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3rd International Conference on Smart Manufacturing, Industrial and Logistics engineering, SMILE 2023 and the 7th Asian Conference of Management science and Applications, ACMSA 2023

作者： Jing, Sheng Han, Wenmin Zhou, Honggen Fu, Wenhan School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States School of Economics and Management Jiangsu University of Science and Technology Zhenjiang212100 China School of Mechanical Engineering Jiangsu University of Science and Technology Zhenjiang212100 China Business School University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9789819701933

Wireless charging system without wires and mechanical connectors and related infrastructure is environment and user friendly that widely applied in electric vehicles. It is crucial to establish a systematic performance improvement framework for the wireless charging systems of electric vehicles. This study proposes a Six Sigma wireless charging system evaluation framework with define-measure-analyze-improve-control management process, embedded with failure mode and effects analysis, Markov chain and quality control techniques to empower manufacturing operational excellence. The proposed framework can achieve the viability of intelligent decision-making to enhance the empirical implementation through continuous process improvement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Six sigma

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Machine learning applied to property prediction of metal additive manufacturing products with textural features extraction

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International Journal of Advanced Manufacturing Technology 2024年第1-2期132卷 83-98页

作者： Chang, Lien-Kai Chen, Ri-Sheng Tsai, Mi-Ching Lee, Rong-Mao Lin, Ching-Chih Huang, Jhih-Cheng Chang, Tsung-Wei Horng, Ming-Huwi Electrical Motor Technology Research Center National Cheng Kung University Tainan700 Taiwan Department of Mechanical Engineering National Cheng Kung University Tainan700 Taiwan Department of Intelligent Automation Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan700 Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan700 Taiwan

Laser powder bed fusion (LPBF) is one of the common metal additive manufacturing technologies, which has been increasingly applied across various industries, including healthcare, manufacturing, and aerospace, owing to its advantages in customization and faster prototyping. However, acquiring accurate product properties necessitates repetitive and time-consuming measurements, which risk damaging the product. Thus, there is a pressing need to develop an automated method for predicting product properties. In this study, to forecast these properties, we documented details related to metal additive manufacturing products, encompassing both the process parameters and textural features. These features were extracted from layer-by-layer images using the gray-level co-occurrence matrix (GLCM). Subsequently, we employed machine learning (ML) models, such as support vector regression (SVR), XGBoost, and LightGBM, to predict product properties and compare their performance. The experimental results reveal stronger correlations between process parameters and texture features of three-dimensional co-occurrence matrices of the product images, compared to two-dimensional ones. Additionally, the models exhibit high predictive accuracy, especially XGBoost, and LightGBM, with R2 scores approaching 0.9 for all properties. These findings highlight the superiority and feasibility of the proposed approach. Moreover, this proposed approach holds promise in accurately predicting diverse product properties, meeting the demands of multiple application contexts. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： 3D printing

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Towards Automatic Characterization of Nano-Energetic Material Response to Directed Energy 13

Towards Automatic Characterization of Nano-Energetic Materia...

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Energy Harvesting and Storage: Materials, Devices, and Applications XIII 2023

作者： Gaines, Timothy Boyle, Camden Hurt, J. Alex Maschmann, Matthew R. Keller, James M. Scott, Grant J. Price, Stanton R. Electrical Engineering and Computer Science Department University of Missouri-Columbia ColumbiaMO65211 United States Mechanical and Aerospace Engineering Department University of Missouri-Columbia ColumbiaMO65211 United States U.S. Army Engineer Research and Development Center VicksburgMS39180 United States

ISBN: (数字)9781510661417

ISBN: (纸本)9781510661400

Nanoenergetic materials offer high-density energy storage that may be reacted to produce heat and release gaseous products. However, the fundamental reaction mechanisms of isolated nanoenergetic fuel particles (typically aluminum - AL) remain poorly understood. In this study, the structure-property relationships of photothermally heated AL nanoparticles are explored using an optical microscope setup and laser-based photothermal heating. Our research explores optical imaging and computer vision techniques to measure distinctive features from images captured before and after directed energy excitation of nanoenergetic particles. These features are used to describe the reactions in the pursuit of creating an automated nanoenergetic material reaction characterization model. Specifically, optical imagery of nano-aluminum particle clusters is taken before and after the reaction is initiated via laser irradiation. Through image preprocessing and registration, we remove untargeted nanoparticle clusters and align the images. We then classify particle reactions into three classes, Spallation, Sintering, or a Combination of both, through an examination of various features derived from our preprocessed imagery. These techniques serve as tools to aid researchers in quantitatively measuring reaction properties, such as loss of mass, and accelerating the search for optimized reaction parameters. © 2023 SPIE.

