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Free vibration analysis of a skew sandwich plate with bamboo biocomposite and polylactic acid core under temperature and moisture conditions

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Noise and Vibration Worldwide 2023年第9期54卷 434-459页

作者： Gawande, Atul S Kattimani, Subhaschandra Murigendrappa, S.M. Nguyen-Thoi, T. Kamyab, Hesam Althamer, Saeed Department of Mechanical Engineering National Institute of Technology Karnataka Mangalore Surathkal India Institute of Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Mechanical Engineering King Khalida University Abha Saudi Arabia

This article investigates the impact of moisture and temperature on vibration characteristics of bio-composite skew-laminated composite sandwich (SLCS) plates. The bio-composite SLCS plates with bamboo face sheets and polylactic acid (PLA) cores are biodegradable, radiolucent, lightweight, high strength, and withstand vibrations. The coupled thermo-elastic and hygro-elastic finite element (FE) model of the SLCS plate is derived using the higher-order shear deformation theory (HSDT). An initial stress stiffness matrix is developed using the nonlinear strain-displacement relations to incorporate the effect of temperature and moisture in FE modeling. Temperature and moisture-dependent material properties of bamboo fiber-reinforced biocomposite (BFB) and PLA core are employed for the analysis. A comprehensive investigation has been carried out to study the impact of moisture and temperature situations with different geometrical parameters and various skew angles on the frequency of the SLCS plate. The results indicate that the biocomposite sandwich material has excellent potential for structural applications under different environmental conditions in various applications like food processing, and biomedical applications, including MRI and CT scan beds. © The Author(s) 2023.

关键词： Laminated composites

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Clustering Method of Driving Scenes based on Road Shape by using Road Map Images 4

Clustering Method of Driving Scenes based on Road Shape by u...

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4th International Conference on electrical, Communication and computer engineering, ICECCE 2023

作者： Hashimoto, Kohjiro Yanagihara, Daichi Kuniyuki, Hiroshi Doki, Kae Funabora, Yuki Doki, Shinji Dept. Applied Information Engineering Suwa University of Science Nagano Chino city Japan Dept. Mechanical and Electrical Engineering Suwa University of Science Nagano Chino city Japan Dept. Electrical Engineering Aichi Institute of Technology Aichi Toyota city Japan Dept. Electrical Engineering and Computer Science Nagoya University Chikusa-ku Aichi Nagoya Japan

ISBN: (纸本)9798350369694

Currently, vehicles are equipped with various sensors that can collect a series of driving behavior data while driving. The driving behavior data obtained in this way can be used to analyze the causes of traffic accidents and evaluate driver skills. However, when performing the above analysis, a specific driving scene is defined and driving behavior data for that driving scene is required. On the other hand, the series of driving behavior data obtained during one driving operation is enormous, and it is not easy to pick up desired driving scene data from among them. This research aims to establish a driving scene clustering method for a series of driving behavior data. In this paper, a driving scene is defined as a scene in which road shape and operation are associated, and propose a driving scene clustering method considered road shape. Here, a road map image is acquired from position information obtained from a GPS sensor, and the relationship between the road map image and operation data is clustered based on Coupled-GP-HSMM. © 2023 IEEE.

关键词： Deep learning

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Assessing Feasibility in Resource Limited Genetic Networks 63

Assessing Feasibility in Resource Limited Genetic Networks

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Junior, Carlos Eduardo Celeste Grunberg, Theodore W. Vecchio, Domitilla Del Massachusetts Institute of Technology Department of Mechanical Engineering 77 Massachusetts Ave CambridgeMA02139 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 77 Massachusetts Ave CambridgeMA02139 United States

ISBN: (纸本)9798350316339

The design of biological systems is a challenging endeavor due to the lack of modularity caused by context effects, such multiple gene expression modules sharing a limited pool of resources. This work considers the problem of determining when specifications on the steady state system behavior can be met for suitable parameter choices, while accounting for resource sharing. We establish necessary and sufficient conditions for the feasibility of a specification for a given network of subsystems that share both production and degradation resources. This extends previous work that considered just production resource sharing and thus lays the foundation for the development of co-design techniques forgenetic networks with both production and degradation resources, where one may be able to mitigate the effects of one type of resource sharing by tuning the other. © 2024 IEEE.

