General Design Problem Research Articles

Shape Morphing of Tubular Structures with Tailorable Mechanical Properties

Tubular structures are in high demand in robotics, medicine, and electronics. They are expected to match different shapes under loading and simultaneously exhibit certain mechanical behavior, e.g., specific radial strength and local flexibility, that poses a complex engineering design task. Herein, strategies to achieve programmable shape morphing in patterned tubular structures are explored, whose mechanical properties for all types of deformation modes can be tailored on demand. The general design problem is formulated, and the programmable response for fundamental—expansion, bending, and twisting—modes activated by tension and pressurization is demonstrated. The design problem for expansion modes is solved analytically; the numerical results agree well with the experimental data for stereolithography three‐dimensional printed cellular tubes. The effects of loading and boundary conditions on the deformed shapes and the structural mechanical response are analyzed. Algorithm‐based design strategies are proposed to achieve quantitative and automatic design for complex deformation modes. The possible use of the proposed structures is also discussed with respect to several applications. The findings pave the way for multifunctional tubular structures by exploring the pluridimensional space of the geometric parameters of metamaterial patterns.

Implementing dynamic subset sensitivity analysis for early design datasets

Engaging with performance feedback in early building design often involves building a custom parametric model and generating large datasets, which is not always feasible. Alternatively, large parametric datasets of general design problems and filtering methods could be used together to explore specific design decisions. This paper investigates the generalizability of a method that dynamically assesses variable importance and likely influence on performance objectives as a precomputed design space is filtered down. The method first trains linear model trees to predict building performance objectives across a generic design space. Leaf node models are then aggregated to provide feedback on variable importance in different design space regions. This approach is tested on three design problems that vary in number of variables, samples, and design space structure to reveal advantages and potential limitations of the method. Algorithm improvements are proposed, and general recommendations are developed to apply it on future datasets.

Multi-configuration rigidity: Theory for statically determinate structures

This paper introduces the concept of multi-configuration rigidity for kinematically indeterminate structures with elastic springs and unilateral constraints. A simple example is provided by a structure with a single mechanism and a spring that engages two different unilateral constraints. In each of these configurations, the structure can rigidly support loads up to a critical magnitude at which the unilateral constraints become inactive. The general design problem of embedding springs throughout a structure to achieve multi-configuration rigidity, with multiple unilateral constraints and springs, is studied. This problem is cast as a linear program that maximizes the critical loads required to break free from the unilateral constraints, in all target configurations. This problem can be efficiently solved with guarantees of optimality. The formulation is generally applicable to a variety of discrete structures (e.g., linkages, pin-jointed bars, or origami) with unilateral constraints (e.g., contacts or cables).

Приближенное определение потерь характеристической скорости и приращения высоты полета верхних ступеней многоступенчатых ракет-носителей

An important role is assigned to design-ballistic calculations at the initial stage of developing a new type of the launch vehicle. Results of such calculations are a necessary condition in solving a more general multidimensional boundary value problem of the ballistic design. It should be noted that ballistic conditions determine the design-ballistic parameters ratio to solve the boundary value problem, and its algorithms could be used as components of the higher-level optimization problems. In order to save computer time, development of the so-called fast algorithms implemented using the analytical approaches to solving a ballistic problem becomes expedient. In the logical chain of calculation dependencies making part of the ballistic calculation algorithms, the main are those relations that connect losses in the characteristic velocity with the design and ballistic parameters. They are understood as the minimum set of parameters that uniquely determine the rocket trajectory. Analytical calculated dependences of the characteristic velocity losses and of the flight altitude increments on the design-ballistic parameters of the second and third stages of three-stage launch vehicles were derived.

