http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Mohammadhassan Behroozi (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
-
Robust Near Optimal Sub-Motions for Differentially-Driven Mobile Robots
Mohammadhassan Behroozi,Khalil Alipour,Behrouz Mashhadi,Samaneh Arabi 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
In the present study, the main concern regarding application requirements was to devise and introduce a motion planning method to guarantee system robustness against uncertainties due to any disturbances or inaccuracies in the system dynamics. The robot motion planning has been divided in two different problems: path planning and velocity planning. In the former, use was made of a odular”path planner, each module consisting of pure displacement and pure rotation. In the latter, the velocity planning problem was transformed into an integrated planning and control one. Care is taken for energy being conserved as much as possible within each module of the path. Since an open-loop optimal control may suffer from the lack of robustness, and owing to the fact that the two-point-boundary-value problem that it leads to may not be solvable, a Neural Network system has been introduced to close the control loop. The Ritz method as a direct approach in the calculus of variations is proposed to train the neural network, thus introducing a robust closed-loop control. In this study, optimization of the sub-motions of the total motion was addressed. Furthermore, a combination of optimal Ritz-based method and neural network was suggested as a robust motion planning approach for differentially-driven mobile robots.
-
Amin Fakhri,Mohammadhassan Behroozi,Fatemeh Alimadadi,Hossein Sadati 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
Obtaining physical reservoir characteristics is extremely important and necessary to determine the correlations, productions and field development. Reservoir characteristics include porosity, permeability, cementation, and the like which are obtained from petrophysic and petrographic analyses. From these properties porosity is the most important static property of petroleum reservoirs that can be used to perceive permeability, fluid behaviors, capillary pressure, and sedimentological interpretations. One of the goals of prediction, accomplished in this paper, is to find out the missed porosity logs to interpret a gas reservoir in the well due to available and suitable petrophysical logs gathered from near wells. In some wells, we cannot measure a number of petrophysical properties whereas wells are maybe washed out or the borehole tools are not available for old wells. Therefore, petroleum geologist should pursue some methods to transfer accessible data into faulty wells. It means that they predict missed data using information which is available in its near wells. For prediction purposes of this property, “esistivity Logs” “amma Ray Log” and “onic Log”will have to be used as input information. The relationships of porosity logs versus the logs mentioned above are absolutely nonlinear. Soft computing methods are one of the powerful approaches used to identify lost data.
-
Ehsan Samadani,Mohammadhassan Behroozi,Amirhossein Shamekhi,Reza Chini 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
In diesel engines, applying design techniques such as computer simulations has become a necessity in view of the fact that these methods can result in small amounts of NOx and SOOT and a reasonable fuel economy. To achieve such a target, multi-objective optimization methodology is a good choice In this paper, this technique is implemented on a closed cycle two-zone combustion model of a DI diesel engine. The combustion model is developed by Matlab programming and validated by a single cylinder Ricardo data obtained from the engine. The main outputs of this model are NOx, SOOT and engine performance. The optimization goal is to minimize NOx and SOOT at the same time while maximizing engine performance. Injection timing, injection duration and AFR (Air-fuel ratio) are selected from engine inputs as design variables. A neural network model of the engine is developed based on model data as an alternative for the complicated and time-consuming combustion model in a wide range of engine operation. Design variables are optimized using GA (Genetic Algorithm). Here, three common algorithms for multi-objective optimization, MOGA, NSGA-II, and SPEA2+ are applied and the results are compared.
-
Reza Chini,Mohammadhassan Behroozi,Amir Hossein Shamekhi,Ehsan Samadani 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
One essential part of automated diagnosis systems for SI engines is due to elements of air path system. The faults occur in this subsystem can result in deviation of air-fuel ratio, which causes increased emissions due to incomplete combustion, misfire and especially loss of power and drivability problems. In this article, a model-based diagnosis system for air-path of an SI engine is constructed. Thus, an adiabatic nonlinear four-state dynamic model of an SI engine is utilized for fault simulations. In the next step, a diagnosis system is designed in the framework of Multilayer Perceptron (MLP) Artificial Neural Network (ANN) classifier. Simulation results show that the constructed diagnosis system for six fault modes considering all three kinds of common faults is applied effectively. In this paper, the Manifold Air Temperature (MAT) sensor, Fuel Injector (FAG) and Throttle Actuator (THAG) faults which comparatively have been evaluated less than other elements in previous relative neural network based works, are also taken into account. As another remarkable aspect of this work, all classes of faults are diagnosed in their full possible over reading (positive) and under reading (negative) ranges.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
