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Study of monolithically integrated 940 nm AlGaAs distributed Bragg reflectors on graded GaAsP/bulk Si substrates

Optical Materials Express 2023年第4期13卷 1077-1091页

作者： Guo, Jia Zhao, Yunlong Feifel, Markus Cheng, Hao-Tien Yang, Yun-Cheng Chrostowski, Lukas Lackner, David Wu, Chao-Hsin Xia, Guangrui Department of Materials Engineering The University of British Columbia VancouverBCV6 T 1Z4 Canada Fraunhofer Institute for Solar Energy Systems ISE Heidenhofstraße 2 Freiburg79110 Germany Graduate Institute of Electronics Engineering National Taiwan University Taiwan Graduate Institute of Photonics and Optoelectronics National Taiwan University Taiwan Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6 T 1Z4 Canada

We report the fabrication of monolithically integrated 940 nm AlGaAs distributed Bragg reflectors (DBRs) on graded GaAsP/Si substrates. Low-density surface bumps and cross-hatch patterns were observed on the DBR surfaces. Cross-sectional DBR layers are smooth and flat. The reflectance spectra of the GaAsP/Si DBRs have lower intensities than those of the GaAs DBRs and have double peaks. Transfer matrix method calculations, surface scratch and polishing tests were conducted, which suggest that the surface cross-hatch was the cause of the inferior DBR reflectance spectra. © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： III-V semiconductors

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Allocation of Control Authority Between Dynamic Inversion and Reinforcement Learning for Autonomous Helicopter Aerial Refueling

Allocation of Control Authority Between Dynamic Inversion an...

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American Control Conference (ACC)

作者： Damsara Jayarathne Santiago Paternain Sandipan Mishra department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute Troy NY Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

The typical control architecture for autonomous helicopter operations consists of an inner loop for attitude stabilization and an outer loop control for lateral/longitudinal dynamics. In this paper, we focus on the design of the outer loop controller for helicopter aerial refueling. In order to alleviate the drawbacks of solely model-based or pure data-driven controllers, a residual reinforcement learning framework is presented where a model-based controller is combined with a reinforcement learning (RL) controller. These two controllers work in tandem to leverage the advantages of both the model-based and data-driven components. However, this requires appropriately allocating control authority to each controller (the maximum/minimum control authority and/or controller parameters), which are typically determined through time-consuming heuristic methods. Recognizing the need for a systematic approach to achieve reasonable harmony between the two control strategies, here we present an analytical (model-based) approach that facilitates the selection of the outer loop control gains for the model-based (dynamic inversion) controller along with appropriate bounds for the (data-driven) RL control input. We explicitly incorporate uncertainty bounds on the drogue motion and the maximum allowable acceleration of the aircraft into the control design. Finally, we showcase the applicability of the methodology through a simulation study.

关键词： Analytical models Uncertainty Systematics Control design Atmospheric modeling Helicopters Dynamics

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Deep nonparametric estimation of operators between infinite dimensional spaces

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 1239-1305页

作者： Hao Liu Haizhao Yang Minshuo Chen Tuo Zhao Wenjing Liao Department of Mathematics Hong Kong Baptist University Hong Kong Department of Mathematics and Department of Computer Science University of Maryland College Park Department of Electrical and Computer Engineering Princeton University School of Industrial and Systems Engineering Georgia Institute of Technology School of Mathematics Georgia Institute of Technology

Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

关键词： deep neural networks nonparametric estimation operator learning generalization error analysis

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Transfer Learning for a Class of Cascade Dynamical systems

arXiv

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arXiv 2024年

作者： Rabiei, Shima Mishra, Sandipan Paternain, Santiago Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute United States

This work considers the problem of transfer learning in the context of reinforcement learning. Specifically, we consider training a policy in a reduced order system and deploying it in the full state system. The motivation for this training strategy is that running simulations in the full-state system may take excessive time if the dynamics are complex. While transfer learning alleviates the computational issue, the transfer guarantees depend on the discrepancy between the two systems. In this work, we consider a class of cascade dynamical systems, where the dynamics of a subset of the state-space influence the rest of the states but not vice-versa. The reinforcement learning policy learns in a model that ignores the dynamics of these states and treats them as commanded inputs. In the full-state system, these dynamics are handled using a classic controller (e.g., a PID). These systems have vast applications in the control literature and their structure allows us to provide transfer guarantees that depend on the stability of the inner loop controller. Numerical experiments on a quadrotor support the theoretical findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Self-Organized Sensor Eggs for Decentralized Localization and Sensing on Vulcano Island – A Glimpse into Future Space Exploration with Swarms

Self-Organized Sensor Eggs for Decentralized Localization an...

