• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.7
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• pp.1-11
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• 2006

Through the analysis of conventional TVD limiters, a new multi-dimensional limiting function is derived for an oscillation control in multi-dimensional flows. Then, Multi-dimensional Limiting Process (MLP) is developed with the multi-dimensional limiting function. The major advantage of MLP is to prevent oscillations across a multi-dimensional discontinuity, and it is readily compatible with more than 3rd order spatial interpolation. Moreover, MLP shows a good convergence characteristic in a steady problem and it is very simple to be implemented. Through numerical test cases, it is verified that MLP substantially improves accuracy, efficiency and robustness both in continuous and discontinuous flows.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.10
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• pp.831-841
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• 1991

A combined model of probabilistic and MLP(multi layer perceptron) model is proposed for the pattern classification of EMG( electromyogram) signals. The MLP model has a problem of not guaranteeing the global minima of error and different quality of approximations to Bayesian probabilities. The probabilistic model is, however, closely related to the estimation error of model parameters and the fidelity of assumptions. A proper combination of these will reduce the effects of the problems and be robust to input variations. Proposed model is able to get the MAP(maximum a posteriori probability) in the probabilistic model by estimating a priori probability distribution using the MLP model adaptively. This method minimize the error probability of the probabilistic model as long as the realization of the MLP model is optimal, and this is a good combination of the probabilistic model and the MLP model for the usage of MLP model reliability. Simulation results show the benefit of the proposed model compared to use the Mlp and the probabilistic model seperately and the average calculation time fro classification is about 50ms in the case of combined motion using an IBM PC 25 MHz 386model.

• Journal of Korea Water Resources Association
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• v.56 no.1
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• pp.63-74
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• 2023

High-reliability prediction of dam inflow is necessary for efficient dam operation. Recently, studies were conducted to predict the inflow of dams using Multi Layer Perceptron (MLP). Existing studies used the Gradient Descent (GD)-based optimizer as the optimizer among MLP operators to find the optimal correlation between data. However, the GD-based optimizers have disadvantages in that the prediction performance is deteriorated due to the possibility of convergence to the local optimal value and the absence of storage space. This study improved the shortcomings of the GD-based optimizer by developing Adaptive moments combined with Improved Harmony Search (AdamIHS), which combines Adaptive moments among GD-based optimizers and Improved Harmony Search (IHS). In order to evaluate the learning and prediction performance of MLP using AdamIHS, Daecheong Dam inflow was learned and predicted and compared with the learning and prediction performance of MLP using GD-based optimizer. Comparing the learning results, the Mean Squared Error (MSE) of MLP, which is 5 hidden layers using AdamIHS, was the lowest at 11,577. Comparing the prediction results, the average MSE of MLP, which is one hidden layer using AdamIHS, was the lowest at 413,262. Using AdamIHS developed in this study, it will be possible to show improved prediction performance in various fields.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.61-66
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• 2006

We propose a new fragment assembly program MLP (mate-based layout with PHRAP). MLP consists of PHRAP, repeat masking, and a new layout algorithm that uses the mate pair information. Our experimental results show that by using MLP instead of PHRAP, we can significantly reduce the difference between the assembled sequence and the original genome sequence.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1277-1280
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• 1998

We propose a structural learning method of MLP classifiers for a given application using PfSGA (parameter-free species genetic algorithm), which is a combining of species genetic algorithm(SGA) and parameter-free genetic algorithm(PfGA). experimental results show that PfSGA can reduce the learing time of SGA and has no influence of parameter values on structural learning. And we also convince that PfSGA is more efficient than the other methods in the aspect of misclassification ratio, learning rate, and complexity of MLP structure.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.107-115
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• 2009

Skin color detection is used to classify input pixels into skin and non skin area, and it requires the classifier to have a high classification rate. In previous work, most classifiers used single color model for skin color detection. However the classification rate can be increased by using more than one color model due to the various characteristics of skin color distribution in different color models, and the MLP is also invested as a more efficient classifier with less parameters than other classifiers. But the input dimension and required parameters of MLP will be increased when using two color models in skin color detection, as a result, the increased parameters will cause the huge teaming time in MLP. In this paper, we propose a MLP based classifier with less parameters in two color models. The proposed partially connected MLP based on two color models can reduce the number of weights and improve the classification rate. Because the characteristic of different color model can be learned in different partial networks. As the experimental results, we obtained 91.8% classification rate when testing various images in RGB and CbCr models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.887-895
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• 2014

Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.

