bluuebunny/crossref_metadata_2025_split huggingface dataset:
Kinematic Calibration of a 3rRPS Metamorphic Parallel Mechanism
Authors: Chai Xuheng , Nurahmi Latifah , Dai Jian S. , Gan Dongming 
Published: November 2020

Abstract
In this paper, a kinematic calibration method is developed for a 3rRPS metamorphic parallel mechanism with respect to all unknown parameters. Each limb of the 3rRPS mechanism is composed of (rR), P, and S joints. Two joints are actuated, namely P and r joints, hence the mechanism is able to switch between the 3RPS parallel mechanism and 3US parallel mechanism. The geometric constraint equations of the 3rRPS mechanism are initially established. Then, the optimization problems for the base, platform and actuated prismatic lengths during given trajectory are formulated by using the global search optimization algorithm. A physical model of the 3rRPS metamorphic parallel mechanism is built and an experiment is setup to validate the proposed calibration and optimization models. The external device, i.e., the OptiTrack is used during the experiment for motion capture system. All unknown parameters are identified and optimized by dint of the geometric properties of this mechanism and nonlinear optimization algorithms. The experimental results demonstrate that the proposed calibration method is valid and effective.
Towards Creating Inter-Finger Kinematic Models for Natural Coordination Derived From Grasping of Virtual Objects
Authors: Robson Nina , Chavez Brianda , Won Jong-Seob 
Published: November 2020

Abstract
This paper builds up on a recently developed novel planar inter-joint finger kinematic model for human hand configurations, which applies to the realization of a naturalistic human finger motion. The model is a function of the finger anthropomorphic data and is derived based on the geometry of a hand shape grasping a virtual cylindrical object. Unlike well-known inverse kinematics models, the inter-joint model is capable of describing the natural rotation configuration of the joints of a long finger independently by a single parameter, the radius of the cylindrical object. A novel inter-finger coordination model, based on the inter-joint model is proposed and experimentally tested. The preliminary results show that the model has the potential to simulate naturalistic human hand grasping motion. The merit of the proposed inter-finger coordination model is in its simplicity when used in hand-exoskeleton design assessment and naturalistic hand trajectory planning applications, among others. We would like to note that this paper is the first step towards exploring future simplified combined design-control strategies for the development and assessment of mechanical limbs for naturalistic movement.
Designing Deep Transfer Networks for Bearing Fault Diagnosis With Heterogeneous Data Fusion
Authors: Su Yunsheng , Wang Zequn 
Published: November 2020

Abstract
Accurate fault defection of bearing is critical in condition-based maintenance to improve system reliability and reduce operational cost. This paper introduces a deep transfer learning-based approach for bearing fault diagnosis by fusing heterogeneous information from multiple sources. Convolutional neural networks (CNN) are first designed to extract critical features by mapping extremely high-dimensional signals such as vibration and images to a much lower dimensional latent space. By partially retaining the resultant CNN architectures and parameters, it becomes possible to transfer and fuse the knowledge gained from multiple heterogeneous sources to improve the robustness and accuracy of fault diagnosis of bearings. With the prior knowledge, a deep transfer learning (DTL) architecture is designed to incorporate the heterogeneous data and trained to detect bearing faults. To future improve the performance of bearing fault diagnosis, a performance-driven optimization approach is developed to optimize the validation accuracy of bearing diagnosis by successively designing the architectures of the deep transfer networks. The CWRU experimental data is utilized to demonstrate the performance of the proposed approach.
A Set Based Design Method Using Bayesian Active Learning
Authors: Shintani Kohei , Sugai Tomotaka , Ishizaki Keisuke , Knudde Nicolas , Couckuyt Ivo , Dhaene Tom 
Published: November 2020

Abstract
The purpose of this paper is to propose a new Set-based concurrent engineering method using Bayesian active learning and to show an application to a multi-disciplinary design optimization problem. In the early stages of the system design process, it is required to set a target value considering the uncertainty of design conditions. If any change of design condition occurs by an external factor in the later development process, the predefined target value cannot be held, and critical rework can be inevitable. To avoid this issue, it is important in the early design stage to solve not only a single target solution but also feasible design solutions that satisfy all multi-disciplinary requirements. In order to discover the feasible region with limited resources, an efficient sampling strategy using CAE simulation is necessary. In this study, a sampling strategy based on Bayesian active learning is proposed to discover a feasible region of multi-disciplinary constraints concurrently. In the proposed method, Gaussian Process models of the multi-disciplinary constraints are trained. Based on posterior distributions of trained Gaussian Processes, new acquisition function by combining two different types of acquisition functions, Probability of Feasibility and Entropy Search is proposed and maximized to generate new sampling points to improve the prediction accuracy of feasible region effectively. To show the effectiveness of the proposed Set-based concurrent engineering method to a multi-disciplinary design problem, a suspension design problem is demonstrated.
Attention Routing: Track-Assignment Detailed Routing Using Attention-Based Reinforcement Learning
Authors: Liao Haiguang , Dong Qingyi , Dong Xuliang , Zhang Wentai , Zhang Wangyang , Qi Weiyi , Fallon Elias , Kara Levent Burak 
Published: November 2020

