Analog CPPN-based optimal road boundary detection algorithm for autonomous vehicle is proposed. The CPPN is a massively connected analog parallel array processor. In the paper, the dynamic programming which is an efficient algorithm to find the optimal path is implemented with the CPPN algorithm. If the image of road-boundary information is utilized as an inter-cell distance, and goals and start lines are positioned at the top and the bottom of the image, respectively, the optimal path finding algorithm can be exploited for optimal road boundary detection. By virtue of the parallel and analog processing of the CPPN and the optimal solution of the dynamic programming, the proposed road boundary detection algorithm is expected to have very high speed and robust processing if it is implemented into circuits. The proposed road boundary algorithm is described and simulation results are reported.

In this paper, a detection technique of initial rotor position at a standstill in Toroidal Switched Reluctance Motor(TSRM) with built-in interior central pole search coil is proposed. In case search coils are used as a position sensor, it has many advantages like low cost, decrease in the volume, high robust characteristics and wide applications. However, the initial rotor position detection is very difficult because the search coil's EMF is not exist at a standstill. In this paper, a new detection method of initial rotor position is suggested using interior central pole search coil. The simulation and experiment for the proposed method and their results support the validity of the proposed scheme by verifying the operation of a 6/4 TSRM using search coils.

The descriptor system model has a high ability in representing dynamical systems. It can preserve physical parameters in the coefficient matrices, and describe the dynamic part, static part, and even the improper part of the system in the same form. The design of mixed $H_{2}/H_{\infty}$ controllers for linear time-invariant descriptor systems is considered in this paper. Firstly, an $H_2$ and $H_{\infty}$ synthesis problems fur a descriptor system are presented separately in terms of linear matrix inequalities (LMIs) based on the bounded real lemma. Then, we show that the existence of a mixed $H_2/H_{\infty}$ controller by which the $H_2$ norm of the second channel is minimized while keeping the $H_2$ norm bound of the first channel less than ${\gamma}$, is reduced to the linear objective minimization problem. The class of desired controllers that are assumed to have the same structure as the plant is parameterized by using the linearizing change of variables.

This paper considers a hybrid PWM(pulse width modulation) method which can be used in the brushless DC motor controller. Due to many disadvantages of bipolar PWM method, unipolar PWM method is mostly used in industrial field. In constant speed control application, the unipolar PWM method shows the good performance of speed control. But in the wide range of speed control application, it shows poor performance especially when deceleration is needed. So we propose the hybrid PWM method that utilizes both of bipolar and unipolar PWM methods according to the sign of the speed controller output. Simulation and experimental result show that the proposed method improves speed control performance of the brushless DC motor which is applied to the industrial sewing machines.

In this paper, a coordinated scheduling algorithm is proposed to reduce end-to-end delay in distributed control of systems. For the algorithm, the analysis of practical end-to-end delay in the worst case is performed priory with considering implementation of the systems. The end-to-end delay is composed of the delay caused by multi-task scheduling of operating systems, the delay caused by network communications, and the delay caused by asynchronous timing between operating systems and network communications. Through some simulation tests based on CAN(Controller Area Network), the proposed worst case end-to-end delay analysis is validated. Through the simulation tests, it is also shown that a real-time distributed control system designed to existing worst case delay cannot guarantee end-to-end time constraints. With the analysis, a coordinated scheduling algorithm is proposed here. The coordinated scheduling algorithm is focused on the reduction of the delay caused by asynchronous timing between operating systems and network communications. Online deadline assignment strategy is proposed for the scheduling. The performance enhancement of the distributed control systems by the scheduling algorithm is shown through simulation tests.

This paper proposes an image processing algorithm for detecting obstacles on road using DLI(disparity of lane-related information) that is generated by stereo images acquired from dual cameras mounted on a moving vehicle. The DLI is a disparity that is acquired using a single lane information from road lane detection. For the purpose to reduce processing time, we use small block of edge-histogram based blocking logic. This algorithm detects moving objects such as preceding vehicles and obstacles. The proposed algorithm has been implemented in a personal computer with the road image data of a typical highway. We successfully performed experiments under a wide variety of road conditions without changing parameter values or adding human intervention. Experimental results also showed that the proposed DLI is quite successful.

For real world applications of reinforcement learning techniques, function approximation or generalization will be required to avoid curse of dimensionality. For this, an improved function approximation-based reinforcement teaming method is proposed to speed up convergence by using CA-CMAC(Credit-Assigned Cerebellar Model Articulation Controller). To show that our proposed CACRL(CA-CMAC-based Reinforcement Learning) performs better than the CRL(CMAC- based Reinforcement Learning), computer simulation and experiment results are illustrated, where a swing-up control Problem of an acrobot is considered.

A classifier was constructed by using a generalized regression neural network (GRU) and random generator (RG), which was applied to classify DNA sequences. Three data sets evaluated are eukaryotic and prokaryotic sequences (Data-I), eukaryotic sequences (Data-II), and prokaryotic sequences (Data-III). For each data set, the classifier performance was examined in terms of the total classification sensitivity (TCS), individual classification sensitivity (ICS), total prediction accuracy (TPA), and individual prediction accuracy (IPA). For a given spread, the RG played a role of generating a number of sets of spreads for gaussian functions in the pattern layer Compared to the GRNN, the RG-GRNN significantly improved the TCS by more than 50%, 60%, and 40% for Data-I, Data-II, and Data-III, respectively. The RG-GRNN also demonstrated improved TPA for all data types. In conclusion, the proposed RG-GRNN can effectively be used to classify a large, multivariable promoter sequences.

A new method is presented to construct a classifier. This was accomplished by combining a generalized regression neural network (GRNN) and a genetic algorithm (GA). The classifier constructed in this way is referred to as a GA-GRNN. The GA played a role of controlling training factors simultaneously. The GA-GRNN was applied to classify 4 different Promoter sequences. The training and test data were composed of 115 and 58 sequence patterns, respectively. The classifier performance was investigated in terms of the classification sensitivity and prediction accuracy. Compared to conventional GRNN, GA-GRNN significantly improved the total classification sensitivity as well as the total prediction accuracy. As a result, the proposed GA-GRNN demonstrated improved classification sensitivity and prediction accuracy over the convention GRNN.

In this paper, we proposed a new method of modeling a neuro-fuzzy system using a hybrid clustering algorithm. The initial parameters and the number of clusters of the proposed system are optimally chosen simultaneously with respect to the process of regression, which is a unique characteristics of the proposed system. The proposed algorithm presented in this work improves the overall performance of the proposed a neuro-fuzzy system by choosing a proper number of clusters adaptively according the characteristics of given data. The process of clustering is performed by deciding on the number of classes, which yields the property of convergence of the system. In experiments, the superiority of the proposed neuro-fuzzy system is demonstrated, especially the process of optimizing parameters and clustering of learning speed.