In this paper, the nearly H∞ optimal control solution for discrete-time (DT) constrained input nonlinear systems is considered. First, to deal with the input constraints, a quasi-norm performance index function is in...
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An aerial work platform (AWP) is a type of off highway vehicle with a long beam to provide temporary access to inaccessible areas [1]. The motivation of the research is to increase its productivity, safety and reduce ...
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In micro-blogging, people talk about their daily life and change minds freely, thus by mining people's interest in micro-blogging, we will easily perceive the pulse of society. In this paper, we catch what people ...
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ISBN:
(纸本)9781450312301
In micro-blogging, people talk about their daily life and change minds freely, thus by mining people's interest in micro-blogging, we will easily perceive the pulse of society. In this paper, we catch what people are caring about in their daily life by discovering meaningful communities based on probabilistic factor model (PFM). The proposed solution identifies people's interest from their friendship and content information. Therefore, it reveals the behaviors of people in micro-blogging naturally. Experimental results verify the effectiveness of the proposed model and show people's social life vividly. Copyright is held by the author/owner(s).
Ground-based cloud classification plays an essential role in meteorological research and has received great concern in recent years. In this paper, a novel algorithm named multiple random projections (MRP) is proposed...
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ISBN:
(纸本)9781467312745;9781467312721
Ground-based cloud classification plays an essential role in meteorological research and has received great concern in recent years. In this paper, a novel algorithm named multiple random projections (MRP) is proposed for ground-based cloud classification. The proposed algorithm uses an ensemble approach of MRP to obtain an optimized textons. Based on the textons, discriminative features can be obtained for classification. A series of experiments on two ground-based cloud databases (Kiel and IapCAS-E) are conducted to evaluate the efficiency of our proposed method. In addition, three current state-of-the-art methods, which include Patch, PCA, single random projection (SRP), are selected for comparison purpose. The experimental results show that our MRP method can achieved the best classification performance.
This paper presents a two-impulse input shaping method using off-line learning method to suppress the residual vibration of a flexible joint robot which is considered as perform repetitive tasks. It has been proved th...
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ISBN:
(纸本)9781467313988
This paper presents a two-impulse input shaping method using off-line learning method to suppress the residual vibration of a flexible joint robot which is considered as perform repetitive tasks. It has been proved that the two-impulse input shaping method has the ability to suppress time-varying or nonlinear residual vibration. However, the parameters of the input shaper are difficult to select. In this paper, a method based on the off-line learning is presented to determine the proper parameters. We found that the torque of the joint can reflect the residual vibration through analysis of relations between the residual vibration and torque of the flexible joint robot. Thus, the torque signal of the joint is used to measure the vibration magnitude and no additional sensors for vibration measurement are required. For the nonmeasurable of the phase of the residual vibration, only the vibration magnitude is used to update the parameters of the input shaper off-line until the minimum vibration is obtained. The initial parameters of the input shaper also are estimated in this paper. Simulations are conducted to suppress residual vibration of a flexible joint robot, thereby demonstrating the effectiveness of the off-learning learning input shaping method.
Background modeling from a stationary camera is a crucial component in video surveillance. Traditional methods usually adopt single feature type to solve the problem, while the performance is usually unsatisfactory wh...
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ISBN:
(纸本)9780769547978
Background modeling from a stationary camera is a crucial component in video surveillance. Traditional methods usually adopt single feature type to solve the problem, while the performance is usually unsatisfactory when handling complex scenes. In this paper, we propose a multi-scale strategy, which combines both texture and color features, to achieve a robust and accurate solution. Our contributions are two folds: one is that we propose a novel texture operator named Scale-invariant Center-symmetric Local Ternary Pattern, which is robust to noise and illumination variations;the other is that a multi-scale fusion strategy is proposed for the issue. Our method is verified on several complex real world videos with illumination variation, soft shadows and dynamic backgrounds. We compare our method with four state-of-the-art methods, and the experimental results clearly demonstrate that our method achieves the highest classification accuracy in complex real world videos.
Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. ...
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ISBN:
(纸本)9780769547978
Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. Yet such a method neglects the constraints that attributes impose on classes which may fail to constrain the semantic relationship between the attribute and object classifiers. In this paper, we explicitly consider such attribute-object relationship, and correspondingly, we modify the multi-task learning model by adding attribute regularization. In this way, the learned model not only shares the low-level features, but also gets regularized according to the semantic constrains. Our method is verified on two challenging datasets (KTH and Olympic Sports), and the experimental results demonstrate that our method achieves better results than previous methods in human action recognition.
With the rapid development of the Internet and E-commerce, online shopping sites are becoming a popular platform for products selling. Shopping sites such as ***, dangdang. com provide consumers with a hierarchical na...
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ISBN:
(纸本)9780769549255;9781467351645
With the rapid development of the Internet and E-commerce, online shopping sites are becoming a popular platform for products selling. Shopping sites such as ***, dangdang. com provide consumers with a hierarchical navigation for selecting products easily from overwhelming amount of products. However, those man-made navigations are so general and professional that consumers still need to spend much time in filtering out their own undesired products personally. Shopping sites provide abundant textual product descriptions for most products, which describes the details of the product. In this paper, we propose a novel model to build a topic hierarchy from the detailed product descriptions, which can automatically model words into a tree structure by hierarchical Latent Dirichlet Allocation (hLDA), besides, our model can also augment words level allocations with the conceptual relation between words in WordNet automatically. Each node in the hierarchical tree contains some relevant keywords of product descriptions, thus clarifying the meaning of the concept in the node. Therefore, consumers can pick out their interested products by using the discovered descriptive and valuable navigation of products. The experimental results on amazon. com, one of the most popular shopping sites in America, demonstrate the efficiency and effectiveness of our proposed model.
Traditional sparse coding has been successfully applied in texture and image classification in the past years. Yet such kind of method neglects the influence of the signs of coding coefficients, which may cause inform...
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ISBN:
(纸本)9784990644109;9781467322164
Traditional sparse coding has been successfully applied in texture and image classification in the past years. Yet such kind of method neglects the influence of the signs of coding coefficients, which may cause information loss in the sequential max pooling. In this paper, we propose a novel coding strategy for ground-based cloud classification, which is named soft-signed sparse coding. In our method, a constraint on the signs is explicitly added to the objective function of traditional sparse coding model, which can effectively regulate the ratio between the number of positive and negative non-zero coefficients. As a result, the proposed method can not only obtain low reconstruction error but also consider the influence of the signs of coding coefficients. The strategy is verified on two challenging cloud datasets, and the experimental results demonstrate the superior performance of our method compared with previous ones.
The k-nearest neighbor (k-NN) nonparametric regression is a classic model for single point short-term traffic flow forecasting. The traffic flows of the same clock time of the days are viewed as neighbors to each othe...
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