The vast volumes of seismic data being recorded by both permanent and temporary networks operating all over the world provide exciting opportunities for studying the Earth’s interior and earthquake source characteris...
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The vast volumes of seismic data being recorded by both permanent and temporary networks operating all over the world provide exciting opportunities for studying the Earth’s interior and earthquake source characteristics. As a result, the development of efficient computer algorithms and procedures capable of automatically extracting and processing such long streams of data is one of the most challenging issues facing modern seismological research. Valoroso et al. (2013) obtained an extraordinary degree of detail in the anatomy of the normal-fault system of the l’Aquila earthquake after processing around 64,000 aftershocks (extracted, picked, and located) via an automated procedure. Spectral analysis of K-NET and KiK-net data in Japan was carried out by Oth et al. (2011) on the basis of more than 67,000 records analyzed via an automated procedure that included phase picking, earthquake location, and coda identification.
Community structure is one of the most fundamental and important topology characteristics of complex networks. The research on community structure has wide applications and is very important for analyzing the topology...
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Community structure is one of the most fundamental and important topology characteristics of complex networks. The research on community structure has wide applications and is very important for analyzing the topology structure, understanding the functions, finding the hidden properties, and forecasting the time-varying of the networks. This paper analyzes some related algorithms and proposes a new algorithm-CN agglomerative algorithm based on graph theory and the local connectedness of network to find communities in network. We show this algorithmis distributed and polynomial;meanwhile the simulations show it is accurate and fine-grained. Furthermore, we modify this algorithm to get one modified CN algorithm and apply it to dynamic complex networks, and the simulations also verify that the modified CN algorithm has high accuracy too.
This paper presents a new near lossless compression algorithm for hyperspectral images based on distributed source coding. The algorithm is performed on blocks that have the same location and size in each band. Becaus...
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This paper presents a new near lossless compression algorithm for hyperspectral images based on distributed source coding. The algorithm is performed on blocks that have the same location and size in each band. Because the importance varies from block to block along the spectral orientation, an adaptive rate allocation algorithm that weights the energy of each block under the target rate constraints is introduced. A simple linear prediction model is employed to construct the side information of each block for Slepian-Wolf coding. The relationship between the quantized step size and the allocated rate of each block is determined under the condition of correct reconstruction with the side information at the Slepian-Wolf decoder. Slepian-Wolf coding is performed on the quantized version of each block. Experimental results show that the performance of the proposed algorithm is competitive with that of state-of-the-art compression algorithms, making it appropriate for on-board compression. (C) 2014 Elsevier Ltd. All rights reserved.
In order to model emergency rescue location problem with uncertain rescue time, an uncertain expected cost minimization model is proposed under uncertain environment. For solving this model, we convert the uncertain m...
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In order to model emergency rescue location problem with uncertain rescue time, an uncertain expected cost minimization model is proposed under uncertain environment. For solving this model, we convert the uncertain model to its equivalent deterministic form. Finally, a numerical example has been presented to illustrate the model. The computational results which were solved by the down mountain algorithm are provided to demonstrate the effectiveness of the model.
A linear combination of Gaussian components, i.e. a Gaussian 'mixture', is used to represent the target probability density function (pdf) in Multiple Hypothesis Tracking (MHT) systems. The complexity of MHT i...
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A linear combination of Gaussian components, i.e. a Gaussian 'mixture', is used to represent the target probability density function (pdf) in Multiple Hypothesis Tracking (MHT) systems. The complexity of MHT is typically managed by 'reducing' the number of mixture components. Two complementary MHT mixture reduction algorithms are proposed and assessed using a simulation involving a cluttered infrared (IR) seeker scene. A simple means of incorporating intensity information is also derived and used by both methods to provide well balanced peak-to-track association weights. The first algorithm (MHT-2) uses the Integral Squared Error (ISE) criterion, evaluated over the entire posterior MHT pdf, in a guided optimization procedure, to quickly fit at most two components. The second algorithm (MHT-PE) uses many more components and a simple strategy, involving Pruning and Elimination of replicas, to maximize hypothesis diversity while keeping computational complexity under control. (C) 2013 Elsevier Ltd. All rights reserved.
