Design Problem Research Articles

Design of multi-hidden-layer Bayesian neural networks for model updating

Structural health monitoring (SHM) based on artificial neural networks (ANN) has received a lot of attention in recent decades, especially the Bayesian neural network (BNN) approach, which is more robust to noise for training and generalization. Since building a high-quality BNN architecture suitable for a specific task depends highly on user experience, the existing literature generally studies BNNs with simple single-hidden-layer architectures designed by empirical formulas or very few tailor-made algorithms. Multiple hidden layer BNN (MBNN) has a more complex architecture than single hidden layer BNN (SBNN) and is expected to achieve better generalization performance for complex problems. But the corresponding architectural design problem is much more complex, and there is no previous experience in the literature. To achieve this for MBNN, this paper proposes two feasible algorithms to optimize the number of neurons in each hidden layer simultaneously, where a logarithmic evidence metric is introduced to quantitatively characterize the performance of MBNN for a given multi-hidden-layer architecture. The feasibility and effectiveness of the proposed method are verified by the finite element (FE) model updating of both a planar truss model and a real-life pedestrian bridge. The verification results show that the second algorithm is much more computationally efficient for the optimal design of the MBNN architecture and has the potential for practical application.

Double machine learning and design in batch adaptive experiments

Abstract We consider an experiment with at least two stages or batches and O ( N ) O\left(N) subjects per batch. First, we propose a semiparametric treatment effect estimator that efficiently pools information across the batches, and we show that it asymptotically dominates alternatives that aggregate single batch estimates. Then, we consider the design problem of learning propensity scores for assigning treatment in the later batches of the experiment to maximize the asymptotic precision of this estimator. For two common causal estimands, we estimate this precision using observations from previous batches, and then solve a finite-dimensional concave maximization problem to adaptively learn flexible propensity scores that converge to suitably defined optima in each batch at rate O p ( N − 1 ⁄ 4 ) {O}_{p}\left({N}^{-1/4}) . By extending the framework of double machine learning, we show this rate suffices for our pooled estimator to attain the targeted precision after each batch, as long as nuisance function estimates converge at rate o p ( N − 1 ⁄ 4 ) {o}_{p}\left({N}^{-1/4}) . These relatively weak rate requirements enable the investigator to avoid the common practice of discretizing the covariate space for design and estimation in batch adaptive experiments while maintaining the advantages of pooling. Our numerical study shows that such discretization often leads to substantial asymptotic and finite sample precision losses outweighing any gains from design.

Integrated multi-manned disassembly line balancing problem with reverse supply chain design strategies by considering lot sizing

ABSTRACT Due to strict governmental regulations, manufacturers have increasingly focused on recovering end-of-life (EoL) products through disassembly lines, aiming to achieve economic benefits while addressing the environmental aspects of supply chain sustainability. Disassembly line balancing (DLB) has become a key challenge for enterprises seeking to perform environmentally conscious manufacturing as part of a closed-loop supply chain policy. Therefore, designing a sustainable closed-loop supply chain requires analyzing interrelated problems by focusing on strategic-, tactical-, and operational-level decisions simultaneously. This study introduces an integrated problem that addresses multi-manned DLB, lot sizing, and reverse supply chain (RSC) design problems. To mathematically represent the problem, a generic optimization model is developed to minimize overall costs, including station opening, inventory holding, shortage, transportation, and collection center/facility opening costs. This study addresses several research questions to explore the impact of distribution strategies (centralized and decentralized) on multi-manned DLB and lot size decisions within the context of reverse supply chain design.

A Secure Auditable Remote Registry Pattern for IoT Systems

In software engineering, pattern papers serve the purpose of providing a description of a generalized, reusable solution to recurring design problems, based on practical experience and established best practices. This paper presents an architectural pattern for a Secure Auditable Registry service based on Message-Oriented Middleware to be used in large-scale IoT systems that must provide auditing capabilities to external entities. To prepare the pattern, the direct experience in applying the pattern solution in an industry-funded R&D project has been a key aspect because it has allowed us to gain a deep understanding of the problem and the solution, and it has contributed to the correctness and real-world applicability of the pattern as described. To further improve the quality of the paper, we have followed the commonly accepted practices in pattern development (including peer reviews) to ensure that the core aspects of the solution are correctly represented and that the description allows it to be applicable to similar problems in other domains, such as healthcare, autonomous devices, banking, food tracing or manufacturing to name a few. The work done in applying this pattern confirms that it solves a recurring problem for IoT systems, but also that it can be adopted in other domains, providing an effective solution in order to achieve enhancement of the auditability capabilities of the target systems. This pattern will be part of a pattern language (i.e., a family of related patterns) that we are developing for transitioning from legacy systems to IoT with an emphasis on security.

