bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploiting Multi-Modal Sensing for Increased Detection Fidelity of Pipeline Leakage
Authors: Lanjile Apeksha , Younis Mohamed , Kim Seung-Jun , Lee Soobum 
Published: November 2019

Abstract
Long distance transportation of various fluid commodities like water, oil, natural gas liquids is achieved through a distribution network of pipelines. Many of these pipelines operates unattended in harsh environments. Therefore, pipes are often susceptible to corrosion, leakage, cracking and third party damage leading to economic and resource infrastructure losses. Thus, early detection and prevention of any further losses is very important. Although many pipeline monitoring techniques exist, the majority of them are based on single sensing modality like acoustic, accelerometer, ultrasound, pressure. This makes the existing techniques unreliable, sensitive to noise and costly. This paper describes a methodology to combine accelerometer and acoustic sensors to increase the detection fidelity of pipeline leakages. The sensors are mounted on the pipe wall at multiple locations. Vibrational and acoustic characteristics obtained from these sensors are fused together through wavelet analysis and classified using kernel SVM and Logistic Regression in order to detect small bursts and leaks in the pipe. The simulation results have confirmed the effectiveness of proposed methodology yielding 90% leak detection accuracy.
Programmable Kirigami: Cutting and Folding in Science, Technology and Architecture
Authors: Liu Jingyang , Chuang Grace , Sang Hun Chun , Sabin Jenny E. 
Published: November 2019

Abstract
This paper investigates the potential of kirigami-folding with the addition of strategically placed cuts at multiple scales through both computational design and physical prototyping. The study develops a novel method and workflow for generating two-dimensional (2D) kirigami patterns developed from doubly curved three-dimensional (3D) surfaces (Inverse process). Corresponding simulations of the kirigami folding motion from 2D pattern to 3D goal shape are presented (Forward process). The workflow is based on a reciprocal feedback loop including computational design, finite element analysis, dynamic simulation and physical prototyping. Extended from previous research on kirigami geometry, this paper incorporates material properties into the folding process and successfully develops active kirigami models from the DNA scale to human scale. The results presented in this paper provide an attractive method for kirigami design and fabrication with a wide range of scales and applications.
Meshing Theory of ZC1 Worm Drive
Authors: Zhao Yaping 
Published: November 2019

Abstract
The toroidal enveloping cylindrical worm drive, also called the ZC1 worm drive, is grinded by the toroidal grinding wheel. In this paper, the meshing theory for this worm drive is systematically established. According to this meshing theory, the meshing function, the meshing limit function, the equations of the worm helicoid and the worm gear tooth surface are obtained. A method for computing the normal vector of the instantaneous line of the ZC1 worm pair is proposed. Due to this method, the curvature interference limit function and the meshing quality parameters can be more simply and clearly obtained. Based on above results, the methods of the numerical calculation of the instantaneous lines and the conjugate zone are proposed. The initial values of the nonlinear equation systems, computed the conjugate zone and the contact lines, are detected and solved by the method based on the elimination method and geometric construction. The results of numerical example clearly reflect that the conjugate zone can almost cover the whole tooth surface of the worm gear and the effective working length of the worm cannot nearly exceed the half of its thread length. The values of the induced principle curvature and the sliding angle show that the lubrication performance is poor and the stress level is higher, near the meshing limit line and at the dedendum of the worm gear.
An Incremental Harmonic Balance Method With Two Time Scales for Quasi-Periodic Motion of Nonlinear Systems With Cubic Nonlinearity
Authors: Huang Jianliang , Zhu Weidong , Chen Shuhui 
Published: November 2019

Abstract
A new incremental harmonic balance (IHB) method with two time scales procedure is used to analyze quasi-periodic motion of multiple degrees of freedom systems with cubic nonlinearity. An amplitude increment algorithm is adapted to deal with cases where the two frequencies are unknown a priori, in order to automatically trace frequency response of quasi-periodic motion of the system and accurately calculate all frequency components and their corresponding amplitudes. Results of application of the present IHB method to quasi-periodic free vibration of the nonlinear system are shown and compared with previously published results with Lau method and those from numerical integration. While differences are noted between results predicted by the present IHB method and Lau method, excellent agreement is achieved between results from the present IHB method and numerical integration even in cases of strongly nonlinear vibration.
Computer-Aided Design Ideation Using InnoGPS
Authors: Luo Jianxi , Sarica Serhad , Wood Kristin L. 
Published: November 2019

