Adaptive Design Scheme Research Articles

Combining Bayesian active learning and conditional Gaussian process simulation for propagating mixed uncertainties through expensive computer simulators

Resulted from the limited information on both parameters and excitation at the early design stage of aerospace structures, evaluating the reliability with high accuracy has been recognized as a challenging task. Imprecise probability models have been widely developed and accepted due to their flexibility in separating the aleatory and epistemic uncertainties, and then the potential of estimating the reliability with high confidence. However, the propagation of these models through expensive-to-evaluate simulators remains to be a challenge due to the hierarchical model structure. To fill this gap, a new Bayesian active learning method is devised for efficiently learning the functional behavior of the failure probability and response variance over the epistemic input parameters. This information is especially useful for evaluating the safety of structures and for managing the uncertainties during the design process. The proposed method is based on training/updating a Gaussian Process Regression (GPR) model in the augmented space of aleatory and epistemic parameters, with the training data actively produced using two well-designed acquisition functions. The induced posterior features of the quantities of interest are inferred numerically based on efficient simulation of the GPR model. Benefiting from the decoupling scheme and the Bayesian adaptive design strategy, the proposed method is extremely efficient and provides accuracy guarantee for the numerical results. The effectiveness and superiority of the proposed method are demonstrated with numerical and engineering benchmarks, including the dynamic reliability analysis of a satellite structure.

Projection and Barrier Lyapunov-Based Controller Update Laws in MRAC Structure for Flexible Satellite Launch Vehicles

This paper investigates adaptive flight control design strategies which are robust to a wide range of both parametric and non-parametric uncertainties for a highly non-linear, time-varying and under-actuated launch vehicle system. In general, scheduled PID controllers are designed for the rigid body dynamics of the launch vehicle and structural filters/compensators are used to stabilize underactuated dynamics such as slosh and flexibility. In this work, two stable Lyapunov-based adaptive controllers are designed with full-state feedback. In the first controller, adapted gains are constrained to be within the limits defined by the user using a rectangular projection operator. In the second controller, tracking error is also constrained using a barrier Lyapunov function. States of the under-actuated dynamics and rigid body dynamics are estimated from the noisy attitude and attitude rate measurements using an extended Kalman filter. Tracking and regulation control studies are conducted to show the efficiency of the proposed controller update laws using different command signals.

Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models

This paper deals with the problem of adaptive output regulation for multivariable nonlinear systems by presenting a learning-based adaptive internal model-based design strategy. The approach builds on the recently proposed adaptive internal model design techniques based on the theory of nonlinear Luenberger observers, and the adaptation side is approached as a probabilistic regression problem. Unlike the previous approaches in the field, here only coarse assumptions about the friend structure are required, making the proposed approach suitable for applications where the exosystem is highly uncertain. The paper presents performance bounds on the attained regulation error and numerical simulations showing how the proposed method outperforms previous approaches.

Definition of Urban Built Environment Climate Adaptive Design Actions Aided by Environmental Data-Driven Design Processes

Environmental and technological design for climate adaptation in the urban built environment can no longer be separated from the generation, collection, or use of data (big data). ICT tools (Information and Communication Technologies), for the modelling and simulation of the built urban environment are identified as measuring devices and provide knowledge on the impacts of climate change in design practice based on an environmentally data-driven approach. This study aims to define a framework for the evaluation of environmental health and comfort parameters applicable to simulation tools, with a specific focus on thermal and environmental exchanges between indoor and outdoor spaces, to define those factors that affect the perception of user’s well-being in thermal stress conditions (e.g., heatwaves), both indoor and outdoor. Through the definition of two study cases in the city of Naples, Italy, special attention was paid to investigating the interaction between outdoor and indoor performance when urban temperatures rise. A comparison between a daily survey for occupants and simulations was conducted to confirm the validity of the data obtainable from the perceived thermal sensations. The obtained results show that the designed framework can reliably simulate real outdoor and indoor conditions according to comfort indices such as the predicted mean vote and adaptive comfort model. The methodological framework developed can guarantee the interoperability of data to simulate indoor and outdoor environments responding to real conditions and determine a favourable condition for the development of urban redevelopment interventions through the application of climate adaptive design strategies.

