A Blockchain-Based Access Control Scheme for Reputation Value Attributes of the Internet of Things

The Internet of Things(IoT)access controlmechanism may encounter security issues such as single point of failure and data tampering.To address these issues,a blockchain-based IoT reputation value attribute access control scheme is proposed.Firstly,writing the reputation value as an attribute into the access control policy,and then deploying the access control policy in the smart contract of the blockchain system can enable the system to provide more fine-grained access control;Secondly,storing a large amount of resources fromthe Internet of Things in Inter Planetary File System(IPFS)to improve system throughput;Finally,map resource access operations to qualification tokens to improve the performance of the access control system.Complete simulation experiments based on the Hyperledger Fabric platform.Fromthe simulation experimental results,it can be seen that the access control system can achieve more fine-grained and dynamic access control while maintaining high throughput and low time delay,providing sufficient reliability and security for access control of IoT devices.

Dynamic optimal allocation of energy storage systems integrated within photovoltaic based on a dual timescale dynamics model

Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

Utility and Application of a Versatile Analytical Method for MMF Calculation in AC Machines

A versatile analytical method(VAM) for calculating the harmonic components of the magnetomotive force(MMF) generated by diverse armature windings in AC machines has been proposed, and the versatility of this method has been established in early literature. However, its practical applications and significance in advancing the analysis of AC machines need further elaboration. This paper aims to complement VAM by augmenting its theory, offering additional insights into its conclusions, as well as demonstrating its utility in assessing armature windings and its application of calculating torque for permanent magnet synchronous machines(PMSM). This work contributes to advancing the analysis of AC machines and underscores the potential for improved design and performance optimization.

Enhanced degradation of tetracycline by gas-liquid discharge plasma coupled with g-C_(3)N_(4)/TiO_(2)

Plasma-catalysis is considered as one of the most promising technologies for antibiotic degradation in water.In the plasma-catalytic system,one of the factors affecting the degradation effect is the performance of the photocatalyst,which is usually restricted by the rapid recombination of electrons and holes as well as narrow light absorption range.In this research,a photocatalyst g-C_(3)N_(4)/TiO_(2) was prepared and coupled with gas-liquid discharge(GLD)to degrade tetracycline(TC).The performance was examined,and the degradation pathways and mechanisms were studied.Results show that a 90%degradation rate is achieved in the GLD with g-C_(3)N_(4)/TiO_(2) over a 10 min treatment.Increasing the pulse voltage is conducive to increasing the degradation rate,whereas the addition of excessive g-C_(3)N_(4)/TiO_(2) tends to precipitate agglomerates,resulting in a poor degradation efficiency.The redox properties of the g-C_(3)N_(4)/TiO_(2) surface promote the generation of oxidizing active species(H2O2,O3)in solution.Radical quenching experiments showed that·OH,hole(h^(+)),play important roles in the TC degradation by the discharge with g-C_(3)N_(4)/TiO_(2).Two potential degradation pathways were proposed based on the intermediates.The toxicity of tetracycline was reduced by treatment in the system.Furthermore,the g-C_(3)N_(4)/TiO_(2) composites exhibited excellent recoverability and stability.

Effect of dielectric material on the uniformity of nanosecond pulsed dielectric barrier discharge

Dielectric barrier discharge(DBD)is considered as a promising technique to produce large volume uniform plasma at atmospheric pressure,and the dielectric barrier layer between the electrodes plays a key role in the DBD processes and enhancing discharge uniformity.In this work,the uniformity and discharge characteristics of the nanosecond(ns)pulsed DBD with dielectric barrier layers made of alumina,quartz glass,polycarbonate(PC),and polypropylene(PP)are investigated via discharge image observation,voltage-current waveform measurement and optical emission spectral diagnosis.Through analyzing discharge image by gray value standard deviation method,the discharge uniformity is quantitatively calculated.The effects of the space electric field intensity,the electron density(Ne),and the space reactive species on the uniformity are studied with quantifying the gap voltage Ug and the discharge current Ig,analyzing the recorded optical emission spectra,and simulating the temporal distribution of Ne with a one-dimensional fluid model.It is found that as the relative permittivity of the dielectric materials increases,the space electric field intensity is enhanced,which results in a higher Ne and electron temperature(Te).Therefore,an appropriate value of space electric field intensity can promote electron avalanches,resulting in uniform and stable plasma by the merging of electron avalanches.However,an excessive value of space electric field intensity leads to the aggregation of space charges and the distortion of the space electric field,which reduce the discharge uniformity.The surface roughness and the surface charge decay are measured to explain the influences of the surface properties and the second electron emission on the discharge uniformity.The results in this work give a comprehensive understanding of the effect of the dielectric materials on the DBD uniformity,and contribute to the selection of dielectric materials for DBD reactor and the realization of atmospheric pressure uniform,stable,and reactive plasma sources.

