An improved interval model updating method via adaptive Kriging models

Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.

Use AMMI model to analyze adaptability of rice varieties

The adaptability of rice varieties is one of themost concerned questions to rice breeders.Inrice breeding,combined variety test and re-gional trials which included multiple genotypesand environments were conducted to identifythe adaptability of new rice varieties.In mostregional trials,interaction between genotypeand environment is significant,so it is impor-tant to analyze the interaction for estimating

Multiple Targets Localization Algorithm Based on Covariance Matrix Sparse Representation and Bayesian Learning

The multi-source passive localization problem is a problem of great interest in signal pro-cessing with many applications.In this paper,a sparse representation model based on covariance matrix is constructed for the long-range localization scenario,and a sparse Bayesian learning algo-rithm based on Laplace prior of signal covariance is developed for the base mismatch problem caused by target deviation from the initial point grid.An adaptive grid sparse Bayesian learning targets localization(AGSBL)algorithm is proposed.The AGSBL algorithm implements a covari-ance-based sparse signal reconstruction and grid adaptive localization dictionary learning.Simula-tion results show that the AGSBL algorithm outperforms the traditional compressed-aware localiza-tion algorithm for different signal-to-noise ratios and different number of targets in long-range scenes.

MULTIVARIABLE MODEL REFERENCE ADAPTIVE CONTROL FOR A TURBOFAN ENGINE

A decentralized model reference adaptive control (MRAC) scheme is proposed and applied to design a multivariable control system of a dual-spool turbofan engine.Simulation studies show good static and dynamic performance of the system over the fullflight envelope. Simulation results also show the good effectiveness of reducing interactionin the multivariable system with significant coupling. The control system developed has awide frequency band to satisfy the strict engineering requirement and is practical for engineering applications.

Robustness Analysis of Discrete-time Indirect Model Reference Adaptive Control with Normalized Adaptive Laws

For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter are also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.

Adaptation models of mountain glacier tourism to climate change: a case study of Mt. Yulong Snow scenic area

Mountain glaciers have an obvious location advantage and tourist market condition over polar and high latitude glaciers. Due to the enormous economic benefit and heritage value, some mountain glaciers will always receive higher attention from commercial media, government departments and mountain tourists in China and abroad. At present, more than 100 glaciers have been devel- oped successfully as famous tourist destinations all over the world. However, global climate change seriously affects mountain glaciers and its surrounding environment. According to the current accelerated retreat trend, natural and cultural landscapes of some glaciers will be weakened, even disappear in the future. Climate change will also inevitably affect mountain ecosystems, and tourism routes under ice and glacier experience activities in these ecosystems. Simultaneously, the disappearance of mountain glaciers will also lead to a clear reduction of tourism and local economic benefits. Based on these reasons, this paper took Mr. Yulong Snow scenic area as an example and analyzed the retreat trend of a typical glacier. We then put forward some scientific and rational response mechanisms and adaptation models based on climate change in order to help future sustainable development of mountain glacier tourism.

Cooperative interception with fast multiple model adaptive estimation

For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.

Intelligent control using multiple models based on on-line learning

In this paper we deal with the problem of plants with large parameter variations under different operating modes. A novel intelligent control algorithm based on multiple models is proposed to improve the dynamical response performance. At the same time adaptive model bank is applied to establish models without prior system information. Multiple models and corresponding controllers are automatically established on-line by a conventionally adaptive model and a re-initialized one. A best controller is chosen by the performance function at every instant. The closed-loop system＇s stability and asymptotical convergence of tracking error can be guaranteed. Simulation results have confirmed the validity of the proposed method.

An Adaptive Substructure-Based Model Order Reduction Method for Nonlinear Seismic Analysis in OpenSees

Structural components may enter an initial-elastic state,a plastic-hardening state and a residual-elastic state during strong seismic excitations.In the residual-elastic state,structural components keep in an unloading/reloading stage that is dominated by a tangent stiffness,thus structural components remain residual deformations but behave in an elastic manner.It has a great potential to make model order reduction for such structural components using the tangent-stiffness-based vibration modes as a reduced order basis.In this paper,an adaptive substructure-based model order reduction method is developed to perform nonlinear seismic analysis for structures that have a priori unknown damage distribution.This method is able to generate time-varying substructures and make nonlinear model order reduction for substructures in the residual-elastic phase.The finite element program OpenSees has been extended to provide the adaptive substructure-based nonlinear seismic analysis.At the low level of OpenSees framework,a new abstract layer is created to represent the time-varying substructures and implement the modeling process of substructures.At the high level of OpenSees framework,a new transient analysis class is created to implement the solving process of substructure-based governing equations.Compared with the conventional time step integration method,the adaptive substructure-based model order reduction method can yield comparative results with a higher computational efficiency.