Increasing the efficiency of cargo handling at a modern warehouse terminal

Problem. The task of increasing the efficiency of cargo handling at a modern warehouse terminal is considered. The need to introduce intelligent cargo handling systems has increased due to the increased demand for supplies and the reduction of personnel in the field of logistics. When analyzing the current state of terminal transportation, it was found that the volume of transportation exceeds the capacity of the terminals. Goal. The goal is to choose a set of technical means of identification and means of cargo transportation for the management of the terminal, which will increase the efficiency and speed of cargo handling operations in the warehouse taking into account the specified criteria and restrictions. Methodology. The task of choosing a identification system is complicated by the ambiguity of various system parameters and characteristics. Their inconsistency complicates the design process. To solve this problem, the principle of system decomposition is used, in which the solution of lower-level tasks, which have their own criteria and restrictions, leads to the solution of the general design problem taking into account these limitations. The task of choosing technical means belongs to the class of multi-criteria and can be solved by methods of multi-criteria optimization of this type of objects. Results. The technology of automated design of the warehouse terminal has been developed. Models for selecting components of the logistic terminal control system have been developed. An example of the selection of the element base of the identification and transportation system according to the specified criteria and restrictions is given. Originality. The originality lies in the fact that the using intelligent components of information processing systems allow to increase warehouse terminal productivity. Practical value. Using the technology of automated design of modern warehouse terminals, a number of issues related to the accuracy, quality and speed of decision-making during cargo handling are solved.

Graph Neural Networks for Wireless Communications: From Theory to Practice

Deep learning-based approaches have been developed to solve challenging problems in wireless communications, leading to promising results. Early attempts adopted neural network architectures inherited from applications such as computer vision. They often yield poor performance in large scale networks (i.e., poor scalability) and unseen network settings (i.e., poor generalization). To resolve these issues, graph neural networks (GNNs) have been recently adopted, as they can effectively exploit the domain knowledge, i.e., the graph topology in wireless communications problems. GNN-based methods can achieve near-optimal performance in large-scale networks and generalize well under different system settings, but the theoretical underpinnings and design guidelines remain elusive, which may hinder their practical implementations. This paper endeavors to fill both the theoretical and practical gaps. For theoretical guarantees, we prove that GNNs achieve near-optimal performance in wireless networks with much fewer training samples than traditional neural architectures. Specifically, to solve an optimization problem on an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -node graph (where the nodes may represent users, base stations, or antennas), GNNs’ generalization error and required number of training samples are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(n)</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(n²)</i> times lower than the unstructured multi-layer perceptrons. For design guidelines, we propose a unified framework that is applicable to general design problems in wireless networks, which includes graph modeling, neural architecture design, and theory-guided performance enhancement. Extensive simulations, which cover a variety of important problems and network settings, verify our theory and the effectiveness of the proposed design framework.

Online Generalized Network Design Under (Dis)Economies of Scale

We consider a general online network design problem in which a sequence of N requests arrive over time, each of which needs to use some subset of the available resources E. The cost incurred by a resource [Formula: see text] is some function fe of the total load [Formula: see text] on that resource. The objective is to minimize the total cost [Formula: see text]. We focus on cost functions that exhibit (dis)economies of scale. These functions are of the form [Formula: see text] if x &gt; 0 (and zero if x = 0), in which the exponent [Formula: see text]. Optimization problems under these functions have received significant recent attention because of applications in energy-efficient computing. Our main result is a deterministic online algorithm with tight competitive ratio [Formula: see text] when αe is constant for all [Formula: see text]. This framework is applicable to a variety of network design problems in undirected and directed graphs, including multicommodity routing, Steiner tree/forest connectivity, and set connectivity. In fact, our online competitive ratio even matches the previous best (offline) approximation ratio. We also demonstrate the practical performance of our online algorithm on instances of multicommodity routing. Funding: This work was supported by the Directorate for Computer and Information Science and Engineering [Grant CCF-1750127] and the Division of Civil, Mechanical and Manufacturing Innovation [Grant CMMI-1940766].

