bluuebunny/crossref_metadata_2025_split huggingface dataset:
Shared Autonomous Vehicle System Design for Battery Electric Vehicle (BEV) and Fuel Cell Electric Vehicle (FCEV)
Authors: Lee Ungki , Jeon Sunghyun , Lee Ikjin 
Published: November 2021

Abstract
Shared autonomous vehicles (SAVs) encompassing autonomous driving technology and car-sharing service are expected to become an essential part of transportation system in the near future. Although many studies related to SAV system design and optimization have been conducted, most of them are focused on shared autonomous battery electric vehicle (SABEV) systems, which employ battery electric vehicles (BEVs) as SAVs. As fuel cell electric vehicles (FCEVs) emerge as alternative fuel vehicles along with BEVs, the need for research on shared autonomous fuel cell electric vehicle (SAFCEV) systems employing FCEVs as SAVs is increasing. Therefore, this study newly presents a design framework of SAFCEV system by developing an SAFCEV design model based on a proton-exchange membrane fuel cell (PEMFC) model. The test bed for SAV system design is Seoul, and optimization is conducted for SABEV and SAFCEV systems to minimize the total cost while satisfying the customer wait time constraint, and the optimization results of both systems are compared. From the results, it is verified that the SAFCEV system is feasible and the total cost of the SAFCEV system is even lower compared to the SABEV system. In addition, several observations on various operating environments of SABEV and SAFCEV systems are obtained from parametric studies.
A Bayesian Approach to Recovering Missing Component Dependence for System Reliability Prediction via Synergy Between Physics and Data
Authors: Li Huiru , Du Xiaoping 
Published: November 2021

Abstract
Predicting system reliability is often a core task in systems design. System reliability depends on component reliability and dependence of components. Component reliability can be predicted with a physics-based approach if the associated physical models are available. If the models do not exist, component reliability may be estimated from data. When both types of components coexist, their dependence is often unknown, and the component states are therefore assumed independent by the traditional method, which can result in a large error. This work proposes a new system reliability method to recover the missing component dependence, thereby leading to a more accurate estimate of the joint probability density (PDF) of all the component states. The method works for series systems whose load is shared by its components that may fail due to excessive loading. For components without physical models available, the load data are recorded upon failure, and equivalent physical models are created; the model parameters are estimated by the proposed Bayesian approach. Then models of all component states become available, and the dependence of component states, as well as their joint PDF, can be estimated. Four examples are used to evaluate the proposed method, and the results indicate that the proposed method can produce more accurate predictions of system reliability than the traditional method that assumes independent component states.
Recent Field Implementation of Contemporary and Smart Farming Technologies at the University of Maryland Eastern Shore
Authors: Nagchaudhuri Abhijit , Hartman Christopher , Ford Travis , Pandya Jesuraj 
Published: November 2021

Abstract
Smart farming experiential learning and research endeavors have been ongoing at the University of Maryland Eastern Shore (UMES) for the past several years. Recent field implementation of contemporary technologies for variable rate fertilizer application based on multispectral drone imagery; deployment of wireless solar powered soil moisture sensor network on a field with subsurface drip and fertigation capability; and development of a sustainable platform integrated with a Cartesian robotic device powered by solar and wind energy that can seed, weed, irrigate, and capture time-lapse photography while servicing a small raised bed for specialty crops and vegetables will be described in this paper. Results from the initial phase of implementation efforts and future goals will also be highlighted.
Short-Term Vehicle Velocity Forecasting via Cycle Segmentation Model Selection
Authors: Liu Yuanzhi , Zhang Jie 
Published: November 2021

Abstract
Vehicle velocity forecasting plays a critical role in scheduling the operations of varying systems and devices in a passenger vehicle. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to contribute to the improvement of vehicle energy efficiency for commuting routes. The generated driving cycles are divided into cycle segments based on intersection/stop identification, deceleration and reacceleration identification, and waiting time estimation, which could be used for better evaluating the effectiveness of model localization. Then, a segment-based vehicle velocity forecasting model is developed, where a machine learning model is trained/developed at each segment, using the hidden Markov chain (HMM) model and long short-term memory network (LSTM). To further improve the forecasting accuracy, a localized model selection framework is developed, which can dynamically choose a forecasting model (i.e., HMM or LSTM) for each segment. Results show that (i) the segment-based forecast could improve the forecasting accuracy by up to 24%, compared the whole cycle-based forecast; and (ii) the localized model selection framework could further improve the forecasting accuracy by 6.8%, compared to the segment-based LSTM model. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.
Transformation Design Principles as Enablers for Designing Reconfigurable Robots
Authors: Kalimuthu M. , Hayat A. A. , Elara M. R. , Wood K. L. 
Published: November 2021