Optimizing the Allocation of Trials to Sub-regions in Crop Variety Testing with Multiple Years and Locations

AbstractField experiments in crop variety testing are conducted in multiple environments. When the targeted growing area can be stratified into sub-regions (zones), a design problem arises regarding the number of trials to be performed in each sub-region. We propose an analytical solution and a computational approach for optimal allocation of trials to sub-regions in multi-environment crop variety testing for multi-year experiments. For our purpose, we use a linear mixed model and we determine allocations that are optimal for the prediction of pairwise linear contrasts of genotype effects. For computations, we use the OptimalDesign package. The analytical results are illustrated by a real data example.

Improved sandcat swarm optimization algorithm for solving global optimum problems

The sand cat swarm optimization algorithm (SCSO) is a metaheuristic algorithm proposed by Amir Seyyedabbasi et al. SCSO algorithm mimics the predatory behavior of sand cats, which gives the algorithm a strong optimized performance. However, as the number of iterations of the algorithm increases, the moving efficiency of the sand cat decreases, resulting in the decline of search ability. The convergence speed of the algorithm gradually decreases, and it is easy to fall into local optimum, and it is difficult to find a better solution. In order to improve the search and movement efficiency of the sand cat, and enhance the global optimization ability and convergence performance of the algorithm, an improved sand cat Swarm Optimization (ISCSO) algorithm was proposed. In ISCSO algorithm, we propose a low-frequency noise search strategy and a spiral contraction walking strategy according to the habit of sand cat, and add random opposition-based learning and restart strategy. The frequency factor was used to control the search direction of the sand cat, and the spiral contraction hunting was carried out, which effectively improved the randomness of the population, expanded the search range of the algorithm, enhanced the moving efficiency of the sand cat, and accelerated the convergence speed of the algorithm. We use 23 standard benchmark functions and IEEE CEC2014 benchmark functions to compare ISCSO with 10 algorithms, and prove the effectiveness of the improved strategy. Finally, ISCSO was evaluated using five constrained engineering design problems. In the results of these problems, using ISCSO has 3.08%, 0.23%, 0.37%, 22.34%, 1.38% improvement compared with the original algorithm respectively, which proves the effectiveness of the improved strategy in practical application problems. The source code website for ISCSO is https://github.com/Ruiruiz30/ISCSO-s-code.

Modified tractive force approach for sediment transport under bank seepage

ABSTRACT The current research focuses on integrating the impact of seepage into the tractive force method for sediment particles situated on porous riverbanks. A formula is developed using the tractive force approach to calculate the critical shear stress acting on sediment particles under steady seepage conditions. The study reveals that the hydraulic gradient influences the critical shear stress, which rises with higher hydraulic gradients. Additionally, application of the derived equation is demonstrated through a hypothetical channel design problem.

Improved Osprey Optimization Algorithm with Multi-Strategy Fusion

The osprey optimization algorithm (OOA) is an effective metaheuristic algorithm. Although the OOA has the characteristics of strong optimality-seeking ability and fast convergence speed, it also has the disadvantages of imbalance between global exploration and local exploitation ability, easily falling into local optima in the later stage, and reduced population diversity and convergence speed. Therefore, this paper proposes an improved osprey optimization algorithm (IOOA) with multi-strategy fusion. First, Fuch chaotic mapping is used to initialize the ospreys’ population and increase the population diversity. Then, an adaptive weighting factor is introduced in the exploration phase of the algorithm to help the algorithm improve the convergence accuracy. The Cauchy variation strategy is integrated in the algorithm’s exploitation stage to enhance the diversity of the ospreys’ population and avoid falling into local optima. Finally, a Warner mechanism for the sparrow search algorithm is introduced to coordinate the algorithm’s local optimization and global search capabilities. The IOOA with various optimization algorithms is tested in a simulation for 10 benchmark test functions and 15 CEC2017 test functions, and non-parametric tests are performed on the IOOA. Experimental results show that the IOOA achieves improved accuracy and stability. The application of the IOOA to the three-bar truss engineering design problem further verifies its superiority in dealing with practical optimization problems.

