• ETRI Journal
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• v.30 no.6
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• pp.765-773
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• 2008

Spatial correlation is a result of insufficient antenna spacing among multiple antenna elements, while temporal correlation is caused by Doppler spread. This paper compares the effect of spatial and temporal correlation in order to investigate the performance of multiuser scheduling algorithms in multiple-input multiple-output (MIMO) broadcast channels. This comparison includes the effect on the ergodic capacity, on fairness among users, and on the sum-rate capacity of a multiuser scheduling algorithm utilizing statistical channel state information in spatio-temporally correlated MIMO broadcast channels. Numerical results demonstrate that temporal correlation is more meaningful than spatial correlation in view of the multiuser scheduling algorithm in MIMO broadcast channels. Indeed, the multiuser scheduling algorithm can reduce the effect of the Doppler spread if it exploits the information of temporal correlation appropriately. However, the effect of spatial correlation can be minimized if the antenna spacing is sufficient in rich scattering MIMO channels regardless of the multiuser scheduling algorithm used.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.16-21
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• 2006

In this paper we proposed of the SEED cipher algorithm which improved cipher intensity. The proposed algorithm has input data of 192bit and key input data of 256bit and it performs 16 Rounds for improvement of cipher intensity. We simulated the algorithm employing C compiler and the Foundation Express Tool so that verified performance of it.

• The Transactions of the Korea Information Processing Society
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• v.4 no.3
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• pp.747-753
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• 1997

Function approximation from a set of input-output parirs has numerous applications in scientiffc and engineer-ing areas.Multiayer feedforward neural networks have been proposed as a good approximator of noninear function.The back propagation (BP) algorithm allows muktiayer feedforward neural networks oro learn input-output mappongs from training samples.However, the mapping acquired through the BP algorithm nay be cor-rupt when errorneous trauning data are employed.In this paper we propose a robust BP learning algorithm that is resistant to the errormeous data and is capable of rejecting gross errors during the approximation process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.7A
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• pp.1076-1083
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• 2000

Vehicle license plate recognition identifies vehicle as a unique, and have many applications in traffic monitoring field. In this paper, a vertical edge based algorithm to extract license plate within input gray-scale image is proposed. A size-and-shape filter based on seed-filling algorithm is applied to remove the edges that are impossible to be the vertical edges of license plate. Then the remaining edges are matched with each other according to some restricted conditions so as to locate license plate in input image. After license plate is extracted. normalized and segmented, the characters on it are recognized by template matching method. Experimental results show that the proposed algorithm can deal with license plates in normal shape effectively, as well as the license plates that are out of shape due to the angle of view.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1195-1200
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• 2019

This paper proposes a detection algorithm for group testing. Group testing is a problem of finding a very small number of defect samples out of a large number of samples, which is similar to the problem of Compressed Sensing. In this paper, we define a noiseless group testing and propose a probabilistic algorithm for detection of defective samples. The proposed algorithm is constructed such that the extrinsic probabilities between the input and output signals exchange with each other so that the posterior probability of the output signal is maximized. Then, defective samples are found in the group testing problem through a simulation on the detection algorithm. The simulation results for this study are compared with the lower bound in the information theory to see how much difference in failure probability over the input and output signal sizes.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.95-103
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• 2006

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize EEG pattern vectors. The frequency and amplitude of alpha rhythms and beta rhythms are used to compose the input pattern vectors. And the algorithm for EEG pattern recognition is used SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of the subclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights between subclass layer and output layer is learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors of EEG, the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.1120-1128
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• 1999

The model-based object recognition is defined as a graph matching process between model images and an input image. In this paper, a graph matching problem is modeled as a n optimization problems and a genetic algorithm is proposed to solve the problems. For this work, fitness function, data structured and genetic operators are developed The simulation results are shown that the proposed genetic algorithm can match feature points between model image and input image for recognition of partially occluded two-dimensional objects. The performance fo the proposed technique is compare with that of a neural network technique.

• International journal of advanced smart convergence
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• v.1 no.2
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• pp.30-33
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• 2012

This paper propose a new design of fuzzy neural networks based on hard partition to generate the rules of the networks. For this we use hard c-means (HCM) clustering algorithm. The premise part of the rules of the proposed networks is realized with the aid of the hard partition of input space generated by HCM clustering algorithm. The consequence part of the rule is represented by polynomial functions. And the coefficients of the polynomial functions are learned by BP algorithm. The number of the hard partition of input space equals the number of clusters and the individual partitioned spaces indicate the rules of the networks. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The proposed networks are evaluated with the use of numerical experimentation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05b
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• pp.90-95
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• 2003

For the safety driving of an automobile which is become individual requisites, a new Neural Network algorithm which recognized the load vehicles in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implemented.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.229-235
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• 2019

Due to the development of video and optical technology today, video equipments are being used in a variety of fields such as identification, security maintenance, and factory automation systems that generate products. In this paper, we investigate an algorithm that effectively recognizes an experimental object in an input image with a partial problem due to the mechanical problem of the input imaging device. The object recognition algorithm proposed in this paper moves and rotates the vertices constituting the outline of the experimental object to the positions of the respective vertices constituting the outline of the DB model. Then, the discordance values between the moved and rotated experimental object and the corresponding DB model are calculated, and the minimum discordance value is selected. This minimum value is the final discordance value between the experimental object and the corresponding DB model, and the DB model with the minimum discordance value is selected as the recognition result for the experimental object. The proposed object recognition method obtains satisfactory recognition results using only partial information of the experimental object.