Inverse Compensation of Hysteresis using Modified Generalized Prandtl-Ishlinskii Hysteresis Model

Smart material based actuators, due to their properties of high precision, fast response, high power density, and small sizes, have become ideal actuators in many industrial applications, i.e. micro positioning, atomic force microscopy, and so forth. However, these smart actuators exhibit hysteresis nonlinear effects, which may worsen tracking performances, lead oscillations or even instabilities. Therefore, the existence of the hysteresis nonlinearities limits the utilization of smart material based actuators, and became the bottleneck of the control strategies development for systems with the smart actuators. In order to overcome the effects of the hysteresis, a number of hysteresis models have been proposed in the literatures. Among them, the Prandtl-Ishlinskii (PI) model, thanks to its significant analytical invertible property, has become one of the most popular hysteresis models. Nevertheless, the PI model can only describe a kind of symmetric, rate-independent, and non-saturated hysteresis, which restricts the use of PI model. Therefore, it requires to generalize the PI model, making it able to represent more complicated hysteresis phenomena, while keeping analytically invertible property. In this thesis, based on the PI model and the Generalized Prandtl-Ishlinskii (GPI) model available in the literature, a modified Generalized Prandtl-Ishlinskii (mGPI) model is proposed, which aims to redefine the play operator in the GPI so as to describe a kind of asymmetric and saturated hysteresis nonlinearities. According to the proposed mGPI model, an analytical inverse model is also derived, which can be used as an inverse compensator of the hysteresis nonlinearities. To validated the proposed inverse model, simulation results are provided confirming the proposed analytical inverse of the mGPI model

Appendix for Nonparametric Multivariate Probability Density Forecast in Smart Grids With Deep Learning

This paper proposes a nonparametric multivariate density forecast model based on deep learning. It not only offers the whole marginal distribution of each random variable in forecasting targets, but also reveals the future correlation between them. Differing from existing multivariate density forecast models, the proposed method requires no a priori hypotheses on the forecasted joint probability distribution of forecasting targets. In addition, based on the universal approximation capability of neural networks, the real joint cumulative distribution functions of forecasting targets are well-approximated by a special positive-weighted deep neural network in the proposed method. Numerical tests from different scenarios were implemented under a comprehensive verification framework for evaluation, including the very short-term forecast of the wind speed, wind power, and the day-ahead forecast of the aggregated electricity load. Testing results corroborate the superiority of the proposed method over current multivariate density forecast models considering the accordance with reality, prediction interval width, and correlations between different random variables

A Web-based Operation Management System for Distributed Divisional Organizations

Operation Management is an important and complex task for a divisional structured organization, especially when the divisions are distributed geographically. In most cases, such organizations didn’t not urge all of it’s divisions to use an integrated information system at the very beginning. But with the development and the expanding of the organization, they sometimes found themselves in the trouble of information exchange and almost lost control of their divisions. At such time, however, on one hand the head quarter inquires more detailed information and more business control on the divisions. On the other hand, some divisions are well built and have its own business processes and information systems. It’s impossible for them to rebuild the information system to integrate with the other divisions and the head quarter as well. Operation Management System (OPMGT) enables real-time inspection of the divisions’ operational data and flexible operation evaluation of each division via the Internet and without much change on the other information systems. The OPMGT presented in this paper was originally developed for the head quarter of a distributed divisional based organization to govern the distributed divisions. System analysis, design and implementation of OPMGT are discussed in detail. Having been developed on the basis of eFramework, a J2EE framework, OPMGT is proved to be highly sufficient in operation management of a distributed divisional structured organization, and it may also do some help to integrate information systems in some degree

Smart Meters Integration in Distribution System State Estimation with Collaborative Filtering and Deep Gaussian Process

