• Journal of Communications and Networks
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• v.5 no.1
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• pp.7-18
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• 2003

In this paper, a decision feedback recurrent neural network equalizer and a modified real time recurrent learning algorithm are proposed, and an adaptive adjusting of the learning step is also brought forward. Then, a complex case is considered. A decision feedback complex recurrent neural network equalizer and a modified complex real time recurrent learning algorithm are proposed. Moreover, weights of decision feedback recurrent neural network equalizer under burst-interference conditions are analyzed, and two anti-burst-interference algorithms to prevent equalizer from out of working are presented, which are applied to both real and complex cases. The performance of the recurrent neural network equalizer is analyzed based on numerical results.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.83-88
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• 2024

In this paper we used classification algorithms on social networking. We are proposing, a new classification algorithm called the improved Decision Tree (IDT). Our model provides better classification accuracy than the existing systems for classifying the social network data. Here we examined the performance of some familiar classification algorithms regarding their accuracy with our proposed algorithm. We used Support Vector Machines, Naïve Bayes, k-Nearest Neighbors, decision tree in our research and performed analyses on social media dataset. Matlab is used for performing experiments. The result shows that the proposed algorithm achieves the best results with an accuracy of 84.66%.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.10
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• pp.1012-1015
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• 2011

The decision-directed blind equalization algorithm is often used due to its simplicity and good convergence property when the eye pattern is open. However, in a channel where the eye pattern is closed, the decision-directed algorithm is not guaranteed to converge. Hence, a modified Bussgang-type algorithm using a hyperbolic tangent function for zero-memory nonlinear(ZNL) function has been proposed and applied to avoid this problem by Filho et al. But application of this algorithm includes the calculation of hyperbolic tangent function and its derivative or a look-up table which may need a large amount of memory due to channel variations. To reduce the computational and/or hardware complexity of Filho's algorithm, in this paper, an improved method for the decision-directed algorithm is proposed. In the proposed scheme, the ZNL function and its derivative are respectively set to be the original signum function and a narrow rectangular pulse which is an approximation of Dirac delta function. It is shown that the proposed scheme, when it is combined with decision-directed algorithm, reduces the computational complexity drastically while it retains the convergence and steady-state performance of the Filho's algorithm.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.358-367
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• 2003

We consider discrete-time factorial Markov Decision Processes (MDPs) in multiple decision-makers environment for infinite horizon average reward criterion with a general joint reward structure but a factorial joint state transition structure. We introduce the "localization" concept that a global MDP is localized for each agent such that each agent needs to consider a local MDP defined only with its own state and action spaces. Based on that, we present a gradient-ascent like iterative distributed algorithm that converges to a local optimal solution of the global MDP. The solution is an autonomous joint policy in that each agent's decision is based on only its local state.cal state.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.2
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• pp.171-182
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• 2000

For the last five years, data mining has drawn much attention by researchers and practitioners because of its many applicable domains. This article presents an adaptive decision tree algorithm for dynamically reasoning machine failure cause out of real-time, large-scale machine status database. Among many data mining methods, intelligent decision tree building algorithm is especially of interest in the sense that it enables the automatic generation of decision rules from the tree, facilitating the construction of expert system. On the basis of experiment using semiconductor etching machine, it has been verified that our model outperforms previously proposed decision tree models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12C
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• pp.753-758
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• 2011

The constant modulus algorithm (CMA) widely used in blind equalization applications minimizes the averaged power of constant modulus error (CME) defined as the difference between an instant output power and a constant modulus. In this paper, a decision feedback version of the linear blind algorithm based on maximization of the zero-error probability for CME is proposed. The Gaussian kernel of the maximum zero-error criterion is analyzed to have the property to cut out excessive CMEs that may be induced from severely distorted channel characteristics. Decision feedback approach to the maximum zero-error criterion for CME is developed based on the characteristic that the Gaussian kernel suppresses the outliers and this prevents error propagation to some extent. Compared to the linear algorithm based on maximum zero-error probability for CME in the simulation of blind equalization environments, the proposed decision feedback version has superior performance enhancement particularly in cases of severe channel distortions.

• Journal of the Korean Operations Research and Management Science Society
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• v.8 no.1
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• pp.31-40
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• 1983

An uncertain dynamic evolving process has been a continuing challenge to decision problems. The dynamic random variable (drv) changes which characterize such a process are very important for the decision-maker in selecting a course of action in a world that is perceived as uncertain, complex, and dynamic. Using this subjective point prediction algorithm based on a modified recursive filter, the decision-maker becomes to have periodically changing plausible points with the passage of time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1405-1409
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• 2004

This paper is studied new block mode decision algorithm for H.264/AVC. The fast block mode decision algorithm is consist of block range decision algorithm. The block range decision algorithm classifies the block over 8$\times$8 size or below for 16${\times}$16 macroblock to decide the size and type of sub blocks. As the sub blocks of 8$\times$8, 8r4, 4$\times$8 and 4$\times$4, which are the blocks below 8$\times$8 size, include important motion information, the exact sub block decision is required. RDC(RDO cost) is used as the matching parameter for the exact sub block decision. RDC is calculated with motion strength which is the mean value of neighbor pixels of each sub block. The sub block range decision reduces encoding arithmetic amount by 34.62% on the average more than the case not using block range decision. The block mode decision using motion strength shows improvement of PSNR of 0.05[dB].

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.7
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• pp.705-710
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• 2011

In this paper, we proposed the low computational complexity soft-decision e-BCH decoding algorithm based on the Chase algorithm. In order to make the test patterns, it is necessary to re-order the least reliable received symbols. In the process of ordering and finding optimal decoding symbols, high computational complexity is required. Therefore, this paper proposes the method of low computational complexity algorithm for soft-decision e-BCH decoding process.

• Journal of Korea Multimedia Society
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• v.7 no.1
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• pp.73-79
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• 2004

In this paper, we propose an efficient soft decision decoding algorithm for linear block codes. A conventional soft decision decoder have to invoke a hard decision decoder several times to estimate its soft decision values. However, in this method, we may not have candidate codewords, thus it is very difficult to produce soft decision values. We solve this problem by introducing an efficient algorithm to search candidate codewords. By using this, we can highly reduce the cases we cannot find candidate codewords. We estimate the performance of the proposed algorithm by using the computer simulations. The simulation is performed for binary (63, 36) BCH code in fading channel.