• Journal of Digital Convergence
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• v.13 no.8
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• pp.289-294
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• 2015

This paper proposes an autonomous machine learning method applicable to the BCI(Brain-Computer Interface) is based on the self-organizing Kohonen method, one of the exemplary method of unsupervised learning. In addition we propose control method of learning region and self machine learning rule using an interactive function. The learning region control and machine learning was used to control the side effects caused by interaction function that is based on the self-organizing Kohonen method. After determining the winner neuron, we decided to adjust the connection weights based on the learning rules, and learning region is gradually decreased as the number of learning is increased by the learning. So we proposed the autonomous machine learning to reach to the network equilibrium state by reducing the flow toward the input to weights of output layer neurons.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.657-663
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• 2016

In this work, a novel silicon (Si) based floating body synaptic transistor (SFST) is studied to mimic the transition from short-term memory to long-term one in the biological system. The structure of the proposed SFST is based on an n-type metal-oxide-semiconductor field-effect transistor (MOSFET) with floating body and charge storage layer which provide the functions of short- and long-term memories, respectively. It has very similar characteristics with those of the biological memory system in the sense that the transition between short- and long-term memories is performed by the repetitive learning. Spike timing-dependent plasticity (STDP) characteristics are closely investigated for the SFST device. It has been found from the simulation results that the connectivity between pre- and post-synaptic neurons has strong dependence on the relative spike timing among electrical signals. In addition, the neuromorphic system having direct connection between the SFST devices and neuron circuits are designed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2264-2275
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• 1994

In this paper, image compression based on neural networks is presented with block classification and coding. Multilayer neural networks with error back-propagation learning algorithm are used to transform the normalized image date into the compressed hidden values by reducing spatial redundancies. Image compression can basically be achieved with smaller number of hidden neurons than the numbers of input and output neurons. Additionally, the image blocks can be grouped for adaptive compression rates depending on the characteristics of the complexity of the blocks in accordance with the sensitivity of the human visual system(HVS). The quantized output of the hidden neuron can also be entropy coded for an efficient transmission. In computer simulation, this approach lie in the good performances even with images outside the training set and about 25:1 compression rate was achieved using the entropy coding without much degradation of the reconstructed images.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.210-216
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• 2015

It is well known that the structure of brain and consciousness of human have a phenomena of complex system. The human emotion have a many kind. The love is one of human emotion, which have been studied in sociology and psychology as a matter of great interested thing. In this paper, we consider a same and different time delay in love equation of Romeo and Juliet. We represent a behavior of love as a time series and phase portrait, and analyze the difference of behaviors between a same and different time delay.

• Journal of Korea Multimedia Society
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• v.2 no.4
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• pp.490-495
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• 1999

In this paper, a method of improving the learning time and convergence rate is proposed to exploit the advantages of artificial neural networks and fuzzy theory to neuron structure. This method is applied to the XOR Problem, n bit parity problem which is used as the benchmark in neural network structure, and recognition of digit image in the vehicle plate image for practical image application. As a result of the experiments, it does not always guarantee the convergence. However, the network showed improved the teaming time and has the high convergence rate. The proposed network can be extended to an arbitrary layer Though a single layer structure Is considered, the proposed method has a capability of high speed 3earning even on large images.

• KIEAE Journal
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• v.15 no.4
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• pp.105-110
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• 2015

Purpose: This study aimed at finding the optimal input variables of the artificial neural network-based predictive model for the optimal controls of the indoor temperature environment. By applying the optimal input variables to the predictive model, the required time for restoring the current indoor temperature during the setback period to the normal setpoint temperature can be more precisely calculated for the cooling season. The precise prediction results will support the advanced operation of the cooling system to condition the indoor temperature comfortably in a more energy-efficient manner. Method: Two major steps employing the numerical computer simulation method were conducted for developing an ANN model and finding the optimal input variables. In the first process, the initial ANN model was intuitively determined to have input neurons that seemed to have a relationship with the output neuron. The second process was conducted for finding the statistical relationship between the initial input variables and output variable. Result: Based on the statistical analysis, the optimal input variables were determined.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1724-1731
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• 2018

The conventional polynomial neural network (PNN) is a classical flexible neural structure and self-organizing network, however it is not free from the limitation of overfitting problem. In this study, we propose a space search-optimized polynomial neural network (ssPNN) structure to alleviate this problem. Ranking selection is realized by means of ranking selection-based performance index (RS_PI) which is combined with conventional performance index (PI) and coefficients based performance index (CPI) (viz. the sum of squared coefficient). Unlike the conventional PNN, L2-norm regularization method for estimating the polynomial coefficients is also used when designing the ssPNN. Furthermore, space search optimization (SSO) is exploited here to optimize the parameters of ssPNN (viz. the number of input variables, which variables will be selected as input variables, and the type of polynomial). Experimental results show that the proposed ranking selection-based polynomial neural network gives rise to better performance in comparison with the neuron fuzzy models reported in the literatures.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.113-118
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• 1996

Principal component analysis(PCA) is an essential technique for data compression and feature extraction, and has been widely used in statistical data analysis, communication theory, pattern recognition, and image processing. Oja(1992) found that a linear neuron with constrained Hebbian learning rule can extract the principal component by using stochastic gradient ascent method. In practice real data often contain some outliers. These outliers will significantly deteriorate the performances of the PCA algorithms. In order to make PCA robust, Xu & Yuille(1995) applied statistical physics to the problem of robust principal component analysis(RPCA). Devlin et.al(1981) obtained principal components by using techniques such as M-estimation. The propose of this paper is to investigate from the statistical point of view how Xu & Yuille's(1995) RPCA works under the same simulation condition as in Devlin et.al(1981).

• Journal of the Korea Computer Industry Society
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• v.2 no.9
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• pp.1191-1198
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• 2001

An increasingly important facet of software development is the ability to estimate the associate cost and effort of development early in the development life cycle. Most of the proposed models are based upon a combination of intuition, expert judgement, and regression analysis of empirical data. Overall, the work has failed to produce any single model that can be applied with a reasonable degree of success to a variety of environments. This paper presents a neural network (NN) model that related software development effort to software size measured in function element types. The heuristic approach is applied to decide the number of hidden neurons in NN from the relationship between input-output pairs. The research describes appropriate NN modeling in the context of a case study for 24 software development projects. Also, this paper compared the NN model with a regression analysis model and found the NN model has better accuracy.

• Progress in Medical Physics
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• v.9 no.2
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• pp.95-104
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• 1998

The dynamic properties of the 3rd-order neuron of the retina was investigated by using conventional intracellular recording techniques. Experiments were performed in the superfused retina-eyecup preparation of the channel catfish, Ictalurus punctatus. The cornea, iris, lens, and vitreous were removed by absorption with Kimwipe tissue under the dissection microscope thereby exposing the retina in a hemi -eyecup. The electrical signal was amplified by electrometer, viewed on oscilloscope. Regular signals from the cells were recorded on a penwriter and stored by data recorder and computer. Full-field, spot or annular light stimuli were generated on a computer monitor and focused onto the retina. Baclofen hyperpolarized the dark membrane potential, suppressed sustained component and enhanced transient component of the ON-sustained cell with a large transient component, but did not affect the surround antagonism of the cell. Baclofen selectively suppressed responses evoked by moving bar light stimuli on the ON-OFF transient cell. The results suggest that transient cells have directional selectivity in the inner retina. These dynamic properties of amacrine and ganglion cells were modulated by baclofen. Therefore, it is presumed that there is baclofen-induced directional selectivity in ON-OFF transient cells in the catfish retina.