bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Study of the Exploratory Creativity Performance Between Machine and Human Designers
Authors: Yin Yuan , Qin Kaitong , Liu Huiting , Childs Peter , Sun Lingyun , Chen Liuqing 
Published: November 2022

Abstract
Exploratory creativity (E-creativity) is used to represent the creative performance behind the exploration process when establishing conceptual space. Researchers have attempted to build computational E-creativity models to help human generate more creative ideas or solutions. This trend sparks the discussion on whether the performance of machine can achieve a similar level to human beings. However, the performance gap of E-creativity between human beings and machine has not been fully studied. This study aims to investigate the E-creativity performance differences between machine and human designers. To be specific, a state-of-the-art model DALL·E is chosen as a representative of machines for generating E-creativity imagery and is compared to novice designers who are the representative for generating E-creativity imagery of humans. Expert designers are recruited as assessors to assess the creativity and E-creativity performance of the collected human and machine data. The experimental results reveal that the creativity level of humans is higher than that of machine. The E-creativity level of machine is higher than that of humans. The textual E-creativity performance is higher than the imagery E-creativity performance of humans while it is lower than the imagery E-creativity performance of machine. The results provide insights for supporting the development of more advanced E-creativity engines and corresponding evaluation methods.
t-METASET: Task-Aware Generation of Metamaterial Datasets by Diversity-Based Active Learning
Authors: Lee Doksoo , Chan Yu-Chin , Chen Wei (Wayne) , Wang Liwei , Chen Wei 
Published: November 2022

Abstract
Inspired by the recent achievements of machine learning in diverse domains, data-driven metamaterials design has emerged as a compelling paradigm that can unlock the potential of the multiscale architectures. The model-centric research trend, however, lacks principled frameworks dedicated to data acquisition, whose quality propagates into the downstream tasks. Built by naive space-filling design in shape descriptor space, metamaterial datasets suffer from property distributions that are either highly imbalanced or at odds with design tasks of interest. To this end, we present t-METASET: an active-learning-based data acquisition framework aiming to guide both balanced and task-aware data generation. Uniquely, we seek a solution to a commonplace yet frequently overlooked scenario at early stages of data-driven design: when a massive shape-only library has been prepared with no properties evaluated. The key idea is to harness a data-driven shape descriptor learned from generative models, fit a sparse regressor as a start-up agent, and leverage metrics related to diversity to drive data acquisition to areas that help designers fulfill design goals. We validate the proposed framework in three deployment cases, which encompass general use, task-specific use, and tailorable use. Two large-scale mechanical metamaterial datasets (∼ O(104)) are used to demonstrate the efficacy. Applicable to general design representations, t-METASET can boost future advancements in data-driven design.
Uncertainty Quantification of Physics-Based Label-Free Deep Learning and Probabilistic Prediction of Extreme Events
Authors: Li Huiru , Yin Jianhua , Du Xiaoping 
Published: November 2022

Abstract
Surrogate models from machine learning regression have been increasingly used in engineering analysis and design. Since surrogate models are usually built using data from solving expensive physical models, label-free machine learning methodologies have been developed to reduce the computational cost. Understanding and quantifying the model (epistemic) uncertainty of surrogate models is critical for their applications with quantified confidence. It is, however, much more computationally expensive, or even impossible to quantifying the model uncertainty for label-free machine learning. In this work we propose an uncertainty quantification method for the epistemic uncertainty of physics-based label-free regression. The method is used after a surrogate model has already been built by deep neural network based on the data of only input variables without labels (data of responses) and a system of physical equations. A surrogate model of the neural network regression model error is built with Gaussian Process regression using the existing training points and the derivatives of the system of physical equations at the training points. The error model is then used to compensate the error of the neural network surrogate model, therefore producing more accurate predictions. With higher accuracy, the proposed method is applied to probabilistic prediction of extreme events where both (aleatory) data uncertainty and model uncertainty coexist, and higher accuracy is required. Its application to time-dependent reliability prediction of a four-bar linkage mechanism demonstrates the high accuracy of the proposed method.
Concreate: Using Data Physicalization to Increase The Understanding and Inspirational Use of Quantitative Data In Data-Driven Design Scenarios
Authors: Spalburg Tiara , Chandrasegaran Senthil , Eikelenberg Nicole , Gonçalves Milene 
Published: November 2022

