The far-field intensity is detected from far-field image to estimate the piston distance between two gratings. The image processing algorithm includes projections along the horizontal and vertical directions, sear...
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The far-field intensity is detected from far-field image to estimate the piston distance between two gratings. The image processing algorithm includes projections along the horizontal and vertical directions, search for each focal spot's centre, feature extraction and intensity computation. Each focal spot's centre can be found with the projections. A self-growing method is used for feature extraction, where the threshold value depends on the gray value of each focal spot's centre. For each focal spot, the sum of gray values within the relevant domain is taken to be its energy intensity. Furthermore, the energy ratio of left and main focal spots (or main and right focal spots) is computed. A formula that expresses the piston distance between two gratings as the function of the energy ratio is fitted with several measured points. Based on this formula, the piston distance is obtained for a new energy-ratio. Finally, the proposed method is verified with a series of experiments.
A game-engine-based modeling and computing platform for artificial transportation systems (ATSs) is introduced. As an important feature, the artificial-population module (APM) is described in both its macroscopic and ...
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Polarity shifting has been a challenge to automatic sentiment classification. In this paper, we create a corpus which consists of polarity-shifted sentences in various kinds of product reviews. In the corpus, both the...
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Polarity shifting has been a challenge to automatic sentiment classification. In this paper, we create a corpus which consists of polarity-shifted sentences in various kinds of product reviews. In the corpus, both the sentimental words and shifting trigger words are annotated. Furthermore, we analyze all the polarity shifted sentences and categorize them into five categories: opinion-itself, holder, target, time and hypothesis. Experimental study shows the agreement of annotation and the distribution of the five categories of polarity shifting.
Wireless sensor networks are characterized by multihop network. Some nodes in network are required to forward a disproportionately high amount of traffic and die early, leaving the unmonitored areas in network and...
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Wireless sensor networks are characterized by multihop network. Some nodes in network are required to forward a disproportionately high amount of traffic and die early, leaving the unmonitored areas in network and leading to the problem of energy hole. This paper investigates a variety of strategies to avoid the energy hole, such as communication power control, data aggregation, nonuniform energy distribution, mobile node auxiliary and clustering. The analysis and comparison of different strategies are given and the advantages and disadvantage of them are discussed in this paper.
Video-based traffic flow monitoring is a fast emerging field based on the continuous development of computer vision. A survey of the state-of-the-art video processing techniques in traffic flow monitoring is presented...
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Video-based traffic flow monitoring is a fast emerging field based on the continuous development of computer vision. A survey of the state-of-the-art video processing techniques in traffic flow monitoring is presented in this paper. Firstly, vehicle detection is the first step of video processing and detection methods are classified into background modeling based methods and non-background modeling based methods. In particular, nighttime detection is more challenging due to bad illumination and sensitivity to light. Then tracking techniques, including 3D model-based, region-based, active contour-based and feature-based tracking, are presented. A variety of algorithms including MeanShift algorithm, Kalman Filter and Particle Filter are applied in tracking process. In addition, shadow detection and vehicles occlusion bring much trouble into vehicle detection, tracking and so on. Based on the aforementioned video processing techniques, discussion on behavior understanding including traffic incident detection is carried out. Finally, key challenges in traffic flow monitoring are discussed.
The ground states of some many-body quantum systems can serve as resource states for the one-way quantum computing model, achieving the full power of quantum computation. Such resource states are found, for example, i...
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The ground states of some many-body quantum systems can serve as resource states for the one-way quantum computing model, achieving the full power of quantum computation. Such resource states are found, for example, in spin-52 and spin-32 systems. It is, of course, desirable to have a natural resource state in a spin-12, that is, qubit system. Here, we give a negative answer to this question for frustration-free systems with two-body interactions. In fact, it is shown to be impossible for any genuinely entangled qubit state to be a nondegenerate ground state of any two-body frustration-free Hamiltonian. What is more, we also prove that every spin-12 frustration-free Hamiltonian with two-body interaction always has a ground state that is a product of single- or two-qubit states. In other words, there cannot be any interesting entanglement features in the ground state of such a qubit Hamiltonian.
Modern power grid is a typical multi-level complex giant system. The conventional analytical methods based on reductionism can't provide sufficient guidance for its operation and management. complex system theory,...
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Modern power grid is a typical multi-level complex giant system. The conventional analytical methods based on reductionism can't provide sufficient guidance for its operation and management. complex system theory, based on holism, has its specific advantages in power grid's research. But, it has some limitations. In this article, we improve complex grid by introducing new parameters which can describe the grid's characters better and using multi-agent theory. As an application, the complex power grid constructed with actual data from North China grid is constructed and its vulnerability has been simulated and analyzed under different attacks.
Patent classification is a large scale hierarchical text classification (LSHTC) task. Though comprehensive comparisons, either learning algorithms or feature selection strategies, have been fully made in the text cate...
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Patent classification is a large scale hierarchical text classification (LSHTC) task. Though comprehensive comparisons, either learning algorithms or feature selection strategies, have been fully made in the text categorization field, few work was done for a LSHTC task due to high computational cost and complicated structural label characteristics. For the first time, this paper compares two popular learning frameworks, namely, hierarchical support vector machine (SVM) and k -nearest neighbor ( k -NN) that are applied to a LSHTC task. Our experimental results show that the latter outperforms the former for the LSHTC task, which is quite different from the existing results for normal text categorization tasks. In addition, this paper compares different similarity measures and ranking strategies in k -NN framework for LSHTC task. From our empirical study, conclusions can be drawn that k -NN is more appropriate for the LSHTC task than hierarchical SVM. BM25 outperforms other similarity measures and ListWeak gains a better performance than other ranking strategies. Our empirical results also indicate that using all the labels of the retrieved neighbors can remarkably improve classification performance over only using the first label of the retrieved neighbors.
This paper presents a discriminative training (DT) approach to irrelevant variability normalization (IVN) based training of feature transforms and hidden Markov models for large vocabulary continuous speech recognitio...
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This paper presents a discriminative training (DT) approach to irrelevant variability normalization (IVN) based training of feature transforms and hidden Markov models for large vocabulary continuous speech recognition. A speaker-clustering based method is used for acoustic sniffing and maximum mutual information (MMI) is used as a training criterion. Combined with unsupervised adaptation of feature transforms, the IVN-based DT approach achieves a 14.5% relative word error rate reduction over an MMI-trained baseline system on a Switchboard-1 conversational telephone speech transcription task.
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