bluuebunny/crossref_metadata_2025_split huggingface dataset:
Examining Social and Environmental Factors for Wearable Data Integrity: a Case Study of Health-CPS
Authors: Wang Ruijing , Liao Ting 
Published: November 2024

Abstract
Wearable devices, with their ability to sense, collect, and transmit large amounts of physiological data, have experienced a recent surge in popularity, especially in the field of health monitoring. Ensuring data integrity is critical to effectively utilizing these devices, especially in the framework of Cyber-Physical Systems (CPS), where the seamless integration of computational algorithms with physical components plays a crucial role. Building on previous insights into the different contextual scenarios and their impact on data reliability, this paper examines the impact of social and environmental factors on the integrity of data collected by wearable devices, with a focus on human-related errors, such as improper wearing, and environmental conditions such as elevated humidity. Through a human-subject experiment, we simulated both prospective influential factors in a controlled lab setting and collected acceleration and heart rate data by utilizing the Apple Watch. To examine data integrity compromises, we proposed a multi-channel anomaly detection framework, and we demonstrated high accuracy rates in detecting anomalies, achieving 88.89% for improper wearing and a perfect 100% for elevated humidity. These findings confirm that human-related and environmental conditions affect data integrity. Our research enhances the reliability and robustness of CPS by quantifying the influence of social and environmental factors on wearable data integrity and providing insights into the system design beyond the technical elements of CPS.
Effects of Different Human-In-The-Loop Approaches on Human-AI Co-Design: A Comparison Between Human-Learning HITL Approach and Machine-Learning HITL Approach
Authors: Yin Yuan , Wang Boheng , Zuo Haoyu , Childs Peter 
Published: November 2024

Abstract
The speed at which the quantity of data is increasing stymies the deployment of some AI tools for design as the basis for training the tools is not stable. Also, selecting which data can be used as training data is a challenge. To address these issues, the concept of human-in-the-loop (HITL) approaches have been proposed. HITL includes human knowledge in the model-building processes to mitigate the high volume requirement of training data. Although how to apply AI in a design process and the effect of Generative AI tools (such as ChatGPT and Midjourney) in design processes has been explored, limited attention has been given to how different HITL approaches affect design performance. This study thus aimed to explore how different HITL approaches (Human-learning HITL approach and Machine-learning HITL approach) affect solutions of human-AI co-design. Fifteen participants were recruited to undertake two design tasks under the guidelines of Human-learning HITL and Machine-learning HITL approach respectively. The solutions from participants were assessed using four criteria (novelty, aesthetics, functionality, and feasibility). The results of the study indicated that the solution generated from Human-learning HITL approach is better compared with that of the Machine-learning HITL approach, especially in terms of aesthetics and functionality of the solution. The study supports the importance of expressing human insights through prompts on improving human-AI co-design solutions compared with providing more training data to AI.
Optimizing 3d Printing Process Parameters to Minimize Surface Roughness Using Bayesian Optimization
Authors: Kolte Akash , Bhaskaran Vignesh , Hoyle Christopher 
Published: November 2024

Abstract
Additive manufacturing techniques, particularly Fused Deposition Modeling have been extensively used in various industries due to their advantages in part design and flexibility over the conventional subtractive manufacturing processes. The process parameters play a crucial role in FDM 3D printing and need to be carefully tuned and optimized to achieve the desired quality, mechanical strength, and accuracy of the printed parts. This paper aims to optimize the process parameters in FDM 3D printing using a Bayesian Optimization (BO) method to minimize the surface roughness of the printed part. Nozzle temperature, layer height, bed temperature, print speed, and infill density are the five continuous input process parameters considered while the surface roughness is the output response. 34 Latin Hypercube Samples (LHS) are printed on a Prusa i3MK3S+ 3D FDM printer to generate the initial samples used in the optimization. Sequential BO with expected improvement as the acquisition function is used to find the optimal process parameters by balancing exploration and exploitation. The overall mean surface roughness (Ra) of the 57 experiments conducted was 6.09 μm and the optimizer produced an optimum value of 2.78 μm when the nozzle temperature was set to 216 °C, bed temperature to 66 °C, print speed to 73 mm/s, infill density to 20% and layer height to 0.123 mm respectively.
On the Prediction of Tremor Dynamics Motion Using Neural Network
Authors: Ding Zijian , Barry Oumar 
Published: November 2024