关键词： Sintering

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Hierarchical Clustering-Based State Grouping Reinforcement Learning for Switching Decision of Autonomous Vehicles

Hierarchical Clustering-Based State Grouping Reinforcement L...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Ouyang, Wenjie Jiao, Yiwen Liu, Yang Li, Yang Hu, Manjiang Qin, Hongmao College of Mechanical and Vehicle Engineering Hunan University Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electrical Mechanical and Computer Science Delft University of Technology Delft Netherlands College of Mechanical and Vehicle Engineering Hunan University State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Changsha China Institute of Hunan University Wuxi Intelligent Control Research Wuxi China

ISBN: (纸本)9798350316308

Reinforcement learning (RL) has gained wide attention, but its implementation in autonomous vehicles is still limited by insufficient sample efficiency and heavy training costs. The training efficiency of RL agents is influenced by the dimension of the state space, which can be partitioned to reduce the complexity of sampling and computation. This study proposes a hierarchical clustering-based state grouping reinforcement learning (HCSG-RL) method for the switching decision of autonomous vehicles. First, we partition the base state space into groups and generate a hierarchical tree of state space groups. Then, we train multiple sub-agents for each node in the hierarchical tree. Finally, we add these trained-well sub-model into master policy. This method allows us to fully explore all state spaces and improve the training efficiency of individual agents, which handles the 'long-tail' issue and the curse of dimensionality issue. We conduct experiments in a simulation environment and results show that the proposed method has 16-72% reward improvement compared to the tree model in different road length. © 2023 IEEE.

关键词： Reinforcement learning

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Online Event-Triggered Switching for Frequency Control in Power Grids with Variable Inertia

arXiv

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arXiv 2024年

作者： Feng, Jie Cui, Wenqi Cortés, Jorge Shi, Yuanyuan The Department of Electrical and Computer Engineering UC San Diego United States The Department of Electrical and Computer Engineering University of Washington Seattle United States The Department of Mechanical and Aerospace Engineering UC San Diego United States

The increasing integration of renewable energy resources into power grids has led to time-varying system inertia and consequent degradation in frequency dynamics. A promising solution to alleviate performance degradation is using power electronics interfaced energy resources, such as renewable generators and battery energy storage for primary frequency control, by adjusting their power output set-points in response to frequency deviations. However, designing a frequency controller under time-varying inertia is challenging. Specifically, the stability or optimality of controllers designed for time-invariant systems can be compromised once applied to a time-varying system. We model the frequency dynamics under time-varying inertia as a nonlinear switching system, where the frequency dynamics under each mode are described by the nonlinear swing equations and different modes represent different inertia levels. We identify a key controller structure, named Neural Proportional-Integral (Neural-PI) controller, that guarantees exponential input-to-state stability for each mode. To further improve performance, we present an online event-triggered switching algorithm to select the most suitable controller from a set of Neural-PI controllers, each optimized for specific inertia levels. Simulations on the IEEE 39-bus system validate the effectiveness of the proposed online switching control method with stability guarantees and optimized performance for frequency control under time-varying inertia. © 2024, CC BY-NC-SA.

关键词： Time varying control systems

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Research on Cancer Prediction and Identification based on Multimodal Medical Image Fusion 24

Research on Cancer Prediction and Identification based on Mu...

