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American Control Conference (ACC)

作者： Rene Mai Sandipan Mishra Agung Julius Department of Mechanical Aerospace and Nuclear Engineering Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY

This paper presents a human-as-advisor architecture for shared human-machine autonomy in dynamic systems. In the human-as-advisor architecture, the human provides suggested control actions to the autonomous system; the system uses a model of the human controller to ascertain the system’s state as perceived by the human. The system combines this information with additional sensor measurements, yielding an improved state estimate. We apply this architecture to the problem of lane-centering an autonomous vehicle in the presence of conflicting lane markings that render the true lane center uncertain. We model conflicting lane markings with a multi-component Gaussian mixture model. The human-suggested course of action is interpreted as an additional sensor measurement, which a Kalman filter is designed to combine with a speedometer and camera for improving the state estimate. With human input from our human-as-advisor architecture, the vehicle centers itself in the lane; without human input, the vehicle does not center itself. We also demonstrate the human-as-advisor architecture is robust to additive output matrix uncertainty and non-linear perturbations in the human model used to interpret the human-suggested control actions.

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Alpha Mini Social Robot as a Fitness Trainer at Home

Alpha Mini Social Robot as a Fitness Trainer at Home

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Berardina De Carolis Giuseppe Palestra Mario Bochicchio Stefano Mazzoleni Department of Computer Science University of Bari A. Moro Italy Department of Electrical and Information Engineering of Polytechnic University of Bari (DEI-POLIBA)

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Engaging in regular exercise offers numerous mental and physical health benefits. Many people, after the Covid-19 pandemic, started to work out at home. However, performing exercises incorrectly can result in injuries. For this reason, technology can be an effective tool for encouraging physical activity at home, and social robots have emerged as a potential solution for providing fitness training in a safe and engaging manner. A physical robot is often perceived as more socially attractive compared to virtual agents. Despite the growing popularity and existing literature on social robotics-based fitness solutions, there is a lack of research examining the accuracy of these systems and interventions at home during physical exercise recognition. In this paper, we propose a novel approach utilizing the Ubtech alpha mini social robot, which possesses motor capabilities to demonstrate exercises. We endowed the robot with the capability to recognize exercise correctness and motivate users to engage in physical activities. In this project, first of all, a new dataset of physical exercises was created, including 1,500 videos of physical exercises. Then, a new approach to recognize physical exercises from image sequences was presented. This method was implemented in a personal social robot to recognize physical exercises using a deep learning model. The results of the study validate the effectiveness of the system in recognizing physical exercises through the camera onboard a personal social robot.

关键词： Training Deep learning Image recognition Pandemics Social robots Robot vision systems Motors Image sequences Injuries Videos

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Zero-shot cross-lingual name retrieval for low-resource languages 2

Zero-shot cross-lingual name retrieval for low-resource lang...

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2nd Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing, DeepLo@EMNLP-IJCNLP 2019

作者： Blissett, Kevin Ji, Heng Computer Science Department Rensselaer Polytechnic Institute Department of Computer Science Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign

ISBN: (纸本)9781950737789

In this paper we address a challenging cross-lingual name retrieval task. Given an English named entity query, we aim to find all name mentions in documents in low-resource languages. We present a novel method which relies on zero annotation or resources from the target language. By leveraging freely available, cross-lingual resources and a small amount of training data from another language, we are able to perform name retrieval on a new language without any additional training data. Our method proceeds in a multi-step process: first, we pretrain a language-independent orthographic encoder using Wikipedia inter-lingual links from dozens of languages. Next, we gather user expectations about important entities in an English comparable document and compare those expected entities with actual spans of the target language text in order to perform name finding. Our method shows 11.6% absolute F-score improvement over state-of-the-art methods. © 2019 Association for Computational Linguistics

关键词： Zero-shot learning

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Phase- and Angle-Sensitive Terahertz Hot-Electron Bolometric Plasmonic Detectors Based on Fets with Graphene Channel and Composite H-BN/Black-P/H-BN Gate Layer

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International Journal of High Speed Electronics and Systems 2024年第4期33卷

作者： Ryzhii, Victor Tang, Chao Otsuji, Taiichi Shur, Michael S. Ryzhii, Maxim Mitin, Vladimir Research Institute For Electrical Communication Tohoku University Miyagi Sendai980-8577 Japan Department Of Electrical Computer And Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Department Of Computer Science And Engineering University Of Aizu Aizu-Wakamatsu Fukushima965-8580 Japan Department Of Electrical Engineering University At Buffalo Suny BuffaloNY United States

In this paper, we propose and analyze the terahertz (THz) bolometric vector detectors based on the graphene-channel field-effect transistors (GC-FET) with the black-P gate barrier layer or with the composite b-BN/black-P/b-BN gate layer. The phase difference between the signal received by the FET source and drain substantially affects the plasmonic resonances. This results in a resonant variation of the detector response on the incoming THz signal phase shift and the THz radiation angle of incidence. © 2024 World Scientific Publishing Company.

