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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory Control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery

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Fundamental Research 2025年

作者： Lan, Wei Tang, Zhentao Liao, Haibo Chen, Qingfeng Chen, Yi-Ping Phoebe Zhang, Zhaolei Wang, Jianxin Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Guangxi 530004 China Department of Computer Science and Information Technology La Trobe University 3086 VIC Australia Donnelly Centre Department of Computer Science and Department of Molecular Genetics University of Toronto M5S 1A1 ON Canada School of Computer Science and Engineering Central South University Hunan 410083 China

Multi-omics cancer data provides complementary views of tumorigenesis and progression. Technical challenges exist in integrating these heterogeneous data into deep learning models to better understand tumorigenesis and predict cancer recurrence. We herein propose a novel end-to-end deep learning method (MULGONET) for cancer recurrence prediction and biomarker discovery. First, MULGONET can effectively solve the curse of dimensionality and the lack of model interpretability in multi-omics data integration. Second, it explores interactions and regulatory relationships between genes and GO terms, thus providing biological insights. Benchmark results show that MULGONET outperforms other contemporary classification methods. It achieves AUPRs of 0.774 ± 0.015, 0.873 ± 0.003 and 0.702 ± 0.011 on the bladder, pancreatic and stomach cancer datasets, respectively. We also show that MULGONET can effectively identify prognostic genes and GO terms associated with cancer recurrence. © 2025

关键词： Biomarker discovery Cancer recurrence Deep learning Interpretability Multi-omics data

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QoE-Guaranteed Optimization in MEC-Enabled Metaverse: An Active Inference Deep Reinforcement Learning Approach

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Du, Jianbo Gong, Jie Chu, Xiaoli Xiong, Zehui Chen, Xianfu Dong, Mianxiong Yu, F. Richard Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an710121 China The University of Sheffield Mappin Street Department of Electronic and Electrical Engineering SheffieldS1 3JD United Kingdom Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore Singapore Shenzhen CyberAray Network Technology Company Ltd Shenzhen518000 China Muroran Institute of Technology Department of Sciences and Informatics Muroran050-8585 Japan Carleton University Systems and Computer Engineering OttawaON Canada

In this paper, we consider a MEC-enabled metaverse scenario which consists of a remote metaverse server and an edge server that cooperates to provide services to mobile users. The edge server is deployed at the base station (BS), serves a dual role: augmenting computational capabilities for user equipment (UE) and pre-caching a portion of the metaverse service contents before each time slot. Moreover, the foreground information and the requested contents generated by the UEs can also be cached to the BS. We formulate a problem to maximize the cache hit number by jointly optimizing contents pre-caching and resource allocation at the BS while considering UEs preference and reducing the UEs total energy consumption, essential for the efficient delivery of services in dynamic MEC environments. To solve this problem, we reformulate it as a partially observable markov decision process and propose an active inference enabled deep reinforcement learning algorithm, which combines active inference with deep reinforcement learning to select the optimal strategy by minimizing the expected free energy. Simulations show that the proposed algorithm can effectively improve the total quality of experience and the cache hit number of UEs, while minimizing the UEs total energy consumption compared with other baseline algorithms © 2015 IEEE.

关键词： Active learning

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Blind Joint Multi-DFO Estimation for High-Mobility OFDM Communication Systems

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IEEE Transactions on Vehicular technology 2025年

作者： Feng, Yunqi Zhu, Zhehua Lu, Weidang Zhao, Nan Nallanathan, Arumugam Zhejiang University of Technology College of Information Engineering Key Laboratory of Communication Networks and Applications of Zhejiang Province Hangzhou310023 China Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

In high-mobility communication systems, the relative motion between the transceivers will cause severe Doppler shifts, resulting in the doubly-selective fading channel. In this correspondence, a blind joint multiple Doppler frequency offsets (DFOs) estimator is proposed for high-mobility communication with orthogonal frequency division multiplexing (OFDM). Both the receive beamforming and multi-beamformer redundancy are employed to estimate and compensate multiple DFOs, thereby effectively mitigating the channel time-variation. Moreover, the inter-channel interference (ICI) has been derived to further analyze the impact of residual DFOs due to the estimation error on the system performance. Numerical results verify that the proposed blind estimator substantially outperforms existing ones. © 1967-2012 IEEE.