Abstract
Traditionally, the ideation of design opportunities and new concepts relies on human expertise or intuition and is faced with high uncertainty. Inexperienced or specialized designers often fail to explore ideas broadly and become fixed on specific ideas early in the design process. Recent data-driven design methods provide external design stimuli beyond one’s own knowledge, but their uses in rapid ideation are still limited. Intuitive and directed ideation techniques, such as brainstorming, mind mapping, Design-by-Analogy, SCAMPER, TRIZ and Design Heuristics may empower designers in rapid ideation but are limited in the designer’s own knowledge base. Herein, we harness data-driven design and rapid ideation techniques to introduce a data-driven computer-aided rapid ideation process using the cloud-based InnoGPS system. InnoGPS integrates an empirical network map of all technology domains based on the international patent classification which are connected according to knowledge distance based on patent data, with a few map-based functions to position technologies, explore neighborhoods, and retrieve knowledge, concepts and solutions in the near or far fields for design analogies and syntheses. The functions of InnoGPS fuse design science, network science, data science and interactive visualization and make the design ideation process data-driven, theoretically-grounded, visually-inspiring, and rapid. We demonstrate the procedures of using InnoGPS as a data-driven rapid ideation tool to generate new rolling toy design concepts.
Resource Allocation for System Reliability Analysis Using Accelerated Life Testing
Authors: Moustafa Kassem , Hu Zhen , Mourelatos Zissimos P. , Baseski Igor , Majcher Monica 
Published: November 2019

Abstract
Accelerated life test (ALT) has been widely used to accelerate the product reliability assessment process by testing product at higher than nominal stress conditions. For a system with multiple components, the tests can be performed at component-level or system-level. The data at these two levels require different amount of resources to collect and carry different values of information for system reliability assessment. Even though component-level tests are cheap to perform, they cannot account for the correlations between the failure time distributions of different components. While system-level tests can naturally account for the complicated dependence between component failure time distributions, the required testing efforts are much higher than that of component-level tests. This research proposes a novel resource allocation framework for ALT-based system reliability assessment. A physics-informed load model is first employed to bridge the gap between component-level tests and system-level tests. An optimization framework is then developed to effectively allocate testing resources to different types of tests. The information fusion of component-level and system-level tests allows us to accurately estimate the system reliability with a minimized requirement on the testing resources. Results of one numerical example demonstrate the effectiveness of the proposed framework.
Generative Design of Multi-Material Hierarchical Structures via Concurrent Topology Optimization and Conformal Geometry Method
Authors: Jiang Long , Chen Shikui , Gu Xianfeng David 
Published: November 2019

Abstract
Topology optimization has been proved to be an automatic, efficient and powerful tool for structural designs. In recent years, the focus of structural topology optimization has evolved from mono-scale, single material structural designs to hierarchical multimaterial structural designs. In this research, the multi-material structural design is carried out in a concurrent parametric level set framework so that the structural topologies in the macroscale and the corresponding material properties in mesoscale can be optimized simultaneously. The constructed cardinal basis function (CBF) is utilized to parameterize the level set function. With CBF, the upper and lower bounds of the design variables can be identified explicitly, compared with the trial and error approach when the radial basis function (RBF) is used. In the macroscale, the ‘color’ level set is employed to model the multiple material phases, where different materials are represented using combined level set functions like mixing colors from primary colors. At the end of this optimization, the optimal material properties for different constructing materials will be identified. By using those optimal values as targets, a second structural topology optimization is carried out to determine the exact mesoscale metamaterial structural layout. In both the macroscale and the mesoscale structural topology optimization, an energy functional is utilized to regularize the level set function to be a distance-regularized level set function, where the level set function is maintained as a signed distance function along the design boundary and kept flat elsewhere. The signed distance slopes can ensure a steady and accurate material property interpolation from the level set model to the physical model. The flat surfaces can make it easier for the level set function to penetrate its zero level to create new holes. After obtaining both the macroscale structural layouts and the mesoscale metamaterial layouts, the hierarchical multimaterial structure is finalized via a local-shape-preserving conformal mapping to preserve the designed material properties. Unlike the conventional conformal mapping using the Ricci flow method where only four control points are utilized, in this research, a multi-control-point conformal mapping is utilized to be more flexible and adaptive in handling complex geometries. The conformally mapped multi-material hierarchical structure models can be directly used for additive manufacturing, concluding the entire process of designing, mapping, and manufacturing.
Build Your Firm With Strangers?: Longitudinal Studies on Open Source Hardware Firm Growth
Authors: Li Zhuoxuan , Seering Warren 
Published: November 2019

