Global State Vector Research Articles

Distributed Multi-View Sparse Vector Recovery

In this paper, we consider a multi-view compressed sensing problem, where each sensor can only obtain a partial view of the global sparse vector. Here the partial view means that some arbitrary and unknown indices of the global vector are unobservable to that sensor and do not contribute to the measurement outputs. The sensors aim to collaboratively recover the global state vector in a decentralized manner. We formulate this recovery problem as a bilinear optimization problem relying on a factored joint sparsity model (FJSM), in which the variables are factorized into a node-specific sparse local masking vector and the desired common sparse global vector. We first theoretically analyze the general conditions guaranteeing the global vector's successful recovery. Then we propose a novel in-network algorithm based on the powerful distributed alternating direction method of multipliers (ADMM), which can reconstruct the vectors and achieve consensus among nodes concerning the estimation of the global vector. Specifically, each node alternately updates the common global vector and its local masking vector, and then it transfers the estimated global vector to its neighboring nodes for further updates. To avoid potential divergence of the iterative algorithm, we propose an early stopping rule for the estimation of the local masking vectors and further conceive an estimation error-mitigation algorithm. The convergence of the proposed algorithms is theoretically proved. Finally, extensive simulations validate their excellent performance both in terms of the convergence and recovery accuracy.

Mode computation of immersed multilayer plates by solving an eigenvalue problem

The aim of this paper is to compute modes of immersed multilayer plates by writing and solving an eigenvalue problem. The method can be applied to any kind of material with layers, i.e., fluid, anisotropic and viscoelastic. The two external interfaces of the plate can be described as either vacuum/vacuum, fluid/vacuum, or fluid/fluid with a single fluid or fluid/fluid with two different fluids. The method is based on the discretization of the plate by using a finite differences scheme in its vertical direction. One global state vector is associated with inner discretized positions of each layer, and two local state vectors characterize the physical state at its bounds. Interfacial state vectors are introduced in certain situations at external and internal plate interfaces. With these state vectors and after pertinent algebraic manipulations, an eigenvalue system is built. Its solutions are searched by fixing the slowness, wavevector or frequency of guided waves. These three parameterizations correspond to three different physical models. For each case, discussions of dispersion curves and attenuation curves are given for guided modes in a plate loaded by fluids at one or two sides. This numerical tool is shown to provide convenience and accuracy.

SENSOR FUSION BASED UPON SELF ORGANIZING MAPS AND LOCAL KALMAN FILTERS FOR LOCAL FAULT DIAGNOSIS

A challenging strategy for fault diagnosis is related to data fusion and feature extraction such as having local models of the plant integrated through patterns. To pursuit this goal a group of Kalman Filters is proposed, one per sensor in order to estimate a state of the plant per sensor. Thereafter, a self-organized map processes the information in order to determine the response areas within the map. Moreover, Kalman filter auto tuning procedure, becomes an interesting issue due to correlated noise amongst them as well as local state dependency at this point this may call a pre-processing stage. Furthermore, once global state vector is produced, the classification stage takes place as long as this vector exists as post processing stage. The resulting maps determine areas of fault free scenarios or nonlinear and no healthy responses classified as faults. This last point is obtained through a high accuracy of the system.

Modified federated Kalman filter for INS/GNSS/CNS integration

This paper presents a modified version of federated Kalman filter (FKF) for INS/GNSS/CNS integration by improving the computational efficiency involved in the FKF's master filter. During the master filtering process, the modified federated Kalman filter (MFKF) firstly decomposes the global state vector into three sub-states according to the characteristics of INS/GNSS/CNS integration. Subsequently, it fuses the sub-state estimations from INS/GNSS and INS/CNS subsystems with the corresponding ones from the time-update solution of the master filter, respectively. Eventually, the fused sub-state estimations are recombined to yield the global state estimation. The proposed MFKF provides the capability of distributed and parallel data processing for the global state fusion to reduce the computational load involved in the master filtering process of the FKF. Experimental results and comparison analysis demonstrate the effectiveness of the proposed MFKF.

