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An efficient echo simulation algorithm considering topography in time domain for bistatic SAR is proposed in this paper. By using subaperture processing and equivalent scatter, this method can implement efficient echo simulation. At last, the effectiveness of this method is verified by numerical simulation.

Structural analysis for fault detection and isolation (FDI) is an efficient method for designing structured residual generators for model-based diagnosis. It generates a number of diagnostic test candidates, whose number may be large in case of complex systems. This paper proposes a method for selecting residual generators from a possibly large number of candidate equation sets using a diagnosability index that measures the degree of diagnosability in a diagnostic system. The selection process of residual generators is aimed at reducing the solution sets for achieving the optimal diagnostic performance, i.e, maximizing the detectability and isolability of various faults. The approach for selecting residual generators is applied to a permanent magnet synchronous machine (PMSM) drive system in an electrified vehicle to show that the proposed methodology is effective in automatically downsizing the candidate equation sets for diagnostic tests from a relatively larger number of choices derived from the structural analysis algorithms.

Torque security is an important element for future automobile development such as for electric and and hybrid electric vehicles (EV/HEVs). For Drive-by-Wire systems that are commonly used in modern vehicles, pedal position signals are critical for ensuring correct calculation of torque request to the rest of the powertrain. This paper develops a model based fault diagnostic scheme for electrified vehicle powertrain torque security problems, with special focus on pedal position sensor faults. After the design of the diagnostic strategy, a fault tolerant control strategy is proposed to mitigate the effect of the faults. The torque security study is applied to a case study of a prototype vehicle developed by the OSU EcoCAR2 team. The diagnostic and fault tolerant control scheme are tested and validated through Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL) simulations.

Fault diagnosis and fault tolerant control are essential to guarantee safety and continuity of operation for HEV/EVs. The electric drive system, which is a key subsystem in electric vehicles, requires a reliable and robust diagnosis system to detect various faults that may occur during operation. This paper proposes a diagnostic strategy based on structural analysis for an electric vehicle driven by a permanent magnet synchronous machine (PMSM). Structural analysis analyzes the structural model represented by Incidence Matrix of a system, using graphical tools to find the analytic redundancy of the model where it is possible to generate residuals. Structural analysis is useful for pre-analysis of fault detectablity and isolability properties without accurate knowledge of system parameters. This paper shows that by properly selecting sequential residual generators, structural analysis approach is effective to identify and isolate multiple sensor faults in the PMSM drive system.

In order to protect legitimate vehicles from cheating by spurious alert messages, we propose a general filter model for Vehicular Ad-hoc Network (VANET) to distinguish spurious messages from valid ones. It is a two-layer filter, the coarse filter is responsible for rapid filtration and the fine filter is for accurate filtration. The data flow should pass through them to get the classification results. The coarse filter makes a judgment by combining several sources of information such as timeliness of the report and correlation of the accident location while the fine filter is based on Back Propagation Neural Network (BPNN) which includes an incremental learning part. The BPNN module refers to vehicles' reputations and behaviors in response to an event, and the support from neighbors will also be a great help. In this paper, we compare the filtering effect of incremental BPNN with several commonly used decision logics including majority voting, weighted voting and Bayesian method. The simulation results show that our scheme performs better both in filtering reliability and stability.

A PDM-based system for manufacturing process analysis and optimization is presented in this paper. The system consists of a standard (commercial) CAD/CAE/CAM system integrated with a PDM (product data management) module. The resulting system is then augmented with additional virtual models, some of which are available commercially and others developed by the authors. The final system allows for the simulation and evaluation of engineering products and systems that involve both mechanical and electrical components. The simulation is more comprehensive allowing for an evaluation that is more accurate and reliable than one achieved using existing state-of-the-art CAD/CAE/CAM/PDM systems. In particular, the extended virtual manufacturing capabilities allow for an accurate prediction of the different machining process parameters. The details of the development, as well as the work accomplished to date to test and validate the extended machining modeling and simulation capabilities, are presented.

Presents a dynamic equilibrium principle for running a CDEES (complex dynamic equilibrium enterprise system) more efficiently. A framework for the dynamic equilibrium system (DES) is described. Two concepts called DEM (dynamic equilibrium module) and OPES (one person enterprise system) are presented based on that theory, and then their characteristics and applications are discussed. Algorithms for capability evaluation and equilibrium functional deployment for dynamic systems are presented. Dynamic contradiction/bottleneck and efficiency analysis methods are presented in the latter part of this paper. The present research works (for the project of Beijing Renmin Printing Machine Inc.'s computer integrated manufacturing system BR-CIMS) of the author are briefly described.