Two-stage robust multimodal hub network design under budgeted demand uncertainty: A Benders decomposition approach and a case study

The widening use of hub networks in urban agglomeration freight systems requires several actual extensions in conventional hub network design problems. For this purpose, we introduce a two-stage robust multimodal hub network design problem for the urban agglomeration freight system by considering incomplete hub network topology, multiple transportation modes, travel time limit and discuss the uncertainty in the constructed network from the demand point of view. Particularly, we model the demand uncertainty for the considered problem in two different ways. The basic model supposes that interval-budgeted uncertainty set is adopted to characterize uncertain demand, while the expanded model additionally considers possible states of the uncertain demand and weights summation of performances over multiple uncertainty sets, namely state-wise budgeted uncertainty set. By using a min–max criterion, we develop the path-based mixed-integer programming formulations for the proposed problem, which can significantly decrease the number of required integer variables and constraints. To handle large-sized problems, we propose an improved Benders decomposition algorithm, in which the master problem is implemented in a branch-and-bound framework and the subproblem is optimality solved by a customized two-step strategy. In addition to evaluating on the standard CAB, TR and AP datasets, we conduct a real-world case study of the Beijing-Tianjin-Hebei urban agglomeration freight system to explore the effect of incorporating uncertainty and showcase the superior performance of the proposed methods.

Parallel-transmission spatial spectral pulse design with local specific absorption rate control: Demonstration for robust uniform water-selective excitation in the human brain at 7 T.

To propose a novel method for parallel-transmission (pTx) spatial-spectral pulse design and demonstrate its utility for robust uniform water-selective excitation (water excitation) across the entire brain. Our design problem is formulated as a magnitude-least-squares minimization with joint RF and k-space optimization under explicit specific-absorption-rate constraints. For improved robustness against off-resonance effects, the spectral component of the excitation target is prescribed to have a water passband and a fat stopband. A two-step algorithm was devised to solve our design problem, with Step 1 aiming to solve a reduced problem to find a sensible start point for Step 2 to solve the original problem. The efficacy of our pulse design was evaluated in simulation, phantom, and human experiments using the commercial Nova head coil. Universal pulses were also designed based on a 10-subject training data set to demonstrate the utility of our method for plug-and-play pTx. For kT-points and spiral nonselective parameterizations, our design method outperformed the pTx interleaved binomial approach, reducing RMS error by up to about 35% for water excitation and about 97% for fat suppression (over a 200-Hz bandwidth) while decreasing local specific absorption rate by about 30%. Both our subject-specific and universal pulses improved water excitation, restoring signal loss in the cerebellum while suppressing fat signal even in regions of large susceptibility-induced off-resonances. Demonstrated useful for 4D (3D spatial, one-dimensional spectral) pTx spatial-spectral pulse design, our proposed method provides an effective solution for robust volumetric uniform water excitation, holding a promise to many ultrahigh-field applications.

Research on the Packaging Design Practices of Agricultural Products in the Context of Rural Revitalization: A Case Study of Zhaoqing Emperor Oranges

To carry out the packaging design of agricultural products around rural revitalization, and to study the innovative application of brand culture in the packaging of agricultural products. Through the case method, Zhaoqing Emperor Citrus is selected as an example for design analysis and practice. Firstly, through the domestic and foreign related literature and data research, the current situation of agricultural product packaging design is sorted out, and the existing packaging problems are summarized; secondly, the user needs are obtained through the qualitative analysis of user interviews, and the near-design orientation is made; finally, countermeasure strategies and design solutions are put forward in combination with the current problems of agricultural product packaging design. In this topic, design practice is carried out for the packaging design of agricultural products, design reference is provided, and relevant design strategies are proposed based on the background of rural revitalization. The innovative storytelling as well as visualization of the culture outside the product can better empower the value of agricultural products, thus contributing to rural revitalization.

Adaptive Free-Form Deformation Parameterization Based on Spring Analogy Method for Aerodynamic Shape Optimization

An adaptive Free-Form Deformation parameterization method based on a spring analogy is presented for aerodynamic shape optimization problems. The proposed method effectively incorporates the gradients of the objective and constraint functions, achieving automatic control point adjustment based on variances in design variable components. To evaluate the performance of the adaptive FFD parameterization method, two 2D airfoil optimization design problems are examined. The optimization of the RAE2822 airfoil with 12, 18 and 24 design variables demonstrates superior results for the adaptive method compared to uniform parameterization. The adaptive method requires fewer iterations and achieves lower objective function values. Additionally, the optimization design from NACA0012 to RAE2822 airfoil with 18 design variables shows that the adaptive parameterization method achieves a lower drag coefficient while satisfying the optimization objective. This validates the method’s capability to finely adjust airfoil shapes and capture more optimal design points by exerting stronger control over local shapes. The proposed adaptive FFD parameterization method proves highly effective for optimizing aerodynamic shapes, offering stability and efficiency in the early stages of optimization, even with a limited number of design variables.