Randomized controlled trials on Roy adaptation model nursing in patients with acute myocardial infarction in china: a systematic review

Objective:This study was to evaluate the quality of the randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction in China.Methods:We systematically searched the Cnki,Wanfang and Vipdatabases,to get randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction.The search period was from inception to October 2020.According to the Cochrane risk bias assessment tool,the quality of the studies included was appraised.Results:A total of 55 studies were retrieved,and 11 were eventually included in the study.Among the studies included,the first study was published in 2008.The overall quality of the 11 studies included was relatively low.Conclusions:The overall quality of the randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction was not high,which would hinder the evidence transformation as well as clinical practice.

Direct model reference adaptive control using K_p = LDU factorization for multivariable discrete-time plants

For a large class of discrete-time multivariable plants with arbitrary relative degrees, the design and analysis of the direct model reference adaptive control scheme are investigated under less restrictive assumptions. The algorithm is based on a new parametrization derived from the high frequency gain matrix factorization Kp=LDU under the condition that the signs of the leading principal minors of/fp are known. By reproving the discrete-time Lp and L2σ norm relationship between inputs and outputs, establishing the properties of discrete-time adaptive law, defining the normalizing signal, and relating the signal with all signals in the closed-loop system, the stability and convergence of the discrete-time multivariable model reference adaptive control scheme are analyzed rigorously in a systematic fashion as in the continuous-time case.

System Design of a Reference Model Adaptive Control for Radial Plasma Position on HL-2A Tokamak

The design of the control system for radial plasma position on HL-2A based on model reference adaptive control (MRAC) principle is presented in this paper. The simulated results show that it can be used to improve the performance of the system greatly. Compared with the classical PID control system, it has obvious advantages in the better dynamic response, the smaller quantity of calculation and the better robustness.

A Multi-Agent Based Model for User Interest Mining on Sina Weibo

User interest mining on Sina Weibo is the basis of personalized recommendations,advertising,marketing promotions,and other tasks.Although great progress has been made in this area,previous studies have ignored the differences among users:the varied behaviors and habits that lead to unique user data characteristics.It is unreasonable to use a single strategy to mine interests from such varied user data.Therefore,this paper proposes an adaptive model for user interest mining based on a multi-agent system whose input includes self-descriptive user data,microblogs and correlations.This method has the ability to select the appropriate strategy based on each user’s data characteristics.The experimental results show that the proposed method performs better than the baselines.

Model Free Adaptive Predictive Control of Desulfurization Slurry pH Based on CPS Framework

In order to improve the slurry pH control accuracy of the absorption tower in the wet flue gas desulfurization process,a model free adaptive predictive control algorithm for the desulfurization slurry pH which is based on a cyber physical systems framework is proposed.First,aiming to address system characteristics of non-linearity and pure hysteresis in slurry pH change process,a model free adaptive predictive control algorithm based on compact form dynamic linearization is proposed by combining model free adaptive control algorithm with model predictive control algorithm.Then,by integrating information resources with the physical resources in the absorption tower slurry pH control process,an absorption tower slurry pH optimization control system based on cyber physical systems is constructed.It is turned out that the model free adaptive predictive control algorithm under the framework of the cyber physical systems can effectively realize the high-precision tracking control of the slurry pH of the absorption tower,and it has strong robustness.

An Optimization Method for Simulator Using Probability Statistic Model

An optimization method was presented to be easily applied in retargetable simulator. The substance of this method is to reduce the redundant information of operation code which is caused by the variety of execution frequencies of instructions. By recoding the operation code in the loading part of simulator, times of bit comparison in identification of an instruction will get reduced. Thus the performance of the simulator will be improved. The theoretical analysis and experimental results both prove the validity of this method.

New model reference adaptive control with input constraints

A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and conventional direct model reference adaptive control scheme,various modifications have been employed to achieve the goal. ＂C omposite model reference adaptive control＂of higher performance is seam-lessly combined with ＂positive μ-mod＂,which consequently results in a smooth tracking trajectory despite of the input constraints. In addition,bounded-gain forgetting is utilized to facilitate faster convergence of parameter estimates. The stability of the closed-loop systemcan be guaranteed by using Lyapunov theory.The merits and effectiveness of the proposed method are illustrated by a numerical example of the longitudinal dynamical systems of a fixed-wing airplane.