Dynamic incorporation of real world evidence within the framework of adaptive design

ABSTRACT For the clinical studies in rare diseases or small patient populations, having an adequately powered randomized controlled trial is further complicated by variability. As such, sample size re-estimation can be a useful tool if at an interim look the trial sample size needs to be increased to achieve adequate power to reject the null hypothesis. Meanwhile, borrowing or extrapolating information from real-world data or real-world evidence has gained increasing use in trial design and analysis since 2014. Combining these two strategies, high-quality real-world data, if leveraged properly, has the potential to generate real-world evidence that can assist interim decision-making, lower enrollment burden, and reduce study timeline and costs. With proper borrowing from historical control, some of the challenges in these high unmet medical need studies could be resolved considerably. We examine the incorporation of real-world evidence within the framework of adaptive design strategy in pediatric type II diabetes trials where recruitment has been challenging and the completion is hardly on time. Simulations under various scenarios are conducted to assess the borrowing strategy, i.e., the matching method in combination of sample size re-estimation. Comparisons of performance metrics are presented to showcase the advantages of proposed method.

High-dimensional Bayesian optimization of 23 hyperparameters over 100 iterations for an attention-based network to predict materials property: A case study on CrabNet using Ax platform and SAASBO

Expensive-to-train deep learning models can benefit from an optimization of the hyperparameters that determine the model architecture. We optimize 23 hyperparameters of a materials informatics model, Compositionally-Restricted Attention-Based Network (CrabNet), over 100 adaptive design iterations using two models within the Adaptive Experimentation (Ax) Platform. This includes a recently developed Bayesian optimization (BO) algorithm, sparse axis-aligned subspaces Bayesian optimization (SAASBO), which has shown exciting performance on high-dimensional optimization tasks. Using SAASBO to optimize CrabNet hyperparameters, we demonstrate a new state-of-the-art on the experimental band gap regression task within the materials informatics benchmarking platform, Matbench (∼4.5% decrease in mean absolute error (MAE) relative to incumbent). Characteristics of the adaptive design scheme as well as feature importances are described for each of the Ax models. SAASBO has great potential to both improve existing surrogate models, as shown in this work, and in future work, to efficiently discover new, high-performing materials in high-dimensional materials science search spaces.

VRDN-001: a Novel IGF-1R Monoclonal Antibody for Treatment of Thyroid Eye Disease: Adaptive Design Strategy for Clinical Development (P16-12.002)

VRDN-001: a Novel IGF-1R Monoclonal Antibody for Treatment of Thyroid Eye Disease: Adaptive Design Strategy for Clinical Development (P16-12.002)

Quantifying the Benefits of Geosynthetics Reinforcement in Flexible Pavement Design Using Cyclic Plate Load Testing

Development of pavement design over the past decades has focused on moving from empirical design equations to more powerful and adaptive design schemes. The AASHTO mechanistic–empirical pavement design guide (MEPDG) has been developed to model pavement structure and predict its service life more accurately. Although MEPDG has been widely implemented to design conventional pavement, it is not yet capable of predicting the service life of pavement reinforced with geosynthetics. Given the above concerns, seven full-scale test sections that were constructed at Louisiana Transportation Research Center-Pavement Research Facility (LTRC-PRF) were devoted to a structural experiment to investigate the performance of geosynthetic reinforced pavements. The benefits of using geosynthetics to enhance the performance of pavements constructed over soft subgrades was evaluated using cyclic plate load testing. Cyclic load at a frequency of 0.77 Hz was applied through a 305 mm diameter steel plate. The test results clearly show the benefits of geosynthetics in significantly reducing pavement rutting. The test section with double geosynthetic layers performed better than the other six sections studied. After eliminating the effect of variations in construction, the benefits of geosynthetic reinforcement are quantified within the context of the AASHTOWare Pavement ME Design Guide. The developed design procedure is capable of quantifying the contribution of geosynthetics in pavements to base reinforcement as well as subgrade stabilization. A design methodology is proposed that falls within the context of MEPDG.

Mechanics prediction of 2D architectured cellular structures using transfer learning

Two-dimensional (2D) architectured cellular structures exhibit outstanding mechanical properties unmatched by their bulk counterparts and show promising outlooks in electronic applications. Understanding of the relationship between their mechanical properties and structure patterns has yet to be fully explored. Also, traditional design rules in 2D architectured structures requiring prior knowledge of geometric parameters impose fundamental challenges for achieving desired performance within a rapid optimization process. Here, by taking full advantage of unsupervised generative adversarial network-based transfer learning (TL) and high-performing coarse-grained molecular dynamics (CGMD), we propose an adaptive design strategy to predict the mechanical performance of 2D architectured cellular structures as well as unravel hidden design rules for maximizing specific tensile strength. Results indicate that the established TL model is accurate enough to predict the mechanical properties of graphene kirigami, in which [Formula: see text] is 0.994 and 0.985 for specific strength and yield strain, respectively. The proposed design method combining machine learning with CGMD extends the ability of physical simulation beyond performance prediction, optimizing fracture mechanical properties by screening through the entire geometric design space of the architected 2D structures. Overall, this work proves that the design method based on TL can effectively obtain the power of new physical insights for structure design and optimization of interest.

