bluuebunny/crossref_metadata_2025_split huggingface dataset:
Self Learning Design Agent (SLDA): Enabling Deep Learning and Tree Search in Complex Action Spaces
Authors: Raina Ayush , Cagan Jonathan , McComb Christopher 
Published: November 2022

Abstract
Building an AI agent that can design on its own has been a goal since the 1980s. Recently, deep learning has shown the ability to learn from large-scale data, enabling significant advances in data-driven design. However, learning over prior data limits us only to solve problems that have been solved before and biases us towards existing solutions. The ultimate goal for a design agent is the ability to learn generalizable design behavior in a problem space without having seen it before. We introduce a self-learning agent framework in this work that achieves this goal. This framework integrates a deep policy network with a novel tree search algorithm, where the tree search explores the problem space, and the deep policy network leverages self-generated experience to guide the search further. This framework first demonstrates an ability to discover high-performing generative strategies without any prior data, and second, it illustrates a zero-shot generalization of generative strategies across various unseen boundary conditions. This work evaluates the effectiveness and versatility of the framework by solving multiple versions of the truss design problem without retraining. Overall, this paper presents a methodology to self-learn high-performing and generalizable problem-solving behavior in an arbitrary problem space, circumventing the needs for expert data, existing solutions, and problem-specific learning.
Ship Deck Object Placement Optimization Using a Many-Objective Bilevel Approach
Authors: Bagazinski Noah J. , Ahmed Faez 
Published: November 2022

Abstract
The placement of objects on a ship is critical to many facets of the performance of a ship. Most notably, the mass distribution properties of objects in a ship affect the ship’s stability, trim, and structural loading. Information gathered from object placement optimization can allow naval architects to further optimize the design of the whole ship by potentially reducing the structural weight of the vessel, and adjusting the shape of the hull or the general arrangements based on available space in the ship. This paper presents a novel, many-objective bin packing problem for object placement across multiple decks on a ship. This problem is also highly constrained to avoid object intersection and protrusion. The problem was optimized with the NSGA-II algorithm, utilizing a heuristic population initialization and by separating the objectives into a bilevel optimization scheme. The bilevel scheme decouples certain objectives and design variables from the rest of the problem and sequences the evaluation for the objectives in a two-stage process. The hypervolume of the final population measured the performance of the optimization test. The results indicate that sequencing the objectives with a bilevel scheme produces an 80.3% larger hypervolume than an all-in-one optimization for the same problem. The findings from this study provide a systematic way by combining concepts from many-objective optimization, bin packing heuristics, and bilevel optimization to sequence the optimization of many-objective, object placement problems.
Modeling and Optimizing Multi-Stage Design With Gaussian Process Based on Surrogate Model Chain
Authors: Yang Siyu , Jia Liangyue , Hao Jia , Alizadeh Reza 
Published: November 2022

Abstract
Continued progress in the surrogate-model-based evaluation for the single-stage has been explored, but multistage has higher dimension and uncertainty. High dimension and low overall data of multi-stage leads to low accuracy of prediction, and cannot characterize the uncertainty of the final prediction performance. We propose a Gaussian Process-based surrogate model chain (GP-SMC) to evaluate the performance of multi-stage. Also, we combine the GP-SMC with the quasi-Newton method (L-BFGS-B), make full use of the gradient information of the GP-SMC to get an optimization solution rapidly. The MAE (Mean Absolute Error) and MRE (Mean Relative Error) and STD (standard deviation) of GP-SMC’s predicted value are 10% of the prediction of a single surrogate model, which achieves a significant improvement in prediction accuracy and a significant reduction in uncertainty. Compared with the original optimization results, the average performance is improved by 21.05%. Based on the optimal solution and GP-SMC, the confidence interval of the final performance under the optimal solution is obtained, and the confidence level is 99%. The truth probability of GP-SMC is 91.25% in the test dataset, which is higher than single GP’s 85% truth probability. The technology is used in the case of Hot Rod Rolling, and can also be applied to complex product design with multi-stage.
Exploring the Usefulness of Agent-Based Product Social Impact Modeling Through a Systematic Literature Review
Authors: Mabey Christopher S. , Mattson Christopher A. , Salmon John L. 
Published: November 2022

