In recent years, the Bag-of-Words (BoW) model has been widely used in most state-of-the-art large-scale image re-trieval systems. However, the standard BoW based systems suffer from low discriminative power of local f...
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OBJECTIVE:To design a face gloss classification model and to provide an automatic and quantitative approach for the diagnosis of Chinese medicine (CM) based on the face images.METHODS:To classify the face gloss images...
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OBJECTIVE:To design a face gloss classification model and to provide an automatic and quantitative approach for the diagnosis of Chinese medicine (CM) based on the face images.
METHODS:To classify the face gloss images into two groups (gloss and non-gloss), feature extraction methods were applied to the original images. The original images were supposed to obtain a more ideal representation in which gloss information was better revealed in four color spaces [including red, green, blue (RGB), hue, saturation, value (HSV), Gray and Lab]. Principal component analysis (PCA), 2-dimensional PCA (2DPCA), 2-directional 2-dimensional PCA [(2D)PCA], linear discriminant analysis (LDA), 2-dimensional LDA (2DLDA), and partial least squares (PLS) were used as the feature extraction methods of face gloss. k nearest neighbor was used as the classifification method.
RESULTS:All the six feature extraction methods were useful in extracting information of face gloss, especially LDA, which had the best prediction accuracy in the 4 color spaces. The average accuracy of LDA in the Lab was 7%-10% higher than that of PCA, 2DPCA, (2D)PCA and 2DLDA P<0.05). The prediction accuracy of LDA reached 98% in the Lab color space and showed practical usage in clinical diagnosis. The consistent rate between the CM experts and the facial diagnosis system was 81%.
CONCLUSION:A computer-assisted classifification model was designed to provide an automatic and quantitative approach for the gloss diagnosis of CM based on the face images.
In this paper,the robust adaptive control is proposed for a class of parametric uncertain nonhnear system with asymmetric non-smooth input *** introducing a well-defined smooth function,a robust adaptive control algor...
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ISBN:
(纸本)9781467397155
In this paper,the robust adaptive control is proposed for a class of parametric uncertain nonhnear system with asymmetric non-smooth input *** introducing a well-defined smooth function,a robust adaptive control algorithms are developed to deal with the problem of asymmetric non-smooth *** most of the existing control schemes for the uncertain nonhnear system,which requires an assumption that the control gain is an unknown constant,the developed controller only needs that the control gain is a bounded and unknown time-varying *** this work,the robust adaptive controller can guarantee global uniform boundedness of the closed-loop system signals as rigorously proved by Lyapunov analysis.A simulation results is presented to illustrate the effectiveness of the proposed adaptive control technique.
Consider to the defect of traditional features which have high empirical components. A vehicle classification algorithm based on the fusion of higher-layer features of a deep network and traditional features was propo...
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Consider to the defect of traditional features which have high empirical components. A vehicle classification algorithm based on the fusion of higher-layer features of a deep network and traditional features was proposed. Firstly, the traditional features of PHOG and LBP-EOH were extracted. Secondly, the higher-layer features excavated from the vehicle pictures by deep belief networks were added, making these three kinds of features together by feature fusion. Finally, support vector machine is used to train and classify the vehicle. When the number of training samples is large enough, the algorithm has a significant effect compared to those with traditional features. It can achieve the accuracy of 95% in the six categories of vehicles.
With the exploding growth of data, the computational complexity required by learning Support Vector Machine (SVM) lays a heavy burden on real-world applications. To address this issue, parallel computational technique...
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ISBN:
(纸本)9781479986989
With the exploding growth of data, the computational complexity required by learning Support Vector Machine (SVM) lays a heavy burden on real-world applications. To address this issue, parallel computational techniques can be employed such as the Graphics Processing Units (GPUs) and MapReduce model. As it is well known, GPUs are microprocessors on a multi-core architecture which reveal high performance in mass data parallel computing, and MapReduce allows computational tasks to be divided into a plurality of parts, distributed to various computing nodes and combined on a single node. In this paper, we propose a GPU-based MapReduce framework to accelerate SVM learning by jointly utilizing the parallel computing power of GPU and MapReduce. Extensive experimental results have verified the effectiveness and efficiency of the proposed approach.
With the explosive growth of Internet information,it is more and more important to fetch real-time and related *** it puts forward higher requirement on the speed of webpage classification which is one of common metho...
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With the explosive growth of Internet information,it is more and more important to fetch real-time and related *** it puts forward higher requirement on the speed of webpage classification which is one of common methods to retrieve and manage *** get a more efficient classifier,this paper proposes a webpage classification method based on locality sensitive hash *** which,three innovative modules including building feature dictionary,mapping feature vectors to fingerprints using Localitysensitive hashing,and extending webpage features are *** compare results show that the proposed algorithm has better performance in lower time than the na?ve bayes one.
It is estimated that the breakthrough in the broad deployment of Internet of Things (IoT) could come from smart cars. Indeed, we have seen multi-facet advances around cars: new material, in vehicle infotainment, drive...
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ISBN:
(纸本)9781479989386
It is estimated that the breakthrough in the broad deployment of Internet of Things (IoT) could come from smart cars. Indeed, we have seen multi-facet advances around cars: new material, in vehicle infotainment, driverless cars, smart transportation, electrical vehicles, etc. However, in-vehicle-networking has been mainly by wire;the wiring for a car is largely prebuilt during the design phase. With more and more things networked within a car, wiring has taken up 1-2 percent of the total weight. This translates into burning up to 0.1 kilogram fuel over 100 kilometers. On the other hand, the advances in wireless technology, especially the broad acceptance of WirelssHART in the industrial settings, has proved its capability in harsh environments. This paper studies what could happen if we use WirelessHART mesh network for in-vehicle communication. While new wireless network protocols are needed to perform the task of CAN, the dominant in-vehicle fieldbus, WirelessHART could take on the work performed by LIN, the fieldbus for peripheral devices. A detailed study is provided to compare these buses. Road tests were performed, in which a WirelessHART network keeps running for the whole 20 minute period.
Link similarity is widely applied in measuring the similarity between objects, e.g., web pages, scientific papers and social networks. However, there are a lot of drawbacks in existing methods of measuring link simila...
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ISBN:
(纸本)9781479973507
Link similarity is widely applied in measuring the similarity between objects, e.g., web pages, scientific papers and social networks. However, there are a lot of drawbacks in existing methods of measuring link similarity. In brief, these methods can not handle some semantic-similar content. Moreover, the computation of them are not accurate in some scenes. In this paper, we present a novel method of measuring link similarity called HSim. It introduces the semantic similarity to calculate the similarity between objects, and overcomes the drawback that existing methods ignore the semantic information of objects. We also develop a novel computation function to make the result of similarity more accurate.
In this paper, the synchronization problem for linearly coupled complex networks is investigated, which is denoted by ordinary differential equations with complex-values. The coupling topology can be asymmetric, and t...
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ISBN:
(纸本)9781467391054
In this paper, the synchronization problem for linearly coupled complex networks is investigated, which is denoted by ordinary differential equations with complex-values. The coupling topology can be asymmetric, and the time delay also exists in the network model. By pinning some external aperiodically intermittent control on the network nodes, we will prove that under some conditions, the complete synchronization will be finally realized exponentially. Moreover, applying the adaptive technique on the coupling strength, we will also present an adaptive rule, whose validity is rigorously proved. Finally, some numerical simulations will be given to illustrate the correctness of these obtained results.
Top-n recommendation technology has recently received a lot of attention in information service community. In this paper, we study the problem of top-n recommendation under the cloud data. Firstly, we propose a multil...
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