Abstract
In the physical design of integrated circuits, global and detailed routing are critical stages involving the determination of the interconnected paths of each net on a circuit while satisfying the design constraints. Existing actual routers as well as routability predictors either have to resort to expensive approaches that lead to high computational times, or use heuristics that do not generalize well. Even though new, learning-based routing methods have been proposed to address this need, requirements on labelled data and difficulties in addressing complex design rule constraints have limited their adoption in advanced technology node physical design problems. In this work, we propose a new router — attention router, which is the first attempt to solve the track-assignment detailed routing problem by applying reinforcement learning. Complex design rule constraints are encoded into the routing algorithm and an attention-model-based REINFORCE algorithm is applied to solve the most critical step in routing: sequencing device pairs to be routed. The attention router and its baseline genetic router are applied to solve different commercial advanced technologies analog circuits problem sets. The attention router demonstrates generalization ability to unseen problems and is also able to achieve more than 100× acceleration over the genetic router without severely compromising the routing solution quality. Increasing the number of training problems greatly improves the performance of attention router. We also discover a similarity between the attention router and the baseline genetic router in terms of positive correlations in cost and routing patterns, which demonstrate the attention router’s ability to be utilized not only as a detailed router but also as a predictor for routability and congestion.
Risk-Averse Optimization for Resilience Enhancement Under Uncertainty
Authors: Wu Jiaxin , Wang Pingfeng 
Published: November 2020

Abstract
With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable towards external disturbances. Hence, managing potential disruptive events during design, operating, and recovery phase of an engineered system therefore improving the system’s resilience is an important yet challenging task. In order to ensure system resilience after the occurrence of failure events, this study proposes a mixed integer linear programming (MILP) based restoration framework using heterogenous dispatchable agents. Scenario based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from the nature. Moreover, different from conventional SO using deterministic equivalent formulations, additional risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves with a large-scale MILP problem and thus an adequate decompaction technique, i.e., modified Langragian Relaxation, is also proposed in order to achieve tractable time complexity. Case study results based on the IEEE 37-buses test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.
Hybrid Soft-Rigid Deployable Structure Inspired by Thick-Panel Origami
Authors: Liu Chenying , Maiolino Perla , Yang Yunfang , You Zhong 
Published: November 2020

Abstract
Recently, the techniques of origami have become the subject of scientific research. Such methods of folding plates are suitable for practical engineering applications. This paper proposes a novel structure, inspired by thick-panel origami, with hybrid rigid bodies and flexible hinges. Able to be expanded, flipped, and rotated, the waterbomb origami pattern has been chosen to produce a large number of configurations. The mechanism and motion analysis of a single unit and its basic assembly are conducted theoretically and also simulated. An additive fabrication method based on 3D printing makes it a one-step process to achieve a balance between rigidity and flexibility in the structure. Different configurations are demonstrated in three assemblies that exhibit good transformability, reconfigurability, and scalability. With the expansion/packaging ratio ranging from 0.11 to 7.2 in a modular unit, a mechanical metamaterial of negative Poisson’s ratio can be obtained at any spatial size. In addition, our design’s potential for robotic applications is also validated by an adaptable gripper with tendon-driven systems.
Designing Excitation Maneuvers With Maximal Parameter Sensitivity for an X-by-Wire Autonomous Tricycle
Authors: Chen Yi-Ping , Chan Kuei-Yuan 
Published: November 2020

Abstract
Validation in vehicle engineering identifies and quantifies the differences between simulation models and experiment data. In this work we consider these differencesthe lack of ability to model uncertainties and to identify unknown parameters values, especially for coupled complex systems such as vehicles. Effects of unknown model parameters vary under different maneuvers and the ability to excite a source of uncertainty is the focus of this study. We propose an optimization method to generate a proper maneuver that maximize the sensitivity of uncertain parameters based on global sensitivity analysis (GSA). Sensitivities with respect to individual uncertain parameters and those that consider coupled effects are all included. We utilize Kriging-based metamodels to improve the efficiency of the GSA problems with computationally expensive simulations. The optimal design of excitation maneuvers to create the most sensitive performances can then be obtained. The applicability and the accuracy of the proposed method are assessed via a math model and a practical application on a x-by-wire autonomous tricycle. Results show that our proposed method can assist in providing a suitable maneuver as an alternative validation to uncertain parameters in a vehicle system.
Analysis of Extrusion Parameters for the Fused Deposition Modeling Process
Authors: Yu Wenqiang , Nie Zhengwei , Lin Yuyi , Jiang Huakai 
Published: November 2020

