Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

Multimodel Ensemble Forecast of Global Horizontal Irradiance at PV Power Stations Based on Dynamic Variable Weight

In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.

A high resolution inversion method for fluid factor with dynamic dryrock V_(P)/V_(S) ratio squared

As an important indicator parameter of fluid identification,fluid factor has always been a concern for scholars.However,when predicting Russell fluid factor or effective pore-fluid bulk modulus,it is necessary to introduce a new rock skeleton parameter which is the dry-rock VP/VS ratio squared(DVRS).In the process of fluid factor calculation or inversion,the existing methods take this parameter as a static constant,which has been estimated in advance,and then apply it to the fluid factor calculation and inversion.The fluid identification analysis based on a portion of the Marmousi 2 model and numerical forward modeling test show that,taking the DVRS as a static constant will limit the identification ability of fluid factor and reduce the inversion accuracy.To solve the above problems,we proposed a new method to regard the DVRS as a dynamic variable varying with depth and lithology for the first time,then apply it to fluid factor calculation and inversion.Firstly,the exact Zoeppritz equations are rewritten into a new form containing the fluid factor and DVRS of upper and lower layers.Next,the new equations are applied to the four parameters simultaneous inversion based on the generalized nonlinear inversion(GNI)method.The testing results on a portion of the Marmousi 2 model and field data show that dynamic DVRS can significantly improve the fluid factor identification ability,effectively suppress illusion.Both synthetic and filed data tests also demonstrate that the GNI method based on Bayesian deterministic inversion(BDI)theory can successfully solve the above four parameter simultaneous inversion problem,and taking the dynamic DVRS as a target inversion parameter can effectively improve the inversion accuracy of fluid factor.All these results completely verified the feasibility and effectiveness of the proposed method.

DYNAMIC MODEL AND SIMULATION OF A NOVEL ELECTRO-HYDRAULIC FULLY VARIABLE VALVE SYSTEM FOR FOUR-STROKE AUTOMOTIVE ENGINES

In modem four-stroke engine technology, variable valve timing and lift control offers potential benefits for making a high-performance engine. A novel electro-hydraulic fully variable valve train for four-stroke automotive engines is introduced. The construction of the nonlinear mathematic model of the valve train system and its dynamic analysis are also presented. Experimental and simulation results show that the novel electro-hydraulic valve train can achieve fully variable valve timing and lift control. Consequently the engine performance on different loads and speeds will be significantly increased. The technology also permits the elimination of the traditional throttle valve in the gasoline engines and increases engine design flexibility.

Hybrid dynamic model of polymer electrolyte membrane fuel cell stack using variable neural network

The polymer electrolyte membrane（PEM） fuel cell has been regarded as a potential alternative power source,and a model is necessary for its design,control and power management.A hybrid dynamic model of PEM fuel cell,which combines the advantages of mechanism model and black-box model,is proposed in this paper.To improve the performance,the static neural network and variable neural network are used to build the black-box model.The static neural network can significantly improve the static performance of the hybrid model,and the variable neural network makes the hybrid dynamic model predict the real PEM fuel cell behavior with required accuracy.Finally,the hybrid dynamic model is validated with a 500 W PEM fuel cell.The static and transient experiment results show that the hybrid dynamic model can predict the behavior of the fuel cell stack accurately and therefore can be effectively utilized in practical application.

