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Annual Allerton Conference on Communication, Control, and Computing

作者： Ahmed S. Alahmed Guido Cavraro Andrey Bernstein Lang Tong School of Electrical and Computer Engineering Cornell University Ithaca NY USA Power System Engineering Center National Renewable Energy Laboratory Golden CO USA

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users’ revenue meters. Given the operating envelopes, we propose an incentive-aligned community pricing mechanism under which the decentralized optimization of community members’ benefit implies the optimization of overall community welfare. The proposed pricing mechanism satisfies the cost-causation principle and ensures the stability of the energy community in a coalition game setting. Numerical examples provide insights into the characteristics of the proposed pricing mechanism and quantitative measures of its performance.

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A Hybrid Reactive Routing Protocol for Decentralized UAV Networks

arXiv

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arXiv 2024年

作者： Garg, Shivam Ihler, Alexander Bentley, Elizabeth Serena Kumar, Sunil United States The Donald Bren Hall School of Information and Computer Sciences University of California Irvine United States The Air Force Research Laboratory RomeNY United States The Electrical and Computer Engineering Department SDSU San Diego United States

Wireless networks consisting of low SWaP (size, weight, and power), fixed-wing UAVs (unmanned aerial vehicles) are used in many applications, such as monitoring, search, and surveillance of inaccessible areas. A decentralized and autonomous approach ensures robustness to failures;the UAVs explore and sense within the area and forward their information, in a multihop manner, to nearby aerial gateway nodes. However, the unpredictable nature of the events, relatively high speed of the UAVs and dynamic trajectories cause the network topology to change significantly over time, resulting in frequent route breaks. A holistic routing approach is needed to support multiple traffic flows in these networks to provide mobility- and congestion-aware, high-quality routes when needed, with low control and computational overhead, using the information collected in a distributed manner. Existing routing schemes do not address all the mentioned issues. This paper presents a hybrid reactive routing protocol for decentralized UAV networks called Hyd-AODV. It searches routes on-demand (using a multi-metric route selection), monitors a region around the selected route (the "pipe"), and proactively switches to an alternative route before the current route’s quality degrades below a threshold. The impact of pipe width is empirically and theoretically evaluated to find alternate high-quality routes within the pipe and the overhead required to maintain the pipe. A queue management scheme is also incorporated to prioritize packet transmissions based on their age of information (AoI). Compared to existing reactive routing schemes, the proposed approach achieves higher throughput and reduces the number of route discoveries, overhead, and resulting flow interruptions at different traffic loads, node density, and speeds. Despite having limited network topology information and low overhead and route computation complexity, the proposed scheme achieves a superior throughput to proactive optimized l

关键词： Unmanned aerial vehicles (UAV)

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Grid Export Reduction Based on Time-Scheduled Charging of Residential Battery Energy Storage Systems—A Case Study in Cyprus 12th

Grid Export Reduction Based on Time-Scheduled Charging of Re...

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12th KES International Conference on Sustainability and Energy in Buildings, SEB 2020

作者： Chatzigeorgiou, Nikolas G. Yerasimou, Yerasimos P. Florides, Michalis A. Georghiou, George E. FOSS Research Centre for Sustainable Energy/PV Technology Laboratory Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (纸本)9789811587825

Photovoltaic (PV) installations grow exponentially, and the mismatch between generation and demand will adversely affect the stability of the grid. This paper presents a modified operational mode of a grid-connected hybrid PV and battery energy storage system (BESS) in Cyprus. The BESS is coupled with residential rooftop PVs and is functioning under an operational mode aiming to decrease the peak power fed into the grid by storing the excess PV generation. The stored energy is then used to cover the mismatch between generation and demand, thus also increasing the on-site PV self-consumption. Given the favourable environmental conditions in Cyprus, with increased PV generation especially during summer, a significant amount of excess PV generation exported to the grid is still observed, as the BESS is usually fully charged before noon. For this reason, a modification of the operational mode of the aforementioned BESS was performed, aiming to reduce the building’s interaction with the electricity grid during peak export hours by delaying the BESS charging process. An analysis of the system’s operation during that period and its contribution towards grid interaction limitation is presented. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Battery storage

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A coherent approach-based fine-tuning of segment anything model plus watershed algorithm for instance segmentation of mitochondria in electron microscopy images

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IEEE Access 2025年 13卷 98088-98105页

