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Power Electronic and Drive systems and Technologies Conference (PEDSTC)

作者： Vahid Rafiee Jawad Faiz Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering. College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728158495

ISBN: (纸本)9781728158501

There is always uncertainty in all aspects of the real life problems. Deterministic optimizations do not take into account the tolerance of input parameters and also the imperfections in operation condition. These uncertainties may cause failure in product line. To produce a product with the least possible failure, the uncertainties must be considered in design and optimization process. This paper presents deterministic and robust design optimization of an outer rotor permanent magnet motor. To reduce the computational burden of finite element method (FEM) application in the optimization process of motor response surface methodology (RSM) surrogate model is adopted. The robust optimization of the motor is based on design for six sigma (DFSS) methodology and the samples are generated using descriptive Monte Carlo sampling (MCS). Finally, the deterministic and robust motors are compared using FEM and the mass production of both motors is simulated.

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Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model

Elucidating Spatial Complex Structures from Mass Spectrometr...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Jiangnan Cui Junjie Xia Xuan Li Yan Wang Fufang Qiu Yaochen Xu Keren Xu Chunman Zuo School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Department of Neurosurgery Huashan Hospital Shanghai China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms.

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Semi-supervised learning for COVID-19 image classification via ResNet

arXiv

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arXiv 2021年

作者： Nwosu, Lucy Li, Xiangfang Qian, Lijun Kim, Seungchan Dong, Xishuang Center of Computational Systems Biology Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States Center of Excellence in Research and Education for Big Military Data Intelligence CREDIT Center Center of Computational Systems Biology Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States Center of Computational Systems Biology Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories, which has resulted in a great public health concern across the international community. Analysis of X-ray imaging data can play a critical role in timely and accurate screening and fighting against COVID-19. Supervised deep learning has been successfully applied to recognize COVID-19 pathology from X-ray imaging datasets. However, it requires a substantial amount of annotated X-ray images to train models, which is often not applicable to data analysis for emerging events such as COVID-19 outbreak, especially in the early stage of the outbreak. To address this challenge, this paper proposes a two-path semi-supervised deep learning model, ssResNet, based on Residual Neural Network (ResNet) for COVID-19 image classification, where two paths refer to a supervised path and an unsupervised path, respectively. Moreover, we design a weighted supervised loss that assigns higher weight for the minority classes in the training process to resolve the data imbalance. Experimental results on a large-scale of X-ray image dataset COVIDx demonstrate that the proposed model can achieve promising performance even when trained on very few labeled training images. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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Cybersecurity and privacy in smart bioprinting

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Bioprinting 2023年 36卷

作者： Isichei, Joan C. Khorsandroo, Sajad Desai, Salil Department of Industrial and Systems Engineering 1601 E Market Street Greensboro 27401 NC United States Department of Computer Science North Carolina A&T State University Greensboro 27411 NC United States Center of Excellence in Product Design and Advanced Manufacturing North Carolina A&T State University 1601 E Market Street Greensboro 27401 NC United States

Bioprinting is a versatile technology gaining rapid adoption in healthcare fields such as tissue engineering, regenerative medicine, drug delivery, and surgical planning. Although the current state of the technology is in its infancy, it is envisioned that its evolution will be enabled by the integration of the following technologies: Internet of Things (IoT), Cloud computing, Artificial Intelligence/Machine Learning (AI/ML), NextGen Networks, and Blockchain. The product of this integration will eventually be a smart bioprinting ecosystem. This paper presents the smart bioprinting ecosystem as a multilayered architecture and reviews the cyber security challenges, vulnerabilities, and threats in every layer. Furthermore, the paper presents privacy preservation solutions and provides a purview of the open research challenges in the smart bioprinting ecosystem. © 2023 Elsevier B.V.

