A method of direction-of-arrival (DOA) estimation using array interpolation is proposed in this paper to increase the number of resolvable sources and improve the DOA estimation performance for coprime array configura...
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A method of direction-of-arrival (DOA) estimation using array interpolation is proposed in this paper to increase the number of resolvable sources and improve the DOA estimation performance for coprime array configuration with holes in its virtual array. The virtual symmetric nonuniform linear array (VSNLA) of coprime array signal model is introduced, with the conventional MUSIC with spatial smoothing algorithm (SS-MUSIC) applied on the continuous lags in the VSNLA;the degrees of freedom (DoFs) for DOA estimation are obviously not fully exploited. To effectively utilize the extent of DoFs offered by the coarray configuration, a compressing sensing based array interpolation algorithm is proposed. The compressing sensing technique is used to obtain the coarse initial DOA estimation, and a modified iterative initial DOA estimation based interpolation algorithm (IMCA-AI) is then utilized to obtain the final DOA estimation, which maps the sample covariance matrix of the VSNLA to the covariance matrix of a filled virtual symmetric uniform linear array (VSULA) with the same aperture size. The proposed DOA estimation method can efficiently improve the DOA estimation performance. The numerical simulations are provided to demonstrate the effectiveness of the proposed method.
In many practical cases, it is necessary to characterize the explored area with a regular set of geodata. Regular matrix data (e.g., ordinary maps) are calculated via existing data interpolation and extrapolation. For...
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In many practical cases, it is necessary to characterize the explored area with a regular set of geodata. Regular matrix data (e.g., ordinary maps) are calculated via existing data interpolation and extrapolation. For low frequency (oversampled) data acquired within a dense profile net (e.g., seismic three-dimensional structural or gravity mapping), this procedure is mathematically more or less stable and, to a certain extent, unique since we might neglect discrepancies resulting from different interpolations. The situation is quite different for high-resolution and high-frequency contaminated data (e.g., raw seismic attributes or geochemistry measurements) represented by sparse profiling. Considering the variety of exploration cases, the investigation of different interpolation algorithm efficiency seems very important. Since it is impossible to compare all algorithms by means of formal mathematics, we have designed a test program. A representative set of seismic attribute maps has been artificially destroyed by introducing blank values (from 20% up to 95%) and then restored by different interpolation algorithms bicubic, bilinear, nearest neighbor, and smart averaging. Smart averaging interpolation is done in a live window. The position, form, and size of the window are determined by some mathematical criterion on a trial-and-error basis. Discrepancies between restored and initial (true) data have been assessed and analysed. It is shown that the total (absolute) efficiency and comparative (relative) efficiency of the algorithms depend mostly upon the initial interpolant data characteristics. Identifying the best interpolation algorithm for all interpretive cases seems impossible. Some aspects of data processing are discussed in connection with interpolation accuracy.
Craig interpolation is a standard method to construct and refine abstractions in model checking. To obtain abstractions that are suitable for the verification of software programs or hardware designs, model checkers r...
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Craig interpolation is a standard method to construct and refine abstractions in model checking. To obtain abstractions that are suitable for the verification of software programs or hardware designs, model checkers rely on theorem provers to find the right interpolants, or interpolants containing the right predicates, in a generally infinite lattice of interpolants for any given interpolation problem. We present a semantic and solver-independent framework for systematically exploring interpolant lattices, based on the notion of interpolation abstraction. We discuss how interpolation abstractions can be constructed for a variety of logics, and how they can be applied in the context of software model checking.
Dissolved oxygen (DO) content is a significant aspect of water quality in aquaculture. Prediction of dissolved oxygen may timely avoid the financial loss caused by inappropriate dissolved oxygen content and three-dime...
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Dissolved oxygen (DO) content is a significant aspect of water quality in aquaculture. Prediction of dissolved oxygen may timely avoid the financial loss caused by inappropriate dissolved oxygen content and three-dimensional prediction can achieve more accurate and overall guidance. Therefore, this study presents a three-dimensional short-term prediction model of dissolved oxygen in crab aquaculture ponds based on back propagation artificial neural network (BPANN) optimized by particle swarm optimization (PSO), which coupled with Kriging method. In this model, wavelet analysis is adopted for denoising, BPANN optimized by PSO is utilized for data analysis and one-dimensional prediction, and Kriging method is used for three-dimensional prediction. Compared with traditional one-dimensional prediction model, three-dimensional model has more real reaction of dissolved oxygen content in crab growth environment. In particular, the merits of PSO are evaluated against genetic algorithm (GA). The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for PSO model are 0.136445, 0.90534, and 0.15384, respectively, while for the GA model the values are 2.04184, 1.18316, and 0.21014, respectively. Furthermore, results of cross validation experiment show that the average error of this model is 0.0705 (mg/L). Consequently, this study suggests that the prediction model operates in a satisfactory manner.
A method for reconstructing the digital real tooth surfaces of hypoid gears can be a significant foundation for a variety of dynamic performance and lifetime prediction. This study demonstrates a new digital real toot...
