bluuebunny/crossref_metadata_2025_split huggingface dataset:
Model Order Reduction for a Piecewise Linear System Based on Dynamic Mode Decomposition
Authors: Saito Akira 
Published: November 2021

Abstract
This paper presents a data-driven model order reduction strategy for nonlinear systems based on dynamic mode decomposition (DMD). First, the theory of DMD is briefly reviewed and its extension to model order reduction of nonlinear systems based on Galerkin projection is introduced. The proposed method utilizes impulse response of the nonlinear system to obtain snapshots of the state variables, and extracts dynamic modes that are then used for the projection basis vectors. The equations of motion of the system can then be projected onto the subspace spanned by the basis vectors, which produces the projected governing equations with much smaller number of degrees of freedom (DOFs). The method is applied to the construction of the reduced order model (ROM) of a finite element model (FEM) of a cantilevered beam subjected to a piecewise-linear boundary condition. First, impulse response analysis of the beam is conducted to obtain the snapshot matrix of the nodal displacements. The DMD is then applied to extract the DMD modes and eigenvalues. The extracted DMD mode shapes can be used to form a reduction basis for the Galerkin projection of the equation of motion. The obtained ROM has been used to conduct the forced response calculation of the beam subjected to the piecewise linear boundary condition. The results obtained by the ROM agree well with that obtained by the full-order FEM model.
Investigate the Influence of Online Ratings and Reviews in Purchase Behavior Using Customer Choice Sets
Authors: Lin Kangcheng , Kim Harrison M. 
Published: November 2021

Abstract
The exponentially growing online reviews have become a great wealth of information into which many researchers have started tapping. Using online reviews as a source of customer feedback, product designers are able to better understand customers’ preferences and improve product design accordingly. However, while predicting future product demand as a function of product attributes and customer heterogeneity has proved to be effective, not many literatures have studied the impact of non-product-related features, such as number of reviews and average ratings, on product demand using a large-scale dataset. As such, this paper proposes a data-driven methodology to investigate the influence of online ratings and reviews in purchase behavior by using discrete choice analysis. In the absence of information about the true customer choice set, we generate an estimated customer choice set based on a probability sampling using customer clustering and product clustering. In order to examine the effect of number of reviews and average rating, we have computed, for all the laptops in the choice set of each customer, the number of reviews and thus average rating at the date of this particular customer’s review. Using laptops for our case study, our experiment has shown that the number of reviews and average ratings are statistically significant, and the inclusion of these features will greatly improve the predictive ability of the model.
Exploration of the Digital Innovation Process in the Smart Product-Service System
Authors: Saymeh Haneen A. F. , Zhang Xiangying , Peng Tao , Tang Renzhong , Wang Zuoxu , Zheng Pai 
Published: November 2021

Abstract
The dissemination of digital technologies enabled massive data generation and new forms of cooperation. Hence, a new type of innovation named digital innovation emerged. Digital innovation has evolved in different domains and contexts, one of which is called Smart Product-Service System (Smart PSS). Many research works have been done on the development of Smart PSS by leveraging various digital technologies and exploiting its digital capabilities along its lifecycle to generate personalized and innovative solutions. However, there are scarcely any works to explore how the digital innovation process can be manifested to fully exploit digital capabilities throughout Smart PSS development. Aiming to fill this gap, this research provides a theoretical basis for digital innovation in Smart PSS and investigates how it can be in line with the development of Smart PSS from an engineering lifecycle perspective. This work also provides a definition of Smart PSS from the context of digital innovation. A case study on smart office chair is employed to demonstrate the digital innovation process in the usage stage. This work can provide insight and guidance for Smart PSS development and further harness its digital innovation process.
There Is No “I” in Team but There Is in Innovation: How Individual Attributes Impact Team Ideation and Selection Practices
Authors: Peng Aoran , Hunter Sam , Miller Scarlett R. 
Published: November 2021