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3rd International Symposium on Robotics, Artificial Intelligence and Information engineering, RAIIE 2024

作者： Diao, Su Wan, Yajie Huang, Shijia Ma, Haowei Department of Industrial & Systems Engineering Auburn University Alabama36849 United States Department of Computer Science Brown University ProvidenceRI02912 United States Fu Foundation School of Engineering and Applied Science Columbia University New York10027 United States Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States

ISBN: (纸本)9798400718311

Multimodal medical image fusion plays a crucial role in computer-aided medical diagnosis, especially in the field of medical image research. Different medical images of the same cell, tissue, bone, organ, and system often show different imaging results due to differences in imaging parameters and mechanisms between different imaging techniques. As a result, single-modal medical images often have limited information and are difficult to provide comprehensive diagnostic information. Multimodal medical image fusion technology has been widely used in the prediction and recognition of cancer, improving the accuracy and efficiency of diagnosis. In this study, a new cancer prediction and recognition method was developed based on multimodal medical image fusion technology. Firstly, the deep learning algorithm is used to integrate data from multiple imaging modes to extract key features through an optimized convolutional neural network model. Secondly, an improved image registration technology is introduced to ensure the accurate alignment between images of different modes, so as to enhance the comprehensive expression ability of image data. Finally, the fused image data was classified to achieve efficient prediction and identification of cancer. Experimental analysis shows that the proposed method is superior to the traditional single-modal image analysis method in terms of accuracy and sensitivity of cancer identification. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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Recent advances in microsystem approaches for mechanical characterization of soft biological tissues

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Microsystems & Nanoengineering 2022年第4期8卷 1-16页

作者： Enming Song Ya Huang Ningge Huang Yongfeng Mei Xinge Yu John A.Rogers Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of OptoelectronicsFudan UniversityShanghai 200433China International Institute of Intelligent Nanorobots and Nanosystems Fudan UniversityShanghai 200433China Department of Biomedical Engineering City University of Hong KongHong Kong 999077China Department of Materials Science Fudan UniversityShanghai 200433China Querrey Simpson Institute for Bioelectronics Department of Materials Science and EngineeringDepartments of Biomedical EngineeringNeurological SurgeryChemistryMechanical EngineeringElectrical Engineering and Computer ScienceNorthwestern UniversityEvanstonIL 60208USA

Microsystem technologies for evaluating the mechanical properties of soft biological tissues offer various capabilities relevant to medical research and clinical diagnosis of pathophysiologic *** progress includes(1)the development of tissue-compliant designs that provide minimally invasive interfaces to soft,dynamic biological surfaces and(2)improvements in options for assessments of elastic moduli at spatial scales from cellular resolution to macroscopic areas and across depths from superficial levels to deep *** review summarizes a collection of these technologies,with an emphasis on operational principles,fabrication methods,device designs,integration schemes,and measurement *** core content begins with a discussion of platforms ranging from penetrating filamentary probes and shape-conformal sheets to stretchable arrays of ultrasonic *** sections examine different techniques based on planar microelectromechanical system(MEMS)approaches for biocompatible interfaces to targets that span scales from individual cells to *** highlighted example includes miniature electromechanical devices that allow depth profiling of soft tissue biomechanics across a wide range of *** clinical utility of these technologies is in monitoring changes in tissue properties and in targeting/identifying diseased tissues with distinct variations in *** results suggest future opportunities in engineered systems for biomechanical sensing,spanning a broad scope of applications with relevance to many aspects of health care and biology research.

关键词： advances identifying utility

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Data Driven Methods to Predict Vertical Profiles of the Rails: A Case Study 10

Data Driven Methods to Predict Vertical Profiles of the Rail...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Nugraha, Ade Chandra Supangkat, Suhono Harso Nugraha, Igb Baskara Handoko, Yunendar Aryo Purwadinata, Awan Hermawan Saputro, Dimas Wisnu Bandung State of Polytechnic School of Electrical Engineering and Informatics Bandung Institute of Technology Department of Computer Engineering and Informatics Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Smart City and Community Innovation Center Bandung Indonesia Bandung Institute of Technology Faculty of Mechanical and Aerospace Engineering Bandung Indonesia PT Kereta Api Indonesia Managing Director of Infrastructure Bandung Indonesia Bandung State of Polytechnic Department of Computer Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350339543

