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This book is about the cooperation of AIAA and IEEE, two major engineering organizations from two distinct focus points of technologies: intelligent aero-engine and electrified aviation. AIAA and IEEE both have their intrinsic needs for each other and their co-working is a must-have in the rest of 21st century. AIAA needs IEEE to become smarter and greener and IEEE needs a much broader scope to enlarge its marketplace and playground. The topics related to AIAA's and IEEE's co-project are highly multi- and inter-disciplinary related and highly goal-oriented. The target audience of this book is IEEE, AIAA members and other related professionals from universities, industries and institutes in the fields of AI-driven smart systems and electric airplanes with the associated new electric aero-engines and mobile aviation electric powers. The key contents When AIAA is Meeting IEEE AIAA vs. IEEE How to interact and what to achieve The mindset analysis of AIAA and IEEE The smarter AIAA The AI - Smart brain, IoT, e-devices The smart sensors for AIAA -scenarios, fabrication, challenges, and testings Electric aviation Versatile, smarter and green The evolution of aero-engines - pistol, gas turbine, electric aero-engine The integration of aero-engines and aero-craft Delta VTOLer and STOL for B787 Rotatable wing and VTOL operation The RDF jet a new electric aero-engine The features: small, light, thrust The architecture: motor, fan, jet The principle: rim driven, Tai Chi fan, duct, and jet.

ABSTRACTWhen the conditions for applying Bellman's principle of optimality hold, the pre‐committed optimal policy derived by dynamic programming at initial time is time consistent, that is, the policy remains to be optimal for any state resulted in at later stages. In multi‐objective optimization with a general separable structure, the pre‐committed optimal policy derived by multi‐objective dynamic programming is time‐consistent in efficiency, that is, the policy derived at initial time remains to be efficient for any possible state at later stages, albeit not time‐consistent in general. However, when a multi‐objective dynamic optimization problem is not separable in the sense of multi‐objective dynamic programming, the derived pre‐committed policy is not time‐consistent in efficiency, as witnessed in the multi‐period mean‐variance portfolio selection problem studied in this paper, thus leading to some irrational decision behaviours. This revealed phenomenon recognizes the importance of the time consistency issue and calls our attentions to construct more suitable decision criteria in multi‐objective optimization. Copyright © 2011 John Wiley & Sons, Ltd.