Multi-stack fuel cell system(MFCS) are an important basis for large-scale application of solid oxide fuel cell(SOFC) technology, MFCS can provide higher system power and longer service life. As the number of stacks in...
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It is well known that the backgrounds or the targets always change in real scenes, which weakens the effectiveness of classical tracking algorithms because of frequent model mismatches. In this paper, an object tracki...
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DNA origami is one of the powerful techniques that utilize DNA as building blocks to synthesize nanostructures. Traditionally, through introducing different numbers of insertions and deletions of base pairs in DNA hel...
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Target detection and location in infrared clutter background is very important to infrared search and track system. Especially for small target detection in infrared image in background of sea and sky, there are no ge...
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Two types of novel cellular neural networks based on mem-elements are proposed, namely, MC-CNN and *** MC-CNN lets a memcapacitor replace the conventional linear capacitor of a cellular neural network cell. This impro...
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ISBN:
(纸本)9781467374439
Two types of novel cellular neural networks based on mem-elements are proposed, namely, MC-CNN and *** MC-CNN lets a memcapacitor replace the conventional linear capacitor of a cellular neural network cell. This improvement takes advantage of the nanoscale of memcapacitor and its natural nonlinearity, which makes the MC-CNN more compact and the output function simplified. Mathematical analysis of stability and simulation of imageprocessing is presented to verify the feasibility and performance of MC-CNN. The EM-CNN is an economical improvement of the memristor synapse cellular neural network. In the EM-CNN, based on the symmetry of CNN templates, the amount of memristors and voltage-controlled current source is largely reduced. Thus, the EM-CNN is not only economical on the fabricating cost of CNN but also possesses a simpler cell structure which is beneficial to better implementation of CNN.
A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not ...
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A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not required. By using problem-specific chromosome structure and genetic operators, the routes are generated in real time, with different mission constraints such as minimum route leg length and flying altitude, maximum turning angle, maximum climbing/diving angle and route distance constraint taken into account.
In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconve...
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A method used for recognition and understanding of airfield based on mathematical morphology is proposed in this paper. The new approach can he divided into three steps. First, to extract the typical geometric structu...
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A method used for recognition and understanding of airfield based on mathematical morphology is proposed in this paper. The new approach can he divided into three steps. First, to extract the typical geometric structure features of airfield, a segmentation method called recursive Otsu algorithm is employed on an airfield image. Second, thinning and shrinking algorithms are utilized to obtain the contour of airfield with single pixel and to remove diffused small particles. Finally, Radon transform is adopted to extract two typical and important components, primary and secondary runways of airfield exactly. At the same time, region growing algorithm is exploited to get the other components such as parking apron and garages. The experimental results demonstrate that the proposed method gives good performance.
This paper investigates the parameter identification of a state-of-charge dependent equivalent circuit model (ECM) for Lithium-ion batteries. Different from most existing ECM identification methods, we focus on identi...
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This paper investigates the parameter identification of a state-of-charge dependent equivalent circuit model (ECM) for Lithium-ion batteries. Different from most existing ECM identification methods, we focus on identifying the functional relations between ECM parameters and state-of-charge (SOC). By transforming the ECM into an ARX model, a Gaussian process regression (GPR) approach is proposed, without using parametric functions to describe the SOC dependence of ARX coefficients. The proposed approach derives the posterior distributions of ECM parameters, thus is capable to quantify the estimation uncertainties. Another advantage lies in the flexibility of incorporating the knowledge of batteries into the prior distributions used in GPR, which enhances the estimation performance in the presence of noises. The effectiveness of the proposed GPR approach is illustrated by simulation examples under both low and high noise levels.
This paper studies the observer-based leader-following consensus of a linear multiagent system on switching networks, in which the input of each agent is subject to saturation. Based on a low-gain output feedback meth...
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