Adaptive Design Methodology Research Articles

Loss-Optimized Design of Magnetic Devices.

Maximizing efficiency, power density, and reliability stands as paramount objectives in the advancement of power electronic systems. Notably, the dimensions and losses of magnetic components emerge as primary constraints hindering the miniaturization of such systems. Researchers have increasingly focused on the design of loss minimization and size optimization of magnetic devices. In this paper, with the objective of minimizing the loss of magnetic devices, an optimal design method for the winding structure of devices is proposed based on the coupling relationship between the loss prediction model and the design variables. The method examines the decoupling conditions between the design variables and the loss model, deriving optimized design closure equations for the design variables. This approach furnishes a technical foundation for the miniaturized design of miniature apparatuses incorporating magnetic components, offering a straightforward and adaptable design methodology. The finite element method simulation results and experimental measurement data verify the accuracy of the prediction of the proposed method and the validity of the optimal design theory of device loss.

Adaptive Study Design Methodology vs. Standard Randomized Clinical Trial in Different Therapeutic Area: A Review

Adaptive Study Design Methodology vs. Standard Randomized Clinical Trial in Different Therapeutic Area: A Review

Adaptive Optimization Design of Building Energy System for Smart Elderly Care Community Based on Deep Deterministic Policy Gradient

In smart elderly care communities, optimizing the design of building energy systems is crucial for improving the quality of life and health of the elderly. This study pioneers an innovative adaptive optimization design methodology for building energy systems by harnessing the cutting-edge capabilities of deep reinforcement learning. This avant-garde method initially involves modeling a myriad of energy equipment embedded within the energy ecosystem of smart elderly care community buildings, thereby extracting their energy computation formulae. In a groundbreaking progression, this study ingeniously employs the actor–critic (AC) algorithm to refine the deep deterministic policy gradient (DDPG) algorithm. The enhanced DDPG algorithm is then adeptly wielded to perform adaptive optimization of the operational states within the energy system of a smart retirement community building, signifying a trailblazing approach in this realm. Simulation experiments indicate that the proposed method has better stability and convergence compared to traditional deep Q-learning algorithms. When the environmental interaction coefficient and learning ratio is 4, the improved DDPG algorithm under the AC framework can converge after 60 iterations. The stable reward value in the convergence state is −996. When the scheduling cycle of the energy system is between 0:00 and 8:00, the photovoltaic output of the system optimized by the DDPG algorithm is 0. The wind power output fluctuates within 50 kW. This study realizes efficient operation, energy saving, and emission reduction in building energy systems in smart elderly care communities and provides new ideas and methods for research in this field. It also provides an important reference for the design and operation of building energy systems in smart elderly care communities.

Tackling Modeling and Kinematic Inconsistencies by Fixed Point Iteration-Based Adaptive Control

The Fixed Point Iteration-based Adaptive Control design methodology is an alternative to the Lyapunov function-based technology. It contains higher-order feedback terms than the standard resolved acceleration rate control. This design approach strictly separates the kinematic and dynamic issues. At first, a purely kinematic prescription is formulated for driving the components of the tracking error to zero. Then an available approximate dynamic model is used to calculate the approximated necessary control forces. Before exerting on the controlled system, these forces are adaptively deformed in order to precisely obtain the prescribed kinematic behavior. The necessary deformation is iteratively found by the use of a contractive map that results in a sequence that converges to the unique fixed point of this map. In the case of underactuated systems, when the relative order of the control task also increases, the highest-order time-derivative depends on the lower-order ones according to the dynamic model of the system. This makes it impossible to realize the arbitrarily constructed kinematic design. In the paper, a resolution to this discrepancy is proposed. The method is demonstrated using two non-linear paradigms, a three-degree-of-freedom robot arm, and a two-degree-of-freedom system, i.e., two coupled non-linear springs. The operation of the method was investigated via simulations made by the use of Julia language and simple sequential programs. It was found that the suggested solution could be considered as a new variant of the fixed point iteration-based model reference adaptive control that is applicable for underactuated systems even if the relative order of the task is increased.

