A novel driver assist stability system for all-wheel-drive electric vehicles

A novel driver-assist stability system for all-wheel-drive electric vehicles is introduced. The system helps drivers maintain control in the event of a driving emergency, including heavy braking or obstacle avoidance. The system comprises a fuzzy logic system that independently controls wheel torque to prevent vehicle spin. Another fuzzy wheel slip controller is used to enhance vehicle stability and safety. A neural network is trained to generate the required reference for yaw rate. Vehicle true speed is estimated by a sensor data fusion method. The intrinsic robustness of fuzzy controllers allows the system to operate in different road conditions successfully. Moreover, the ease of implementing fuzzy controllers gives a potential for vehicle stability enhancement.     

Prediction of ultrasonic wave velocity in particleboard and fiberboard

Ultrasonic wave velocities were determined at parallel and perpendicular to manufacturing direction and at the interval angles of 15° in clockwise and counterclockwise directions of particleboard and fiberboard. The experimental results were compared with the predicted values using some empirical formulae such as Hankinson and Jacoby equations. The results showed that the ultrasonic wave velocity were the highest in parallel direction in particleboard and fiberboard and decreases with increase of angle and the lowest values occurred in perpendicular direction. The predicted ultrasonic velocity using Hankinson and Jacoby equations are in close agreement with the measured values. Relationship between ultrasonic wave velocities and particles and fibers angle could be successfully presented by cubic and quadratic regression equations as well.     

Reliability assessment of an automated distribution system

Automation can greatly enhance distribution-network reliability by speeding up service restoration and thus significantly reduce customer-outage time. The paper presents an approach to assess quantitatively the adequacy of a particular automated distribution scheme designated as the `low interruption system' (LIS). Owing to the use of a high-speed communication system and line sensors, this automated scheme can reduce drastically the number of interruptions, the service interruption time and also the area affected by the fault. This scheme provides a simple and cost-effective way to automate distribution systems in which the remotely controlled switches speed up isolation of faulted sections and the restoration of healthy sections through alternative routes. The step-by-step calculation procedure is presented using a typical small automated distribution system. The proposed technique is then applied to a larger distribution system to examine the effectiveness of the technique and also to examine the level of reliability improvement achieved by automation     

Development and characterization of magnetorheological polymer gels

Magnetorheological materials have been used in many applications in recent years. To develop new materials, polyurethane and silicone polymer gels were investigated. Rheology is qualitatively controlled for each system by controlling the concentration of reactants and diluents. The resulting polymers have solid, gel, or liquid states, depending on the crosslinking and dilution. The gels were characterized through kinetic analysis. Differential scanning calorimetry (DSC) was used with analysis methods to find the kinetic properties for diluted and undiluted polyurethane systems. Heat of reaction, order of reaction, preexponential constant, and activation energy were obtained from the experimental DSC data. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 84: 2733–2742, 2002     

Contextual void patching for digital elevation models

Digital terrain models can be created by gathering a set of measurements from geometric objects. For various reasons, these models may be incomplete and thus fail to meet the requirements defined by their potential applications. In this work, we develop a novel multiresolution approach to repair the voids commonly found in digital elevation models (DEM). We use the overall shape and structure of the surrounding terrain to build a smooth patch for the void. Then, using a multiresolution approach obtained from reverse Chaikin subdivision, we extract the low-scale characteristics from the surrounding terrain and apply them to the smooth patch. The results demonstrate that our approach is effective in synthesizing models with realistic characteristics.     

Applying dual-tree complex discrete wavelet transform and gamma modulating function for simulation of ground motions

The aim of this paper is to develop a stochastic-parametric model for the generation of synthetic ground motions (GMs) which are in accordance with a real GM. In the proposed model, the dual-tree complex discrete wavelet transform (DT-CDWT) is applied to real GMs to decompose them into several frequency bands. Then, the gamma modulating function (GMF) is used to simulate the wavelet coefficients of each level. Consequently, synthetic wavelet coefficients are generated using extracted model parameters and then synthetic GM is extracted by applying the inverse DT-CDWT to synthetic wavelet coefficients. This model simulates the time–frequency distribution of both wide-frequency and narrow-frequency bandwidth GMs. Besides being less time consuming, it simulates several dominant frequency peaks at any moment in the time duration of GM, because each frequency band is separately simulated by the gamma function. Moreover, the inelastic response spectra of synthetic GMs generated by the proposed model are a good estimate of target ones. Using the random sign generator in the proposed model, it is possible to generate any number of synthetic GMs in accordance with a recorded one. Because of these advantages, the proposed model is suitable for using in performance-based earthquake engineering.