Abstract
The design of cleaning and maintenance (CaM) robots are generally limited by their fixed morphologies. Contrary to the fixed robotic systems, design for reconfiguration in robots presents unique challenges. Reconfigurable robots pose the challenge of designing their subsystems and functionalities such that a robot meets its system, performance, and other fixed requirements, while providing reconfiguration capabilities to increase functionality and to provide innovative operational scenarios. Established transformation or reconfiguration principles, namely, expand/collapse, expose/cover, and fuse/divide, observed in several products-services-systems, can be adopted to design subsystems and system for reconfiguration in robots. Essentially these principles in many robots may be governed and implemented. The heuristic approach to design the reconfigurable robotic systems using three layers namely input, formulation and output layer is proposed. This paper used the design principles and associated facilitators and abstracts them to build a reconfigurable pavement cleaning robot named Panthera. Moreover, need, challenges, and design strategies for system and subsystem levels are presented. The system-level reconfiguration is to expand/collapse, whereas the subsystems, namely, i) Varying footprint, ii) Transmission, iii) Storage bin, iv) Cleaning brushes, v) Vacuum/Suction and blowing, and vi) Outer skin or cover are explained. The step-by-step illustration for reconfiguring the system and subsystem of Panthera is done by referring to the transformation principles, precedence, and mechanism adopted to achieve reconfiguration requirements.
Fuzzy Evaluation of Kansei Attributes Using Convolutional Neural Networks
Authors: Jiang Wei , Zhang Kai , Zhao Wu , Guo Xin 
Published: November 2021

Abstract
The emotional needs for products have increased significantly with the recent improvements in living standards. Attribute evaluation forms the core of Kansei engineering in emotion-oriented products, and is practically quite subjective in nature. Essentially, attribute evaluation is a fuzzy classification task, whose quantitative results change slightly with statistical time and statistical objects, making it difficult to accurately describe using standard mathematical models. In this paper, we propose a novel deep-learning-assisted fuzzy attribute-evaluation (DLFAE) method, which could generate quantitative evaluation results. In comparison to existing methods, the proposed method combines subjective evaluation with convolutional neural networks, which facilitates the generation of quantitative evaluation results. Additionally, this strategy has better transferability for different situations, increasing its versatility and applicability. This, in turn, reduces the computational burden of evaluation and improves operational efficiency.
Using Machine Learning for the Classification of the Remaining Useful Cycles in Lithium-Ion Batteries
Authors: Coutts Harry , Wang Qing 
Published: November 2021

Abstract
In order to keep up with the increasing focus on renewable energy, the demand for new battery technology and peripherals has likewise increased greatly. Given the relatively slow rate of change of new battery chemistry and technology, it is the peripherals to the batteries that are often relied upon to provide this necessary increase in performance. The 18650 battery with Lithium-Ion internal chemistry is one of the most widely used batteries and is depended upon in many industries to provide power portability and storage. Using an extensive freely available dataset compromising of the charge cycles of 121 18650 batteries, this paper evaluates multiple algorithms’ effectiveness at predicting the remaining useful cycles of a battery from a single discharge curve. Upon evaluation of the algorithms, ‘Weighted K Nearest Neighbours’ was shown to be the most accurate model and was further improved to ensure that the maximum accuracy was acquired. Finally, a user interface was created to allow for the demonstration of a potential use case for the model. This model and user interface show the potential for easy testing of batteries to determine the number of remaining useful cycles. This makes the possibility of re-purposing or extending the initial purpose of these batteries much greater, which is preferable from both an economic standpoint and an ecological one.
Modular Foldable Airship Concept for Subterranean Exploration
Authors: Salas Gordoniz Jorge Esteban , Reeves Nicolas , St-Onge David 
Published: November 2021

Abstract
The exploration of new lands has always been a source of motivation for mankind. Despite the common idea that our planet is fully known, a huge number of inaccessible places still remains unvisited today, especially below the surface. Recent advances in robotics allow some of these locations to be explored by unmanned vehicles. This paper presents the design of a 3-modules lighter-than-air vehicle specifically conceived to autonomously explore inaccessible caves and underground environments. The design is inspired from an arthropod, scutigera coleoptrata, a long-legged centipede commonly found in our houses. Instead of crawling on walls like its biological counterpart, the robotic scutigera hovers and flies in cave tunnels. The aim is to develop a flexible semi-rigid, segmented airship that can withstand long, smooth explorations of caves while transmitting in real-time the images and sounds that it captures. To develop the model of the system, the kinematics of the modules are obtained in the inertial frame, and the dynamic derivation of the vehicle is obtained using Kane’s equations, which can also be extended for an n-bodies system. Its motion is illustrated with a couple of simplified scenarios in the horizontal plane and only having actuation in the front, or first module, of the airship. A structural design of the modules is presented and supported with a proof-of-concept prototype.
Predicting Mechanical Properties of 3D Printed Lattice Structures
Authors: Ma Shuai , Tang Qian , Liu Ying , Feng Qixiang 
Published: November 2021