Detecting communities within networks is of great importance to understand the structure and organizations of real-world systems. To this end, one of the major challenges is to find the local community from a given no...
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Detecting communities within networks is of great importance to understand the structure and organizations of real-world systems. To this end, one of the major challenges is to find the local community from a given node with limited knowledge of the global network. Most of the existing methods largely depend on the starting node and require predefined parameters to control the agglomeration procedure, which may cause disturbing inference to the results of local community detection. In this work, we propose a parameter-free local community detecting algorithm, which uses two self-adaptive phases in detecting the local community, thus comprehensively considering the external and internal link similarity of neighborhood nodes in each clustering iteration. Based on boundary nodes identification, our self-adaptive method can effectively control the scale and scope of the local community. Experimental results show that our algorithm is efficient and well-behaved in both computer-generated and real-world networks, greatly improving the performance of local community detection in terms of stability and accuracy.
A double evolutionary pool memetic algorithm is proposed to solve the examination timetabling problem. To improve the performance of the proposed algorithm, two evolutionary pools, that is, the main evolutionary pool ...
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A double evolutionary pool memetic algorithm is proposed to solve the examination timetabling problem. To improve the performance of the proposed algorithm, two evolutionary pools, that is, the main evolutionary pool and the secondary evolutionary pool, are employed. The genetic operators have been specially designed to fit the examination timetabling problem. A simplified version of the simulated annealing strategy is designed to speed the convergence of the algorithm. A clonal mechanism is introduced to preserve population diversity. Extensive experiments carried out on 12 benchmark examination timetabling instances show that the proposed algorithm is able to produce promising results for the uncapacitated examination timetabling problem.
A new approach regarding real-time low-level image and signal processing is presented. This new approach deals with the philosophy of finding a method for designing and executing algorithms (filters), using the most m...
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A new approach regarding real-time low-level image and signal processing is presented. This new approach deals with the philosophy of finding a method for designing and executing algorithms (filters), using the most minimum possible complexity. The proposed method is based on a special formation of coordinate logic filters (CLFs), the coordinate logic order statistics (CL-OS) filters. CL-OS introduces a minimalistic approach in filter design followed by the greatest possible efficiency in terms of speed, quality, complexity and energy consumption. Moreover CL-OS reveals new signal information;this information gives a different interpretation of the original signal, it could be said that it acts like a function applying a nonlinear quantisation in order to approximate the desired output.
We present an image denoising method using the edge map of an image. The denoised image is considered as a linear combination of the observed image and its average value, where the coefficients are controlled by a loc...
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We present an image denoising method using the edge map of an image. The denoised image is considered as a linear combination of the observed image and its average value, where the coefficients are controlled by a local edge detector. The parameters are set on suitable values related to noise energy computed by the curvature norm of the original image. Implementation can be done in a single iteration and the speed of the process is reasonably high. Noise reduction quality of the introduced method is compared with Wiener and Total Variation based filters for some images. The method appears to be easy, fast and useful for very noisy images. The differences between our method and the patent 6229578 "Edge Detection Based Noise Removal Algorithm" are explained. (C) 2013 Elsevier Ltd. All rights reserved.
The critical external pressure stability calculation of stiffened penstock in the hydroelectric power station is very important work for penstock design. At present, different assumptions and boundary simplification a...
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The critical external pressure stability calculation of stiffened penstock in the hydroelectric power station is very important work for penstock design. At present, different assumptions and boundary simplification are adopted by different calculation methods which sometimes cause huge differences too. In this paper, we present an immune based artificial neural network model via the model and stability theory of elastic ring, we study effects of some factors (such as pipe diameter, pipe wall thickness, sectional size of stiffening ring, and spacing between stiffening rings) on penstock critical external pressure during huge thin-wall procedure of penstock. The results reveal that the variation of diameter and wall thickness can lead to sharp variation of penstock external pressure bearing capacity and then give the change interval of it. This paper presents an optimizing design method to optimize sectional size and spacing of stiffening rings and to determine penstock bearing capacity coordinate with the bearing capacity of stiffening rings and penstock external pressure stability coordinate with its strength safety. As a practical example, the simulation results illustrate that the method presented in this paper is available and can efficiently overcome inherent defects of BP neural network.
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