41,619 research outputs found
Water-pumping permanent magnet synchronous motor optimization based on customized torque-speed operating area and performance characteristics
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents a novel methodology for optimizing Permanent Magnet Synchronous Motors for Water-Pumping applications. The algorithm is designed to start the optimization process from a predefined torque-speed area, its desired envelope, and the performance characteristics of the motor to be obtained after the optimization process, providing the information in an efficiency map, according to a predefined control strategy (MTPA, MTPV, etc.). This work also implements an image comparison technique based on the structural similarity index to evaluate the objective function.Peer ReviewedPostprint (author's final draft
Macroservers: An Execution Model for DRAM Processor-In-Memory Arrays
The emergence of semiconductor fabrication technology allowing a tight coupling between high-density DRAM and CMOS logic on the same chip has led to the important new class of Processor-In-Memory (PIM) architectures. Newer developments provide powerful parallel processing capabilities on the chip, exploiting the facility to load wide words in single memory accesses and supporting complex address manipulations in the memory. Furthermore, large arrays of PIMs can be arranged into a massively parallel architecture. In this report, we describe an object-based programming model based on the notion of a macroserver. Macroservers encapsulate a set of variables and methods; threads, spawned by the activation of methods, operate asynchronously on the variables' state space. Data distributions provide a mechanism for mapping large data structures across the memory region of a macroserver, while work distributions allow explicit control of bindings between threads and data. Both data and work distributuions are first-class objects of the model, supporting the dynamic management of data and threads in memory. This offers the flexibility required for fully exploiting the processing power and memory bandwidth of a PIM array, in particular for irregular and adaptive applications. Thread synchronization is based on atomic methods, condition variables, and futures. A special type of lightweight macroserver allows the formulation of flexible scheduling strategies for the access to resources, using a monitor-like mechanism
Neural Network Memory Architectures for Autonomous Robot Navigation
This paper highlights the significance of including memory structures in
neural networks when the latter are used to learn perception-action loops for
autonomous robot navigation. Traditional navigation approaches rely on global
maps of the environment to overcome cul-de-sacs and plan feasible motions. Yet,
maintaining an accurate global map may be challenging in real-world settings. A
possible way to mitigate this limitation is to use learning techniques that
forgo hand-engineered map representations and infer appropriate control
responses directly from sensed information. An important but unexplored aspect
of such approaches is the effect of memory on their performance. This work is a
first thorough study of memory structures for deep-neural-network-based robot
navigation, and offers novel tools to train such networks from supervision and
quantify their ability to generalize to unseen scenarios. We analyze the
separation and generalization abilities of feedforward, long short-term memory,
and differentiable neural computer networks. We introduce a new method to
evaluate the generalization ability by estimating the VC-dimension of networks
with a final linear readout layer. We validate that the VC estimates are good
predictors of actual test performance. The reported method can be applied to
deep learning problems beyond robotics
Approximating Mexican highways with slime mould
Plasmodium of Physarum polycephalum is a single cell visible by unaided eye.
During its foraging behavior the cell spans spatially distributed sources of
nutrients with a protoplasmic network. Geometrical structure of the
protoplasmic networks allows the plasmodium to optimize transport of nutrients
between remote parts of its body. Assuming major Mexican cities are sources of
nutrients how much structure of Physarum protoplasmic network correspond to
structure of Mexican Federal highway network? To find an answer undertook a
series of laboratory experiments with living Physarum polycephalum. We
represent geographical locations of major cities by oat flakes, place a piece
of plasmodium in Mexico city area, record the plasmodium's foraging behavior
and extract topology of nutrient transport networks. Results of our experiments
show that the protoplasmic network formed by Physarum is isomorphic, subject to
limitations imposed, to a network of principle highways. Ideas and results of
the paper may contribute towards future developments in bio-inspired road
planning
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