Ecological Design Strategies and Theory for Urban Parks in Seoul, 1990s–Present

This study explores the characteristics of and changes in Korean landscape architects’ attitudes toward ecological design strategies and theories over the last three decades. Methodologically, this study includes a literature review and incorporates data from case studies and site visits. It discusses Seoul-specific contexts regarding environmental conditions, urban morphology, administrative agency, and design theory and practice. It redefines ecological parks, expanding their scope using physical and non-physical ecological processes. Considering this redefinition, this study categorizes the five main attitudes of contemporary Korean landscape architects towards ecological design: providing wildlife habitat, constructing aesthetic experiences, the phasing strategy, developing environmental learning programs, and designers’ metaphoric expression. Through these attitudes, this study chronologically explores gradual and constant changes in design strategies and the discourse on ecological design. Specifically, in the 1990s, landscape architects emphasized the representation of ecosystems by constructing wildlife habitats. In the early 2000s, ecological parks were artistically designed as urban parks by reusing post-industrial landscapes. Around the 2010s, landscape architects developed resilient and adaptive design strategies to flexibly respond to uncertain changes in natural and urban ecological circumstances. Since the 2010s, landscape architects have continually expanded the scope of ecology to cover physical, non-physical, urban, and social infrastructures, including public transportation, as well as political, social, and cultural structures and virtual and augmented landscapes. This study can contribute to the field literature while adding a valuable overview of the understudied Korean context.

Sequential adaptive design for jump regression estimation

Selecting input variables or design points for statistical models has been of great interest in adaptive design and active learning. Motivated by two scientific examples, this article presents a strategy of selecting the design points for a regression model when the underlying regression function is discontinuous. The first example we undertook was to accelerate imaging speed in high-resolution material imaging, and the second was to use sequential design for mapping a chemical phase diagram. In both examples, the underlying regression functions have discontinuities, and thus many existing design optimization approaches cannot be used, as they assume a continuous regression function. Although some existing adaptive design strategies developed from the treed regression models can handle the discontinuities, the related Bayesian model estimation approaches come with computationally expensive Markov Chain Monte Carlo algorithms for posterior inferences and the subsequent design point selections, which may not be applicable for the first motivating example that requires the computation to be faster than the original imaging speed. In addition, the treed models are based on domain partitioning and are inefficient in cases when the discontinuities occur at complex sub-domain boundaries. In this article, we propose a simple and effective adaptive design strategy for regression analysis with discontinuities. After some statistical properties of the estimated regression model are derived in cases with a fixed design, a new criterion for sequentially selecting the design points is suggested. The suggested sequential design selection procedure is then evaluated using a comprehensive simulation study and demonstrated using two motivating examples.

Promoting sustainable carbon sequestration of plants in urban greenspace by planting design: A case study in parks of Beijing

Urban greenspace has been highlighted as a means of offsetting carbon dioxide (CO2) emissions and mitigating climate change in cities. However, it is still challenging to design for sustainable carbon sequestration. This study developed a transdisciplinary framework to link carbon sequestration research with planting design practices. Taking 28 urban parks in Beijing as a case, we integrated field investigation and remote sensing images to analyze the relationship between planting design indicators and carbon sequestration efficiency, and the change of carbon sequestration efficiency with the growth of plant biomass. The results showed that carbon sequestration efficiency varied over the urban parks with a mean value of 8.72 gC m−2d-1, and varied with plant growth. The difference was associated with individual plant properties and biotope structure. Medium-sized plants and biotopes with partly open structure are proved cost-effective for sustainable carbon sequestration of urban greenspace. We summarized adaptive planting design strategies for different biotope types and emphasized the benefits of nurturing growing and partly open biotopes structured with different sizes of plants. The results will be possible to produce information for landscape architects for improving sustainable carbon sequestration not only in the study area but also in other cities with similar climatic characteristic.