Bifurcation analysis and control study of improved full-speed differential model in connected vehicle environment

In recent years, the traffic congestion problem has become more and more serious, and the research on traffic system control has become a new hot spot. Studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable pivots can alleviate the traffic congestion problem from a new perspective. In this work, the full-speed differential model considering the vehicle network environment is improved in order to adjust the traffic flow from the perspective of bifurcation control, the existence conditions of Hopf bifurcation and saddle-node bifurcation in the model are proved theoretically, and the stability mutation point for the stability of the transportation system is found. For the unstable bifurcation point, a nonlinear system feedback controller is designed by using Chebyshev polynomial approximation and stochastic feedback control method. The advancement, postponement, and elimination of Hopf bifurcation are achieved without changing the system equilibrium point, and the mutation behavior of the transportation system is controlled so as to alleviate the traffic congestion. The changes in the stability of complex traffic systems are explained through the bifurcation analysis, which can better capture the characteristics of the traffic flow. By adjusting the control parameters in the feedback controllers, the influence of the boundary conditions on the stability of the traffic system is adequately described, and the effects of the unstable focuses and saddle points on the system are suppressed to slow down the traffic flow. In addition, the unstable bifurcation points can be eliminated and the Hopf bifurcation can be controlled to advance, delay, and disappear,so as to realize the control of the stability behavior of the traffic system, which can help to alleviate the traffic congestion and describe the actual traffic phenomena as well.

Stochastic programming based coordinated expansion planning of generation,transmission,demand side resources,and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.

Improved combustion stability of biogas at different CO_(2) concentrations using inhomogeneous partially premixed stratified flames

Biogas is a renewable and clean energy source that plays an important role in the current environment of lowcarbon transition.If high-content CO_(2) in biogas can be separated,transformed,and utilized,it not only realizes high-value utilization of biogas but also promotes carbon reduction in the biogas field.To improve the combustion stability of biogas,an inhomogeneous,partially premixed stratified(IPPS)combustion model was adopted in this study.The thermal flame structure and stability were investigated for a wide range of mixture inhomogeneities,turbulence levels,CO_(2) concentrations,air-to-fuel velocity ratios,and combustion energies in a concentric flow slot burner(CFSB).A fine-wire thermocouple is used to resolve the thermal flame structure.The flame size was reduced by increasing the CO_(2) concentration and the flames became lighter blue.The flame temperature also decreased with increase in CO_(2) concentration.Flame stability was reduced by increasing the CO_(2) concentration.However,at a certain level of mixture inhomogeneity,the concentration of CO_(2) in the IPPS mode did not affect the stability.Accordingly,the IPPS mode of combustion should be suitable for the combustion and stabilization of biogas.This should support the design of highly stabilized biogas turbulent flames independent of CO_(2) concentration.The data show that the lower stability conditions are partially due to the change in fuel combustion energy,which is characterized by the Wobbe index(WI).In addition,at a certain level of mixture inhomogeneity,the effect of the WI on flame stability becomes dominant.

Machine learning-driven optimization of plasma-catalytic dry reforming of methane

This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data.To address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML model.The ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM process.The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance.We found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/min.Furthermore,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM process.The results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction performance.This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

Decentralized Optimal Control and Stabilization of Interconnected Systems With Asymmetric Information

The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information.Compared with previous work,a closed-loop optimal solution to the control problem and sufficient and necessary conditions for the stabilization problem of the interconnected systems are given for the first time.The main challenge lies in three aspects:Firstly,the asymmetric information results in coupling between control and estimation and failure of the separation principle.Secondly,two extra unknown variables are generated by asymmetric information(different information filtration)when solving forward-backward stochastic difference equations.Thirdly,the existence of additive noise makes the study of mean-square boundedness an obstacle.The adopted technique is proving and assuming the linear form of controllers and establishing the equivalence between the two systems with and without additive noise.A dual-motor parallel drive system is presented to demonstrate the validity of the proposed algorithm.

