bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reliability Analysis and Random Vibration of Nonlinear Systems Using the Adjoint Method and Projected Differentiation
Authors: Papadimitriou Dimitrios , Mourelatos Zissimos P. , Hu Zhen 
Published: November 2020

Abstract
This paper proposes a new methodology for time-dependent reliability and random vibrations of nonlinear vibratory systems using a combination of a time-dependent adjoint variable (AV) method and a projected differentiation (PD) method. The proposed approach is called AV-PD. The vibratory system is excited by stationary Gaussian or non-Gaussian input random processes. A Karhunen-Loeve (KL) expansion expresses each input random process in terms of standard normal random variables. The nonlinear equations of motion (EOM) are linearized using a Taylor expansion using the first-order derivatives of the output with respect to the input KL random variables. An adjoint approach obtains the output derivatives accurately and efficiently requiring the solution of as many sets of EOM as the number of outputs of interest, independently of the number of KL random variables. The proposed PD method then computes the autocorrelation function of each output process at an additional cost of solving as many sets of EOM as the number of outputs of interest, independently of the time horizon (simulation time). A time-dependent reliability analysis is finally performed using a KL expansion of the output processes and Monte Carlo Simulation (MCS). The number of solutions of the EOM scales only with the number of output random processes which is commonly much smaller than the number of input KL random variables. The efficiency and accuracy of the proposed approach is demonstrated using a four degree-of-freedom (DOF) half-car vibratory problem.
Period-3 Motions in a Parametrically Exited Inverted Pendulum
Authors: Luo Albert C. J. , Guo Chuan 
Published: November 2020

Abstract
In this paper, period-3 motions in a parametrically exited inverted pendulum are analytically investigated through a discrete implicit mapping method. The corresponding stability and bifurcation conditions of the period-3 motions are predicted through eigenvalue analysis. The symmetric and asymmetric period-3 motions are obtained on the bifurcation tree, and the period-doubling bifurcations of the asymmetric period-3 motions are observed. The saddle-node and Neimark bifurcations for symmetric period-3 motions are obtained. The saddle-bifurcations of the symmetric period-3 motions are for symmetric motion appearance (or vanishing) and onsets of asymmetric period-3 motion. Numerical simulations of the period-3 motions in the inverted pendulum are completed from analytical predictions for illustration of motion complexity and characteristics.
Sensor Based Target Tracking With Application to Autonomous Docking and Self-Reconfigurability
Authors: Sohal Shubhdildeep S. , Ben-Tzvi Pinhas 
Published: November 2020

Abstract
This paper presents a target detection technique, which combines a supervised learning model with sensor data to eliminate false positives for a given input image frame. Such a technique aids with selective docking procedures where multiple robots are present in the environment. Hence the sensor data provides additional information for this decision making process. Senor accuracy plays a crucial role when the motion of the robot is defined by the use of data recorded by its sensors. The uncertainties in the sensory data can cause misalignments due to poor calibration of the sensor, which can result in poor positioning of the robot relative to its target. Such misalignments can play a significant role where certain accuracy is desired. Therefore, it is necessary to minimize such misalignments to achieve certainty for the robot interaction with its target. The work proposed in this paper allows achieving such accuracy using a vision-based approach by eliminating all false occurrences leading to selective interactions with the target. The proposed methodology is validated using a self-reconfigurable mobile robot capable of hybrid Wheeled-Tracked mobility, as an application towards autonomous docking of mobile robotic modules.
A Machine Learning-Based Design Recommender System for Additive Manufacturing
Authors: Ghiasian Seyedeh Elaheh , Lewis Kemper 
Published: November 2020

Abstract
To appropriately leverage the benefits of Additive Manufacturing (AM), it would be advantageous if a printing could be guaranteed before allocating the necessary resources. Further, when considering AM for an inventory of existing components traditionally fabricated through traditional means, such a guarantee could result in significant technical and economic advantages. To realize such advantages, this paper presents a platform that allows for a successful and efficient transition of part-inventories to AM. This is accomplished using a novel design recommender system supported by machine learning, capable of making suggestions towards effective design modifications. This system uses an automatic AM-feasibility analysis of existing parts and a clustering of the parts based on similarities in their AM-feasibilities to develop a set of recommendations for those part clusters whose current designs are deemed as infeasible and/or inefficient for AM. The design modifications leverage a re-design algorithm to address not only problematic geometric issues but also potential infeasibilities associated with resource consumption. The utility of the presented modification algorithm is demonstrated using a number of case studies.
Criticality of Hopf Bifurcation in Precision Motion Stage With PID and Time-Delayed Feedback Controls
Authors: Gupta Sunit K. , Wang Jiamin , Barry Oumar R. 
Published: November 2020