关键词： Specifications

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Multistep Model Predictive Torque Control for Induction Motor via Imitation Learning 49

Multistep Model Predictive Torque Control for Induction Moto...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Zhang, Yudi Gao, Shuhua Ge, Jian Ji, Hongfeng Xiang, Cheng Electrical & Computer Engineering National University of Singapore Singapore Control Science & Engineering Shandong University Jinan China Jiangsu Hengkang Mechanical and Electrical Co. Ltd Jiangsu China Shandong Sanjian Construction Engineering Co. Ltd. Jinan China

ISBN: (纸本)9798350331820

This article proposes a novel approach utilizing imitation learning to address the computational challenge of multistep model predictive torque control (MPTC). The long prediction horizon of multistep MPTC usually contributes to better performance for induction motor control. Nevertheless, the computational burden increases exponentially with the length of the prediction horizon, leading to considerable difficulties in its real time implementation. MPTC essentially solves an optimization problem, which is time-consuming for a long prediction horizon. To overcome the computational difficulty, we replace the expensive numerical solver with a cheap deep neural network (DNN) following the idea of imitation learning. The DNN's output approximates the optimal solution after training. The proposed method achieves comparable steady-state performance to the ideal multistep MPTC, with lower computational complexity, especially when the switching frequency is limited. Moreover, this strategy demonstrates notable control performance improvements in contrast to the conventional one-step MPTC. Overall, the proposed method has great potential for real-time multistep MPTC implementation in typical induction motor drives. © 2023 IEEE.

关键词： Deep neural networks

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Thermoeconomic assessment of the air-cooled tropical data center through energy-saving strategies

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Energy and Built Environment 2024年

作者： Zhou, Jing Kanbur, Baris Burak Van Le, Duc Tan, Rui Duan, Fei School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Rapid developments in the electronic information industry drive the increased energy usage and carbon emission of data center buildings, prompting the focus on the energy efficiency and environmental sustainability. Expanded operation envelopes of tropical data centers is assessed to analyze the potential for the building energy savings and carbon emission reduction through collaborative analysis of operation modes (OMs), supply air temperature (SAT), and outdoor air temperature (OAT). The OMs of compression vary with the setpoints of SAT, in which the average exergy efficiency of compressors at alternate operation mode is 6.8 % and 8.0 % lower than that of double and single compression operations. As SAT rises from 20 °C to 32 °C, the system exergoeconomic factor increases from 5.4 % to 8.0 %, and the average carbon cost decreases by 36.5 %. Additionally, with just an 8.5 % increase in exergy cost (i.e., Case 8) at OAT rising from 30 to 34 °C, the high SAT and low refrigerant charges provide considerable exergy cost advantages versus resisting the OAT fluctuations. Dynamic operation strategies are also proposed and compared to cope with the impacts of tropical environments. Compared to the 26 °C SAT baseline, the average energy savings are 9.1–14.7 %, indicating the ability to fully utilize outdoor and indoor conditions. © 2024

关键词： Energy efficiency

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A Generative Adversarial Network-Based Method for LiDAR-Assisted Radar Image Enhancement 27

A Generative Adversarial Network-Based Method for LiDAR-Assi...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Thilakanayake, Thakshila De Silva, Oscar Wanasinghe, Thumeera R. Mann, George K. Jayasiri, Awantha Memorial University of Newfoundland Department of Mechanical and Mechatronics Engineering St. John'sNL Canada Memorial University of Newfoundland St. John's Department of Electrical and Computer Engineering NL Canada Aerospace Research Centre National Research Council Canada OttawaON Canada

ISBN: (纸本)9798331505929

This paper presents a generative adversarial network (GAN) based approach for radar image enhancement. Although radar sensors remain robust for operations under adverse weather conditions, their application in autonomous vehicles (AVs) is commonly limited by the low-resolution data they produce. The primary goal of this study is to enhance the radar images to better depict the details and features of the environment, thereby facilitating more accurate object identification in AVs. The proposed method utilizes high-resolution, two-dimensional (2D) projected light detection and ranging (LiDAR) point clouds as ground truth images and low-resolution radar images as inputs to train the GAN. The ground truth images were obtained through two main steps. First, a LiDAR point cloud map was generated by accumulating raw LiDAR scans. Then, a customized LiDAR point cloud cropping and projection method was employed to obtain 2D projected LiDAR point clouds. The inference process of the proposed method relies solely on radar images to generate an enhanced version of them. The effectiveness of the proposed method is demonstrated through both qualitative and quantitative results. These results show that the proposed method can generate enhanced images with clearer object representation compared to the input radar images, even under adverse weather conditions. © 2024 IEEE.

关键词： Radar imaging

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A Comparison of Relative Seebeck Coefficients for Screen Printed Flexible Thermocouples Using Commercially Available Conductive Inks 5

A Comparison of Relative Seebeck Coefficients for Screen Pri...

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5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023

作者： Anderson, Cameron Fan, Z. Hugh Richstein, Jorg Sussman, Mark Nishida, Toshikazu University of Florida Department of Electrical and Computer Engineering GainesvilleFL United States University of Florida Department of Mechanical and Aerospace Engineering GainesvilleFL United States Jabil Inc. St. PetersburgFL United States

ISBN: (纸本)9781665457330

Thermocouples prepared on flexible substrates using additive manufacturing techniques can provide a path forward to strain independent temperature sensors for applications in a variety of wearable devices. An evaluation of 3 conductive ink pairings (C/ Ag, Ag/Ni, C/Ni) fabricated using a screen-printed process is presented. The relative Seebeck coefficients of each ink pair was measured to be 9μ V/°C+/-2μ V, 16 μ V/°C+/-2μ V and 27μ V/°C+/-6μ V respectively. Response time of the sensors were also evaluated to characterize the capability of real-time measurement with these devices. © 2023 IEEE.

