• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.25-29
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• 2019

Microstructure pattern generation on light guides in backlight unit (BLU) is an essential process for designing flat panel display, but efficient designing algorithm is still limited to achieve uniform luminescence while maintaining fully random distribution to avoid interference effects. In this study, a molecular dynamics model based pattern generation algorithm has been developed. The proposed algorithm allows a fast and efficient distribution of patterns at specified density within the user-defined computational cells, and its efficiency and performance has been demonstrated with sample cases.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.51-56
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• 2022

This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.6
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• pp.1512-1532
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• 2013

A convertible undeniable signature requires a verifier to interact with the signer to verify a signature and furthermore allows the signer to convert a valid one to publicly verifiable signature. In 2007, Yuen et al. proposed a convertible undeniable signature without random oracles in pairings. However, it is recently shown that Yuen et al.'s scheme is not invisible for the standard definition of invisibility. In this paper, we propose a new improvement by using extended Euclidean algorithm that can overcome the visibility attack. The proposed scheme has been evaluated based on computation and communication complexities and the performance comparisons of Yuen et al.'s scheme and various convertible undeniable signature schemes are provided. Moreover, it has been observed that the proposed algorithm reduces the computation and communication times significantly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2298-2304
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• 2015

In this paper, we provide a new approach which optimizes the vertical tilting angle of the base station for multi-cell multiple-input single-output (MISO) downlink active antenna systems (AAS). Instead of the conventional optimal algorithm which requires an exhaustive search, we propose simple and near optimal algorithms. First, we represent a large system approximation based vertical beamforming algorithm which is applied to the average sum rate by using the random matrix theory. Next, we suggest a signal-to-leakage-and-noise ratio (SLNR) based vertical beamforming algorithm which simplifies the optimization problem considerably. In the simulation results, we demonstrate that the performance of the proposed algorithms is near close to the exhaustive search algorithm with substantially reduced complexity.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.83-91
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• 1995

A neural network with one or more layers of hidden units can be trained using the well-known error back propagation algorithm. According to this algorithm, the synaptic weights of the network are updated during the training by propagating back the error between the expected output and the output provided by the network. However, the error back propagation algorithm is characterized by slow convergence and the time required for training and, in some situation, can be trapped in local minima. A theoretical formulation of a new fast learning method based on tabu search method is presented in this paper. In contrast to the conventional back propagation algorithm which is based solely on the modification of connecting weights of the network by trial and error, the present method involves the calculation of the optimum weights of neural network. The effectiveness and versatility of the present method are verified by the XOR problem. The present method excels in accuracy compared to that of the conventional method of fixed values.

• Industrial Engineering and Management Systems
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• v.8 no.1
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• pp.22-28
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• 2009

We consider facility layout problems, where mn facility units are assigned into mn cells. These cells are arranged into a rectangular pattern with m rows and n columns. In order to solve this cell type facility layout problem, many approximation algorithms with improved local search methods were studied because it was quite difficult to find exact optimum of such problem in case of large size problem. In this paper, new algorithms based on Simulated Annealing (SA) method with two neighborhood generation methods are proposed. The new neighborhood generation method adopts the exchanging operation of facility units in accordance with adjacent preference. For evaluating the performance of the neighborhood generation method, three algorithms, previous SA algorithm with random 2-opt neighborhood generation method, the SA-based algorithm with the new neighborhood generation method (SA1) and the SA-based algorithm with probabilistic selection of random 2-opt and the new neighborhood generation method (SA2), are developed and compared by experiment of solving same example problem. In case of numeric examples with problem type 1 (the optimum layout is given), SA1 algorithm could find excellent layout than other algorithms. However, in case of problem type 2 (random-prepared and optimum-unknown problem), SA2 was excellent more than other algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.177-183
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• 2022

There is no known polynomial time algorithm for QAP that is a NP-complete problem. This paper suggests O(n²) polynomial time algorithm for random type quadratic assignment problem (QAP). The proposed algorithm suggests incremental augmenting matching strategy that is to set the matching set M={(l_i,f_j)} from l_i with minimum sum of distance in location matrix L and f_j with maximum sum of quantity in facility matrix F, and incremental augmenting of matching set M from M to l_i with minimum sum of distance and to f_j with maximum sum of quantity. Finally, this algorithm performs swap strategy that is to reflect the complex correlations of distances in locations and quantities in facilities. For the experimental data, this algorithm, in spite of O(n²) polynomial time algorithm, can be improve the solution than genetic algorithm a kind of metaheuristic method.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.747-750
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• 1998

This paper deals with the vehicle arbitration algorithm used in communication system between vehicles and a roadside control unit. To Improve the performance of vehicle arbitration, a random delay counter method is taken into account and modified to select the optimal maximum count value according to the vehicle arrival rate. The suggested algorithm is tested by computer simulation andthe enhanced performance was shown. This method could be applied to various systems which include the communications between transponders and a control unit.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.49-58
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• 2004

In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.