Bayesian Adaptive Randomization with Compound Utility Functions

Bayesian adaptive designs formalize the use of previous knowledge at the planning stage of an experiment, permitting recursive updating of the prior information. They often make use of utility functions, while also allowing for randomization. We review frequentist and Bayesian adaptive design methods and show that some of the frequentist adaptive design methodology can also be employed in a Bayesian context. We use compound utility functions for the Bayesian designs, that are a trade-off between an optimal design information criterion, that represents the acquisition of scientific knowledge, and some ethical or utilitarian gain. We focus on binary response models on two groups with independent Beta prior distributions on the success probabilities. The treatment allocation is shown to converge to the allocation that produces the maximum utility. Special cases are the Bayesian Randomized (simply) Adaptive Compound (BRAC) design, an extension of the frequentist Sequential Maximum Likelihood (SML) design and the Bayesian Randomized (doubly) Adaptive Compound Efficient (BRACE) design, a generalization of the Efficient Randomized Adaptive DEsign (ERADE). Numerical simulation studies compare BRAC with BRACE when D-optimality is the information criterion chosen. In analogy with the frequentist theory, the BRACE-D design appears more efficient than the BRAC-D design.

Distributed adaptive formation tracking using quantized feedback communication for networked mobile robots with unknown wheel slippage

In this study, we consider a quantized-feedback-communication-based control design problem for the distributed adaptive formation tracking of multiple nonholonomic mobile robots with unknown slippage constraints under capacity-limited network control environments. Uniform-hysteretic quantizers are employed to quantize all the inputs and states of robots and the quantized position information of each robot is only transmitted to neighboring robots through directed networks. Compared with existing literature related to the robot formation, the primary contribution of this paper lies in establishing a novel local adaptive control design methodology to deal with the discontinuity problem caused by using the quantized states of each follower and the quantized position communication of neighboring robots. In the proposed strategy, the communication of the orientations and velocities of neighboring robots is not required for the local control design of follower robots. Moreover, quantized-states-based adaptive compensation schemes are constructed for the effects of signal quantization and wheel slippage. Based on the analysis of quantization errors, the practical stability strategy of the overall closed-loop formation system is derived with the convergence of local tracking errors. Simulation results clarify the proposed formation strategy.

Adaptive design methodology of segmented non-uniform fin arrangements for trans-critical natural gas in the printed circuit heat exchanger

As the core heat transfer equipment of a floating storage regasification unit (FSRU), the printed circuit plate heat exchanger (PCHE) has a significant impact on the gasification performance of liquefied natural gas (LNG). During LNG regasification process, natural gas undergoes a trans-critical process and its thermal properties such as density, viscosity and specific heat capacity vary considerably especially near the pseudo-critical point, which will probably lead to more volatile flow heat transfer mechanism. The traditional design scheme of uniform finned channel is difficult to give full play to the efficiency and compactness of PCHE because of lacking considering the local fluid thermodynamic variation. Therefore, this paper provides a novel design method for optimizing the flow channel profile of PCHE by applying adaptive segmented non-uniform finned micro-channels. Firstly, the entire flow channels are segmented by qualifying the gradient of density with temperature ∂ρ/∂t of trans-critical natural gas. Subsequently, within the constraints of heat exchange requirements, maximum allowable pressure drop, existing manufacturing techniques and structural strength, a mathematical optimization design model considering non-uniform fin arrangements and interlinked thermal states among these segmented channels is established. Then, to comprehensively improve entropy generation number and volume, Pareto solutions covering local flow channel parameters are obtained based on multi-objective genetic algorithm. Finally, the actual performance differences between two optimization methods respectively derived from the conventional uniform fin channels and the proposed non-uniform fin arrangements are compared based on distributed parameter model. The results show that the deviation of the entropy generation number between the optimized and validated values for the non-uniform design is very small at 6.5%, much less than that of 31.4% in the uniform design. Moreover, under the premise of the similar heat transfer efficiency, the volume for the PCHE by the non-uniform design can be reduced by 9.3%. These indicate that the proposed design methodology of segmented non-uniform fin arrangements indeed makes the PCHE more compact and enables the thermal-hydraulic performance better. The current work will serve as a framework for the design and optimization of PCHEs with drastic changes in fluid physical properties.

INSP-04. Confirmatory adaptive designs for survival trials with several time-to-event endpoints

Confirmatory adaptive designs comprise a range of statistical methods that allow to modify the sample size of an ongoing trial in a data-dependent way without compromising control of the type I error rate. For short-term endpoints (e.g., 3-month response rate), comprehensive methodology of adaptive designs exists. However, clinical trials in oncology often have a special focus on long-term outcome and therefore often choose a time-to-event endpoint as the primary endpoint. Typical examples are progression-free survival (PFS) or overall survival (OS). But subtle statistical problems arise when adaptively analysing survival trials. Classical designs for survival trials are therefore commonly limited to a single primary endpoint, which combines the occurrence of progression, toxicities, deaths, and other events of potential interest into a single statistical measure (composite endpoint). However, the complexity of oncological diseases can be mapped more accurately using multi-stage models, where the occurrence of progressions, toxicities and deaths is modelled jointly instead of combining them into a single composite endpoint. We present and discuss adaptive design methodology for single-arm phase II survival trials for testing hypotheses on the joint distribution of several time-to-event endpoints in the context of multi-state models. We illustrate the methodology using the example of adaptive hypothesis tests for the joint distribution of progression-free survival (PFS) and overall survival (OS) in the context of an illness-death model. The methodology is motivated from application in pediatric oncology.