关键词： Graphene

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TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator

arXiv

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arXiv 2024年

作者： Zhang, Meng Yin, Dennis Gangi, Nicholas Begović, Amir Chen, Alexander Huang, Zhaoran Rena Gu, Jiaqi Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Electronic-photonic computing systems offer immense potential in energy-efficient artificial intelligence (AI) acceleration tasks due to the superior computing speed and efficiency of optics, especially for real-time, low-energy deep neural network (DNN) inference tasks on resource-restricted edge platforms. However, current optical neural accelerators based on foundry-available devices and conventional system architecture still encounter a performance gap compared to highly customized electronic counterparts. To bridge the performance gap due to lack of domain specialization, we present a time-multiplexed dynamic photonic tensor accelerator, dubbed TeMPO, with cross-layer device/circuit/architecture customization. At the device level, we present foundry-compatible, customized photonic devices, including a slow-light electro-optic modulator with experimental demonstration, optical splitters, and phase shifters that significantly reduce the footprint and power in input encoding and dot-product calculation. At the circuit level, partial products are hierarchically accumulated via parallel photocurrent aggregation, lightweight capacitive temporal integration, and sequential digital summation, considerably relieving the analog-to-digital conversion bottleneck. We also employ a multi-tile, multi-core architecture to maximize hardware sharing for higher efficiency. Across diverse edge AI workloads, TeMPO delivers digital-comparable task accuracy with superior quantization/noise tolerance. We achieve a 368.6 TOPS peak performance, 22.3 TOPS/W energy efficiency, and 1.2 TOPS/mm2 compute density, pushing the Pareto frontier in edge AI hardware. This work signifies the power of cross-layer co-design and domain-specific customization, paving the way for future electronic-photonic accelerators with even greater performance and efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Memory architecture

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FAKEY: Fake Hashed Key Attack on Payment Channel Networks

FAKEY: Fake Hashed Key Attack on Payment Channel Networks

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IEEE Conference on Communications and Network Security (CNS)

作者： Alvi Ataur Khalil Mohammad Ashiqur Rahman Hisham A. Kholidy Department of Electrical and Computer Engineering Florida International University USA Department of Network and Computer Security SUNY Polytechnic Institute USA

Although blockchain’s immutability and decentralized capabilities make monetary transactions more secure than ever, its inherent scalability problem hinders its utilization in myriad applications. Payment channel networks (PCNs), one of the prominent solutions to blockchain’s scalability issue, reduce the load on the blockchain by performing the transactions offchain. However, malicious PCN participants can delay the block delivery by slightly misbehaving in the initialization phase. In this work, we introduce FAKEY, a fake hashed key-based attack on PCN that can block up a whole set of channels carrying a transaction for a certain period (via collateral lock), depending on the hashed timelock contract (HTLCs) in the attacked payment path. For the targeted attack setup, the effect of the FAKEY is more severe, as PCN throughput can be reduced significantly. Moreover, the attacker can gain direct monetary benefit by carefully choosing one or more victim nodes. We utilize one of the state-of-the-art PCN simulators, PCN sim, to perform multiple experiments with different attack strategies and calculate profit accumulation scenarios. To demonstrate the attack impacts in detail, we perform a couple of case studies using the simulator. Finally, to validate the attack effect and adversarial benefits in a real-world PCN, we utilize the Bitcoin Lightning Network snapshots spanning over a period of 18 months and calculate the exact monetary impact in satoshi units.

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Determination of Parameters of Time-delayed Embedding Algorithm Using Koopman Operator-based Model Predictive Frequency Control

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CSEE Journal of Power and Energy Systems 2021年第6期7卷 1140-1151页

作者： Xiawen Li Chetan Mishra Shichao Chen Yajun Wang Jaime De La Ree Dominion Energy RichmondVAUSA Thorlabs Quantum Electronics. JessupMDUSA Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State UniversityBlacksburgVAUSA

Power systems around the world have been registering a degenerating inertial response in view of the growth of inverter-based resources along with the withdrawal of conventional coal ***,there is a need for swift frequency support and its control,preferably by means of power electronic-interfaced storage devices,owing to their beneficial *** being particularly efficient,pragmatically,the traditional model-based non-linear control techniques are not highly popular in power system control design,primarily due to the complications faced in obtaining accurately suitable models for certain power system ***,the modelfree Koopman operator-based model predictive control(KMPC)has proven to be highly conducive for data-driven non-linear control *** principle behind KMPC is to change the coordinates in a manner to get an approximately linear model,which can then be controlled using a linear model predictive *** this study,we employed time-delayed embedding of measurements to reconstruct a new set of preferable coordinates,thereby suggesting an approach for finding the optimal number of time lags and the embedding dimensions which are the key parameters of this *** efficacy of this KMPC framework is established by adopting a decentralized frequency control problem through a decoupled synchronous machine system,which we proposed for both the Kundur two-area system as well as the IEEE 39-bus test system.