Abstract
The use of precision motion stages is very popular among advanced manufacturing and machining industries. However, the performance of these motion stages is usually undermined by friction-induced vibration. In this paper, we propose the use of time-delayed feedback control to minimize the undesirable effects of friction-induced vibrations. The use of time-delayed feedback control is well established in the literature; however, the use of time-delayed feedback control in PID controlled motion-stages has not been explored yet. Here, we consider a lumped parameter model of the PID controlled precision motion stage with a linear time-delayed state feedback control. The dynamical friction in the systemis modeled using the LuGre model. Stability and nonlinear analysis of the system are carried out using analytical methods. The stability analysis reveals the existence of multiple stability lobes and codimension-2 Hopf points for a given choice of system parameters. Also, the nature of Hopf bifurcation is determined by using the method of multiple scales. We observe the existence of both subcritical and supercritical Hopf bifurcations in the system, depending on the choice of control parameters. This observation implies that the nonlinearity in the system could both be stabilizing or destabilizing in nature.
Autonomous Cricothyroid Membrane Detection Using Neural Networks for First-Aid Surgical Airway Management
Authors: Han Xiaoxue , Ren Hailin , Ben-Tzvi Pinhas 
Published: November 2020

Abstract
Airway management is one of the most important priorities when dealing with patients with severe injuries, but knowledge of the important anatomy and physiology is needed for providers to perform a successful surgery. This paper provides a solution for the precise cricothyroid membrane detection problem for real-time surgical airway management applications. With a commercial compact and portable cricothyrotomy kit, the proposed method will enable providers with general knowledge to perform successful first-aid airway management. In this paper, we propose a Hybrid Neural Network (HNNet), consisting of two parallel computing ensembles. The first ensemble takes as an input a low-resolution global image and outputs the Region-of-Interest (ROI) from the predefined grids. The high-resolution image is then cropped according to the ROI, and fed into the second ensemble to achieve precise keypoint detection. Global features and their spatial information from the first ensemble are also fed into the second ensemble to improve the precision. A dataset that consists of over 16,000 images from 13 subjects is built, and the location of the cricothyroid membrane in each image is precisely labeled by medical experts. The training results are presented to show both the efficiency and improved performance of our proposed method compared to existing ones.
Multiscale Off-Road Mobility Simulation With Computational Load Balancing for Lower-Scale Discrete-Element Models
Authors: Chen Guanchu , Yamashita Hiroki , Ruan Yeefeng , Jayakumar Paramsothy , Sugiyama Hiroyuki 
Published: November 2020

Abstract
Scalable parallel computing schemes play an important role in physics-based off-road mobility simulations due to complexities in modeling soil behavior for vehicle-terrain interaction. With the hierarchical multiscale off-road mobility simulation capability, limitations of existing computational deformable terrain models can be eliminated, including the use of phenomenological constitutive assumptions in finite element (FE) approaches as well as high computational intensity of discrete element (DE) models. However, parallel computing algorithms for multiscale simulations need to be carefully developed due to possible unbalanced computational loads occurring in lower-scale RVE simulations, which prevents desirable computational speedup. Therefore, this study aims to develop a scalable hybrid MPI-OpenMP parallel computing framework for hierarchical FE-DE multiscale off-road mobility simulations with a special focus on computational load balancing for the lower-scale DE models.
Structure Synthesis of Multi-DOF Planar Metamorphic Mechanisms With a Single Driver
Authors: Yang Qiang , Murray Andrew P. , Myszka David H. , Li Shujun 
Published: November 2020

Abstract
This paper approaches the type synthesis of multi-degree of freedom planar metamorphic mechanisms with a single driver in a systematic process. The process is facilitated by implementing a constraint status matrix and a equivalent resistance matrix as a method for identifying an appropriate structure of planar metamorphic mechanisms with a single driver. Multi-structures can be obtained from the same source metamorphic mechanism by designing different constraint architectures of metamorphic joints. To determine the constraint architectures of metamorphic joints and their different assembly combinations, the constraint status matrix is built based on the task-based metamorphic cyclogram of a source mechanism. According to the equivalent resistance gradient model and the constraint status matrix, an equivalent resistance matrix for the metamorphic joints is proposed. A structural synthesis matrix of the metamorphic mechanism is then obtained from the equivalent resistance matrix by deducing the constraint-form vectors of the metamorphic joints. Furthermore, an effective kinematic diagram synthesis of the source mechanism of the planar metamorphic mechanism is proposed which is based only on the 14 one or zero degree-of-freedom (DOF) linkage groups. The entire structural design method of a metamorphic mechanism is based on the structural synthesis matrix and given in steps. Finally, a proposed structural design approach is illustrated by two examples.
Semi-Supervised Deep Learning for High-Dimensional Uncertainty Quantification
Authors: Wang Zequn , Li Mingyang 
Published: November 2020

