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A novel hybrid ensemble approach for wind speed forecasting with dual-stage decomposition strategy using optimized GRU and transformer models

引用

Energy 2025年 329卷

作者： Ullah, Sajid Chen, Xi Han, Han Wu, Junhao Dong, Jinghan Liu, Ruiqing Ding, Weijie Liu, Min Li, Qingli Qi, Honggang Huang, Yonggui Yu, Philip Lh East China Normal University Shanghai200241 China School of Geographic Sciences East China Normal University Shanghai200241 China Software Engineering Institute East China Normal University Shanghai200026 China State Key Laboratory of Estuarine and Coastal Research East China Normal University Shanghai200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Peking University Chongqing Research Institute of Big Data Chongqing High-tech Zone Science Valley Building 10 China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong

Wind energy has attracted global interest owing to its sustainable and environmentally friendly characteristics. Nevertheless, precisely forecasting wind speed can be challenging due to its volatile and unpredictable nature. This paper presents a new hybrid forecasting approach based on dual stage decomposition mechanism, namely TMQGDT for wind speed prediction. At first, a decomposition technique called time-varying filtered based empirical mode decomposition (TVFEMD) is utilized to decompose the original wind speed data into several intrinsic mode functions (IMFs). Afterwards, multi-scale permutation entropy (MPE) is used to assess the complexity of each IMF. Based on the entropy values, the IMFs are further classified into high-frequency and low-frequency IMFs. To address the significant volatility observed in the high-frequency IMFs, discrete wavelet transform (DWT) method is employed to perform secondary decomposition. The low-frequency IMFs are forecasted using gated recurrent unit (GRU) model optimized with quantum particle swarm optimization (QPSO) algorithm, while the high-frequency IMFs are forecasted with the Transformer model. The proposed model is trained and validated using four wind speed time series datasets collected from Germany and China. Five individual models and six hybrid models are compared against the proposed model to validate the forecasting performance of the proposed TMQGDT model. The prediction outcomes reveals that the R2 of the model is 0.973, 0.968, 0.956, and 0.996 on the four dataset test sets, which has improved by 3.39 %, 3.93 %, 5.53 %, and 0.50 %, respectively, compared to the TVFEMD-MPE-QPSO-GRU-DWT-Autoformer model. The excellent accuracy performance of the TMQGDT model indicates that developing a hybrid model based on deep learning techniques using secondary decomposition mechanism and optimization algorithm can enhance the precision of wind speed prediction. © 2025

关键词： Particle swarm optimization (PSO)

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DFPL: Decentralized Federated Prototype Learning Across Heterogeneous data Distributions

arXiv

引用

arXiv 2025年

作者： Zhang, Hongliang Xu, Fenghua Yu, Zhongyuan Hu, Chunqiang Pang, Shanchen Wang, Xiaofen Yu, Jiguo School of Computer Science and Technology Qilu University of Technology Shandong Jinan250353 China Cyber Security Institute University of Science and Technology of China Anhui Hefei230026 China School of Information Science and Engineering Lanzhou University Gansu Lanzhou730000 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China Shandong Qingdao266580 China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Federated learning is a distributed machine learning paradigm that enables the collaborative training of multiple clients through centralized model aggregation. However, standard federated learning relies on a centralized server, making it vulnerable to server failures. While existing solutions utilize blockchain technology to implement Decentralized Federated Learning (DFL), the statistical heterogeneity of data distributions among clients severely degrades the DFL’s performance. Driven by this issue, this paper proposes a decentralized federated prototype learning framework, named DFPL, which significantly improves the performance of distributed machine learning across heterogeneous data distributions. Specifically, our framework introduces prototype learning into DFL to address statistical heterogeneity, which greatly reduces the number of parameters exchanged between clients. Additionally, blockchain is embedded into our framework, enabling the training and mining processes to be implemented at each client. From a theoretical perspective, we provide convergence guarantee of DFPL by combining resource allocation for training and mining. The experiments highlight the superiority of our DFPL framework in communication efficiency and test performance across three benchmark datasets with heterogeneous data distributions. © 2025, CC BY.

