bluuebunny/crossref_metadata_2025_split huggingface dataset:
A New Look to the Three Axes Theorem
Authors: Valderrama-Rodríguez Juan Ignacio , Rico José M. , Cervantes-Sánchez J. Jesús , Pérez-Zamudio Fernando Tomás 
Published: November 2019

Abstract
This paper analyzes the well known three axes theorem under the light of the Lie algebra se(3) of the Euclidean group, SE(3) and the symmetric bilinear forms that can be defined in this algebra. After a brief historical review of the Aronhold-Kennedy theorem and its spatial generalization, the main hypothesis is that the general version of the Aronhold-Kennedy theorem is basically the application of the Killing and Klein forms to the equation that relates the velocity states of three bodies regardless if they are free to move in the space, independent of each other, or they form part of a kinematic chain. Two representative examples are employed to illustrate the hypothesis, one where the rigid bodies are free to move in the space without any connections among them and other concerning a RCCC spatial mechanism.
Loaded Tooth Contact Pattern Analysis for Strain Wave Gear With Non-Elliptical Wave Generator
Authors: Yu Zhiyuan 
Published: November 2019

Abstract
This paper presents a new non-elliptical wave generator for strain wave gear to improve its contact pattern quality. The new wave generator has a polynomial profile at one cross section, then crowned along the lead direction. The lead crowning uses a parabolic function with crowning amount controlled by parabolic coefficient. Loaded tooth contact pattern analysis based on finite element method is used to evaluate the new design. The result shows that the new design will avoid the edge contact between wave generator and flexspline, which reduces contact pressure and improve the wearing life of the gear. It also improves the contact pattern quality of the tooth surface. Comparing with elliptical wave generator, the new wave generator with polynomial profile and lead parabolic crowned surface offers more design freedom to improve strain wave gear’s performance. The parametric equation of the new wave generator is defined intuitively, and it can be easily adapted for any type of strain wave gear. Furthermore, the finite element model for the strain wave gear is a new development and application for Loaded Tooth Contact Analysis (LTCA).
Estimation of Uncertainty Change of Reliability in Adaptive Sampling Under Prediction Uncertainty of Gaussian Process
Authors: Bae Sangjune , Kim Nam H. 
Published: November 2019

Abstract
A novel approach is introduced to estimate the change in the variance of the probability of failure by adding a sample to the Gaussian process (GP) in a conservative manner. Uncertainty in probability stems from prediction uncertainty and GP is used to represent the uncertainty. In the estimation of variance, a single-loop Monte Carlo Simulation (MCS) alleviates the computational burden. The result shows that the proposed methodology well predicts the change by a sample, maintaining the conservativeness by ignoring correlation in GP, yet the computational cost is at the same level as single-loop MCS.
Making Trimmed B-Spline B-Reps Watertight With a Hybrid Representation
Authors: Song Yang , Cohen Elaine 
Published: November 2019

Abstract
Watertightness is an important property for CAD models, yet it is not generally available in commercial systems. This paper introduces watertight hybrid B-spline based B-reps that maintain the original parameterization and representation except in narrow regions around trimming curves, where Bézier triangle type regions seal the model. The new representation matches the model space trimming curve exactly. It can be discretized for analysis and fabrication in a straightforward manner without requiring any post modeling repair operations. We apply this representation to mechanical models and more sculptured models to demonstrate small parametric distortion and arbitrarily small geometric error.
Powered Ankle-Foot Prostheses: A Survey on Sensing Systems and Control Strategies
Authors: Chumacero-Polanco Erik , Yang James 
Published: November 2019

