• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.611-615
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• 2004

A neural prostheses can be designed to permit stimulation of specific sites in the nervous system to restore their functions, lost due to disease or trauma. This study focuses on the feasibility of optoelecronic stimulation into nervous system. Optoelectronic stimulation supplies, power and signal into the implanted optical detector inside the body by optics. It can be effective strategy especially on the retinal prosthesis, because it enables the non-invasive connection between the external source and internal detector through natural optical window 'eye'. Therefore, we designed an effective neural stimulating setup by optically based stimulation. Stimulating on the sciatic nerve of a rat with proper depth probe through optical stimulation needs higher ratio of current spreading through the neural surface, because of high impedance of neural interface. To increase the insertion current spreading into the neuron, we used a parallel low resistance compared to load resistance organic interface and calculated the optimized outer parallel resistance for maximum insertion current with the assumption of limited current by photodiode. Optimized outer parallel resistance was at a range of 500Ω-700Ω and a current was at a level between 580uA and 650uA. Stimulating current efficiency from initial photodiode induced current was between 47.5 and 59.7%. Various amplitude and frequency of the optical stimulation on the sciatic nerve showed the reliable visual tremble, and the action potential was also recorded near the stimulating area. These result demonstrate that optoelectronic stimulation with no bias can be applied to the retinal prosthesis and other neuroprosthetic area.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.609-614
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• 2014

Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebella ataxia patients has limited effectiveness, presumably because an inappropriate closed-loop cerebellar response to the inevitable observed error confounds motor learning mechanisms. Recent studies have validated the age-old technique of employing motor imagery training (mental rehearsal of a movement) to boost motor performance in athletes, much as a champion downhill skier visualizes the course prior to embarking on a run. Could the use of EEG based BCI provide advanced biofeedback to improve motor imagery and provide a "backdoor" to improving motor performance in ataxia patients? In order to determine the feasibility of using EEG-based BCI control in this population, we compare the ability to modulate mu-band power (8-12 Hz) by performing a cued motor imagery task in an ataxia patient and healthy control.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.233-242
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• 2021

In this paper, as a liberal arts course for non-majors, we proposed a supervised learning artificial neural network parameter optimization method and a basic education method for activation function to design a basic artificial neural network subject curriculum. For this, a method of finding a parameter optimization solution in a spreadsheet without programming was applied. Through this training method, you can focus on the basic principles of artificial neural network operation and implementation. And, it is possible to increase the interest and educational effect of non-majors through the visualized data of the spreadsheet. The proposed contents consisted of artificial neurons with sigmoid and ReLU activation functions, supervised learning data generation, supervised learning artificial neural network configuration and parameter optimization, supervised learning artificial neural network implementation and performance analysis using spreadsheets, and education satisfaction analysis. In this paper, considering the optimization of negative parameters for the sigmoid neural network and the ReLU neuron artificial neural network, we propose a training method for the four performance analysis results on the parameter optimization of the artificial neural network, and conduct a training satisfaction analysis.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.65-74
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• 2010

We propose a system that automatically recognizes the tense or relaxed condition of scrolling-shooting game subject that plays. Existing study compares the changed values of source of stimulation to the player by suggesting the source, and thus involves limitation in automatic classification. This study applies SOM of unsupervised learning for automatic classification and recognition of player's condition change. Application of SOM for automatic recognition of tense and relaxed condition is composed of two steps. First, ECG measurement and analysis, is to extract characteristic vector through HRV analysis by measuring ECG after having the player play the game. Secondly, SOM learning and recognition, is to classify and recognize the tense and relaxed conditions of player through SOM learning of the input vectors of heart beat signals that the characteristic extracted. Experiment results are divided into three groups. The first is HRV frequency change and the second the SOM learning results of heart beat signal. The third is the analysis of match rate to identify SOM learning performance. As a result of matching the LF/HF ratio of HRV frequency analysis to the distance of winner neuron of SOM based on 1.5, a match rate of 72% performance in average was shown.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.123-136
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• 2005

We propose a recognition system for superimposed patterns based on selective attention model and SVM which produces better performance than artificial neural network. The proposed selective attention model includes attention layer prior to SVM which affects SVM's input parameters. It also behaves as selective filter. The philosophy behind selective attention model is to find the stopping criteria to stop training and also defines the confidence measure of the selective attention's outcome. Support vector represents the other surrounding sample vectors. The support vector closest to the initial input vector in consideration is chosen. Minimal euclidean distance between the modified input vector based on selective attention and the chosen support vector defines the stopping criteria. It is difficult to define the confidence measure of selective attention if we apply common selective attention model, A new way of doffing the confidence measure can be set under the constraint that each modified input pixel does not cross over the boundary of original input pixel, thus the range of applicable information get increased. This method uses the following information; the Euclidean distance between an input pattern and modified pattern, the output of SVM, the support vector output of hidden neuron that is the closest to the initial input pattern. For the recognition experiment, 45 different combinations of USPS digit data are used. Better recognition performance is seen when selective attention is applied along with SVM than SVM only. Also, the proposed selective attention shows better performance than common selective attention.

