bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors Hindering the Implementation of Prototyping in the Early Stages of New Business Development
Authors: Mitomi Keita , Honmmi Masashi , Yasugi Masao , Tanaka Satoshi , Ohnaga Nobuyuki , Shirasaka Seiko 
Published: November 2023

Abstract
In order to commercialize a new technology as a product, t is necessary to decide which customers to target and identify their needs. Furthermore, prototyping is an effective way to identify customer needs. In recent years, there has been an increasing number of studies on prototyping in the private sector, and we are beginning to understand what types of prototyping are effective. However, trying to use prototyping in the private sector, cultural differences, and other aspects can be a disincentive to using prototyping in the private sector, and it does not always work. In other words, prototyping is necessary to develop a business using new technology within a company, but it is not enough to simply implement prototyping; we need to understand what is holding it back. Therefore, this study explores the factors that hinder effective prototyping in the early stages of new business development with new technologies in the private sector. Specifically, we used a grounded theory approach to interview members of new business development projects using new technologies at a leading Japanese manufacturer, as well as the collection and analysis of documents. The results of the study suggested two inhibiting phenomena: 1) prototyping skills depend on the individual, and implementation methods often involve tacit knowledge, and 2) development teams are reluctant to communicate with customers. Then, we presented hypotheses for countermeasures against each of these phenomena.
An Experimental Study of Drag Coefficients of a Quadrotor Airframe
Authors: Morris Daniel , Pretty Christopher 
Published: November 2023

Abstract
This paper investigates the aerodynamic drag coefficients of a standard cross-geometry quadrotor airframe at varying pitch and yaw angles, using a wind tunnel. The quadrotor airframe was mounted in the wind tunnel at fixed pitch and yaw angles, and subjected to a range of wind flow speeds. Drag forces created by the mounting systems used were measured and removed from subsequent drag force measurements. The drag force on the airframe at these speeds and attitudes was measured, and used to identify lumped drag-area coefficients. The variability of these coefficients was observed both with and without unpowered propellers mounted on the airframe. It was found that these parameters increased with increasing angle, and were larger with propellers mounted on the airframe. Measurements were also taken at mixed pitch/yaw orientations to determine whether a relationship exists between these drag-area coefficients and ones taken at individual pitch and yaw angles. Validation was performed using computer vision estimates of projected areas and comparison with drag coefficients of geometric shapes. Fourier series models were fit to the drag-area coefficient trends to create estimator functions. The pitch axis estimator functions had adjusted R2 coefficients of 0.976 and 0.932, while the adjusted R2 coefficients for the yaw axis were 0.733 and 0.508 (without and with propellers respectively). The measured drag-area parameter values and fitted estimator functions provide reference values for quadrotor airframe design, simulation and controller development for model-based controllers.
Mitigating the Effects of Source-Dependent Bias and Noise on Multi-Source Bayesian Optimization: Application to Materials Design
Authors: Zanjani Foumani Zahra , Yousefpour Amin , Shishehbor Mehdi , Bostanabad Ramin 
Published: November 2023

Abstract
Bayesian optimization (BO) is a sequential optimization strategy that is increasingly employed in a wide range of areas including materials design. In real world applications, acquiring high-fidelity (HF) data through physical experiments or HF simulations is the major cost component of BO. To alleviate this bottleneck, multi-fidelity (MF) methods are increasingly used to forgo the sole reliance on the expensive HF data and reduce the sampling costs by querying inexpensive low-fidelity (LF) sources whose data are correlated with HF samples. Existing multi-fidelity BO (MFBO) methods operate under the following two assumptions: (1) Leveraging global (rather than local) correlation between HF and LF sources, and (2) Associating all the data sources with the same noise process. These assumptions dramatically reduce the performance of MFBO when LF sources are only locally correlated with the HF source or when the noise variance varies across the data sources. To dispense with these incorrect assumptions, we propose an MF emulation method that (1) learns a noise model for each data source, and (2) enables BO to leverage highly biased LF sources which are only locally correlated with the HF source. We illustrate the performance of our method through analytical examples and engineering problems on materials design.
Deep Generative Model-Based Synthesis of Four-Bar Linkage Mechanisms Considering Both Kinematic and Dynamic Conditions
Authors: Lee Sumin , Kim Jihoon , Kang Namwoo 
Published: November 2023