Abstract
People who have suffered a transtibial amputation show diminished ambulation and impaired quality of life. Powered ankle foot prostheses (AFP) are used to recover some mobility of transtibial amputees (TTAs). Powered AFP is an emerging technology that has great potential to improve the quality of life of TTAs with important avenues for research and development in different fields. This paper presents a survey on sensing systems and control strategies applied to powered AFPs. Sensing kinematic and kinetic information in powered AFPs is critical for control. Ankle angle position is commonly obtained via potentiometers and encoders directly installed on the joint, velocities can be estimated using numerical differentiators, and accelerations are normally obtained via inertial measurement units (IMUs). On the other hand, kinetic information is usually obtained via strain gauges and torque sensors. On the other hand, control strategies are classified as high- and low-level control. The high-level control sets the torque or position references based on pattern generators, user’s intent of motion recognition, or finite-state machine. The low-level control usually consists of linear controllers that drive the ankle’s joint position, velocity, or torque to follow an imposed reference signal. The most widely used control strategy is the one based on finite-state machines for the high-level control combined with a proportional-derivative torque control for low-level. Most designs have been experimentally assessed with acceptable results in terms of walking speed. However, some drawbacks related to powered AFP’s weight and autonomy remain to be overcome. Future research should be focused on reducing powered AFP size and weight, increasing energy efficiency, and improving both the high- and the low-level controllers in terms of efficiency and performance.
Convolutional Neural Network-Based Intention Forecasting and Lane Change Path Predicting of the Human Driver
Authors: Yang Kaiming , Yan Zhanhong , Chen Di , Ji Xuewu , Nakano Kimihiko 
Published: November 2019

Abstract
The intention forecasting of the human driver plays an essential role in improving the safety and driver-machine coordination of the co-piloting vehicle, which is the decision-making foundation for driver assistance system (DAS). However, the quantitative prediction of the lane change decision, which determines the occasion and shape of the lane change path, has drawn little attention by the prior works. In this paper, a novel convolutional neural network (CNN)-based predictor that forecasts the driver’s lane change path is proposed. By including the driver’s eye-gazing and head-heading information, an earlier and more accurate prediction of driver’s time to lane change (TLC) and lane change duration (LCD), and we use the positive TLC and negative TLC to represent the classification results LC and RC, respectively. The classification and regression results validated that the inclusion of driver’s gazing information achieved an earlier and more precise detection of driver’s lane change intention. Furthermore, owning to the hierarchical structure of the CNN model and the parallel computing devices, the execution time of the proposed model is shorter and exhibits its powerful real-time computing performance. The estimated TLC and LCD are adopted to predict the driver’s target lane change trajectory with satisfactory accuracy. Therefore, the proposed model enables the DAS to generate a human-like lane change decision to minimize the driver-machine conflicts during lane change.
A Cloud-Based Healthcare Infrastructure for Neonatal Intensive-Care Units
Authors: Migliorelli Lucia , Cenci Annalisa , Bernardini Michele , Romeo Luca , Moccia Sara , Zingaretti Primo 
Published: November 2019

Abstract
Intensive medical attention of preterm babies is crucial to avoid short-term and long-term complications. Within neonatal intensive care units (NICUs), cribs are equipped with electronic devices aimed at: monitoring, administering drugs and supporting clinician in making diagnosis and offer treatments. To manage this huge data flux, a cloud-based healthcare infrastructure that allows data collection from different devices (i.e., patient monitors, bilirubinometers, and transcutaneous bilirubinometers), storage, processing and transferring will be presented. Communication protocols were designed to enable the communication and data transfer between the three different devices and a unique database and an easy to use graphical user interface (GUI) was implemented. The infrastructure is currently used in the “Women’s and Children’s Hospital G.Salesi” in Ancona (Italy), supporting clinicians and health opertators in their daily activities.
Semi-Autonomous Teleoperation, Guidance, and Obstacle Avoidance With Path Adherence
Authors: Budolak Daniel , Chauhan Raghuraj , Ben-Tzvi Pinhas 
Published: November 2019