Combining Farmers' Preferences With Evidence-Based Strategies to Prevent and Lower Farmers' Distress: Co-design and Acceptability Testing of ifarmwell.

BackgroundFarming is physically and psychologically hazardous. Farmers face many barriers to help seeking from traditional physical and mental health services; however, improved internet access now provides promising avenues for offering support.ObjectiveThis study aims to co-design with farmers the content and functionality of a website that helps them adopt transferable coping strategies and test its acceptability in the broader farming population.MethodsResearch evidence and expert opinions were synthesized to inform key design principles. A total of 18 farmers detailed what they would like from this type of website. Intervention logic and relevant evidence-based strategies were mapped. Website content was drafted and reviewed by 2 independent mental health professionals. A total of 9 farmers provided detailed qualitative feedback on the face validity of the draft content. Subsequently, 9 farmers provided feedback on the website prototype. Following amendments and internal prototype testing and optimization, prototype usability (ie, completion rate) was examined with 157 registered website users who were (105/157, 66.9%) female, aged 21-73 years; 95.5% (149/156) residing in inner regional to very remote Australia, and 68.2% (107/157) “sheep, cattle and/or grain farmers.” Acceptability was examined with a subset of 114 users who rated at least module 1. Interviews with 108 farmers who did not complete all 5 modules helped determine why, and detailed interviews were conducted with 18 purposively sampled users. Updates were then made according to adaptive trial design methodology.ResultsThis systematic co-design process resulted in a web-based resource based on acceptance and commitment therapy and designed to overcome barriers to engagement with traditional mental health and well-being strategies—ifarmwell. It was considered an accessible and confidential source of practical and relevant farmer-focused self-help strategies. These strategies were delivered via 5 interactive modules that include written, drawn, and audio- and video-based psychoeducation and exercises, as well as farming-related jokes, metaphors, examples, and imagery. Module 1 included distress screening and information on how to speak to general practitioners about mental health–related concerns (including a personalized conversation script). Modules were completed fortnightly. SMS text messages offered personalized support and reminders. Qualitative interviews and star ratings demonstrated high module acceptability (average 4.06/5 rating) and suggested that additional reminders, higher quality audio recordings, and shorter modules would be useful. Approximately 37.1% (52/140) of users who started module 1 completed all modules, with too busy or not got to it yet being the main reason for non-completion, and previous module acceptability not predicting subsequent module completion.ConclusionsSequential integration of research evidence, expert knowledge, and farmers’ preferences in the co-design process allowed for the development of a self-help intervention that focused on important intervention targets and was acceptable to this difficult-to-engage group.Trial RegistrationAustralian New Zealand Clinical Trials Registry ACTRN12617000506392; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=372526

Novel Adaptive Design Methodology of Segmented Non-Uniform Fin Arrangements for Trans-Critical Natural Gas in the Printed Circuit Heat Exchanger

Novel Adaptive Design Methodology of Segmented Non-Uniform Fin Arrangements for Trans-Critical Natural Gas in the Printed Circuit Heat Exchanger

Novel Adaptive Design Methodology of Segmented Non-Uniform Fin Arrangements for Trans-Critical Natural Gas in the Printed Circuit Heat Exchanger

Novel Adaptive Design Methodology of Segmented Non-Uniform Fin Arrangements for Trans-Critical Natural Gas in the Printed Circuit Heat Exchanger

P‐6.4: Comprehensive optimum and adaptable design methodology for the Display Support Structure Based on ANSYS Simulation

The back cover is mainly used to strengthen the display product strength, increase rigidity, and prevent product deformation and distortion during assembly, use or transportation. This thesis studies the influence of different back cover thickness, backplane material and stiffener position, number of stiffeners, and stiffener structure on the strength of the backplane. Good display support structures are required that not only can offer better sustentation results, but also can reduce the negative effect with lesser back cover materials. By simulating and optimizing the design of the backplane, the strength of the product is increased and the unit price of the product is reduced.

Adaptive trial designs for spinal cord injury clinical trials directed to the central nervous system.

