bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Intelligent Prediction for Detecting Bearing Vibration Characteristics Using a Machine Learning Model
Authors: Salunkhe Vishal G. , Desavale R. G. 
Published: January 2021

Abstract
Bearing failure in the heavy rotating machines results in shut down of many other machines and affects the overall cost and quality of the product. Condition monitoring of bearing systems acts as a preventive and corrective measure as it avoids breakdown and saves maintenance time and cost. This research paper proposes advanced strategies for early detection and analysis of taper rolling bearings. In view of this, mathematical model-based fault diagnosis and support vector machining (SVM) are proposed in this work. A mathematical model using dimension analysis by the matrix method (Dimension Analysis Method (DAMM)) and SVM is developed that can be used to predict the vibration characteristic of the rotor-bearing system. Types of defects are created using electrical discharge machining (EDM) and analyzed, and correlation is established between dependent and independent parameters. Experiments were performed to evaluate the rotor dynamic characteristic of healthy and unhealthy bearings. Experimental results are used to validate the model obtained by the DAMM and SVM. Experimental results showed that the vibration characteristic could be evaluated by using a theoretical model and SVM. Efforts have been made to extend the service life of the machines and the assembly lines and to improve their efficiency, so as to reduce bearing failure; what provides novelty to these efforts is the use of four machine learning techniques. Thus, an automatic online diagnosis of bearing faults has been made possible with the developed model based on DAMM and SVM.
Passive Multi-Degree-of-Freedom Stabilization of Ultra-High-Speed Maglev Vehicles
Authors: Circosta Salvatore , Galluzzi Renato , Amati Nicola , Tonoli Andrea , Bonfitto Angelo , Lembke Torbjörn A. , Kertész Milan 
Published: January 2021

Abstract
Over the last decades, the search for fast and efficient transportation systems has raised the interest toward maglev technologies. In this scenario, the Hyperloop paradigm is regarded as a breakthrough for future mobility. However, its practical implementation requires the solution of key shortcomings. Among these, the stability of the electrodynamic levitation system remains partially unexplored. The state of the art presents numerous attempts to attain stable behavior. In recent works, the stabilization of maglev vehicles has been addressed only for the vertical dynamics. Nevertheless, stable operation of all degree-of-freedom is required for a successful implementation of these transportation systems. The present paper addresses the full stabilization of a downscaled vehicle where levitation and guidance are provided by electrodynamic means. To this end, a design methodology supported by analytical modeling is proposed, where the degree-of-freedom are stabilized by suitably introducing secondary suspension elements. The design of the secondary suspension and the guidance system is obtained through the optimization of stability and dynamic performance. Then, a multibody model is developed. Both numerical approaches are compared in the frequency domain for validation purposes. Finally, the multibody model is simulated in the time domain to assess system performance in the presence of track irregularities and evaluate coupling effects between the degree-of-freedom.
Adaptive Neural Network Based Predictive Control of Nonlinear Systems With Slow Dynamics
Authors: Spiller Mark , Bakhshande Fateme , Söffker Dirk 
Published: January 2021

Abstract
In this paper a data-driven approach for model-free control of nonlinear systems with slow dynamics is proposed. The system behavior is described using a local model respectively a neural network. The network is updated online based on a Kalman filter. By predicting the system behavior two control approaches are discussed. One is obtained by calculating a control input from the one step ahead prediction equation using least squares, the other is obtained by solving a standard linear model predictive control problem. The approaches are tested on a constrained nonlinear MIMO system with slow dynamics.
Real-Time Applicable Power Management of Multi-Source Fuel Cell Vehicles Using Situation-Based Model Predictive Control
Authors: Ali Ahmed M. , Söffker Dirk 
Published: January 2021

Abstract
Power management in all-electric powertrains has a significant potential to optimally handle the limited energy and power density of electric power sources. Situation-based power management strategies (SB-PMSs), defining optimized solutions related to specific vehicle situations, offer the ability to reduce computational requirements and enhance the solution optimality of simple rule-based algorithms. Moreover, the local optimality of SB-PMSs can be addressed by considering online optimization of the situated solutions for limited horizons. This paper presents a novel PMSs using model predictive control (MPC) to define optimal control strategies based on situated solutions for fuel cell hybrid vehicles. Vehicle states are defined in terms of multiple characteristic variables and power management decisions are optimized offline for each vehicle states. Prediction of vehicle states is conducted using statistical predictive model based on state transitions in a number of driving cycles. Preoptimized solutions related to predicted states are iterated online to achieve better optimality over the look-ahead horizon. Results analysis from online testing revealed the ability of SB-MPC to improve the optimality of situation-based solutions and hence reduce total energy cost in different driving cycles.
A Lateral Motion Planning Method for Automated Vehicles Based on Sinusoids
Authors: Wang Wei , Wang Zejiang , Chen Xinbo , Wang Junmin 
Published: January 2021