作者： Faska, Zahra Khrissi, Lahbib Mountasser, Imadeddine Haddouch, Khalid Akkad, Nabil El Alshathri, Samah El-Shafai, Walid Sidi Mohamed Ben Abdellah University Laboratory of Engineering Systems and Applications ENSA Fez Morocco Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Computer Science Department Riyadh11586 Saudi Arabia Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt

Increasingly available ultrastructural data from a continuously growing diversity of experimental conditions are driving new opportunities for fruitful neuroscientific hypotheses tested in intracellular compartments such as the nanoscale roles of, e.g., the mitochondria. Reliable morphological statistics are based on achieving highly accurate semantic segmentations of EM images. The state-of-the-art deep CNNs can be somewhat brittle;they tend to provide coarse and high-frequency-oscillatory solutions with discontinuities and false positives even for simple mitochondria segmentation. Historically, the current state-of-the-art in medical image segmentation would involve some variant of the encoder-decoder architecture, such as the U-Net architecture. The SAM does not perform as well, since it has not been explicitly trained for the task and does not demonstrate user-interactive, over one billion annotations mostly for natural images. However, the SAM may be applied to segment anything, including medical image segmentation challenging new datasets. This work is aimed at the difficult task of implementing domain adaptation in mitochondria segmentation within EM images obtained from various tissues and species, using deep learning. We do a systematic study to assess SAM's ability to perform segmentation in medical images, measure its performance on volumetric EM datasets, and show that it is powerful at segmenting instances even under challenging imaging conditions. We provide a fine-tuning SAM which can be naturally trained by SAM at an exemplary scale, benefiting from a diverse and large dataset over one million image masks in 11 modalities. This model would be able to perform precise segmentation for a wide range of targets under various imaging conditions, at the level of performance of specialized U-Net models, or even better. A visual comparison is shown between our fine-tuning SAM model and U-Net, along with an examination of different watershed post-processing st

关键词： Semantic Segmentation

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A Robust Graph Convolutional Neural Network-Based Classifier for Automatic Modulation Recognition

A Robust Graph Convolutional Neural Network-Based Classifier...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Pejman Ghasemzadeh Michael Hempel Hamid Sharif Department of Electrical and Computer Engineering Advanced Telecommunication Engineering Laboratory (TEL) University of Nebraska-Lincoln USA

The procedure of automatically recognizing the modulation scheme of the received signal without any knowledge of the communications parameters employed by the transmitter has gained tremendous attention for developing various applications such as dynamic spectrum access in next generation communications systems and in electronic warfare (EW) applications. Amongst the proposed approaches for this procedure, the feature-based approach working on the principles of deep learning models has consistently demonstrated more favorable properties for its integration with real-world applications and realtime operations. However, this approach faces the challenges of low performance efficiency caused by low classification accuracy and high computational complexity in environments characterized by low signal-to-noise (SNR) ratio conditions. Therefore, in this paper, we present our research findings on a deep learning-based classifier for Automatic Modulation Recognition (AMR) with robust classification accuracy and lower computational complexity, thus leading to higher overall performance efficiency. The feature extraction stage of the proposed classifier operates on the translation of received signal constellations into a graph, which then follows a process of extracting graph information. Mapping the extracted information into features is implemented by a convolutional block. This design employed in our feature extraction stage is the main element for the robustness of our proposed classifier. For our performance evaluations we selected 8QAM, 64QAM and 256QAM, representing candidates for low-, medium- and high-order modulation schemes, respectively. Our simulation results exhibit higher classification accuracy compared to related literature works, by an average of 11 percentage points (p.p.), 11.31 p.p. and 9.3 p.p., respectively, for the successful classification of 8QAM, 64QAM and 256QAM across an SNR range from −10 to 30 dB. The computational complexity analysis indicates t

关键词： Wireless communication Transmitters Simulation Modulation Feature extraction Data mining Convolutional neural networks

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Sub-micron Ultracold Neutron Position Resolution using Chip Based Optical Neural Network

Sub-micron Ultracold Neutron Position Resolution using Chip ...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Lin, S. Zhu, H. Clayton, S. Morris, C.L. Tang, Z. Wang, Z. Chen, R.T. Los Alamos National Laboratory Los AlamosNM87545 United States Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78705 United States Microelectronics Research Center The University of Texas at Austin AustinTX78758 United States Omega Optics Inc. AustinTX78757 United States

High spatial resolution of ultracold neutron (UCN) measurements of 1 µm or less is highly desired for many UCN experiments. Optical neural networks are potential radiation-hard hardware platforms for real-time, energy-efficient analysis of UCN hits.(LA-UR-23-33103). © Optica Publishing Group 2024, © 2024 The Author(s)

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Three-Dimensional Nuclei Synthesis for Fluorescence Microscopy Image Analysis

Three-Dimensional Nuclei Synthesis for Fluorescence Microsco...

