Unified Design Framework Research Articles

Determining doses for backfill cohorts based on patient-reported outcome

BackgroundIncorporating backfill cohorts in phase I oncology trials is a recently developed strategy for dose optimization. However, the efficacy assessment window is long in general, causing a lag in identifying ineffective doses and more patients being backfilled to those doses. There is necessity to investigate how to use patient-reported outcomes (PRO) to determine doses for backfill cohorts.MethodsWe propose a unified Bayesian design framework, called ‘Backfill-QoL’, to utilize patient-reported quality of life (QoL) data into phase I oncology trials with backfill cohorts, including methods for trial monitoring, algorithm for dose-finding, and criteria for dose selection. Simulation studies and sensitivity analyses are conducted to evaluate the proposed Backfill-QoL design.ResultsThe simulation studies demonstrate that the Backfill-QoL design is more efficient than traditional dose-expansion strategy, and fewer patients would be allocated to doses with unacceptable QoL profiles. A user-friendly Windows desktop application is developed and freely available for implementing the proposed design.ConclusionsThe Backfill-QoL design enables continuous monitoring of safety, efficacy and QoL outcomes, and the recommended phase II dose (RP2D) can be identified in a more patient-centered perspective.

A comprehensive control paradigm for prescribed performance attainment in complex nonlinear systems

This paper presents a comprehensive control framework tailored for achieving Prescribed Performance Control (PPC) in the context of complex nonlinear systems, addressing multifaceted challenges prevalent in control design. The focus is on a control scenario characterized by the simultaneous presence of nonlinearity, external disturbances, time delay, and unknown control direction—issues that pose considerable obstacles for existing solutions. To surmount these challenges, our proposed approach integrates well-established techniques, including neural networks, Nussbaum-type gains, and adaptive control strategies within a unified control design framework. The specific technical challenge addressed in this work involves the effective management of these intricate complexities in Single-Input Single-Output (SISO) systems. Our contributions extend the theoretical foundations, presenting an ideal PPC control design and introducing two adaptive neural network-based control methods capable of accommodating both known and unknown control directions. Utilizing Lyapunov–Krasovskii functionals, we showcase a unique integration that surpasses a mere combination of individual techniques. This work advances the theoretical underpinnings of control engineering tailored for real-world scenarios. The proposed controller’s efficacy is validated through rigorous simulations and compared with recent results and benchmark PPC controllers, establishing its superiority in addressing the intricacies of complex control scenarios.

Toward sustainable recycled methanol production from CO2 and steel by-product gases in South Korea; process design and assessment

Methanol is receiving considerable attention as an alternative fuel because of its potential role as a transitional option in mitigating greenhouse gas emissions and enabling the transition towards biofuels. Recycled methanol, produced using captured CO2 or industrial by-product gases, emerges as an attractive alternative in South Korea, especially considering the excessive emissions from its energy and industrial sectors. However, assessing technological superiority amongst a range of alternatives remains challenging. This study aims to assess and compare various methanol production pathways utilizing captured CO2 and by-product gases from steel industry within context of South Korea. The selected technologies are either currently in industrial use or pivotal for future industrial applications. A unified process design and assessment framework was employed to model and simulate eight technological conversion processes. These processes underwent evaluation based on key performance indicators, accompanied by comprehensive techno-economic and environmental life cycle assessments. Additionally, comparisons were made with conventional fossil-based methanol production, considering different hydrogen and electricity production routes. This study provides valuable insights into the viability of recycled methanol pathways in terms of process performance, economic and environmental feasibility and will facilitate the practical implementation of recycled methanol, contributing towards achieving carbon neutrality objectives.

