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Pacific-Rim Laser Damage 2023: Optical Materials for High-Power Lasers

作者： Xiang, Chengjiang Liu, Xiaofeng Tao, Chunxian Li, Dawei Zhao, Yuan’an Sun, Jian Zhang, Weili Shao, Yuchuan Shao, Jianda School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Laboratory of Thin Film Optics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Key Laboratory of High Power Laser Materials Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China

ISBN: (纸本)9781510672826

Time-resolved pump-probe technology is an effective method to study the dynamic damage process of optics. In this paper, the dynamic damage process of HfO2/SiO2 anti-reflection coating, for the cases that the coating located on the laser incidence (forward process) and exit (reverse process) surface, irradiated by a 1064nm nanosecond laser was studied based on the time-resolved pump-probe technology of intensified charge-coupled device (ICCD). Under the irradiation of the same fluence (52J/cm2), pits without and with the layer peeling existed in both forward and reverse processes. However, the lateral size and depth of the small pits generated by the reverse process are larger than those of the forward process. The finite element analysis shows that the electric field intensity (EFI) on the substrate-coating interface for the forward and reverse processes is similar, which is not enough to form the difference in damage morphology. These results indicate the develop process of the plasma after its formation under the subsequent laser pulse irradiation determines the damage difference in the above two cases. The time-resolved study of anti-reflective coating damage is of great significance for its damage mechanism analysis and practical application. © 2023 SPIE.

关键词： Laser damage

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Stochastic model of noise for a quantum thermal transistor

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Physical Review B 2023年第23期108卷 235421-235421页

作者： Uthpala N. Ekanayake Sarath D. Gunapala Malin Premaratne Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA

Our focus in this investigation lies in developing a noise model for a quantum thermal transistor model inspired by its electronic counterpart, with the primary aim of establishing a platform for constructing analogous models. Previous studies on coupled two-level systems-based thermal transistors were focused on their average energy exchange. In this paper, we shift our attention to exploring the stochastic behavior of such thermal transistors due to the disturbances caused to their environment, such as continuous measurements. In the literature, the master equation for the transistor model is derived using the reduced dynamics method. This way, it masks the study of the stochastic nature of the energy flows in the system due to disturbances to the environment. In this paper, we describe a quantum trajectory under measurement theory whose ensemble average unravels the master equation for a quantum thermal transistor. This allows us to analyze the fluctuations and noise levels in the transistor model with greater detail. Then, we produce a numerical solution for the transistor dynamics based on Euler-Maruyama approximation. This helps to establish a model for the thermal transistor, drawing parallels to the small-signal/noise model in an electronic transistor. We define two parameters, thermal conductance and output thermal resistance, to describe the small signal-like model for the thermal transistor. Through these investigations, we seek to gain insights that can help design advanced heat management devices at the quantum level.

关键词： Open quantum systems Quantum gates Thermal properties Nanotechnology Transistors

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An Iterative Method for a Frequency Domain PID Controller Synthesis

An Iterative Method for a Frequency Domain PID Controller Sy...

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SICE Annual Conference

作者： Kenjiro Minamikawa Yasushi Kami Eitaku Nobuyama Atsushi Hosokawa Department of Mechanical and Electronic System Engineering Advanced Course of National Institute of Technology Akashi College Hyogo Japan Department of Electrical Systems Engineering Hiroshima Institute of Technology Hiroshima Japan Department of Intelligent and Control Systems Kyushu Institute of Technology Fukuoka Japan Department of Electrical and Computer Engineering National Institute of Technology Akashi College Hyogo Japan

This study is concerned with a control system design method with frequency response data named a finite number of frequency responses(an FNFR) model. In this paper a numerical descent direction is introduced and an iterative method for solving control problems formulated with the FNFR model is proposed with the descent direction. One feature of our approach is to update controller variables so that they approach to a feasible region of considering control problems from the outside. The efficiency of our method is shown by numerical examples.

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Amplified Cooling of Light via Emergent Onsager Irreversible Thermodynamics

Amplified Cooling of Light via Emergent Onsager Irreversible...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Xiong, Zhongfei Wu, Fan O. Liu, Yang Jiang, Jian-Hua Christodoulides, Demetrios N. Chen, Yuntian School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Ming Hsieh Department of Electrical and Computer School of Physical Science and Technology Soochow University Suzhou215006 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Department of Electrical Engineering University of Southern California Los AngelesCA90089 United States Wuhan National Laboratory of Optoelectronics Huazhong University of Science and Technology Wuhan430074 China

While conventional cooling schemes solely depend on temperature differences, here we demonstrate that in photonics configurations, amplified cooling can be achieved by judiciously manipulating Onsager coefficients thr... 详细信息

ISBN: (纸本)9781957171395

关键词： Photonics

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Nanophotonic neural probe enabled light-sheet light-field fluorescence imaging microendoscope

