Importance Analysis of a Multi-state System Based on Direct Partial Logic Derivatives and Multi-valued Decision Diagrams

Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.

Reliability Evaluation and Sensitivity Analysis for Multi-state System Based on Time Degradation Measures

The performance and state of multi-state system depend on its structure and different state combinations of the components. In order to evaluate the reliability of multi-state system effectively,a reliability evaluation and sensitivity analysis method based on the time degradation measures was proposed. The equivalence sets of the multi-state system under different output performances were established. The state combinations were classified according to the performance level. The degradation probability models under different states were established,and the new reliability measures,such as dynamic probability of multi-state system,holding time in each state,dynamic expectation function and integrated expectation function of the performance,were proposed and used to implement the dynamic reliability evaluation and sensitivity analysis. A certain diesel engine fuel feeding system was taken as an application example to illustrate the proposed method. The results show that not only the holding time in the desired state of the components and the system can be predicted,but also the best state component in a certain time period can be obtained.

Preparation of entangledW states based on the cavity QED system

We present a qubit-loss-free(QLF)fusion scheme for generating large-scale atom W states in cavity quantum electrodynamics(QED)system.Compared to the most current fusion schemes which are conditioned on the case where one particle can be extracted from each initial W state to the fusion process,our scheme will access one or two particles from each W state.Based on the atom–cavity-field detuned interaction,three jWin+m+t states can be generated from the jWin,jWim,and jWit states with the help of two auxiliary atoms,and three jWin+m+t+q states can be generated from jWin,jWim,jWit,and a jWiq state with the help of three auxiliary atoms.Comparing the numerical simulations of the resource cost of fusing three small-size W states based on the previous schemes,our fusion scheme seems to be more efficient.This QLF fusion scheme can be generalized to the case of fusing k different or identical particle W states.Furthermore,with no qubit loss,it greatly reduces the number of fusion steps and prepares W states with larger particle numbers.

Pure State Feedback Switching Control Based on the Online Estimated State for Stochastic Open Quantum Systems

For the n-qubit stochastic open quantum systems,based on the Lyapunov stability theorem and LaSalle’s invariant set principle,a pure state switching control based on on-line estimated state feedback(short for OQST-SFC)is proposed to realize the state transition the pure state of the target state including eigenstate and superposition state.The proposed switching control consists of a constant control and a control law designed based on the Lyapunov method,in which the Lyapunov function is the state distance of the system.The constant control is used to drive the system state from an initial state to the convergence domain only containing the target state,and a Lyapunov-based control is used to make the state enter the convergence domain and then continue to converge to the target state.At the same time,the continuous weak measurement of quantum system and the quantum state tomography method based on the on-line alternating direction multiplier(QST-OADM)are used to obtain the system information and estimate the quantum state which is used as the input of the quantum system controller.Then,the pure state feedback switching control method based on the on-line estimated state feedback is realized in an n-qubit stochastic open quantum system.The complete derivation process of n-qubit QST-OADM algorithm is given;Through strict theoretical proof and analysis,the convergence conditions to ensure any initial state of the quantum system to converge the target pure state are given.The proposed control method is applied to a 2-qubit stochastic open quantum system for numerical simulation experiments.Four possible different position cases between the initial estimated state and that of the controlled system are studied and discussed,and the performances of the state transition under the corresponding cases are analyzed.

Numerical simulation for the initial state of avalanche in polydisperse particle systems

Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle systems.Nucleation and propagation processes are illustrated for pentadisperse and triadisperse particle systems,respectively.In these processes,particles involved in the avalanche grow slowly in the early stage and explosively in the later stage,which is clearly different from the continuous and steady growth trend in the monodisperse system.By examining the avalanche propagation,the number growth of particles involved in the avalanche and the slope of the number growth,the initial state can be divided into three stages:T1(nucleation stage),T2(propagation stage),T3(overall avalanche stage).We focus on the characteristics of the avalanche in the T2 stage,and find that propagation distances increase almost linearly in both axial and radial directions in polydisperse systems.We also consider the distribution characteristics of the average coordination number and average velocity for the moving particles.The results support that the polydisperse particle systems are more stable in the T2 stage.

Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.

State Estimation Method for GNSS/INS/Visual Multi-sensor Fusion Based on Factor Graph Optimization for Unmanned System

With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information continuously.However,when this system is applied to indoor or GNSS-denied environments,such as outdoor substations with strong electromagnetic interference and complex dense spaces,it is often unable to obtain high-precision GNSS positioning data.The positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios.This paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source optimization.Through the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.