Abstract
Conventional uncertainty quantification methods usually lacks the capability of dealing with high-dimensional problems due to the curse of dimensionality. This paper presents a semi-supervised learning framework for dimension reduction and reliability analysis. An autoencoder is first adopted for mapping the high-dimensional space into a low-dimensional latent space, which contains a distinguishable failure surface. Then a deep feedforward neural network (DFN) is utilized to learn the mapping relationship and reconstruct the latent space, while the Gaussian process (GP) modeling technique is used to build the surrogate model of the transformed limit state function. During the training process of the DFN, the discrepancy between the actual and reconstructed latent space is minimized through semi-supervised learning for ensuring the accuracy. Both labeled and unlabeled samples are utilized for defining the loss function of the DFN. Evolutionary algorithm is adopted to train the DFN, then the Monte Carlo simulation method is used for uncertainty quantification and reliability analysis based on the proposed framework. The effectiveness is demonstrated through a mathematical example.
Linearization of Characteristic Response of a Capacitive MEMS Pressure Sensor by Patterning the Dielectric Layer
Authors: Ebrahimpour Tolouei Nadia , Shavezipur Mohammad 
Published: November 2020

Abstract
The present work introduces a novel design that linearizes the characteristic capacitance-pressure (C-P) response of the pressure sensor in contact mode. The design relies on patterning the insulating (dielectric) layer that separates the two electrodes of the device when the device is in contact mode. Since the capacitance is inversely proportional to the gap between the electrodes and the dielectric constant of the insulating layer is several times more than that of air (or vacuum), the contact region of the two electrodes makes more significant contribution to the overall capacitance of the system. Therefore, if the dielectric layer is properly patterned, the shape of C-P response can be controlled. In this work, we focus on linearity of the sensor response, and design and optimize dielectric pattern to achieve the highest linearity. Finite element simulations are used to demonstrate the applicability of the design concept. Different sensor designs are modeled and simulated using ANSYS® Multiphysics solver and their responses are compared to that of a conventional capacitive pressure sensor. Coefficient of linear correlation between pressure and capacitance is used as a quantitative measure for improvement of linearity. The simulation results show that the linearity of the C-P response improves from 0.930 in a 600 μm-diameter conventional design to 0.978 for a sensor with patterned dielectric layer. Moreover, a smaller sensor with 300 μm diameter display linearity of 0.999 over a 1.25 MPa – 5.0 MPa pressure range.
Dynamic Modeling and Transient Analysis of a Deployable Miura-Origami Tube
Authors: Wu Haiping , Xu Jian , Chen Lifen , Fang Hongbin 
Published: November 2020

Abstract
Origami provides a rich library and unique benefits for developing deployable structures. Comparing with the vast amount of progress in pattern design, static configuration analysis, and folding kinematics, research on the dynamics of origami deployable structures remains at the early stage. This paper presents our effects in developing an effective and addressable dynamic model for studying the transient dynamics of a Miura-origami tube consisting of stacked Miura-ori (SMO) cells. The Miura-ori tube, in an ideal scenario, is rigid-foldable and flat-foldable, and its folding can be described via a single-degree-of-freedom (DOF) mechanism. However, practically, these features cannot be fully satisfied in a real prototype. In this research, five assumptions are proposed for dynamic modeling purposes, which, on one hand, retain the key characteristics of folding, and on the other hand, significantly simplify the problem. With the five assumptions and based on the Lagrange Equation for the general case, the governing equation of the Miura-ori tube can be derived. Taking a six-cell Miura-ori tube under free deployment as an example, numerical analyses reveal that in addition to the decayed vibrations in the deploying direction, the tube would also exhibit significant transverse vibrations. The transient dynamics in both the deploying and the transverse directions can be quantified by the overshoot values and the settling times. Moreover, by increasing the additionally-introduced crease torsional stiffness, which is used to constrain the deviation between the folding of adjacent half SMO cells, the multiple-DOF dynamic model would degenerate into the single-DOF dynamic model. In such a scenario, only vibrations in the deploying direction are possible. The constructed model and the preliminary understanding of the transient dynamics could provide useful guidelines for designing and optimizing origami-based tubular deployable structures.
Bioinspired Origami: Case Studies Using a Keyword Search Algorithm
Authors: Morris Elissa , McAdams Daniel A. 
Published: November 2020

