作者：
Lidong HeXiaofan WangDepartment of Automation
Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China
We consider the discrete linear state estimation problem over a packet-dropping network. Before transmitting the data, the linear measurement combination is designed at the local sensor side. In order to improve the r...
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We consider the discrete linear state estimation problem over a packet-dropping network. Before transmitting the data, the linear measurement combination is designed at the local sensor side. In order to improve the remote estimation performance, whether transmitting the current data or the combination depends on whether the previous packet is successfully received or not. A linear minimum mean square error estimation algorithm is proposed for the novel scheme. Moreover, the recursive expression between estimate and smooth error covariance of the last step, as well as the current estimation error covariance is explicitly established via properly constructing a state observer. The equivalency of the two methods is shown via some basic transformations in algebraic Riccati equation theory. The optimal weight for minimizing the estimation error covariance is derived for scalar systems.
Cutting Stock Problems (CSP) arise in many production industries where large stock sheets must be cut into smaller pieces. An irregular-shaped nesting approach for two-dimensional cutting stock problem is constructed ...
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Cutting Stock Problems (CSP) arise in many production industries where large stock sheets must be cut into smaller pieces. An irregular-shaped nesting approach for two-dimensional cutting stock problem is constructed in this research. We present a heuristic based on Particle Swarm Optimization Algorithm (PSO) for irregular-shaped two-dimensional cutting stock problem, where PSO is utilized to search optimal solution. Furthermore, the proposed approach combines a grid approximation method with Bottom-Left-Fill heuristic placement strategy to allocate irregular items. We evaluate the proposed approach using 15 revised benchmark problems available from the EURO Special Interest Group on Cutting and Packing. The performance illustrates the effectiveness and efficiency of our approach in solving irregular cutting stock problems.
Weak boundary contrast, inhomogeneous background and overlapped intensity distributions of the object and background are main causes that may lead to failure of boundary detection for many traditional active contour m...
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ISBN:
(纸本)9781479923427
Weak boundary contrast, inhomogeneous background and overlapped intensity distributions of the object and background are main causes that may lead to failure of boundary detection for many traditional active contour methods. In this paper, we propose a region-based active contour model to address these problems in both local and global ways. A localized active contour framework is developed, in which two local boundary measures are introduced for the evolution of the level set function. These measures are used to select the boundary candidates for boundary preservation such that the evolution of the contour is guided in a reasonable way. The object boundary is determined by a global boundary measure which evaluates the boundary completeness during the entire evolution process. The experiments demonstrate that our method works well against weak boundary contrast, inhomogeneous background and overlapped intensity distributions.
As maritime weather and other environmental changes are more and more complex and frequent, ships traveling along established routes may encounter some cases where people are unable to react quickly. The navigational ...
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An intelligent wheelchair JiaoLong with multi-mode is developed for the handicapped and the *** is designed of two manipulate modes according to the user's disability and the environments for *** on the dynamic lo...
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An intelligent wheelchair JiaoLong with multi-mode is developed for the handicapped and the *** is designed of two manipulate modes according to the user's disability and the environments for *** on the dynamic localizability matrix,an improved particle filter localization algorithm is proposed in this *** results of experiments show the practicability of the system design and the effectiveness provided by the improved localization method.
Crowded scene analysis is becoming increasingly popular in computer vision field. In this paper, we propose a novel approach to analyze motion patterns by clustering the hybrid generative-discriminative feature maps u...
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ISBN:
(纸本)9781479923427
Crowded scene analysis is becoming increasingly popular in computer vision field. In this paper, we propose a novel approach to analyze motion patterns by clustering the hybrid generative-discriminative feature maps using unsupervised hierarchical clustering algorithm. The hybrid generative-discriminative feature maps are derived by posterior divergence based on the tracklets which are captured by tracking dense points with three effective rules. The feature maps effectively associate low-level features with the semantical motion patterns by exploiting the hidden information in crowded scenes. Motion pattern analyzing is implemented in a completely unsupervised way and the feature maps are clustered automatically through hierarchical clustering algorithm building on the basis of graphic model. The experiment results precisely reveal the distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.
This paper designs a mixed H 2 /H ∞ model predictive algorithm for systems with energy-bounded disturbance. The formulation of H 2 /H ∞ performance in previous work is improved by introducing a slack variable. Fur...
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This paper designs a mixed H 2 /H ∞ model predictive algorithm for systems with energy-bounded disturbance. The formulation of H 2 /H ∞ performance in previous work is improved by introducing a slack variable. Furthermore, we decouple the formulation of H 2 /H ∞ performance from the formulation of input and state constraints by dilated LMI technique. These two aspects are combined to develop a less conservative control algorithm. Recursive feasibility of the algorithm is able to be proved and stability of the controlled system is ensured. Numerical example shows the effectiveness of the proposed algorithm.
Current freeway traffic flow prediction techniques pay attention to time series prediction or introduce the upstream adjacent road segments in the short-term prediction model. In this paper, all of the road segments o...
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ISBN:
(纸本)9781479929153
Current freeway traffic flow prediction techniques pay attention to time series prediction or introduce the upstream adjacent road segments in the short-term prediction model. In this paper, all of the road segments on the freeway are considered as candidates of the independent variables fed into the prediction model. A spatio-temporal multivariate adaptive regression splines (MARS) approach is proposed for the road network analysis and to predict the short-term traffic volume at the observation stations on the freeway. The actual traffic data are collected from a series of observation stations along a freeway in Portland every 15 minutes. In the first phase, the macroscopic dependency relationships of the stations on the freeway are investigated via MARS method. Subsequently the stations most related to the object station are selected and fed into the MARS prediction model to generate the short-term volume. The experiments are carried out on the actual traffic data and the results indicate that the proposed spatio-temporal MARS model can generate superior prediction accuracy in contrast with the historical data based MARS model, the parametric ARIMA, and the nonparametric PPR methods.
Multi-cell multi-user downlink beamforming schemes considering transceiver impairments were developed, with the objective of minimizing the maximum mean square error (MSE) and the sum of MSE of all users. The optimiza...
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Multi-cell multi-user downlink beamforming schemes considering transceiver impairments were developed, with the objective of minimizing the maximum mean square error (MSE) and the sum of MSE of all users. The optimization problem was transformed into a second-order cone programming (SOCP) standard form, and a hierarchical iterative algorithm was developed to solve the original problem. Compared with the traditional beamforming methods developed for ideal transceivers, numerical results show that our proposed optimization algorithms greatly reduce the impact of impairments and improve the system performance.
In this paper, T-G-P model is built to find maximum power point according to light intensity and temperature, making it easier and more clearly for photovoltaic system to track the MPP. A predictive controller conside...
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