Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

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.

Complex adaptive system theory,agent-based modeling,and simulation in dominant technology formation

Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.

Projection-Based Dimensional Reduction of Adaptively Refined Nonlinear Models

Adaptive mesh refinement (AMR) is fairly practiced in the context of high-dimensional, mesh-based computational models. However, it is in its infancy in that of low-dimensional, generalized-coordinate-based computational models such as projection-based reduced-order models. This paper presents a complete framework for projection-based model order reduction (PMOR) of nonlinear problems in the presence of AMR that builds on elements from existing methods and augments them with critical new contributions. In particular, it proposes an analytical algorithm for computing a pseudo-meshless inner product between adapted solution snapshots for the purpose of clustering and PMOR. It exploits hyperreduction—specifically, the energy-conserving sampling and weighting hyperreduction method—to deliver for nonlinear and/or parametric problems the desired computational gains. Most importantly, the proposed framework for PMOR in the presence of AMR capitalizes on the concept of state-local reduced-order bases to make the most of the notion of a supermesh, while achieving computational tractability. Its features are illustrated with CFD applications grounded in AMR and its significance is demonstrated by the reported wall-clock speedup factors.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance:A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows,comprising human-driven vehicles(HDVs)and connected and autonomous vehicles(CAVs),face challenges from uncertain disturbances,such as sensor faults,inaccurate driver operations,and mismatched model errors.Furthermore,misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’perception and platoon safety.In this paper,we develop a two-dimensional robust control method for a mixed platoon,including a single leading CAV and multiple following HDVs that incorpo-rate robust information sensing and platoon control.To effectively detect and locate unknown obstacles ahead of the leading CAV,we propose a cooperative vehicle-infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV.This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner.Additionally,we propose a distributed car-following control scheme with robustness to guarantee the following HDVs,considering uncertain disturbances.We also provide theoretical proof of the string stability under this control framework.Finally,extensive simulations are conducted to validate our approach.The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers,significantly reduce the mean-square deviation in obstacle sensing,and approach the theoretical error lower bound.Moreover,the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

Multiple model PHD filter for tracking sharply maneuvering targets using recursive RANSAC based adaptive birth estimation

An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles.The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper,we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.

Adaptive Random Effects/Coefficients Modeling

Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.

Change in self-image pressure level before and after autologous fat breast augmentation and its effect on social adaptability

BACKGROUND There is an increasingly strong demand for appearance and physical beauty in social life,marriage,and other aspects with the development of society and the improvement of material living standards.An increasing number of people have improved their appearance and physical shape through aesthetic plastic surgery.The female breast plays a significant role in physical beauty,and droopy or atrophied breasts can frequently lead to psychological inferiority and lack of confidence in women.This,in turn,can affect their mental health and quality of life.AIM To analyze preoperative and postoperative self-image pressure-level changes of autologous fat breast augmentation patients and their impact on social adaptability.METHODS We selected 160 patients who underwent autologous fat breast augmentation at the First Affiliated Hospital of Xinxiang Medical University from January 2020 to December 2022 using random sampling method.The general information,selfimage pressure level,and social adaptability of the patients were investigated using a basic information survey,body image self-assessment scale,and social adaptability scale.The self-image pressure-level changes and their effects on the social adaptability of patients before and after autologous fat breast augmentation were analyzed.RESULTS We collected 142 valid questionnaires.The single-factor analysis results showed no statistically significant difference in the self-image pressure level and social adaptability score of patients with different ages,marital status,and monthly income.However,there were significant differences in social adaptability among patients with different education levels and employment statuses.The correlation analysis results revealed a significant correlation between the self-image pressure level and social adaptability score before and after surgery.Multiple factors analysis results showed that the degree of concern caused by appearance in selfimage pressure,the degree of possible behavioral intervention,the related distress caused by body image,and the influence of body image on social life influenced the social adaptability of autologous fat breast augmentation patients.CONCLUSION The self-image pressure on autologous fat breast augmentation patients is inversely proportional to their social adaptability.