Abstract
Pathological tremors significantly affect the quality of life for patients worldwide. Rehabilitation exoskeletons serve as one of the solutions to alleviate these pathological tremors, and voluntary motion prediction-based motion planning has been employed to enhance the performance of these devices. This paper presents a method for predicting future voluntary movement in tremor-alleviating rehabilitation exoskeletons that use voluntary motion prediction-based motion planning. In this study, a Convolutional Neural Network and Transformer architecture based neural network work with EMG sensors to predict future voluntary movements. The results show that approach performs well in predicting future voluntary movements, but there is still a limitation to filter out the tremors completely. In summary, we provide a concept for predicting future voluntary movement, which has the potential to improve the effectiveness of rehabilitation exoskeletons in tremor alleviation.
Impact of Social Learning on Teamwork Efficiency in Learning-Based Self-Organized Systems
Authors: Huang Bingling , Ji Hao , Jin Yan 
Published: November 2024

Abstract
Self-organized systems (SOSs) possess the great potential to adapt to dynamic environments, and thus, advance the level of autonomy in engineering systems. Learning-based approaches, particularly multiagent reinforcement learning (MARL), have empowered the design freedom of SOSs while enhancing the system to be more intelligent. This is primarily because MARL encourages agents to learn from past failures and data through training. However, the training process is conducted in a “black box”, whose embeddings are uninterpretable for humans yet. This poses significant challenges for system designers, especially when task complexity reaches a certain extent, and agents experience a shortfall in learning due to limited available information. Therefore, in order to address how to steer more effective learning, we broaden the views of individual agents, enabling them to recognize the presence and actions of other members within a certain range of distance. This approach fosters social learning within AI agent groups. Prior work, however, has shown the intricacy of the social mechanism. Inappropriate social abilities can undermine a team’s performance. Thus, it is imperative to delve into the effects of social learning to facilitate the system design, further narrowing down the understanding gap between humans and AI agents. In this study, we train agent teams to engage in social learning to complete an assembly task involving collision avoidance. We aim to answer how social abilities influence teamwork at both the team and individual agent levels in terms of energy utilization. By examining the impact of team energy efficiency and teamwork division, our findings indicate a mapping linking appropriate social abilities to tasks of a certain complexity level. Suitable social abilities contribute to elevated team intelligence, more effective teamwork division, and higher energy efficiency, while insufficient social abilities may hinder cooperation and reduce energy efficiency.
Enhancing Vehicle Braking Performance on U-Split Road Through Active Steering Intervention
Authors: Hu Ruibo , Yu Liangyao , Huang Yong , Gao Dawei 
Published: November 2024

Abstract
Active steering technology offers an innovative approach to enhance vehicle safety by enabling maneuvers beyond the driver’s reaction speed. This proactive control not only prevents potential hazards but also maximizes ground friction utilization, thereby elevating overall safety. The focus of this paper is on improving vehicle braking performance through active front wheel steering control, using braking on uneven road surfaces as an illustrative example. While historical research primarily concentrated on post-instability measures in braking dynamics, specifically on returning a vehicle to a stable state after instability, limited exploration has been undertaken regarding control strategies when a vehicle encounters hazardous road conditions. At this initial stage, various vehicle states remain within a relatively stable range, allowing for rational intervention and more effective control. Utilizing obstacle recognition and distance estimation, considering estimable distances between the vehicle and the uneven road surface, the study introduces a Model Predictive Control (MPC) algorithm with a time margin for optimal steering input computation. The algorithm incorporates a high-fidelity nonlinear vehicle model and the Magic Formula tire model as state equation constraints, integrating the constraints of the actual steering actuation system. Simulation results demonstrate that implementing the MPC algorithm with a time margin effectively reduces lateral deviation and yaw angle during braking on uneven road surfaces, ultimately enhancing overall braking stability.
Design and Modeling of Compliant Four-Bar Mechanisms With Variable Stiffness Links for Multi-Output Trajectory
Authors: Luo Jiayu , Alvi Muhammad Hammad , Lin Han , Huang Xiaotong , Gan Dongming 
Published: November 2024

Abstract
This research presents a novel design of a four-bar mechanism featuring a variable stiffness link (VSL) as the output component, aimed at enabling diverse end-effector trajectories without modifying the link length or moment input. By employing both single-beam and multi-section beam configurations within a large deflection model, the study investigates the effect of varying link stiffness under constant load and geometric conditions on the mechanism’s trajectory outcomes. The proposed design was validated through both numerical modeling and experimental testing of a built prototype. The findings confirm the prototype’s alignment with theoretical predictions, highlighting the VSL’s key role in significantly enhancing the adaptability and application range of four-bar mechanisms. This advancement circumvents the traditional constraints of fixed-trajectory mechanisms, proposing a versatile, efficient, and cost-effective solution for complex motion applications in compliant mechanism design.
A Study on Combined Lithium and Sodium-Ion Hybrid Energy Storage Systems in Battery Electric Vehicles
Authors: Foti Orazio , Pakštys Saulius , Bonfitto Angelo 
Published: November 2024

