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Directional Sparsity Based Statistical Channel Estimation for 6D Movable Antenna Communications

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arXiv 2025年

作者： Shao, Xiaodan Zhang, Rui Park, Jihong Quek, Tony Q.S. Schober, Robert Shen, Xuemin Department of Electrical and Computer Engineering University of Waterloo Canada School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Information System Technology and Design Pillar Singapore University of Technology and Design Singapore Institute for Digital Communications Friedrich-Alexander University of Erlangen-Nürnberg Germany

Six-dimensional movable antenna (6DMA) is an innovative and transformative technology to improve wireless network capacity by adjusting the 3D positions and 3D rotations of antennas/surfaces (sub-arrays) based on the channel spatial distribution. For optimization of the antenna positions and rotations, the acquisition of statistical channel state information (CSI) is essential for 6DMA systems. In this paper, we unveil for the first time a new directional sparsity property of the 6DMA channels between the base station (BS) and the distributed users, where each user has significant channel gains only with a (small) subset of 6DMA position-rotation pairs, which can receive direct/reflected signals from the user. By exploiting this property, a covariance-based algorithm is proposed for estimating the statistical CSI in terms of the average channel power at a small number of 6DMA positions and rotations. Based on such limited channel power estimation, the average channel powers for all possible 6DMA positions and rotations in the BS movement region are reconstructed by further estimating the multi-path average power and direction-of-arrival (DOA) vectors of all users. Simulation results show that the proposed directional sparsity-based algorithm can achieve higher channel power estimation accuracy than existing benchmark schemes, while requiring a lower pilot overhead. Copyright © 2025, The Authors. All rights reserved.

关键词： Signaling

Multi-level Coordinated Energy Management for Energy Hub in Hybrid Markets with Distributionally Robust Scheduling

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arXiv 2022年

作者： Cao, Jiaxin Yang, Bo Zhu, Shanying Chung, Chi Yung Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China Department of Electrical and Computer Engineering University of Saskatchewan SaskatoonSKS7N 5A9 Canada

Maintaining energy balance and economical operation is significant for multi-energy systems such as the energy hub (EH). However, it is usually challenged by the frequently changing and unpredictable uncertain parameters at different timescales. Under this scope, this paper investigates the coordinated energy management problem for day-ahead and intra-day conditions considering uncertainties of source-load and market prices concurrently. Note that the precise knowledge of distributions about uncertainties may be unaccessible before the decision-making in day-ahead phase. A two-stage chance-constrained model based on distributionally robust approach with ambiguous moment information is proposed to immunize scheduling strategies against the worst-case probability distributions. The first stage is dedicated to obtaining more energy arbitrage and operation flexibility by optimizing bidding strategies in day-ahead power, natural gas and carbon trading markets. The second stage focuses on the optimization of the worst-case expected operation cost. It provides a robust energy equipment and load scheduling strategy for the reference of subsequent intra-day arrangements. With respect to different variations of electrical and thermal components, an intra-day two-timescale coordination is implemented step by step. The energy scheduling is re-dispatched circularly to minimize the operation and penalty costs. The possible energy imbalance is also compensated by this way. As the energy management program is nonlinear, chance-constrained and multi-stage, some linearization and dual transformation techniques are designed to enhance tractability of the program. Experimental results show that the developed multi-level framework results in a carbon emission decrease of 37%, and reduces energy cost averagely 3% compared with corresponding contrasting cases. The obtained strategy validates a good tradeoff between robustness and optimality. Copyright © 2022, The Authors. All rights reser

关键词： Power markets

Trends in Glucagon-Like Peptide-1 Receptor Agonist Social Media Posts Using Artificial Intelligence

