• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2562-2567
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• 2005

This paper suggests a new method discretization of nonlinear system using Taylor series expansion and zero-order hold assumption. This method is applied into the sampled-data representation of a nonlinear system with input time delay. Additionally, the delayed input is time varying and its amplitude is bounded. The maximum time-delayed input is assumed to be two sampling periods. Them mathematical expressions of the discretization method are presented and the ability of the algorithm is tested for some of the examples. And 'hybrid' discretization scheme that result from a combination of the ‘scaling and squaring' technique with the Taylor method are also proposed, especially under condition of very low sampling rates. The computer simulation proves the proposed algorithm discretized the nonlinear system with the variable time-delayed input accurately.

• Journal of Communications and Networks
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• v.18 no.4
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• pp.649-657
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• 2016

In this paper, we investigate energy efficiency (EE) of the traditional co-located and the distributed massive multiple-input multiple-output (MIMO) systems. First, we derive an approximate EE expression for both the idealistic and the realistic power consumption models. Then an optimal energy-efficient remote access unit (RAU) selection algorithm based on the distance between the mobile stations (MSs) and the RAUs are developed to maximize the EE for the downlink distributed massive MIMO systems under the realistic power consumption model. Numerical results show that the EE of the distributed massive MIMO systems is larger than the co-located massive MIMO systems under both the idealistic and realistic power consumption models, and the optimal EE can be obtained by the developed energy-efficient RAU selection algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.9C
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• pp.876-883
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• 2003

In this paper, we propose an algorithm for organ recognition in ultrasound images using log power spectrum. The main procedure of the algorithm consists of feature extraction and feature classification. In the feature extraction, as a translation invariant feature, log power spectrum is used for extracting the information on echo of the organs tissue from a preprocessed input image. In the feature classification, Mahalanobis distance is used as a measure of the similarity between the feature of an input image and the representative feature of each class. Experimental results for real ultrasound images show that the proposed algorithm yields the improvement of maximum 30% recognition rate than the recognition algorithm using power spectrum and Euclidean distance, and results in better recognition rate of 10-40% than the recognition algorithm using weighted quefrency complex cepstrum.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.21-31
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• 1998

In this paper, a new algorithm is proposed which distributes multicast cells in a copy network. The dual copy network is composed of running adder network, distributor, dummy address encoder, and broadcasting network. It is operated lower input address and higher one simultaneously by the distribution algorithm. As a result, for each input has a better equal opportunity of processing, cell delay and hardware complexity are reduced in copy network. Also, for it adopts the broadcasting network from an expansion Banyan network with binary tree and Banyan network, overflow probability is reduced to a half in that network. As a result of computer simulation, the copy network processed by the distribution algorithm is remarkably improved in cell delay of input buffer according to all input loads.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.123-131
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• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.977-985
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• 2020

Aiming at the problem of complex feature extraction and low accuracy in human action recognition, this paper proposed a network structure combining batch normalization algorithm with GoogLeNet network model. Applying Batch Normalization idea in the field of image classification to action recognition field, it improved the algorithm by normalizing the network input training sample by mini-batch. For convolutional network, RGB image was the spatial input, and stacked optical flows was the temporal input. Then, it fused the spatio-temporal networks to get the final action recognition result. It trained and evaluated the architecture on the standard video actions benchmarks of UCF101 and HMDB51, which achieved the accuracy of 93.42% and 67.82%. The results show that the improved convolutional neural network has a significant improvement in improving the recognition rate and has obvious advantages in action recognition.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2060-2071
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• 2000

Scheduling parallel tasks, represented as a Directed Acyclic Graph (DAG) or task graph, on a multiprocessor system has been an important research area in the past decades. List scheduling algorithms assign priorities to a node or an edge in an input DAG, and then generate a schedule according to the assigned priorities. This appear proposes a list scheduling algorithms with effective method of priority assignments. The paper also analyzes the worst case performance and optimality condition for the proposed algorithm. The performance comparison study shows that the proposed algorithms outperforms existing scheduling algorithms especially for input DAGs with high communication overheads. The performance improvement over existing algorithms becomes larger as the input DAG becomes more dense and the level of parallelism in the DAG is increased.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12A
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• pp.1238-1243
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• 2007

The computational complexity for the zero-forcing (ZF)-based multiuser (MU) multiple-input multiple-output(MIMO) preprocessing matrices can be immoderately large as the number of transmit antennas or users increases. In this paper, we show that the span of singular vector space of a matrix can be obtained from the singular vectors of the parted rows of that matrix with computational saving and propose a computationally efficient recursive-algorithm for achieving the ZF-based preprocessing matrices. Analysis about the complexities shows that a new recursive-algorithm can lighten the computational load.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6A
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• pp.494-499
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• 2009

In this paper, we provide an efficient method of user selection for achieving the maximum system throughput in downlink multiuser Multiple-Input Multiple-Output (MIMO) systems. A proposed method is for selecting a fine user set only with powers of each user channel and angles between them. This algorithm is simpler than SUS because there is no considering about the optimal value of correlation. The proposed method finds the user set toward maximizing system throughput, so it has high performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.490-496
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• 2005

In this paper, we propose a self-constructed clustering algorithm based on inference information of the fuzzy model. This method makes it possible to automatically detect and optimize the number of cluster and parameters by using input-output data. The propose method improves the performance of clustering by extended supervised learning technique. This technique uses the output information as well as input characteristics. For effect the similarity measure in clustering, we use the TSK fuzzy model to sent the information of output. In the conceptually, we design a learning method that use to feedback the information of output to the clustering since proposed algorithm perform to separate each classes in input data space. We show effectiveness of proposed method using simulation than previous ones