Entanglement dynamics of electron-electron scattering in low-dimensional semiconductor systems

We perform the quantitative evaluation of the entanglement dynamics in scattering events between two indistinguishable electrons interacting via the Coulomb potential in one- and two-dimensional semiconductor nanostructures. We apply a criterion based on the von Neumann entropy and the Schmidt decomposition of the global state vector suitable for systems of identical particles. From the time-dependent numerical solution of the two-particle wave function of the scattering carriers we compute their entanglement evolution for different spin configurations: two electrons with the same spin, with different spin, and singlet and triplet spin states. The procedure allows us to evaluate the mechanisms that govern entanglement creation and their connection with the characteristic physical parameters and initial conditions of the system. The cases in which the evolution of entanglement is similar to the one obtained for distinguishable particles are discussed.

An asynchronous integration and event detection algorithm for simulating multi-agent hybrid systems

A simulation algorithm is presented for multi-agent hybrid systems---systems consisting of many sets of nonsmooth differential equations---such as systems involving multiple rigid bodies, vehicles, or airplanes. The differential equations are partitioned into coupled subsystems, called "agents"; and the conditions which trigger the discontinuities in the derivatives, called "events", may depend on the global state vector. Such systems normally require significant computational resources to simulate because a global time step is used to ensure the discontinuity is properly handled. When the number of systems is large, forcing all system to be simulated at the same rate creates a computational bottleneck, dramatically decreasing efficiency. By using a control systems approach for selecting integration step sizes, we avoid using a global time step. Each subsystem can be simulated asynchronously when the state is away from the event. As the state approaches the event, the simulation is able to synchronize each of the local time clocks in such a way that the discontinuities are properly handled without the need for "roll back". The algorithm's operation and utility is demonstrated on an example problem inspired by autonomous highway vehicles. Using a combination of stochastic modelling and numerical experiments we show that the algorithm requires significantly less computation time when compared with traditional simulation techniques for such problems, and scales more favorably with problem size.

General criterion for the entanglement of two indistinguishable particles

We relate the notion of entanglement for quantum systems composed of two identical constituents to the impossibility of attributing a complete set of properties to both particles. This implies definite constraints on the mathematical form of the state vector associated with the whole system. We then analyze separately the cases of fermion and boson systems, and we show how the consideration of both the Slater-Schmidt number of the fermionic and bosonic analog of the Schmidt decomposition of the global state vector and the von Neumann entropy of the one-particle reduced density operators can supply us with a consistent criterion for detecting entanglement. In particular, the consideration of the von Neumann entropy is particularly useful in deciding whether the correlations of the considered states are simply due to the indistinguishability of the particles involved or are a genuine manifestation of the entanglement. The treatment leads to a full clarification of the subtle aspects of entanglement of two identical constituents which have been a source of embarrassment and of serious misunderstandings in the recent literature.

Near-field optics and quantum optics: an assignation arranged by four kinds of photons.

In the process of emergence a photon emitted from an atom (or a molecule) is usually no better localized in space than to the near-field zone of the source. Near-field optics therefore is of central importance for understanding fundamental statistical aspects related to single-photon tunnelling, the space-time description of photon dynamics, and the photon position-operator problem. In the present work an attempt is made to study the microscopic near-field optical interaction from a quantum statistical point of view. In near-field quantum electrodynamics (QED) scalar and longitudinal photons always are involved and this makes the covariant formulation of QED attractive also in the low-energy regime. We show that the Lorenz gauge condition on the global state vector relates to the near-field electrodynamics of the d-photons. The gauge photon is shown to be of no importance in near-field interactions. To understand the role of the lack of photon localizability we finally study near-field quantum optical correlations in a new so-called propagator gauge.

Observability, controllability and decentralized control of interconnected power systems

An examination is made of the structural properties of observability and controllability for a class of interconnected power system models. A state-space representation which is both completely observable and controllable is developed for a two-area interconnected power system (TAIPS). These properties of the new representation may be exploited in order to generate, for each area, controls which are based upon area output-feedback only. With the use of reduced-order observers, controls which are linear functionals of the global state-vector may be generated within each area. The proposed scheme therefore provides for the complete decentralization of a global state-feedback control policy (e.g. pole assignment; optimal control), in the sense that the area control feedback loops are completely decoupled.