L₁-Adaptive Robust Control Design for a Pressurized Water-Type Nuclear Power Plant

This work proposes adaptive control-based design strategies to control a pressurized water reactor (PWR) nuclear power plant (NPP). An L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -adaptive-based state-feedback control technique is proposed using the linear quadratic Gaussian control and projection-based adaptation laws. The control scheme possesses good robustness capabilities in handling disturbances and uncertainties. A robust L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -adaptive control technique is also proposed by combining the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -adaptive control with the loop transfer recovery (LTR) technology. The framework hence gives the strengthened robust set-point tracking performance given the matched and unmatched uncertainties and disturbances. The NPP model employed in this article is defined by five inputs, five outputs, and 38 state variables. A linear model for controller design is obtained by linearizing the nonlinear NPP model at operating conditions. Various simulations are carried out on subsystems of the NPP to verify the effectiveness of the proposed scheme. Numerical and statistical measures are computed for quantitative analysis of the controllers' performance. Several classical control design techniques are also implemented, and their performance is compared with the proposed adaptive control techniques.

Quantized-states-based adaptive control against unknown slippage effects of uncertain mobile robots with input and state quantization

An adaptive tracking design strategy based on quantized state feedback is developed for uncertain nonholonomic mobile robots with unknown wheel slippage effects. All state variables and control torques are assumed to be quantized by the state and input quantizers, respectively, in a network control environment. Thus, the quantized state feedback information is only available for the tracking control design. An approximation-based adaptive controller using quantized states is recursively designed to ensure the robust adaptive tracking against unknown wheel slippage effects where the quantized-states-based adaptive mechanism is derived to compensate for unknown wheel slippage effects, system nonlinearities, and quantization errors. The boundedness of the quantization errors and estimated parameters in the closed-loop system is analyzed by presenting some theoretical lemmas. Based on these lemmas, we prove the uniform ultimate boundedness of closed-loop signals and the convergence of the trajectory tracking error in the presence of wheel slippage effects. Simulations verify the effectiveness of the resulting tracking scheme.

Fast Parameter Estimation for Adaptive Maximum Torque Per Ampere Control of Interior Permanent Magnet Synchronous Motor Drives

This paper proposes a fast adaptive parameter estimation method for maximum torque per ampere control (MTPA) of interior permanent magnet synchronous motor (IPMSM) drives. The adaptive features that enable fast parameter estimation are investigated, and an adaptive observer with a structure capable of achieving fast estimation is proposed. A cascade adaptive observer design strategy is presented in order to obtain the benefit of fast estimation from the proposed adaptive scheme. The MTPA is achieved through the solution of the stator current optimization problem combined with the IPMSM estimated parameters. Simulation and experimental results are provided in order to validate the proposed fast estimation adaptive MTPA algorithm.

Decentralized event-triggered adaptive control of a class of uncertain interconnected nonlinear systems using local state feedback corrupted by unknown injection data

This paper investigates a decentralized event-triggered adaptive control problem of uncertain interconnected lower-triangular nonlinear systems using corrupted local state feedback. The unknown injection data such as sensor faults or cyber attacks are assumed to be added to full state feedback measurements of each subsystem with unknown nonlinearities. Under this assumption, exactly measured local state variables are unknown during Lyapunov-stability-based recursive control design steps. The primary contributions of this study are (i) to develop a recursive adaptive control design strategy for achieving decentralization of unmatched interconnected nonlinearities using corrupted local state feedback information and (ii) to design a decentralized asynchronous event-triggering mechanism using the corrupted local state variables. To this end, local injection data compensators using neural networks and adaptive tuning laws are designed to compensate for unknown injection data and nonlinear interaction effects. It is shown that the proposed decentralized control system via the corrupted local state feedback ensures the practical stability of the total closed-loop system and the exclusion of Zeno behavior.

Neural-Network-Based Distributed Asynchronous Event-Triggered Consensus Tracking of a Class of Uncertain Nonlinear Multi-Agent Systems.

This article proposes a neural-network-based adaptive asynchronous event-triggered design strategy for the distributed consensus tracking of uncertain lower triangular nonlinear multi-agent systems under a directed network. Compared with the existing event-triggered recursive consensus tracking designs using multiple neural networks for each follower and continuous communications among followers, the primary contribution of this study is the development of an asynchronous event-triggered consensus tracking methodology based on a single-neural network for each follower under event-driven intermittent communications among followers. To this end, a distributed event-triggered estimator using neighbors' triggered output information is developed to estimate a leader signal. Subsequently, the estimated leader signal is used to design local trackers. Only a triggering law and a single-neural network are used to design the local tracking law of each follower, irrespective of unmatched unknown nonlinearities. The information of each follower and its neighbors is asynchronously and intermittently communicated through a directed network. Thus, the proposed asynchronous event-triggered tracking scheme can save communicational and computational resources. From the Lyapunov stability theorem, the stability of the entire closed-loop system is analyzed and the comparative simulation results demonstrate the effectiveness of the proposed control strategy.

