• Journal of Sensor Science and Technology
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• v.29 no.1
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• pp.19-26
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• 2020

Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.600-602
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• 1997

In this study, a hybrid neural net consisting of an Adaptive LVQ(ALVQ) algorithm and MLP is proposed to perform speaker identification task. ALVQ is a new learning procedure using adaptively feature vector sequence instead of only one feature vector in training codebooks initialized by LBG algorithm and the optimization criterion of this method is consistent with the speaker classification decision rule. ALVQ aims at providing a compressed, geometrically consistent data representation. It is fit to cover irregular data distributions and computes the distance of the input vector sequence from its nodes. On the other hand, MLP aim at a data representation to fit to discriminate patterns belonging to different classes. It has been shown that MLP nets can approximate Bayesian "optimal" classifiers with high precision, and their output values can be related a-posteriori class probabilities. The different characteristics of these neural models make it possible to devise hybrid neural net systems, consisting of classification modules based on these two different philosophies. The proposed method is compared with LBG algorithm, LVQ algorithm and MLP for performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.200-207
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• 2001

In this paper, we propose an HMM(Hidden Markov modeJ)-MLP(Multi-layer perceptron) hybrid model for recognizing legal words on the English bank check. We adopt an explicit segmentation-based word level architecture to implement an HMM engine with nonscaled and non-normalized symbol vectors. We also introduce an MLP for implicit segmentation-based word recognition. The final recognition model consists of a hybrid combination of the HMM and MLP with a new hybrid probability measure. The main contributions of this model are a novel design of the segmentation-based variable length HMMs and an efficient method of combining two heterogeneous recognition engines. ExperimenLs have been conducted using the legal word database of CENPARMI with encouraging results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.105-113
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• 1996

In this paper, a new korean digita speech recognition technique was proposed using muktolayer perceptron (MLP). In spite of its weakness in dynamic signal recognition, MLP was adapted for this model, cecause korean syllable could give static features. It is so simle in its structure and fast in its computing that MLP was used to the suggested system. MLP's input vectors was transformed using karhunen-loeve transformation (KLT), which compress signal successfully without losin gits separateness, but its physical properties is changed. Because the suggested technique could extract static features while it is not affected from the changes of syllable lengths, it is effectively useful for korean numeric recognition system. Without decreasing classification rates, we can save the time and memory size for computation using KLT. The proposed feature extraction technique extracts same size of features form the tow same parts, front and end of a syllable. This technique makes frames, where features are extracted, using unique size of windows. It could be applied for continuous speech recognition that was not easy for the normal neural network recognition system.

• Journal of Microbiology
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• v.37 no.4
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• pp.229-233
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• 1999

The mitochondrial plasmid pMLP1 from a white-rot fungus, Pleurotus ostreatus, is a double-stranded DNA containing 381 bp terminal inverted repeat (TIR) whose 5'-ends are covalently bound by terminal proteins. The plasmid contains two major open reading frames (ORFs), encoding putative DNA and RNA polymerases, and a minor ORF encoding a small, highly basic protein. To identify the DNA binding activity that recognizes the TIR region of pMLP1, gel retardation assays were performed with mitochondrial extracts. A specific protein binding to a region between 123 and 248 nt within TIR was observed. We examined whether the gene product of mORF1 bindes to this region specifically. E. coli cell extract which contains an overproduced mORF1 protein formed a complex specific to the region between 123 and 248 nt. Inclusion of mORF1 protein in the specific complex formed between P. ostreatus mitochondrial extract and TIR was confirmed by a supershift assay using polyclonal antibodies against the mORF1 protein. Our result suggest that the product of mORF1 may function as a terminal region recognition factor (TRF), recognizing an internal region in TIR.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.111-117
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• 2022

Many studies have been conducted on software fault prediction models for decades, and the models using machine learning techniques showed the best performance. Deep learning techniques have become the most popular in the field of machine learning, but few studies have used them as classifiers for fault prediction models. Some studies have used deep learning to obtain semantic information from the model input source code or syntactic data. In this paper, we produced several models by changing the model structure and hyperparameters using MLP with three or more hidden layers. As a result of the model evaluation experiment, the MLP-based deep learning models showed similar performance to the existing models in terms of Accuracy, but significantly better in AUC. It also outperformed another deep learning model, the CNN model.