Abstract
While teaming is a vital component of engineering, it is important to remember that there is no team without individuals and individual behavior can drive team outputs. One of these individual factors that may manifest itself at the team level is individual risk-taking attitudes, which can be impacted by personality and preferences for creativity. However, a gap exists in research on the impact of the team composition in these factors on creative outputs, as previous research has found that team composition plays a key role in team performance. The current work was developed to examine how the elevation and diversity of team personality and preferences for creativity impact the novelty and quality of ideas generated and selected by the team through a simulation study of 60,831 simulated teams. The results of this study show that the novelty and quality of ideas generated and selected can be predicted by the elevation and diversity of the simulated teams’ personality and PCS factors. In addition, it was found that the impact of the individual elevation and diversity factors can vary depending on the ideation trait (novelty and quality) and design stage (concept generation and selection). However, there was one variable that had a consistent positive impact on team behavior — the elevation of the Creative Confidence of the Simulated Team Members. These findings provide preliminary evidence on how individual attributes manifest themselves at the team level to impact team ideation and selection performances.
Toward Computer Aided Visual Analogy Support (CAVAS): Augment Designers Through Deep Learning
Authors: Zhang Zijian , Jin Yan 
Published: November 2021

Abstract
The goal of this research is to develop a computer-aided visual analogy support (CAVAS) framework that can augment designers’ visual analogical thinking by providing relevant visual cues or sketches from a variety of categories and stimulating the designer to make more and better visual analogies at the ideation stage of design. The challenges of this research include what roles a computer tool should play in facilitating visual analogy of designers, what the relevant and meaningful visual analogies are at the sketching stage of design, and how the computer can capture such meaningful visual knowledge from various categories through analyzing the sketches drawn by the designers. A visual analogy support framework and a deep clustering model, called Cavas-DL, are proposed to learn a latent space of sketches that can reveal the shape patterns for multiple categories of sketches and at the same time cluster the sketches to preserve and provide category information as part of visual cues. The latent space learned serves as a visual information representation that captures the learned shape features from multiple sketch categories. The distance- and overlap-based similarities are introduced and analyzed to identify long- and short-distance analogies. Extensive evaluations of the performance of our proposed methods are carried out with different configurations, and the visual presentations of the potential analogical cues are explored. The evaluation results and the visual organizations of information have demonstrated the potential of the usefulness of the Cavas-DL model.
Forward Kinematics for Suspended Under-Actuated Cable-Driven Parallel Robots: A Neural Network Approach
Authors: Mishra Utkarsh A. , Caro Stéphane 
Published: November 2021

Abstract
Kinematic analysis of under-constrained Cable-Driven Parallel Robots has been a topic of interest because of the inherent coupling between the loop-closure and static equilibrium equations. The paper proposes an unsupervised neural network algorithm to perform real-time forward geometrico-static analysis of such robots in a suspended configuration under the action of gravity. The formulation determines a non-linear function approximation to model the problem and proves to be efficient in solving for consecutive and close waypoints in a path. The methodology is applied on a six-degree-of-freedom (6-DOF) spatial under-constrained suspended cable-driven parallel robot. Specific comparison results to show the effectiveness of the proposed method in tracking a given path and degree of constraint satisfaction are presented against the results obtained from non-linear least-square optimization.
Towards a Rational, Narrative-Based Design Framework for Navigating Radical Uncertainty in Engineering Design
Authors: Bryden Kenneth M. , Ferguson Scott 
Published: November 2021

Abstract
This paper examines decision making under radical uncertainty in engineering design, that is, engineering decision making in those situations where it is not possible to know the outcomes and/or construct the utility functions and probabilities needed to support rational-human decision making. In these situations, despite being faced with radical uncertainty, engineers do (and must) proceed forward in a linear, clear, and predictable manner. Yet, they may not proceed in a manner that is well described by current engineering design frameworks. Examining the role of decision making in business and other social enterprises, Tuckett and Nikolic [1] have proposed conviction narrative theory (CNT) to describe how rational decision-makers confronted with situations in which insufficient information is available to support traditional decision-making tools use narrative and intuition to reach convincing and actionable decisions. This paper proposes that, in a manner similar to what is described in CNT, narrative and engineering judgment play a critical role in engineering design situations dominated by radical uncertainty. To that end, this paper integrates the traditional rational-human view of decision making as expressed by Hazelrigg in the well-known Decision-Based Design (DBD) framework and CNT as proposed by Tuckett and Nikolic. In the resulting rational, narrative-based design framework, narrative structures are used to describe and develop design alternatives and provide the ideas, beliefs, and preferences needed by the DBD framework. The resulting preferred design is expressed as a narrative and tested using engineering judgement. Specifically, the goal of the design process is expressed as a high-level guiding narrative that fosters the development of design narratives (design alternatives), and ultimately results in a convincing narrative that describes the preferred design. The high-level guiding narrative outlines the event(s), entity(s), preferences, and beliefs needed to support the design. The design narratives are narrative fragments that are nested within the high-level narrative and include the proposed action (idea), the specific challenges that the design faces, and the possible (but not yet verified) outcomes. The convincing narrative is the validated, preferred option that results from the DBD analysis and optimization process and is reviewed using engineering judgement. Following development of the rational, narrative-based design framework, the value of the framework is discussed within the context of practical engineering design.
Is Expensive More Environment Friendly? Comparative LCA of Three Home Appliances
Authors: Shaukat M. Mobeen , Masood Hammad , Merah Neçar , Al-Badour Fadi 
Published: November 2021