Abstract
The success of many open source software projects revealed the power of voluntary collaborative production of large/complex software systems. The research community is therefore curious about the viability of open source projects in other areas. In around 2000, open source practices started to take place in the commercial hardware realm, and so far, the phenomenon has not been fully explored. Using grounded theory, the authors studied 31 firms for an average of 2.3 years, discovering a 4-phase growth pattern of open source hardware firms, including starting the firm, identifying core competencies, business model improvement and business maturation. The firms behaviors in each stage are reported, as well as the evolution of community demography, behaviors and impact in different growth phases.
A Differential Game Approach to Dynamic Competitive Decisions Toward Human-Computer Collaboration
Authors: Bayrak Alparslan Emrah , McComb Christopher , Cagan Jonathan , Kotovsky Kenneth 
Published: November 2019

Abstract
Partnership between humans and computers has a significant potential to extend the ability of humans to address complex design problems. This paper presents a decision-making process for computers to effectively collaborate with humans in the solution of complex problems under dynamic competition. In the proposed process, the computers learn strategies and objectives from prior experimental data and provide strategy suggestions to human collaborators. The study integrates clustering and sequential learning methods from machine learning with a differential game formulation based on model predictive control to find dynamic Nash equilibrium solutions to zero-sum games. The application of the proposed approach is demonstrated on the real-time strategy game Starcraft II that offers a dynamic competitive problem comparable in complexity to real-world applications. The results show that the proposed approach can successfully identify a variety of opening strategies in the experimental data for the initial phase of the process. The game-theoretic strategies in the later phases provide useful suggestions for low-performing players but are unnecessarily conservative for high-performing players where there is little opportunity for improvement. These results suggest a need for an assessment of the opponent expertise and a human intuition to judge the appropriateness of the game-theoretic suggestions for further improvement.
Thermomechanical Topology Optimization of Lattice Heat Transfer Structure Including Natural Convection and Design Dependent Heat Source
Authors: Wu Tong , Najmon Joel C. , Tovar Andres 
Published: November 2019

Abstract
Lattice Heat Transfer (LHT) structures provide superior structural support while improving the heat transfer coefficient through their high surface-to-volume ratios. By using current Additive Manufacturing (AM) technologies, LHT with highly complex structures is possible. In this study, the design concept of LHT is further improved by implementing a thermomechanical topology optimization method. With utilization of design-dependent heat source, the method can be applied to generate stiffer LHT structures under mechanical and thermomechanical loads, without decreasing their thermal performance; relative to a design made of a uniform LHT having the same mass fraction. Two numerical examples are presented to illustrate how to use the proposed approach to design LHT sections. The results show that the mechanical performance can be improved more than 50% compared to a uniform LHT with the same mass fraction, without decreasing the thermal performance. The method does not require a fluid mechanics model, thus it is computational effective and particularly suitable for the conceptual design stage. The resulting optimized lattice is made possible by utilizing additive manufacturing technologies.
Modified Model-Free Adaptive Control Method Applied to Vibration Control of an Elastic Crane
Authors: Pham Hoang Anh , Söffker Dirk 
Published: November 2019

Abstract
Model-free adaptive control (MFAC) is a data-driven control approach receiving increased attention in the last years. Different model-free-based control strategies are proposed to design adaptive controllers when mathematical models of the controlled systems should not be used or are not available. Using only measurements (I/O data) from the system, a feedback controller is generated without the need of any structural information about the controlled plant. In this contribution an improved MFAC is discussed for control of unknown multivariable flexible systems. The main improvement in control input calculation is based on the consideration of output tracking errors and its variations. A new updated control input algorithm is developed. The novel idea is firstly applied for controlling vibrations of a MIMO ship-mounted crane. The control efficiency is verified via numerical simulations. The simulation results demonstrate that vibrations of the elastic boom and the payload of the crane can be reduced significantly and better control performance is obtained when using the proposed controller compared to standard model-free adaptive and PI controllers.
A Modular Approach to Level Curve Tracking With Two Nonholonomic Mobile Robots
Authors: Chatterjee Sarthak , Wu Wencen 
Published: November 2019