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A method for reconstructing the digital real tooth surfaces of hypoid gears can be a significant foundation for a variety of dynamic performance and lifetime prediction. This study demonstrates a new digital real tooth surfaces modeling approach for hypoid gears based on non-geometric-feature segmentation and interpolation algorithm. In this method, the discrete data points, which are obtained by using acoordinate measure machine (CMM), are segmented in the form of Delaunay triangular meshes. In order to identify irregular local micro-geometry features, the segmentation method starts with a feature detection based on normal vectors of Delaunay triangular meshes, identifying wear regions around each discrete data point, and is followed by region growing steps to divide tooth surface. In addition, a revised interpolation algorithm is applied to describe local micro-geometry features on wear regions via weighted factors to locally qualify the triangular vertexes. And the revised fairing algorithm minimizes the effect of noisy points. Experimental results from reconstruction of real tooth surface after wear test demonstrate that our method can improve the computation precision of wear region on actual tooth surfaces.
Cyber-Physical Systems (CPS) are often composed of a great number of mobile, wireless networked devices. In order to guarantee the system performing, management policies focused on becoming transparent to high-level a...
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Cyber-Physical Systems (CPS) are often composed of a great number of mobile, wireless networked devices. In order to guarantee the system performing, management policies focused on becoming transparent to high-level applications, the changes in the hardware platform have to be implemented. However, traditional reactive methodologies and basic proposed predictive solutions are not valid either due to the extremely dynamical behavior of CPS or because the high number of involved devices prevents fulfill the timing requirements. Therefore, in this paper, we present an advance predictive solution for managing the mobility and device lifecycle, being able to meet all requirements of CPS. The solution is based on an infinite loop, which calculates, in each iteration, a sequence of future system states using a CPS simulator and interpolation algorithms. Furthermore, an experimental validation is provided in order to determine the performing of the proposed solution.
The integration of stochastic wind power has accentuated a challenge for power system stability assessment. Since the power system is a time-variant system under wind generation fluctuations, pure time-domain simulati...
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The integration of stochastic wind power has accentuated a challenge for power system stability assessment. Since the power system is a time-variant system under wind generation fluctuations, pure time-domain simulations are difficult to provide real-time stability assessment. As a result, the worst-case scenario is simulated to give a very conservative assessment of system transient stability. In this study, a probabilistic contingency analysis through a stability measure method is proposed to provide a less conservative contingency analysis which covers 5-min wind fluctuations and a successive fault. This probabilistic approach would estimate the transfer limit of a critical line for a given fault with stochastic wind generation and active control devices in a multi-machine system. This approach achieves a lower computation cost and improved accuracy using a new stability measure and polynomial interpolation, and is feasible for online contingency analysis.
A method of extracting bare-earth points from photogrammetric point clouds by partially using an existing lower resolution digital terrain model (DTM) is presented. The bare-earth points are extracted based on a thres...
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A method of extracting bare-earth points from photogrammetric point clouds by partially using an existing lower resolution digital terrain model (DTM) is presented. The bare-earth points are extracted based on a threshold defined by local slope. The local slope is estimated from the lower resolution DTM. A gridded DTM is then interpolated from the extracted bare-earth points. Five different interpolation algorithms are implemented and evaluated to identify the most suitable interpolation method for such non-uniformly scattered data. The algorithm is tested on four test sites with varying topographic and ground cover characteristics. The results are evaluated against a reference DTM created using aerial laser scanning. The deviations of the extracted bare-earth points, and the interpolated DTM, from the reference DTM increases with increasing forest canopy density and terrain roughness. The DTM created by the method is significantly closer to the reference DTM than the lower resolution national DTM. The ANUDEM (Australian National University Digital Elevation Modelling) interpolation method is found to be the best performing interpolation method in terms of reducing the deviations and in terms of modelling the terrain realistically with minimum artefacts, although the differences among the interpolation methods are not considerably large.
Instrumentation to measure microclimate in greenhouses is a task to developing before starting the identification and control moisture and temperature in greenhouses, a variable which best describe the microclimate an...
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We further study the properties of the back and forth error compensation and correction (BFECC) method for advection equations such as those related to the level set method and for solving Hamilton-Jacobi equations on...
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We further study the properties of the back and forth error compensation and correction (BFECC) method for advection equations such as those related to the level set method and for solving Hamilton-Jacobi equations on unstructured meshes. In particular, we develop a new limiting strategy which requires another backward advection in time so that overshoots/undershoots on the new time level get exposed when they are transformed back to compare with the solution on the old time level. This new technique is very simple to implement even for unstructured meshes and is able to eliminate artifacts induced by jump discontinuities in derivatives of the solution as well as by jump discontinuities in the solution itself (even if the solution has large gradients in the vicinities of a jump). Typically, a formal second order method for solving a time dependent Hamilton-Jacobi equation requires quadratic interpolation in space. A BFECC method on the other hand only requires linear interpolation in each step, thus is local and easy to implement even for unstructured meshes.
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