Interface state-based bound states in continuum and below-continuum-resonance modes with high-Q factors in the rotational periodic system

We have introduced a new approach to calculate the orbital angular momentum(OAM)of bound states in continuum(BICs)and below-continuum-resonance(BCR)modes in the rotational periodic system nested inside and outside by transforming the Bloch wave number from the translational periodic system.We extensively classify and study these BICs and BCR modes,which exhibit high-quality(high-Q)factors,in different regions relative to the interface of the system.These BICs and BCR modes with a high-Q factor have been studied in detail based on distinctive structural parameters and scattering theory.The outcomes of this research break the periodic limitation of interface state-based BICs,and realize more and higher symmetry interface state-based BICs and BCR modes.Moreover,we can control the region where light is captured by adjusting the frequency,and show that the Q factor of BICs is more closely related to the ordinal number of rings and the rotational symmetry number of the system.

Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

Analytical Verification of Performance of Deep Neural Network Based Time-synchronized Distribution System State Estimation

Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input mea-surements.It has already been shown that evaluating perfor-mance based only on the test dataset might not effectively indi-cate the ability of a trained DNN to handle input perturbations.As such,we analytically verify the robustness and trustworthi-ness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)problems.The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted.The framework is val-idated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.

A Health State Prediction Model Based on Belief Rule Base and LSTM for Complex Systems

In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.

Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying Systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying systems.Due to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential eavesdroppers.To avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption scheme.The purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is achieved.Sufficient conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state estimator.Finally,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

Initial-state dependence of phase behaviors in a dense active system

There are rich emergent phase behaviors in non-equilibrium active systems.Flocking and clustering are two representative dynamic phases.The relationship between both the phases is still unclear.Herein,we numerically investigate the evolution of flocking and clustering in a system consisting of self-propelled particles with active reorientation.We consider the interplay between flocking and clustering phases with different initial configurations,and observe a domain in steady state order parameter phase diagrams sensitive to the choice of initial configurations.Specifically,by tuning the initial degree of polar ordering,either a more ordered flocking or a disordered clustering state can be observed in the steady state.These results enlighten us to manipulate emergent behaviors and collective motions of an active system,and are qualitatively different from the emergence of a new bi-stable regime observed in aligned active particles due to an explicit attraction[New J.Phys.14073033(2012)].

Dual-Ion Co-Regulation System Enabling High-Performance Electrochemical Artificial Yarn Muscles with Energy-Free Catch States

Artificial yarn muscles show great potential in applications requiring low-energy consumption while maintaining high performance. However, conventional designs have been limited by weak ion-yarn muscle interactions and inefficient “rocking-chair” ion migration. To address these limitations, we present an electrochemical artificial yarn muscle design driven by a dual-ion co-regulation system. By utilizing two reaction channels, this system shortens ion migration pathways, leading to faster and more efficient actuation. During the charging/discharging process, PF_6~- ions react with carbon nanotube yarn, while Li~+ ions react with an Al foil. The intercalation reaction between PF_6~- and collapsed carbon nanotubes allows the yarn muscle to achieve an energy-free high-tension catch state. The dual-ion coordinated yarn muscles exhibit superior contractile stroke, maximum contractile rate, and maximum power densities, exceeding those of “rocking-chair” type ion migration yarn muscles. The dual-ion co-regulation system enhances the ion migration rate during actuation, resulting in improved performance. Moreover, the yarn muscles can withstand high levels of isometric stress, displaying a stress of 61 times that of skeletal muscles and 8 times that of “rocking-chair” type yarn muscles at higher frequencies. This technology holds significant potential for various applications, including prosthetics and robotics.

Maximum Correntropy Kalman Filtering for Non-Gaussian Systems With State Saturations and Stochastic Nonlinearities

This paper tackles the maximum correntropy Kalman filtering problem for discrete time-varying non-Gaussian systems subject to state saturations and stochastic nonlinearities. The stochastic nonlinearities, which take the form of statemultiplicative noises, are introduced in systems to describe the phenomenon of nonlinear disturbances. To resist non-Gaussian noises, we consider a new performance index called maximum correntropy criterion(MCC) which describes the similarity between two stochastic variables. To enhance the “robustness” of the kernel parameter selection on the resultant filtering performance, the Cauchy kernel function is adopted to calculate the corresponding correntropy. The goal of this paper is to design a Kalman-type filter for the underlying systems via maximizing the correntropy between the system state and its estimate. By taking advantage of an upper bound on the one-step prediction error covariance, a modified MCC-based performance index is constructed. Subsequently, with the assistance of a fixed-point theorem, the filter gain is obtained by maximizing the proposed cost function. In addition, a sufficient condition is deduced to ensure the uniqueness of the fixed point. Finally, the validity of the filtering method is tested by simulating a numerical example.