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JACC: Advances

JACC: Advances 2024年第9期3卷 101182-101182页

作者： Javaid, Aamir Baviriseaty, Sruthika Javaid, Rehan Zirikly, Ayah Kukreja, Harshita Kim, Chang H. Blaha, Michael J. Blumenthal, Roger S. Martin, Seth S. Marvel, Francoise A. Ciccarone Center for the Prevention of Cardiovascular Disease Division of Cardiology Department of Medicine Johns Hopkins University School of Medicine Baltimore MD United States Digital Health Innovation Laboratory Johns Hopkins University School of Medicine Baltimore MD United States Whiting School of Engineering Department of Computer Science Johns Hopkins University Baltimore MD United States Computer Science Department at New York University NYU Graduate School of Arts & Science New York NY United States

Background: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have surged in popularity in recent years, with discussions about their on-label and off-label use spilling into the public forum. No study has analyzed online discussions about GLP-1RAs. Objectives: The purpose of this study was to analyze perceptions of GLP-1RAs on social media. Methods: We analyzed GLP-1RA-related posts on Reddit between May 28, 2013, and June 1, 2023. All posts were identified that included generic or brand names of GLP-1RAs. Post volume on Reddit was compared to search interest on Google over time. An artificial intelligence (AI) pipeline consisting of a semi-supervised natural language processing model (Bidirectional Encoder Representations from Transformers [BERT]), a dimensionality reduction technique, and a clustering algorithm was used to cluster posts into related topics. Discussion sentiment was classified using a pretrained BERT model and assessed qualitatively. Results: 14,390 GLP-1RA-related Reddit posts by 8,412 authors were identified. Ninety-four percent of posts were created after 2021, consistent with search interest trend on Google. We used the AI model to categorize posts into 30 topics which were hierarchically grouped by the model based on shared content. Posts were identified among communities for individuals with diabetes and obesity, as well as for diseases without a Food and Drug Administration-approved indication. Most posts had a negative sentiment using the pretrained model, acknowledging the pretrained model is at risk for misclassifying posts. Conclusions: AI can generate insights on perceptions of GLP-1RAs on social media. Common themes included success stories of improving diabetes and obesity management, struggles with insurance coverage, and questions regarding diet, side effects, and medication administration. © 2024 The Authors

关键词： artificial intelligence GLP-1RA natural language processing ozempic social media

Bird Detection Algorithm for Transmission Line Based on Improved YOLOv7

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Bird Detection Algorithm for Transmission Line Based on Impr...

Power and Energy (PandaFPE), Panda Forum on

作者： Yong Kang Zheng Jia Lin Liu Chao Wang Xu Xu Li Xin Yu Tang Ling Hao Zhang Yong Liu System Technology Center State Grid Sichuan Electric Power Research Institute Chengdu China School of Electrical Engineering Northeast Electric Power University Jilin China Equipment Department State Grid Sichuan electric power company Chengdu China Artificial Intelligence Key Laboratory of Sichuan Province Sichuan University of Science & Engineering Yibin China Power Internet of Things Key Laboratory of Sichuan Province State Grid Sichuan Electric Power Research Institute Chengdu China Digital Department ABA power supply company of State Grid Sichuan electric power company Chengdu China

With the increase in the number of birds, the life of birds will seriously endanger the safety of transmission lines or transmission towers. To ensure peaceful coexistence between transmission towers or lines and birds, this paper proposes to identify birds based on the improved YOLOv7 network structure, which lays the foundation for driving or guiding different birds. Replace part of the Conv layer in YOLOv7 with DOConv layer, improve the network performance through deeply over-parameterized convolution, and do not over-parameterize in reasoning; Add SPDConv layer in small target recognition to improve the recognition rate of small target; SimAM attention mechanism is added to feature extraction to enhance feature extraction. Some birds fly at high speed, resulting in blurred pictures taken by traditional cameras, which will greatly reduce the recognition rate of birds. In this paper, motion blur processing of data sets is beneficial to the recognition of blurred bird images. The model can accurately identify birds in the collected images of power transmission lines, providing a reference for the prevention and control of bird-related faults on power transmission lines.