Abstract
Mechanisms are essential components designed to perform specific tasks in various mechanical systems. However, designing a mechanism that satisfies both kinematic and dynamic requirements is a challenging task. The kinematic requirements may include the workspace of a mechanism, where the maximum scalar displacement of the mechanism can be derived from this region. On the other hand, the dynamic requirements of a mechanism may include its torque transmission, which refers to the ability of the mechanism to transfer power and torque effectively. In this paper, we propose a deep generative model that can generate multiple crank-rocker four-bar linkage mechanism samples that satisfy both the kinematic and dynamic requirements aforementioned. The proposed model is based on a conditional generative adversarial network (cGAN) with some modifications for mechanism synthesis, which is trained to learn the relationship between the requirements of a mechanism with respect to linkage lengths, and generates multiple mechanism samples that satisfy given requirements. The results demonstrate that our proposed method can successfully generate multiple mechanisms that satisfy specific requirements. Our approach has several advantages over traditional design methods. It enables designers to explore a larger design space and efficiently generate multiple diverse and feasible designs. Also, the proposed method considers both the kinematic and dynamic requirements, which can lead to more efficient and effective mechanisms for real-world use, making it a promising tool for linkage mechanism design.
Implementing Application-Driven Data Analytics in Engineering and Engineering Technology Courses With Student Feedback Assessment
Authors: Yu Zhiyuan , Liu Chenang 
Published: November 2023

Abstract
The integration of Application-Driven Data Analytics into Engineering and Engineering Technology courses has become increasingly important due to the growing significance of data in the modern industry. This research aims to explore the potential of introducing new application-driven data analytics modules to advance the teaching effectiveness of the existing fundamental Engineering and Engineering Technology courses in undergraduate education. This pilot project involves one study module called “The Value of Data Analytics in Engineering” in a Machine Design course at Miami University Engineering Technology Department and a Data Analytics Programming course at Oklahoma State University Industrial Engineering & Management School. The lecture in this module includes topics of data collection, manipulation, analysis, and visualization. A lab session is also conducted to analyze the collected tensile test data using Python programming, where various analyses are performed to understand the material properties of the test samples. These analyses include stress-strain curves learning and visualization, material properties calculation, and results comparison to reference data. To gather feedback on the effectiveness of this new study module, students are given surveys after the lectures and lab sessions. The surveys focus on the outcomes of this approach in helping students understand the course material, the ease of tools usage, and the student’s interests in learning further of Data Analytics topics in their future courses. In total 39 students are surveyed for feedback on the lecture and lab session. Overall, the feedback gathered from the students indicates that the approach has been effective. The survey results show that 72% of the students have gained better understanding of data analytics, and 79% of the students are interested in integrating more of the topics into existing curriculum. The use of Python in Google Colab has also been well received by the students, with many finding them easy to use with limited prior data analytics programming experience. Additionally, the students have expressed the usefulness of the skills acquired in their future careers and are interested in learning more about the topics in their curriculum. Therefore, this pilot studies recommend that an application-driven data analytics module can be effectively integrated into more courses in Engineering and Engineering Technology curricula. The future research involves courses such as Introduction to Programming, Manufacturing Processes, Quality Control, Machine Design, Thermodynamics, and Materials Science.
A Multi-Fidelity Gaussian Process Regression Method for Probabilistic Wind Farm Power Curve Estimation
Authors: Li Honglin , Feng Cong , Zhang Jie 
Published: November 2023

Abstract
Accurate estimation of the power curve for wind turbines or wind farms is crucial to ensure their efficient operation and management. However, conventional methods for power curve estimation rely either on expensive and infrequent measurements or on low-quality numerical simulations. Moreover, the majority of previous studies on power curve estimation for wind turbines or wind farms focused on deterministic estimation, which provides a point estimate of the relationship between wind speed and power generation. Nevertheless, the deterministic approach fails to consider the inherent uncertainty associated with wind energy production resulting from varying turbine characteristics. This can lead to inaccurate power generation estimation and suboptimal decisions regarding energy management. In this paper, a kernel density estimation (KDE) based Multi-Fidelity Gaussian Process Regression (MFGPR) model is proposed to fuse theoretical power curve data and the ground true measurements to create a mapping of wind speed and wind power. By conducting a case study on an actual wind farm in China, the efficacy of the proposed MFGPR model was demonstrated in characterizing the variability of wind power. The probabilistic MFGPR model was also able to generate confidence intervals that encompassed the measured power, thereby improving the accuracy and confidence in wind power estimation or wind resource assessment. Overall, the proposed MFGPR model offers a reliable approach to integrate high-fidelity ground measurements and theoretical power curve data, resulting in precise wind resource assessment and power estimation.
A Backbone-Driven Approach for the Positioning of Continuum Robots
Authors: Li Yucheng , Murray Andrew P. , Myszka David H. 
Published: November 2023