Abstract
With an end-goal of carbon neutrality set by many nations across the world, various industries face the challenging task of incorporating novel technologies in their products while remaining economically sustainable. This is particularly evident in the automotive sector, where diversifying the product portfolio of many Original Equipment Manufacturers to meet carbon emission targets is a primary goal, investing in research ranging from hydrogen fuel propulsion to energy storage systems. It is in this latter area in which this paper provides an insightful analysis in the coupling of two rechargeable energy storage systems based on different chemical technologies. The work herein evaluates a hybrid energy storage system for a subcompact crossover sport utility vehicle that includes a lithium-ion (LIB) and sodium-ion battery (NaIB) pack, with varying divisions of the total energy between the two. The aim is to exploit the NaIB pack to the greatest possible degree such that the longevity of the LIB pack is extended, given that NaIBs are more forgiving in their response to high C-rates. The division is performed on the basis of two methodologies: 1) maintaining a constant mass of the combined battery pack and 2) maintaining a constant energy of the hybrid energy storage system. The analysis considers two energy management strategies that are both deterministic rule-based, the first termed as a Load Follower and the second as a Range Extender. The analysis of a selection of energy division and energy management strategies shows that a hybrid battery system comprising of 70% of energy provided by a LIB and 30% by a NaIB coupled with a Range Extender energy management provides the best trade-off between C-rate reduction, range penalization and cost reduction. Such a solution allows for up to 46% reduction in LIB C-rate and 7.6% reduction in cost with respect to the vehicle mounted with the baseline LIB.
Path Optimization From En Route 3d Printed Aircraft Using Physics-Based Estimation Methods and Surrogate Models
Authors: Dickerson Tevin , Salmon John L. , Mattson Christopher A. 
Published: November 2024

Abstract
In a previous study, a general approach to increasing the range capability of an unmanned aerial system was assessed by 3D printing components or parts for the UAS while a carrier platform, of both the printer and UAS systems, travels to a launch point for the UAS. As a result, the platform could launch the UAS from a distance further away from the location of interest. This paper outlines the parameters of a physics-based model to determine the actual range increase of an attritable aircraft, constrained by the amount of time available for printing while en route. It develops the formulation of the UAS range increase from the lift-to-drag ratio increases caused by the physical wing section enhancements printed in a constrained amount of time while the carrier platform travels from one location to another. The relationship is leveraged to optimize the shortest path necessary for the carrier platform to access or reach the set of locations of interest in an operational environment. Scenarios of up to 15 locations are explored by first optimizing the sequence of the locations in a traveling salesman problem and then optimizing the actual path taken by the carrier platform along that sequence. Statistical results are presented to summarize the benefits of 3D printing en route across different scenarios.
Metareasoning to Adapt Path Planning Decision Making for Different Modalities of Uncertainty
Authors: Sathyamurthy Eashwar , Richardson John , Herrmann Jeffrey W. , Raglin Adrienne 
Published: November 2024

Abstract
Determining the appropriate level and composition of reasoning for decision-making is a critical component of variable autonomy, as an autonomous system might shift the responsibility for decision-making to its human teammate when it cannot solve the problem that has appeared. Metareasoning (reasoning about reasoning) can provide the autonomous system with a way to adapt its own reasoning (path planning) processes so that it relies less upon its human teammate. A use case was selected to study metareasoning for the problem of path planning in the presence of different types of uncertainty. We created and evaluated the performance of metareasoning policies that changed the path-planning approach for a set of simulated autonomous systems for diverse and dynamic uncertainties. The initial simulation included a single vehicle navigating from a start location to a specified goal while avoiding obstacles. Various iterations of the simulation were conducted, including missions with no uncertainty, uncertainty of obstacle locations, and uncertainty of obstacle status. Our simulation results showed that the vehicle used shorter paths when employing metareasoning, which also reduced total computational effort. These results suggest that metareasoning is a feasible strategy for variable autonomy human-robot teaming and that autonomous systems can use metareasoning to reduce the dependency on human intervention.
Improving Climbing Performance Using Passive Tail in Lizard-Inspired Robot
Authors: Pranav Kodidasu , Sankar Kadali Mani , Thakur Atul 
Published: November 2024