Predicting the value of railroad track geometry is needed to reduce the cost and time of rail condition measurements that have been carried out periodically every three months by railroad operators in Indonesia (PT Kereta Api Indonesia/PT KAI). Four track geometry parameters - vertical profile, horizontal alignment, twist, and gauge - are the reference measurements that will be mapped with alternative measurement results using accelerometers installed on regular trains (in-service trains). In this paper, eight prediction algorithms from the machine learning model are applied to map the vertical profile with 73 kilometers of measurement data in the Jakarta to Padalarang railroad segment, precisely on the Jatinegara-Cikampek measuring trip conducted from April 2019 to June 2022. The evaluation performance of the prediction results has been measured with the GadientBoosting Regressor algorithm is the best algorithm with MSE = 0.791, R2 = 0.507 and feature importance on the Train speed and Az attributes. The research can be continued to detect the correlation of three other track geometry parameters referring to the dataset of rail geometry condition measurement results through accelerometers on regular trains (in-service trains). © 2023 IEEE.

关键词： Forecasting

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Dynamic Path Planning for Multiple Robots Transporting Objects in a Deformable Sheet

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IFAC-PapersOnLine 2023年第3期56卷 505-510页

作者： Kyle Hunte Craig J. Ramlal Jingang Yi Department of Electrical & Computer Engineering Rutgers University Piscataway New Jersey 08854 Department of Electrical & Computer Engineering University of The West Indies Trinidad & Tobago Department of Mechanical & Aerospace Engineering Rutgers University Piscataway New Jersey 08854

This paper presents the design of a path-planning algorithm for a multi-robot transportation system that uses a deformable sheet to transport and manipulate objects. The algorithm aims to develop a path that navigates around obstacles and considers the size of the transportation system in the planning process. The algorithm employs a polynomial function to represent the path coordinates between the system's current position and the desired destination. The coefficients of the polynomial function are adjusted to satisfy two competing objectives. These objectives are 1) minimizing the path's length and 2) maintaining a minimum safe distance away from obstacles while the system moves along the path. The paper represents the problem as an optimization task whose solution achieves an equilibrium between these two competing objectives. By iteratively solving for the optimal coefficients of the polynomial function, the algorithm dynamically adapts the path to achieve the shortest path length and safe obstacle avoidance. We present simulation results that explain how the path is determined as the robots move toward the destination. Experimental results show the algorithm's use during the transportation and manipulation of a spherical object.

关键词： multi-robot object transportation soft manipulation motion planning mobile robot multi-robot teams

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Efficient Task Scheduling in Cloud Environment Based on Hyper Min Max Task Scheduling 2

Efficient Task Scheduling in Cloud Environment Based on Hype...

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2nd IEEE International Conference on Distributed Computing and electrical Circuits and Electronics, ICDCECE 2023

作者： Gnanaprakasam, D. Mohanraj, M. Srinivas, T. Aditya Sai Bhaggiaraj, S. Baskaran, J. Sivankalai, S. Dr. Mahalingam College of Engineering and Technology Department of Electrical and Electronics Engineering Pollachi India Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India Ashoka Women's Engineering College Department of Computer Science and Engineering India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Krishna College of Engineering and Technology Department of Mechanical Engineering Coimbatore India Chennai India

ISBN: (纸本)9798350347456

The growing field of cloud computing deals with large tasks for processing resources. From the application point of view, the research on task scheduling mechanism of data transfer in large-scale cloud computing environment is relatively poor. Unbalanced scheduling leads to traffic overload, energy loss, and failure of hardware control. In addition, residential appliances do not consider delay reduction in power consumption. Hence, Internet of Things (IoT) dominates the current trends in the Internet. The large number of things (things) associated with the Internet creates a large amount of information that requires a lot of effort and work preparation to make it valuable. To resolve this problem, we propose a Hyper Min max task scheduling (HMMTS) based in cascade shrink priority (CSP) to allocate task to optimize the scheduling. With intent a Changeover Load Balancer (CLB) and The Preemptive Flow Manager (PFM) is responsible for the application of load balancing strategy based on the mixed load balancing algorithm improves the task allocation better to balance load to improve the response time. Experimental results have been demonstrated with respect to better load balancing, lower power rate, and time consumption rate in both phase and random uniform propagation. Simulated results performance of this process can reduce data processing time and achieve load neutralization. © 2023 IEEE.

关键词： Cloud computing

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