Due to the unmanned aerial vehicle remote sensing images (UAVRSI) within rich texture details of ground objects and obvious phenomenon, the same objects with different spectra, it is difficult to effectively acquire t...
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Due to the unmanned aerial vehicle remote sensing images (UAVRSI) within rich texture details of ground objects and obvious phenomenon, the same objects with different spectra, it is difficult to effectively acquire the edge information using traditional edge detection operator. To solve this problem, an edge detection method of UAVRSI by combining Zernike moments with clustering algorithms is proposed in this study. To begin with, two typical clustering algorithms, namely, fuzzy c-means (FCM) and K-means algorithms, are used to cluster the original remote sensing images so as to form homogeneous regions in ground objects. Then, Zernike moments are applied to carry out edge detection on the remote sensing images clustered. Finally, visual comparison and sensitivitymethods are adopted to evaluate the accuracy of the edge information detected. Afterwards, two groups of experimental data are selected to verify the proposed method. Results show that the proposed method effectively improves the accuracy of edge information extracted from remote sensing images.
In this paper, the recently developed physically inspired non-gradient algorithm is employed for structural optimization with frequency constraints. The algorithm being called vibrating particles system (VPS) mimics t...
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In this paper, the recently developed physically inspired non-gradient algorithm is employed for structural optimization with frequency constraints. The algorithm being called vibrating particles system (VPS) mimics the free vibration of single degree of freedom systems with viscous damping. Truss optimization with frequency constraints has attracted substantial attention recently in order to enhance the dynamic performance of structures. These kinds of problems are believed to represent nonlinear and non-convex search spaces with several local optima and therefore are suitable for examining the capabilities of the new algorithms. A set of five truss design problems are considered for evaluating the VPS in this article. The numerical results demonstrate the efficiency and robustness of the new method and its competitive performance to other algorithms for structural optimization problems.
It is often desirable that referring expressions be chosen in such a way that their referents are easy to identify. This article focuses on referring expressions in hierarchically structured domains, exploring the hyp...
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It is often desirable that referring expressions be chosen in such a way that their referents are easy to identify. This article focuses on referring expressions in hierarchically structured domains, exploring the hypothesis that referring expressions can be improved by including logically redundant information in them if this leads to a significant reduction in the amount of search that is needed to identify the referent, Generation algorithms are presented that implement this idea by including logically redundant information into the generated expression, in certain well-circumscribed situations. To test our hypotheses, and to assess the performance of our algorithms, two controlled experiments with human subjects were conducted. The first experiment confirms that human judges have a preference for logically redundant expressions in the cases where our model predicts this to be the case. The second experiment suggests that readers benefit from the kind of logical redundancy that our algorithms produce, as measured in terms of the effort needed to identify the referent of the expression.
For wireless network microseismic monitoring and the problems of low compression ratio and high energy consumption of communication, this paper proposes a segmentation compression algorithm according to the characteri...
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For wireless network microseismic monitoring and the problems of low compression ratio and high energy consumption of communication, this paper proposes a segmentation compression algorithm according to the characteristics of the microseismic signals and the compression perception theory (CS) used in the transmission process. The algorithm will be collected as a number of nonzero elements of data segmented basis, by reducing the number of combinations of nonzero elements within the segment to improve the accuracy of signal reconstruction, while taking advantage of the characteristics of compressive sensing theory to achieve a high compression ratio of the signal. Experimental results show that, in the quantum chaos immune clone refactoring (Q-CSDR) algorithm for reconstruction algorithm, under the condition of signal sparse degree higher than 40, to be more than 0.4 of the compression ratio to compress the signal, the mean square error is less than 0.01, prolonging the network life by 2 times.
Most adaptive problems of interest are non-linear, however, the application of non-linear, or pole-zero filter to these problems have been restricted due to the poor convergence properties of adaptive pole-zero algori...
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Most adaptive problems of interest are non-linear, however, the application of non-linear, or pole-zero filter to these problems have been restricted due to the poor convergence properties of adaptive pole-zero algorithms that often result in an unstable solution. Here, we present a stable pole-zero filter model for such applications. In a given example, this filter resulted in significant, up to 4 times reduction in computations and up to 7 time reduction in memory bandwidth when compared to a linear filter for the same problem. The above algorithm has been used for echo cancellation and demonstrated in a directory assistance system that has been deployed in carrier networks and also for channel equalization in a V.17 modem(1).
Within the framework of constrained density functional theory (CDFT), the diabatic or charge localized states of electron transfer (ET) have been constructed. Based on the diabatic states, inner reorganization energy ...