Abstract
Lizards are excellent climbers and effectively utilize their tail to actively counter the destabilizing pitching moment during climbing. Taking inspiration from the use of a tail for imparting stability we designed a compliant passive tail to improve the climbing performance of a wall climbing lizard-inspired robot developed in the past by our research group [1, 2]. This paper presents the incorporation of the spring-actuated tail into climbing robots to improve their stability and climbing capabilities. A spring-actuated tail helps in regaining the stability during momentary adhesive failure making the robot more reliable. However, there is no external support for the tail to avoid slipping during momentary adhesive failure. To improve the climbing performance and reduce the chances of failure, A flexible pressure-sensitive adhesive tip has been incorporated on the compliant passive tail to enhance the climbing stability. The climbing capabilities of the proposed design are compared with two different conditions: a robot with and without a tail under two different terrains: hard (Acrylic sheet) and soft (Fitness mat). The experimental results demonstrate that the robot equipped with a tail featuring a flexible adhesive tip achieves the best climbing performance on hard terrains with a maximum slope of 65°, while a tail lacking a flexible adhesive tip achieves superior climbing performance on soft terrains with a maximum slope of 40°. This approach enables a climbing robot to navigate and ascend various terrains, thereby expanding its operational capabilities to encompass areas with different terrains. This versatility is especially valuable in tasks such as inspection, exploration, and construction.
Bluff Body, Piezoelastic Oscillator: Reduced-Order Model, Vortex-Induced Vibrations, and Energy Harvesting
Authors: Bellei Andrés , Balachandran Balakumar 
Published: November 2024

Abstract
Energy harvesting from flow-induced vibrations has gained substantial attention in the last two decades due to the rising demand for renewable and sustainable energy sources, as well as the widely availability of these sources, offering a viable alternative in areas where other ambient energy sources may not be readily accessible. Flow-induced vibrations of bluff bodies are characterized by complex nonlinear dynamics, for which accurate models are currently lacking.
In this work, a circular cylinder attached to the free end of a piezoelastic cantilever is considered for energy harvesting. When placed in a flow, this system undergoes vortex-induced vibrations. A reduced-order model is developed to understand fluid-structure interactions of this system. A wake oscillator has been used to describe vortex-induced vibrations and a finite-element model has been used to model the piezoelastic cantilever. The developed model is used to explore the interplay amongst the fluid, structure, and piezoelectric element. The results obtained are compared to experimental data from literature, in terms of the vibration amplitude, vibration frequency, and power obtained. Modifications to the wake oscillator model are examined to better reflect the fluid-structure interactions. It is found that there is a trade-off between accurately predicting the vibration amplitude and accurately predicting the vibration frequency.
Extending the Product Architecture Strategies and Effects Database Through Large Language Models
Authors: Rice Scott E. , Sannar Benjamin C. , Mattson Christopher A. , Anderson Michael A. 
Published: November 2024

Abstract
There are more than 4000 archival publications on the topic of product architecture. Manually synthesizing such a vast body of literature is prohibitive. To ease the burden and bring insight from the literature to product design engineers, we introduced the Product Architecture Strategies and Effects (PASE) database in a previous publication. This database is a collection of relationships between product architecture decisions and their effects as contained in a selection of the literature. Despite the discovery of over 400 relationships, the PASE database was constructed manually from only 17 publications. The present paper describes a way to expand the PASE database, utilizing the data it currently contains to train a set of cascading large language models (LLMs) to rapidly expand the PASE database. In this paper, we carry out the first two steps of the proposed approach and show that it has potential to reduce the effort required to add to the PASE database by orders of magnitude, compared to the manual approach.
Navigating the Tradespace: Empowering Decision-Making With Data Manipulation
Authors: Turner Cameron J. , Raj Nikhil , Hartman Greg , Castanier Matt , Rapp Stephen 
Published: November 2024

Abstract
In engineering design and decision-making, the exploration of tradespace is crucial for arriving at optimal solutions. Traditional methods, such as morphological charts, have been instrumental in organizing design possibilities. Our approach integrates functional objectives, enhancing focus on performance and efficiency. While morphological charts have been fundamental, our work introduces advanced techniques like ANOVA and PCA, expanding tradespace exploration. This empowers researchers to uncover insights and correlations and make informed decisions, driving innovation.
We illustrate how methodologies like ANOVA, PCA and correlation open new horizons in tradespace exploration, capturing the richness of design alternatives and understanding relationships between variables. Embracing functional objectives aligns engineering design with evolving technological demands. As graduate students and researchers tackle complex challenges, our work bridges the past and future of design methodologies.
The US Army DEVCOM GVSC invested years to develop a comprehensive tradespace encompassing vast vehicle configurations. A ‘Testbed’ tradespace with about 13 variables has been formulated to facilitate smaller-scale exploration. It serves as a resource for future studies, providing a standardized framework for advancing engineering design optimization. This collaborative effort highlights the need for effective tradespace exploration approaches to inform decision-making.