Abstract
Lattice structures (LS) manufactured by 3D printing are widely applied in many areas, such as aerospace and tissue engineering, due to their lightweight and adjustable mechanical properties. It is necessary to reduce costs by predicting the mechanical properties of LS at the design stage since 3D printing is exorbitant at present. However, predicting mechanical properties quickly and accurately poses a challenge. To address this problem, this study proposes a novel method that is applied to different LS and materials to predict their mechanical properties through machine learning. First, this study voxelised 3D models of the LS units and then calculated the entropy vector of each model as the geometric feature of the LS units. Next, the porosity, material density, elastic modulus, and unit length of the lattice unit are combined with entropy as the inputs of the machine learning model. The sample set includes 57 samples collected from previous studies. Support vector regression was used in this study to predict the mechanical properties. The results indicate that the proposed method can predict the mechanical properties of LS effectively and is suitable for different LS and materials. The significance of this work is that it provides a method with great potential to promote the design process of lattice structures by predicting their mechanical properties quickly and effectively.
Structures With Multiple Rigid Configurations Due to Prestress and Unilateral Contacts
Authors: Dorn Charles , Li Yang , Pellegrino Sergio 
Published: November 2021

Abstract
This paper presents structures with multiple equilibrium configurations arising from the combination of a state of prestress and unilateral contacts. A design problem is posed where preloaded elastic springs and unilateral constraints are embedded throughout a mechanism. The spring parameters are designed such that multiple target configurations are immobilized due to contact. In each of these configurations, the spring forces maintain compressive reaction forces, immobilizing the structure. Each immobilized configuration can rigidly resist perturbation forces up to some finite magnitude where contact is lost. Hence, this case of multiple configurations in equilibrium due to the combination of prestress and contact is referred to as multi-configuration rigidity. Two examples of structures exhibiting multi-configuration rigidity are presented. First, a four bar linkage with a single kinematic degree of freedom is used to introduce the concept. In the context of the linkage, multi-configuration rigidity is compared to multi-stability, exhibiting the key differences between the two concepts. Then, a 24-degree-of-freedom kirigami surface is presented that can morph between flat and spherical configurations, motivated by RF antenna applications. By embedding torsional springs and fold angle stops throughout the structure, flat and spherical configurations are made rigid. Actuation between the configurations can easily be achieved by snapping the structure between the rigid configurations.
A Survey of Important Factors in Human - Artificial Intelligence Trust for Engineering System Design
Authors: Lotfalian Saremi Mostaan , Bayrak Alparslan Emrah 
Published: November 2021

Abstract
Incorporating user trust in the development of intelligent systems is one of the new challenges in engineering design field. Trust in human-intelligent system interaction determines how much user relies on the system and directly influences the benefits that an intelligent system provides to human decision-making. This paper reviews the existing literature on trust in human-AI interaction to highlight key areas for engineering system design research to address the overarching issue of user trust in system development. We present how trust influences the use of an intelligent system, describe multiple contexts that users interact with intelligent systems and categorize ways in which trust is formed based on the literature. We classify the key factors that are critical in the formation of user trust in three categories of human user attributes, design of the intelligent system, and task characteristics. We also present the analytical models that exist in the literature used to evaluate and predict trust. This paper is not intended to be a thorough literature review but rather a position paper that provides a structure to the existing literature as a reference for engineering design research. We propose future directions for engineering system design community based on the gaps and open questions identified in the literature.
FMEA-Inspired Analysis for Social Impact of Engineered Products
Authors: Armstrong Andrew G. , Mattson Christopher A. , Salmon John L. , Dahlin Eric C. 
Published: November 2021

Abstract
Social Impact has been widely discussed by the engineering community, but studies show that there is currently little systematic consideration of the social impact of products in both academia and in industry beyond social impacts on health and safety. This paper illustrates how Failure Mode and Effect Analaysis (FMEA) style analysis can be applied to evaluating the social impact of products. The authors propose a new method titled Social Impact Effects Analysis (SIEA), describe how it is performed, and explain the benefits of performing SIEA.
Digital Twin Behavior Matching of Gas Plumes Using a Fixed Sensor Mesh and Dynamic Mode Decomposition
Authors: Hollenbeck Derek , Chen YangQuan 
Published: November 2021

