bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implications of Competitor Representation on Optimal Design
Authors: Yip Arthur H. C. , Michalek Jeremy J. , Whitefoot Kate S. 
Published: November 2019

Abstract
We investigate the effect of competitor product representation on optimal design results in profit-maximization studies. Specifically, we study the implications of replacing a large set of product alternatives available in the marketplace with a reduced set of selected competitors or with composite alternatives, as is common in the literature. We derive first-order optimality conditions and show that optimal design (but not price) is independent of competitors under the logit and nested logit models (where preference coefficients are homogeneous), but optimal design results may depend on competitor representation in latent class and mixed logit models (where preference coefficients are heterogeneous). In a case study of automotive powertrain design using mixed logit demand, we find some change in the optimal acceleration performance value when competitors are modeled using a small set of alternatives rather than the larger set. The magnitude of this change depends on the specific form and parameters of the cost and demand functions assumed, ranging from 0% to 3% in our case study. We find that the magnitude of the change in optimal design variables induced by competitor representation in our case study increases with the heterogeneity of preference coefficients across consumers and changes with the curvature of the cost function.
Effects of Surface Condition on Traction of Two-Wheeled Inverted-Pendulum Transporters
Authors: Castro Arnoldo , Adams Christopher , Singhose William 
Published: November 2019

Abstract
Self-balancing transporters apply wheel torque in order to accelerate, turn, and regain balance. However, these control efforts require sufficient traction between the wheels and the ground so that appropriate wheel-ground forces are generated. When this is not the case, the stability is compromised and machine behavior can be unexpected and destabilizing. Experiments were performed to quantify the reduction in wheel-ground friction that can occur in common real-life conditions, such as on wet surfaces or surfaces with debris. A simulation has been developed to show that common transporter motions that are safe on dry surfaces lead to unstable or unpredictable behavior when the available wheel-ground friction is reduced by amounts similar to those seen in the experiments.
Periodic-Doubling Bifurcation of a Circuit With a Fractional-Order Memristor
Authors: Yu Yajuan , Chen Yangquan 
Published: November 2019

Abstract
A new fractional-order current-controlled memristor is proposed by the fact of the memory loss. Excited by sinusoidal current, the generalized hysteresis loops of the new fractional-order memristor are no longer symmetrical to the origin and the time to reach the steady state is longer than the integer-order memristor’s. The dynamical behaviors of a new fractional-order memristive circuit system whose state variables have different derivation orders are investigated by theoretical analyses and simulated numerically. It is shown that the new fractional-order memristive circuit system goes into chaos by period-doubling bifurcation; the periodic windows are induced by the discontinuous change of derivative order between variables.
Effect of Structural Nonlinearity on a Piecewise Linear Aeroelastic System
Authors: Lelkes János , Kalmár-Nagy Tamás 
Published: November 2019

Abstract
Aeroelasticity is the study of the interaction of aerodynamic, elastic and inertia forces. When flexible structures, such as an airfoil, undergo wind excitation, divergence or flutter instability may arise. We study the dynamics of a two-degree-of-freedom (pitch and plunge) aeroelastic system with cubic structural nonlinearities. The aerodynamic forces are modeled as a piecewise linear function of the effective angle of attack. Stability and bifurcations of equilibria are analyzed. The effect of the structural nonlinearity is investigated. We find border collision, rapid, Hopf, saddle-node and pitchfork bifurcations. Bifurcation diagrams of the system were calculated utilizing MatCont and Mathematica.
Maximizing the Power Extraction From Train Suspension Energy Harvesting System
Authors: Costanzo Luigi , Vitelli Massimo , Pan Yu , Zuo Lei 
Published: November 2019

Abstract
In this paper, an equivalent electric model for railway vehicle suspension harvester is proposed. With reference to the maximization of the extracted power, the open issues for such kinds of systems are reviewed. In particular, it is evidenced that the use of passive diode bridge rectifiers unavoidably leads to a reduction of power with respect to both the resistive load case and the theoretical optimal load case. Therefore, suitable average power maximization techniques and power electronics architectures are needed. In this paper, two techniques are proposed. The first technique is based on the adoption of additional passive components and, in the considered test cases, it allows the increase of the power output up to 45%. The second technique is based on the adoption of a properly controlled active power electronic interface and it is much more efficient since, in the considered test cases, it allows the increase of the power output up to 135%.
Bio-Inspired Human Network Design: A Multi-Currency Robustness Metric Inspired by Ecological Network Analysis
Authors: Chatterjee Abheek , Layton Astrid 
Published: November 2019

