This paperpresents an optimal vehicle and seat suspension design for a half-car vehicle model to reduce human-body vibration (whole-body vibration). Agenetic algorithm is applied to search for the optimal parameters of the seat and vehicle suspension. The desired objective is proposed as the minimization of a multi-objectivefunction formed by the combination of seat suspension working space (seat suspension deflection), head acceleration, and seat mass acceleration to achieve the best comfort of the driver. With the aid of Matlab/Simulinksoftware, a simulation model isachieved. In solving this problem, the genetic algorithms have consistently found near-optimal solutions within specified parameters ranges for several independent runs. For validation, the solution obtained by GA was compared to the ones of the passive suspensions through sinusoidal excitation of the seat suspension system for the currently used suspension systems.
Several studies have been carried on sign language recognition systems, however, practically deployable system for real-time use is still a challenge. Traditionally, sign language recognition systems have either used sensor gloves or digital cameras to acquire and process hand gestures. Both approaches exhibit some disadvantages for real time deployment that hindered it large scale adoption. With the growth witnessed in gaming systems, two new instruments have been introduced namely, the Microsoft Kinect (MK) and the leap motion controller. The MK system has been developed to interact with video games by tracking full body movements and gestures. To overcome some of the disadvantages of the classical methods, we propose here to develop an Arabic sign language recognition system based on MK system. The developed system was tested with 20 signs from the Arabic sign language dictionary. Therefore,in this paper, we present our experiment carried out using the MK setup on 20 Arabic sign language words. Video samples of both true color images and depth images were collected from native deaf signer. Linear Discriminant analysis was used for feature dimension reduction and sign classification. Furthermore, fusion from RGB and depth sensor was carried at feature and decision level giving an overall best accuracy of 99.8%.
A technique f o r assigning the eigenvalues of a system to any desired position through selecring rhe state weighring matrix Q) of a lineor quadratic regiilator (LQR) is presented. This method is capnble of ylncing both r e d arid complex eigenvalues to any desired location Mv'thout distrirbitig the rest of the eigenvalues.
This paper combined artificial neural network and regression modeling methods to predict electrical load. We propose an approach for specific day, week and/or month load forecasting for electrical companies taking into account the historical load. Therefore, a modified technique, based on artificial neural network (ANN) combined with linear regression, is applied on the KSA electrical network dependent on its historical data to predict the electrical load demand forecasting up to year 2020. This technique was compared with extrapolation of trend curves as a traditional method (Linear regression models). Application results show that the proposed method is feasible and effective. The application of neural networks prediction shows the capability and the efficiently of the proposed techniques to obtain the predicting load demand up to year 2020.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.