Abstract
For automated driving applications, exact motion planning is pivotal to obstacle-avoidance and local driving, particularly in sophisticated surroundings. Treating this problem from a differential geometric viewpoint aids in simplifying the control and planning processes. Different from the classic differential geometric methods that result in piece-wise solutions with both forward and backward moving, this paper explores the application of sinusoids in exact motion planning regarding both the positive/continuous speed requirements and the system non-holonomic constraint. By converting the vehicle kinematic model to a similar chained form, we apply sinusoidal inputs with biases to generate the reference trajectory and discuss the principles of parameter selection for a given lateral displacement. To overcome the extra constraint brought by l’Hospital rule, we introduce a pair of entry/leave transition periods to ensures an adjustable speed profile and design flexibility. The resulting trajectory reflects an strong applicability to lane-changing or evasive steering scenarios. Since the proposed method does not account for the small-time controllability because of the positive/continuous speed constraint, the reachability problem is further investigated. We provide an analytic estimation of the reachable area edge and compare it with the numerical simulation results. Finally, some indoor field-tests verified the feasibility of the proposed method.
Inverse Reinforcement Learning Based Driver Behavior Analysis and Fuel Economy Assessment
Authors: Ozkan Mehmet Fatih , Ma Yao 
Published: January 2021

Abstract
Human drivers have different driver behaviors when operating vehicles. These driving behaviors, including the driver’s preferred speed and rate of acceleration, impose a major impact on vehicle fuel consumption consequently. In this study, we proposed a feature-based driver behavior learning model from demonstrated driving data utilizing the Inverse Reinforcement Learning (IRL) approach to analyze various driver behaviors and their impacts on vehicle fuel consumption. The proposed approach models the individual driving style as cost function which is a linear combination of the features and their corresponding weights. The proposed IRL framework is used to find the model parameters that fit the observed driving style best. By using the learned driving behavior model, the most likely trajectories are computed and the optimized feature weights are used to analyze different driver behaviors. The different driver behaviors and their impacts on vehicle fuel consumption are then analyzed in real-world driving scenarios. Results show that the proposed IRL framework can successfully learn individual driver behaviors using vehicle trajectory data demonstrated by different real drivers. The learned driver behaviors promise a significant correlation between driving behavior and fuel consumption.
Motion Planning for Autonomous Driving With Extended Constrained Iterative LQR
Authors: Shimizu Yutaka , Zhan Wei , Sun Liting , Chen Jianyu , Kato Shinpei , Tomizuka Masayoshi 
Published: January 2021

Abstract
Autonomous driving planning is a challenging problem when the environment is complicated. It is difficult for the planner to find a good trajectory that navigates autonomous cars safely with crowded surrounding vehicles. To solve this complicated problem, a fast algorithm that generates a high-quality, safe trajectory is necessary. Constrained Iterative Linear Quadratic Regulator (CILQR) is appropriate for this problem, and it successfully generates the required trajectory in realtime. However, CILQR has some deficiencies. Firstly, CILQR uses logarithmic barrier functions for hard constraints, which will cause numerical problems when the initial trajectory is infeasible. Secondly, the convergence speed is slowed with a bad initial trajectory, which might violate the real-time requirements. To address these problems, we propose the extended CILQR by adding two new features. The first one is using relaxed logarithmic barrier functions instead of the standard logarithmic barrier function to prevent numerical issues. The other one is adding an efficient initial trajectory creator to generate a good initial trajectory. Moreover, this initial trajectory helps CILQR to converge to a desired local optimum. These new features extend CILQR’s usage to more practical autonomous driving applications. Simulation results show that our algorithm is effective in challenging driving environments.
Suggestion-Based Fuel Efficient Control of Connected and Automated Vehicles
Authors: Vellamattathil Baby Tinu , Karimi Shahri Pouria , Ghasemi Amir H. , HomChaudhuri Baisravan 
Published: January 2021

Abstract
This paper presents a suggestion-based fuel efficient controller for connected and automated vehicles (CAVs) in presence of human-driven vehicles (HDVs). The suggestion-based controller, apart from evaluating the fuel efficient control solution of the host CAV, provides suggested velocity commands to the HDVs so that the fuel efficiency of itself or the group can improve. We assume that in the connected vehicle system, the HDVs are also able to receive information though Vehicle to Vehicle (V2V) communication and they always try to follow the suggested commands. The suggestion-based control provides additional decision variables to the CAVs with which they can influence the actions of the HDVs and hence improve the fuel efficiency of the whole group. The controller is implemented in a model predictive control (MPC) framework where the suggested command velocities are held constant over some prescribed time so that the driver gets enough time to reach the suggested command velocities. For this control method to function, we present a model that captures the response of a HDV to different suggested-commands. The parameters of the model is obtained from a table-top drive simulator. The accuracy of this model is also validated with the experimental data (table-top drive simulator) and the results are presented in this paper. Simulation studies for the control strategies show the efficacy of the proposed control strategy when compared with existing baseline methods.
An Ultrasound Imaging Based Observer for Estimating NMES-Induced Muscle Fatigue: Theory and Simulation
Authors: Sheng Zhiyu , Kim Kang , Sharma Nitin 
Published: January 2021