Abstract
Decreasing user effort and automating subtasks such as obstacle avoidance and user guidance has shown to increase the effectiveness and utility of teleoperation. Extending the capabilities of teleoperation remains a critical research topic for tasks that need to leverage user knowledge, or for unstructured environments that autonomous solutions are not robust enough to handle. Previous methods have focused individually on joint space tasks, regression or training based user intention recognition and intervention, or application specific solutions. To overcome the limitations of these methods, this paper proposes the use of path planning based gross motion assistance with a projection based user intention recognition method, for improving task execution in semi-autonomous teleoperation. The proposed solution synthesizes an assistive architecture that leverages the benefit of supervisory level task identification with semi-autonomous trajectory tracking. With the proposed method, continuous and more immersive teleoperation is achieved, as control states are user selected and task execution is informed from the operator’s motion. The effectiveness of the proposed method is validated with a user study.
Human Computer Interface Using Electroencephalography for Driver Behavior Classification
Authors: Manchala Vamsi K. , Clara Alvaro V. , Subramanian Susheelkumar C. , Redkar Sangram , Sugar Thomas 
Published: November 2019

Abstract
It is important to know and be able to classify the drivers’ behavior as good, bad, keen or aggressive, which would aid in driver assist systems to avoid vehicle crashes. This research attempts to develop, test, and compare the performance of machine learning methods for classifying human driving behavior. It also proposes to correlate driver affective states with the driving behavior. The major contributions of this work are to classify the driver behavior using Electroencephalograph (EEG) while driving simulated vehicle and compare them with the behavior classified using vehicle parameters and affective states. The study involved both classical machine learning techniques such as k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) and latest “unsupervised” Hybrid Deep Learning techniques, and compared the accuracy of classification across subjects, various driving scenarios and affective states.
Gaussian Process Based Crack Initiation Modeling for Design of Battery Anode Materials
Authors: Zheng Zhuoyuan , Xu Yanwen , Chen Bo , Wang Pingfeng 
Published: November 2019

Abstract
Silicon-based anode is one of the promising candidates for the next generation lithium ion batteries (LIBs) to achieve high power/energy density. However, the major drawback limiting the practical application of Si anode is that Si experiences significant volume change during its lithiation/de-lithiation cycles, which induces high stress and causes degradation and pulverization of the anode. This study focuses on the crack initiation performances of Si anode during the de-lithiation process. A multi-physics based finite element (FE) model is built to simulate the electrochemical process and crack generation during de-lithiation. On top of that, a Gaussian Processes (GP) based surrogate model is developed to assist the exploration of the crack initiation performances within the anode design space. It is found that, the thickness of the Si coating layer TSi, the yield strength σFc of Si material, the cohesive strength between Si and substrate σFs, and the curvature of the substrate ρ have large impacts on the cracking behavior of Si. This coupled FE simulation-GP surrogate model framework is also applicable to other types of LIB electrodes.
Design for Nonassembly: Current Status and Future Directions
Authors: Jung Sangjin , Laureijs Rianne E. , Combemale Christophe , Whitefoot Kate S. 
Published: November 2019

Abstract
In this paper, we review the literature on design for nonassembly (DFNA) and the broader literature on design for manufacturing that has design guidelines and metrics applicable to nonassembled products, including both monolithic single-part products and nonassembly mechanisms. Our review focuses on guidelines that apply across multiple manufacturing processes. We identify guidelines and metrics that seek to reduce costs as well as provide differentiated products across a product family. We find that existing DFNA guidelines fall into four main categories pertaining to: (1) geometry and size, (2) material, (3) production process, and (4) clearance and tolerances. We also identify existing product family metrics that can be modified for nonassembled products to capture some aspects of these categories. Finally, we discuss possible future research directions to more accurately characterize the relationships between design variables and manufacturing costs, including investigating factors related to the complexity of operations at particular process steps and across process steps.
Efficient Finite Element Formulation for Geometrically Exact Beams
Authors: Bauchau Olivier A. , Han Shilei 
Published: November 2019