Abstract
Fused deposition modeling (FDM) is an additive manufacturing technology commonly used for three-dimensional printing. The screw rod is an important part of the extrusion nozzle in the FDM manufacturing. The technical parameters of the screw rod, such as the screw pitch, screw angle, groove width, and length-diameter ratio (or aspect ratio), etc., directly determine the speed of extrusion and the precision of modeling. To improve the printing speed and quality of FDM models, this work analyzed the influence of extrusion screw’s parameters on the extrusion flow rate and proposed the best parameters for the screw rod of the FDM process. The results of this work have a crucial significance on the modeling speed and accuracy of the FDM.
Task Taxonomy for Autonomous Unmanned Aerial Manipulator: A Review
Authors: Coulombe Charles , Gamache Jean-François , Barron Olivier , Descôteaux Gabriel , Saussié David , Achiche Sofiane 
Published: November 2020

Abstract
The development of unmanned aerial manipulators (UAMs) allows a novel class of flying robots to carry out a wide variety of tasks in difficult environments due to their versatility and autonomy. However, the different tasks that can be carried out might call for different control strategies. To this end, one needs to categorize the possible tasks accomplishable by UAMs. This paper proposes a novel taxonomy, which is the result of a video information acquisition methodology combined with a review of research works in the literature. The different elements of the taxonomy are separated using a higher level of abstraction in a way that the general description of the tasks are considered and not its operational details. To illustrate the fact that algorithms must adapt to different tasks, a description of the usual UAM architecture is carried out. Four categories of criteria are used in the taxonomy to differentiate all possible tasks. These categories are the interaction type, the actual task definition, the environment condition and the time sensitivity of the task. This taxonomy forms the basis for possible machine-learning-based task classifiers that could be used in autonomous UAMs control and mission planning. Multiple tasks defined in the taxonomy can be combined to accomplish complex missions.
Optimal Trajectory Generation Using an Improved Probabilistic Road Map Algorithm for Autonomous Driving
Authors: Feraco Stefano , Bonfitto Angelo , Khan Irfan , Amati Nicola , Tonoli Andrea 
Published: November 2020

Abstract
This paper presents a technique based on the probabilistic road map algorithm for trajectory planning in autonomous driving. The objective is to provide an algorithm allowing to compute the trajectory of the vehicle by reducing the distance traveled and minimizing the lateral deviation and relative yaw angle of the vehicle with respect to the reference trajectory, while maximizing its longitudinal speed. The vehicle is considered as a 3 Degree-of-Freedom bicycle model and a Model Predictive Control algorithm is implemented to control the lateral and longitudinal dynamics. Both the control and trajectory generation algorithms take the road lane boundaries as the only input from the surrounding environment exploiting a simulated camera. The performance of the technique is compared with the case in which the reference trajectory is the central line between the lane boundaries. The proposed algorithm is validated in a simulated driving scenario.
On Supporting the Learning of Biomechanics Using Multidisciplinary Physical Prototyping
Authors: Hernandez Sebastian , Achiche Sofiane , Spooner Daniel , Vadean Aurelian , Raison Maxime 
Published: November 2020

Abstract
Over the last decades, the use of multibody dynamics in biomechanics research has grown considerably and holds significant promises for the health and biomedical industries. Nowadays, it allows estimating internal data of the body that would be impractical or impossible to obtain experimentally, e.g. individual muscle forces. Also, multibody dynamics simulation allows one to constrain virtually any apparatus to the musculoskeletal system, helping to understand and improve the patient’s dynamic interactions with the device. The modeling and validation of human multibody models remain a tedious task to achieve for the research community and can vary significantly depending on the applications. Despite the advantages offered by the multibody modeling of the human body, its introduction in the biomedical engineering curriculum is not widespread. The present paper aims to evaluate the feasibility and the interest of introducing multibody modeling into multidisciplinary, real-world projects using 3D printed prototypes to add an experimental understanding of the difficulties and validation of the human body modeling. The proposed methodology is based on a literature review of the multibody dynamics teaching methods used in mechanical engineering, followed by a first pilot project and feedback from students and professors of the community through interviews. Finally, a project is proposed, using physical prototyping to support the learning.
A Model Predictive Control-Based Energy Management Strategy Considering Electric Vehicle Battery Thermal and Cabin Climate Control
Authors: Liu Yuanzhi , Zhang Jie 
Published: November 2020