Abstract
The Ecological Network Analysis (ENA) metric ecological robustness quantifies the unique balance that biological food webs have between their pathway efficiency and redundancy, enabling them to maximize their robustness to system disturbances. This robustness is a potentially desirable quality for human systems to mimic. Modeling the interactions between actors in human networks as predator-prey type exchanges (of a medium or currency rather than caloric exchanges) enables an ENA analysis. ENA has been shown to be a useful tool in improving the design of human networks because it allows the characteristics of biological networks to be mimicked. The application of these metrics is, however, limited to networks with only one flow type. Human networks are composed of many different types of flow interactions and thus a biologically-inspired indicator of total system robustness must take into account all of these interactions. This work further develops the traditional ENA ecological robustness metric to accommodate various flows between actors in multi-currency human networks. Two novel methods for quantifying multi-currency flow network robustness are introduced. The mathematical formulation for these new metrics is presented. The water network for the Kalundborg Eco-Industrial Park (EIP) is used as a case study to determine the benefits of the proposed robustness metrics. The results obtained using the single-currency robustness and the two multi-currency robustness metrics are compared using the case study. Based on the analysis of the results obtained at the system level, as well as at the sub-levels, both multi-currency metrics showed the ability to predict systems characteristics for the multi-currency Kalundborg EIP. While both of these are promising, more research regarding these metrics is needed in order to develop an elegant and comprehensive total system robustness metric.
Online Identification of Uncertain Fractional-Order Nonlinear Systems Using a Reinforced Differential Evolution Optimizer
Authors: Wei Jiamin , Yu Yongguang , Chen YangQuan 
Published: November 2019

Abstract
Parameter identification as known as a significant issue is investigated in this paper. The research focus on online identifying unknown parameters of uncertain fractional-order chaotic and hyperchaotic systems, which shows great potential in recent applications. Up to now, most of the existing online identification methods only focus on integer-order systems, thus, it’s necessary to expand these fundamental results to uncertain fractional-order nonlinear dynamic systems and adopt an effective optimizer to deal with the model uncertainties. Motivated by this consideration, this research introduces an efficient optimizer to offline and online parameter identification of the fractional-order chaotic and hyperchaotic systems through non-Lyapunov way. For problem formulation, a multi-dimensional optimization problem is converted into from the problem of parameter identification, where both systematic parameters and fractional derivative orders are considered as independent unknown parameters to be estimated. The experimental results illustrate that SHADE is significantly superior to the other compared approaches. In this case, online identification is conducted via SHADE, the simulation results further indicate that success-history based adaptive differential evolution (SHADE) algorithm is capable of detecting and determining the variations of parameters in uncertain fractional-order chaotic and hyperchaotic systems, and also is supposed to be a successful and potentially promising method for handling the online identification problems with high efficiency and effectiveness.
Designing Self-Organizing Systems With Deep Multi-Agent Reinforcement Learning
Authors: Ji Hao , Jin Yan 
Published: November 2019

Abstract
Self-organizing systems (SOS) are able to perform complex tasks in unforeseen situations with adaptability. Previous work has introduced field-based approaches and rule-based social structuring for individual agents to not only comprehend the task situations but also take advantage of the social rule-based agent relations in order to accomplish their overall tasks without a centralized controller. Although the task fields and social rules can be predefined for relatively simple task situations, when the task complexity increases and task environment changes, having a priori knowledge about these fields and the rules may not be feasible. In this paper, we propose a multi-agent reinforcement learning based model as a design approach to solving the rule generation problem with complex SOS tasks. A deep multi-agent reinforcement learning algorithm was devised as a mechanism to train SOS agents for acquisition of the task field and social rule knowledge, and the scalability property of this learning approach was investigated with respect to the changing team sizes and environmental noises. Through a set of simulation studies on a box-pushing problem, the results have shown that the SOS design based on deep multi-agent reinforcement learning can be generalizable with different individual settings when the training starts with larger number of agents, but if a SOS is trained with smaller team sizes, the learned neural network does not scale up to larger teams. Design of SOS with a deep reinforcement learning model should keep this in mind and training should be carried out with larger team sizes.
A Novel Variable Stiffness Compliant Robotic Gripper Based on Layer Jamming
Authors: Gao Yuan , Huang Xiguang , Mann Ishan Singh , Su Hai-Jun 
Published: November 2019