MIDAS-2: an enhanced Bayesian platform design for immunotherapy combinations with subgroup efficacy exploration

ABSTRACT Although immunotherapy combinations have revolutionised cancer treatment, the rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging. This necessitates innovative, integrated, and efficient trial designs. In this study, we extend the MIDAS design to include subgroup exploration and propose an enhanced Bayesian information borrowing platform design called MIDAS-2. MIDAS-2 enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We use a regression model to characterize the efficacy pattern in subgroups. Information borrowing is applied through Bayesian hierarchical modelling to improve trial efficiency considering the limited sample size in subgroups. Time trend calibration is also employed to avoid potential baseline drifts. Simulation results demonstrate that MIDAS-2 yields high probabilities for identifying the effective drug combinations as well as promising subgroups, facilitating appropriate selection of the best treatments for each subgroup. The proposed design is robust against small time trend drifts, and the type I error is successfully controlled after calibration when a large drift is expected. Overall, MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion.

Optimizing wavefront coding for extended depth of field: a synchronous algorithm for optical element and decoding optimization.

Wavefront coding (WFC) combines phase mask design and image restoration algorithm to extend the depth of field (DOF) for various applications. However, discrete design limits finding globally optimal solutions, increasing the complexity of system design, and affecting the accuracy and robustness of image restoration. An end-to-end imaging system design has emerged to break through these limitations by integrating optical design and image processing algorithms. In this study, we propose an algorithm that synchronously optimizes the optical elements and decoding algorithm in WFC using ray-tracing simulation. We also derive formulas for the optical layer's forward and backward propagation for joint optimization of the optical layer and decoding algorithm. Experimental verification demonstrates the algorithm's effectiveness in optimizing the WFC system and offers improved performance under a unified design framework.

A Bifurcation Theoretic Integrated Methodology for Aircraft Conceptual Design

A bifurcation theoretic integrated methodology for aircraft conceptual design is presented in this paper. The methodology uses the extended bifurcation analysis technique to formulate a unified design framework that can be applied for configuration sizing of aircraft, assessment of aircraft performance and stability, and evaluation of aircraft maneuverability. Unlike conventional approaches for conceptual design, the proposed methodology incorporates a full six degrees-of-freedom flight dynamics model for stability and control analysis. Maneuverability evaluation is done with the help a suitable controller. Configuration sizing of a six-seater general aviation aircraft, followed by performance, stability and maneuverability assessment of the aircraft is successfully carried out in this paper to demonstrate the effectiveness of the proposed methodology.

Asynchronous stabilization for hidden Markov jump linear systems with complex transition probabilities

This study is intended to investigate the asynchronous stabilization problem for discrete-time Markov jump linear systems (MJLSs) with complex mode transition probabilities (C-TPs). A practical scenario is considered in this study. First, the system mode may not always be precisely detected in practice, therefore a hidden Markov model (HMM) is used to characterize the mismatched mode phenomenon. Second, some mode TPs associated with the Markov process of the original system may be unknown or inaccurate, rendering the C-TPs scenario. This issue could also exist in the conditional TPs associated with the joint process of the HMM. A mode separation strategy is proposed to separate the Markov process and the joint process such that we can deal with the C-TPs associated with them, respectively. Based on the above considerations, a unified controller design framework is established for MJLSs with or without C-TPs. Moreover, the results developed in this study can cover some existing works as special cases without introducing additional conservativeness. A DC motor device is used to demonstrate the effectiveness of the proposed design method.

Differential geometric guidance law design for varying-speed missile

A unified differential geometric guidance law (DGGL) design framework for varying-speed missiles against stationary targets is proposed in this paper. The varying-speed missile guidance model is established in the arc-length domain with the help of the classical differential geometry curve theory. It is strictly proven that DGGL is a natural and intuitive choice for varying-speed missiles when intercepting stationary targets since the influence of missile speed is directly eliminated in the guidance model and also during the guidance law design process. Hence, the guidance performance of DGGL will remain the same, no matter how the missile speed changes. After that, the error dynamics approach, which is widely used in the guidance law design field for constant-speed missiles in the time domain, is extended to the arc-length domain for DGGL design. Besides, the nonlinearity of the guidance model is fully considered when designing DGGL, hence the guidance errors caused by linearizations and small-angle assumptions are avoided. Then, taking the representative optimal error dynamics (OED) as an example, the corresponding OED-based DGGLs for zero-effort-miss-control, impact-angle-control, and flying-range-control are respectively derived. Finally, the derived illustrative guidance laws are simulated and compared with their time-domain counterparts to verify the effectiveness and superiority of the proposed unified DGGL design framework.

