Slow eye movement as a possible predictor of reaction delays to auditory warning alarms in a drowsy state

In recently developed intelligent vehicles, warning alarms are often used to prompt avoidance behaviours from drivers facing imminent hazardous situations. However, when critical reaction delays to auditory stimulation are anticipated, the alarm should be activated earlier to compensate for such delays. It was found that reaction times to an auditory stimulus significantly increased in the presence of slow eye movement (SEM), which is known to occur frequently during the wake-sleep transition. The reaction delay could not be attributed to temporal effects such as fatigue and was invariant regardless of response effectors (finger or foot). Moreover, it was found that applied pedal force decreased immediately after an auditory stimulus interrupted SEM. Consequently, it was concluded that SEM can be a good predictor of reaction delays to auditory warning alarms when drivers are in a drowsy state. STATEMENT OF RELEVANCE: The present study demonstrated that simple auditory reaction time significantly increased when SEM emerged. In the design of vehicle safety systems using warning alarms to prompt avoidance behaviours from drivers, such reaction delays during SEM must be taken into account.     

A global structural optimization technique using an interval method

This paper deals with a global optimization scheme for structural systems that require finite element analysis to evaluate the constraints or the objective function. The paper proposes a strategy for finding the global optimum using an interval method in conjunction with a multipoint function approximation. The highly nonlinear and nonconvex objective and constraint functions are first represented in the design space using linear and adaptive local approximations and these approximations are blended globally with the use of proper weighting functions. The interval method is then employed to trace the global optimum in the approximated function space. The procedure is tested with several examples with known global solutions and it is successfully applied to optimize the fiber-orientation angles of laminated composite plates for minimum deflections. Received December 22, 2000     

Empirical data modeling in software engineering using radial basisfunctions

Many empirical studies in software engineering involve relationships between various process and product characteristics derived via linear regression analysis. We propose an alternative modeling approach using radial basis functions (RBFs) which provide a flexible way to generalize linear regression function. Further, RBF models possess strong mathematical properties of universal and best approximation. We present an objective modeling methodology for determining model parameters using our recent SG algorithm, followed by a model selection procedure based on generalization ability. Finally, we describe a detailed RBF modeling study for software effort estimation using a well-known NASA dataset     

Real-time transport of MPEG video with a statistically guaranteedloss ratio in ATM networks

Unlike deterministic real-time communication in which excessive resources may be required for “absolute” performance guarantees, statistical real-time communication seeks to achieve both probabilistic performance guarantees and efficient resource sharing. This paper presents a framework for statistical real-time communication in ATM networks, providing delay-guaranteed transport of MPEG-coded video traffic with a statistically-guaranteed cell-loss ratio. Delay-guaranteed communication is achieved with a modified version of Traffic-Controlled Rate-Monotonic Priority Scheduling (TCRM). A set of statistical real-time channels that share similar traffic characteristics are multiplexed into a common macrochannel. Those statistical real-time channels which are multiplexed together share the resources of a macrochannel, and individual statistical real-time channels are given timeliness and probabilistic cell-loss guarantees. A macrochannel is serviced by the modified TCRM which improves link utilization and makes channel management simpler. Based on the analysis of an M/D/1/N queueing system, we propose a procedure for determining the transmission capacity of a macrochannel necessary to statistically guarantee a cell-loss ratio bound. Our extensive trace-driven simulation has shown the superiority of the proposed framework to the other approaches. The overall cell-loss ratios for multihop statistical real-time channels are shown to be smaller than the predetermined bounds, thus verifying our analytical results     

Reconfigurable Service Composition and Categorization for Power-Aware Mobile Computing

The requirement of agile adaptation to varying resource constraints in mobile systems motivates the use of a service-oriented architecture (SOA), which can support the composition of two or more services to form a complex service. In this paper, we propose SOA-based middleware to support QoS control of mobile applications and to configure an energy-efficient service composition graph. We categorize services into two layers:functionality-centric services, which are connected to create a complex service to meet the user's intentions, and resource-centric services, which undertake distributed functionality-centric services in a way that increases the success rate of service composition while reducing contention at specific service nodes. We also present a service routing algorithm to balance the resource consumption of service providers on a service-overlay network. Through simulation of power-aware service composition using a realistic model based on ns-2 and traced data, we demonstrate that our approach can help both the mobile devices and the servers in a service-overlay network to reduce energy consumption without an increase in response time.     

Learning type PID control system using input dependence reinforcement scheme

PID control has widely used in the field of process control and a lot of methods have been used to design PID parameters. When the characteristic values of a controlled object are changed due to a change over the years or disturbance, the skilled operators observe the feature of the controlled responses and adjust the PID parameters using their knowledge and know-how, and a lot of labors are required to do it. In this research, we design a learning type PID control system using the stochastic automaton with learning function, namely learning automaton, which can autonomously adjust the control parameters updating the state transition probability using relative amount of controlled error. We show the effectiveness of the proposed learning type PID control system by simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Interactive classification for pre-integrated volume rendering of high-precision volume data

The pre-integrated volume rendering technique is widely used for creating high quality images. It produces good images even though the transfer function is nonlinear. Because the size of the pre-integration lookup table is proportional to the square of data precision, the required storage and computation load steeply increase for rendering of high-precision volume data. In this paper, we propose a method that approximates the pre-integration function proportional to the data precision. Using the arithmetic mean instead of the geometric mean and storing opacity instead of extinction density, this technique reduces the size and the update time of the pre-integration lookup table so that it classifies high-precision volume data interactively. We demonstrate performance gains for typical renderings of volume datasets.     

Suboptimal filter for continuous‐time linear systems with unknown parameters

The filtering problem for continuous‐time linear systems with unknown parameters is considered. A new suboptimal filter is herein proposed. It is based on the optimal mean‐square linear combination of the local Kalman filters. In contrast to the optimal weights, the suboptimal weights do not depend on current observations; thus, the proposed filter can easily be implemented in real‐time. Examples demonstrate high accuracy and efficiency of the suboptimal filter. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society