Modified preview repetitive control with equivalent-input-disturbance based on two-dimensional model

This paper deals with the problem of two-dimensional (2D) system-based modified preview repetitive control (MPRC) with equivalent-input-disturbance (EID) for continuous-time systems. First, an EID-based MPRC law is used to improve the tracking performance and disturbance attenuation. Next, by using a new equality constraint, a continuous-discrete 2D system is established and the design problem of the EID-based MPRC law is transformed into the stability problem of the 2D system. Then, we employ the 2D system theory together with the linear matrix inequality (LMI) approach, a sufficient condition is derived to ensure the stability of the closed-loop system. By solving an LMI, the gains of the controller and state observer can be obtained. Finally, two numerical examples are provided to illustrate the effectiveness and superiority of the developed controller design technique.

A Closed-Form Solution to Observer Design Problem for Ostensible Metzler Takagi-Sugeno Systems

This paper addresses the state estimation problems related to the generalized fuzzy observer design for ostensible Metzler Takagi-Sugeno (T-S) systems. Attention is focused on design constraints for the concept of diagonal stabilization and positivity of observer gain matrices. On the basis of some new interpretations, the parameterizations of ostensible Metzler T-S fuzzy systems is presented, which opens the way to the solution of the design problem using only the principle of linear matrix inequalities. The same approach is intended to ensure the stability of the dynamics of the estimation error. The presented method extends and generalizes the results that have been presented in the literature so far.

Using of Grasshopper Optimization Algorithm Approach for Optimal Weight Design Problem of the Spur Gear

Gears are undoubtedly the most important parts of motion transmission. One way to increase the motion performance of the system is to reduce the weights of the gears without sacrificing the strength capability. Today, various optimization techniques that rely heavily on analytical and heuristic approaches are used for this problem. In this study, the gear optimization problem in accordance with the minimum weight was solved with Grasshopper Optimization Algorithm (GOA). Introduced in recent years, GOA is a meta-heuristic optimization technique that stands out with its successful performance in engineering applications. This approach was used in a spur gear formation case as per the minimum weight for the first time. Compared to previous studies, the results obtained in this paper show that it is possible to design a lighter gear with GOA.

Introduction to the Investigation of reproduction of the real geometry of UBM panels

The article concisely describes UBM technology (Ultimate Building Machine), that can be used as a solution for buildings and roof structures. The structure of this technology is made of double corrugated thin-walled steel profiles manufactured on the construction site by a self-contained UBM manufacturing factory on wheels. These panels serve as both the building envelope and the structural system. The specificity of the construction poses many design problems, especially the determination of the strength parameters and stiffness of the double-corrugated panels from which the structure is made. The article presents the results of spatial scanning tests of double-corrugated steel sheets, which were carried out using commonly available 3D scanning devices: Leica 3D Disto and MagiScan app. Additionally, results of numerical analyses performed on scanned samples and a comparison of these results with preliminary laboratory tests have been presented in the article. The purpose of scanning was to obtain an accurate and real geometry of the UBM panels, to implement it into a numerical software, and then to perform numerical analyses. Commonly available 3D scanning devices were used because using advanced 3D scanners is not popular nowadays for economic reasons, and hand-built geometric models pose a lot of problems and are not accurate enough. Promising results were obtained, which form the basis for further research.

It takes three: Parental hostility, brain morphology and child externalizing problems in a parent-offspring neuroimaging trio design.