关键词： Frequency allocation

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Resource and Trajectory Optimization for Secure Enhancement in IRS-Assisted UAV-MEC Systems

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IEEE Transactions on Vehicular technology 2025年

作者： Zhang, Fuyuqi Ding, Yu Cao, Mingfeng Wu, Mengru Lu, Weidang Nallanathan, Arumugam Zhejiang University of Technology College of Information Engineering Hangzhou310023 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggido Yongin-si17104 Korea Republic of

Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle-mobile edge computing (UAV-MEC) systems have brought great advancements in future networks. However, due to the line-of-sight transmission, confidential task information is highly susceptible to interception by malicious UAV eavesdroppers, posing a significant security challenge. To overcome this challenge, we aim to design a joint resource and trajectory optimization scheme to enhance the secure performance of the IRS-assisted UAV-MEC system. Specifically, the minimum secrecy capacity of users is maximized by optimizing time allocation, transmit power, local computation CPU frequency, IRS phase shifts, and UAV trajectory while satisfying users' task processing capacity requirements. To address the non-convex optimization problem, we first employ mathematical techniques to simplify it into a more tractable form, and subsequently decompose it into several sub-problems. These sub-problems can be solved iteratively through combining phase alignment with successive convex approximation. Simulation results verify that our proposed scheme can improve the secrecy capacity performance of the considered IRS-assisted UAV-MEC system compared with benchmarks. © 1967-2012 IEEE.

关键词： Convex optimization

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Facial age estimation by group-centric feature learning

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Expert Systems with Applications 2025年 285卷

作者： Zhao, Qilu Li, Yante College of Computer Science and Technology Qingdao University 308 Ningxia Road Qingdao266000 China Department of Information Technology and Electronic Engineering University of Oulu Pentti Kaiteran katu 1 Oulu90570 Finland

Predicting age from facial images presents a significant challenge due to the intricate nature of the facial aging process. Human faces undergo gradual changes as they age, causing faces to appear very similar at adjacent ages. However, notable age differences result in significant appearance changes, as the human face matures in different ways at different ages, e.g., bone growth in childhood and skin wrinkles in adulthood. In a word, the process of facial aging is continuous but also non-stationary. To address these challenges, our approach is grounded in the insight that the non-stationary aging process can be decomposed into a series of stationary subprocesses. Using a divide-and-conquer strategy, we divide ages into several overlapping groups to facilitate group-centric feature learning. Within each age group, where the aging dynamics are relatively stable, we employ a specialized deep neural network, termed an "expert", to extract group-centric ordinal features. The overlapping of age groups allows each expert to capture the continuity between adjacent groups. The group-centric features are then fused into universal representations, with an attention layer employed to selectively emphasize the most salient feature pertinent to the final age estimation task. For the age estimator, we leverage a hybrid method involving expectation regression to alleviate the inconsistency between training and evaluation stages found in traditional label distribution learning estimators. Extensive experiments involving several famous datasets (MORPH2, CACD, and AFAD) for age estimation have demonstrated the superior performance of our proposed model over several existing state-of-the-art methods. © 2025 Elsevier Ltd

关键词： Self-supervised learning

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Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks

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IEEE Transactions on Communications 2025年

作者： Yang, Li Naser, Shimaa Shami, Abdallah Muhaidat, Sami Ong, Lyndon Debbah, Merouane Ontario Tech University Faculty of Business and Information Technology OshawaONL1G 0C5 Canada Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Western University Department of Electrical and Computer Engineering LondonONN6A 3K7 Canada Ciena HanoverMD21076 United States

The transition from fifth-generation (5G) to sixth-generation (6G) mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017) datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity. © 1972-2012 IEEE.