Nanophotonic neural probe enabled light-sheet light-field fl...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Peisheng Ding Hannes Wahn Fu-Der Chen Xianshu Luo Guo-Qiang Lo Joyce K. S. Poon Wesley D. Sacher Max Planck Institute of Microstructure Physics Halle Germany Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada Advanced Micro Foundry Pte Ltd Singapore

We demonstrate a lensless volumetric imaging microendoscope for deep-brain fluorescence microscopy. It consists of a nanophotonic neural probe for light-sheet illumination and an image fiber bundle for light-field flu... 详细信息

ISBN: (纸本)9781957171258

关键词： Fiber bundles Fiber coatings Fluorescence microscopy Image quality Multicore fibers Plenoptic imaging

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Hybrid surface waves in twisted anisotropic heterometasurfaces

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Physical Review Applied 2024年第6期21卷 064034-064034页

作者： Xinyan Zhang Chenxu Bian Zheng Gong Ruoxi Chen Tony Low Hongsheng Chen Xiao Lin Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation International Joint Innovation Center The Electromagnetics Academy at Zhejiang University Department of Electrical and Computer Engineering Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Shaoxing Institute of Zhejiang University

Recent advances in twisted photonic structures (e.g., twisted bilayer graphene and twisted anisotropic metasurfaces) have enabled many exotic phenomena of light-matter interactions, such as topological transition of the isofrequency contour of surface waves at the magic angle, photonic flatbands, and localization-to-delocalization transition of light. In general, the optical features of these twisted photonic structures are sensitive to the interlayer twist angle. Here we investigate the mode hybridization of surface waves supported by twisted bilayer anisotropic heterometasurfaces, whose two constituent metasurfaces are nonidentical and support surface waves with similar spatial confinements but distinct polarizations. Counterintuitively, we find that the shape of the isofrequency contour of these hybrid surface waves—eigenmodes with both transverse magnetic and transverse electric wave components—can be insensitive to the twist angle, due to the weak interlayer electromagnetic coupling. Moreover, the interlayer distance provides a unique route to reshape the dispersion of one specific kind of hybrid surface wave, while it has negligible influence on the other kinds. Our finding enriches the physics of light-matter interactions in twisted photonic structures and may open new possibilities to mold the flow of light at the subwavelength scale.

关键词： Classical optics Light-matter interaction Metamaterials Metasurfaces Near-field optics Photonics Plasmonics Van der Waals interaction

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Deep Learning PNN Based Fault Monitoring System for Three Phase Industrial Drive System

Deep Learning PNN Based Fault Monitoring System for Three Ph...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Chempavathy B K. David Raju P. K. Mani S. Vijayalakshmi N. Janaki D. Karthikeyan Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of Electrical and Electronics Engineering Malla Reddy Engineering College For Women Hyderabad India Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Electrical and Electronics Engineering K. Ramakrishnan College of Technology Samayapuram Tiruchirappalli Tamil Nadu Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Asynchronous motors are widely used in a variety of sectors. Even though induction motors are strong and dependable, they are susceptible to a variety of problems. Faults in induction motors can result in horrible events such as operator injuries, manufacturing disruption, and raw material loss. As a result, defect identification has become more crucial in induction motor management. Among the different problems that can occur in the motor, bearing failure is a severe issue that can cause catastrophic damage to the machine if not detected at an early stage of the fault. As consequently, the functioning of bushings in induction machinery must be checked on a regular basis. This research proposes a novel method for analyzing speed using discrete wavelet transform (DWT) and identifying bearing difficulties using Probabilistic Neural Network (PNN) optimized by improved salp swarm algorithm. The stator electricity is examined and classified when an induction motor is operating under varied loading conditions with good and troublesome bearings. Scale-Invariant Feature Transform (SIFT) is a feature extraction method that condenses visual content into a set of points that may be used to find similar patterns in other images. The proposed PNN classifier can classify different types of bearing faults, and the empirical findings validate the created approach. PNN-based motor bearing recognizing defects and diagnosis outperforms SVM and ANN classifiers in this regard. The Shortest path accuracy of the PNN Classifier is 98.5% respectively.

关键词： Support vector machines Visualization Induction motors Accuracy Neural networks Transforms Feature extraction

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Improving Adversarial Transferability Through Hybrid Augmentation

SSRN

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SSRN 2023年

作者： Zhu, Peican Fan, Zepeng Guo, Sensen Tang, Keke Li, Xingyu Xi’an China School of Computer Science NWPU Xi’an China School of Cybersecurity NWPU Xi’an China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Many works have shown that the adversarial examples being generated on a known substitute model have the ability to mislead other unknown black-box models, which has attracted widespread attention. Recently, many model augmentation methods have been presented to boost the corresponding transferability of adversarial examples, which usually attack a set of models simulated with augmented images. However, existing model augmentation methods focus on augmentations within a single domain to simulate models, thereby restricting the diversity of the simulated models. In this manuscript, we present a novel model augmentation method named Hybrid Augmentation Method (HAM), which combines our proposed channel-wise scaling (CS) and spectrum masking (SM) to enhance the diversity of simulated models. Specifically, we first scale the image by channel with CS, which augments them in spatial domain. Then we randomly remove some frequency information of the image with our SM, further augmenting the image in frequency domain. Instead of confining the augmentation to a single domain, we augment the image both in spatial and frequency domain simultaneously. This enables us to fully exploit the properties of image in different domains and largely increases the diversity of simulated models to create more powerful adversarial examples. Later, we conduct extensive experiments to demonstrate the superiority of our method on both undefended and defense models, which outperforms the considered attacks by a large extent. Moreover, our HAM can be integrated with other attacks to further enhance adversarial transferability. © 2023, The Authors. All rights reserved.