Comparing dynamical systems concepts and techniques for biomechanical analysis

Traditional biomechanical analyses of human movement are generally derived from linear mathematics.While these methods can be useful in many situations,they do not describe behaviors in human systems that are predominately nonlinear.For this reason,nonlinear analysis methods based on a dynamical systems approach have become more prevalent in recent literature.These analysis techniques have provided new insights into how systems(1) maintain pattern stability,(2) transition into new states,and(3) are governed by short-and long-term(fractal) correlational processes at different spatio-temporal scales.These different aspects of system dynamics are typically investigated using concepts related to variability,stability,complexity,and adaptability.The purpose of this paper is to compare and contrast these different concepts and demonstrate that,although related,these terms represent fundamentally different aspects of system dynamics.In particular,we argue that variability should not uniformly be equated with stability or complexity of movement.In addition,current dynamic stability measures based on nonlinear analysis methods(such as the finite maximal Lyapunov exponent) can reveal local instabilities in movement dynamics,but the degree to which these local instabilities relate to global postural and gait stability and the ability to resist external perturbations remains to be explored.Finally,systematic studies are needed to relate observed reductions in complexity with aging and disease to the adaptive capabilities of the movement system and how complexity changes as a function of different task constraints.

MODELING, VALIDATION AND OPTIMAL DESIGN OF THE CLAMPING FORCE CONTROL VALVE USED IN CONTINUOUSLY VARIABLE TRANSMISSION

Associated dynamic performance of the clamping force control valve used in continuously variable transmission （CVT） is optimized. Firstly, the structure and working principle of the valve are analyzed, and then a dynamic model is set up by means of mechanism analysis. For the purpose of checking the validity of the modeling method, a prototype workpiece of the valve is manufactured for comparison test, and its simulation result follows the experimental result quite well. An associated performance index is founded considering the response time, overshoot and saving energy, and five structural parameters are selected to adjust for deriving the optimal associated performance index. The optimization problem is solved by the genetic algorithm （GA） with necessary constraints. Finally, the properties of the optimized valve are compared with those of the prototype workpiece, and the results prove that the dynamic performance indexes of the optimized valve are much better than those of the prototype workpiece.

Variable dimensional state space based global path planning for mobile robot

A variable dimensional state space（VDSS） has been proposed to improve the re-planning time when the robotic systems operate in large unknown environments.VDSS is constructed by uniforming lattice state space and grid state space.In VDSS,the lattice state space is only used to construct search space in the local area which is a small circle area near the robot,and grid state space elsewhere.We have tested VDSS with up to 80 indoor and outdoor maps in simulation and on segbot robot platform.Through the simulation and segbot robot experiments,it shows that exploring on VDSS is significantly faster than exploring on lattice state space by Anytime Dynamic A＊（AD＊） planner and VDSS is feasible to be used on robotic systems.

Routine laboratory parameters in patients with necrotizing pancreatitis by the time of operative pancreatic debridement:Food for thought

BACKGROUND Timing of invasive intervention such as operative pancreatic debridement(OPD)in patients with acute necrotizing pancreatitis(ANP)is linked to the degree of encapsulation in necrotic collections and controlled inflammation.Additional markers of these processes might assist decision-making on the timing of surgical intervention.In our opinion,it is logical to search for such markers among routine laboratory parameters traditionally used in ANP patients,considering simplicity and cost-efficacy of routine laboratory methodologies.AIM To evaluate laboratory variables in ANP patients in the preoperative period for the purpose of their use in the timing of surgery.METHODS A retrospective analysis of routine laboratory parameters in 53 ANP patients undergoing OPD between 2017 and 2020 was performed.Dynamic changes of routine hematological and biochemical indices were examined in the preoperative period.Patients were divided into survivors and non-survivors.Survivors were divided into subgroups with short and long post-surgery length of stay(LOS)in hospital.Correlation analysis was used to evaluate association of laboratory variables with LOS.Logistic regression was used to assess risk factors for patient mortality.RESULTS Seven patients(15%)with severe acute pancreatitis(SAP)and 46 patients(85%)with moderately SAP(MSAP)were included in the study.Median age of participants was 43.2 years;33(62.3%)were male.Pancreatitis etiology included biliary(15%),alcohol(80%),and idiopathic/other(5%).Median time from diagnosis to OPD was≥4 wk.Median postoperative LOS was at the average of 53 d.Mortality was 19%.Progressive increase of platelet count in preoperative period was associated with shortened LOS.Increased aspartate aminotransferase and direct bilirubin(DB)levels the day before the OPD along with weak progressive decrease of DB in preoperative period were reliable predictors for ANP patient mortality.CONCLUSION Multifactorial analysis of dynamic changes of routine laboratory variables can be useful for a person-tailored timing of surgical intervention in ANP patients.

