Recently, it is still difficult to extract interested object from complex background. In this field, interactive image segmentation method has attracted much attention in the vision. In this paper, we propose a new al...
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Vehicle scheduling plays a crucial role in public transport bus companies. An efficient schedule can help bus companies reduce operating costs while being an essential guide to daily operations. However, the precompil...
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ISBN:
(纸本)9781538629185
Vehicle scheduling plays a crucial role in public transport bus companies. An efficient schedule can help bus companies reduce operating costs while being an essential guide to daily operations. However, the precompiled schedule is usually hard to be adhered to in practice due to the diversity of traffic and driving conditions. Therefore, dynamic vehicle scheduling becomes an important supplement to the daily operations. In this paper, a dynamic vehicle scheduling approach based on Hierarchical Task Network(HTN) is proposed. In the approach, two dynamic vehicle scheduling strategies are devised according to the practical scheduling philosophy. The first is to reschedule for individual vehicle independently, the objective is to maximize the execution of the precompiled schedule. The second is to reschedule for multiple vehicles simultaneously, which aims to maintain the scheduled headways. The two strategies are achieved in the HTN planning through different task decomposition processes, which are constrained by vehicle resources currently available. To verify the feasibility, this approach is implemented based on the Simple Hierarchical Ordered Planner 2(SHOP2), which is a domainindependent and state-based forward HTN planner. Experimental results show that the approach has good adaptability to solve dynamic vehicle schedule problem, meanwhile, it can be helpful to deal with the abnormal services agilely and hence to increase the service quality of public transit.
Steel billet recognition is an urgent requirement in the steel industry of heavy rail line. Due to high temperature and complex scene in the rolling line, the recognition at the end of billet is quite different from o...
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The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufa...
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The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a general data-driven,end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on 10 representative data sets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts,indicating its potential use as a critical cornerstone in smart manufacturing.
The edge and contour details in SAR images are important for subsequent processing tasks. The multiscale geometric analysis method — Nonsubsampled contourlet transform(NSCT) is able to capture the geometric informati...
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The edge and contour details in SAR images are important for subsequent processing tasks. The multiscale geometric analysis method — Nonsubsampled contourlet transform(NSCT) is able to capture the geometric information of SAR images effectively. Describing the aggregation behavior of the neighborhoods coefficients, the scale mixtures of Gaussians model has exhibited favorable performances. A novel SAR image despeckling method is presented by constructing the scale mixtures of Gaussians model of NSCT. This method models the SAR images using the multiscale and multidirection information in NSCT domain. The dependency relationship of NSCT neighborhoods coefficients are also taken into consideration in our model. The speckle noise coefficients are shrinkaged by statistical prior estimation based on SAR image model constructed. Experimental results demonstrate that our method is advantageous at directional information preservation and the speckle restraint.
Bearing remaining useful life(RUL) prediction is critical for safe operation of rotating *** this paper,we propose a combined RUL prediction approach that leverages both trajectory similarity and relevance vector mach...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
Bearing remaining useful life(RUL) prediction is critical for safe operation of rotating *** this paper,we propose a combined RUL prediction approach that leverages both trajectory similarity and relevance vector machine(RVM).The similarity based prediction relies on historical degradation trajectories that are highly similar to the online data,hence would perform poorly if all historical trajectories have low similarity with the online *** RVM based prediction relies solely on a regression model learned from the available online data,thus gives an inaccurate prediction when insufficient data are available in the early stage of degradation.A weighted sum of these above two predictions is proposed to address the limitation of each single prediction method,whose weights are determined by solving a non-negative least squares fitting *** further improve RUL prediction accuracy,we distinguish between fast and slow degradation modes,so that each mode uses a different set of historical degradation trajectories and kernel *** doing so,we predict RUL under the identified *** case study using the PHM2012 dataset demonstrates the effectiveness of the proposed RUL prediction approach.
We consider the problem of looking for small universal spiking neural P systems with exhaustive use of rules, which was formulated as an open problem by Andrei PǍun and Gheorghe PǍun in a survey paper. Here, spiking...
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There have been increasing interests in studying multiplex dynamical networks *** paper focuses on topology identiflcation of two-layer multiplex networks with peer-to-peer interlayer *** a two-layer network model in ...
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There have been increasing interests in studying multiplex dynamical networks *** paper focuses on topology identiflcation of two-layer multiplex networks with peer-to-peer interlayer *** a two-layer network model in which different layers have different coupling patterns,we propose novel methods to recover unknown topological structure of one layer,using the information of the other layer known as a *** proposed methods make full use of the measured evolutional states of the multiplex network itself,and treat the layer with a known structure as an auxiliary layer which is designed to identify the unknown topological *** with the traditional synchronization-based identiflcation method,the proposed methods are in no need of constructing an additional auxiliary network to identify the unknown topological layer,and thus greatly reduce the cost of topology ***,numerical simulations validate the effectiveness of the proposed methods.
In formal language theory of two-dimensions, 2D picture grammars are powerful tools to generate picture languages. In this work, we incorporate the idea of membrane systems (also called P systems) into 2D picture gram...
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It is difficult to guide the entry vehicle to prescribed area due to the disperse of environment and kinematics. Through predicted residual range at the current state based on drag acceleration, we developed a predict...
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