• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1A
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• pp.114-121
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• 2000

The problem of an input-buffered switch is the HOL(head-of-line) blocking which limits the maximum throughput but it is easy to implement in hardware. However, HOL blocking can be eliminated using aVOQ(virtual-output-queueing) technique. 0 this paper, we propose a new cell-scheduling algorithm for aninput-buffered ATM switch. The proposed algorithm, called PPIM(Probabilistic Parallel Iterative Matching), imposesa weight to every request based on the reserved bandwidth. It is shown that the input-buffered ATM switch withthe proposed PPIM algorithm not only provides high throughput and low delay but it also reduces the jitter,compared with the existing WPIM(Weighted PIM).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11C
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• pp.94-101
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• 2001

In this paper, we propose WRR cell scheduling algorithm that improve current smoothing scheme. In proposed cell scheduling algorithm, using the number of practical input cell in each VC and variable thats indicate weight and state of queue, we could service VC of buffer efficiently that input cells over weight value and input cells smaller than weight value. And, we could service multimedia data by providing remained bandwidth after that allocate to real time traffic with non-real time traffic. In this result, the number of serviceable average cells were increased and length of buffer was decreased. Through the computer simulation, we evaluated the performance of proposed algorithm. According to the results, the proposed algorithm showed good performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.995-1000
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• 2016

The maximum cross-correntropy (MCC) algorithm with a set of random symbols keeps its optimum weights undisturbed from impulsive noise unlike MSE-based algorithms and its main factor has been known to be the input magnitude controller (IMC) that adjusts the input intensity according to error power. In this paper, a normalization of the step size of the MCC algorithm by the power of IMC output is proposed. The IMC output power is tracked recursively through a single-pole low-pass filter. In the simulation under impulsive noise with two different multipath channels, the steady state MSE and convergence speed of the proposed algorithm is found to be enhanced by about 1 dB and 500 samples, respectively, compared to the conventional MCC algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.474-476
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• 2009

In the radio communication, such as echo cancellation and channel equalization, adaptive filtering is very practical. Its convergence behavior that is used for updating the weights depends on the correlation of the input signal and length of adaptive filter. Highly correlated input and long length of adaptive filter deteriorate the convergence behavior. To solve this problem, recently, subband affine projection algorithm which pre-whiten the correlation of the input and update the weights in subband structure has been presented. This paper presents convergence analysis method of multiple constrained subband affine projection algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1401-1414
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• 2018

The k-means clustering algorithm groups input data with the number of groups represented by variable k. In fact, this algorithm is particularly useful in market segmentation and medical research, suggesting its wide applicability. In this paper, we propose a privacy-preserving clustering algorithm that is appropriate for outsourced encrypted data, while exposing no information about the input data itself. Notably, our proposed model facilitates encryption of all data, which is a large advantage over existing privacy-preserving clustering algorithms which rely on multi-party computation over plaintext data stored on several servers. Our approach compares homomorphically encrypted ciphertexts to measure the distance between input data. Finally, we theoretically prove that our scheme guarantees the security of input data during computation, and also evaluate our communication and computation complexity in detail.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.201-203
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• 2007

The paper concerns the successive optimization for structure and parameters of fuzzy inference systems that is based on parallel Genetic Algorithms (PGA) and information data granulation (IG). PGA is multi, population based genetic algorithms, and it is used tu optimize structure and parameters of fuzzy model simultaneously, The granulation is realized with the aid of the C-means clustering. The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly' organized based on center points of data group extracted by the C-Means clustering, It concerns the fuzzy model related parameters such as the number of input variables to be used in fuzzy model. a collection of specific subset of input variables, the number of membership functions according to used variables, and the polynomial type of the consequence part of fuzzy rules, The simultaneous optimization mechanism is explored. It can find optimal values related to structure and parameter of fuzzy model via PGA, the C-means clustering and standard least square method at once. A comparative analysis demonstrates that the Dnmosed algorithm is superior to the conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1785-1794
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• 1999

The affine projection algorithm has known t require less computational complexity than RLS but have much faster convergence than NLMS for speech-like input signals. But the affine projection algorithm is still much more computationally demanding than the LMS algorithm because it requires the matrix inversion. In this paper, we show that the affine projection algorithm can be realized with the Gram-Schmidt orthogonalizaion of input vectors. Using the derived relation, we propose an approximate but much more efficient implementation of the affine projection algorithm. Simulation results show that the proposed algorithm has the convergence speed that is comparable to the affine projection algorithm with only a slight extra calculation complexity beyond that of NLMS.

• The Journal of Information Technology
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• v.9 no.4
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• pp.49-55
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• 2006

In this paper, a new fragile watermarking algorithm for digital image is presented, which makes resolving the security and forgery problem of the digital image to be possible. The most suitable watermarking method that verifies the authentication and integrity of the digital image is the Wong's method, which invokes the hash function (MD5). The algorithm is safe because this method uses the hash function of the cryptology. The operations such as modulus, complement, shift, bitwise exclusive-or, bitwise inclusive-or are necessary for calculating the value of hash function. But, in this paper, an Arithmetic encoding method that only includes the multiplication operation is adopted. This technique prints out accumulative probability interval, which is obtained by multiplying the input symbol probability interval. In this paper, the initial probability interval is determined according to the value of the key, and the input sequence of the symbols is adjusted according to the key value so that the accumulative probability interval will depend on the key value. The integrity of the algorithm has been verified by experiment. The PSNR is above the 51.13db and the verifying time is $1/3{\sim}1/4$ of the verifying time of using the hash function (MD5), so, it can be used in the real-time system.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1033-1043
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• 2007

The multi layer perceptron for segmenting objects in images only uses the input windows that are made from a image in a fixed size. These windows are recognized so each independent learning data that they make the performance of the multi layer perceptron poor. The poor performance is caused by not considering the position information and effect of input windows in input images. So we propose a new approach to add the position information and effect of input windows to the multi layer perceptron#s input layer. Our new approach improves the performance as well as the learning time in the multi layer perceptron. In our experiment, we can find our new algorithm good.