This paper proposes a novel grid-connected to rotor type doubly fed induction machine (DFIM) in which the rotor winding is connected to the grid instead of the stator winding. The size and weight of the stator can be ...
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This paper considers the design of linear time-varying networks and of nonlinear time-invariant networks, the latter being operated in the small-signal mode. In the first part, the design example considered is a netwo...
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This paper considers the design of linear time-varying networks and of nonlinear time-invariant networks, the latter being operated in the small-signal mode. In the first part, the design example considered is a network whose time delay is a prescribed function of time. A quadratic performance criterion 'is formulated and the design is obtained iteratively by steepest descent. The second part of the paper considers the design of a nonlinear timeinvariant network with variable bias sources whose small-signal equivalent network is identical with a given linear time-varying network. Explicit conditions are given under which this can be done.
作者：
LEVINE, GHAWKINS, SGeorge Levine
presently a Vice President of Robert Taggart Inc. holds a B. S. degree in Naval Architecture and Marine Engineering from Webb Institute and a Masters degree in Mechanical Engineering from Northeastern University. Levine is also a registered Professional Engineer in New York State. His experience covers a broad range of technical disciplines for diverse types of marine vehicles including: resistance propulsion stability and control computer aided ship design and ship vulnerability. In addition to analytical studies he has had extensive experience in model and full scale testing of marine vehicles. Seth Hawkins
also a Vice President of Robert Taggart Inc. holds Bachelors degrees in Marine Transportation and Marine Engineering from the State University of New York Maritime College. In addition to having sailed in the deck and engineering departments of merchant and naval ships his experience includes managing and participating in numerous research projects in the areas of ship resistance and propulsion ship maneuverability and control seakeeping underwater acoustics oceanographic survey fleet requirements ship trials hydrofoil design and various aspects of underwater engineering.
Drones have drawn considerable attention as the agents in wireless data collection for agricultural applications, by virtue of their three-dimensional mobility and dominant line-of-sight communication channels. Existi...
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Drones have drawn considerable attention as the agents in wireless data collection for agricultural applications, by virtue of their three-dimensional mobility and dominant line-of-sight communication channels. Existing works mainly exploit dedicated drones via deployment and maintenance, which is insufficient regarding resource and cost-efficiency. In contrast, leveraging existing delivery drones for the data collection on their way of delivery, called delivery drones’ piggybacking, is a promising solution. For achieving such cost-efficiency, drone scheduling inevitably stands in front, but the delivery missions involved have escalated it to a wholly different and unexplored problem. As an attempt, we first survey 514 delivery workers and conduct field experiments; noticeably, the collection cost, which mostly comes from the energy consumption of drones’ piggybacking, is determined by the decisions on package-route scheduling and data collection time distribution. Based on such findings, we build a new model that jointly optimizes these two decisions to maximize data collection amount, subject to the collection budget and delivery constraints. Further model analysis finds it a Mixed Integer Non-Linear Programming problem, which is NP-hard. The major challenge stems from interdependence entangling the two decisions. For this point, we propose Delta, a \(\frac{1}{9+\delta }\)-approximation delivery drone scheduling algorithm. The key idea is to devise an approximate collection time distribution scheme leveraging energy slicing, which transforms the complex problem with two interdependent variables into a submodular function maximization problem only with one variable. The theoretical proofs and extensive evaluations verify the effectiveness and the near-optimal performance of Delta.
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