Abstract
A key part of an engineer’s purpose is to create products and services that benefit society, or, in other words, create products with a positive social impact. While engineers have many predictive models to aid in making design decisions about the performance or safety of a product, very few models exist for estimating or planning for the wide range of social impacts an engineered product can have. To model social impact, a model must contain representations of the product and society. Agent-based modeling is a tool that can model society and incorporate social impact factors. In this paper, we investigate factors that have historically limited the usefulness of product adoption agent-based models, and predictive social impact models through a systematic literature review. Common themes of limiting factors are identified and steps are presented to improve the usefulness of agent-based product adoption models and predictive social impact models. The goal of a predictive social impact model is to help an engineer/designer make better decisions. Predictive social impact models can help identify areas in the design space for improving the social impact of products. When coupled with existing design methods, agent-based predictive social impact models can help increase the probability that a product achieves positive social impact.
A Comparative Study of Surrogate Modeling of Nonlinear Dynamic Systems
Authors: Zhao Ying , Jiang Chen , Vega Manuel A. , Todd Michael D. , Hu Zhen 
Published: November 2022

Abstract
Surrogate models play a vital role in overcoming the computational challenge in designing and analyzing nonlinear dynamic systems, especially in the presence of uncertainty. This paper presents a comparative study of different surrogate modeling techniques for nonlinear dynamic systems. Four surrogate modeling methods, namely Gaussian process (GP) regression, a long short-term memory (LSTM) network, a convolutional neural network (CNN) with LSTM (CNN-LSTM), and a CNN with bidirectional LSTM (CNN-BLSTM), are studied and compared. All these model types can predict future behavior of dynamic systems over long periods based on training data from relatively short periods. The multidimensional inputs of surrogate models are organized in a nonlinear autoregressive exogenous model (NARX) scheme to enable recursive prediction over long periods, where current predictions replace inputs from the previous time window. The Bouc-Wen nonlinear dynamic model, which can flexibly capture the behavior of many inelastic material models, is used to compare the performance of the four surrogate modeling techniques. The results show that the GP-NARX surrogate model tends to have more stable performance than the other three deep learning-based methods for this particular example. The tuning effort of GP-NARX is also much lower than its deep learning-based counterparts.
STEP-NC Compliant Data Representations for Powder Bed Fusion in Additive Manufacturing
Authors: Milaat Fahad , Witherell Paul , Hardwick Martin , Yeung Ho , Ferrero Vincenzo , Monnier Laetitia , Brown Matthew 
Published: November 2022

Abstract
Powder bed fusion (PBF) is an additive manufacturing (AM) technology that uses powerful beams to fuse powder material into layers of scanned patterns, thus producing parts with great geometric complexity. For PBF, process parameters, environmental control, and machining functions play critical roles in maintaining fabrication consistency and reducing potential part defects such as pores and grain growth. However, a major contributor to such defects can be attributed to poor data representations in the form of tessellated geometry and incoherent process plans. To address this issue, the Standard for the Exchange of Product model data Numerical Control (STEP-NC) recently added standardized data elements, entities, and attributes specifically for AM applications. Yet, the current STEP-NC data representations for AM lack definitions of process parameters and scan strategies that are commonly used in PBF processes. Therefore, characterization of the relationship between joint features, especially for PBF in AM, is missing. To bridge this gap, in this paper, an amended STEP-NC compliant data representation for PBF in AM is proposed. Specifically, the characteristics of the interlayer relationships in PBF, along with beam technology and AM strategy controls, are defined. Simulation results demonstrate the feasibility of granular process planning control, and the potential for producing high-quality parts with exact geometry and tolerance.
Information Fusion-Based Meta-Learning for Few-Shot Fault Diagnosis Under Different Working Conditions
Authors: Xie Tingli , Huang Xufeng , Choi Seung-Kyum 
Published: November 2022

Abstract
With the development of deep learning and information technologies, intelligent fault diagnosis has been further developed, which achieves satisfactory identification of mechanical faults. However, the lack of labeled samples and complex working conditions can hinder the improvement of diagnostics models. In this article, a novel method called Information Fusion-based Meta-Learning (IFML) is explored for fault diagnosis with few-shot problems under different working conditions. Firstly, an information fusion and embedding module is applied to perform both data- and feature-level fusion of multi-source. The embedding module only contains one input layer and multiple convolutions, residual and batch normalization (BN) layers, which has the advantage of low computational cost and high generalization. Then the prototypical module is proposed to reduce the influence of domain-shift caused by different working conditions using the fusion representation, which can improve the performance of fault diagnosis. The approach is verified on artificial and real faults under 4 different working conditions from the KAt-DataCenter at Paderborn University. For the 3-way 1-shot classification on Task T1, the average testing accuracy of the proposed method is 97.14%. For the K-shot classification on different tasks, the proposed method achieves the highest average testing accuracy of 94.21%. The results show the proposed method outperforms other typical meta-learning methods in terms of testing accuracy and generalization capability.
Effects of Augmented Information System on Design Communication: A Human-Subject Study Using Aircraft Design Studio
Authors: Thekinen Joseph , Grogan Paul T. 
Published: November 2022