关键词： Artificial intelligence

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A disposable tartrazine sensor fabricated with a synchronously activated nanocomposite comprising gadolinium molybdate nanoflowers anchored on functionalized carbon nanofibers

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New Journal of Chemistry 2023年第6期48卷 2660-2672页

作者： Jayapaul, Abishek Sakthivel, Rajalakshmi Lin, Yu-Chien Dhawan, Udesh Liu, Xinke Wen, Hsiao-Wei Chung, Ren-Jei No. 1 Section 3 Zhongxiao E. Rd. Taipei10608 Taiwan Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Food Science and Biotechnology National Chung Hsing University Taichung402202 Taiwan Food and Animal Product Safety Inspection Center National Chung Hsing University Taichung402202 Taiwan Taipei10608 Taiwan

Tartrazine (TRZ) is a synthetic food dye considered noxious to human health and a toxic environmental contaminant owing to the presence of a harmful azo group (-N 00000000 00000000 00000000 00000000 11111111 00000000 11111111 00000000 00000000 00000000 N-) in its chemical structure. The global necessity to prevent food contamination has led scientific researchers to develop disposable sensing platforms. This study aims to integrate nanotechnology with electrochemistry to fabricate a disposable screen-printed carbon electrode (SPCE) with a nanocomposite composed of gadolinium molybdate (Gd2MoO6) nanoflowers decorated on an enmeshed matrix of acid-functionalized carbon nanofibers (f-CNF). The hierarchal arrangement of distinct components in the nanocomposite is promoted by synergism through the formation of a hybrid heterojunction. This heterojunction in the electrode modifier activates the electrode to sense TRZ with a remarkable limit of detection (0.8 nmol L−1) and quantification (2 nmol L−1) over a broad linear range of 0.025 μM to 216.45 μM. Furthermore, the functional sensing ability of the proposed sensor was analyzed in various commercial food products. © 2024 The Royal Society of Chemistry.

关键词： Nanocomposites

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STARGATE: Spatial Transcriptomic Analysis with Recurrent and Graph Attention Techniques using Ensemble Learning

STARGATE: Spatial Transcriptomic Analysis with Recurrent and...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ning Li Jiayidaer Badai Dengjie Chen Ming Xiao Le Zhang College of Computer Science Sichuan University Chengdu China College of Mechanical and Electrical Engineering Fujian A&F University Fuzhou China College of Computer Science Sichuan University Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Chengdu China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Previous research has identified hematoma as the primary cause of mortality and disability in intraventricular hemorrhage (IVH), yet neurologic outcomes post-IVH have not significantly improved following hematoma evacuation. Therefore, utilizing spatial transcriptomics sequencing technology to acquire and analyze IVH data is of significant importance for the early diagnosis and treatment of acute cerebral hemorrhage. However, existing spatial transcriptomics clustering algorithms often overlook local information, fail to integrate multimodal data, and suffer from performance instability. To address these issues, we developed a multimodal spatial transcriptomics clustering algorithm named STARGATE, which initially captures the global spatial topology and local long-sequence gene set features of cells by integrating Graph Convolutional Networks (GCNs) based on self-attention mechanisms and Long Short-Term Memory networks (LSTMs), subsequently fuses gene expression, tissue imagery, morphological, and spatial coordinate features using Gated Recurrent Units (GRUs), and finally enhances the accuracy and robustness of predictions through ensemble learning methods. Our key experimental results include: (1) In clustering experiments on our proprietary mouse IVH spatial transcriptomics dataset, the STARGATE model achieved Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and Fowlkes-Mallows Index (FMI) scores of 0.80, 0.84, and 0.82, respectively, all superior to classic algorithms such as stLearn and CCST. (2) In clustering experiments on the mouse IVH dataset, the STARGATE model reached an accuracy of 89.63%, with an excellent confusion matrix, demonstrating outstanding accuracy and stability. (3) In clustering experiments on the public dataset DLFPC, which is known for its high data complexity leading to generally lower clustering metrics, the STARGATE model's ARI, NMI, and FMI indicators still outperformed classic algorithms such as SpaGCN and Seurat.

关键词： Accuracy Transcriptomics Stability criteria Clustering algorithms Data models Spatial databases Topology Indexes Ensemble learning Hemorrhaging

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