This paper presents investigations into the development of an assistive device for elderly mobility. An exoskeleton is designed as an assistive device, to enhance the lower extremity and provide supporting torque to a...
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This study introduces a hybrid multi-objective evolutionary algorithm (MOEA) for the optimization of aircraft control system design. The strategy suggested here is composed mainly of two stages. The first stage consis...
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Many physical systems that occur in applications are naturally passive, for example, mechanical systems with dual sensors and actuators, and electrical circuits with passive components. Taking advantage of this proper...
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Many physical systems that occur in applications are naturally passive, for example, mechanical systems with dual sensors and actuators, and electrical circuits with passive components. Taking advantage of this property, many controller schemes have been proposed with the property that the controller is strictly positive real. Due to design and implementation considerations, the plant or the controller may need to be approximated by a lower-order system. It is highly desirable for the reduced-order system to also possess the positive realness property to guarantee that the resulting closed-loop system remains stable. Motivated by this problem, this paper considers the general model-reduction problem for a positive real system under the constraint that the reduced system is also positive real. We present a solution based on the balanced stochastic truncation. When the higher-order system is strictly positive real, we derive an H(infinity) norm bound on the approximation error. We also consider alternate approaches of approximating the spectral factors with associated H(infinity) norm error bounds. An example is included to show the efficacy of this method and comparison with other approaches.
Rolling bearings play a significant role in the operation of rotating machinery. Finding bearing faults in the early stage can not only keep machinery running safely but also avoid economic loss. Traditional machine l...
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Model structure selection is crucial in system identification and data-driven modelling. Many spurious candidate variables can influence the determination of model structures due to the lack of prior knowledge of the ...
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Camera calibration is a necessary step in 3D modeling in order to extract metric information from images. Computed camera parameters are used in a lot of computer vision applications which involves geometric computati...
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Cargo screening is an important process to inspect illegal contraband, such as drugs, nuclear materials, weapons and explosives at seaports and airports. A great deal of research has been carried out to address the pr...
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Manufacturing efficiency and transport operations are being significantly improved by mobile robots. As the implementation of a configurable, lightweight, and stateof-the-art robotic system is required for current man...
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Blood is a vital source for delivering oxygen and nutrients to trillions of cells in the body;this makes the function of the cardiovascular system essential to our existence. In the last few years, research interests ...
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Decomposition based approaches are known to perform well on many-objective problems when a suitable set of weights is provided. However, providing a suitable set of weights a priori is difficult. This study proposes a...
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ISBN:
(纸本)9781450319645
Decomposition based approaches are known to perform well on many-objective problems when a suitable set of weights is provided. However, providing a suitable set of weights a priori is difficult. This study proposes a novel algorithm: preference-inspired co-evolutionary algorithm using weights (PICEA-w), which co-evolves a set of weights with the usual population of candidate solutions during the search process. The co-evolution enables suitable sets of weights to be constructed along the optimization process, thus guiding the candidate solutions toward the Pareto optimal front. Experimental results show PICEA-w performs better than algorithms embedded with random or uniform weights.
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