Abstract
In today’s world, the advantages of data-enabled design are undeniable, increasing the performance of organisations drastically by informing and inspiring the design process. While organisations seem to be more experienced with quantitative data for evaluative purposes, they do struggle to use data as creative material to inspire the design process. Choosing the right type of data representation is critical for using data for creative purposes. Data visualization has proven to be highly effective in increasing understanding of data, as it is fast, accurate and flexible. Data physicalization, on the other hand, remains unexplored in comparison, especially its effect on creativity. This paper presents the results of two studies (one preliminary and one follow-up study), which explored the use of data physicalization in creative settings. The preliminary study enabled to collect initial requirements for the development of a physicalization toolkit, while the follow-up study investigated its impact on the design process, in comparison to data visualization. From the studies, we developed Concreate, a collaborative data physicalization toolkit designed to lead to creative insights from quantitative data. Our results show that Concreate can potentially stimulate creative thinking, by encouraging intense, tangible interaction with data leading to increased reflection-in-action and a deeper understanding of data. The two studies and toolkit development were carried out at a multinational automotive company, interested in innovating by incorporating data as creative material. Besides the immediate practical implications, we conclude this paper with a discussion on future recommendations for using data physicalization in the design process.
Facial Expression Recognition as a Measure of User-Designer Empathy
Authors: Salmi Aleksi , Li Jie , Holtta-Otto Katja 
Published: November 2022

Abstract
In human-centered product design and development, understanding the users is essential. Empathizing with the user can help designers to gain deeper insights into the user experience and their needs. However, capturing real time empathy during user interaction and the degree to which empathy enhances user understanding remains unclear. To narrow this gap, this study aims to explore the use of facial expression recognition during a videotaped user interview as means to capture empathy. Mimicry and synchrony have been shown to be predictors of empathy in cognitive psychology. In this study, we adapt this method to 46 user-designer interviews. The results show that the user and designer show mimicry in their facial expressions, which indicates that affective empathy can be captured via facial recognition. However, we find that the user’s facial expressions might not represent their actual emotional tone, which can mislead the designer. Further, we do not find a link between the observed mimicry of facial expressions and the understanding of mental contents, which hints that the affective and some cognitive parts of user empathy may not be directly connected. Further studies are needed to understand how facial expression analysis can further be used to study and advance empathic design.
Modeling and Analysis of Uncertain Static Contact Characteristics for Revolute Clearance Joints
Authors: Liu Sheng , Yu Haidong , Gu Bin , Zhao Yong 
Published: November 2022

Abstract
The presence of uncertain clearance in revolute joints affects the motion accuracy and stability of the mechanical system with revolute joints. In this paper, a novel static conformal contact force (CCF) model for revolute clearance joints is developed to evaluate the relationship between contact forces and penetration depth in conformal contact problems based on the geometric constraints and the Winkler elastic foundation theory. Compared to the other two conventional static contact force models, the computational results of the proposed CCF model are more consistent with the FEM simulation results in analyzing the conformal contact problems. Static contact characteristics of the revolute joint during conformal contact are investigated using the established CCF model when considering the fuzzy uncertainty of clearance size and material parameters of the joint. The analysis results may be used to guide the design and the contact analysis of the revolute clearance joints.
Stability of Rotorcraft Ground Resonance by Estimating Lyapunov Characteristic Exponents From Multibody Dynamics
Authors: Cassoni Gianni , Zanoni Andrea , Tamer Aykut , Masarati Pierangelo 
Published: November 2022

Abstract
This work discusses the use of Lyapunov Characteristic Exponents to assess the stability of nonlinear, time-dependent mechanical systems. Specific attention is dedicated to methods capable of estimating the largest exponent without requiring the Jacobian matrix of the problem, which can be applied to time histories resulting from existing multibody solvers. Helicopter ground resonance is analyzed. Improvements over the available literature are: the problem is formulated in physical coordinates, without eliminating periodicity through multiblade coordinates; the rotation of the blades is not linearized; the problem is modeled considering absolute positions and orientations of parts. The dynamic instability that arises at some angular velocities when the isotropy of the rotor is broken (e.g., caused by the failure of one lead-lag damper, a design test condition) is observed to evolve into a large amplitude limit cycle, where the usual Floquet-Lyapunov analysis of the linearized time-periodic simply predicts instability.
Kinematics of an Origami Inspired Millipede Robot
Authors: Liu Chenying , You Zhong , Maiolino Perla 
Published: November 2022