Approximate Submodularity in Network Design Problems

The paper “Approximate Submodularity in Network Design Problems” studies general network design problems where a firm strategically selects its network to better match supply and demand in the future. The paper observes that the arcs in network design problems are cover modular, i.e., approximately substitutes with each other, in the sense that local changes in the objective function can be used to bound global changes. The cover modularity is then applied to prove that a set of simple and intuitive heuristics achieve constant factor approximation guarantees in network design problems. Furthermore, the paper demonstrates cover modularity is also present in a general class of linear programming formulations.

SOS-Based Nonlinear Observer Design for Simultaneous State and Disturbance Estimation Designed for a PMSM Model

In this study, a type of nonlinear observer design is studied for a class of nonlinear systems. For the construction of the nonlinear observer, SOS-based optimization tools are utilized, which for some nonlinear dynamical systems have the advantage of transforming the problem into a more tractable one. The general problem of nonlinear observer design is translated into an SOS polynomial optimization which can be turned into an SDP problem. For a study problem, simultaneous state and disturbance estimation is considered, a cascaded nonlinear observer using a certain parameterization is constructed, and computation techniques are discussed. Cascade nonlinear observer structure is a design strategy that decomposes the problem into its components resulting in dimension reduction. In this paper, SOS-based methods using the cascade design technique are represented, and a simultaneous state and disturbance signal online estimation algorithm is constructed. The method with its smaller components is given in detail, the efficacy of the method is demonstrated by means of numerical simulations performed in MATLAB, and the observer is designed using numerical optimization tools YALMIP, MOSEK, and PENLAB.

Quasi-Polynomial Algorithms for Submodular Tree Orienteering and Directed Network Design Problems

We consider the following general network design problem. The input is an asymmetric metric (V, c), root [Formula: see text], monotone submodular function [Formula: see text], and budget B. The goal is to find an r-rooted arborescence T of cost at most B that maximizes f(T). Our main result is a simple quasi-polynomial time [Formula: see text]-approximation algorithm for this problem, in which [Formula: see text] is the number of vertices in an optimal solution. As a consequence, we obtain an [Formula: see text]-approximation algorithm for directed (polymatroid) Steiner tree in quasi-polynomial time. We also extend our main result to a setting with additional length bounds at vertices, which leads to improved [Formula: see text]-approximation algorithms for the single-source buy-at-bulk and priority Steiner tree problems. For the usual directed Steiner tree problem, our result matches the best previous approximation ratio but improves significantly on the running time. For polymatroid Steiner tree and single-source buy-at-bulk, our result improves prior approximation ratios by a logarithmic factor. For directed priority Steiner tree, our result seems to be the first nontrivial approximation ratio. Under certain complexity assumptions, our approximation ratios are the best possible (up to constant factors).

Analytical approach for designing accelerated degradation tests under an exponential dispersion model

To provide timely lifetime information of manufactured products to potential customers, designing an efficient accelerated degradation test (ADT) is an important task for reliability analysts. In the literature, several papers have addressed a general k-level ADT design problem (including the determinations of the number of stress levels (k), testing stresses, allocation of test units and termination times) when the underlying degradation path follows an exponential dispersion model. The results are practical and interesting. However, most of those studies only addressed the case of k≤4. Generally, if a direct proof (such as using the Karush–Kuhn–Tucker conditions) for the case of k>4 is intractable, we can consider an indirect proof via the general equivalence theorem.In this paper, we first propose a conjecture optimal design for a k-level ADT design problem and then apply the general equivalence theorem to show that this conjecture design turns out to be the global V-optimal design. In addition, an example is used to illustrate the proposed procedure. The main contribution of this work is that this analytical approach can provide reliability analysts with better insight for designing an efficient ADT plan.