Abstract
This paper proposes a new approach to the modeling of geometrically exact beams based on motion interpolation schemes. Motion interpolation schemes yield simple expressions for the sectional strains and linearized strain-motion relationships at the mesh nodes. The classical formulation of the finite element method starts from the weak form of the continuous governing equations obtained from a variational principle. Approximations, typically of a polynomial nature, are introduced to express the continuous displacement field in term of its nodal values. Introducing these approximations into the weak form of the governing equations then yields nonlinear discrete that can be solved with the help of a linearization process. In the proposed approach, the order of the first two steps of the procedure is reversed: approximations are introduced in the variational principle directly and the discrete equations of the problem then follow. This paper has shown that for geometrically exact beams, the discrete equations obtained from the two procedure differ significantly: far simpler discrete equations are obtained from the proposed approach.
Fractional-Order Extreme Learning Machine With Mittag-Leffler Distribution
Authors: Niu Haoyu , Chen Yuquan , Chen YangQuan 
Published: November 2019

Abstract
Extreme Learning Machine (ELM) has a powerful capability to approximate the regression and classification problems for a lot of data. ELM does not need to learn parameters in hidden neurons, which enables ELM to learn a thousand times faster than conventional popular learning algorithms. Since the parameters in the hidden layers are randomly generated, what is the optimal randomness? Lévy distribution, a heavy-tailed distribution, has been shown to be the optimal randomness in an unknown environment for finding some targets. Thus, Lévy distribution is used to generate the parameters in the hidden layers (more likely to reach the optimal parameters) and better computational results are then derived. Since Lévy distribution is a special case of Mittag-Leffler distribution, in this paper, the Mittag-Leffler distribution is used in order to get better performance. We show the procedure of generating the Mittag-Leffler distribution and then the training algorithm using Mittag-Leffler distribution is given. The experimental result shows that the Mittag-Leffler distribution performs similarly as the Lévy distribution, both can reach better performance than the conventional method. Some detailed discussions are finally presented to explain the experimental results.
Finite Element Model of Schallamach Waves in Belt-Drives
Authors: Khattab Mohammed , Wasfy Tamer 
Published: November 2019

Abstract
The objective of this study is to investigate if a high-fidelity finite element model can predict the Schallamach wave phenomenon in belt-drives. To this end a computational model which closely mimics a recently developed one-pulley experimental belt-drive apparatus, was created. The dynamic response predicted by the model is compared to the experiment results in order to demonstrate that the model can be used to predict the Schallamach wave phenomenon. Furthermore, the model is used to investigate the roles of Coulomb friction coefficient, adhesion, and torque direction on stick-slip instability effects.
An Agent-Based Modeling Approach for Clean Technologies Adoption Using Theory of Planned Behavior Based Decision-Making
Authors: Pakravan Mohammad H. , MacCarty Nordica 
Published: November 2019

Abstract
Technology adoption in low-income regions is among the key challenges facing international development projects. Nearly 40% of the world’s population relies on open fires and rudimentary cooking devices exacerbating health outcomes, deforestation, and climatic impacts of inefficient biomass burning. Clean technology alternatives such as clean cookstoves are among the most challenging technologies to approach their target goals through sustainable adoption due to lack of systematic market-driven design for adoption. Thus, a method is needed to provide insight regarding how target customers evaluate and perceive causes for adopting a clean technology. The holistic approach of this study captures the three main aspects of technology adoption through lenses of social networks, individual and society scale beliefs, and rational decision-making behavior. Based on data collected in the Apac region in Northern Uganda, an Agent-Based Model is developed to simulate emerging adoption behavior in a community. Then, four different scenarios investigate how adoption patterns change due to potential changes in technology or intervention strategy. These scenarios include influence of stove malfunctions, price elasticity, information campaigns, and strength of social network. Results suggest that higher adoption rates are achievable if designed technologies are more durable, information campaigns provide realistic expectations for users, policy makers and education programs work toward women’s empowerment, and communal social ties are recognized for influence maximization. Application of this study provides insight for technology designers, project implementers, and policy makers to update their practices for achieving sustainable and to the scale clean technology adoption rates.
Simulation of Nucleation and Grain Growth in Selective Laser Melting of Ti-6Al-4V Alloy
Authors: Liu Dehao , Wang Yan 
Published: November 2019