A Design of Modular Interior Ferrite Magnet Fluxswitching Linear Motor for Track Transport

A novel topology of modular ferrite magnet fluxswitching linear motor(FMFSLM)use for track transport is presented in this paper,which enables more ferrite magnets to be inserted into the primary iron core.The motor has a significant low-cost advantage in long-distance linear drive.The proposed FMFSLM’s structure and working principle were introduced.Further,the thrust force expression of the motor was established.The thrust force components triggering thrust force ripple were investigated,and their expressions can be obtained according to the inductances’Fourier series expressions.Resultantly,the relationship between the harmonics of thrust force and that of self-and mutual inductances was revealed clearly.Based on the relationship,a skewed secondary should be practical to reduce the thrust force ripple.Thus,the effect of employing a skewed secondary to the proposed FMFSLM was investigated,and an optimized skewing span distance was determined.Finite element analysis(FEA)was conducted to validate the exactness of the theoretical analysis.The simulation results indicate that the strategy of suppressing thrust force ripple has a significant effect.Meanwhile,the motor maintains a good efficiency characteristic.The results of the prototype experiment are in good agreement with FEAs,which further verifies the proposed modular interior FMFSLM’s practicability.

Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

Current optimization-based control of dual three-phase PMSM for low-frequency temperature swing reduction

In this paper,a control scheme based on current optimization is proposed for dual three-phase permanent-magnet synchronous motor(DTP-PMSM)drive to reduce the low-frequency temperature swing.The reduction of temperature swing can be equivalent to reducing maximum instantaneous phase copper loss in this paper.First,a two-level optimization aiming at minimizing maximum instantaneous phase copper loss at each electrical angle is proposed.Then,the optimization is transformed to a singlelevel optimization by introducing the auxiliary variable for easy solving.Considering that singleobjective optimization trades a great total copper loss for a small reduction of maximum phase copper loss,the optimization considering both instantaneous total copper loss and maximum phase copper loss is proposed,which has the same performance of temperature swing reduction but with lower total loss.In this way,the proposed control scheme can reduce maximum junction temperature by 11%.Both simulation and experimental results are presented to prove the effectiveness and superiority of the proposed control scheme for low-frequency temperature swing reduction.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

A Catalog of ^(13)CO Clumps from the MWISP in l=10°–20°

In this study, we present a catalog of molecular clumps extracted from ^(13)CO(J=1-0) emission data of the Milky Way Imaging Scroll Painting(MWISP) project. The data covers the inner Milky Way within the longitude range10°≤l≤20° and the latitude strip of |b|≤ 5°. 25. The workflow for the extraction of clumps, namely Facet-SS-3DClump, consists of two parts: the identification of clump candidates and their verification. First, Facet-SS-3DClump employs FacetClumps to identify clump candidates. Subsequently, high-confidence clumps are obtained by cross-matching with the clumps detected by other algorithms, such as dendrogram. Second, these high-confidence clumps are used as prior knowledge to train a semi-supervised deep clustering approach, SS-3D-Clump, which is applied to verify clump candidates detected by FacetClumps, providing confidence levels for the molecular clumps. Finally, the catalog comprising 18,757 molecular clumps was obtained using Facet-SS-3D-Clump, and the catalog is 90% complete above 37 K km s^(-1). We observe a significant deviation of the mean Galactic latitude for clumps within |b|≤ 2° from the midplane, with b=-0°.110. We found that 82.3% of the dust clumps correspond to ^(13)CO clumps by matching with Herschel infrared dust clumps. In the future, Facet-SS-3D-Clump will be applied to detect ^(13)CO clumps in the entire MWISP data.

