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Comparative analysis of carbon dioxide and hydrogen plume migration in aquifers inspired by the FluidFlower benchmark study

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International Journal of Hydrogen Energy 2025年 135卷 56-68页

作者： Misaghi Bonabi, Amin van Rooijen, Willemijn Al Kobaisi, Mohammed Vuik, Cornelis Hajibeygi, Hadi Department of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of Geoscience and Engineering Faculty of Civil Engineering and Geosciences Delft University of Technology Delft Netherlands

Large-scale geological storages of hydrogen (H2) and carbon dioxide (CO2) in saline aquifers present feasible options for a sustainable energy future. We compared the plume migration of CO2 and H2 in aquifers using the FluidFlower benchmark, incorporating the state-of-the-art thermophysical and petrophysical properties. The H2 plume, with its higher buoyancy and mobility compared to CO2, remains predominantly in the gas phase due to its lower solubility, increasing the chances of escaping through fractures or migration to distant regions. This additionally leads to a higher pressurized reservoir, which, along with higher buoyancy, increases the chance of caprock penetration. Dissolution trapping of CO2 into brine increases over time due to its fingering, while H2 does not show fingering. Our findings show that while geological carbon storage (GCS) benefits significantly from all structural, dissolution, and residual trapping, underground hydrogen storage (UHS) relies mainly on structural trapping, making the integrity of sealing elements of the system a key factor in its performance. © 2025 The Authors

关键词： Two phase flow

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CNN-Transformer-Based Cooperative Spectrum Sensing in Cognitive Radio Networks

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IEEE Wireless Communications Letters 2025年第5期14卷 1576-1580页

作者： Fang, Xiangqian Jin, Ming Guo, Qinghua Jiang, Tao Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia

Recently, deep learning (DL)-based data-driven spectrum sensing has attracted much attention due to its powerful capabilities to exploit underlying signal features. Existing DL-based cooperative spectrum sensing (CSS) detectors usually extract the signal features from each secondary user (SU) individually, followed by feature fusion at a central fusion center (FC). However, this cannot extract correlation features across SUs. To address this limitation, this letter proposes a CNN-Transformer neural network to exploit the correlations among the received signals across and within SUs. Leveraging the CNN-Transformer, we propose a posteriori probability ratio detector, which is optimal for the binary hypothesis testing problem under the Neyman-Pearson criterion. Moreover, unlike most DL-based CSS detectors, the proposed detector considers imperfect and independent reporting channels between SUs and the FC, and exploits the temporal correlation of signals among neighboring samples. Numerical results are provided to demonstrate the superior performance of the proposed detector, showing an improvement of approximately 6 dB over traditional model-driven energy detectors. Moreover, the proposed detector improves the detection probability to 0.99 from 0.88 of data-driven detectors under Rician fading channels. © 2012 IEEE.

关键词： Fading channels

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MST-Net: Multispectral transformer network with geo-guided query for multi-label remote sensing image scene classification

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Pan, Yukai Jin, Wei Fu, Randi Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

Multi-label scene classification (MLSC) in remote sensing (RS) plays a crucial role in recognizing the intricate contents of RS images. However, the MLSC task is challenged by the complexity of label combinations and the diversity of visual content. Multispectral (MS) data provide valuable information for scene classification. To achieve accurate classification results, it is important to fully utilize the diverse information contained in MS data. To this end, we propose a multispectral transformer network (MST-Net), which leverages transformer-based architectures to capture the diverse information within MS images and the complex relationships between labels and features. Specifically, MST-Net consists of a feature fusion encoder and a semantic query decoder. Within the encoder, we have developed an MS deformable attention module based on a sampling strategy that reduces focus on redundant spectral areas, allowing for better integration of complementary MS information. In the decoder, geographic information is introduced as an inductive bias, utilizing the unique spatiotemporal characteristics of RS images to learn better class-related features. Extensive experiments were conducted on two RS multi-label datasets, namely, LSCIDMRv2 and BigEarthNet. Comparisons with several state-of-the-art multi-label classification methods demonstrate the effectiveness and superiority of MST-Net. © 2024 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Deep learning

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JLOS: a Cooperative UAV-Based Optical Wireless Communication With Multi-Agent Reinforcement Learning

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1345-1356页

作者： Liu, Jiangang Luo, Hanjiang Tao, Hang Liu, Jiahong Zhou, Jiehan Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland

In maritime Internet of Things (IoT) systems, leveraging a swarm of Unmanned Aerial Vehicles (UAVs) and optical communication can achieve a variety of potential maritime missions. However, due to the high directionality of the optical beam and interference from the marine environment, the optical link via UAVs as relays is prone to interruption. To address this challenge, we propose a Joint Link Optimization Scheme (JLOS) that includes Wind Disturbance Resistance (WDR) and Adaptive Beamwidth Adjustment (ABA). In WDR, we first model the problem as a Partially Observed Markov Decision Process (POMDP), and then design a collaborative Multi-Agent Reinforcement Learning (MARL) approach to control a swarm of UAVs in windy conditions, to maintain mechanical stability and prevent link interruption. Furthermore, in ABA, to reduce uncertainties from control activities and environmental factors like sunlight and fog, we design an adaptive algorithm using distributed MARL. It adjusts beamwidth based on historical UAV locations and link Bit Error Ratio (BER) to improve communication reliability. Numerical simulations confirm its effectiveness in enhancing robust data transmission. © 2004-2012 IEEE.

