genres such as Classic, Hiphop, Jazz, Rock, Speech using digital signal processing approach. From the 20 second query audio file, 54 dimensional feature vectors, including Spectral Centroid, Rolloff, Flux, LPC, MFCC, is extracted from each query audio. For the classification algorithm, k-NN, Gaussian, GMM classifier is used. In order to choose optimum features from the 54 dimension feature vectors, SFS (Sequential Forward Selection) method is applied to draw 10 dimension optimum features and these are used for the genre classification algorithm. From the experimental result, we verify the superior performance of the SFS method that provides near $90{\%}$ success rate for the genre classification which means $10{\%}$-$20{\%}$ improvements over the previous methods

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1467-1486
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• 2017

Fuzzy co-clustering is sensitive to noise data. To overcome this noise sensitivity defect, possibilistic clustering relaxes the constraints in FCM-type fuzzy (co-)clustering. In this paper, we introduce a new possibilistic fuzzy co-clustering algorithm based on information bottleneck (ibPFCC). This algorithm combines fuzzy co-clustering and possibilistic clustering, and formulates an objective function which includes a distance function that employs information bottleneck theory to measure the distance between feature data point and feature cluster centroid. Many experiments were conducted on three datasets and one artificial dataset. Experimental results show that ibPFCC is better than such prominent fuzzy (co-)clustering algorithms as FCM, FCCM, RFCC and FCCI, in terms of accuracy and robustness.

• Korean Journal of Remote Sensing
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• v.11 no.1
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• pp.47-64
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• 1995

In this paper, an algorithm that finds man-made structures in a praylevel aerial images is proposed to perform stereo matching. An extracted contour of buildings must have a high accuracy in order to get a good feature-based stereo matching result. Therefore this study focuses on the use of edge following in the original image rather than use of ordinary edge filters. The Algorithm is composed of two main categories; one is to find candidate regions in the whole image and the other is to extract exact contours of each building which each candidate region.. The region growing method using the centroid linkage method of variance value is used to find candidate regions of building and the contour line tracing algorithm based on an adge following method is used to extract exact contours. The result shows that the almost contours of building composed of line segments are extracted.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.627-631
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• 2004

An efficient algorithm for clustering of GPDFs(Gaussian Probability Density Functions) in a speech recognition model is proposed in this paper. The proposed algorithm is based on CNN with the divergence as its distance measure and is applied to a speech recognition. The algorithm is compared with conventional Dk-means(Divergence-based k-means) algorithm in CDHMM(Continuous Density Hidden Markov Model). The results show that it can reduce about 31.3％ of GPDFs over Dk-means algorithm without suffering any recognition performance. When compared with the case that no clustering is employed and full GPDFs are used, the proposed algorithm can save about 61.8％ of GPDFs while preserving the recognition performance.

• Korean Journal of Optics and Photonics
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• v.19 no.1
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• pp.1-8
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• 2008

Shack-Hartmann sensors are widely employed as a wavefront measuring device in various applications. Adaptive optics is one of the major applications. Since an adaptive optics system should be operated in real-time, high-speed wavefront sensing is essential. In high-speed operation, integration time of an image detector is very short. In this case, noises such as readout noise and photon noise greatly influence the accuracy of wavefront sensing. Therefore a fast and noise-insensitive centroid finding algorithm is required for the real-time wavefront sensing. In this paper, the multi-resolution correlation method is proposed. By employing multi-resolution images, this method greatly reduces the computation time when compared to the fast Fourier transform (FFT) correlation method. The verification is performed through the computational simulation. In this paper, the center of mass method, correlation method and multi-resolution correlation method are employed to compare the measurement accuracy of the centroid finding algorithms. The accuracy of a Shack-Hartmann wavefront sensor using the proposed algorithm is proved to be comparable to that of the conventional correlation method.

• Journal of Digital Convergence
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• v.13 no.7
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• pp.207-212
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• 2015

The sensor network that is component of the Internet of Things require a lot of research to select the best route to send information to the anchor node, to collect a number of environment and cost efficient for communication between the sensor life. On the sensor network in one of the components of IOT's environment, sensor nodes are an extension device with low power low capacity. For routing method for data transmission between the sensor nodes, the connection between the anchor and the node must be accurate with in adjacent areas relatively. Localization CA (Centroid Algorithm) is often used although an error frequently occurs. In this paper, we propose a range-free localization method between sensor nodes based on the Radical Line in order to solve this problem.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.239-247
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• 2014

In modern society, the transportation sector is having difficulties with mobility, safety, energy and environment issues so that solutions should be prepared progressively. Recently developed countries are paying greater attention to car-sharing systems as alternatives for relieving problems caused by increasing number of vehicles. Car-sharing systems are believed to keep the number of personal vehicles down and traffic congestion due to the fact that this system induces efficiency of vehicle operation. In this study, authors focused on solving the imbalance problem of car-sharing systems dynamically, to specifically improve one-way car sharing service. Therefore vehicle relocation algorithm was designed for maximizing profit of car sharing operators. For the application of the vehicle relocation algorithm among rental offices, we selected Haeundae-gu, Busan as the area of study. Rental offices have been designated to the zone centroid and we calculated the shortest path between rental offices by using TransCAD. In the study, we adapted MATLAB for the application of vehicle relocation algorithm. As the results, the total vehicle relocation time was estimated as 59.9 minutes. In addition, vehicle relocation algorithm was verified successfully by using Decision Tree method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2754-2768
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• 2013

Video fingerprints generated from global features are usually vulnerable against general geometric transformations. In this paper, a novel video fingerprinting algorithm is proposed, in which a new spatio-temporal gradient is designed to represent the spatial and temporal information for each frame, and a new partition scheme, based on concentric circle and rings, is developed to resist the attacks efficiently. The centroids of spatio-temporal gradient orientations (CSTGO) within the circle and rings are then calculated to generate a robust fingerprint. Our experiments with different attacks have demonstrated that the proposed approach outperforms the state-of-the-art methods in terms of robustness and discrimination.

• The Journal of the Acoustical Society of Korea
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• v.19 no.7
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• pp.23-27
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• 2000

We provide an useful method to design codebooks with better performance than conventional methods. In the proposed method, new codevectors obtained from learning iterations are not the centroid vectors which are the representatives of partitions, but the vectors manipulated by the distance between new codevectors and old codevectors in the early stages of learning iteration. Experimental results show that the codevectors obtained by the proposed method converge to a locally better optimal codebook.