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Within the framework of constrained density functional theory (CDFT), the diabatic or charge localized states of electron transfer (ET) have been constructed. Based on the diabatic states, inner reorganization energy lambda(in) has been directly calculated. For solvent reorganization energy lambda(s), a novel and reasonable nonequilibrium solvation model is established by introducing a constrained equilibrium manipulation, and a new expression of lambda(s) has been formulated. It is found that lambda(s) is actually the cost of maintaining the residual polarization, which equilibrates with the extra electric field. On the basis of diabatic states constructed by CDFT, a numerical algorithm using the new formulations with the dielectric polarizable continuum model (D-PCM) has been implemented. As typical test cases, self-exchange ET reactions between tetracyanoethylene (TCNE) and tetrathiafulvalene (TTF) and their corresponding ionic radicals in acetonitrile are investigated. The calculated reorganization energies A are 7293 cm(-1) for TCNE/TCNE- and 5939 cm(-1) for TTF/TTF+ reactions, agreeing well with available experimental results of 7250 cm(-1) and 5810 cm(-1), respectively.
Recent advances in laser scanning hardware have allowed rapid generation of high-resolution digital terrain models (DTMs) for large areas. However, the automatic discrimination of ground and non-ground light detection...
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Recent advances in laser scanning hardware have allowed rapid generation of high-resolution digital terrain models (DTMs) for large areas. However, the automatic discrimination of ground and non-ground light detection and ranging (lidar) points in areas covered by densely packed buildings or dense vegetation is difficult. In this paper, we introduce a new hierarchical moving curve-fitting filter algorithm that is designed to automatically and rapidly filter lidar data to permit automatic DTM generation. This algorithm is based on fitting a second-degree polynomial surface using flexible tiles of moving blocks and an adaptive threshold. The initial tile size is determined by the size of the largest building in the study area. Based on an adaptive threshold, non-ground points and ground points are classified and labelled step by step. In addition, we used a multi-scale weighted interpolation method to estimate the bare-earth elevation for non-ground points and obtain a recovered terrain model. Our experiments in four study areas showed that the new filtering method can separate ground and non-ground points in both urban areas and those covered by dense vegetation. The filter error ranged from 4.08% to 9.40% for Type I errors, from 2.48% to 7.63% for Type II errors, and from 5.01% to 7.40% for total errors. These errors are lower than those of triangulated irregular network filter algorithms.
The paper presents a new global three-dimensional model of the Earth thermosphere (for altitudes from 90 to 500 km) with a high spatial resolution. The model uses simple approximations for calculation of solar radiati...
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The paper presents a new global three-dimensional model of the Earth thermosphere (for altitudes from 90 to 500 km) with a high spatial resolution. The model uses simple approximations for calculation of solar radiation and ion-neutral interaction. A detailed description of the algorithm is presented. It is shown that the model represents the main features of the general thermospheric circulation with a satisfactory accuracy.
A novel CT reconstruction model is proposed, and the reconstruction is completed by this kernel-based method. The reconstruction kernel can be obtained by combining the approximate inverse method with the FDK algorith...
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A novel CT reconstruction model is proposed, and the reconstruction is completed by this kernel-based method. The reconstruction kernel can be obtained by combining the approximate inverse method with the FDK algorithm. The computation of the kernel is moderate, and the reconstruction results can be improved by introducing the compact support version of the kernel. The efficiency and the accuracy are shown in the numerical experiments.
High-performance heterogeneous computing systems are achieved by the use of efficient application scheduling algorithms. However, most of the current algorithms have low efficiency in scheduling. Aiming at solving thi...
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High-performance heterogeneous computing systems are achieved by the use of efficient application scheduling algorithms. However, most of the current algorithms have low efficiency in scheduling. Aiming at solving this problem, we propose a novel task scheduling algorithm for heterogeneous computing named HSIP (heterogeneous scheduling algorithm with improved task priority) whose functionality relies on three pillars: (1) an improved task priority strategy based on standard deviation with improved magnitude as computation weight and communication cost weight to make scheduling priority more reasonable;(2) an entry task duplication selection policy to make the makespan shorter;and (3) an improved idle time slots (ITS) insertion-based optimizing policy to make the task scheduling more efficient. We evaluate our proposed algorithm on randomly generated DAGs, using some real application DAGs by comparison with some classical scheduling algorithms. According to the experimental results, our proposed algorithm appears to perform better than other algorithms in terms of schedule length ratio, efficiency, and frequency of best results.
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