Variable length dynamic addressing based on network traffic distribution in wireless sensor networks

In this paper,a novel dynamic addressing scheme for wireless sensor networks(WSNs)is proposed by using variable length coding.A WSN is typically composed of numerous tiny energy-constrained sensor nodes with limited information processing and data storage capabilities;thus,the energy-efficient strategy is the key issue in designing protocols for WSN.Traditional addressing strategies adopt flat addressing(static and uniform addresses)for sensor nodes.However,the proposed variable length dynamic addressing(VLDA)for sensor nodes is based on the fact that different nodes in the network have uneven traffic loads.Therefore,nodes with more data to receive or send are allocated with shorter addresses.Whether a node is busy or not is determined by the network traffic distribution(NTD),which is defined as the number of data packets each node has received or sent in a period of time.Sensor nodes’energy is saved by VLDA scheme;hence,the wireless sensor network’s lifetime is extended.In the simulation,a 20%improvement has been achieved through the addressing scheme compared to traditional flat addressing.

Stochastic fluctuations of permittivity coupling regulate seizure dynamics in partial epilepsy

Partial epilepsy is characterized by recurrent seizures that arise from a localized pathological brain region. During the onset of partial epilepsy, the seizure evolution commonly exhibits typical timescale separation phenomenon. This timescale separation behavior can be mimicked by a paradigmatic model termed as Epileptor, which consists of coupled fast-slow neural populations via a permittivity variable. By incorporating permittivity noise into the Epileptor model, we show here that stochastic fluctuations of permittivity coupling participate in the modulation of seizure dynamics in partial epilepsy. In particular, introducing a certain level of permittivity noise can make the model produce more comparable seizure-like events that capture the temporal variability in realistic partial seizures. Furthermore, we observe that with the help of permittivity noise our stochastic Epileptor model can trigger the seizure dynamics even when it operates in the theoretical nonepileptogenic regime. These findings establish a deep mechanistic understanding on how stochastic fluctuations of permittivity coupling shape the seizure dynamics in partial epilepsy,and provide insightful biological implications.

Analysis on capabilities of density-based solvers within OpenFOAM to distinguish aerothermal variables in difusion boundary layer

Open source feld operation and manipulation（OpenFOAM）is one of the most prevalent open source computational fluid dynamics（CFD）software.It is very convenient for researchers to develop their own codes based on the class library toolbox within OpenFOAM.In recent years,several density-based solvers within OpenFOAM for supersonic/hypersonic compressible flow are coming up.Although the capabilities of these solvers to capture shock wave have already been verifed by some researchers,these solvers still need to be validated comprehensively as commercial CFD software.In boundary layer where diffusion is the dominant transportation manner,the convective discrete schemes＇capability to capture aerothermal variables,such as temperature and heat flux,is different from each other due to their own numerical dissipative characteristics and from viewpoint of this capability,these compressible solvers within OpenFOAM can be validated further.In this paper,frstly,the organizational architecture of density-based solvers within OpenFOAM is analyzed.Then,from the viewpoint of the capability to capture aerothermal variables,the numerical results of several typical geometrical felds predicted by these solvers are compared with both the outcome obtained from the commercial software Fastran and the experimental data.During the computing process,the Roe,AUSM＋（Advection Upstream Splitting Method）,and HLLC（Harten-Lax-van Leer-Contact）convective discrete schemes of which the spatial accuracy is 1st and 2nd order are utilized,respectively.The compared results show that the aerothermal variables are in agreement with results generated by Fastran and the experimental data even if the1st order spatial precision is implemented.Overall,the accuracy of these density-based solvers can meet the requirement of engineering and scientifc problems to capture aerothermal variables in diffusion boundary layer.