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A Multi-Stage Stochastic Dispatching Method for Electricity-Hydrogen Integrated Energy systems Driven by Model and Data

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SSRN

SSRN 2023年

作者： Yang, Zhixue Ren, Zhouyang Li, Hui Sun, Zhiyuan Feng, Jianbing The State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China The State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau 999078 China The State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China Electric Power Research Institute of Guangxi Power Grid Co. Ltd Guangxi Nanning530000 China

To balance the contradiction between economy, security, and computational burden caused by the uncertainty of the electricity-hydrogen integrated energy systems (EH-IESs), a multi-stage coordinated dispatching framework of "day-ahead deterministic dispatching - online security monitoring - intra-day flexible correction" is proposed. The flexibility of the hydrogen energy system at the source-side and load-side is fully exploited and incorporated into the day-ahead dispatching model. To online monitor the future security of the EH-IESs operation in an uncertain environment, a security monitoring method is proposed by combining deep learning and Monte Carlo simulation. The predetermined dispatching scheme may not ensure the security of system operation due to the renewable energy uncertainty. Thus, an intra-day correction method based on chance-constrained model and multi-agent deep reinforcement learning is established to determine the correction scheme. Finally, the effectiveness and adaptability of the proposed method are demonstrated using the data of two EH-IESs based on the standard IEEE 57-node and IEEE 118-node test systems. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

Interpretable breast cancer diagnosis using histopathology and lesion mask as domain concepts conditional simulation ultrasonography

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Information Fusion 2025年 123卷

作者： Dai, Guowei Wang, Chaoyu Tang, Qingfeng Zhang, Yi Dai, Duwei Qiao, Lang Yan, Jiaojun Chen, Hu College of Computer Science Sichuan University Chengdu610065 China National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu610065 China Chengdu University of Traditional Chinese Medicine Chengdu611137 China Institute of Oncology Chengdu University of Traditional Chinese Medicine Chengdu610036 China Digital and Intelligent Health Research Center Anqing Normal University Anqing246133 China School of Cyber Science and Engineering and the Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu610065 China National-Local Joint Engineering Research Center of Biodiagnosis & Biotherapy the Second Affiliated Hospital of Xi'an Jiaotong University Xi'an710004 China Department of Ultrasound Sichuan Integrative Medicine Hospital Chengdu610041 China

Breast cancer diagnosis using ultrasound imaging presents challenges due to inherent limitations in image quality and the complex nature of lesion interpretation. We propose SgmaFuse, a novel interpretable multimodal framework that integrates histopathological concepts and lesion masks information, treated as domain concepts, with ultrasound imaging for accurate and explainable breast cancer diagnosis. At its core, SgmaFuse employs a Spatially Guided Multi-Level Alignment Mechanism (SGMLAM) that orchestrates global–local feature interactions across modalities. This is achieved through a sophisticated hierarchical strategy incorporating cross-modal fusion and attention-based feature correspondence at four distinct levels: global image-report alignment, local mask-guided attention report alignment, local image diagnostic report alignment, and concept-level diagnostic report alignment. Concurrently, a Histological Semantic Activation Vector Learning (HSAVL) module, leveraging kernel Support Vector Machines, learns discriminative semantic concepts directly from histopathological data, thereby bridging the gap between ultrasound imaging features and established pathological patterns via robust concept-level alignment. The framework ability to provide transparent, structured diagnostic explanations through interpretable visual attention maps and semantic concept contributions demonstrates its potential as a reliable clinical decision support tool, particularly in the challenging domain of breast ultrasound diagnosis. © 2025 Elsevier B.V.

