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ABSTRACT This paper presents a new method to characterize the change of microstructure of Styrene-Butadiene-Styrene (SBS) modified asphalt when temperature changes. MATLAB was employed to process microscopic images and to [...]

The functional reliability of the vehicle latch is very important and is determined by the internal mechanism’s motion stability. In this paper, a novel compliant mechanism constructed with a novel compliant joint is applied to the vehicle latch, and multiple mode motions of the mechanism realize the newly addedelectric power release function. The repeatability of mechanism trajectories and phase diagrams of several different periodic motions is creatively regarded as the stability of the compliant mechanism. In this compliant mechanism, the endpoint of the groove-coupler is chosen as a characteristic point and the compliant joint’s spring stiffness kTis taken as the key parameter to obtain trajectories and phase diagram. It is found that all cycle overlapping phase diagrams possess intuitively good repeatability, except in two locally specific regions. Then, Wolf’s numerical calculation method of Lyapunov exponents is utilized to analyze the motion stability of the compliant mechanism. An appropriate range (1.0–1.05 N mm/°) of the joint stiffness kTis obtained, satisfying the stability criterion. Finally, characteristic point trajectory and functional experiments with kT= 1 N mm/° are conducted to verify the motion stability of the compliant mechanism. The method of mechanism motion stability analysis by means of Lyapunov exponents can be applied to other under-actuated mechanisms.

Clinical data are inevitably incomplete, and most knowledge discovery algorithms lack the capability to contend with missing data. Flow-graph confers some distinct advantages in data mining and knowledge discovery. However, flow-graph methodology is not able to comprehensively solve the incomplete data problem. This paper proposes a flow-graph network approach for extracting knowledge from incomplete medical data. The concept of incomplete-medical-diagnosis-flow-graph (IMDFG) was defined. To evaluate the diagnosis rules within the IMDFG, the computing method for the certainty factor and coverage factor are presented. Moreover, the application of flow-graph network can be useful for extracting comprehensibility knowledge from the incomplete medical data. In an illustrative medical example, the clinical diagnosis rules are induced and interpreted in accordance to the generated flow graphs.