• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2A
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• pp.85-90
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• 2009

In this paper, as an alternative to constant modulus algorithm based on MSE, maximization of the probability that equalizer output power is equal to the constant modulus of the transmitted symbols is introduced. The proposed algorithm using the gradient ascent method to the maximum probability criterion has superior convergence and steady-state MSE performance, and the error samples of the proposed algorithm exhibit more concentrated density functions in blind equalization environments. Simulation results indicate that the proposed training has a potential advantage versus MSE training for the constant modulus approach to blind equalization.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.2
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• pp.190-195
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• 2016

In this paper, we are comparative analysis both class method and proposal method in order to estimation of incident signal direction on uniform array antenna system. Proposal method of this paper decrease error probability for a signal direction of arrival estimation using maximum posterior probability estimator. If it decrease to signal estimation direction error probability, signal direction of arrival can correctly estimate. Through simulation, we were comparative analysis proposed method and class method. Also, we were comparative analysis about signal estimation error probability with increasing array antenna element. We show the superior performance of the proposed method relative to the class method to decrease of signal estimation error probability about 12%.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.535-545
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• 2015

In a various range of applications including hydrology, the type-I extreme value distribution has been extensively used as a probabilistic model for analyzing extreme events. In this paper, we introduce methods for estimating the scale parameter of the type-I extreme value distribution. A simulation study is performed to compare the estimators in terms of mean-squared error and bias, and the obtained results are provided.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.65-70
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• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.

• Journal of Communications and Networks
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• v.18 no.1
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• pp.19-26
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• 2016

In this paper, the performance of dual-hop multi-relay maximum ratio transmission (MRT) over Rayleigh flat fading channels is studied with both conventional (all relays participate the transmission) and opportunistic (best relay is selected to maximize the received signal-to-noise ratio (SNR)) relaying. Performance analysis starts with the derivation of the probability density function, cumulative distribution function and moment generating function of the SNR. Then, both approximate and asymptotic expressions of symbol error rate (SER) and outage probability are derived for arbitrary numbers of antennas and relays. With the help of asymptotic SER and outage probability, diversity and array gains are obtained. In addition, impact of imperfect channel estimations is investigated and optimum power allocation factors for source and relay are calculated. Our analytical findings are validated by numerical examples which indicate that multi-relay MRT can be a low complexity and reliable option in cooperative networks.

• 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.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.1-9
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• 2007

A three-state Markov logistic regression model is suggested to forecast the probability of tomorrow's precipitation based on the current meteorological situation. The suggested model turns out to be better than Markov regression model in the sense of the mean squared error of forecasting for the rainfall data of Seoul area.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.5
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• pp.237-243
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• 2014

In this paper, a calculation-efficient method for weight update in the algorithm based on maximization of the zero-error probability (MZEP) is proposed. This method is to utilize the current slope value in calculation of the next slope value, replacing the block processing that requires a summation operation in a sample time period. The simulation results shows that the proposed method yields the same performance as the original MZEP algorithm while significantly reducing the computational time and complexity with no need for a buffer for error samples. Also the proposed algorithm produces faster convergence speed than the algorithm that is based on the error-entropy minimization.

• Journal of Korea Society of Industrial Information Systems
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• v.2 no.2
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• pp.219-228
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• 1997

The Fano metric is the maximum likelihood decoding choice for convlutional code for binary symmetric channel. The Fano metric assumes that it has previous knowledge of channel error probability. However, the bit errors in real channel occur in bursts and the channel error probability can not be known exactly. Thus, the Fano metric is not the maximum likelihood choice for bursty-noise channel. In this paper universal metri which dose not require the previous knowlege of the channel transition probability is used for sequential decoding. It is shown that the complexity of the universal is much less than that of the Fano metric bursty-noise channel, since it is estimated on a branch by branch basis.