关键词： Adaptive algorithms

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Geometric Interpretation of Sensitivity to Structured Uncertainties in Spintronic Networks

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IEEE Control Systems Letters 2025年 9卷 192-197页

作者： O'Neil, S.P. Jonckheere, E.A. Schirmer, S. United States Military Academy Department of Electrical Engineering and Computer Science NY United States University of Southern California Dept of Electrical and Computer Engineering CA United States Swansea University Faculty of Science and Engineering Physics United Kingdom

We present a geometric model of the differential sensitivity of the fidelity error for state transfer in a spintronic network based on the relationship between a set of matrix operators. We show an explicit dependence of the differential sensitivity on the fidelity (error), and we further demonstrate that this dependence does not require a trade-off between the fidelity and sensitivity. We prove that for closed systems, ideal performance in the sense of perfect state transfer is both necessary and sufficient for optimal robustness in terms of vanishing sensitivity. We demonstrate the utility of this geometric interpretation of the sensitivity by applying the model to explain the sensitivity versus fidelity error data in two examples. © 2017 IEEE.

关键词： Sensitivity analysis

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False Data Injection Attack Detection and Localization Framework in Power Distribution Systems Using a Novel Ensemble of CNNs and Explainable Artificial Intelligence

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IEEE Transactions on Industry Applications 2025年第3期61卷 4801-4811页

作者： Dehbozorgi, Mohammad Reza Rastegar, Mohammad Arani, Mohammadreza F. M. Shiraz University School of Electrical and Computer Engineering Shiraz Iran Toronto Metropolitan University Faculty of Engineering and Architectural Science Toronto Canada

Cyber-physical power systems are vulnerable to cyber-attacks, especially false data injection attacks (FDIAs). FDIAs against distribution system state estimation (DSSE), which alter state estimation (SE) by changing meter readings, have received researchers' attention in recent years. A common defense against FDIAs in the literature is the use of labeled data to train classifiers as FDIA detectors. However, this approach's performance can be limited by the highly imbalanced nature of FDIA datasets. The black box characteristics of the machine learning models can make them hard to trust and adopt in important applications. Hence, we propose an innovative explainable artificial intelligence (XAI)-enhanced ensemble-based detection and localization model that leverages convolutional neural networks (CNNs) and support vector machine (SVM). The ensemble model uses SVM to merge various spatiotemporal CNNs' outputs. Training these CNNs on under-sampled subsets of the majority class and using their ensemble addresses class imbalance. This paper leverages XAI to enhance the interpretability of the detection process and improve localization accuracy. The localization process uses the outputs of an XAI technique, gradient-weighted class activation mapping, to aid the majority-voting-based localization ensemble model. Our model can also detect FDIAs during the distribution feeder's topology changes. Extensive simulations on IEEE 13-bus and IEEE 123-bus feeders prove the proposed under-sampling-based detection approach as an alternative to prevalent over-sampling methods like generative adversarial networks (GANs), offering a novel solution to class imbalance challenges. The paper also provides a comprehensive analysis of the proposed spatiotemporal model's performance, demonstrating its superiority over temporal CNNs. © 1972-2012 IEEE.

关键词： DC distribution systems

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AG Codes Have No List-Decoding Friends: Approaching the Generalized Singleton Bound Requires Exponential Alphabets

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IEEE Transactions on Information Theory 2025年第4期71卷 2443-2451页

作者： Alrabiah, Omar Guruswami, Venkatesan Li, Ray University of California at Berkeley Department of Electrical Engineering and Computer Science BerkeleyCA94709 United States University of California at Berkeley Department of Electrical Engineering and Computer Science Department of Mathematics Simons Institute for the Theory of Computing BerkeleyCA94709 United States Santa Clara University Department of Mathematics and Computer Science Santa ClaraCA95053 United States

A simple, recently observed generalization of the classical Singleton bound to list-decoding asserts that rate R codes are not list-decodable using list-size L beyond an error fraction L/L+1 (1-R) (the Singleton bound being the case of L=1 , i.e., unique decoding). We prove that in order to approach this bound for any fixed L > 1 , one needs exponential alphabets. Specifically, for every L > 1 and R ∈ (0,1) , if a rate R code can be list-of-L decoded up to error fraction L/L+1 (1-R - ∈ ) , then its alphabet must have size at least exp (ΩL,R(1\ ∈)) . This is in sharp contrast to the situation for unique decoding where certain families of rate R algebraic-geometry (AG) codes over an alphabet of size O(1\ ∈2) are unique-decodable up to error fraction (1-R-\ ∈ )2 . Our bounds hold even for subconstant ∈ ≥ 1/n , implying that any code exactly achieving the L-th generalized Singleton bound requires alphabet size 2ΩL,R(n) . Previously this was only known only for L=2 under the additional assumptions that the code is both linear and MDS. Our lower bound is tight up to constant factors in the exponent—with high probability random codes (or, as shown recently, even random linear codes) over exp(OL(1\ ∈)) -sized alphabets, can be list-of-L decoded up to error fraction L/L+1 (1-R -∈ ) . © 1963-2012 IEEE.