关键词： 3D printing Artificial intelligence Bioprinting Cybersecurity Digital twin (DT) Internet of things (IoT)

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A Van der Waals Moiré Bilayer Photonic Crystal Cavity

arXiv

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arXiv 2025年

作者： Spencer, Lesley Coste, Nathan Ni, Xueqi Park, Seungmin Schaeper, Otto C. Kim, Young Duck Taniguchi, Takashi Watanabe, Kenji Toth, Milos Zalogina, Anastasiia Tang, Haoning Aharonovich, Igor School of Mathematical and Physical Sciences University of Technology Sydney UltimoNSW2007 Australia ARC Centre of Excellence for Transformative Meta-Optical Systems University of Technology Sydney UltimoNSW2007 Australia Department of Physics National University of Singapore 119077 Singapore Department of Physics Kyung Hee University Seoul02447 Korea Republic of International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States

Enhancing light-matter interactions with photonic structures is critical in classical and quantum nanophotonics. Recently, Moiré twisted bilayer optical materials have been proposed as a promising means towards a tunable and controllable platform for nanophotonic devices, with proof of principle realisations in the near infrared spectral range. However, the realisation of Moiré photonic crystal (PhC) cavities has been challenging, due to a lack of advanced nanofabrication techniques and availability of standalone transparent membranes. Here, we leverage the properties of the van der Waals material hexagonal Boron Nitride to realize Moiré bilayer PhC cavities. We design and fabricate a range of devices with controllable twist angles, with flatband modes in the visible spectral range (~ 450 nm). Optical characterization confirms the presence of spatially periodic cavity modes originating from the engineered dispersion relation (flatband). Our findings present a major step towards harnessing a two-dimensional van der Waals material for the next-generation of on chip, twisted nanophotonic systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Van der Waals forces

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Noise-like pulse generation in erbium-doped fiber laser by hybrid mode-locking of graded index multimode fiber and nonlinear optical loop mirror

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Optical Fiber Technology 2025年 93卷

作者： Hikmat Maad Norita Mohd Yusoff Siti Barirah Ahmad Anas Makhfudzah Mokhtar Mohd Adzir Mahdi Zuraidah Zan Wireless and Photonics Networks Research Center of Excellence Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia 43400 UPM Serdang Selangor Malaysia Institute of Nanoscience and Nanotechnology (ION2) Universiti Putra Malaysia 43400 Serdang Selangor Malaysia

The practical implementation of graded-index multimode fiber (GIMF) as a saturable absorber (SA) in mode-locked fiber lasers is limited by the requirement for extremely short fiber lengths, making its reproducibility and investigation difficult. In this work, we demonstrate stable noise-like pulse (NLP) generation using a hybrid mode-locked fiber laser that integrates a nonlinear optical loop mirror (NOLM) with a 32 m GIMF segment. Nonlinear multimode interference (NL-MMI) and nonlinear phase accumulation within the NOLM bidirectional-loop (bi-loop) were leveraged to enhance the saturable absorption characteristics. Comprehensive measurements comparing the nonlinear transmission behavior of GIMF alone and NOLM-GIMF structures confirmed a substantial improvement in modulation depth. NLP generation was systematically investigated across GIMF lengths from 30 m to 62.5 m, with optimal performance observed at 32 m, yielding a 3-dB bandwidth of 22.38 nm, spike width of 153.9 fs, and pedestal width of 61.73 ps. The obtained pulse is significantly shorter than previously reported NLP, which is typically reported in the nanosecond range for the 1.5 µm regime. Long-term stability testing over 300 min showed minimal spectral and temporal fluctuations, confirming robust operation. The proposed NOLM-GIMF design successfully overcomes the short-length constraint of GIMF-based SAs, offering a scalable and reliable solution for high-energy, broadband NLP fiber lasers in the 1.5 µm regime. This work advances mode-locked fiber laser technology by improving stability and scalability for ultrafast photonics applications.

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Mechanistic Modeling of Copper Corrosions in Datacenter Environments

SSRN

引用

SSRN 2023年

作者： Zhang, Rui Zhang, Jianshun Schmidt, Roger Gilbert, Jeremy L. Department of Mechanical and Aerospace Engineering College of Engineering and Computer Science Syracuse University 263 Link Hall SyracuseNY13244 United States Buildings and Transportation Science Division Oak Ridge National Laboratory Oak RidgeTN37830 United States SyracuseCoE New York Center of Excellence in Environmental and Energy Systems Syracuse University 727 E. Washington Street SyracuseNY13244 United States Department of Mechanical and Aerospace Engineering College of Engineering and Computer Science Syracuse University 212 Link Hall SyracuseNY13244 United States Department of Bioengineering Clemson University Clemson United States Medical University of South Carolina Bioengineering Program CharlestonSC29425 United States