关键词： Screen printing

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Diagnosis of COVID-19 on Chest X-ray (CXR) Images Using CNN with Transfer Learning and Integrated Stacking Ensemble Learning 1

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4th International conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

作者： Low, Wai Sing Chow, Li Sze Solihin, Mahmud Iwan Handayani, Dini Oktarina Dwi Department of Electrical and Electronic Engineering Faculty of Engineering Technology and Built Environment UCSI University Cheras Malaysia Department of Mechanical Engineering Faculty of Engineering Technology and Built Environment UCSI University Cheras Malaysia Computer Science Department Faculty of Information Communication and Technology International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9789819988198

ISBN: (纸本)9789819988181

COVID-19 caused a pandemic outbreak, resulting in many deaths and severe economic damage since 2019. Hence, the diagnosis of COVID-19 has become one of the major fields of research. Although RT-PCR has excellent reliability and precision, it is time-consuming and laborious. Therefore, the chest X-ray was used as an alternative and reliable diagnostic tool for COVID-19. However, it requires a radiologist to analyze the X-ray images, which is limited by the availability of experts and time. Henceforth, many researchers deployed automated computer-aided diagnosis with deep learning neural networks to speed up the diagnosis of COVID-19 with high accuracy and reproducibility. This study applied six state-of-art convolutional neural networks (DenseNet201, MobileNetV2, ResNet101V2, VGG16, InceptionNetV3, and Xception) with transfer learning. An integrated stacking ensemble method was used to concatenate DenseNet201, MobileNetV2, VGG16, and Xception to produce a robust and accurate diagnostic model for COVID-19. The proposed ensembled CNN model in this study produced a test accuracy of 0.9725, sensitivity of 0.9749, and F1-score of 0.9724. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Triple strong coupling and Rabi splitting enhancement in multi-layer waveguides

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Physica B: Condensed Matter 2025年 712卷

作者： Sun, Miaowenhao Xie, Suxia Zeng, Zhaoyou Chen, Jianghao Chu, Zeyang Zhuo, Junwu Cui, Jie Zhang, Meidi Gao, Man Shao, Yue School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

Bound states in the continuum (BIC) within continuous spectra exhibit exceptional quantum efficiency, enabling precise control in strong coupling systems. However, the limited tunability of strong coupling restricts its broad applicability. This paper presents a novel method for tuning strong coupling interactions by modifying the number and characteristics of waveguide layers. We demonstrate single, double, and triple strong coupling using a perovskite waveguide, a bilayer waveguide composed of perovskite and WS2, and a trilayer waveguide consisting of perovskite, WS2, and WSe2. Two strong coupling modes are identified: quasi-bound states in the continuum (QBIC) and guided mode resonance (GMR). We observe a transition of GMR to the off-Γ BIC mode. By adjusting the exciton oscillation strength of the waveguide materials, the Rabi splitting for single strong coupling is enhanced from 134 meV to 189.7 meV. In double strong coupling, Rabi splitting reaches 175.8 meV and 342 meV, respectively. The emergence of triple Rabi splitting significantly broadens the available configurations for tuning exciton oscillation strength. Through our tuning strategy, we achieve triple strong coupling with six distinct Rabi splitting values: 114.3 meV, 135.4 meV, 148 meV, 112.6 meV, 130.8 meV, and 146 meV. This work establishes a new platform for strong coupling, with potential applications in advanced photonic devices, such as dual-wavelength and tri-wavelength polarized lasers and light absorbers. © 2025

关键词： Ridge waveguides

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A SiC-Based 243 kW 60 kW/L Electric Vehicle Traction Inverter

A SiC-Based 243 kW 60 kW/L Electric Vehicle Traction Inverte...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Wu, Chih-Chiang Rout, Uma Sankar Chou, Hsin-Ping Hu, Jwu-Sheng Industrial Technology Research Institute Mechanical and Mechatronics Systems Research Laboratories Hsinchu Taiwan National Yang Ming Chiao Tung University Department of Electrical Engineering & Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350376067

This paper investigates the evolving landscape of EV traction inverters, addressing key technical goals outlined by US Drive and analyzing parameters such as voltage, power density, and efficiency. Through innovative methodologies, a high-efficiency SiC-based traction inverter is developed to overcome challenges like spatial constraints. Leveraging international benchmarks, a cutting-edge SiC-based 800V 243 kW traction inverter with a power density of 60 kW/L is presented, poised to surpass US Drive's 2025 targets. This study emphasizes SiC technology's role in advancing EV propulsion systems towards sustainable and efficient transportation solutions. © 2024 IEEE.

关键词： power density silicon carbide high-efficiency sustainable transportation traction inverter

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