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IEEE International Symposium on Biomedical Imaging

作者： Liming Wu Alain Chen Paul Salama Kenneth W. Dunn Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis Indiana USA Division of Nephrology School of Medicine Indiana University Indianapolis Indiana USA

Three-dimensional tissue cytometry is an important technique for quantitative analysis of cell structures in large fluorescence microscopy volumes. Accurate nuclei detection and segmentation is an important step for 3D tissue cytometry. Deep learning methods have shown promising results for nuclei detection and segmentation. However, manually annotating ground truth for training deep learning methods is labor-intensive and not practical for large 3D volumes. In this paper, we propose a 3D nuclei synthesis method, known as 3DSpCycleGAN, for generating 3D ground truth volumes along with corresponding synthetic microscopy volumes. Experimental results using fluorescence microscopy volumes demonstrate that our method generates more realistic 3D volumes when evaluated both visually and quantitatively than previously reported. We also show that using the synthetic volumes generated by 3DSpCycleGAN as training data improves segmentation accuracy for deep learning segmentation techniques.

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Multipartite Nonlocality in Clifford Networks

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Physical Review Letters 2023年第24期130卷 240802-240802页

作者： Amanda Gatto Lamas Eric Chitambar Department of Physics University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA

We adopt a resource-theoretic framework to classify different types of quantum network nonlocality in terms of operational constraints placed on the network. One type of constraint limits the parties to perform local Clifford gates on pure stabilizer states, and we show that quantum network nonlocality cannot emerge in this setting. Yet, if the constraint is relaxed to allow for mixed stabilizer states, then network nonlocality can indeed be obtained. We additionally show that bipartite entanglement is sufficient for generating all forms of quantum network nonlocality when allowing for postselection, a property analogous to the universality of bipartite entanglement for generating all forms of multipartite entangled states.

关键词： Nonlocality Quantum information theory Quantum networks Resource theories

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Topology-Aware Reinforcement Learning for Voltage Control: Centralized and Decentralized Strategies

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IEEE Transactions on Industry Applications 2025年

作者： Hossain, Rakib Gautam, Mukesh MansourLakouraj, Mohammad Livani, Hanif Benidris, Mohammed University of Nevada Reno Department of Electrical and Biomedical Engineering RenoNV89557 United States Pacific Northwest National Laboratory Electricity Infrastructure and Buildings Division RichlandWA99352 United States Michigan State University Department of Electrical and Computer Engineering East LansingMI48824 United States

Volt-VAR control (VVC) methods based on deep reinforcement learning (DRL) can effectively control distribution grid voltage and minimize power loss by implementing corrective and preventive control measures on the reactive power output of inverter-based distributed energy resources (DERs). However, model-free DRL-based VVC approaches usually cannot capture the important topological feature of the power system since they use a fully-connected network (FCN) to deliver the action. Therefore, this paper proposes a graph convolutional network (GCN)-based DRL approach that can employ the topological information of the network to take better control action for regulating the voltage. Our implementation allows for both centralized and decentralized configurations, utilizing a single agent and multiple agents respectively. Although the centralized GCN-based DRL approach has its advantages of minimizing voltage fluctuation and power loss, it is not suitable for large scale power systems due to its challenges in terms of scalability, computation speed and potential single points of failure. Therefore, these problems can be resolved using the decentralized GCN-based DRL approach. Moreover, to ensure the safe operation of the model, our proposed approach incorporates an exponential barrier function while formulating the reward function for each agent. To validate performance of the proposed approaches, the proposed model is tested on modified IEEE test systems and the performances are measured in terms on voltage fluctuation reduction, minimization of power loss and computational speed. The results show that the proposed topology-aware approach outperforms the FCN-based DRL approach in terms of reducing voltage fluctuation and minimizing power loss of the network. Moreover, it is shown that the decentralized GCN-based DRL has faster computational speed than other approaches. © 2025 IEEE.

关键词： Decentralized control

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Sub-micron Ultracold Neutron Position Resolution using Chip Based Optical Neural Network

Sub-micron Ultracold Neutron Position Resolution using Chip ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Semiconductor lasers

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