An Adaptive Lasso Grey Model for Regional FDI Statistics Prediction

To overcome the deficiency of traditional mathematical statistics methods,an adaptive Lasso grey model algorithm for regional FDI(foreign direct investment)prediction is proposed in this paper,and its validity is analyzed.Firstly,the characteristics of the FDI data in six provinces of Central China are generalized,and the mixture model’s constituent variables of the Lasso grey problem as well as the grey model are defined.Next,based on the influencing factors of regional FDI statistics(mean values of regional FDI and median values of regional FDI),an adaptive Lasso grey model algorithm for regional FDI was established.Then,an application test in Central China is taken as a case study to illustrate the feasibility of the adaptive Lasso grey model algorithm in regional FDI prediction.We also select RMSE(root mean square error)and MAE(mean absolute error)to demonstrate the convergence and the validity of the algorithm.Finally,we train this proposedal gorithm according to the regional FDI statistical data in six provinces in Central China from 2006 to 2018.We then use it to predict the regional FDI statistical data from 2019 to 2023 and show its changing tendency.The extended work for the adaptive Lasso grey model algorithm and its procedure to other regional economic fields is also discussed.

An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT

Social Edge Service(SES)is an emerging mechanism in the Social Internet of Things(SIoT)orchestration for effective user-centric reliable communication and computation.The services are affected by active and/or passive attacks such as replay attacks,message tampering because of sharing the same spectrum,as well as inadequate trust measurement methods among intelligent devices(roadside units,mobile edge devices,servers)during computing and content-sharing.These issues lead to computation and communication overhead of servers and computation nodes.To address this issue,we propose the HybridgrAph-Deep-learning(HAD)approach in two stages for secure communication and computation.First,the Adaptive Trust Weight(ATW)model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead.Second,a Quotient User-centric Coeval-Learning(QUCL)mechanism to formulate secure channel selection,and Nash equilibrium method for optimizing the communication to share data over edge devices.The simulation results confirm that our proposed approach has achieved effective communication and computation performance,and enhanced Social Edge Services(SES)reliability than state-of-the-art approaches.

Speed Identification of Speed Sensorless Linear Induction Motor Based on MRAS

In the linear induction motor control system,the optical grating speed transducer is susceptible to strong magnetic field interference.What's more,it may reduce motor integration and raise device costs.Therefore a speed identification method to replace grating speed transducer is studied in this article.This speed identification method for linear induction motor mainly adopts Model Reference Adaptive Method(Abbreviated as MRAS)and Popov Hyperstability Theory.The research content of this paper can be divided into four parts.First,the mathematical model of the motor based on the model reference adaptive system structure is deduced.Second,the adaptive law of the estimated speed is solved by Popov hyper-stability theory,which ensures the stability of the system.Third,the simulation model of the linear induction motor speed identification control system based on model reference adaptation is built in the MATLAB environment.Finally,the simulation test and analysis are carried out.The simulation results show that the speed identification control system can track the actual speed of the linear induction motor well in the no-load operation and the load operation,and the stability of the system is guaranteed in the full speed range.

Composite Adaptive Control of Belt Polishing Force for Aero-engine Blade

The existing methods for blade polishing mainly focus on robot polishing and manual grinding.Due to the difficulty in high-precision control of the polishing force,the blade surface precision is very low in robot polishing,in particular,quality of the inlet and exhaust edges can not satisfy the processing requirements.Manual grinding has low efficiency,high labor intensity and unstable processing quality,moreover,the polished surface is vulnerable to burn,and the surface precision and integrity are difficult to ensure.In order to further improve the profile accuracy and surface quality,a pneumatic flexible polishing force-exerting mechanism is designed and a dual-mode switching composite adaptive control(DSCAC) strategy is proposed,which combines Bang-Bang control and model reference adaptive control based on fuzzy neural network(MRACFNN) together.By the mode decision-making mechanism,Bang-Bang control is used to track the control command signal quickly when the actual polishing force is far away from the target value,and MRACFNN is utilized in smaller error ranges to improve the system robustness and control precision.Based on the mathematical model of the force-exerting mechanism,simulation analysis is implemented on DSCAC.Simulation results show that the output polishing force can better track the given signal.Finally,the blade polishing experiments are carried out on the designed polishing equipment.Experimental results show that DSCAC can effectively mitigate the influence of gas compressibility,valve dead-time effect,valve nonlinear flow,cylinder friction,measurement noise and other interference on the control precision of polishing force,which has high control precision,strong robustness,strong anti-interference ability and other advantages compared with MRACFNN.The proposed research achieves high-precision control of the polishing force,effectively improves the blade machining precision and surface consistency,and significantly reduces the surface roughness.