MDD: A Unified Model-Driven Design Framework for Embedded Control Software

Model-driven methods are widely used in embedded control software development. Current design tools, such as Ptolemy-II and Simulink, have strong modeling capability but their simulation and code generation functionalities are challenged by the increasing complexity of control requirements. For simulation, emulating the triggering of the actor leads to additional time overhead and speed degradation. For code generation, generating redundant content degrades the code quality. Besides, current tools do not have a unified interface, which makes it difficult to cooperation. In this article, we propose a unified model-driven design framework MDD to facilitate embedded control software development. MDD can support the unification of models built by different modeling tools for high-efficiency simulation and high-quality code generation. The MDD framework supports the expansion of more modeling tools, and also supports the expansion of more uses, such as unified testing and verification. First, it offers a model intermediate representation (MIR) and several corresponding parsers, which facilitate a unified representation and cooperation for different design tools. Then, based on data flow schedule analysis of the original MIR, intermediate code representation will be generated for optimized code synthesis. Finally, a variety of code translators will synthesize the intermediate code representation into the code of actual use, such as code for simulation and code for deployment. For evaluation, we enhance two widely used design tools in industry, Ptolemy-II and Simulink, and apply them on the implementation of several benchmark models and a real-world self-driving control software of our industrial collaborator. Using MDD can help reduce their simulation time by 98.9% and 92.6%, the generated code by 99.7% and 69.9% in the number of lines, and 94.3% and 34.3% in code execution time, respectively.

Multi-Level Over-the-Air Aggregation of Mobile Edge Computing Over D2D Wireless Networks

In this paper, we consider a wireless multihop device-to-device (D2D) based mobile edge computing (MEC) system, where the destination wireless device (WD) is scheduled to compute nomographic functions. Under the MapReduce framework and motivated by reducing communication resource overhead, we propose a new multi-level over-the-air (OTA) aggregation scheme for the destination WD to collect the individual partially aggregated intermediate values (IVAs) for reduction from multiple source WDs in the data shuffling phase. For OTA aggregation per level, the source WDs employ a truncated channel-inverse structure multiplied by their individual transmit coefficients in transmission over the same time-frequency resource blocks, and the destination WD finally uses a receive filtering factor to construct the aggregated IVA. Under this setup, we develop a unified transceiver design framework that minimizes the mean squared error (MSE) of the aggregated IVA at the destination WD subject to the source WDs’ individual power constraints, by jointly optimizing the individual transmit coefficients of the source WDs and the receive filtering factor of the destination WD. The formulated power-constrained MSE minimization problem is non-convex. First, based on the primal decomposition method, we derive the closed-form solution under the special case of a common transmit coefficient. This shows that the common transmit coefficient of the source WDs is determined by the minimal transmit power budget among them. Next, for the general case, we transform the original problem into a quadratic fractional programming problem, and then develop a low-complexity algorithm to obtain the (near-) optimal solution by leveraging Dinkelbach’s algorithm along with the Gaussian randomization method. Numerical results are provided to demonstrate the significant performance gains achieved by the proposed multi-level OTA aggregation scheme over various existing schemes.

A unified fixed-time framework of adaptive fuzzy controller design for unmodeled dynamical systems with intermittent feedback

Although conventional dynamic surface filters can eliminate the issue of complexity explosion in backstepping design, the convergence of filter errors significantly influences the overall control performance. This paper proposes a unified design framework of an event-triggered adaptive fuzzy design scheme with fixed-time performance for unmodeled strict-feedback nonlinear systems, where the tracking performance, filter error, and parameter learning convergence are considered simultaneously. The unified framework guarantees the smoothness of all the closed-loop signals. A novel dynamic surface filter is designed to avoid the repeated differentiation of recursive virtual control while the filter error converges in fixed time. Compared with the conventional backstepping design, the proposed adaptive fuzzy controller design in this paper can guarantee fixed-time tracking performance. An event-triggered condition with intermittent feedback is developed to decrease the computational and communication burden. Two simulation examples are provided to validate the effectiveness of the unified fixed-time framework.