Offset-Free Model Predictive Control for a cone-shaped active magnetic bearing system

Active Magnetic Bearings (AMB) are mechatronic systems that support a rotating shaft using magnetic levitation. The standard AMB architecture includes two radial actuators and one for the axial direction. An alternative geometry with cone-shaped magnetic cores allows for a more compact layout without a dedicated axial actuator. However, this configuration reduces the axial force generation capability and requires a more complex control architecture due to the inherent coupling of the axial and radial control actions. When using decentralized control, effective handling of the coil current limitations together with the axial disturbance rejection is difficult to achieve. In this context, the present paper demonstrates the benefits of applying Offset-Free Model Predictive Control (OF-MPC) for a cone-shaped AMB system. A procedure for the overall design is presented and supported by the experimental work conducted in a scaled machine that reproduces an on-board turbo-compressor unit for an aircraft. The modeling of the system is described together with the design of the OF-MPC in all its parts: general control architecture, disturbance model and observer design, target calculation and control problem formulation. An OF-MPC variant with reduced control horizon is proposed and implemented in real time. Experimental results demonstrate that the prototype is compliant with application-specific stability requirements from the ISO 14839-3:2006 standard. In addition, experiments show that OF-MPC outperforms decentralized PID controllers in terms of axial disturbance rejection. OF-MPC yields a favorable constrained optimal control technique for cone-shaped AMBs because intrinsic coupling and current saturation are optimally handled by the controller.

Human-centered XAI: Developing design patterns for explanations of clinical decision support systems

Much of the research on eXplainable Artificial Intelligence (XAI) has centered on providing transparency of machine learning models. More recently, the focus on human-centered approaches to XAI has increased. Yet, there is a lack of practical methods and examples on the integration of human factors into the development processes of AI-generated explanations that humans prove to uptake for better performance. This paper presents a case study of an application of a human-centered design approach for AI-generated explanations. The approach consists of three components: Domain analysis to define the concept & context of explanations, Requirements elicitation & assessment to derive the use cases & explanation requirements, and the consequential Multi-modal interaction design & evaluation to create a library of design patterns for explanations. In a case study, we adopt the DoReMi-approach to design explanations for a Clinical Decision Support System (CDSS) for child health. In the requirements elicitation & assessment, a user study with experienced paediatricians uncovered what explanations the CDSS should provide. In the interaction design & evaluation, a second user study tested the consequential interaction design patterns. This case study provided a first set of user requirements and design patterns for an explainable decision support system in medical diagnosis, showing how to involve expert end users in the development process and how to develop, more or less, generic solutions for general design problems in XAI.

When Can Decision Makers Learn from Financial Market Prices?

AbstractI analyze a general setting where a policymaker needs information that financial market traders have in order to implement optimal policy, and prices can potentially reveal this information. Policy decisions, in turn, affect asset values. I derive a condition for the existence of fully revealing equilibria in competitive financial markets, which identifies all situations where learning from prices for policy purposes works. I discuss the possibility of using market information for banking supervision and central banking, and the general problem of asset design. I also demonstrate that some corporate prediction markets are ill‐designed, and show how to fix it.

Why appealing to the virtues of scientific theory (and method) is necessary but insufficient for effecting systemic change: Commentary on Fergnani &amp; Chermack, 2021

Why appealing to the virtues of scientific theory (and method) is necessary but insufficient for effecting systemic change: Commentary on Fergnani &amp; Chermack, 2021

Approximate submodularity in network design problems

Network design problems, such as flexibility design, are ubiquitous in modern marketplaces where firms constantly innovate new ways to match supply and demand. We develop a primal-dual based approach to analyze the flexibility design problem, i.e., allocating subsets of limited resources to each of multiple demand classes, and establish that the problem possesses a novel structural property. The property, which we call cover modularity, can be interpreted as an approximate form of submodularity, in the sense that local changes in the objective function can be used to bound global changes. We use this structure to analyze a class of greedy heuristics and establish the first constant factor approximation guarantee for solving the general flexibility design problem. Further, we identify a significant practical byproduct of our primal-dual analysis: the dual solutions we construct can be used as surrogates to guide the heuristics, leading to order of magnitude gains in computational efficiency, without loss of optimization performance. Finally, we extend our analysis by demonstrating the presence of cover modularity in a general class of linear programming formulations, indicating applicability of our approach to a wide range of network design problems distinct from flexibility design.