Hostility often co-occurs in parents and associates with increased aggression and inattention problems in children. In this population-based cohort of 484 mother-father-child neuroimaging trios, we investigated the degree to which associations of prenatal and childhood parental hostility would be associated with maternal, paternal and child brain structural differences. Also, we examined whether hippocampal volumes of the parents or child mediate the association of prenatal parental hostility with child externalizing behaviors. Maternal and paternal hostility was assessed with the hostility subscale of the Brief-Symptom-Inventory at three time points: prenatally at 30 weeks gestation, and when the child was 3 and 10 years old. During adolescence assessment wave (age 14), maternal, paternal, and offspring assessment included a magnetic-resonance-imaging (MRI). Child externalizing problems were assessed with Youth-Self-Report-Child-Behavior-Checklist.Our findings suggest that maternal and paternal hostility were each associated with smaller gray matter, white matter, and hippocampal volumes of their own and their partner's brain. Prenatal maternal but not paternal hostility was associated with smaller total gray matter, white matter, and hippocampal volumes in the offspring. The child's hippocampal volumes partially mediated the associations of prenatal parental hostility (latent-construct) with adolescent externalizing behavior, even after adjusting for prior child externalizing problems. Moreover, parental psychopathology may have long-lasting neurodevelopmental correlates in children that underlie the intergenerational transmission of behavioral problems. The behavior of family members results from a system of interdependent dyadic relationships over time that associate with specific brain structural differences.Significance statement Parental hostility often co-occurs in the parents. Research suggests that what transpires in one family subsystem, e.g. hostility among parents, is related to what transpires in other subsystems, e.g. mother-child or father-child, and can negatively impact child development. To understand the neurobiological effects of parental hostility on the families, these can best be studied with trio analysis as parents and children may all be affected. Overall, the findings elucidate how hostility of a parent negatively relates to different family subsystems and associated brain characteristic, such as the hippocampal volume. Our findings suggest that the behavior of family members results from a system of interdependent dyadic relationships over time that associate with specific brain structural differences.

Compliant Mechanism Synthesis Using Nonlinear Elastic Topology Optimization With Variable Boundary Conditions

ABSTRACTIn topology optimization of compliant mechanisms, the specific placement of boundary conditions strongly affects the resulting material distribution and performance of the design. At the same time, the most effective locations of the loads and supports are often difficult to find manually. This substantially limits topology optimization's effectiveness for many mechanism design problems. We remove this limitation by developing a method which automatically determines optimal positioning of a prescribed input displacement and a set of supports simultaneously with an optimal material layout. Using nonlinear elastic physics, we synthesize a variety of compliant mechanisms with large output displacements, snap‐through responses, and prescribed output paths, producing designs with significantly improved performance in every case tested. Compared to optimal designs generated using manually designed boundary conditions used in previous studies, the mechanisms presented in this paper see performance increases ranging from 47% to 380%. The results show that nonlinear mechanism responses may be particularly sensitive to boundary condition locations and that effective placements can be difficult to find without an automated method.

Chaotic-Based Improved Henry Gas Solubility Optimization Algorithm: Application to Electric Motor Control

This study presents a novel meta-heuristic optimization method that combines the Henry Gas Solubility Optimization (HGSO) technique with symmetric chaotic systems. By leveraging the randomness of chaotic systems, the parameters of the HGSO algorithm that require random generation are produced through chaotic processes, allowing the algorithm to exhibit chaotic behavior in its pursuit of optimal values. This innovative approach is termed Chaotic Henry Gas Solubility Optimization (CHGSO), with the primary objective of enhancing the performance of the HGSO method. The randomness of the data obtained from chaotic systems was validated using NIST-800-22 tests. The CHGSO method was applied to both 47 benchmark functions and the optimization of parameters for a PID controller utilized in the speed control of a DC motor. To evaluate the effectiveness of the proposed method, it was compared with several widely recognized algorithms in the literature, including PSO, WOA, GWO, EA, SA, and the original HGSO algorithm. The results demonstrate that the proposed method achieved the best performance in 43 of the benchmark functions, outperforming the other algorithms. In the context of controller design, the PID parameters were optimized using the error-based ITSE objective function. According to the controller responses, the proposed method has achieved the best results in the simulation studies, with a settling time of 0.035 and a rise time of 0.014 without overshooting, and in the experimental studies, with a settling time of 0.15 and a settling time of 1.4%. When the results are examined, it is observed that it has achieved successful results in the controller design problem.

A semi-definite optimization method for maximizing the shared band gap of topological photonic crystals

Topological photonic crystals (PCs) can support robust edge modes to transport electromagnetic energy in an efficient manner. Such edge modes are the eigenmodes of the PDE operator for a joint optical structure formed by connecting together two photonic crystals with distinct topological invariants, and the corresponding eigenfrequencies are located in the shared band gap of two individual photonic crystals. This work is concerned with maximizing the shared band gap of two photonic crystals with different topological features in order to increase the bandwidth of the edge modes. We develop a semi-definite optimization framework for the underlying optimal design problem, which enables efficient update of dielectric functions at each time step while respecting symmetry constraints and, when necessary, the constraints on topological invariants. At each iteration, we perform sensitivity analysis of the band gap function and the topological invariant constraint function to linearize the optimization problem and solve a convex semi-definite programming (SDP) problem efficiently. Numerical examples show that the proposed algorithm is superior in generating optimized optical structures with robust edge modes.