Mimetic Neural Networks: A Unified Framework for Protein Design and Folding.

Recent advancements in machine learning techniques for protein structure prediction motivate better results in its inverse problem–protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in tandem, allowing to improve protein backbone design when the structure is better estimated. We use the ProteinNet data set and show that the state of the art results in protein design can be met and even improved, given recent architectures for protein folding.

Arbitrary-Order Fixed-Time Differentiators

Differentiation is an important task in control, observation, and fault detection. Levant’s differentiator is unique, since it is able to estimate exactly and robustly the derivatives of a signal with a bounded high-order derivative. However, the convergence time, although finite, grows unboundedly with the norm of the initial differentiation error, making it uncertain when the estimated derivative is exact. In this article, we propose an extension of Levant’s differentiator so that the worst-case convergence time can be arbitrarily assigned independently of the initial condition, i.e., the estimation converges in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fixed time</i> . We propose also a family of continuous differentiators and provide a unified Lyapunov framework for analysis and design.

Observer Based Generalized Active Damping for Voltage Source Converters With LCL Filters

An observer-based active damping (AD) controller is proposed along with a unified design and implementation framework for LCL-equipped voltage source converters. The AD controller uses feedback of either of the converter-side current or the grid-side current along with that of the grid voltage. The state of the arts offer observer-based AD only for current-mode control and are limited by their inflexibility to be used in conjunction with established supplementary control methods or by the lack of damping efficacy for all configurations of LCL resonance frequency and the measured current (grid-side vs. converter-side). The proposed AD method is identically applicable for current-mode control and virtual oscillator control (VOC) where explicit voltage/current tracking loops are not used. The proposed AD controller thus overcomes a major limitation of VOC which otherwise offers robust synchronization in reduced/zero-inertia networks and enhanced fault ride-through capability. Simplified design guidelines are presented through comprehensive small-signal analysis including the observer dynamics. The proposed method is shown to be effective for both converter-side and grid-side current measurements irrespective of the LCL resonance frequency relative to the critical frequency. The analysis and design methods are validated through laboratory hardware experiments.

A Passivity-Based Sliding Mode Controller for a Class of Electro-Mechanical Systems

This letter proposes a novel sliding mode control method for a class of electro-mechanical systems using passivity based approach. For mechanical systems, the authors proposed a passivity based sliding mode controller based on the kinetic potential energy shaping method which allows one to obtain a wider class of Lyapunov function candidates. This letter extends this framework to cope with a class of electro-mechanical systems and gives a new class of energy based Lyapunov function candidates. Based on this technique, a novel sliding mode controller with an explicit energy based Lyapunov function is proposed. It is expected to provide a new basis for a unified control design framework for both passivity based control and sliding mode control. Furthermore, a numerical example demonstrates the effectiveness of the proposed method.

A design framework for absorption and diffusion panels with sustainable materials

Architectural acoustics has not traditionally had unified design methods that specify acoustical performance, visual appearance, and sustainable material selection, leading to underperforming products that contribute to a waste stream of petro-chemical foam and fiberglass materials. The evolution of design, materials, and manufacturing techniques in recent years has created new opportunities to reimagine acoustic diffusers and absorbers. Previous work by the authors have demonstrated a unifying framework for design and collaboration in architectural acoustics. The framework uses visually-driven computational design method inspired by shape grammars that generate a wide range of acoustic phase grating diffuser arrays that display unique visual and performative qualities. Simulation and evaluation metrics to assess the complexity of each design are rated in terms of their diffusion and absorption coefficients and a visual aesthetic coefficient. This paper extends the framework to include digital fabrication protocols and sustainable material specifications - including the use of fungi-based materials. Built prototypes demonstrate an expanded acoustic design space that gives acousticians the potential to create custom diffuser shapes with precise acoustical response. The innovative combination of computational design methods and sustainable fabrication protocols will be discussed, and the acoustic properties of arrays will be evaluated and compared to simulations of corresponding designs.