To evaluate the reliability of information technology equipment under various thermal and air-pollution conditions, a mechanistic model based on multi-ion transport and chemical reactions was developed. The model was used to predict Cu corrosion caused by Cl2-containing pollutant mixtures. It also accounted for the effects of temperature (25°C and 28°C), relative humidity (50%, 75%, and 95%), and synergism. It also identified higher air temperature as a corrosion barrier and higher relative humidity as a corrosion accelerator, which agreed well with the experimental results. The average root mean square error of the prediction was 13.7 Å. © 2023, The Authors. All rights reserved.

关键词： Copper corrosion

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Analytical Solution of Two-Conductor Transmission Lines Excited by External Electromagnetic Field

Analytical Solution of Two-Conductor Transmission Lines Exci...

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Iranian Conference on Electrical engineering (ICEE)

作者： M. S. Khatami J. A. Rashed Mohassel School of Electrical and Computer Engineering University of Tehran Tehran Iran Center of Excellence on Applied Electromagnetics Systems School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172972

In this paper, an analytical approach is presented to calculate the field-to-transmission line coupling. Specifically, Green functions of the telegraphic equations are presented for a simple case of two transmission lines. Then, unknown coefficients in the Green functions are represented in matrix forms to be solved by a MATLAB code. Finally, the results are compared with those of Finite Different Time Domain method and an excellent agreement is shown.

关键词： Mathematical model Couplings Green's function methods Power transmission lines Finite difference methods Time-domain analysis Numerical models

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Retraction notice to “Increasing electricity generation—Installing photovoltaic cells coupled to a battery pack, to provide the electricity” [J. Energy Storage 54 (2022) 105264]

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Journal of Energy Storage 2024年 94卷

作者： Ahmad H. Milyani Elias M. Salilih Mohammed N. Ajour Hesham A. Alhumade Nidal H. Abu-Hamdeh Arash Karimipour Center of Research Excellence in Renewable Energy and Power Systems Energy Efficiency Group Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems Energy Efficiency Group Department of Mechanical Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems Energy Efficiency Group Department of Chemical and Materials Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Mechanical Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah 21589 Saudi Arabia Energy Efficiency Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Dipartimento di Ingegneria Astronautica Elettrica ed Energetica Sapienza Universita di Roma Via Eudossiana 18 Roma 00184 Italy

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Quantum sensing and imaging with spin defects in hexagonal boron nitride

arXiv

引用

arXiv 2023年

作者： Vaidya, Sumukh Gao, Xingyu Dikshit, Saakshi Aharonovich, Igor Li, Tongcang Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States School of Mathematical and Physical Sciences University of Technology Sydney UltimoNSW2007 Australia ARC Centre of Excellence for Transformative Meta-Optical Systems University of Technology Sydney UltimoNSW2007 Australia Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States

Color centers in hexagonal boron nitride (hBN) have recently emerged as promising candidates for a new wave of quantum applications. Thanks to hBN’s high stability and 2-dimensional (2D) layered structure, color centers in hBN can serve as robust quantum emitters that can be readily integrated into nanophotonic and plasmonic structures on a chip. More importantly, the recently discovered optically addressable spin defects in hBN provide a quantum interface between photons and electron spins for quantum sensing applications. The most well-studied hBN spin defects, the negatively charged boron vacancy (VB−) spin defects, have been used for quantum sensing of static magnetic fields, magnetic noise, temperature, strain, nuclear spins, paramagnetic spins in liquids, RF signals, and beyond. In particular, hBN nanosheets with spin defects can form van der Waals (vdW) heterostructures with 2D magnetic or other materials for in situ quantum sensing and imaging. This review summarizes the rapidly evolving field of nanoscale and microscale quantum sensing with spin defects in hBN. We introduce basic properties of hBN spin defects, quantum sensing protocols, and recent experimental demonstrations of quantum sensing and imaging with hBN spin defects. We also discuss methods to enhance their sensitivity. Finally, we envision some potential developments and applications of hBN spin defects. Copyright © 2023, The Authors. All rights reserved.

关键词： Boron nitride

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