• The Korean Journal of Physiology and Pharmacology
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• v.1 no.1
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• pp.97-105
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• 1997

The regulatory role of cyclic nucleotides in the expression of neutrophil responses has been examined. fMLP-stimulated superoxide production in neutrophils was inhibited by dibutyryl adenosine 3',5'-cyclic monophosphate (DBcAMP), histamine, adenosine + theophylline, cAMP elevating agents, and 8-bromoguanosine 3' ,5' -cyclic monophosphate (8-BrcGMP) and sodium nitroprusside, cGMP elevating agents. Staurosporine, a protein kinase C inhibitor, genistein, a protein tyrosine kinase inhibitor and chlorpromazine, a calmodulin inhibitor, inhibited superoxide production by fMLP, but they did not further affect the action of DBcAMP on the stimulatory action of fMLP. DBcAMP, histamine, adenosine+theophylline and genistein inhibited myeloperoxidease release evoked by fMLP, whereas BrcGMP, sodium nitroprusside and staurosporine did not affect it. The elevation of $[Ca^{2+}]_i$ evoked by fMLP was inhibited by genistein and chlorpromazine but was not affected by staurosporine. DBcAMP exerted little effect on the initial peak in $[Ca^{2+}]_i$ response to fMLP but effectively inhibited the sustained rise. On the other hand, BrcGMP significantly inhibited both phases. fMLP-induced $Mn^{2+}$ influx was inhibited by either DBcAMP or BrcGMP. These results suggest that fMLP-stimulated neutrophil responses may be regulated by cAMP more than cGMP. cAMP and cGMP appear not affect stimulated responses by direct protein kinase C activation. Their regulatory action on the stimulated neutrophil responses may be not influenced by other activation processes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.922-929
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• 2005

In this paper, we propose the MRS p:Ittern classification techniques by the fusion scheme based on the SpPCA and MLP. A conventional PCA teclulique for the dimension reduction has the problem that it can't find a optimal transformation matrix if the property of input data is nonlinear. To overcome this drawback we extract features by the SpPCA technique which use the local patterns rather than whole patterns. In a next classification step, individual classifier based on MLP calculates the similarity of each class for local features. Finally, MRS patterns is classified by the fusion scheme to effectively combine the individual information. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.

• The Journal of the Acoustical Society of Korea
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• v.19 no.1
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• pp.4-10
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• 2000

The work presented in this paper is about a postprocessor, which improves the performance of automatic speech segmentation system by correcting the phoneme boundary errors. We propose a postprocessor that reduces the range of errors in the auto labeled results that are ready to be used directly as synthesis unit. Starting from a baseline automatic segmentation system, our proposed postprocessor trains the features of hand labeled results using multi-layer perceptron(MLP) algorithm. Then, the auto labeled result combined with MLP postprocessor determines the new phoneme boundary. The details are as following. First, we select the feature sets of speech, based on the acoustic phonetic knowledge. And then we have adopted the MLP as pattern classifier because of its excellent nonlinear discrimination capability. Moreover, it is easy for MLP to reflect fully the various types of acoustic features appearing at the phoneme boundaries within a short time. At the last procedure, an appropriate feature set analyzed about each phonetic event is applied to our proposed postprocessor to compensate the phoneme boundary error. For phonetically rich sentences data, we have achieved 19.9 % improvement for the frame accuracy, comparing with the performance of plain automatic labeling system. Also, we could reduce the absolute error rate about 28.6%.