Abstract
In this paper, we consider the problem of tracking noisy two-dimensional level curves using only the instantaneous measurements of the field, taken by two mobile agents, without the need of estimating the field gradient. To do this, we propose a dual-control-module structure consisting of the formation control and curve tracking modules. The former uses the linear velocity of the agents to generate the angular velocities, which are then used to maintain a constant distance between the two agents. The latter uses the instantaneous field measurements to generate the linear velocities of the two agents to successfully track level curves. The modular approach decouples the problems of formation control and curve tracking, thus allowing the seamless design of the two modules. We show that the proposed dual-module control structure allows fast and accurate tracking of planar level curves.
A Framework of Multi-Fidelity Topology Design and its Application to Optimum Design of Flow Fields in Battery Systems
Authors: Yaji Kentaro , Yamasaki Shintaro , Tsushima Shohji , Fujita Kikuo 
Published: November 2019

Abstract
We propose a novel framework based on multi-fidelity design optimization for indirectly solving computationally hard topology optimization problems. The primary concept of the proposed framework is to divide an original topology optimization problem into two subproblems, i.e., low- and high-fidelity design optimization problems. Hence, artificial design parameters, referred to as seeding parameters, are incorporated into the low-fidelity design optimization problem that is formulated on the basis of a pseudo-topology optimization problem. Meanwhile, the role of high-fidelity design optimization is to obtain a promising initial guess from a dataset comprising topology-optimized design candidates, and subsequently solve a surrogate optimization problem under a restricted design solution space. We apply the proposed framework to a topology optimization problem for the design of flow fields in battery systems, and confirm the efficacy through numerical investigations.
Improving the Method for Kinematic Analysis of Mechanisms That Was Based on Parametric Polynomial System With Gröbner Cover
Authors: Arikawa Keisuke 
Published: November 2019

Abstract
A polynomial system that contains parameters is termed a parametric polynomial system (PPS). We had previously proposed a method of kinematic analysis of mechanisms based on PPS with Gröbner cover, where the parameters are used to express link lengths, displacements of active joints, and so on. Calculating Gröbner cover of PPS that expresses kinematic constraints, and interpreting the segments of the parameter space that are generated by Gröbner cover, it is possible to gain an insight for comprehensively understanding kinematic properties of mechanisms characterized by the parameters. In this study, certain improvements to the method were made to enhance its practical application. The validity check of the segments in the real domain using quantifier elimination provides an automatic reliable check even for a large number of segments. The evaluation of the solution spaces in the segments using primary decomposition facilitates the kinematic interpretation of the complex solution spaces. The active joint selection based on the variable order in Gröbner cover enables the analyses without explicitly specifying active joints. Moreover, the alternative algebraic formulation of kinematic problems based on a homogeneous transformation matrix provides further insight regarding the mechanisms containing zero-length links. The effectiveness of these improvements was verified by the analyses of the configurations of 3RPR mechanism and five-bar linkage.
Model-Based Combustion Duration and Ignition Timing Prediction for Combustion Phasing Control of a Spark-Ignition Engine Using In-Cylinder Pressure Sensors
Authors: Wang Xin , Khameneian Amir , Dice Paul , Chen Bo , Shahbakhti Mahdi , Naber Jeffrey D. , Archer Chad , Qu Qiuping , Glugla Chris , Huberts Garlan 
Published: November 2019

Abstract
Combustion phasing, which can be defined as the crank angle of fifty percent mass fraction burned (CA50), is one of the most important parameters affecting engine efficiency, torque output, and emissions. In homogeneous spark-ignition (SI) engines, ignition timing control algorithms are typically map-based with several multipliers, which requires significant calibration efforts. This work presents a framework of model-based ignition timing prediction using a computationally efficient control-oriented combustion model for the purpose of real-time combustion phasing control. Burn duration from ignition timing to CA50 (ΔθIGN-CA50) on an individual cylinder cycle-by-cycle basis is predicted by the combustion model developed in this work. The model is based on the physics of turbulent flame propagation in SI engines and contains the most important control parameters, including ignition timing, variable valve timing, air-fuel ratio, and engine load mostly affected by combination of the throttle opening position and the previous three parameters. With 64 test points used for model calibration, the developed combustion model is shown to cover wide engine operating conditions, thereby significantly reducing the calibration effort. A Root Mean Square Error (RMSE) of 1.7 Crank Angle Degrees (CAD) and correlation coefficient (R2) of 0.95 illustrates the accuracy of the calibrated model. On-road vehicle testing data is used to evaluate the performance of the developed model-based burn duration and ignition timing algorithm. When comparing the model predicted burn duration and ignition timing with experimental data, 83% of the prediction error falls within ±3 CAD.