Abstract
In this paper, we present a novel compliant robotic gripper with three variable stiffness fingers. While the shape morphing of the grippers is cable-driven, the stiffness variation is enabled by layer jamming. The inherent flexibility makes compliant grippers suitable for tasks such as grasping soft and irregular objects. However, their relatively low load capacity due to low structural stiffness limits their applications. Variable stiffness robotic grippers have the potential to address this challenge as their stiffness can be tuned on demand based on the needs of tasks. Layer jamming is an emerging method for variable stiffness due to its advantages of light weight, simple and quick actuation. In our design, the compliant backbone of the fingers is made of 3d printed PLA material. Four thin film materials are attached to each side of the skeleton. The working process of the robotic gripper follows two basic steps. First, the compliant skeleton is bent to a desired shape by actuating a tension cable via a servo motor. Second, upon application of a negative pressure by a vacuum pump, the finger is stiffened up owing to the increasing of the friction between contact surfaces of layers preventing their relative movement.
Since the structural stiffness of the fingers is increased, their load capacity will be increased proportionally. When the air pressure is sufficiently large, the morphed shape can even be locked (no slipping). Test for stiffness of individual finger and load capacity of the robotic gripper are conducted to validate capability of the design. The results showed a 69-fold increase in stiffness of individual finger and a 30-fold increase in gripper’s load capacity.
Reconstruction of Gap-Free Intersections for Trimmed NURBS Surfaces
Authors: Urick Benjamin , Crawford Richard H. , Hughes Thomas J. R. , Cohen Elaine , Riesenfeld Richard F. 
Published: November 2019

Abstract
The modern engineering technologies of Computer-Aided Design (CAD), Computer-Aided Engineering (CAE) and Computer-Aided Manufacturing (CAM) are ubiquitous in engineering design. They are focused on creating, analyzing, and fabricating objects represented as geometric models. Historically, these technologies developed independently, such that their geometric representations are customized to the needs of the technology. As a result, combined use of these technologies has led to differences in data structures, file formats, software constraints, and user knowledge and practice, requiring translation of representations between systems to support interoperability. Complicating this situation is the approximate nature of modeling operations in CAD systems, which can result in gaps at the boundary curves between mating trimmed surfaces of a model. The research presented here is aimed at removing the gaps between trimmed surfaces, resulting in a “watertight” model that is suitable for use directly by downstream applications. A three-step algorithm is presented that includes analysis of the parametric space of the trimming curves, reparameterization to create a global parameter space, and reconstruction of the intersecting surfaces to ensure continuity at the trimming curve.
Data-Driven Dynamic Network Modeling for Analyzing the Evolution of Product Competitions
Authors: Xie Jian , Bi Youyi , Sha Zhenghui , Wang Mingxian , Fu Yan , Contractor Noshir , Gong Lin , Chen Wei 
Published: November 2019

Abstract
Understanding the impact of engineering design on product competitions is imperative for product designers to better address customer needs and develop more competitive products. In this paper, we propose a dynamic network based approach to modeling and analyzing the evolution of product competitions using multi-year product survey data. We adopt Separate Temporal Exponential Random Graph Model (STERGM) as the statistical inference framework because it considers the evolution of dynamic networks as two separate processes: formation and dissolution. This treatment allows designers to investigate why two products enter into competition and why a competitive relationship preserves or dissolves over time. In an open market, the available products to customers are continuously changing over the time, posing challenges for conventional modeling methods concerning fixed product input. Consequently, we propose to leverage “structural zeros” in STERGM to tackle the problem of modeling varying product competitors as nodes in dynamic networks. We use China’s automotive market as a case study to illustrate the implementation of the proposed approach and its benefits compared to the static network modeling approach based on Exponential Random Graph Model (ERGM). The results show that our approach identifies the driving factors associated with product attributes and current market competition structures for the change of competition in both formation and dissolution processes. The insights gained from this paper can help designers better interpret the temporal changes of product competition relations and make product design decisions with the aid of dynamic network-based models.
Direct Energy Deposition 3D Printing of Thermoelectric Materials: Simulation and Experiments
Authors: Sun Kan , Wu Yongjia , Qi Huan , Wu Zhiwei , Zuo Lei 
Published: November 2019

Abstract
The thermoelectric material can directly convert energy between thermal and electric without involving any moving parts. Due to the limitation in geometry and efficiency of fabricating the thermoelectric modules in conventional methods, additive manufacturing is now becoming a promising solution. Direct Energy Deposition (DED) is superior for its large dimension limitation, fast processing speed, and independence from any powder bed. A physical model of DED fabrication with magnesium silicide (Mg2Si) powder was developed, and validation experiments were conducted. In the model and the experiments, a set of parameters and settings, including laser power, scanning speed, material feeding rate, and so on, were applied to investigate their influences on the products and to validate the simulation. The result of this research can give a reference for the further attempt of DED thermoelectric fabrication.
Factors Leading to Sustainable Social Impact on the Affected Communities of Engineering Service Learning Projects
Authors: Armstrong Andrew G. , Mattson Christopher A. , Lewis Randy S. 
Published: November 2019