Abstract
Neuroprosthetic devices that use transcutaneous neuromuscular electrical stimulation (NMES) are potential interventions to restore skeletal muscle function in people with neurological disorders. As commonly noted, how to assess the NMES-induced muscle fatigue is a critical problem. This is because the capability of fatigue assessment is a necessary precursor for optimally modulating the NMES dosage to improve the control performance of a neuroprosthesis and ensure user’s safety. To effectively estimate the NMES-induced muscle fatigue, this paper proposes a novel state observer that combines a mathematical predictive fatigue model and intermittent feedback from ultrasound-derived strain images. The strain images quantify muscle contractility during NMES. Principal component regression (PCR) is used to derive a relationship between the strain images and instantaneous muscle force production. Lyapunov stability analysis was performed to obtain the convergence property of the designed observer. A globally uniformly ultimately bounded (GUUB) result was obtained. Simulations based on pre-recorded data from a human experiment were also conducted to demonstrate the performance of the designed observer.
Inverse Kinematic Analysis of Muscular Hydrostat Inspired Soft Robot With Chain-Like Optimization With an Embedded Controller
Authors: Zamanian Amir Hosein , Richer Edmond 
Published: January 2021

Abstract
In this study, we present a fast computing algorithm for the inverse kinematic analysis of a dexterous robot inspired by muscular hydrostats. First, the forward kinematics based on piecewise constant curvature (PCC) theory was established. Second, an inverse kinematic algorithm based on a chain-like connection comprising rigid link elements with revolute joints is proposed. The inverse kinematic algorithm starts with a solution derived from the Euler-Bernoulli beam equation and then performs an iterative constrained optimization with an embedded integral controller. The algorithm can reach the desired positions and orientations of the end effector with theoretically “zero” error. The simulation results showed that the algorithm can reach all positions and orientations in the continuous workspace with a small number of iterations.
Minimum Safety Distances for Emergency Braking Maneuvers in Car-Following Applications
Authors: Schafer Devin , Chen Pingen 
Published: January 2021

Abstract
Platooning/car following has been considered as a promising approach for improving vehicle efficiency due to the reduction of aerodynamic force when closely following a pilot vehicle. However, safety is a major concern in the close car platooning/following. This paper investigates the minimum inter-vehicle distances required for a passenger vehicle to safely travel behind a heavy-duty truck with three different types of emergency maneuvers. The three emergency maneuvers considered are braking only, steering only, and braking then steering, where steering refers to a single lane change maneuver. Numerical analysis is conducted for deriving the clearance space in the braking only scenario. In addition, simulations are conducted in MATLAB/Simulink, using a bicycle model for the vehicle dynamics, to examine the minimum safe following distance for the other two scenarios. The simulation results show that, for initial vehicle speeds greater than 8 m/s, a lane change maneuver requires the shortest safety distance. Braking followed by lane changing usually requires the largest minimum safety distance.
Optimal Realization of Endpoint Stiffness in Static Human Arm Postures
Authors: Iqbal Kamran 
Published: January 2021

Abstract
The endpoint stiffness, i.e., stiffness displayed by the wrist amid perturbations to the arm, has been used to assess the mechanical stability of the arm posture. The aim of this study is to develop an algorithm to optimally realize a desired end-point stiffness by minimizing muscle forces. The neuro-muscular behavior of the human arm during posture maintenance tasks is approximated by a two-link eight-muscle arm model. The model parameters reflect physiological data taken from published literature. The endpoint stiffness is shown to be a linear function of muscle activations. It is shown that the problem to minimize muscle activations while satisfying torque constraint at the joints can be solved by using non-negative least-squares method. Alternatively, linear programming can be used for this purpose. The biomechanical model is used to demonstrate how endpoint stiffness of desired magnitude, orientation, and eccentricity can be synthesized by activating arm muscles with minimal energy expenditure. Our simulation results suggest that bi-articular muscles play a major role in developing the desired endpoint stiffness. The model can be scaled up to three-dimensions by adding muscle groups.
Cooperation and Null-Space Control of Networked Omni-Directional Mobile Manipulators
Authors: Chua Michael John , Liu Yen-Chen 
Published: January 2021