Backstepping Sliding Mode Control Based on Extended State Observer for Hydraulic Servo System

Hydraulic servo system plays an important role in industrial fields due to the advantages of high response,small size-to-power ratio and large driving force.However,inherent nonlinear behaviors and modeling uncertainties are the main obstacles for hydraulic servo system to achieve high tracking perfor-mance.To deal with these difficulties,this paper presents a backstepping sliding mode controller to improve the dynamic tracking performance and anti-interfer-ence ability.For this purpose,the nonlinear dynamic model is firstly established,where the nonlinear behaviors and modeling uncertainties are lumped as one term.Then,the extended state observer is introduced to estimate the lumped distur-bance.The system stability is proved by using the Lyapunov stability theorem.Finally,comparative simulation and experimental are conducted on a hydraulic servo system platform to verify the efficiency of the proposed control scheme.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Semi-analytical solution for drained expansion analysis of a hollow cylinder of critical state soils

The expansion of a thick-walled hollow cylinder in soil is of non-self-similar nature that the stress/deformation paths are not the same for different soil material points.As a result,this problem cannot be solved by the common self-similar-based similarity techniques.This paper proposes a novel,exact solution for rigorous drained expansion analysis of a hollow cylinder of critical state soils.Considering stress-dependent elastic moduli of soils,new analytical stress and displacement solutions for the nonself-similar problem are developed taking the small strain assumption in the elastic zone.In the plastic zone,the cavity expansion response is formulated into a set of first-order partial differential equations(PDEs)with the combination use of Eulerian and Lagrangian descriptions,and a novel solution algorithm is developed to efficiently solve this complex boundary value problem.The solution is presented in a general form and thus can be useful for a wide range of soils.With the new solution,the non-self-similar nature induced by the finite outer boundary is clearly demonstrated and highlighted,which is found to be greatly different to the behaviour of cavity expansion in infinite soil mass.The present solution may serve as a benchmark for verifying the performance of advanced numerical techniques with critical state soil models and be used to capture the finite boundary effect for pressuremeter tests in small-sized calibration chambers.

Association of DNA methylation/demethylation with the functional outcome of stroke in a hyperinflammatory state

Inflammation is closely related to stroke prognosis, and high inflammation status leads to poor functional outcome in stroke. DNA methylation is involved in the pathogenesis and prognosis of stroke. However, the effect of DNA methylation on stroke at high levels of inflammation is unclear. In this study, we constructed a hyperinflammatory cerebral ischemia mouse model and investigated the effect of hypomethylation and hypermethylation on the functional outcome. We constructed a mouse model of transient middle cerebral artery occlusion and treated the mice with lipopolysaccharide to induce a hyperinflammatory state. To investigate the effect of DNA methylation on stroke, we used small molecule inhibitors to restrain the function of key DNA methylation and demethylation enzymes. 2,3,5-Triphenyltetrazolium chloride staining, neurological function scores, neurobehavioral tests, enzyme-linked immunosorbent assay, quantitative reverse transcription PCR and western blot assay were used to evaluate the effects after stroke in mice. We assessed changes in the global methylation status by measuring DNA 5-mc and DNA 5-hmc levels in peripheral blood after the use of the inhibitor. In the group treated with the DNA methylation inhibitor, brain tissue 2,3,5-triphenyltetrazolium chloride staining showed an increase in infarct volume, which was accompanied by a decrease in neurological scores and worsening of neurobehavioral performance. The levels of inflammatory factors interleukin 6 and interleukin-1 beta in ischemic brain tissue and plasma were elevated, indicating increased inflammation. Related inflammatory pathway exploration showed significant overactivation of nuclear factor kappa B. These results suggested that inhibiting DNA methylation led to poor functional outcome in mice with high inflammation following stroke. Further, the effects were reversed by inhibition of DNA demethylation. Our findings suggest that DNA methylation regulates the inflammatory response in stroke and has an important role in the functional outcome of hyperinflammatory stroke.

THE EXISTENCE OF GROUND STATE NORMALIZED SOLUTIONS FOR CHERN-SIMONS-SCHRODINGER SYSTEMS

In this paper,we study normalized solutions of the Chern-Simons-Schrödinger system with general nonlinearity and a potential in H^(1)(ℝ^(2)).When the nonlinearity satisfies some general 3-superlinear conditions,we obtain the existence of ground state normalized solutions by using the minimax procedure proposed by Jeanjean in[L.Jeanjean,Existence of solutions with prescribed norm for semilinear elliptic equations,Nonlinear Anal.(1997)].