Abstract
Selective laser melting (SLM) builds parts by selectively melting metallic powders layer by layer with a high-energy laser beam. It has a variety of applications in aerospace, medical device, and other low-volume manufacturing. Nevertheless, the lack of fundamental understanding of the process-structure-property (P-S-P) relationship for better quality control inhibits wider applications of SLM. Recently, a mesoscale simulation approach, called phase-field and thermal lattice Boltzmann method (PF-TLBM), was developed to simulate microstructure evolution of alloys in SLM melt pool with simultaneous consideration of solute transport, heat transfer, phase transition, and latent heat effect. In this paper, a nucleation model is introduced in the PF-TLBM model to simulate heterogeneous nucleation at the boundary of melt pool in SLM. A new method is also developed to estimate the thermal flux out of the SLM melt pool model given a constant cooling rate. The effects of latent heat and cooling rate on dendritic morphology and solute distribution are studied. The simulation results of Ti-6Al-4V alloy suggest that the inclusion of latent heat is necessary because it reveals the details of the formation of secondary arms, reduces overestimated microsegregation, and provides more accurate kinetics of dendritic growth.
Visualizing and Evaluating High-Dimensional Mappings of Sets of High Performance Designs
Authors: Morris Clinton B. , Haberman Michael R. , Seepersad Carolyn C. 
Published: November 2019

Abstract
Design space exploration can reveal the underlying structure of design problems. In a set-based approach, for example, exploration can map sets of designs or regions of the design space that meet specific performance requirements. For some problems, promising designs may cluster in multiple regions of the input design space, and the boundaries of those clusters may be irregularly shaped and difficult to predict. Visualizing the promising regions can clarify the design space structure, but design spaces are typically high-dimensional, making it difficult to visualize the space in three dimensions. To convey the structure of such high-dimensional design regions, a two-stage approach is proposed to (1) identify and (2) visualize each distinct cluster or region of interest in the input design space. This paper focuses on the visualization stage of the approach. Rather than select a singular technique to map high-dimensional design spaces to low-dimensional, visualizable spaces, a selection procedure is investigated. Metrics are available for comparing different visualizations, but the current metrics either overestimate the quality or favor selection of certain visualizations. Therefore, this work introduces and validates a more objective metric, termed preservation, to compare the quality of alternative visualization strategies. Furthermore, a new visualization technique previously unexplored in the design automation community, t-Distributed Neighbor Embedding, is introduced and compared to other visualization strategies. Finally, the new metric and visualization technique are integrated into a two-stage visualization strategy to identify and visualize clusters of high-performance designs for a high-dimensional negative stiffness metamaterials design problem.
Designing and Manufacturing a Super Excellent and Ultra-Cheap Energy Absorber by Origami Engineering
Authors: Zhao Xilu , Hagiwara Ichiro 
Published: November 2019

Abstract
Current vehicle energy absorbers have two defects during collision, such as only 70 % collapsed in its length and high initial peak load. It is because present energy absorbed column is the most primitive from the point of Origami structure. We developed the column so called Reversed Spiral Origami Structure; RSO which solves these 2 defects. However, for RSO, the manufacturing cost of hydroforming in the existing technology is too expensive to be applied in real vehicle structure. To address the problems, a new structure, named Reversed Torsion Origami Structure (RTO), has been developed, which can be manufactured at a low cost by using simple torsion. This structure is possible to replace conventional energy absorbers and it is expected to be widely used such as not only in automobile structures but also in building ones.