• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2113-2115
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• 2006

This paper proposes a spatio-temporal correlation algorithm that takes advantage of the spatial and temporal correlations in video streams for error concealment. The spatio-temporal correlation algorithm sets the neighborhood area of the damaged part as a reference window, then finds the area that best matches the reference window in the previous frame. The best-matched area in the previous frame replaces the damaged part in the current frame. The results of ten variations of the proposed algorithm are compared with conventional error concealment methods. These methods include the ones applicable to P-frames as well as I-frames. The comparison results show that the proposed algorithm is very efficient for l-frame error concealment with a large motion between frames.

• Current Optics and Photonics
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• v.2 no.1
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• pp.69-78
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• 2018

This paper presents a precise edge detection algorithm for the critical dimension (CD) measurement of a Thin-Film Transistor Liquid-Crystal Display (TFT-LCD) pattern. The sigmoid surface function is proposed to model the blurred step edge. This model can simultaneously find the position and geometry of the edge precisely. The nonlinear least squares fitting method (Levenberg-Marquardt method) is used to model the image intensity distribution into the proposed sigmoid blurred edge model. The suggested algorithm is verified by comparing the CD measurement repeatability from high-magnified blurry and noisy TFT-LCD images with those from the previous Laplacian of Gaussian (LoG) based sub-pixel edge detection algorithm and error function fitting method. The proposed fitting-based edge detection algorithm produces more precise results than the previous method. The suggested algorithm can be applied to in-line precision CD measurement for high-resolution display devices.

• ETRI Journal
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• v.17 no.3
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• pp.45-63
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• 1995

This paper proposes an algorithm to improve the correlation between giga-hertz transverse electromagnetic (GTEM) cell and open area test site (OATS). It is based on the dipole modeling process of an unknown source object in a GTEM cell and on the evaluation of the approximate far field equations correlated with measured GTEM powers at output port of the GTEM cell. In this algorithm, the relative phase differences between dipole moments play an important part in modeling the test object as a set of dipoles and offer stable calculation of emission values. The radiated emission test using this algorithm requires fifteen orientations of equipment under test, but the increased orientations as compared with the previous method have little effect on the time needed for testing. Radiation from a notebook computer has been tested for statistical analysis of the correlation between GTEM data and OATS data. The emission test results of the notebook computer show that the mean, the standard deviation, and the correlation coefficient are -0.62, 1.99, and +0.85, respectively. These figures indicate that this algorithm provides improved accuracy in the measurement of electromagnetic emissions over the previous method.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3
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• pp.111-118
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• 2008

In this paper, we propose a new automatic taxonomy generation algorithm for the audio genre classification. The proposed algorithm automatically generates hierarchical taxonomy based on the estimated classification accuracy at all possible nodes. The estimation of classification accuracy in the proposed algorithm is conducted by applying the training data to classifier using k-fold cross validation. Subsequent classification accuracy is then to be tested at every node which consists of two clusters by applying one-versus-one support vector machine. In order to assess the performance of the proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigated classification performance using the proposed algorithm and previous flat classifiers. The classification accuracy reaches to 89 percent with proposed scheme, which is 5 to 25 percent higher than the previous flat classification methods. Using low-dimensional feature vectors, in particular, it is 10 to 25 percent higher than previous algorithms for classification experiments.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.709-717
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• 1997

For an undirected graph G=(V, E), the Rural Postman Problem (RPP) is a problem that finds a minimum cost tour that must pass edges in E'($\subseteq$ E) at least once. RPP, such as Traveling Salesman Problem (TSP), is known as an NP. Complete problem. In the previous study of RPP, he structure of the chromosome is constructed by E' and the direction of the edge. Hence, the larger the size of IE' I is, the larger the size of the chromosome and the size of the solution space are. In this paper, we transform the RPP into a Hamiltonian graph and use a genetic algorithm to solve the transformed problem using restructured chromosomes. In the simulations, we analyze our method and the previous study. From the simulation results, it is found that the results of the proposed method is better than those of the previous method and the proposed method also obtains the near optimal solution in earlier generations than the previous study.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.3
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• pp.273-281
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• 2004

