• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.516-516
• /
• 2000

We propose a neuron model that is possible to learn three-dimensional movement. The neuron model by imitating structure of a neuron, has the system resemble a neuron. We considered a neuron system based on the arguments, and wished to examine whether the system had reasonable function. Koch, Poggio and Torre believed that inhibition signal would shunt excitation signal on the dendrites. They believed that excitation signal operated input-signals and inhibition did as delayed ones. Thus, they were sure that function for directional selectivity was arisen by the shunting. Koch's concept is so important; therefore, we construct the neuron system with their concept. The neuron system makes the shunting function; thus, the model may have a function for directional selectivity. We initialized the connections and the dendrites by random data, and trained them by the back-propagation algorithm for three-dimensional movement. We made sure the defection of three-dimensional movement in the system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.1
• /
• pp.28-35
• /
• 2012

This paper presents the method for $H_{\infty}$ fuzzy controller design of discrete-time fuzzy Markovian jump systems with state and input time delays. The Takagi and Sugeno fuzzy model is employed to represent a delayed nonlinear system that possesses Markovian jump parameters. A stochastic mode dependent Lyapunov function is employed to analyze the stability and $H_{\infty}$ disturbance attenuation performance of the fuzzy Markovian jump systems with state and input time delays. A sufficient condition for the existence of fuzzy $H_{\infty}$ controller is given in terms of matrix inequalities. Also numerical example is presented to illustrate the efficiency of the proposed design method.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.21 no.4
• /
• pp.62-69
• /
• 2013

Visual demands associated with in-vehicle display usage and text messaging distract a driver's visual attention from the roadway. To minimize eyes-off-the-road demands, voice interaction systems are widely introduced. Under cognitively distracted condition, however, awareness of the operating environment will be degraded although the driver remains oriented to the roadway. It is also know that the risk of inattentive driving varies with age, thus systematic analysis of driving risks is required for the older drivers. This paper aims to understand the age-related driving performance degradation and visual attention changes under auditory cognitive demand which consists of three graded levels of cognitive complexity. In this study, two groups, aged 25-35 and 60-69, engaged in a delayed auditory recall task, so called N-back task, while driving a simulated highway. Comparisons of younger and older drivers' driving performance including mean speed, speed variability and standard deviation of lane position, and gaze dispersion changes, which consist of x-axis and y-axis of visual attention, were conducted. As a result, it was observed that gaze dispersion decreased with each level of demand, demonstrating that these indices can correctly rank order cognitive workload. Moreover, gaze dispersion change patterns were quite consistent in younger and older age groups. Effects were also observed on driving performance measures, but they were subtle, nonlinear, and did not effectively differentiate the levels of cognitive workload.

• Journal of Intelligence and Information Systems
• /
• v.5 no.1
• /
• pp.103-123
• /
• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.