Abstract
This paper performs a human subject study to investigate the effect of an augmented design interface on the communication behavior and performance of multidisciplinary design teams. We frame an aircraft design task as an iterative multidisciplinary design process with 12 design parameters shared across four design actors, each in charge of one discipline, implemented in a browser-based virtual design studio. We conduct a baseline experiment to collect observational data on design communication and performance and recommend two augmentations to improve team performance: a flexible design timer to control design iterations and a history catalog that quantifies the quality of previous design states. Results of an augmented experiment show the new features increase design iteration rate and decrease chat message rate, enabling the most successful teams to complete the task faster. We discuss the implications of our results in the broader context of digitalization and transformations due to information systems.
Concurrent Shape and Topology Optimization of Metamaterials Based on Periodic Surface Modeling
Authors: Lu Yanglong , Wang Yan 
Published: November 2022

Abstract
In the most recent decades, structural optimization (SO) methods including shape and topology optimization have been employed in designing metamaterials. However, shape optimization and topology optimization are usually performed separately. Conventional topology optimization techniques are limited by high computational cost because of the high-dimensional search space. Maintaining the structural continuity and smooth boundaries of metamaterials is also challenging. In this paper, a new SO method based on periodic surface (PS) modeling is proposed to optimize the shape and topology of metamaterials concurrently. The PS model can represent a wide variety of topology with only a small number of design parameters, including periodic moments, basis vectors, and scale parameters. By limiting the number of available basis vectors to choose from, the search efficiency of topology optimization is significantly improved. To solve the mix-integer optimization problem, a mixed-integer Bayesian optimization method is also developed with a new Gaussian process kernel, which is customized for the design parameters in the PS model. The new SO approach is applied to design mechanical metamaterials with high strength-weight ratio and negative Poisson’s ratio. The comparison with other topology optimization methods shows the high efficiency of the proposed approach.
The Use of Additive Manufactured Plastic in Small-Scale Poppet Valves and Pressure Vessels
Authors: Deibert Brendan , Scott Sophia , Dolovich Allan , Wiens Travis 
Published: November 2022

Abstract
Small-scale (&lt; 100 W), low-pressure (4 MPa), low-cost hydraulic components, such as pumps and cylinders, have recently become more available. These components have potential uses in demanding applications such as in a pump-controlled electrohydrostatic actuators (EHA)s. This is limited by the fact that the unbalanced flows of a single-rod cylinder require a valve to reconcile the imbalance, which is commercially unavailable in this size and price range. We have hypothesized that it would be feasible to produce this component using additive manufactured (3D printed) plastic. Such a system would be relatively low cost with a high specific power, and could have applications in hand tools, prosthetics, robotics, and more.
This paper focuses on some of the challenges in the use of 3D printed plastic for small-scale poppet valves and pressure vessels. The objectives of this research include the investigations of the sealing performance of 3D printed plastic poppet valves and the mechanical strength of 3D printed plastic pressure vessels. Experimental results included in this paper reveal the effects of surface finish and poppet and seat geometry on sealing performance. The influences of print process, material, and orientation on the strength of a 3D printed pressure vessel are examined and the results can inform valve casing design considerations. Mechanical tensile testing of fused deposition modelling (FDM) printed polyethylene terephthalate glycol (PETG) and stereolithography (SLA) printed acrylonitrile butadiene styrene (ABS)-like test specimens provided insight to the corresponding burst strength of that material and print process. The work presented in this paper advances the state-of-the-art of using 3D printed plastic for the construction of small-scale hydraulic components.
Research on High Precision Control of Maximum Power Point Tracking for Offshore Hydraulic Wind Turbine
Authors: Zhang Jiarui , Chen Wenting , Ai Chao , Yang Yue , Shang Shuming , Zhou Qin 
Published: November 2022

Abstract
This research takes the fixed displacement pump-variable displacement motor hydraulic transmission system of offshore hydraulic wind turbine (HWT) as the research object, to solve the parameter uncertainly problem of maximum power point tracking (MPPT) control of HWT. The feedback linearization method is used to deal with the nonlinear state space model of the system. Aiming at the problem that the feedback linearization method needs an accurate mathematical model and the hydraulic transmission system of the HWT has the characteristics of parameter uncertainly, the RBF neural network adaptive control theory is used to deduce the control strategy. Taking the pump speed and power as the output respectively, the MPPT control strategy is realized. The theory proposed above is verified by Matlab/Simulink. The simulation results show that the control strategy with pump power as output is more smooth than that with speed as output, which is friendly to the grid. The simulation results under fluctuating wind speed show that the MPPT control strategy has good robustness. By comparing the simulation results, the control strategy proposed in this research solves the problem of parameter uncertainty, and does not cause the system out of control like only using feedback linearization.
Design and Analysis of a Digital Hydrostatic Transmission for Wind Turbines
Authors: Manosso Lucas Zanatta , De Negri Victor J. 
Published: November 2022