Narrative review. To provide an overview of adaptive trial designs, and describe how adaptive methods can address persistent challenges encountered by randomized controlled trials of people with spinal cord injury (SCI). With few exceptions, adaptive methodologies have not been incorporated into clinical trial designs of people with SCI. Adaptive methods provide an opportunity to address high study costs, slow recruitment, and excessive amount of time needed to carry out the trial. The availability of existing SCI registries are well poised to support modeling and simulation, both of which are used extensively in adaptive trial designs. Eight initiatives for immediate advancement of adaptive methods in SCI were identified. Although successfully applied in other fields, adaptive clinical trial designs in SCI clinical trial programs have been narrow in scope and few in number. Immediate application of several adaptive methods offers opportunity to improve efficiency of SCI trials. Concerted effort is needed by all stakeholders to advance adaptive clinical trial design methodology in SCI.

Adaptable SVC Design Methodology for Power Quality Improvements in Steel Melting Shops

In 2019, India was the second-largest steel producer with total crude steel production of 112.3 metric ton [12]. There were lots of development actions taken in the starting of 90’s to promote more investments on producing steel and making it a bigger industry supporting country’s economy. Even though large amount of produced steel is utilized within the country for infrastructure, automobile and other consumable industries, still India is the seventh-largest exporter of steel. Also, Steel industries are not new to India. The oldest was TISCO and it started its production in 1907. Being said that, we have come long way in technology and science that all the steel plants need to be modernized and adapted to become more efficient, economical and productive. This paper presents one of such technology that being developed in the modern engineering word to make it adaptable in the steel industries where – Efficiency, energy consumption, quality and production can be improved significantly.

Intelligent decision-support system to plan, manage and optimize water quality monitoring programs: design of a conceptual framework

This paper presents the conceptual framework of a holistic, intelligent decision-support system (IDSS) to plan, manage and optimize water quality monitoring programs (WQMPs) for surface waters. WQMPs are a crucial component of water management because information on water quality is essential when taking action such as legislative compliance, environmental projects, urban and infrastructure development. Planning, managing and optimizing WQMPs is a complex process and requires multiple variables, rules and subject matter expert knowledge. The specific goals of this paper were to (1) assess to what extent the subject domain (WQMPs) is deemed appropriate for an IDSS; (2) describe the design process of the conceptual framework; (3) present the main elements of the system architecture; (4) present two case studies that served as potential end users; (5) illustrate the applicability of the IDSS and (6) describe subsequent steps to further test the IDSS. The IDSS was developed on the premise that the proposed system could (1) improve quality, (2) capture undocumented expertise that is perishable or in short supply (tacit knowledge), (3) provide accessible expertise to novice users, (4) have a training effect on users, and (5) show that the system, even partially complete, could still be useful. Our initial assumptions regarding these points were validated through interviews with subject matter experts. The conceptual framework was designed based on a literature review, interviews with 44 subject matter experts from Europe, Canada and the United States, interaction with end users from two case studies in the Province of Quebec, Canada, and five information technology experts from Canada and Germany. The IDSS presented in this paper will facilitate the planning, management and optimization of WQMPs. It will be exportable to various watersheds and consider the WQMP planner’s need to update the network rapidly if changes occur in human, financial and technical resources. HIGHLIGHTS Holistic and adaptable decision-support system to plan, manage and optimize surface water quality monitoring programs Management and decision support system for knowledge acquisition processes on water quality System integrates tacit and explicit knowledge on water quality monitoring challenges Management and decision support system based on tacit knowledge from 44 experts and two case studies Adapted design methodology for an Intelligent decision-support system

An N-Path Filter Design Methodology With Harmonic Rejection, Power Reduction, Foldback Elimination, and Spectrum Shaping

In this paper, an adaptive design methodology for synthesizing a harmonic free N-path filter with reduced frequency folding is presented. System level analysis of proposed architecture shows that by adding a few extra paths with proper weights to a conventional N-path filter, several characteristics such as harmonic rejection, power reduction, foldback elimination and spectrum shaping can be achieved. The designed filter is reconfigurable to be a band-pass filter (BPF) or a band-reject filter (notch), based on the requirements. By using the n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> harmonic of Local Oscillator (LO) signal, instead of the fundamental harmonic, the required input clock frequency in N-phase clock generator is reduced by a factor of n. As a proof of concept, a 0.1-5 GHz RF filter with 75 dB and 82 dB harmonic rejection at 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> and 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> order harmonics respectively is analyzed and simulated using MATLAB and Cadence Spectre-RF. Post-layout simulations are performed using CMOS 180 nm technology with 1.8 V supply voltage. The total power consumption of the chip is less than 8.5 mW while occupying a silicon area of 0.2 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Furthermore, Noise Figure (NF) of the circuit is shown to be between 3.5 and 4.7 dB and its out-of-band IIP3 is +6 dBm.