Abstract
The area of continuum robotics is showing potential in a variety of applications including surgical robotics, search and rescue operations, manufacturing, and assembly. Shape-changing mechanisms provide the theoretical tools for the synthesis of spatial chains of rigid bodies that position themselves along curves of any complexity. This article introduces a method for generating the backbone of a continuum robot that closely approximates a given spatial curve. The approach combines the kinematic model of the continuum robot, which operates under the constant curvature assumption, with the design profile and target profile concepts from established spatial shape-changing mechanism theory. This approach involves an analysis of the accessibility of the terminal orientation of the spatial curve, which subsequently identifies a rapid synthesis method. This method facilitates the creation of a backbone with a predetermined number of segments that closely aligns with the tangential direction of the reference points of the target profile while simultaneously approximating the target profile itself. Optimization techniques demonstrate the efficacy of using the generated backbone of the rapid approximation method as the initial design for the optimization. In a situation where a close approximation is not needed at interim steps, optimization is not needed to provide a displacement sequence to move the robot.
CyberRiskDELPHI: Towards Objective Cyber Risk Assessment for Complex Systems
Authors: Papakonstantinou Nikolaos , Van Bossuyt Douglas L. , Hale Britta , Arlitt Ryan , Salonen Jarno , Suomalainen Jani 
Published: November 2023

Abstract
Risk assessment is an essential step for architecting the resilience (safety/security) of a mission critical software-intensive system as well as a regular maintenance procedures. It closely relates to estimating the (cyber) insurance needs of the system. Managing of cyber risk involves gathering threat intelligence, prioritizing the current threats against the system of interest, and planning mitigation strategies. While reliability engineering can rely on a relatively stable set of failure modes and statistical data related to their probabilities of occurrence, security deals with a dynamic threat environment. This reality has dictated the use of qualitative methods (like STRIDE and DREAD), relying on the experience and the specific background of the person performing the study. This subjectivity leads to criticism, since results calculated by different experts for the same system can vary significantly. This challenge has been addressed in the past with a method called DELPHI aiming to reduce subjectivity using a group of experts. The scientific contribution of this paper is the development of the CyberRiskDELPHI, a modified version of original DELPHI method for the identification and prioritization of cyber risks. It is demonstrated over a case study of a 5G tactical bubble covering the communication needs of a critical operation. An early evaluation of the use of a large language model (ChatGPT) in risk identification and prioritization for this case study is also included as a complementary side-activity giving an indication of future developments in the risk assessment domain.
Cross-Domain Health Conditions Identification Based on Joint Distribution Modeling of Fused Prototypes
Authors: Xie Tingli , Huang Xufeng , Choi Seung-Kyum 
Published: November 2023

Abstract
Intelligent diagnostic models with deep learning networks have been widely used for health conditions identification of complex mechanical systems. However, the distributional shift and data scarcity phenomenon inevitably exist in practical and real-world scenarios, which further affect the generalization and representation capability of neural networks. Recently, meta-learning has provided an effective way to solve domain shifts and extremely limited data problems. By introducing non-parametric similarity measures into meta-knowledge learning, metric-based meta-learning can seek embedding spaces to compare class prototypes to identify unseen testing samples. This article proposes a novel meta-neural network based on joint distribution modeling of fused prototypes (IFJPN) to alleviate distribution discrepancy in embedding spaces between different domains. First, multi-source time-series data are transformed into images for modality alignment and information aggregation. Second, a feature embedding module with multiple convolutions is developed to obtain fused prototypes from converted images. Then, the Brownian distance covariance is used for efficient joint distribution modeling of fused prototypes, which improves the discriminative capability of cross-domain health conditions with extremely limited data. Extensive experiments on a benchmark dataset in different cross-domain scenarios under two limited data settings show that the proposed IFJPN has more robust recognition and generalization capability than typical transfer-learning or meta-learning methods.
Identification of the Piezoelectric Properties of Materials From Impulsive Tests on Cantilever Harvesters
Authors: Tommasino Domenico , Tonan Michele , Moro Federico , Doria Alberto 
Published: November 2023

Abstract
There are many promising applications of vibration energy harvesting based on piezoelectric materials, but some efficient piezoelectric materials e.g. PZT being used inside MFCs include Pb and other elements that are harmful for the environment. Hence, there is a great research effort to develop new eco-friendly piezoelectric materials. In order to make the best use of these materials, a complete characterization of their piezoelectric and mechanical properties is needed. In this paper an alternative identification methodology for the characterization of piezoelectric materials is proposed. A layer of piezoelectric material is bonded to a structural layer making up a cantilever harvester that is excited with impulsive forces. The identification is carried out in the frequency domain, because on the one hand the impact force generates a significant excitation in a wide frequency band and on other hand the cantilever harvester is characterized by a well-distinct resonance peak. The measured frequency response function is fitted with a mathematical model that includes the unknown parameters of the material, which are reconstructed by using an optimization process. In the paper the proposed method is validated identifying the properties of well-known harvesters. Finally, piezoelectric properties obtained by applying an impulsive excitation are compared with those obtained by applying a harmonic excitation generated by a shaker.
Hydroplaning of Tires: A Review of Numerical Modeling and Novel Sensing Methods
Authors: Vilsan Alexandru , Sandu Corina 
Published: November 2023