Abstract
A millipede is a worm-like arthropod with a relatively hard exoskeleton, a segmented body, and numerous pairs of legs. Its appendage helps to coordinate the behaviour of multiple body segments to generate a peristaltic wave, so that efficient locomotion can be achieved on uneven surfaces and limited space. When sensing danger or disturbed, the millipede curls up into a coil shape to protect itself against predators. The curling behaviour of a millipede has also been found in some of its close relatives in the arthropod family, such as pill bugs and lobsters. Inspired by its peristaltic and curling behaviours, we propose an origami-based concept for a robot that imitates the motion of a millipede. The mechanism is made with a chain of interlinked modules, mimicking the segments in a millipede. Each module is an assembly of rigid facets with a uniform and finite thickness, reducing the overall degrees of freedom (DoF) to a manageable number. Given its unique geometric design, the assembly can contain any number of modules without an excessively large DoF. Its curling and peristaltic motions are quantified kinematically in the paper, which paves the way for the millipede robot design with actuation and control components integrated. In addition, a few other configurations of the proposed origami mechanism and its geometric variants are presented, thus opening up design space for other possible applications.
Stress Trajectory Guided Structural Design and Topology Optimization
Authors: Wang Junpeng , Wu Jun , Westermann Rüdiger 
Published: November 2022

Abstract
Density-based topology optimization using global and local volume constraints is a key technique to automatically design lightweight structures. It is known that stiffness optimal structures comprise spatially varying geometric patterns that span multiple length scales. However, both variants of topology optimization have challenges to efficiently converge to such a structural layout. In this paper, we investigate material layouts that are generated from stress trajectories, i.e., to compile a globally consistent structure by tracing the stress trajectories from finite element simulation of the solid design domain under external loads. This is particularly appealing from a computational perspective, since it avoids iterative optimization that involves finite element analysis on fine meshes. By regularizing the thickness of each trajectory using derived strain energy measures along them, stiff structural layouts can be generated in a highly efficient way. We then shed light on the use of the resulting structures as initial density fields in density-based topology optimization, i.e., to generate an initial density field that is then further optimized via topology optimization. We demonstrate that by using a stress trajectory guided density initialization in lieu of a uniform density field, convergence issues in density-based topology optimization can be significantly relaxed at comparable stiffness of the resulting structural layouts.
On the Effect of Nonlinear Energy Sink Damping in Seismic Vibration Attenuation
Authors: Motato Eliot , Guerrero Fabio 
Published: November 2022

Abstract
Nonlinear Energy Sinks (NES) are passive vibration absorbers composed of a small inertia and a smooth nonlinear stiffness with linear viscous damping. The main advantage of a NES is its lack of preferential natural frequency, then NESs can resonate over a wide range of frequencies and with any mode of the primary structure to which they are connected. These kind of absorbers however, are energy-dependent devices limited by energy thresholds where optimal design regions can be very close to low effective ones. It is then frequent that changes in the exciting energy levels can significantly reduce the performance of previously tuned NESs. The NES energy sensibility, limit its capabilities to control vibrations in structures subject to seismic induced excitations. Preliminary studies have shown that this sensitivity can be reduced by properly tuning the NES damping but no formal procedure exist. In this work we present a numerical methodology to tune the NES parameters by defining an index that quantifies the effect that NES damping has on the absorber robustness. A three-story shear building model subject to random seismic ground excitation is used to introduce the concepts and the methodology.
Extending and Bending Robotic Limbs Using Tape Springs for Mobility and Manipulation: Preliminary Investigations
Authors: Quan Justin , Hong Dennis 
Published: November 2022

Abstract
Conventional mobile robots have difficulty navigating highly unstructured spaces such as caves. In these environments, an extendable/retractable mechanism could be useful for deploying hooks to climb over terrain, or for reaching hard-to-access sites for sample collection. Spooled tape spring mechanisms offer long reach in a compact package, but have not been widely explored for use in mobility and manipulation tasks. This paper proposes a new form of multimodal mobile robot that utilizes a novel tape spring limb named EEMMMa (Elastic Extending Mechanism for Mobility and Manipulation). For mobility, the limb can extend prismatically to deploy grappling hook anchors to suspend and transport the main body, or even serve as legs. For manipulation, the limb can morph its shape to bend around or over obstacles and extend to reach into tight spaces. This can allow the limb to retrieve distant objects, position cameras around corners, or place grappling anchors above an overhang such as a table or cliff. The EEMMMa-1 prototype detailed in this paper can climb ladders and shelves, and exhibit shape morphing to bend over obstacles. The extendable limb uses a simple braking function as a mechanical multiplexer, which can change the limb’s kinematics to control a second rotational DOF using only a single motor. The paper concludes by detailing potential applications and configurations of future EEMMMa robotic systems.
Incorporating Performance Portability and Data-Oriented Design in Phase-Field Modeling
Authors: Yenusah Caleb , Stone Tonya W. , Morgan Nathaniel R. , Robey Robert W. , Liu Yucheng , Chen Lei 
Published: November 2022