A Practical Perspective: Application of the Generalized Approach for Adaptive Design.

Adaptive design methodology has been well studied for continuous, binary, and survival outcomes for decades. However, for complicated endpoints such as recurrent hospitalization in the joint frailty setting and composite endpoint in the win-ratio setting, adaptive design is not intuitive because of sophistication in existing methods to perform sample size re-estimation. The objective of this paper is to propose a practical generalized approach to implement the above activities at the interim stage through approximation so that sample size re-estimation becomes easily understood and readily amenable. Through simulations on representative complex situations, the proposed method can maintain the planned statistical power by sample size re-estimation while controlling the type I error. The proposed adaptive approach is easy to implement in general sample size re-estimation situations. Its validity can be verified through simulation under varying scenarios. In summary, this approach offers a transparent communication channel with regulatory agencies to facilitate clinical trial development regardless of the complexity of the underlying situations.

Adaptive trial designs in diagnostic accuracy research.

The aim of diagnostic accuracy studies is to evaluate how accurately a diagnostic test can distinguish diseased from nondiseased individuals. Depending on the research question, different study designs and accuracy measures are appropriate. As the prior knowledge in the planning phase is often very limited, modifications of design aspects such as the sample size during the ongoing trial could increase the efficiency of diagnostic trials. In intervention studies, group sequential and adaptive designs are well established. Such designs are characterized by preplanned interim analyses, giving the opportunity to stop early for efficacy or futility or to modify elements of the study design. In contrast, in diagnostic accuracy studies, such flexible designs are less common, even if they are as important as for intervention studies. However, diagnostic accuracy studies have specific features, which may require adaptations of the statistical methods or may lead to specific advantages or limitations of sequential and adaptive designs. In this article, we summarize the current status of methodological research and applications of flexible designs in diagnostic accuracy research. Furthermore, we indicate and advocate future development of adaptive design methodology and their use in diagnostic accuracy trials from an interdisciplinary viewpoint. The term "interdisciplinary viewpoint" describes the collaboration of experts of the academic and nonacademic research.

Adaptive designs in multi-reader multi-case clinical trials of imaging devices.

Evaluation of medical imaging devices often involves clinical studies where multiple readers (MR) read images of multiple cases (MC) for a clinical task, which are often called MRMC studies. In addition to sizing patient cases as is required in most clinical trials, MRMC studies also require sizing readers, since both readers and cases contribute to the uncertainty of the estimated diagnostic performance, which is often measured by the area under the ROC curve (AUC). Due to limited prior information, sizing of such a study is often unreliable. It is desired to adaptively resize the study toward a target power after an interim analysis. Although adaptive methods are available in clinical trials where only the patient sample is sized, such methodologies have not been established for MRMC studies. The challenge lies in the fact that there is a correlation structure in MRMC data and the sizing involves both readers and cases. We develop adaptive MRMC design methodologies to enable study resizing. In particular, we resize the study and adjust the critical value for hypothesis testing simultaneously after an interim analysis to achieve a target power and control the type I error rate in comparing AUCs of two modalities. Analytical results have been derived. Simulations show that the type I error rate is controlled close to the nominal level and the power is adjusted toward the target value under a variety of simulation conditions. We demonstrate the use of our methods in a real-world application comparing two imaging modalities for breast cancer detection.

An additive boundary for group sequential designs with connection to conditional error.

Group sequential designs allow stopping a clinical trial for meeting its efficacy objectives based on interim evaluation of the accumulating data. Various methods to determine group sequential boundaries that control the probability of crossing the boundary at an interim or the final analysis have been proposed. To monitor trials with uncertainty in group sizes at each analysis, error spending functions are often used to derive stopping boundaries. Although flexible, most spending functions are generic increasing functions with parameters that are difficult to interpret. They are often selected arbitrarily, sometimes using trial and error, so that the corresponding boundaries approximate the desired behavior numerically. Lan and DeMets proposed a spending function that approximates in a natural way the O'Brien-Fleming boundary based on the Brownian motion process. We extend this approach to a general family that has an additive boundary for the Brownian motion process. The spending function and the group sequential boundary share a common parameter that regulates how fast the error is spent. Three subfamilies are considered with different additive terms. In the first subfamily, the parameter has an interpretation as the conditional error rate, which is the conditional probability to reject the null hypothesis at the final analysis. This parameter also provides a connection between group sequential and adaptive design methodology. More choices of designs are allowed in the other two subfamilies. Numerical results are provided to illustrate flexibility and interpretability of the proposed procedures. A clinical trial is described to illustrate the utility of conditional error in boundary determination.