关键词： Ultrasonography

Spatiotemporal Photonic Emulator of Potential-free Schrödinger Equation

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arXiv 2025年

作者： Cao, Qian Zhang, Nianjia Chong, Andy Zhan, Qiwen School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Zhangjiang Laboratory Shanghai China University of Shanghai for Science and Technology Shanghai Key Laboratory of Modern Optical System Shanghai China Pusan National University Department of Physics Busan Korea Republic of Institute for Future Earth Pusan National University Busan46241 Korea Republic of Westlake Institute for Optoelectronics Hangzhou Fuyang311421 China Hiroshima University Hiroshima Higashihiroshima739-8526 Japan

Photonic quantum emulator utilizes photons to emulate the quantum physical behavior of a complex quantum system. Recent study in spatiotemporal optics has enriched the toolbox for designing and manipulating complex spatiotemporal optical wavepackets, bringing new opportunities in building such quantum emulators. In this work, we demonstrate a new type of photonic quantum emulator enabled by spatiotemporal localized wavepackets with spherical harmonic symmetry. The spatiotemporal field distribution of these wavepackets has the same distributions of the wavefunction solutions to the potential-free Schrödinger equation with two controllable quantum numbers. A series of such localized wavepackets are experimentally generated with their localized feature verified. These localized wavepackets can propagate invariantly in space-time like particles, forming a new type of photonic quantum emulator that may provide new insight in studying quantum physics and open up new applications in studying light-matter interactions and quantum optics. © 2025, CC BY.

关键词： Schrodinger equation

Communication Compression for Distributed Nonconvex Optimization

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arXiv 2022年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Chai, Tianyou Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers distributed nonconvex optimization with the cost functions being distributed over agents. Noting that information compression is a key tool to reduce the heavy communication load for distributed algorithms as agents iteratively communicate with neighbors, we propose three distributed primal–dual algorithms with compressed communication. The first two algorithms are applicable to a general class of compressors with bounded relative compression error and the third algorithm is suitable for two general classes of compressors with bounded absolute compression error. We show that the proposed distributed algorithms with compressed communication have comparable convergence properties as state-of-the-art algorithms with exact communication. Specifically, we show that they can find first-order stationary points with sublinear convergence rate O(1/T) when each local cost function is smooth, where T is the total number of iterations, and find global optima with linear convergence rate under an additional condition that the global cost function satisfies the Polyak–Lojasiewicz condition. Numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

Development and performance analysis of machine learning methods for predicting depression among menopausal women

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Healthcare Analytics 2023年 3卷 100202-100202页

作者： Ali, Md. Mamun Algashamy, Hussein Ali A. Alzidi, Enas Ahmed, Kawsar Bui, Francis M. Patel, Shobhit K. Azam, Sami Abdulrazak, Lway Faisal Moni, Mohammad Ali Department of Software Engineering (SWE) Daffodil International University (DIU) Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh Medical laboratory techniques Al-Rafidain University College Baghdad Iraq Computer Technologies Engineering Al-Turath University College Baghdad Iraq Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon SK S7N 5A9 Canada Group of Biophotomatiχ Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Department of Computer Engineering Marwadi University Gujarat Rajkot 360003 India College of Engineering IT and Environment Charles Darwin University Casuarina NT 0909 Australia Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya 46001 Iraq Artificial Intelligence & Digital Health School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD 4072 Australia

Menopause is an obligatory phenomenon in a woman's life. Some women face mental and physical issues during their menopausal period. Depression is one of the issues some women struggle with during their menopausal period. The scarcity of specialists, lack of knowledge, and awareness is the motivating factor in this research to predict depression among menopausal women and enhance their quality of life. The prediction of depression symptoms among menopausal women with machine learning techniques is promising and challenging in artificial intelligence. This study develops a system with significant accuracy using a supervised machine-learning approach. Various classification algorithms are used to determine the best-performing classifier by evaluating multiple parameters, including accuracy, sensitivity, specificity, precision, recall, F-Measure, Receiver Operating Characteristic (ROC), Precision–Recall Curve (PRC), and Area Under the Curve (AUC). We found that Random Forest and XGBoost classifiers are the performers with 99.04% accuracy employing the 14 most significant features. © 2023 The Author(s)

关键词： Artificial intelligence Depression Machine learning Menopause Quality of life Random Forest