In this paper, we considered the design of gain scheduled controllers for linear systems with saturating actuators. Our basic idea is to design a control that uses higher control gain when the states are smaller, and lower gain when it is higher. By doing this, we can avoid the saturation and we can improve the performance. First, we derive a control and a reachable set expressed as LMI form, which minimizes not only the L$_2$ gain from the disturbance to the measured output but also the control is never saturated within this reachable set. Next, the reachable set is divided as nested subsets, and at each nested subset, the control gain is designed to minimize the L$_2$ gain and it is never saturated. Finally, the control gain is scheduled according to the status of states, i.e., the subset in which the states are located. A numerical example is presented to show that our gain scheduled control significantly improves the performance.

A robust controller for large space structures(LSS) is studied from passivity point of view. While velocity sensors are commonly used for proportional-derivative (PD) control law to stabilize large space structures, if the structure can be controlled without velocity measurements, it is desirable against the failure of velocity sensors and for the cost reduction of the sensing system. In a recent result a dynamic output feedback control law has been provided using only displacement measurements. This paper presents a passivity-based controller design method and provides an alternative stability analysis tool for the previous displacement feedback robust control law. The closed-loop system can be viewed as a feedback interconnection of a passivated large space structure (LSS) and a strictly positive real (SPR) system.

This paper describes a recognition method of a road direction for an autonomous vehicle based on magnetic sensors. Using the sensors mounted on a vehicle and the magnetic markers embedded along the center of road, the autonomous vehicle can recognize a road direction and control a steering angle. Using the front lateral deviation of a vehicle and the rear one, the road direction is calculated. The analysis of magnetic field, the acquisition technique of training data, the training method of neural network and the computer simulation are presented. According to the computer simulation, the proposed method is simulated, and its performance is verified. Also, the experimental test is confirmed its reliability.

Reinforcement learning (RL) has been widely used as a learning mechanism of an artificial life system. However, RL usually suffers from slow convergence to the optimum state-action sequence or a sequence of stimulus-response (SR) behaviors, and may not correctly work in non-Markov processes. In this paper, first, to cope with slow-convergence problem, if some state-action pairs are considered as disturbance for optimum sequence, then they no to be eliminated in long-term memory (LTM), where such disturbances are found by a shortest path-finding algorithm. This process is shown to let the system get an enhanced learning speed. Second, to partly solve a non-Markov problem, if a stimulus is frequently met in a searching-process, then the stimulus will be classified as a sequential percept for a non-Markov hidden state. And thus, a correct behavior for a non-Markov hidden state can be learned as in a Markov environment. To show the validity of our proposed learning technologies, several simulation result j will be illustrated.

A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

In this paper, we designed the PC-based analyzer of the power spectral density that could estimate the heart rate variability from time series data of R-R interval. The power spectral density estimated that it applied the autoregressive model to the measured electrocardiogram during a short period. Also, the characteristics of the designed analyzer are that it could process of the signal filtering, the generation and recomposition of time series and the feature extraction at the same time. Especially the analyzer reconstructed which applied the lowpass filter of the time series composed by the linear interpolation so as to enhance the signal-to-noise feature. We could estimate the power spectral density that confirmed a variety of power peak with low frequency range and high frequency rang of autonomic nerve by the heart rate variability.

In this paper the possibility of snorer-dependent snore recognition using cepstral coefficients was suggested. We assumed that snore and speech sounds have some similarities and we used cepstral coefficients which are widely used for speech recognition. Snoring data were acquired from 18 persons including 5 patients diagnosed as snore patient. To evaluate the performance of proposed method, the distance ratio based on LPC cepstral coefficients was selected as an index for snorer-dependent snore recognition. As a result, distance ratio of 3 was selected as optimal value showing the most efficient snorer-dependent snore recognition, which is high accuracy of 95.05％ on average. In conclusion, the proposed method showed the possibilities to be applied in clinical applications for snorer-dependent snore recognition.

EMG recordings of the electrical activity of muscle have proved to be a valuable tool in studying muscle function and reflex activity. SP(silent period) is elicited by a electrical stimulation on the chin during isometric contraction of jaw muscle. This paper proposes a model of the inhibitory reflex mechanism of jaw muscle after electrical stimulation. The SPs of jaw muscle after a electrical stimulation to the chin were divided into SP1 and SP2. SP1 is produced by the activation of periodontal receptors. The activation of nociceptors contributes to the SP2. As a result, the EMG signal generated by a proposed a model of inhibitory reflex mechanism is similar to real both EMG signal including SP1 and SP2. The present study have shown differences of SP1 and SP2 induced by inhibitory reflex mechanism.