Dynamic alterations of amplitude of low-frequency fluctuations in patients with chronic neck pain

Background:The pathogenesis of neck pain in the brain,which is the fourth most common cause of disability,remains unclear.Furthermore,little is known about the characteristics of dynamic local functional brain activity in cervical pain.Objective:The present study aimed to investigate the changes of local brain activity caused by chronic neck pain and the factors leading to neck pain.Methods:Using the amplitude of low-frequency fluctuations(ALFF)method combined with sliding window approach,we compared local brain activity that was measured by the functional magnetic resonance imaging(fMRI)of 107 patients with chronic neck pain(CNP)with that of 57 healthy control participants.Five pathogenic factors were selected for correlation analysis.Results:The group comparison results of dynamic amplitude of low-frequency fluctuation(dALFF)variability showed that patients with CNP exhibited decreased dALFF variability in the left inferior temporal gyrus,the middle temporal gyrus,the angular gyrus,the inferior parietal marginal angular gyrus,and the middle occipital gyrus.The abnormal dALFF variability of the left inferior temporal gyrus was negatively correlated with the average daily working hours of patients with neck pain.Conclusions:The findings indicated that the brain regions of patients with CNP responsible for audition,vision,memory,and emotion were subjected to temporal variability of abnormal regional brain activity.Moreover,the dALFF variability in the left inferior temporal gyrus might be a risk factor for neck pain.This study revealed the brain dysfunction of patients with CNP from the perspective of dynamic local brain activity,and highlighted the important role of dALFF variability in understanding the neural mechanism of CNP.

Optimal two-impulse space interception with multiple constraints

We consider optimal two-impulse space interception problems with multiple constraints.The multiple constraints are imposed on the terminal position of a space interceptor,impulse and impact instants,and the component-wise magnitudes of velocity impulses.These optimization problems are formulated as multi-point boundary value problems and solved by the calculus of variations.Slackness variable methods are used to convert all inequality constraints into equality constraints so that the Lagrange multiplier method can be used.A new dynamic slackness variable method is presented.As a result,an indirect optimization method is developed.Subsequently,our method is used to solve the two-impulse space interception problems of free-flight ballistic missiles.A number of conclusions for local optimal solutions have been drawn based on highly accurate numerical solutions.Specifically,by numerical examples,we show that when time and velocity impulse constraints are imposed,optimal two-impulse solutions may occur;if two-impulse instants are free,then a two-impulse space interception problem with velocity impulse constraints may degenerate to a one-impulse case.

Multi-scale variability of the tropical Indian Ocean circulation system revealed by recent observations

The tropical Indian Ocean circulation system includes the equatorial and near-equatorial circulations, the marginal sea circulation, and eddies. The dynamic processes of these circulation systems show significant multi-scale variability associated with the Indian Monsoon and the Indian Ocean dipole. This paper summarizes the research progress over recent years on the tropical Indian Ocean circulation system based on the large-scale hydrological observations and numerical simulations by the South China Sea Institute of Oceanology(SCSIO), Chinese Academy of Sciences. Results show that:(1) the wind-driven Kelvin and Rossby waves and eastern boundary-reflected Rossby waves regulate the formation and evolution of the Equatorial Undercurrent and the Equatorial Intermediate Current;(2) the equatorial wind-driven dynamics are the main factor controlling the inter-annual variability of the thermocline in the eastern Indian Ocean upwelling;(3) the equatorial waves transport large amounts of energy into the Bay of Bengal in forms of coastal Kelvin and reflected free Rossby waves. Several unresolved issues within the tropical Indian Ocean are discussed:(i) the potential effects of the momentum balance and the basin resonance on the variability of the equatorial circulation system, and(ii) the potential contribution of wind-driven dynamics to the life cycle of the eastern Indian Ocean upwelling. This paper also briefly introduces the international Indian Ocean investigation project of the SCSIO, which will advance the study of the multi-scale variability of the tropical Indian Ocean circulation system, and provide a theoretical and data basis to support marine environmental security for the countries around the Maritime Silk Road.