Software development using design patterns

Information systems have become an integral part of our lives. The demands for software that helps us accomplish our daily tasks are ever-increasing, considering the great technological momentum around the globe. Software engineering is the process of analyzing user requirements and designing and developing software applications. Each user request is a problem that an individual or a business has encountered in the daily work processes. The goal of software engineering is to provide an optimal and efficient solution to these problems to increase the overall productivity of employees in the respective industries and at the end of the day, to increase profits. Providing these solutions is no easy feat, as the problems are often complex and in addition to requiring careful analysis, they also need smart solutions. Fortunately, we have the ability to learn from experience and apply our knowledge in different contexts to achieve our goals. As in any field of life, problems have a recursive nature and it often happens that the same problem is encountered in different contexts. Naturally, we can think that similar problems have similar solutions. The set of solutions to general software design problems in a specific context constitutes what are called design patterns. Design patterns are structures of how some objects dialogue with each other to provide a specific solution to a problem. They are ready-made solutions to known problems, and the real challenge with them is not in their construction, but in the intuitive ability to associate a design problem with the corresponding pattern that offers the most optimal solution. This paper will deal with the development of an information system for an ATM Exchange system, which carries the functionalities of an ATM cash machine and exchange rate chart. The development of this software will be totally based on design patterns, more specifically the “Observer” pattern and “Chain of Responsibility” pattern. This paper aims to emphasize the advantages of using design patterns and highlight their potential to solve general problems in a specific context. It also focuses on software engineers, information systems developers, and software engineering students. This paper will serve as a manual of best software development practices, and emphasize the principles of flexibility and reusability of information systems development components.

Graph-theoretical formulation of the generalized epitope-based vaccine design problem.

Epitope-based vaccines have revolutionized vaccine research in the last decades. Due to their complex nature, bioinformatics plays a pivotal role in their development. However, existing algorithms address only specific parts of the design process or are unable to provide formal guarantees on the quality of the solution. We present a unifying formalism of the general epitope vaccine design problem that tackles all phases of the design process simultaneously and combines all prevalent design principles. We then demonstrate how to formulate the developed formalism as an integer linear program, which guarantees optimality of the designs. This makes it possible to explore new regions of the vaccine design space, analyze the trade-offs between the design phases, and balance the many requirements of vaccines.

Intermodal Hinterland Network Design Games

Intermodal hinterland transportation is becoming increasingly critical for global container supply chains. Managing intermodal hinterland networks is challenging because multiple actors often interact in practice. The intermodal hinterland network design games that we propose enable assessing the impact of having noncooperative users in intermodal networks. The games fall into the class of network design games but have key distinctive features. We provide some general results as well as an instance without a pure Nash equilibrium for the general case. Subsequently, we focus on the special case with a single intermodal connection available. We show that a pure Nash equilibrium always exists but that this one is not always unique. We additionally identify key structural properties for this single-hub game. These properties enable us to identify all pure Nash equilibria and a system-optimal solution in polynomial time. We illustrate our results with an application related to the development of an extended gate in the Netherlands and derive a series of insights. Overall, the results show that the multiple user feature of intermodal hinterland networks is critical and needs to be accounted for at the network design stage. We believe that this latter statement holds for general network design problems with multiple users.

Policy Announcement Design

We study the general problem of information design for a policymaker—a central bank—that communicates its private information (the ``state) to the public. We show that it is optimal for the policymaker to partition the state space into a finite number of ``clusters” and to communicate to the public to which cluster the state belongs. Optimal communication is more precise when the policymaker's beliefs conform with prior public expectations, whereas it is more vague in the case of divergence. We characterize the policymaker's trade-offs via a novel object—the information relevance matrix—and label its eigenvectors as principal information components (PICs). PICs with the highest eigenvalues determine the dimensions of information with the highest welfare sensitivity and, hence, are the ones about which the policymaker should be most precise.