Reconstruction of enterprise debt networks based on compressed sensing

In this letter, the compressive sensing (CS) is utilized to accelerate the matrix filling procedure of the traditional method of moments (MoM). Instead of constituting a fully populated matrix as the traditional MoM typically does, the CS technology allows one to establish an underdetermined linear system, and the unknown induced currents can be reconstructed by finding the smallest possible $\ell _1 $ norm solutions to the underdetermined linear system. Thus, the number of computations required for constructing the impedance matrix and for solving the unknown induced currents can be reduced dramatically. The ideas can be exploited in the context of bistatic scattering from an arbitrary target. Numerical simulations are presented, and the results are compared to those obtained from traditional MoM to illustrate the validity and the efficiency of the proposed method.

The alpha approximation method is known to be effective and simple for soil moisture retrieval from time series of synthetic aperture radar data. However, its accuracy is usually degraded by the scattering from vegetation, and it entails working with an underdetermined linear system when solving the unknown surface parameters. In this work, we study how the availability of fully polarimetric data and a diversity in incidence angles can help this method for soil moisture estimation. Results are obtained using data from the Soil Moisture Active Passive Validation Experiment 2012 campaign acquired by an air-borne L-band radar system. The assessment of the performance is based on in situ measurements over agricultural fields corresponding to five different crop types: bean, soybean, canola, corn, and wheat. The validation shows that, compared with the original method, the retrieval accuracy can be improved when the polarimetric decomposition is included in the approach. The combination of polarimetric decomposition and multi-incidence observations of enriched data provides the best performance, with a decrease in the final root-mean-square error between 0.4% and 5% with respect to single-pol and single-incidence data. Compared with HH, the results obtained for VV data present a higher accuracy for the overall crop types. The most noticeable improvement is achieved for corn, soybean and wheat, demonstrating the contribution of this extension of the original approach.

In this article, we present a novel method that results in efficient minimum norm solution for the rate coordination problem in redundant manipulators. The theory is developed based upon a geometric interpretation that, for minimum norm criterion, vectors orthogonal to constraint space should pass through the origin of the solution space. It is shown that, for any spatial manipulator with 1, 2, or 3 degrees of redundancy, the minimum norm rate solution can be derived in analytic closed form. An example of the analytic formulation is given for a 3R planar case, substantiated with simulation results. The behavior of this algorithm in nonredundant and near singular situations is also discussed. The method offers an equivalent but much more efficient alternative to using the pseudoinverse in redundancy resolution and, in fact, is applicable to any underdetermined linear system. An alternative formulation of pseudoinverse arrived at in the course of the development is also presented. © 1992 John Wiley & Sons, Inc.

Compressive sensing relies on the sparse prior imposed on the signal of interest to solve the ill-posed recovery problem in an under-determined linear system. The objective function used to enforce the sparse prior information should be both effective and easily optimizable. Motivated by the entropy concept from information theory, in this paper we propose the generalized Shannon entropy function and R\'{e}nyi entropy function of the signal as the sparsity promoting regularizers. Both entropy functions are nonconvex, non-separable. Their local minimums only occur on the boundaries of the orthants in the Euclidean space. Compared to other popular objective functions, minimizing the generalized entropy functions adaptively promotes multiple high-energy coefficients while suppressing the rest low-energy coefficients. The corresponding optimization problems can be recasted into a series of reweighted $l_1$-norm minimization problems and then solved efficiently by adapting the FISTA. Sparse signal recovery experiments on both the simulated and real data show the proposed entropy functions minimization approaches perform better than other popular approaches and achieve state-of-the-art performances.

This paper addresses the problem of feature selection for the classification of ground penetrating radar signals. We propose a new classification approach based on time-frequency analysis and sparse kernel feature selection. In the proposed approach, a time-frequency or a time-scale transform is first applied to the one-dimensional radar trace. Sparse kernel feature selection is then employed to extract an optimum set of features for classification. The sparse kernel method is formulated as an underdetermined linear system in a high-dimensional space, and the category labels of the training samples are used as measurements to select the most informative features. The proposed approach is evaluated through an industrial application of assessing railway ballast fouling conditions. Experimental results show that the proposed combination of sparse kernel feature selection and support vector machine classification yields very high classification rates using only a small number of features.