关键词： Federated learning

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Digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach

arXiv

引用

arXiv 2024年

作者： He, Long Sun, Geng Sun, Zemin Wang, Jiacheng Du, Hongyang Niyato, Dusit Liu, Jiangchuan Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada R&D Department Jiangxing Intelligence Inc. Nanjing210000 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

The emergence of space-air-ground integrated multi-access edge computing (SAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various applications by offering efficient communication and computing services. However, the heterogeneous nature of SAGIMEC networks, coupled with the stringent computational and communication requirements of diverse applications in the LAE, introduces considerable challenges in integrating SAGIMEC into the LAE. In this work, we first present a digital twin-assisted SAGIMEC paradigm for LAE, where digital twin enables reliable network monitoring and management, while SAGIMEC provides efficient computing offloading services for Internet of Things sensor devices (ISDs). Then, a joint satellite selection, computation offloading, communication resource allocation, computation resource allocation and UAV trajectory control optimization problem (JSC4OP) is formulated to maximize the quality of service (QoS) of ISDs. Given the complexity of JSC4OP, we propose an online decentralized optimization approach (ODOA) to address the problem. Specifically, JSC4OP is first transformed into a real-time decision-making optimization problem (RDOP) by leveraging Lyapunov optimization. Then, to solve the RDOP, we introduce an online learning-based latency prediction method to predict the uncertain system environment and a game theoretic decision-making method to make real-time decisions. Finally, theoretical analysis confirms the effectiveness of the ODOA, while the simulation results demonstrate that the proposed ODOA outperforms other alternative approaches in terms of overall system performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Quality of service

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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

引用

arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Cooperative Sensing and Heterogeneous Information Fusion in VCPS: A Multi-agent Deep Reinforcement Learning Approach

arXiv

引用

arXiv 2022年

作者： Xu, Xincao Liu, Kai Dai, Penglin Xie, Ruitao Cao, Jingjing Luo, Jiangtao The College of Computer Science Chongqing University Chongqing400040 China The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Transportation and Logistics Engineering Wuhan University of Technology Hubei430063 China The Electronic Information and Networking Research Institute Chongqing University of Posts and Telecommunications Chongqing400065 China

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent deep reinforcement learning solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Correction: Uncertainty Modelling in Performability Prediction for Safety-Critical Systems

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Arabian Journal for science and engineering 2024年第2期50卷 1353-1353页

作者： Ahamad, Shakeel Gupta, Ratneshwer Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Software Quality Assurance Lab School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

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Attentive Prototype Few-Shot Learning with Capsule Network-Based Embedding 1

引用

16th European Conference on Computer Vision, ECCV 2020

作者： Wu, Fangyu Smith, Jeremy S. Lu, Wenjin Pang, Chaoyi Zhang, Bailing Department of Computer Science and Software Engineering Xi’an Jiaotong-liverpool University SuZhouJiangSu Province China Department of Electrical Engineering and Electronic University of Liverpool Liverpool United Kingdom School of Computer and Data Engineering Zhejiang University Ningbo Institute of Technology NingboZhejiang Province China

ISBN: (数字)9783030586041

ISBN: (纸本)9783030586034

Few-shot learning, namely recognizing novel categories with a very small amount of training examples, is a challenging area of machine learning research. Traditional deep learning methods require massive training data to tune the huge number of parameters, which is often impractical and prone to over-fitting. In this work, we further research on the well-known few-shot learning method known as prototypical networks for better performance. Our contributions include (1) a new embedding structure to encode relative spatial relationships between features by applying a capsule network;(2) a new triplet loss designated to enhance the semantic feature embedding where similar samples are close to each other while dissimilar samples are farther apart;and (3) an effective non-parametric classifier termed attentive prototypes in place of the simple prototypes in current few-shot learning. The proposed attentive prototype aggregates all of the instances in a support class which are weighted by their importance, defined by the reconstruction error for a given query. The reconstruction error allows the classification posterior probability to be estimated, which corresponds to the classification confidence score. Extensive experiments on three benchmark datasets demonstrate that our approach is effective for the few-shot classification task. © 2020, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Collaborative Filtering Recommendation Algorithm Based on Multi-relationship Social Network

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Ingenierie des Systemes d'Information 2020年第3期25卷 359-364页

作者： Liu, Yue Yang, Hua Sun, Gengxin Bin, Sheng Institute of Information Engineering Kaifeng University Kaifeng475000 China School of Data Science and Software Engineering Qingdao University Qingdao266071 China

For solving the data sparsity of traditional algorithms, this paper proposes a novel collaborative filtering recommendation algorithm based on multi-relationship social network. On the basis of traditional matrix decomposition model, the proposed algorithm obtains the trust and trust feature matrix by integrating the user preferences of multi-relationship social network, and then, the rating of the commodity is predicted by the social feature matrix, the commodity feature matrix, and the similarity of user rating preference. In order to verify the reliability of the proposed algorithm, the Epinions dataset is used to compare the performance of the algorithm with that of the existing social network recommendation algorithms. According to the experimental results, the proposed algorithm had smaller mean absolute error (MAE) and root mean square error (RMSE), indicating that it has effectively reduced the impact of data sparsity on recommendation results and improved the recommendation accuracy. © 2020 International Information and engineering Technology Association. All rights reserved.