Abstract
In this article, previous literature on tire hydroplaning has been reviewed, especially focusing on real-time estimation methodologies and numerical modeling of the partial and total hydroplaning phenomenon. Hydroplaning is a challenging problem for the passenger car tire industry. The current active safety features that equip the most technologically advanced passenger cars cannot predict and prevent the occurrence of the hydroplaning phenomenon. Hydroplaning is a complex phenomenon that depends on a multitude of factors. The total hydroplaning is a phenomenon which occurs when the tire is no longer capable of evacuating the water from the grooves, resulting in total loss of control of the motor vehicle. This represents a situation where the entire contact patch is lifted from the ground to the layer of water that was formed on the road. However, the reduction of the contact between the tire and the road surface is progressive, this situation being called partial hydroplaning. The threshold speed between the partial and total hydroplaning is called the critical hydroplaning speed. These concepts are universally accepted by every researcher in the hydroplaning field, however in our literature review we discovered that the detection of the critical hydroplaning speed threshold varies from one experimental investigation to another.
Knowledge Transfer in Self-Organizing Systems and Impact of Social Ability
Authors: Huang Bingling , Jin Yan 
Published: November 2023

Abstract
Multiagent systems have evolved in both research and practice domains over the past decades. Application domains have also broadened into robotic and vehicular systems, among others. A self-organizing system comprises multiple agents and can adapt to changing operational environments. Early work on the self-organizing system has tackled the system design problem by introducing information fields and logical rules. As tasks become more complex, the difficulty of devising the fields and the rules has become harder. To overcome this limitation, a multiagent reinforcement learning based approach has been explored, aiming to let agents acquire the task knowledge by themselves. The research results thus far have verified the effectiveness of the learning-based approach. However, the training process of reinforcement learning takes a long time and a vast amount of computational resources. The question arises: can the previously trained team knowledge, embedded in neural networks, be reused so that the new training time can be significantly reduced? This paper addresses this overall question by investigating the effectiveness of transferring the knowledge of neural networks learned by a certain agent team to another team of the same domain but with different team sizes. Furthermore, the learning team has social abilities: they not only focus on the task states of the environment while learning but also observe the behavior of other agents. The results of this study have demonstrated the potential benefits as well as limitations of knowledge transfer between teams of the same domain but with different team sizes.
Heat Sink Design Optimization via GAN-CNN Combined Deep-Learning
Authors: Flynn Nathan , Qian Xiaoping 
Published: November 2023

Abstract
This work proposes a combined deep learning based approach to improve thermal component heat sinks involving turbulent fluid flow. A Generative Adversarial Network (GAN) is trained to learn and recreate the new ellipse based heat sinks. Simulation data for new designs is efficiently generated using OpenFOAM 7 (Open Source Computational Fluid Dynamics software) along with high throughput computing. To improve the speed of design evaluation, a Convolutional Neural Network (CNN) is trained to predict the entire temperature field for a given design. The trained CNN is able to predict the entire temperature field for the design with a mean average error of 1.140 degrees kelvin in 0.04 seconds (22,500 times faster than the simulation). A combined model is formed using the trained CNN and GAN networks to create and simulate new designs. The combined model optimizes the latent representation of 64 random designs on a Graphical Processing Unit (GPU) in ten minutes. The optimized designs perform fourteen degrees kelvin better on average than the non-optimized designs. The highest preforming design outperforms any design in the training data by 1.83 degrees kelvin.
A Closed-Form Command Shaping Control of a Rotating Flexible Beam With Nonzero Initial Conditions
Authors: Alhazza Khaled A. , Alfares Mohammed , Alawadhi Abdulaziz 
Published: November 2023

Abstract
This paper proposes a single-mode smooth wave-form command shaping controller to eliminate residual vibrations induced by rest-to-rest maneuver of rotating flexible beams. The proposed shaper is designed to eliminate residual vibration when nonzero initial conditions exist at the start of rest-to-rest maneuver. The nonlinear equation of motion of a rotating flexible beam is derived, discretized, and finally linearized. To eliminate the residual vibrations, the solution of the linear system, with the suggested shaper as an input, is used to determine the shaper parameters analytically. The shaper parameters were calculated by considering the effect of the initial deflection of the beam tip. To demonstrate the shaper performance, two numerical simulations with different initial conditions are conducted. The results of these simulations show the ability of the proposed shaper to eliminate the first-mode residual vibrations of a flexible rotating beam with nonzero initial conditions.