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International Conference on Information Fusion

作者： Fabio Broghammer Thomas Wiedemann Siwei Zhang Armin Dammann Christian Gentner Petar Djuric Institute of Communications and Navigation German Aerospace Center (DLR) Germany Perception for Intelligent Systems Munich Institute of Robotics and Machine Intelligence TU Munich Germany Department of Electrical and Computer Engineering Stony Brook University USA

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Robotic swarms or portable sensor networks are emerging technologies for sensing physical processes that are spatially distributed- and temporally dynamic, both on Earth and in future Moon/Mars exploration missions. We develop a portable network composed of a multitude of self-organized “sensor eggs”. These eggs are equipped with ultra-wideband (UWB) transceivers, providing precise time and position information without additional infrastructures like Global Navigation Satellite systems (GNSSs). Each egg is additionally equipped with environmental sensors, for example, a Sulfur dioxide gas sensor to explore volcanic activity. We use a real time decentralized particle filter (DPF) to estimate the a-posteriori probability density functions (PDFs) of the egg positions. These PDFs are then used in a static state binary Bayes filter for estimating the gas sources with potentially complex structures such as cracks on the volcano surface. The proposed sensor network is verified with an in-field experiment at La Fossa volcano on the island of Vulcano, Italy, in 2023.

关键词： Location awareness Volcanic activity Probability density function Robot sensing systems Particle filters Transceivers Sensors Timing Surface cracks Ultra wideband technology

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Deep-Learning Based Classification of Engagement for Child-Robot Interaction

Deep-Learning Based Classification of Engagement for Child-R...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Gabriela Simões Ana Lopes Carlos Carona Ricardo Pereira Urbano J. Nunes Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Polytechnic Institute of Tomar Portugal Center for Research in Neuropsychology and Cognitive and Behavioral Intervention University of Coimbra

This work aims to describe the research and development of an engagement classification system based on emotion recognition through the use of facial expressions, to be used in robotic applications for children interacting with a NAO humanoid robot during therapy sessions. An emotion classification model based on different Convolution Neural Networks (CNN) architectures was first fed by faces provided from different public datasets to train and validate the emotion classification models. Two engagement classification methodologies are proposed: Method-1 first classifies 7-classes of emotions, using the ConvNeXt network, and then infers the levels of engagement based on the affective model developed by [1]; Method-2 employs the same classification network to directly classify the four levels of engagement. The results obtained for both engagement classification methodologies using the CAFE dataset proved their effectiveness, however Method-1 performs slightly better in terms of engagement classification accuracy. A novel child-robot activity with a NAO humanoid robot, that consists in a breathing-based relaxation exercise, is also proposed and the results of offline classification system showed to be promising, and with potential to be applied in psychology therapies in the future.

关键词： Emotion recognition Convolution Neural networks Medical treatment Humanoid robots Psychology Human-robot interaction

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Programmable Switching of Fluids via Pre-Twisting

Programmable Switching of Fluids via Pre-Twisting

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Yijia Wu Markus P. Nemitz Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Mechanical and Materials Engineering Worcester Polytechnic Institute Worcester MA USA Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA USA

Manipulating airflow is important for controlling pneumatically actuated soft robots, however, current switching techniques suffer from leakage under high pressure ( $> 200$ kPa) or require a complex fabrication process. We propose a new method for reliably and repeatably cutting off airflow by harnessing pre-loaded torsional forces applied to our tubing. The switching distance and hysteresis of our pre-twisted tubing are programmable by varying the tube length and the twisting angle. Our experiments demonstrate the use of pre-twisted tubing to implement CMOS equivalent fluidic switches configured as NOT-, AND-, and OR-gates, and a distance sensor for feedback control for the oscillation of a PneuNet. Our approach of pre-loading tubes with a torsional force allows for simplicity, integrated functionality, and the capability of manipulating high-pressure, fluidic signals mainly at the cost of tubing.