关键词： Errors

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Fluid Antenna-Assisted Simultaneous Wireless Information and Power Transfer Systems

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8285-8290页

作者： Zhou, Liaoshi Yao, Junteng Wu, Tuo Jin, Ming Yuen, Chau Adachi, Fumiyuki Ningbo University faculty of Electrical Engineering and Computer Science Ningbo315211 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Sendai Japan

This paper examines a fluid antenna (FA)-assisted simultaneous wireless information and power transfer (SWIPT) system. Unlike traditional SWIPT systems with fixed-position antennas (FPAs), our FA-assisted system enables dynamic reconfiguration of the radio propagation environment by adjusting the positions of FAs. This capability enhances both energy harvesting and communication performance. The system comprises a base station (BS) equipped with multiple FAs that transmit signals to an energy receiver (ER) and an information receiver (IR), both equipped with a single FA. Our objective is to maximize the communication rate between the BS and the IR while satisfying the harvested power requirement of the ER. This involves jointly optimizing the BS's transmit beamforming and the positions of all FAs. To address this complex convex optimization problem, we employ an alternating optimization (AO) approach, decomposing it into three sub-problems and solving them iteratively using first and second-order Taylor expansions. Simulation results validate the effectiveness of our proposed FA-assisted SWIPT system, demonstrating significant performance improvements over traditional FPA-based systems. © 2025 IEEE.

关键词： Convex optimization

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JC-GN: multi-scene joint conditional network for remote sensing sequence imagery generation under spatio-temporal variation

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Journal of King Saud University - computer and Information sciences 2025年第3期37卷 1-13页

作者： Zhao, Zi-Yi Jin, Xing Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo 315211 China

Remote sensing imagery plays an important role in the research and application of environmental change, such as phenology changes, land surface parameter relationships, land degradation, etc. However, remote sensing observation data are often missing due to sensor resolution limitations and environmental factors. The missing images may occur randomly or continuously, which is difficult to deal with this situation with conventional methods. To address this issue, we perform the multi-scene joint conditional generation network (JC-GN) based on radiation properties to learn the complex mapping between phase images. The proposed model introduces multi-scene joint conditional loss to constrain the spatio-temporal characteristics of the generated image. Our experiments, conducted utilizing unmanned aerial vehicle (UAV) datasets and the Landsat-8 datasets, which are located in the southeast of Gansu Province (34.73∘N, 105.50∘E), demonstrate that the proposed methodology achieves promising performance in terms of both precision (RMSE decreases by about 2.5 points, PSNR increases by about 10 points, and SSIM increases by about 0.03 points) and effectiveness (time cost savings of about 50 percent), and data-driven model exhibits a superior capacity to simulate intricate information with greater fidelity. © The Author(s) 2025.

关键词： Data driven model Image generation Landsat-8 Multi-scene joint conditional network Unmanned Aerial Vehicle (UAV)

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Particle-based Data-driven Nonlinear State Estimation of Model-free Process from Nonlinear Measurements

Particle-based Data-driven Nonlinear State Estimation of Mod...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ghosh, Anubhab Eldar, Yonina C. Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Mathematics and Computer Science The Weizmann Institute of Science Israel

ISBN: (纸本)9798350368741

We consider the problem of causal filtering of a model-free process from (noisy) nonlinear measurements. The 'model-free process' means that we do not have a state-space model (SSM) of the process dynamics, limiting the use of traditional model-driven filters, such as unscented Kalman filter (UKF) and particle filter (PF). To address the problem we propose a particle-based data-driven nonlinear state estimation (pDANSE) method. In pDANSE, a recurrent neural network (RNN) provides the statistical parameters of a Gaussian prior of the underlying state, and particles are then drawn from the prior to compute the posterior moments. pDANSE is typically trained in a semi-supervised fashion. For our experiments we study the use of half-wave rectification as a nonlinear transformation of measurements. We first show that an unsupervised learning-based method under-performs, and subsequently the semi-supervised learning-based pDANSE performs satisfactorily. Using Lorenz-63 system as benchmark, pDANSE is found to be competitive against a model-driven PF that knows the exact SSM. © 2025 IEEE.

关键词： deep neural network Kalman filter particle filter semi-supervised learning State estimation

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