Abstract
The energy management strategy plays a critical role in scheduling the operations and enhancing the overall efficiency for electric vehicles. This paper proposes an effective model predictive control-based (MPC) energy management strategy to simultaneously control the battery thermal management system (BTMS) and the cabin air conditioning (AC) system for electric vehicles (EVs). We aim to improve the overall energy efficiency, while retaining soft constraints from both BTMS and AC systems. It is implemented by optimizing the operation and discharging schedule to avoid peak load and by directly utilizing the regenerative power instead of recharging. Compared to the systematic performance without any control coordination between BTMS and AC, results reveal that there are a 4.3% reduction for the recharging energy, and a 6.5% improvement for the overall energy consumption that gained from the MPC-based energy management strategy. Overall the MPC-based energy management is a promising solution to enhance the efficiency for electric vehicles.
Lattice Structure Optimization With Orientation-Dependent Material Properties
Authors: Sharpe Conner , Seepersad Carolyn Conner 
Published: November 2020

Abstract
Advances in additive manufacturing techniques have enabled the production of parts with complex internal geometries. However, the layer-based nature of additive processes often results in mechanical properties that vary based on the orientation of the feature relative to the build plane. Lattice structures have been a popular design application for additive manufacturing due to their potential uses in lightweight structural applications. Many recent works have explored the modeling, design, and fabrication challenges that arise in the multiscale setting of lattice structures. However, there remains a significant challenge in bridging the simplified computational models used in the design process and the more complex properties actually realized in fabrication. This work develops a design approach that captures orientation-dependent material properties that have been observed in metal AM processes while remaining suitable for use in an iterative design process. Exemplar problems are utilized to investigate the potential design changes and performance improvements that can be attained by taking the directional dependence of the manufacturing process into account in the design of lattice structures.
Lightweight Design of a Multi-Material Suspension Lower Control Arm
Authors: Messana Alessandro , Sisca Lorenzo , Ferraris Alessandro , Airale Andrea Giancarlo , Carello Massimiliana 
Published: November 2020

Abstract
This paper is focused on the design, analysis and testing of a multi-material (carbon fibre and steel) Lower Control Arm (LCA) of a McPherson suspension for a C segment vehicle. Therefore, starting from the existing component (made of steel), the LCA mass has been reduced by using a hybrid technology, diminishing the steel thickness and adding a carbon fiber tailored cover without compromising the mechanical performance in terms of stiffness and stress distribution. In so doing, it has been possible to evaluate the potential and the capabilities of the hybridization without re-designing the component totally. In particular, it has been developed a specific methodology that combines both virtual and experimental procedures to face the hybridization challenges of mechanical coupling, safety and lightweight. For these reasons, the multi-material lower control arm represents a noticeable case study in which this methodology has been applied, correlated and validated.
A LIDAR-Based Clustering Technique for Obstacles and Lane Boundaries Detection in Assisted and Autonomous Driving
Authors: Feraco Stefano , Bonfitto Angelo , Amati Nicola , Tonoli Andrea 
Published: November 2020

Abstract
This paper presents a clustering technique for the detection of the obstacles and lane boundaries on a road. The algorithm consists of two nested clustering stages. The first stage is based on hierarchical clustering, and the second on k-means clustering. The method exploits a preliminary ground-plane filtering algorithm to process the raw LIDAR point cloud, that is based on the semantic segmentation of point clouds. The clustering algorithm estimates the position of the obstacles that define the race track. Once the race track is sensed, the lane boundaries are detected. The method is validated experimentally on a four-wheel drive electric vehicle participating in the Formula SAE events. The validation environment is structured with traffic cones to define the race track.
Optimizing an Algorithm for Data Mining a Design Repository to Automate Functional Modeling
Authors: Mikes Alex , Edmonds Katherine , Stone Robert B. , DuPont Bryony 
Published: November 2020

Abstract
The purpose of this research is to find the optimum values for threshold variables used in a data mining and prediction algorithm. We also minimize and stratify a training set to find the optimum size based on how well it represents the whole dataset. Our specific focus is automating functional models, but the method can be applied to any dataset with a similar structure. We iterate through different values for two of the threshold variables in this process and cross-validate to calculate the average accuracy and find the optimum values for each variable. We optimize the training set by reducing the size by 78% and stratifying the data, whereby we achieve an accuracy that is 96% as good as the whole training set and takes 50% less time. These optimum values can be used to better predict the functions and flows of any future product based on its constituent components, which can be used to generate a complete functional model.