This paper proposes a novel permanent magnet planar motor with moving multilayer orthogonal overlapping windings. This novel motor topology can achieve a five-degrees-of-freedom drive using two sets of x-direction win...
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This paper proposes novel descriptors that integrate information from multiple views of a 3D object, called Temporal Ensemble of Shape Functions (TESF) descriptors. The TESF descriptors are built by combining per-view...
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Based on the concept of optimizing the efficiency of the automated solar system in residential buildings application, this paper proposed a High efficiency solar Cut-Off charge controller as an alternative to the main...
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The paper deals with some ideas of the current trends in home rehabilitation - rehabilitation robotics and wearable sensors. In the first section, introduction and motivation for research is described, followed by sec...
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In this paper,a group consensus problem is investigated for multiple networked agents with parametric uncertainties where all the agents are governed by the Euler-Lagrange system with uncertain *** the group consensus...
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In this paper,a group consensus problem is investigated for multiple networked agents with parametric uncertainties where all the agents are governed by the Euler-Lagrange system with uncertain *** the group consensus problem,the agents asymptotically reach several different states rather than one consistent state.A novel group consensus protocol and a time-varying estimator of the uncertain parameters are proposed for each agent in order to solve the couple-group consensus *** is shown that the group consensus is reachable even when the system contains the uncertain ***,the multi-group consensus is discussed as an extension of the couple-group consensus,and then the group consensus with switching topology is *** results are finally provided to validate the effectiveness of the theoretical analysis.
It has been recognized by many researchers that accurate bus travel time prediction is critical for successful deployment of traffic signal priority (TSP) systems. Although there exist a lot of studies on travel time ...
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It has been recognized by many researchers that accurate bus travel time prediction is critical for successful deployment of traffic signal priority (TSP) systems. Although there exist a lot of studies on travel time prediction for Advanced Traveler Information Systems (ATIS), this problem for TSP purpose is a little different and the amount of literature is limited. This paper proposes a deep learning based approach for continuous travel time prediction problem. Parameters of the deep network are fine-tuned following a layer-by-layer pre-training procedure on a dataset generated by traffic simulations. Variables that may affect continuous travel time are selected carefully. Experiments are conducted to validate the performance of the proposed model. The results indicate that the proposed model produces prediction with mean absolute error less than 4 seconds, which is accurate enough for TSP operations. This paper also reveals that, except for obvious factors like speed, travel distance and traffic density, the signal time when the prediction is made is also an important factor affecting travel time.
In this article, a method to obtain spatial coordinate of spherical robot's moving platform using a single camera is proposed, and experimentally verified. The proposed method is an accurate, flexible and low-cost...
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ISBN:
(纸本)9781467372350
In this article, a method to obtain spatial coordinate of spherical robot's moving platform using a single camera is proposed, and experimentally verified. The proposed method is an accurate, flexible and low-cost tool for the kinematic calibration of spherical-workspace mechanisms to achieve the desired accuracy in position. The sensitivity and efficiency of the provided method is thus evaluated. Furthermore, optimization of camera location is outlined subject to the prescribed cost functions. Finally, experimental analysis of the proposed calibration method on ARAS Eye surgery Robot (DIAMOND) is presented;In which the accuracy is obtained from three to six times better than the previous calibration.
To better analyze images with the Gaussian white noise, it is necessary to remove the noise before image processing. In this paper, we propose a self-Adaptive image denoising method based on bidimensional empirical mo...
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Because the new approach cannot be applied directly in the hot-rolled strip laminar cooling process, a simulation is necessary to verify the new approach effectively which can improve the strip quality and production ...
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Based on the recent success of Low-Rank matrix Representation(LRR),we propose a novel classification method for robust face recognition,named LRR-based Classification(LRRC).By the ideal that if each data class is line...
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ISBN:
(纸本)9781479947249
Based on the recent success of Low-Rank matrix Representation(LRR),we propose a novel classification method for robust face recognition,named LRR-based Classification(LRRC).By the ideal that if each data class is linearly spanned by a subspace of unknown dimensions and the data are noiseless,the lowest-rank representations of a set of test vector samples with respect to a set of training vector samples have the nature of being both dense for within-class affinity and almost zero for between-class ***,the LRR exactly reveals the classification of the *** experimental results demonstrate that LRRC has competitive with state-of-the-art classification methods.
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