Abstract
This paper presents cooperation and null-space control for networked mobile manipulators with high degrees of freedom (DOFs). First, kinematic model and Euler-Lagrange dynamic model of the mobile manipulator, which has an articulated robot arm mounted on a mobile base with omni-directional wheels, have been presented. Then, the dynamic decoupling has been considered so that the task-space and the null-space can be controlled separately to accomplish different missions. The motion of the end-effector is controlled in the task-space, and the force control is implemented to make sure the cooperation of the mobile manipulators, as well as the transportation tasks. Also, the null-space control for the manipulator has been combined into the decoupling control. For the mobile base, it is controlled in the null-space to track the velocity of the end-effector, avoid other agents, avoid the obstacles, and move in a defined range based on the length of the manipulator without affecting the main task. Numerical simulations have been addressed to demonstrate the proposed methods.
Adaptive Impedance Control for the Haptic Shared Driving Task Based on Nonlinear MPC
Authors: Izadi Vahid , Ghasemi Amir H. 
Published: January 2021

Abstract
This paper presents an adaptive haptic shared control framework wherein a driver and an automation system are physically connected through a motorized steering wheel. The automation system is modeled as an intelligent agent that is not only capable of making decisions but also monitoring the human’s behavior and adjusting its behavior accordingly. To enable the automation system to smoothly exchange the control authority with the human partner, this paper introduces a novel self-regulating impedance controller for the automation system. To determine an optimal modulation policy, a cost function is defined. The terms of the cost function are assigned to minimize the performance error and reduce the disagreement between the human and automation system. To solve the optimal control problem, we employed a nonlinear model predictive approach and used the continuation generalized minimum residual method to solve the nonlinear cost function. To demonstrate the effectiveness of the proposed approach, simulation studies consider a scenario where the human and the automation system both detect an obstacle and negotiate on controlling the steering wheel so that the obstacle can be avoided safely. The simulations involve four interaction modes addressing the cooperation status (cooperative and uncooperative) and the desired direction of the control transfer (active safety and autopilot). The results of the numerical studies show that when the automation system acts as autopilot, using the proposed modulation method, the automation adopts smaller impedance controller gains, which results in a smaller disagreement between the human and automation systems. On the other hand, when the human’s control command is insufficient, by modulating and adopting larger values for the impedance controller parameters, the automation system gains the control authority and ensures the safety of the obstacle avoidance task.
A new model for the optimal structural context for giant porphyry copper deposit formation
Authors: Piquer José , Sanchez-Alfaro Pablo , Pérez-Flores Pamela 
Published: January 2021

AbstractPorphyry-type deposits are the main global source of copper and molybdenum. An improved understanding of the most favorable structural settings for the emplacement of these deposits is necessary for successful exploration, particularly considering that most future discoveries will be made under cover based on conceptual target generation. A common view is that porphyry deposits are preferentially emplaced in pull-apart basins within strike-slip fault systems that favor local extension within a regional compressive to transpressive tectonic regime. However, the role of such a structural context in magma storage and evolution in the upper crust remains unclear. In this work, we propose a new model based on the integration of structural data and the geometry of magmatic-hydrothermal systems from the main Andean porphyry Cu-Mo metallogenic belts and from the active volcanic arc of southern Chile. We suggest that the magma differentiation and volatile accumulation required for the formation of a porphyry deposit is best achieved when the fault system controlling magma ascent is strongly misoriented for reactivation with respect to the prevailing stress field. When magmas and fluids are channeled by faults favorably oriented for extension (approximately normal to σ3), they form sets of parallel, subvertical dikes and veins, which are common both during the late stages of the evolution of porphyry systems and in the epithermal environment. This new model has direct implications for conceptual mineral exploration.
Transport and accumulation of plastic litter in submarine canyons—The role of gravity flows
Authors: Zhong Guangfa , Peng Xiaotong 
Published: January 2021

Abstract
Manned submersible dives discovered plastic litter accumulations in a submarine canyon located in the northwestern South China Sea, ∼150 km from the nearest coast. These plastic-dominated litter accumulations were mostly concentrated in two large scours in the steeper middle reach of the canyon. Plastic particles and fragments generally occurred on the upstream-facing sides of large boulders and other topographic obstacles, indicating obstruction during down-valley transportation. Most of the litter accumulations were distributed in the up-valley dipping slopes downstream of the scour centers. This pattern is tentatively linked to turbidity currents, which accelerated down the steep upstream slopes of the scours and underwent a hydraulic jump toward the scour centers before decelerating on the upstream-facing flank. Associated seabed sediment consisted of clayey and sandy silts, with unimodal or bimodal grain-size distributions, which are typical for turbidites. The focused distribution of the litter accumulations is therefore linked to turbidity currents that episodically flush the canyon. Our findings provide evidence that litter dispersion in the deep sea may initially be governed by gravity flows, and that turbidity currents efficiently transfer plastic litter to the deeper ocean floor.