Approximate message passing-based compressed sensing reconstruction with generalized elastic net prior

Forecasting Networks Links with Laplace Characteristic and Geographical Information in Complex Networks

Link prediction aims to predict undetected or unknown links through network information. Most prediction indicators depend on local topology but ignore the pivot role of nodes. Aiming at the problem of topological sparseness in some directed scale-free networks, a preference attachment method based on Shannon theory is proposed to measure the relevance of information from the perspective of nodes. This paper defined the nodes self-information while analyzing nodes interaction process and the conditional information while analyzing the conditional probability of effective interaction based on information theory, and use them designed a new predict indicator. Experiments on 9 scale-free networks show that the proposed method has a better improvement under the Precision metrics.

Due to the expanding trend of smart homes, electromobiles and renewable and alternative energy sources, there is the possibility of linking these systems. In order to optimize and interconnect these systems, all input and output information from these systems must be known. The define main and the secondary optimization criteria and then design the control system according to this optimization criterion. This master control system must be able to work with all on-line inputs and outputs information in real-time, as well as with the prediction and statistics of already accumulated data.

Fuzzy controlled robot arm playing two-dimensional ping-pong game

Multi-modal simultaneous machine translation fusion of image information

Network reconstruction is significant in a variety of domains including physics, biology, engineering and social science. Although a lot of effort has been put into static network reconstructions, time-varying networks are rarely studied. Existing studies of time-varying networks usually assume nodes' dynamics to be linear or consider nodes' nonlinear dynamics as prior knowledge. In this paper, identification of time-varying weighted networks is discussed under the condition where network topology, link weights and nodes' nonlinear dynamics are all unknown or unavailable. In fact, networks in real world are naturally sparse which enables the transformation from network reconstruction problem into sparse signal recovering, so that $l_{1}$ regularization can be effectively applicable. Based on the assumption that most natural networks can be mathematically expressed by only a few important analytical functions, we construct a function set consisting of both linear and nonlinear candidate functions. Then LASSO is applied to select the possible function assemblies from the proposed set and optimize the coefficients of the corresponding function terms in order to best describe time-varying structures as well as nodes' nonlinear dynamics. To prove the feasibility and effectiveness of this reconstruction method, both two complex models are testified in networks of different sizes. And this method shows potential ability applying to a wide range of dynamical time-varying networks.

Robust DC/DC converter control for polymer electrolyte membrane fuel cell application

Classification can also take advantage of links in heterogeneous information networks. Knowledge can be effectively propagated across a heterogeneous network because the nodes of the same type that are linked similarly via the same typed links are likely to be similar. Moreover, following the idea of ranking-based clustering, one can explore ranking-based classification since objects highly ranked in a class are likely to play a more important role in classification. In this chapter, we show that by distinguishing the types of links in the networks during class label propagation, the classification accuracy can be significantly enhanced.

Increasing evidence has shown that gene-gene interactions have important effects in biological processes of human diseases. Due to the high dimensionality of genetic measurements, interaction analysis usually suffers from a lack of sufficient information and has unsatisfactory results. Biological network information has been massively accumulated, allowing researchers to identify biomarkers while taking a system perspective, conducting network selection (of functionally related biomarkers), and accommodating network structures. In main-effect-only analysis, network information has been incorporated. However, effort has been limited in interaction analysis. Recently, link networks that describe the relationships between genetic interactions have been demonstrated as effective for revealing multiscale hierarchical organizations in networks and providing interesting findings beyond node networks. In this study, we develop a novel structured Bayesian interaction analysis approach to effectively incorporate network information. This study is among the first to identify gene-gene interactions with the assistance of network selection, while simultaneously accommodating the underlying network structures of both main effects and interactions. It innovatively respects multiple hierarchies among main effects, interactions, and networks. The Bayesian technique is adopted, which may be more informative for estimation and prediction over some other techniques. An efficient variational Bayesian expectation-maximization algorithm is developed to explore the posterior distribution. Extensive simulation studies demonstrate the practical superiority of the proposed approach. The analysis of TCGA data on melanoma and lung cancer leads to biologically sensible findings with satisfactory prediction accuracy and selection stability.