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Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Assessing the Robustness of nnU-Net in the Detection of Pros...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos Kalantzopoulos, Charalampos Pezoulas, Vasileios C. Koutsouris, Dimitrios D. Matsopoulos, George K. Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. National Technical University of Athens School of Electrical and Computer Engineering Biomedical Engineering Laboratory AthensGR15773 Greece Forth Biomedical Research Institute Ioannina Greece University of Ioannina Ioannina Greece Forth Institute of Computer Science Heraklion Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete Heraklion Greece FORTH-IMBB Ioannina Greece University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department IoanninaGR 45110 Greece

ISBN: (纸本)9798350383386

Within the scope of prostate cancer diagnostic imaging, distinguishing lesions is challenging due to their subtle appearance and the prostate gland's complexity. This study employed the nnU-Net, a state-of-the-art medical image segmentation model, on a dataset of 301 patients from 2 openly available datasets to identify whether lesion sizes are affecting the model's performance. By applying t-SNE dimensionality reduction algorithm among dice score and respective lesion sizes, we found that nnU-Net behaves differently for lesions smaller than 9mm compared to lesions larger than 15mm. These insights can inform specialized training approaches for future deep learning models in prostate lesion *** Relevance: The main contributions of this study is, to investigate whether lesion sizes affects nnU-Net's deficiency leading to the development of more specialized models in respect to these factors. © 2023 IEEE.

关键词： Urology

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Improved Feed-Forward Control of Active Power Decoupler for PV Microinverter With Non-Sinusoidal Power Decoupling Capability

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IEEE Transactions on Power Electronics 2025年

作者： Shen, Yidi Hasnain, Arafat Zakzewski, Daniel Resalayyan, Rakesh Khaligh, Alireza University of Maryland Department of Electrical and Computer Engineering Maryland Power Electronics Laboratory Institute for Systems Research College ParkMD20742 United States

In single-phase power conversion systems, there is an inherent difference between the dc-side constant and ac-side oscillating power, and power decoupling is required especially in applications like photovoltaic (PV) inverters. Active power decoupling (APD) converters are often used to balance instantaneous power while reducing the size of dc link capacitors and improving the system reliability. Conventionally, APD converters are controlled to decouple only sinusoidal power at double-line frequency. However, the control is further complicated when non-sinusoidal power is delivered due to non-linear circuit components, non-linear loads, or grid distortions. This paper proposes an improved feed-forward APD control scheme with the capability of decoupling non-sinusoidal power, which has application in single-phase systems, particularly all types of PV microinverters. The proposed feed-forward control includes the capacitor voltage calculation under arbitrary power harmonics and a dead-time compensation to increase the accuracy of duty prediction. The proposed algorithm can work as open-loop control to eliminate APD sensors and can serve as a feed-forward term in closed-loop control to improve power decoupling performance, thereby simplifying the controller design. The proposed approach is experimentally verified using a dc-parallel APD prototype, a 50 V 250 W microinverter with non-linear ac capacitor, and a diode rectifier load. © 1986-2012 IEEE.

关键词： Closed loop control systems

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Water droplets play a role in Internet of Things applications

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Droplet 2024年第3期3卷 27-48页

作者： Feng Wen Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore Center for Intelligent Sensors and MEMS(CISM) National University of SingaporeSingapore i-Lab Key Laboratory of Multifunctional Nanomaterials and Smart SystemsSuzhou Institute of Nano-Tech and Nano-Bionics(SINANO)Chinese Academy of Sciences(CAS)SuzhouChina NUS Graduate School—Integrative Sciences and Engineering Programme(ISEP) National University of SingaporeSingapore

Water droplets help life in nature survive,thrive,and *** water droplet serv-ing as one of the indispensable elements in the Internet of Things(IoT),many droplet-oriented technologies,such as microfluidics,droplet manipulation,electrowetting,and energy harvesting,make rapid progress driven by material science,computer science,and ***-based wearable devices are endowed with advantages such as flexibility,sensing ability,and automation for various parameter ***,the continuous exploration of droplet manipulation has led to the emergence of a wide variety of manipulation ***,electrowetting that utilizes exter-nal fields modifying liquid–solid surfaces has found its applications in various areas,including droplet transportation,microfabrication,and *** energy gener-ation from water droplets also presents exciting opportunities for the development of novel electricity *** approaches for droplet utilization underscore the immense potentials and versatilities of droplet-based technologies in the IoT ***,this mini review presents the fundamental droplet-based technologies by summarizing their working mechanisms and methods,device structures,and *** the challenges in materials,fabrication,and system integration,this review shows the overall development roadmap in terms of improved functionality and performance and highlights the opportunities toward multifunctional,self-sustainable,and intelligent systems,which is called for IoT construction.

关键词： IoT utilize fabrication

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