Abstract
Numerous folding patterns, structures, and behaviors exist in nature that may provide design solutions to engineering problems. While applying biological solutions to engineering design is evidently valuable, the retrieval of useful design inspiration remains a primary challenge preventing the transfer of knowledge from biology to the engineering domain. In prior research, information retrieval techniques are employed to retrieve useful biological design solutions and a text-based search algorithm is developed to return passages where folding in nature is observed. The search algorithm, called FoldSearch, integrates tailored biological keywords and filtering methods to retrieve passages from an extensive biological corpus. The objective of this paper is two-fold — 1) to demonstrate the functionality of FoldSearch, and 2) to create abstract models of the retrieved biological systems from FoldSearch which can be used for the development of novel origami crease patterns and foldable structures. In this paper, the utility of FoldSearch is demonstrated through two case studies where the retrieved biological examples undergo a design abstraction process that leads to the development of bioinspired origami crease patterns and novel foldable structures. The abstraction process is presented as a systematic design methodology for bioinspired origami for the growing research field of origami engineering.
Bio-MEMS Circular Plate Sensors Under Electrostatic Hard Excitations: Frequency Response of Superharmonic Resonance of Fourth Order
Authors: Beatriz Julio , Caruntu Dumitru I. 
Published: November 2020

Abstract
This paper investigates the frequency-amplitude response of electrostatically actuated Bio-MEMS clamped circular plates under superharmonic resonance of fourth order. The system consists of an elastic circular plate parallel to a ground plate. An AC voltage between the two plates will lead to vibrations of the elastic plate. Method of Multiple Scales, and Reduced Order Model with two modes of vibration are the two methods used in this work. The two methods show similar amplitude-frequency response, with an agreement in the low amplitudes. The difference between the two methods can be seen for larger amplitudes. The effects of voltage and damping on the amplitude-frequency response are reported. The steady-state amplitudes in the resonant zone increase with the increase of voltage and with the decrease of damping.
On the Dynamics of a Quadratic-Oscillator-Based, Infinite-Equilibrium System
Authors: Xing Siyuan , Luo Albert C. J. , Huang Jianzhe 
Published: November 2020

Abstract
In this paper, the local and global dynamics of a periodically forced, quadratic-oscillator-based, infinite-equilibrium system is discussed. The local analysis of regular equilibriums and infinite-equilibriums is completed, and the global responses of the periodically forced infinite-equilibrium system are presented through numerical simulations. Near the infinite-equilibrium surface, the periodically forced infinite-equilibrium system can be reduced to a one-dimensional system and new contraction regions can be formed. The infinite-equilibrium surface can be artificially designed to control the motions of the original quadratic nonlinear oscillator. Such a property is like a discontinuous dynamical system, which can be used for controller design in nonlinear systems.
Basic Performance of Natural Fiber Bevel Gears Made From Only Bamboo Fibers Extracted With a Machining Center
Authors: Kawabata Tetsuya , Hirogaki Toshiki , Aoyama Eiichi , Nakagawa Masao , Nobe Hiromichi 
Published: November 2020

Abstract
Metal gears can transmit large torque. The disadvantages associated with metal gears are the noise produced and necessity of lubricants. Plastic gears are advantageous because they are lightweight and can be used without lubricants. However, plastic gears have relatively low strengths and damaging environmental effects. We propose the development of a new type of gear that overcomes the disadvantages associated with metal and plastic gears whilst maintaining their advantages. To address environmental issues within manufacturing, it is particularly important to utilize sustainable and reproducible natural materials. Therefore, we devised a method for extracting high-quality and high-precision bamboo fibers using a machining center. Bamboo bevel gears, which are complex-shaped mechanical elements, were manufactured using the hot-pressing method. This paper outlines the performance and characteristics of the molded bamboo bevel gears. We investigated the degree of burning and strength of the bamboo fiber gears at various cutting and forming conditions. The results demonstrated that the degree of burning (black color) did not affect the gear strength, and the gear strength was at a maximum when the fiber length was 50% of the module size.
Invariant Manifolds in Human Joint Angle Analysis During Walking Gait
Authors: Bhat Sandesh G. , Sugar Thomas G. , Redkar Sangram 
Published: November 2020

Abstract
The complex dynamics of human gait is yet to be completely understood. Researchers have quantified stability of walking gait using Floquet multipliers as well as Lyapunov exponents. In this article, we utilize the techniques and tools from dynamical system theory and invariant manifolds to map the gait data onto a time invariant representation of a dynamical system. As an example, the complex behavior of the joint angle during walking was studied using a conformal mapping approach that transformed the time periodic system into a time invariant linear system. Time-delay embedding was used to reconstruct the dynamics of the original gait system with time series kinematic data. This minimal realization of the system was used to construct a Single Degree of Freedom (SDOF) oscillator. The time evolution of the linear oscillatory system was mapped back using the conformal mapping derived using Lyapunov-Floquet Theory. This algorithm was verified for walking gait kinematics data for two healthy human subjects. A comparison was drawn between the phase space behavior of the original time periodic system and the remapped time invariant system. The two systems showed good correlation. The algorithm resulted in a well correlated phase space representation.