• 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 KIISE:Software and Applications
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• v.37 no.7
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• pp.491-502
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• 2010

When human beings acquire and develop knowledge through education, their prior knowledge influences the next learning process. As this is a fact that should be considered in machine learning, we need to examine the effects of controlling the order of training sequence on machine learning. In this research, the role of the supervisor is extended to control the order of training samples, in addition to just instructing the target values for classification problems. The supervisor sequences the training examples categorized by SOM to the learning model which in this case is MLP. The proposed method is distinguished in that it selects the most instructive example from categories formed by SOM to assist the learning progress, while others use SOM only as a preprocessing method for training samples. The result shows that the method is effective in terms of the number of samples used and time taken in training.

• The Korean Journal of Mycology
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• v.15 no.1
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• pp.1-8
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• 1987

The purpose of this experiment is to examine if G. lucidum extracts could have an effect on leukocyte migration and to select the best chemotactic factor for leukocyte migration. When chemotactic factors such as TC-199 medium, filtrate of E. coli culture, N-fMLP, and G. lucidum extracts were used, N-fMLP, G. lucidum extracts and filtrate of E. coli were positive effect, but TC-199 medium was less than others. The various concentrations of the chemotactic factors such as G. lucidum extracts(1.00%, 0.10% and 0.01%) were stimulated for the leukocytes migration in the modified Boyden chambers, however, the lowest concentration such as 0.01% was more effective than others for the chemoattractant of the leukocyte migration When the leukocyte was treated with G. lucidum extracts(1.00%, 0.10%, and 0.01%) at room temperature for 120 minutes, the chemotactic activities had a good effect at 0.01%. Therefore, leukocyte was affected by G. lucidum extracts.

• Journal of Microbiology and Biotechnology
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• v.14 no.5
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• pp.1031-1037
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• 2004

The interaction of chemoattractant receptors and $G{\alpha}_{16}$ was studied to provide the molecular basis to elucidate the interaction of chemoattractant receptors with $G{\alpha}_{16}$ subunit, thereby possibly contributing to finding novel targets for designing new type of G protein antagonists with anti-inflammatory effects. Experiments were performed to characterize the $G{\alpha}_{16}$ subunit domains responsible for efficient coupling to chemoattractant receptors. Thus, a series of chimeric $G{\alpha}_{11}G{\alpha}_{16}$ and $G{\alpha}_{16}G{\alpha}_{11}$ cDNA constructs were expressed, and the ability of chimeric proteins to mediate C5a, IL-8, and fMLP-induced release of inositol phosphate in transfected Cos-7 cells was tested. The results showed that short stretches of residues 154 to residue 167 and from residue 174 to residue 195 of $G{\alpha}_{16}$ contribute to efficient coupling to the C5a receptor. On the other hand, a stretch of amino acid residues 220-240 of $G{\alpha}_{16}$ that is necessary for interacting with C5a receptor did not play any role in the interaction with IL-8 receptor. However, a stretch from residue 155 to residue 195 of $G{\alpha}_{16}$ was found to be crucial for efficient coupling to IL-8 receptor in concert with C-terminal 30 amino acid residues of this ${\alpha}$ subunit. Coupling profiles of a variety of chimeras, composed of $G{\alpha}_{11}G{\alpha}_{16}$ to fMLP receptor indicate that the C-terminal 30 amino acids are most critical for the coupling of $G{\alpha}_{16}$ to fMLP receptor. Taken together, $G{\alpha}_{16}$ subunit recruits multiple and distinctive coupling regions, depending on the type of receptors, to interact.