Assistive Devices and Clothing: Exploring Adaptive Clothing Needs for Women with Lower Limb Prostheses Using the FEA Model

This study aimed to comprehensively investigate the essential considerations in designing adaptive clothing for women with lower limb prostheses in Saudi Arabia. Employing a qualitative methodology, the research entailed semi-structured, in-depth interviews with women utilizing lower limb prostheses and prosthetic specialists. This approach was selected to unearth pivotal design prerequisites and comprehend the specific challenges these women encounter within the realm of clothing. The utilization of selective sampling facilitated the collection of intricate and valuable insights. A Functional, Expressive, and Aesthetic (FEA) User Needs model was utilized to scrutinize participant feedback. Functional requisites encompass ease of dressing and undressing, accessibility to the prosthetic limb, comfort, mobility with the prosthesis, and appropriate fit. Additionally, participants highlighted various expressive needs, including privacy preservation, modesty, camouflaging disability appearances, maintaining alignment with non-disabled women’s fashion, and considerations about the aesthetic aspects of garments.

Adapter Based on Pre-Trained Language Models for Classification of Medical Text

We present an approach to classify medical text at a sentence level automatically.Given the inherent complexity of medical text classification,we employ adapters based on pre-trained language models to extract information from medical text,facilitating more accurate classification while minimizing the number of trainable parameters.Extensive experiments conducted on various datasets demonstrate the effectiveness of our approach.

A Lane Change Model Considering the Stability of Cooperative Adaptive Cruise Control Platoon Fleet

In this article,lane change models for mixed traffic flow under cooperative adaptive cruise control(CACC)platoon formation are established.The analysis begins by examining the impact of lane changes on traffic flow stability.The influences of various factors such as lane change locations,timing,and the current traffic state on stability are discussed.In this analysis,it is assumed that the lane change location and the entry position in the adjacent lane have already been selected,without considering the specific intention behind the lane change.The speeds of the involved vehicles are adjusted based on an existing lane change model,and various conditions are analyzed for traffic flow disturbances,including duration,shock amplitude,and driving delays.Numerical calculations are provided to illustrate these effects.Additionally,traffic flow stability is factored into the lane change decision-making process.By incorporating disturbances to the fleet into the lane change income model,both a lane change intention model and a lane change execution model are constructed.These models are then compared with a model that does not account for stability,leading to the corresponding conclusions.

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.

Application of PPE Model in Land Adaptability Appraisal in Small Basin Based on RAGA

The paper applies multi-dimension to lower technology-projection pursuit evaluation model in the conservation of water and soil discipline domain, optimizes the projection direction using the improved acceleration genetic algorithms, transforms the multi-dimensional data target to lower sub-space, and values soil adaptability of Dongdagou basin in Keshan County by searching the optimal projection direction and the projection function data. The paper provides a new notion and method for the conservation of water and soil in small basin.

A self-adaptive grey forecasting model and its application

GM(1,1)models have been widely used in various fields due to their high performance in time series prediction.However,some hypotheses of the existing GM(1,1)model family may reduce their prediction performance in some cases.To solve this problem,this paper proposes a self-adaptive GM(1,1)model,termed as SAGM(1,1)model,which aims to solve the defects of the existing GM(1,1)model family by deleting their modeling hypothesis.Moreover,a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed,the proposed multi-parameter optimization scheme adopts machine learning ideas,takes all adjustable parameters of SAGM(1,1)model as input variables,and trains it with firefly algorithm.And Sobol’sensitivity indices are applied to study global sensitivity of SAGM(1,1)model parameters,which provides an important reference for model parameter calibration.Finally,forecasting capability of SAGM(1,1)model is illustrated by Anhui electricity consumption dataset.Results show that prediction accuracy of SAGM(1,1)model is significantly better than other models,and it is shown that the proposed approach enhances the prediction performance of GM(1,1)model significantly.