Decentralized event-triggered adaptive control for interconnected nonlinear dynamics of constrained air supply and thermal management systems of PEMFCs

This paper investigates a decentralized event-triggered adaptive control problem for uncertain interconnected air supply and thermal management nonlinear dynamics of proton exchange membrane fuel cells (PEMFCs). In addition, the constraint problems of the oxygen excess ratio (OER) and stack temperature (ST) are addressed for preventing oxygen starvation, parasitic loss, membrane dehydration, and electrode flooding of PEMFCs. First, the practical nonlinear interaction problem between the air pressure of the air supply subsystem and the ST of the thermal management subsystem is formulated. Then, a decentralized adaptive control design strategy is established to guarantee the constraint satisfaction of each subsystem and the asynchronously event-triggered regulation of the OER and ST. In contrast to the previous studies on the control of PEMFCs, this study first addresses the nonlinear interaction problem of the air supply and thermal management systems and their decentralized control design in the control field of PEMFCs. Based on the Lyapunov stability theorem, the stability of the resulting overall control scheme is analyzed.

A World for Reactive Phenotypes

Humans currently occupy all continents and by doing so, modify the environment and create novel threats to many species; a phenomenon known as human-induced rapid environmental changes (HIREC). These growing anthropogenic disturbances represent major and relatively new environmental challenges for many animals, and invariably alter selection on traits adapted to previous environments. Those species that survive often have moved from their original habitat or modified their phenotype through plasticity or genetic evolution. Based on the most recent advances in this research area, we predict that wild individuals with highly plastic capacities, relatively high basal stress level, and that are generally shy—in other words, individuals displaying a reactive phenotype—should better cope with sudden and widespread HIREC than their counterparts' proactive phenotypes. If true, this selective response would have profound ecological and evolutionary consequences and can therefore impact conservation strategies, specifically with respect to managing the distribution and abundance of individuals and maintaining evolutionary potential. These insights may help design adaptive management strategies to maintain genetic variation in the context of HIREC.

Adaptive Elements in Internet-Delivered Psychological Treatment Systems: Systematic Review.

BackgroundInternet-delivered psychological treatments (IDPTs) are built on evidence-based psychological treatment models, such as cognitive behavioral therapy, and are adjusted for internet use. The use of internet technologies has the potential to increase access to evidence-based mental health services for a larger proportion of the population with the use of fewer resources. However, despite extensive evidence that internet interventions can be effective in the treatment of mental health disorders, user adherence to such internet intervention is suboptimal.ObjectiveThis review aimed to (1) inspect and identify the adaptive elements of IDPT for mental health disorders, (2) examine how system adaptation influences the efficacy of IDPT on mental health treatments, (3) identify the information architecture, adaptive dimensions, and strategies for implementing these interventions for mental illness, and (4) use the findings to create a conceptual framework that provides better user adherence and adaptiveness in IDPT for mental health issues.MethodsThe review followed the guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The research databases Medline (PubMed), ACM Digital Library, PsycINFO, CINAHL, and Cochrane were searched for studies dating from January 2000 to January 2020. Based on predetermined selection criteria, data from eligible studies were analyzed.ResultsA total of 3341 studies were initially identified based on the inclusion criteria. Following a review of the title, abstract, and full text, 31 studies that fulfilled the inclusion criteria were selected, most of which described attempts to tailor interventions for mental health disorders. The most common adaptive elements were feedback messages to patients from therapists and intervention content. However, how these elements contribute to the efficacy of IDPT in mental health were not reported. The most common information architecture used by studies was tunnel-based, although a number of studies did not report the choice of information architecture used. Rule-based strategies were the most common adaptive strategies used by these studies. All of the studies were broadly grouped into two adaptive dimensions based on user preferences or using performance measures, such as psychometric tests.Conclusions Several studies suggest that adaptive IDPT has the potential to enhance intervention outcomes and increase user adherence. There is a lack of studies reporting design elements, adaptive elements, and adaptive strategies in IDPT systems. Hence, focused research on adaptive IDPT systems and clinical trials to assess their effectiveness are needed.