Abstract
Due to rise in income and living standards in the developing world, there is a substantial growth in the use of home appliances. This growth is responsible for several environmental issues. Hence, there is a growing demand for energy efficient, environment friendly, and sustainable appliances. This study compares the environmental impacts of three home appliances using Life Cycle Assessment (LCA). Three irons of same power rating but with different prices (low, medium, and high) were selected for this study. First, energy consumption of these irons was measured and then they were disassembled to generate a detailed list of components, materials, and processes used to manufacture them. Next, LCA was conducted using SimaPro to compare the environmental impacts of these irons. Life cycle stages including material extraction, component manufacturing, assembly, distribution, and use were considered in LCA. Both ReCiPe mid-point and end-point environmental impacts were calculated. The results of this study showed that high-price iron was more environment friendly than the low-price iron.
Frequency Tunable Phononic Crystal Flat Lens for Subwavelength Imaging
Authors: Danawe Hrishikesh , Tol Serife 
Published: November 2021

Abstract
In this paper, we present a thickness-contrast based flat lens for subwavelenth imaging in an aluminum plate. The lens is made of phononic crystal (PC) with a triangular lattice arrangement of through holes drilled over an aluminum plate. Subwave-length imaging is achieved by exploiting the concept of negative refraction of A0 plate mode for the optical dispersion branch of the PC. The wavenumbers are matched at a design frequency by creating a step change in the thickness of the PC-lens and host plate. The thickness-contrast results in refractive index of minus one at the interface of the lens and host plate. Negative refraction-based lens overcomes the diffraction limit and enables focusing of flexural waves in an area less than a square wavelength. We validate the flat lens design at a single design frequency through numerical simulations and experiments. Further, we numerically demonstrate the tunability of the lens design over a broadband frequency range by modifying the thickness-contrast between the lens and host plate. The proposed frequency tunable design is promising for many applications such as ultrasonic inspection, tetherless energy transfer, and energy harvesting, where the localization of wave energy in a small spot is desirable.
The Mixed-Body Model: A Method for Predicting Large Deflections in Stepped Cantilever Beams
Authors: Sargent Brandon S. , Ynchausti Collin R. , Nelson Todd G. , Howell Larry L. 
Published: November 2021

Abstract
This paper presents a method for predicting endpoint coordinates, stress, and force to deflect stepped cantilever beams under large deflections. This method, the Mixed-Body Model or MBM, combines small deflection theory and the Pseudo-Rigid-Body Model for large deflections. To analyze the efficacy of the model, the MBM is compared to a model that assumes the first step in the beam to be rigid, to finite element analysis, and to the numerical boundary value solution over a large sample set of loading conditions, geometries, and material properties. The model was also compared to physical prototypes. In all cases, the MBM agrees well with expected values. Optimization of the MBM parameters yielded increased agreement, leading to average errors of &lt; 0.01 to 3%. The model provides a simple, quick solution with minimal error that can be particularly helpful in design.
Reduction of Whole Body Vibration in a Wide Frequency Range Using Inflation Pressure Control of Air Bladder Cushion
Authors: Nuthi Pavan , Gu Yixin , Nasirian Aida , Lindsay Alexandra , Purandare Himanshu , Haldipurkar Nischita , Shah Kashish , Wijesundara Muthu B. J. 
Published: November 2021