Review of the Analysis and Suppression for High-frequency Oscillations of the Grid-connected Wind Power Generation System

High-frequency oscillation(HFO)of gridconnected wind power generation systems(WPGS)is one of the most critical issues in recent years that threaten the safe access of WPGS to the grid.Ensuring the WPGS can damp HFO is becoming more and more vital for the development of wind power.The HFO phenomenon of wind turbines under different scenarios usually has different mechanisms.Hence,engineers need to acquire the working mechanisms of the different HFO damping technologies and select the appropriate one to ensure the effective implementation of oscillation damping in practical engineering.This paper introduces the general assumptions of WPGS when analyzing HFO,systematically summarizes the reasons for the occurrence of HFO in different scenarios,deeply analyses the key points and difficulties of HFO damping under different scenarios,and then compares the technical performances of various types of HFO suppression methods to provide adequate references for engineers in the application of technology.Finally,this paper discusses possible future research difficulties in the problem of HFO,as well as the possible future trends in the demand for HFO damping.

Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks

A dynamical model is constructed to depict the spatial-temporal evolution of malware in mobile wireless sensor networks(MWSNs). Based on such a model, we design a hybrid control scheme combining parameter perturbation and state feedback to effectively manipulate the spatiotemporal dynamics of malware propagation. The hybrid control can not only suppress the Turing instability caused by diffusion factor but can also adjust the occurrence of Hopf bifurcation induced by time delay. Numerical simulation results show that the hybrid control strategy can efficiently manipulate the transmission dynamics to achieve our expected desired properties, thus reducing the harm of malware propagation to MWSNs.

Transient AC Overvoltage Suppression Orientated Reactive Power Control of the Wind Turbine in the LCC-HVDC Sending Grid

High-voltage direct current(HVDC) transmission is a crucial way to solve the reverse distribution of clean energy and loads. The line commutated converter-based HVDC(LCCHVDC) has become a vital structure for HVDC due to its high technological maturity and economic advantages. During the DC fault of LCC-HVDC, such as commutation failure, the reactive power regulation of the AC grid always lags the DC control process, causing overvoltage in the AC sending grid, which brings off-grid risk to the wind power generation based on power electronic devices. Nevertheless, considering that wind turbine generators have fast and flexible reactive power control capability, optimizing the reactive power control of wind turbines to participate in the transient overvoltage suppression of the sending grid not only improves the operational safety at the equipment level but also enhances the voltage stability of the system. This paper firstly analyses the impact of wind turbine's reactive power on AC transient overvoltage. Then, it proposes an improved voltage-reactive power control strategy, which contains a reactive power control delay compensation and a power command optimization based on the voltage time series prediction. The delay compensation is used to reduce the contribution of the untimely reactive power of wind turbines on transient overvoltage, and the power command optimization enables wind turbines to have the ability to regulate transient overvoltage, leading to the variation of AC voltage, thus suppressing the transient overvoltage. Finally, the effectiveness and feasibility of the proposed method are verified in a ±800kV/5000MW LCC-HVDC sending grid model based on MATLAB/Simulink.

Performance analysis of the tight combination of GPS, BDS, GALILEO, and QzsS mixed frequencies signals

With the gradual development and modernization of satellite navigation systems,using observation information from multi-GNss has become one of the hot-spot issues in recent years.Multi-system loose combinations form double-difference observation equations within their respective systems,and the positioning effect is improved.However,the interchangeability and compatible interoperability between global navigation satellite systems(GNSS)cannot be truly realized.At the same time,when the number of visible satellites decreases abruptly,the positioning performance deteriorates sharply.This paper focuses on the GNsS multi-system tight combination relative positioning technique,gives a mathe-matical model of multi-system tight combination relative positioning considering differential inter-system bias(DISB),and analyzes the time-varying characteristics of DISB at overlapping and non-overlapping frequencies among GPS/Galileo,GPS/BDS,and GPS/QZSS in terms of receiver brand,tem-perature,and receiver restart.The GNsS tight combination relative positioning performance is verified by static data from Curtin University and dynamic data measured at Taiyuan University of Technology.The results show that compared with loose combination,the ambiguity-fixed rate increases from 62.18%to 97.60%for static data and from 74.97%to 99.53%for dynamic data when the elevation mask angle is 50°,resulting in a significant improvement in positioning performance.

Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.