关键词： False nearest neighbors koopman operator model predictive control non-linear control time-delayed embedding

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Hoist Control Stabilization with Real-Time Gyroscopic Data

Hoist Control Stabilization with Real-Time Gyroscopic Data

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IEEE Integrated STEM Education Conference (ISEC)

作者： Howard Yu Rui Peng Meiyu Zheng Zhilin Huang Luyi Tang Mary Lanzerotti Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute & State University Blacksburg VA USA Department of Biomedical Engineering and Mechanics Virginia Polytechnic Institute & State University Blacksburg VA USA

ISBN: (数字)9798350352801

ISBN: (纸本)9798350352818

A typical aerial medical evacuation (MEDEVAC) rescue retrieves a patient into the helicopter by shortening a 6-meter-long cable. This paper is dedicated to the design, construction, testing, and analysis of the swing stabilization of a gyroscope-equipped payload with variable length control of the cable. The stabilization algorithm uses real-time gyroscope data to control the hoist. Tracking software is employed to meticulously acquire experimental data from videos of the rescues. These data sets are subsequently analyzed using MATLAB. This paper aims to emphasize the necessity for enhancements in sensor platforms and to establish precise constraints for future rescue scenarios. Testing results for a rescue scenario show that the 6.0 m cable is shortened to approximately 0.2 m in about 75 s in two phases. In the first phase, the cable length decreases to 3.7 m in ≈72 s by shortening the cable at the ends of the swing. In the second phase, the cable is continuously shortened to ≈0.2 m in length from t ≈ 72 s to t ≈ 75 s. The results demonstrate that swing stabilization with reduced swing angle is achieved using variable length control hoisting.

关键词： Software algorithms Helicopters Real-time systems Software Gyroscopes Lifting equipment Testing

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DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Guided and Class-Balanced Losses

DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Gui...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Biao Yin Nicholas Josselyn Thomas Considine John Kelley Berend Rinderspacher Robert Jensen James Snyder Ziming Zhang Elke Rundensteiner Department of Data Science Worcester Polytechnic Institute Worcester MA Department of Computer Science Worcester Polytechnic Institute Worcester MA Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA

Corrosion is a prevalent issue in numerous industrial fields, causing expenses nearing $3 trillion or 4% of the GDP annually with safety threats and environmental pollution. To timely qualify and validate new corrosion-inhibiting materials on a large scale, accurate and efficient corrosion assessment is crucial. Yet it is hindered by a lack of automatic tools for expert-level corrosion segmentation of material science experimental images. Developing such tools is challenging due to limited domain-valid data, image artifacts visually similar to corrosion, various corrosion morphology, strong class imbalance, and millimeter-precision corrosion boundaries. To help the community address these challenges, we curate the first expert-level segmentation annotations for a real-world image dataset [1] for scientific corrosion segmentation. In addition, we design a deep learning based model, called DeepSC-Edge that achieves guidance of ground-truth edge learning by adopting a novel loss that avoids over-fitting to edges. It also is enriched by integrating a class-balanced loss that improves segmentation with small area but crucial edges of interest for scientific corrosion assessment. Our dataset and methods pave the way to advanced deep-learning models for corrosion assessment and generation – promoting new research to connect computer vision and material science discovery. Once the appropriate approvals have been cleared, we expect to release the code and data at: https://***/

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Interactions Between Wind Turbines and Synchronous Condensers Equipped with Flywheels

Interactions Between Wind Turbines and Synchronous Condenser...

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IEEE Electronic Power Grid (eGrid)

作者： Lawrence Hiquiana Jian Sun John Schmall Scott Zuloaga Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA Electrical Reliability Council of Texas (ERCOT) Taylor TX USA

ISBN: (数字)9798331505493

ISBN: (纸本)9798331505509

Synchronous condensers integrated with a flywheel can be used to enhance system inertia in addition to providing voltage and reactive power support. The added flywheel introduces torsional modes that may interact with the rest of the system in ways like what has been studied for synchronous generators. This paper shows how such interactions may play out in wind power systems. The focus will be 1) torque amplification when a torsional mode is excited by an existing electrical resonance and 2) torsional control interaction when the torsional mode creates a variation in the synchronous condenser’s impedance and forms an unstable oscillation with a nearby wind turbine. Modeling and analysis are performed in the frequency-domain using impedance-based methods and verified by EMT simulation.

关键词： Reactive power Torque Rotors Wind power generation Resonance Synchronous generators Stability analysis Flywheels Wind turbines Oscillators

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