• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.27-33
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• 2011

For compensation of channel distortion from multipath fading and impulsive noise, a decision feedback equalizer (DFE) algorithm based on minimization of Error entropy (MEE) is proposed. The MEE criterion has not been studied for DFE structures and impulsive noise environments either. By minimizing the error entropy with respect to equalizer weight based on decision feedback structures, the proposed decision feedback algorithm has shown to have superior capability of residual intersymbol interference cancellation in simulation environments with severe multipath and impulsive noise.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.7
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• pp.12-18
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• 1993

We have proposed a soft-decision decoding method for block codes. With careful examinations of the first hard-decision decoded results, The candidate codewords are efficiently searched for. Thus, we can reduce the decoding complexity (the number of hard-decision decodings) and lower the block error probability. Computer simulation results are presented for the (23,12) Golay code. They show that the decoding complexity is considerably reduced and the block error probability is close to that of the maximum likelihood decoder.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1998.10a
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• pp.421-431
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• 1998

Error control performance of the (16, 7) minimum-bandwidth binary error cotrol line code (MB-ECLC) according to decoding algorithms is analyzed and compared in this paper. As a result , when retransmission is not allowed or meaningless. to reduce performance degradation ad computational burden. the modified soft decision decoding algorithm using the structure of (16,7) MB-ELEC is proposed. The error cotnrol capability of this modified algorithm is far better than that of a hard decision decoding algorithm, and almost same as that of a full soft decision decoding algorithm. In additino, the number of comparisons for the modified algorithm is decreased more than 5 times as compared with a full soft decision decoding algorithm.

• Journal of Broadcast Engineering
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• v.10 no.4 s.29
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• pp.540-547
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• 2005

In the DFE(Decision Feedback Equalizer) for ATSC DTV receivers, there are decision errors in the slicer or. the simplified trellis decoder, and these decided false data comes to the feedback filter to make the error propagation phenomenon. The error propagation degrades the equalizer performance by increasing residual errors as well as slowing down the convergence rate. In this paper we propose a dual-feedback equalization structure. There are two feedback filters. One is the decision feedback filter which uses the simplified trellis decoder output data, the other is non-decision feedback filter which uses the equalizer output data. The additional non-decision feedback filter doesn't introduce the error propagation, so it can compensate the error propagation. The proposed structure accelerates the convergence rate as well as reduces output men-square error(MSE). We analyzed the performance enhancement of DTV receiver using dual-feedback equalization structure.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.500-509
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• 2022

With the rapid development of educational informatization, teaching methods become diversified characteristics, but a large number of information data restrict the evaluation on teaching subject and object in terms of the effect of English education. Therefore, this study adopts the concept of incremental learning and eigenvalue interval algorithm to improve the weighted decision tree, and builds an English education effect evaluation model based on association rules. According to the results, the average accuracy of information classification of the improved decision tree algorithm is 96.18%, the classification error rate can be as low as 0.02%, and the anti-fitting performance is good. The classification error rate between the improved decision tree algorithm and the original decision tree does not exceed 1%. The proposed educational evaluation method can effectively provide early warning of academic situation analysis, and improve the teachers' professional skills in an accelerated manner and perfect the education system.

• ETRI Journal
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• v.26 no.2
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• pp.101-111
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• 2004

This paper presents a near-optimum blind decision feedback equalizer (DFE) for the receivers of Advanced Television Systems Committee (ATSC) digital television. By adopting a modified trellis decoder (MTD) with a trace- back depth of 1 for the decision device in the DFE, we obtain a hardware-efficient, blind DFE approaching the performance of an optimum DFE which has no error propagation. In the MTD, the absolute distance is used rather than the squared Euclidean distance for the computation of the branch metrics. This results in a reduction of the computational complexity over the original trellis decoding scheme. Compared to the conventional slicer, the MTD shows an outstanding performance improvement in decision error probability and is comparable to the original trellis decoder using the Euclidean distance. Reducing error propagation by use of the MTD in the DFE leads to the improvement of convergence performance in terms of convergence speed and residual error. Simulation results show that the proposed blind DFE performs much better than the blind DFE with the slicer, and the difference is prominent at the trellis decoder following the blind DFE.

• Asia pacific journal of information systems
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• v.3 no.1
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• pp.155-189
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• 1993

Empirical studies have shown that the performance of decision tree induction usually improves when the trees are pruned. Whether these results hold in general and to what extent pruning improves the accuracy of a concept have not been investigated theoretically. This paper provides a theoretical study of pruning. We focus on a particular type of pruning and determine a bound on the error due to pruning. This is combined with PAC (Probably Approximately Correct) Learning theory to determine a sample size sufficient to guarantee a probabilistic bound on the concept error. We also discuss additional pruning rules and give an analysis for the pruning error.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2172-2177
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• 2015

The DF-MZEP-CME (decision feedback - maximum zero-error probability for constant modulus errors) algorithm that makes the probability for constant modulus error (CME) close to zero and employs decision feedback (DF) structures shows more improved performance in channel distortion compensation. However the DF-MZEP-CME algorithm has a computational complexity proportional to a sample size for probability estimation and this property plays a role of an obstacle in practical implementation. In this paper, the gradient of DF-MZEP-CME is proposed to be estimated recursively and shown to solve the computational problem by making the algorithm independent of the sample size. For a sample size N, the conventional method has 10N multiplications but the proposed has only 20 regardless of N. Also the recursive gradient estimation for weight update is kept in continuity from the initial state to the steady state without any error propagation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.2C
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• pp.181-192
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• 2003

Blind equalization of communication channel is important because it does not need training signal, nor does it require a priori channel information. So, we can increase the bandwidth efficiency. The linear prediction error method is perhaps the most attractive in practice due to the insensitive to blind channel equalizer length mismatch as well as for its simple adaptive implementation. Unfortunately, the previous one-step prediction error method is known to be limited in arbitrary decision delay. In this paper, we propose method for fractionally spaced blind equalizer with arbitrary decision delay using one-step forward prediction error filter from second-order statistics of the received signals for SIMO channel. Our algorithm utilizes the forward prediction error as training signal and computes the best decision delay from all possible decision delay. Simulation results are presented to demonstrate the performance of our proposed algorithm.