Abstract
University engineering programs across the USA engage in service learning projects. These projects involve student teams designing and implementing products or solutions for communities in need, often in developing nations. There has been much research done relating to pedagogy and the impact of these programs on student learning. However, less research has been done on measuring the impact of these programs on the affected communities. This paper examines factors that practitioners believe are related to successfully delivering a desirable and transferable solution to affected communities. The authors identified 46 distinct factors from the literature that implicitly or explicitly are suggested to contribute to successful project outcomes. Formed as postulates in this paper, these 46 factors have been separated into 5 categories to assist understanding and implementing these factors into service learning programs. Lastly, different methods of analyzing and measuring project success and impact are discussed. Future methods for proving the viability of the 46 postulates are discussed as well.
A Methodology for Determining Static Mode Shapes of a Compliant Mechanism Using the Pseudo-Rigid-Body Model (PRBM) Concept and the Degrees-of-Freedom Analysis
Authors: Bapat Sushrut G. , Bagivalu Prasanna Pratheek , Midha Ashok 
Published: November 2019

Abstract
Traditionally, the deflected configuration of compliant segments is determined through rigorous mathematical analysis using Newtonian mechanics. Application of these principles in evaluating the deformed configuration of compliant mechanisms, containing a variety of segment types, becomes cumbersome. This paper introduces a methodology to determine the expected deflected configuration(s) of a compliant mechanism, for a given set of load and/or displacement boundary conditions. The method utilizes the principle of minimum total potential energy, in conjunction with the degrees-of-freedom analysis and the pseudo-rigid-body model concept. The static mode shape(s) of compliant segments are integrated in identifying the possible functional configuration(s) of a given compliant mechanism’s structural configuration. The methodology, in turn, also facilitates the in situ determination of the deformed configuration of the constituent compliant segments. It thus assists in the identification of an appropriate pseudo-rigid-body model for design and analysis of a compliant mechanism.
Development of Novel Tooth-Matching Linear Piezoelectric Actuator
Authors: Huang Po-Lin , Chang Jen-Yuan (James) 
Published: November 2019

Abstract
This paper proposes a new concept of piezoelectric actuator. It is different from ordinary piezoelectric actuators which are actuated by friction, and wear becomes a major problem in long-term use. The main purpose of this research is to drive the motor without friction. Hence, the actuator driven by resonance force is proposed here. The foundation of the actuator is based on piezoelectric material which possess the inverse piezoelectric effect itself. The axial deformation of piezoelectric material is worked as excitation here, which makes the stator subjected to mutative equivalent force along the time. At the same time, the input frequency of sinusoidal voltage is controlled and applied to the stators which makes the stators resonated and in contact with motor for pushing the motor forward. In addition to proposing the preliminary design concept of linear piezoelectric actuator, the dynamic model of the piezoelectric actuator system is firstly studied by Hamilton’s Principle. Then, the finite element method is used to calculate the modal analysis of stator. Finally, the prototype is fabricated and experiment platform is established. The vibration response of stators is measured by laser Doppler vibration measuring system, which is able to verify reasonableness of the constructed finite element model and feasibility of linear piezoelectric actuator.
Optimization of Phase-Locked-Loop Based Correction Method for Linear Magnetic Encoding Signals
Authors: Shih Yi-Ting , Chang Jen-Yuan 
Published: November 2019

Abstract
A magnetic encoder is currently used in precision positioning system because it has many advantageous characteristics. The signals generated from magnetic sensor are always disturbed by noise, that is, these signals are not ideal. For the purpose of precise positioning and high resolution in many industrial control systems, this paper proposes an approach, which contains two parts: The first part is “eliminate the harmonic in the signals” by applying an adaptive notch filter. The second part is “the phase tracking and signal reconstruction” by applying software phase-locked loop (SPLL). Through this scheme of encoder signal processing, it could eliminate the noise and improve the accuracy of the system. The simulation results are provided to demonstrate the promotion of the resolution in positioning system.
Systematic Experimental Validation of High-Order Spectral Method for Deterministic Wave Prediction
Authors: Klein Marco , Dudek Matthias , Clauss Günther F. , Hoffmann Norbert , Behrendt Jasper , Ehlers Sören 
Published: November 2019

Abstract
The applicability of the High-Order Spectral Method (HOSM) as a very fast non-linear method for deterministic short-term wave prediction is discussed within this paper. The focus lies on the systematic experimental validation of the HOSM in order to identify and evaluate possible areas of application as well as limitations of use. For this purpose, irregular sea states with varying parameters such as wave steepness and underlying wave spectrum are addressed by numerical simulations and model tests in the controlled environment of a seakeeping basin. In addition, the influence of the propagation distance is discussed. For the evaluation of the accuracy of the HOSM prediction, the surface similarity parameter (SSP) is utilized, allowing a quantitative validation of the results. The results obtained are compared to linear wave prediction to discuss the pros and cons of a non-linear deterministic short-term wave prediction. In conclusion, this paper shows that the non-linear deterministic wave prediction based on HOSM leads to a substantial improvement of the prediction quality for moderate and steep irregular wave trains in terms of individual waves and prediction distance.