• Journal of the Korean Graphic Arts Communication Society
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• v.21 no.2
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• pp.43-53
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• 2003

As information-communication network has been developed rapidly, internet users' circumstances also have been changed for the better, in result, more information can be applied than before. At this moment, there are many differences between real color and reappeared color on the CRT. When we observe a material object, our eyes perceive the multiplied form of light sources and nature spectral reflection. However, when the photographed signal is reappeared, illumination at that time of photographing and spectral reflection of a material object are converted into signal, and this converted RGB signal is observed on the CRT under another illumination. At this time, RGB signal is the reflected result of illumination at that time of photographing Therefore, this signal is influenced by the illumination at present, so it can be perceived another color. To accord the colro reflections of another color source, the study has been reported by S.C.Ahn$^{[1]}$, which study is about the color reapperarance system using neuron network. Furthermore, color reappearing method become independent of its circumstances has been reported by Y.Miyake$^{[2]}$. This method can make the same illuminations even if the observe circumstances are changed. To assume the light sources of observe circumstances, the study about color reappearing system using CCD sensor also have been studied by S.C.Ahn$^{[3]}$. In these studies, a population is fixed, first, on ab coordinates of CIE L${\ast}$a${\ast}$b${\ast}$. Then, color reappearing can be possible using every population and existing digital camera. However, the color is changed curvedly, not straightly, according to value's changes on the ab coordinates of CIE L${\ast}$a${\ast}$b. To solve these problems in this study, first of all, Labeling techniques are introduced. Next, basis color-it is based on Munsell color system-is divided into 10 color fields. And then, 4 special color- skin color, grass color, sky color, and gray-are added to the basis color. Finally, 14 color fields are fixed. After analyzing of the principle elements of new-defined-color fields' population, utility value and propriety value are going to be examined in 3-Band system from now on.

• Korean Journal of Cognitive Science
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• v.25 no.4
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• pp.343-384
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• 2014

In order to unravel the problems of the mind, today's cognitive science has expanded its perspective from the narrow framework of the past computer model or neuronal network model to the wider frameworks of interaction with the brain in interaction with the body in interaction with their environments. The theories of 'the extended mind', 'embodied mind', or 'enactive mind' appeared through such processes are working on a way to move into the environments while the problem to unravel the complex process of interactions between the mind, the body and the environments are left alone. This problem can be traced back as far as to Gibson and Maturana & Varela who tried at first to unravel the problem of the mind in terms of interaction between the brain, the body and there environments in 1960~70s. It's because Gibson stressed the importance of the 'affordance' provided by the environment while Maturana & Varela emphasized the 'autonomy' of auto-poiesis of life. However, it will be proper to say that there are invariants in the affordances provided by the environment as well as the autonomy of life in the state of structural coupling of the environment's variants and life's openness toward the environment. In this case, the confrontational points between Gibson and Maturana & Varela will be resolved. In this article, I propose Benjamin's theory of mimesis as a mediator of both theories. Because Benjamin's concept of mimesis has the process of making a constellation of the embodiment of the affordance and the enaction of new affordance into the environment at the same time, Gibson's concept of the affordance and Maturana & Varela's concept of embodiment and enaction will be so smoothly interconnected to circulate through the medium of Benjamin's concept of mimesis.

• Journal of IKEEE
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• v.18 no.4
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• pp.593-602
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• 2014

One of the purposes of an artificial neural netowrk(ANNet) is to implement the largest number of functions as possible with the smallest number of nodes and layers. This paper presents a CoreNet which has a multi-leveled input value and a multi-leveled output value with a 2-layered ANNet, which is the basic structure of an ANNet. I have suggested an equation for calculating the capacity of the CoreNet, which has a p-leveled input and a q-leveled output, as $a_{p,q}={\frac{1}{2}}p(p-1)q^2-{\frac{1}{2}}(p-2)(3p-1)q+(p-1)(p-2)$. I've applied this CoreNet into the simulation model 1(5)-1(6), which has 5 levels of an input and 6 levels of an output with no hidden layers. The simulation result of this model gives, the maximum 219 convergences for the number of implementable functions using the cot(${\sqrt{x}}$) input leveling method. I have also shown that, the 27 functions are implementable by the calculation of weight values(w, ${\theta}$) with the multi-threshold lines in the weight space, which are diverged in the simulation results. Therefore the 246 functions are implementable in the 1(5)-1(6) model, and this coincides with the value from the above eqution $a_{5,6}(=246)$. I also show the implementable function numbering method in the weight space.

• Biomolecules & Therapeutics
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• v.22 no.6
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• pp.532-539
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• 2014

Peripheral neuropathy induced by human immunodeficiency virus (HIV) infection and antiretroviral therapy is not only difficult to distinguish in clinical practice, but also difficult to relieve the pain symptoms by analgesics because of the severity of the disease at the later stage. Hence, to explore the mechanisms of HIV-related neuropathy and find new therapeutic options are particularly important for relieving neuropathic pain symptoms of the patients. In the present study, primary cultured embryonic rat dorsal root ganglion (DRG) neurons were used to determine the neurotoxic effects of HIV-gp120 protein and/or antiretroviral drug dideoxycytidine (ddC) and the therapeutic actions of insulin-like growth factor-1 (IGF-1) on gp120- or ddC-induced neurotoxicity. DRG neurons were exposed to gp120 (500 pmol/L), ddC ($50{\mu}mol/L$), gp120 (500 pmol/L) plus ddC ($50{\mu}mol/L$), gp120 (500 pmol/L) plus IGF-1 (20 nmol/L), ddC ($50{\mu}mol/L$) plus IGF-1 (20 nmol/L), gp120 (500 pmol/L) plus ddC ($50{\mu}mol/L$) plus IGF-1 (20 nmol/L), respectively, for 72 hours. The results showed that gp120 and/or ddC caused neurotoxicity of primary cultured DRG neurons. Interestingly, the severity of neurotoxicity induced by gp120 and ddC was different in different subpopulation of DRG neurons. gp120 mainly affected large diameter DRG neurons (> $25{\mu}m$), whereas ddC mainly affected small diameter DRG neurons (${\leq}25{\mu}m$). IGF-1 could reverse the neurotoxicity induced by gp120 and/or ddC on small, but not large, DRG neurons. These data provide new insights in elucidating the pathogenesis of HIV infection- or antiretroviral therapy-related peripheral neuropathy and facilitating the development of novel treatment strategies.