Experimental evaluation on well pattern adaptability of ultra-low permeability reservoir using sandstone flat model

As for ultra-low permeability reservoir,the adaptability of common nine-spot well pattern is studied through large-scale flat models made by micro-fractured natural sandstone outcrops.Combined with non-linear porous flow characteristics,the concept of dimensionless pressure sweep efficiency and deliverability index are put forward to evaluate the physical models' well pattern adaptability.Through experiments,the models' pressure distribution is measured and on which basis,the pressure gradient fields are drawn and the porous flow regions of these models are divided into dead oil region,non-linear porous flow region,and quasi-linear porous flow region with the help of twin-core non-linear porous flow curve.The results indicate that rectangular well pattern in fracture reservoirs has the best adaptability,while the worst is inverted nine-spot equilateral well pattern.With the increase of drawdown pressure,dead oil region decreases,pressure sweep efficiency and deliverability index increase; meantime,the deliverability index of rectangular well pattern has much more rational increase.Under the same drawdown pressure,the rectangular well pattern has the largest pressure sweep efficiency.

Optimization Strategy for Passive Form Design of Architectural Grey Space under the Background of Climate Adaptability

Taking the bottom grey space with great influence on outdoor thermal comfort as the research object,this paper summarizes the morphological characteristics and climate response methods of two types of bottom grey space:overhead grey space and canopy grey space.The spatial form indexes that greatly affect the ecological performance of architectural grey space such as ventilation,shading,etc.are discussed,and two passive spatial form indexes of spatial scale and location orientation are studied.According to the research of related scholars and literature summary,the optimization strategies for passive form design of architectural grey space based on climate adaptability are put forward,which will provide a reference for the climate adaptive design of architectural grey space,and helps to improve the outdoor thermal environment from the micro scale and create a better living environment.

Adaptability Feature's Concept, Modeling and Application in Product Design

The current 3D CAD/CAM system, both research prototypes and commercial systems, based on traditional feature modeling are always hampered by the problems in their complicated modeling and difficult maintaining. This paper introduces a new method for modeling parts by using adaptability feature (AF), by which the consistent relationship among parts and assemblies can be maintained in whole design process. In addition, the design process, can be speeded, time-to-market shortened, and product quality improved. Some essential issues of the strategy are discussed. A system, KMCAD3D, by taking advantages of AF has been developed. It is shown that the method discussed is a feasible and effective way to improve current feature modeling technology.

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

PROGRESS OF GREY SYSTEM MODELS

The progress of grey system models is reviewed, and the general grey numbers, the grey sequence op- erators and several most commonly used grey system models are introduced, such as the absolute degree of grey incidence model, the grey cluster model based on endpoint triangular whitenization functions, the grey cluster model based on center-point triangular whitenization functions, the grey prediction model of the model GM （ 1,1）, and the weighted multi-attribute grey target decision model.

Phase-field modeling of dendritic growth under forced flow based on adaptive finite element method

A mathematical model combined projection algorithm with phase-field method was applied. The adaptive finite element method was adopted to solve the model based on the non-uniform grid, and the behavior of dendritic growth was simulated from undercooled nickel melt under the forced flow. The simulation results show that the asymmetry behavior of the dendritic growth is caused by the forced flow. When the flow velocity is less than the critical value, the asymmetry of dendrite is little influenced by the forced flow. Once the flow velocity reaches or exceeds the critical value, the controlling factor of dendrite growth gradually changes from thermal diffusion to convection. With the increase of the flow velocity, the deflection angle towards upstream direction of the primary dendrite stem becomes larger. The effect of the dendrite growth on the flow field of the melt is apparent. With the increase of the dendrite size, the vortex is present in the downstream regions, and the vortex region is gradually enlarged. Dendrite tips appear to remelt. In addition, the adaptive finite element method can reduce CPU running time by one order of magnitude compared with uniform grid method, and the speed-up ratio is proportional to the size of computational domain.