The problem of state estimations for electric distribution system is considered. A collaborative filtering approach is proposed in this paper to integrate the slow time-scale smart meter measurements in the distribution system state estimation, in which the deep Gaussian process is incorporated to infer the fast time-scale pseudo measurements and avoid anomalies. Numerical tests have demonstrated the higher estimation accuracy of the proposed method

Heat Shock Protein 70 Inhibits the Activity of Influenza A Virus Ribonucleoprotein and Blocks the Replication of Virus In Vitro and In Vivo

BACKGROUND: Heat shock protein 70 (Hsp70) was identified as a cellular interaction partner of the influenza virus ribonucleoprotein (RNP) complex. The biological significance of the interaction between Hsp70 and RNP has not been fully investigated. PRINCIPAL FINDINGS: Here we demonstrated that Hsp70 was involved in the regulation of influenza A viral transcription and replication. It was found that Hsp70 was associated with viral RNP by directly interacting with the PB1 and PB2 subunits, and the ATPase domain of Hsp70 was required for the association. Immunofluorescence analysis showed that Hsp70 was translocated from the cytoplasm into the nucleus in infected cells. Then we found that Hsp70 negatively regulated the expression of viral proteins in infected cells. Real-time PCR analysis revealed that the transcription and replication of all eight viral segments were significantly reduced in Hsp70 overexpressed cells and greatly increased as Hsp70 was knocked down by RNA interference. Luciferase assay showed that overexpression of Hsp70 could inhibit the viral RNP activity on both vRNA and cRNA promoters. Biochemical analysis demonstrated that Hsp70 interfered with the integrity of RNP. Furthermore, delivered Hsp70 could inhibit the replication of influenza A virus in mice. SIGNIFICANCE: Our study indicated that Hsp70 interacted with PB1 and PB2 of RNP and could interfere with the integrity of RNP and block the virus replication in vitro and in vivo possibly through disrupting the binding of viral polymerase with viral RNA

Universal Quantum Optimization with Cold Atoms in an Optical Cavity

Cold atoms in an optical cavity have been widely used for quantum simulations of many-body physics, where the quantum control capability has been advancing rapidly in recent years. Here, we show the atom cavity system is universal for quantum optimization with arbitrary connectivity. We consider a single-mode cavity and develop a Raman coupling scheme by which the engineered quantum Hamiltonian for atoms directly encodes number partition problems (NPPs). The programmability is introduced by placing the atoms at different positions in the cavity with optical tweezers. The NPP solution is encoded in the ground state of atomic qubits coupled through a photonic cavity mode, that can be reached by adiabatic quantum computing (AQC). We construct an explicit mapping for the 3-SAT and vertex cover problems to be efficiently encoded by the cavity system, which costs linear overhead in the number of atomic qubits. The atom cavity encoding is further extended to quadratic unconstrained binary optimization (QUBO) problems. The encoding protocol is optimal in the cost of atom number scaling with the number of binary degrees of freedom of the computation problem. Our theory implies the atom cavity system is a promising quantum optimization platform searching for practical quantum advantage.Comment: 13 pages, 2 figure

LEO Satellite-Enabled Grant-Free Random Access with MIMO-OTFS

This paper investigates joint channel estimation and device activity detection in the LEO satellite-enabled grant-free random access systems with large differential delay and Doppler shift. In addition, the multiple-input multiple-output (MIMO) with orthogonal time-frequency space modulation (OTFS) is utilized to combat the dynamics of the terrestrial-satellite link. To simplify the computation process, we estimate the channel tensor in parallel along the delay dimension. Then, the deep learning and expectation-maximization approach are integrated into the generalized approximate message passing with cross-correlation--based Gaussian prior to capture the channel sparsity in the delay-Doppler-angle domain and learn the hyperparameters. Finally, active devices are detected by computing energy of the estimated channel. Simulation results demonstrate that the proposed algorithms outperform conventional methods.Comment: This paper has been accepted for presentation at the IEEE GLOBECOM 2022. arXiv admin note: text overlap with arXiv:2202.1305