There is the temporal correlation of the video sequence between the motion vector of current block and the motion vector of previous block. In this paper, we propose the prediction search algorithm for block matching using the temporal correlation of the video sequence and the center-biased property of motion vectors. The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(sum of absolute difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate point on each search region. Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved up to the 1.06㏈ as depend on the video sequences and improved about 0.19∼0.46㏈ on an average except the full search(FS) algorithm.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.4
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• pp.231-238
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• 2017

Detecting small infrared targets from the low-SCR images at a long distance is very hard. The previous Local Contrast Method (LCM) algorithm based on the human visual system shows a superior performance of detecting small targets by a background suppression technique through local contrast measure. However, its slow processing speed due to the heavy multi-scale processing overhead is not suitable to a variety of real-time applications. This paper presents a lightweight real-time small target detection algorithm, called by the Improved Selective Local Contrast Method (ISLCM), to reduce the scale-invariant computational overhead. The proposed ISLCM applies the improved local contrast measure to the predicted selective region so that it may have a comparable detection performance as the previous LCM while guaranteeing low scale-invariant computational load by exploiting both adaptive scale estimation and small target feature feasibility. Experimental results show that the proposed algorithm can reduce its computational overhead considerably while maintaining its detection performance compared with the previous LCM.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1108-1121
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• 2019

As the quality and level of life rise, many people are doing search for various pieces of information about tourism. In addition, users prefer the search methods reflecting individual opinions such as SNS and blogs to the official websites of tourist destination. Many of previous studies focused on a recommendation system for tourist courses based on the GPS information and past travel records of users, but such a system was not capable of recommending the latest tourist trends. This study thus set out to collect and analyze the latest SNS data to recommend tourist destination of high interest among users. It also aimed to propose an altered TSP algorithm to recommend the optimal routes to the recommended destination within an area and a system to recommend the optimal tourist courses by applying the Elasticsearch engine. The altered TSP algorithm proposed in the study used the location information of users instead of Dijkstra's algorithm technique used in previous studies to select a certain tourist destination and allowed users to check the recommended courses for the entire tourist destination within an area, thus offering more diverse tourist destination recommendations than previous studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3373-3389
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• 2022

Exploration of mobile robot without prior data about environments is a fundamental problem during the SLAM processes. In this work, we propose improved version of previous Rmap algorithm by modifying its Exploration submodule. Despite the previous Rmap's performance which significantly reduces the overhead of the grid map, its exploration module costs a lot because of its rectangle following algorithm. To prevent that, we propose a new Rmap+ algorithm for autonomous path planning of mobile robot to explore an unknown environment. The algorithm bases on paired frontiers. To navigate and extend an exploration area of mobile robot, the Rmap+ utilizes the inner and outer frontiers. In each exploration round, the mobile robot using the sensor range determines the frontiers. Then robot periodically changes the range of sensor and generates inner pairs of frontiers. After calculating the length of each frontiers' and its corresponding pairs, the Rmap+ selects the goal point to navigate the robot. The experimental results represent efficiency and applicability on exploration time and distance, i.e., to complete the whole exploration, the path distance decreased from 15% to 69%, as well as the robot decreased the time consumption from 12% to 86% than previous algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.976-990
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• 2020

The k-means algorithm is widely used in academia and industry due to easy and simple implementation, enabling fast learning for complex datasets. However, k-means struggles to classify datasets without prior knowledge of specific domains. We proposed the repulsive k-means (RK-means) algorithm in a previous study to improve the k-means algorithm, using the repulsive force concept, which allows deleting unnecessary cluster centroids. Accordingly, the RK-means enables to classifying of a dataset without domain knowledge. However, three main problems remain. The RK-means algorithm includes a cluster repulsive force offset, for clusters confined in other clusters, which can cause cluster locking; we were unable to prove RK-means provided optimal convergence in the previous study; and RK-means shown better performance only normalize term and weight. Therefore, this paper proposes the advanced RK-means (ARK-means) algorithm to resolve the RK-means problems. We establish an initialization strategy for deploying cluster centroids and define a metric for the ARK-means algorithm. Finally, we redefine the mass and normalize terms to close to the general dataset. We show ARK-means feasibility experimentally using blob and iris datasets. Experiment results verify the proposed ARK-means algorithm provides better performance than k-means, k'-means, and RK-means.