An integrative framework for geometric and hidden projections in three-level fractional factorial designs

Geometric and hidden projection are two important properties to consider for three-level fractional factorials. The effective development of methods that can address tasks involving both these properties requires a unified design framework that integrates them. We highlight one integrative framework that is based on indicator functions for three-level designs under the linear-quadratic parameterization system. We demonstrate the broad scope and utility of this framework by using it to develop a new procedure for addressing a task comprised of three serial problems in geometric and hidden projection. The first in the series is to construct the sets of smallest follow-up runs to a projection of a three-level design that yield specified aliasing relations. Second is to immediately characterize the partial aliasing relations among factorial effects in the resulting augmented designs. The final problem is to identify the augmented designs that maximize popular optimality criteria with respect to the second-order model among all of the candidate augmentations. The application of our procedure is illustrated with sample projections of definitive screening designs onto four, five, and six factors. The integrative framework that we present can ultimately facilitate the systematic development of methods that advance comprehensive understanding of multiple properties for general three-level designs.

A unified digital twin framework for shop floor design in industry 4.0 manufacturing systems

Digital Twin and Cyber Physical Systems (CPS) are gaining popularity due to their significant impacts on the realization of smart manufacturing within which data and information from different systems is used to increase self-awareness, self-predict, and self-configure functionalities. Throughout the integration of digital twins with CPS, intelligent virtual counterparts are created that represent the behavior of the physical systems and provide predictive insights for advanced decision making. This article provides a systematic framework to integrate digital twins of different perspectives of a shop floor design from unit level, system implementation, cyber-physical integration, and business application all together into an end-to end design solution. We propose a time machine approach to utilize the historical data into the process of digital twin design and implementation and provide a simplified approach to evaluate the suitability of different designs for a given set of tasks and applications.

Unified Virtual Oscillator Control for Grid-Forming and Grid-Following Converters

A unified virtual oscillator controller (uVOC) is proposed, which enables a unified analysis, design, and implementation framework for both grid-forming (GFM) and grid-following (GFL) voltage-source converters (VSCs). Oscillator-based GFM controllers, such as dispatchable virtual oscillator control (dVOC), offer a rigorous analytical framework with enhanced synchronization but lack effective fault handling capability, which severely limits practical application. The proposed uVOC facilitates synchronization with an arbitrarily low grid voltage and fast overcurrent limiting; this enables effective fault ride-through unlike existing GFM controllers which typically switch to a back-up controller during the fault. GFM operation with uVOC is achieved in both grid-connected and islanded modes with seamless transitions between the two. In GFL converters, bidirectional power flow control and dc bus voltage regulation are achieved with uVOC. No phase-locked loop (PLL) is required for either GFL or GFM operation circumventing the synchronization issues associated with PLLs in weak grid applications. Detail small-signal models for GFM and GFL operation have been developed, and the systematic design guidelines for controller parameters are provided. The proposed controller is validated through hardware experiments in a hybrid ac-dc microgrid.

A Unified Framework for Asymptotic Observer Design of Fuzzy Systems With Unmeasurable Premise Variables

This article develops a unified framework to design fuzzy-model-based observers of general nonlinear systems for both discrete-time and continuous-time cases. This observer problem is known as a challenging task due to the mismatch caused by the unmeasurable premise variables. To deal with this major challenge, we propose to rewrite the nonlinear system as a specific fuzzy model with two types of local nonlinearities: measurable and unmeasurable. Then, a differential mean value theorem for vector-valued functions is applied to local unmeasurable nonlinearities. This allows to represent the estimation error dynamics in a special polytopic form involving measurable membership functions and unknown but bounded time-varying parameters. Using Lyapunov-based arguments, design conditions in terms of linear matrix inequalities are derived to guarantee the asymptotic convergence of the estimation error. Three illustrative examples are given to demonstrate the interests of the new fuzzy observer framework in reducing: 1) the design conservatism and 2) the numerical complexity of the fuzzy observer structure for real-world applications.