关键词： Complex networks

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Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence

arXiv

引用

arXiv 2024年

作者： Jiang, Yuncheng Feng, Chun-Mei Ren, Jinke Wei, Jun Zhang, Zixun Hu, Yiwen Liu, Yunbi Sun, Rui Tang, Xuemei Du, Juan Wan, Xiang Xu, Yong Du, Bo Gao, Xin Wang, Guangyu Zhou, Shaohua Cui, Shuguang Goh, Rick Siow Mong Liu, Yong Li, Zhen Shenzhen518172 China Shenzhen518172 China Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Research Institute of Big Data Shenzhen518172 China South China Hospital Health Science Center Shenzhen University Shenzhen518111 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Affiliated Hospital of North Sichuan Medical College Sichuan637000 China North Sichuan Medical College Sichuan637000 China Shenzhen518055 China School of Computer Science Wuhan University Wuhan430072 China Thuwal23955-6900 Saudi Arabia Thuwal23955-6900 Saudi Arabia Beijing University of Posts and Telecommunications Beijing100876 China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, conventional ultrasound diagnostics face several limitations, including high dependence on physician expertise and suboptimal image quality, which complicates interpretation and increases the likelihood of diagnostic errors. Artificial intelligence (AI) has emerged as a promising solution to enhance clinical diagnosis, particularly in detecting abnormalities across various biomedical imaging modalities. Nonetheless, current AI models for ultrasound imaging face critical challenges. First, these models often require large volumes of labeled medical data, raising concerns over patient privacy breaches. Second, most existing models are task-specific, which restricts their broader clinical utility. To overcome these challenges, we present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries, leveraging a dataset of over 1 million ultrasound images covering 19 organs and 10 ultrasound modalities. This extensive and diverse data, combined with a secure training framework, enables UltraFedFM to exhibit strong generalization and diagnostic capabilities. It achieves an average area under the receiver operating characteristic curve (AUROC) of 0.927 for disease diagnosis and a dice similarity coefficient (DSC) of 0.878 for lesion segmentation. Notably, UltraFedFM surpasses the diagnostic accuracy of mid-level ultrasonographers (4–8 years of experience) and matches the performance of expert-level sonographers (10+ years of experience) in the joint diagnosis of 8 common systemic diseases. These findings indicate that UltraFedFM can significantly enhance clinical diagnostics while safeguarding patient privacy, marking a significant advancement in AI-driven ultrasound imaging for future clinical applications. Copyri

关键词： Differential privacy

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Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalization

arXiv

引用

arXiv 2021年

作者： Zhou, Ziqi Qi, Lei Yang, Xin Ni, Dong Shi, Yinghuan The State Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University China The School of Computer Science and Engineering Southeast University China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center The Medical Ultrasound Image Computing Lab The Marshall Laboratory of Biomedical Engineering Shenzhen University China

For medical image segmentation, imagine if a model was only trained using MR images in source domain, how about its performance to directly segment CT images in target domain? This setting, namely generalizable cross-modality segmentation, owning its clinical potential, is much more challenging than other related settings, e.g., domain adaptation. To achieve this goal, we in this paper propose a novel dual-normalization model by leveraging the augmented source-similar and source-dissimilar images during our generalizable segmentation. To be specific, given a single source domain, aiming to simulate the possible appearance change in unseen target domains, we first utilize a nonlinear transformation to augment source-similar and source-dissimilar images. Then, to sufficiently exploit these two types of augmentations, our proposed dual-normalization based model employs a shared backbone yet independent batch normalization layer for separate normalization. Afterward, we put forward a style-based selection scheme to automatically choose the appropriate path in the test stage. Extensive experiments on three publicly available datasets, i.e., BraTS, Cross-Modality Cardiac, and Abdominal Multi-Organ datasets, have demonstrated that our method outperforms other state-of-the-art domain generalization methods. Code is available at https://***/zzzqzhou/Dual-Normalization. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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