关键词： Cybersecurity

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Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 162-184页

作者： Azami, Hamed Mirjalili, Mina Rajji, Tarek K. Wu, Chien-Te Humeau-Heurtier, Anne Jung, Tzyy-Ping Wei, Chun-Shu Trinh, Thanh-Tung Liu, Yi-Hung University of Toronto Centre for Addiction and Mental Health TorontoONM5S 1A1 Canada University of Texas Southwestern Medical Center DallasTX75390 United States University of Florida Department of Occupational Therapy GainesvilleFL32611 United States University of Florida Center for Cognitive Aging and Memory McKnight Brain Institute GainesvilleFL32611 United States University of Angers LARIS SFR MATHSTIC AngersF-49000 France University of California Institute for Neural Computation Institute of Engineering in Medicine La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu City30010 Taiwan National Taipei University of Technology Graduate Institute of Manufacturing Technology College of Mechanical and Electrical Engineering Taipei10608 Taiwan Vietnam National University Department of Information and Communication Technology Faculty of Nontraditional Security HSB Hanoi720325 Viet Nam National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan

Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application. © 2024 IEEE.

关键词： Deep learning

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Pinching Antennas: Principles, Applications and Challenges

arXiv

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arXiv 2025年

作者： Yang, Zheng Wang, Ning Sun, Yanshi Ding, Zhiguo Schober, Robert Karagiannidis, George K. Wong, Vincent W.S. Dobre, Octavia A. Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou350117 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Khalifa University Abu Dhabi United Arab Emirates University of Manchester ManchesterM1 9BB United Kingdom Germany Group Electrical & Computer Engineering Dept. Aristotle University of Thessaloniki Thessaloniki54 124 Greece Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Department of Electrical and Computer Engineering Memorial University St. John’sNL Canada

Flexible-antenna systems, such as fluid antennas and movable antennas, have been recognized as key enabling technologies for sixth-generation (6G) wireless networks, as they can intelligently reconfigure the effective channel gains of the users and hence significantly improve their data transmission capabilities. However, existing flexible-antenna systems have been designed to combat small-scale fading in non-line-of-sight (NLoS) conditions. As a result, they lack the ability to establish line-of-sight links, which are typically 100 times stronger than NLoS links. In addition, existing flexible-antenna systems have limited flexibility, where adding/removing an antenna is not straightforward. This article introduces an innovative flexible-antenna system called pinching antennas, which are realized by applying small dielectric particles to waveguides. We first describe the basics of pinching-antenna systems and their ability to provide strong LoS links by deploying pinching antennas close to the users as well as their capability to scale up/down the antenna system. We then focus on communication scenarios with different numbers of waveguides and pinching antennas, where innovative approaches to implement multiple-input multiple-output and non-orthogonal multiple access are discussed. In addition, promising 6G-related applications of pinching antennas, including integrated sensing and communication and next-generation multiple access, are presented. Finally, important directions for future research, such as waveguide deployment and channel estimation, are highlighted. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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Mobile Edge Computing Networks: Online Low-Latency and Fresh Service Provisioning

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IEEE Transactions on Communications 2025年

作者： Yi, Yuhan Zhang, Guanglin Jiang, Hai Donghua University College of Information Science and Technology Shanghai China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

Edge service caching can significantly mitigate latency and reduce communication and computing overhead by downloading and caching service (application) data from clouds. The freshness of cached service data is critical when providing satisfactory services to users, but has been overlooked in existing research efforts. In this paper, we study the online low-latency and fresh service provisioning in mobile edge computing (MEC) networks. Specifically, we jointly optimize the service caching, task offloading, and resource allocation. To solve the formulated joint online long-term optimization problem, we design a Lyapunov-based online framework that decouples the problem at temporal level into a series of per-time-slot subproblems. For each subproblem, we propose an online integrated optimization-deep reinforcement learning (OIODRL) method, which consists of an optimization stage and a learning stage. In the optimization stage of OIODRL, a quadratically constrained quadratic program (QCQP) transformation and a semidefinite relaxation (SDR) method are utilized;in the learning stage of OIODRL, a deep reinforcement learning (DRL) algorithm is applied. Extensive simulations show that the proposed OIODRL method achieves a near-optimal solution and outperforms benchmark methods. © 1972-2012 IEEE.

关键词： Mobile edge computing

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