Abstract
This paper presents the design, modelling, and simulation of a digital hydrostatic transmission (D-HST) for wind turbines using fixed and variable displacement hydrostatic machines. A sizing algorithm is developed considering real efficiency data in order to optimize the selection of components and set the most energy efficient configuration. The design of a D-HST for a 28.5 kW wind turbine is carried out. Considering the use of a synchronous generator, the best D-HST configuration is set with two fixed pumps and a variable motor. A computational model is developed according to the designed system. The D-HST is compared to a conventional hydrostatic transmission (C-HST), made up by a fixed pump and a variable motor. The model of the C-HST used as a baseline was validated using an experimental setup and that is modified according to the new designed system. Steady-state and dynamic analysis are done, in order to identify the pros and cons of each concept. Furthermore, a comparison with other wind turbine drivetrains is carried out including: a widespread solution drivetrain made up by a gearbox and a doubly-fed induction generator with frequency converter; a digital displacement hydrostatic transmission; and a direct-drive wind turbine.
Ocular features in a patient presenting with a rare combination of multiple phakomatoses
Authors: Senthilkumar Vijayalakshmi A , Kohli Piyush , Mishra Chitaranjan , Mamchisetti Kavitha 
Published: November 2022

Phakomatoses are a group of congenital disorders characterised by hamartomatous lesions of the skin as well as the central and peripheral nervous systems. The presence of naevus flammeus or port-wine stain is a characteristic feature of many such disorders including Sturge-Weber syndrome (SWS), Klippel-Trenaunay syndrome (KTS) and Phakomatosis pigmentovascularis (PPV).We describe the ocular findings in a patient with coexisting PPV, SWS and KTS
Higher risk of severe hypoglycemia in children and adolescents with a rapid loss of C-peptide during the first 6 years after type 1 diabetes diagnosis
Authors: Grönberg Annika , Espes Daniel , Carlsson Per-Ola , Ludvigsson Johnny 
Published: November 2022

IntroductionThe progression to insulin deficiency in type 1 diabetes is heterogenous. This study aimed to identify early characteristics associated with rapid or slow decline of beta-cell function and how it affects the clinical course.Research design and methodsStimulated C-peptide was assessed by mixed meal tolerance test in 50 children (<18 years) during 2004–2017, at regular intervals for 6 years from type 1 diabetes diagnosis. 40% of the children had a rapid decline of stimulated C-peptide defined as no measurable C-peptide (<0.03 nmol/L) 30 months after diagnosis.ResultsAt diagnosis, higher frequencies of detectable glutamic acid decarboxylase antibodies (GADA) and IA-2A (p=0.027) were associated with rapid loss of beta-cell function. C-peptide was predicted positively by age at 18 months (p=0.017) and 30 months duration (p=0.038). BMI SD scores (BMISDS) at diagnosis predicted higher C-peptide at diagnosis (p=0.006), 3 months (p=0.002), 9 months (p=0.005), 30 months (p=0.022), 3 years (p=0.009), 4 years (p=0.016) and 6 years (p=0.026), whereas high HbA1c and blood glucose at diagnosis predicted a lower C-peptide at diagnosis (p=<0.001) for both comparisons. Both GADA and IA-2A were negative predictors of C-peptide at 9 months (p=0.011), 18 months (p=0.008) and 30 months (p<0.001). Ten children had 22 events of severe hypoglycemia, and they had lower mean C-peptide at 18 months (p=0.025), 30 months (p=0.008) and 6 years (p=0.018) compared with others. Seven of them had a rapid decline of C-peptide (p=0.030), and the odds to experience a severe hypoglycemia were nearly fivefold increased (OR=4.846, p=0.04).ConclusionsLow age and presence of multiple autoantibodies at diagnosis predicts a rapid loss of beta-cell function in children with type 1 diabetes. Low C-peptide is associated with an increased risk of severe hypoglycemia and higher Hemoglobin A1C. A high BMISDS at diagnosis is predictive of remaining beta-cell function during the 6 years of follow-up.