Abstract
Several types of interfaces like foam and inflated air cells exist to reduce the effect of mechanical vibration experienced in human-machine interfaces in different scenarios such as transportation. However, their vibration attenuation performance in a wide frequency range relevant to whole body vibration (1–80 Hz) leaves much to be desired. In this study, we investigate the effect of inflation pressure on the vibration attenuation behavior of an air cell cushion. An experimental setup capable of conducting frequency sweep tests and regulating inflation pressure in an air cell array cushion was developed. Frequency sweep tests were conducted at various inflations and the vibration transmissibilities at static inflations were plotted. A dynamic inflation scheme was developed based on the apriori knowledge of inflation dependent transmissibilities. Furthermore, the closed loop behavior of the inflation scheme was evaluated with a frequency sweep test. The resulting closed loop transmissibility indicated better vibration attenuation performance than any single static inflation for the air cell array cushion in the range of frequencies relevant to whole body vibration. This result lays the groundwork for potential air cell cushions which modify their inflation dynamically through a direct feedback from sensors like accelerometers to attenuate vibration in a wide frequency range.
An Enhanced Squared Exponential Kernel With Manhattan Similarity Measure for High Dimensional Gaussian Process Models
Authors: Xu Yanwen , Wang Pingfeng 
Published: November 2021

Abstract
The Gaussian Process (GP) model has become one of the most popular methods and exhibits superior performance among surrogate models in many engineering design applications. However, the standard Gaussian process model is not able to deal with high dimensional applications. The root of the problem comes from the similarity measurements of the GP model that relies on the Euclidean distance, which becomes uninformative in the high-dimensional cases, and causes accuracy and efficiency issues. Limited studies explore this issue. In this study, thereby, we propose an enhanced squared exponential kernel using Manhattan distance that is more effective at preserving the meaningfulness of proximity measures and preferred to be used in the GP model for high-dimensional cases. The experiments show that the proposed approach has obtained a superior performance in high-dimensional problems. Based on the analysis and experimental results of similarity metrics, a guide to choosing the desirable similarity measures which result in the most accurate and efficient results for the Kriging model with respect to different sample sizes and dimension levels is provided in this paper.
Reflections on Designing in the Wild: How Theories of Design Information Manifest in Practice
Authors: Damen Nicole B. , Toh Christine A. 
Published: November 2021

Abstract
Information acquisition, utilization and communication are integral to the design process, but systematic investigation of information behavior is complicated by its variety and the ways in which designers engage with information throughout the design process. Our previous work developed a theoretical framework to categorize the various types of information used during the design process, known as the Information Archetypes Framework. This paper explores how these information dimensions manifest in design practice, as reflected on by experienced practicing designers. Deep qualitative analysis of eight interviews with practicing designers revealed that the designers intentionally adapt their behavior to match situation specific needs, and navigate the tensions between information dimensions through trajectories and loops.
Examining Goal Congruence on Engineering Design and Innovation Student Teams
Authors: Beckman Sara , Jian Alan , Sabharwal Ahan , Goucher-Lambert Kosa 
Published: November 2021

Abstract
Goal congruence, defined as agreement by all members of a team on a common set of objectives, has been positively associated with team cohesion, team performance and team outcomes, including grades earned. Yet there is little in-depth study at scale and across types of engineering design and innovation classes in higher education that examines the goals students set for their work together. This research explores goal congruence in 857 teams involving 1470 students across 18 classes over four years. To examine goal congruence, we use student assessments of their level of agreement on their goals as well as evaluations of their written goal statements. Machine learning techniques are used to automatically identify goal types and congruence between goals. We find that goal congruence on student teams is relatively low, even when they assess it as high, partly due to variety in the types of goals they identify. We categorize the goals students articulate for their teams into grade-, completion-, teaming-, learning-, problem-, output- and outcome-oriented goals and report variance in the types of goals identified in different pedagogical settings. Our findings have implications for how faculty design their classes, link learning outcomes to team projects and facilitate goal setting on student teams.
Multi-UAV Cooperative Transportation Using Dynamic Control Allocation and a Reinforcement Learning Compensator
Authors: Li Shuai , Zanotto Damiano 
Published: November 2021

Abstract
This paper proposes a new trajectory tracking method for a 6 degree-of-freedom (DOF) cable-suspended payload controlled by a team of quadrotors. Using the modeling convention of reconfigurable cable-driven parallel robots (RCDPRs) the coupled dynamics of the payload and the quadrotors are derived. Based on this dynamic model, a new dynamic control allocation approach is introduced to optimally distribute the virtual control input (i.e., the wrench to be exerted on the payload) among the cables and generate reference positions for the quadrotors on-line, while avoiding collisions between quadrotors and accounting for cable tension constraints. Furthermore, a new reinforcement-learning (RL) compensator is proposed to reduce tracking errors caused by the constraints in the quadrotors’ thrusts. Numerical simulations are conducted to validate the proposed approach.