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. 相似文献
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. 相似文献
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 相似文献
The present work aims to investigate the effect adding Ag, Co, Ni, Cd and Pt to copper on ethanol dehydrogenation. The catalysts synthesized by deposition–precipitation method were characterized using various physicochemical methods such as N2 adsorption–desorption, TPR, SEM–EDX, XRD, XPS and TGA–DSC-MS. Catalytic evaluation results revealed that the predominant product of the reaction was acetaldehyde. Monometallic copper or mixed with Cd, Ag or Co show good catalytic performances. Adding nickel to copper improves the process conversion but reduces acetaldehyde selectivity, giving rise to methane in produced hydrogen. Pt-Cu/SiO2 catalyst guides the reaction towards diethyl ether. Time on stream tests performed during 12 h at 260 °C, showed that adding Cd to Cu enhances its stability by over 30% of conversion, this is explained by the reduction of copper crystallites sintering, which makes Cd-Cu/SiO2 a promising catalyst for the production of acetaldehyde by ethanol dehydrogenation.
Localization is a crucial problem in wireless sensor networks and most of the localization algorithms given in the literature are non-adaptive and designed for fixed sensor networks. In this paper, we propose a learning based localization algorithm for mobile wireless sensor networks. By this technique, mobility in the network will be discovered by two crucial methods in the beacons: position and distance checks methods. These two methods help to have accurate localization and constrain communication just when it is necessary. The proposed method localizes the nodes based on connectivity information (hop count), which doesn’t need extra hardware and is cost efficient. The experimental results show that the proposed algorithm is scalable with a small set of beacons in large scale network with a high density of nodes. The given algorithm is fast and free from a pre-deployment requirement. The simulation results show the high performance of the proposed algorithm. 相似文献
Nowadays, there is a growing need to manage trust in open systems as they may contain untrustworthy service providers. Agent Trust Management (ATM) tries to address the problem of finding a set of the most trusted agents in multi agent systems. This paper presents ScubAA, a novel generic ATM framework based on the theory of Human Plausible Reasoning (HPR). For each user’s request, ScubAA determines a ranked list of the most trusted service agents, within the context of the request, and forwards the request to those trusted services only. ScubAA determines an agent’s degree of trust in terms of a single personalized value derived from several types of evidences such as user’s feedback, history of user’s interactions, context of the submitted request, references from third party users as well as from third party service agents, and structure of the society of agents. ScubAA is able to utilize more trust evidences towards a more accurate value of trust. We also propose a function to figure out how similar two users are in a given context. We apply the proposed HPR-based ATM framework to the domain of Web search. The resulting ATM system recommends to the user a list of the most trusted search engines ranked according to the retrieval precision of documents returned in response to the user’s query as well as the degree of trust of the search engines have gained by interacting with other related users within the context of the query. In addition, we conduct a statistical analysis of ScubAA based on ANOVA and by using a data set of forty queries in different domains. This analysis clearly reveals that ScubAA is able to successfully assess the trustworthiness of service agents. 相似文献
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. 相似文献
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. 相似文献