Abstract
Digital twins (DT) have become a useful tool in smart manufacturing, engineering and controls. Behavior matching of DTs to their physical twin counterparts is essential for capturing the evolution of key system parameters. Given that environmental gas emissions are governed by partial differential equations, the behavior matching optimization can often be ill posed and computationally expensive. Stochastic models have shown good agreement to deterministic models while having a significant computational cost reduction. This work presents a method for solving the source localization problem using a DT implementation of a stochastic point source emission with a fixed-mesh of gas sensors. The DT source localization is determined through behavior matching process with low frequency modes after dynamic mode decomposition using spatial interpolation on measured time series data. That is, the minimization of the mismatch between the DT and the unknown physical model can given an estimate of the source location.
A Fractional Order Control and Correction Strategy for EtherCAT Communication Clock Drift
Authors: Sun Jihao , Chen Pengchong , Luo Ying 
Published: November 2021

Abstract
Ethernet Control Automation Technology (EtherCAT) applies distributed clock (DC) to realize synchronization among different slaves. Due to the influence of the crystal oscillator manufacturing process and environment, there is still synchronization error between reference clock and non-reference clock. To solve the clock synchronization problem, this paper proposes a clock drift compensation algorithm based on the idea of closed-loop control. By designing integer-order proportional integral (IOPI) and fractional-order proportional integral (FOPI) controllers, the synchronization error between slaves can be minimized. The IOPI and FOPI controllers designed in this paper are used to eliminate the drift error. This method improves the synchronization accuracy without bringing too much computational load. The results show that the proposed FOPI controller can effectively reduce the synchronization error with even better performance over the IOPI controller.
A Computational Framework for Social Entrepreneurs to Determine Policies for Sustainable Development
Authors: Karkaria Vispi , Das Ashok K. , Yadav Abhishek , Sharma Ayushi , Allen Janet K. , Mistree Farrokh 
Published: November 2021

Abstract
Many villagers in India suffer from multiple socio-economic challenges, such as, low-income, unemployment, and lack of access to clean water and energy that hinder their overall development. Social entrepreneurs can assist with the design and implementation of policies that help villagers achieve overall sustainability. Previously, a framework to support social entrepreneurs and stakeholders in identifying potential challenges and evaluating the impact of solutions concerning sustainable development was proposed. The framework was anchored in a single perspective (thematic area) Dilemma Triangle Method for identification of challenges and System Dynamics Models for evaluation of impacts. Policies for sustainable development, however, require an understanding of the interactions among multiple possibilities and their associated challenges to be viable, feasible, and equitable. Additionally, one key missing feature in the past framework is the evaluation of the economic impact of the solutions or policies.
In this paper, we add new value to the past framework using the Dilemma Triangle Method to integrate more than one perspective, and the System Dynamics Model integrating the economic indicator to get a holistic view of sustainable development. By the addition of more than one thematic area in the Dilemma Triangle Method, the inter-dependency among thematic areas and their associated parameters is understood, which is necessary for identifying problems in complex systems. We include Gross Value Added (GVA) as an economic indicator for evaluating the economic feasibility of the policies identified by using the framework. To illustrate the efficacy of the framework, we implement it for the Kantashol village (panchayat), Jharkhand, India. Based on the Dilemma Triangle Method, multiple policies are proposed, out of which four policies are evaluated in the Systems Dynamic Model for the sustainable development of the village. The policies are currently being applied in the village and the outcome of this framework will be validated in real-time over the years.
A Semi-Autonomous Quadruped Robot for Performing Disinfection in Cluttered Environments
Authors: Chen Yiyu , Pandey Abhinav , Deng Zhiwei , Nguyen Anthony , Wang Ruiqi , Thonapalin Pornrawee , Nguyen Quan , Gupta Satyandra K. 
Published: November 2021

Abstract
The global COVID-19 pandemic has inevitably made disinfection a daily routine to ensure the safety of public and private spaces. However, the existing disinfection procedures are time-consuming and require intensive human labor to apply chemical-based disinfectant onto contaminated surfaces. In this paper, a robot disinfection system is presented to increase the automation of the disinfection task to assist humans in performing routine disinfection safely and efficiently. This paper presents a semi-autonomous quadruped robot called LASER-D for performing disinfection in cluttered environments. The robot is equipped with a spray-based disinfection system and leverages the body motion to control the spray action without an extra stabilization mechanism. The spraying unit is mounted on the robot’s back and controlled by the robot computer. The control architecture is designed based on force control, resulting in navigating rough terrains and the flexibility in controlling the body motion during standing and walking for the disinfection task. The robot also uses the vision system to improve localization and maintain desired distance to the disinfection surface. The system incorporates image processing capability to evaluate disinfected regions with high accuracy. This feedback is then used to adjust the disinfection plan to guarantee that all assigned areas are disinfected properly. The system is also equipped with highly integrated simulation software to design, simulate and evaluate disinfection plans effectively. This work has allowed the robot to successfully carry out effective disinfection experiments while safely traversing through cluttered environments, climb stairs/slopes, and navigate on slippery surfaces.