Abstract
The advancements in modern computer architecture have made it possible for the simulation of large-scale problems that are of industrial application. However, it can be time-intensive to continually port scientific codes to the ever-evolving computer architectures. Therefore, it is important to develop computational tools that make it easy for researchers to port their existing scientific software or develop new software that can run optimally on current and future computer architectures from different vendors. Also, it often advantageous for scientific software to leverage fine-grained parallelism on homogeneous architectures that are comprised of multi-core CPUs and on heterogeneous computer architectures comprised of multi-core CPUs and GPUs. This paper demonstrates the incorporation of performance portability and data-oriented design paradigms in the phase-field modeling of microstructure evolution. To achieve this, we utilized MATAR, a C+ + software library that allows the straightforward creation and usage of multidimensional and multi-size dense or sparse matrix and array data structures that are also portable across disparate architectures using Kokkos. As a case study, we use the phase-field model for spinodal decomposition which numerically solves the Cahn-Hilliard equation using the semi-implicit Fourier spectral method. Performance portability across multi-core CPUs and different GPU architectures are investigated. Additionally, numerical accuracy and performance gains between single and double precision phase-field calculations are investigated.
Origami-Inspired Closeouts for Starshade Inner Disk Optical Shields
Authors: Haraszti Alexandra , Arya Manan 
Published: November 2022

Abstract
A starshade is a concept for a deployable external occulter to a telescope that enables the imaging of exoplanets by blocking out the light of the host star. The starshade architecture has an inner disk structure consisting of a lightweight opacity blanket and a stiff perimeter truss. The opacity blanket is called the optical shield and folds with an origami-inspired spiral wrapping pattern. The inner disk architecture has a physical gap: there is a difference in geometry between the perimeter truss and optical shield in both the stowed and deployed configurations. To demonstrate the deployment of the starshade inner disk at a higher level of fidelity, an opaque closeout must be developed to span the difference in geometric shape. Here we describe the design of an origami-motivated closeout design for the optical shield. We propose several closeout fold patterns, for which we optimized the difference in normalized fold line lengths between the folded and unfolded states to be less than 0.01%. The close-out patterns are then validated by simulation and prototyping. These patterns may be realized with flight-like materials and will enable the technological maturation of the starshade concept.
Anisotropic Approach to Control Viscous Fingering Pattern Generated in Lifting Plate Hele-Shaw Cell
Authors: Bhole Kiran S. , Kale Bharatbhushan S. , Mastud Sachin , Raykar Nilesh , Sharma Chetna , Deshmukh Prashant 
Published: November 2022

Abstract
Stability is one of the important aspects of life, our everyday systems — the permanence of things. When this stability gets disturbed, instability is produced. Sometimes this instability is desirable, and sometimes not. In the crude oil extraction process, fluid instability is observed. Saffman and Taylor explored the concept of the Hele-Shaw cell to study these instabilities. The Hele-Shaw cell involves a high viscous fluid sandwiched between two parallel plates, and the low viscosity fluid enters from the periphery. Insertion of low viscous fluid into a high viscous fluid generates a pattern that is a resemblance to a finger. This phenomenon is called viscous fingering. In this paper, the authors control the instabilities and mimic patterns available in nature. These instabilities can be controlled by controlling one of the fluids in the cell. Here authors control the low viscous fluid (air) by providing anisotropies. Anisotropy means providing holes and slots on any plate of the cell. This anisotropy guides air to interact with high viscous fluid at some desired location. The authors further studied the effect of size, position, the orientation of these holes and slots on the viscous fingering exhaustively.
Trust in Human-Robot Interaction Within Healthcare Services: A Review Study
Authors: Townsend Dedra , MajidiRad AmirHossein 
Published: November 2022

Abstract
There has always been a dilemma of the extent to which human can rely on machines in different activities of daily living. Ranging from riding on a self-driving car to having an iRobot vacuum clean the living room. However, when it comes to healthcare settings where robots are intended to work next to human, making decision gets difficult because repercussions may jeopardize people’s life. That has led scientists and engineers to take one step back and think out of the box. Having concept of trust under scrutiny, this study helps deciphering complex human-robot interaction (HRI) attributes. Screening essential constituents of what shapes the trust in human mind as s/he is working with a robot will provide a more in-depth insight through how to build and consolidate the trust. In physiotherapeutic realm, this feeds into improving safety protocols and level of comfort; as well as increasing the efficacy of robot-assisted physical therapy and rehabilitation. This paper provides a comprehensive framework for measuring trust through introducing several scenarios that are prevalent in rehabilitation environment. This proposed framework highlights importance of clear communication between physicians and how they expect robot to intervene in a human centered task. In addition, it reflects on patients’ perception of robot assistance. Ultimately, recommendations are made in order to maximize trust earned from the patients which then feeds into enhancing efficacy of the therapy. This is an ongoing study; authors are working with a local hospital to implement the know in a real-world application.