     

Direct adaptive fuzzy control with membership function tuning

In this paper, a direct self‐structured adaptive fuzzy control is introduced for the class of nonlinear systems with unknown dynamic models. Control is accomplished by an adaptive fuzzy system with a fixed number of rules and adaptive membership functions. The reference signal and state errors are used to tune the membership functions and update them instantaneously. The Lyapunov synthesis method is also used to guarantee the stability of the closed loop system. The proposed control scheme is applied to an inverted pendulum and a magnetic levitation system, and its effectiveness is shown via simulation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Long‐term water uptake behavior of natural fiber/polypropylene composites

Composites of different natural fibers and polypropylene were prepared and their long‐term water absorption behaviors were studied. Wood flour, rice hulls, newsprint fibers, and kenaf fibers (at 25 and 50% by weight contents) were mixed with polypropylene and 1 and 2% compatibilizer, respectively. Water absorption tests were carried out on injection‐molded specimens at room temperature for 5 weeks. Measurements were made every week and water absorption was calculated. Water diffusion coefficients were also calculated by evaluating the water absorption isotherms. Results indicated a significant difference among different natural fibers, with kenaf fibers and newsprint fibers exhibiting the highest and wood flour and rice hulls the lowest water absorption values, respectively. The difference between 25 and 50% fiber contents for all composite formulations increased at longer immersion times. Water diffusion coefficients of the composites were found to be about 3 orders of magnitude higher than that of pure PP. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci, 2006     

An improved Differential Evolution algorithm using learning automata and population topologies

A new variant of Differential Evolution (DE), called ADE-Grid, is presented in this paper which adapts the mutation strategy, crossover rate (CR) and scale factor (F) during the run. In ADE-Grid, learning automata (LA), which are powerful decision making machines, are used to determine the proper value of the parameters CR and F, and the suitable strategy for the construction of a mutant vector for each individual, adaptively. The proposed automata based DE is able to maintain the diversity among the individuals and encourage them to move toward several promising areas of the search space as well as the best found position. Numerical experiments are conducted on a set of twenty four well-known benchmark functions and one real-world engineering problem. The performance comparison between ADE-Grid and other state-of-the-art DE variants indicates that ADE-Grid is a viable approach for optimization. The results also show that the proposed ADE-Grid improves the performance of DE in terms of both convergence speed and quality of final solution.     

Perceiving,learning, and exploiting object affordances for autonomous pile manipulation

Autonomous manipulation in unstructured environments will enable a large variety of exciting and important applications. Despite its promise, autonomous manipulation remains largely unsolved. Even the most rudimentary manipulation task—such as removing objects from a pile—remains challenging for robots. We identify three major challenges that must be addressed to enable autonomous manipulation: object segmentation, action selection, and motion generation. These challenges become more pronounced when unknown man-made or natural objects are cluttered together in a pile. We present a system capable of manipulating unknown objects in such an environment. Our robot is tasked with clearing a table by removing objects from a pile and placing them into a bin. To that end, we address the three aforementioned challenges. Our robot perceives the environment with an RGB-D sensor, segmenting the pile into object hypotheses using non-parametric surface models. Our system then computes the affordances of each object, and selects the best affordance and its associated action to execute. Finally, our robot instantiates the proper compliant motion primitive to safely execute the desired action. For efficient and reliable action selection, we developed a framework for supervised learning of manipulation expertise. To verify the performance of our system, we conducted dozens of trials and report on several hours of experiments involving more than 1,500 interactions. The results show that our learning-based approach for pile manipulation outperforms a common sense heuristic as well as a random strategy, and is on par with human action selection.