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Multi-Scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-Charge in Battery Energy Storage Systems

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 405-414页

作者： Hao Liu Fengwei Liang Tianyu Hu Jichao Hong Huimin Ma School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Mechanical Engineering University of Science and Technology BeijingBeijingChina IEEE

Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric *** overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical *** correlation analysis is first employed to identify SOC-related *** parameters are then input into a multi-layer GRU for point-wise feature ***,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time ***,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are *** extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.

关键词： Electric vehicle battery energy storage system(BESS) state-of-charge(SOC)prediction gated recurrent unit(GRU) multi-scale fusion(MSF).

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Intelligent integrated sensing and communication:a survey

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Science China(Information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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Local-global dynamic correlations based spatial-temporal convolutional network for traffic flow forecasting

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High technology Letters 2024年第4期30卷 370-379页

作者： ZHANG Hong GONG Lei ZHAO Tianxin ZHANG Xijun WANG Hongyan College of Computer and Communication Lanzhou University of TechnologyLanzhou 730050P.R.China

Traffic flow forecasting plays a crucial role and is the key technology to realize dynamic traffic guidance and active traffic control in intelligent traffic systems(ITS).Aiming at the complex local and global spatial-temporal dynamic characteristics of traffic flow,this paper proposes a new traffic flow forecasting model spatial-temporal attention graph neural network(STA-GNN)by combining at-tention mechanism(AM)and spatial-temporal convolutional *** model learns the hidden dynamic local spatial correlations of the traffic network by combining the dynamic adjacency matrix constructed by the graph learning layer with the graph convolutional network(GCN).The local tem-poral correlations of traffic flow at different scales are extracted by stacking multiple convolutional kernels in temporal convolutional network(TCN).And the global spatial-temporal dependencies of long-time sequences of traffic flow are captured by the spatial-temporal attention mechanism(STAtt),which enhances the global spatial-temporal modeling and the representational ability of *** experimental results on two datasets,METR-LA and PEMS-BAY,show the proposed STA-GNN model outperforms the common baseline models in forecasting accuracy.

关键词： traffic flow forecasting graph convolutional network(GCN) temporal convolu-tional network(TCN) attention mechanism(AM)

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Modeling and Analysis of OFDMA-NOMA-RA Protocol Considering Imperfect SIC in Multi-User Uplink WLANs

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computers, Materials & Continua 2024年第6期79卷 5273-5294页

作者： Hailing Yang Suoping Li Duo Peng School of Science Lanzhou University of TechnologyLanzhou730050China School of Computer&Communication Lanzhou University of TechnologyLanzhou730050China

To address the problems of network congestion and spectrum resources shortage in multi-user large-scale scenarios,this paper proposes a twice random access OFDMA-NOMA-RA protocol combining the advantages of orthogonal frequency division multiple access(OFDMA)and non-orthogonal multiple access(NOMA).The idea of this protocol is that OFMDA is used to divide the entire frequency field into multiple orthogonal resource units(RUs),and NOMA is used on each RU to enable more users to access the channel and improve spectrum *** on the protocol designed in this paper,in the case of imperfect successive interference cancellation(SIC),the probability of successful competition subchannels and the outage probability are derived for two scenarios:Users occupy the subchannel individually and users share the ***,when two users share the channel,the decoding order of the users and the corresponding probabilities are ***,the system throughput is *** achieve better outage performance in the system,the optimal power allocation algorithm is proposed in this paper,which enables the optimal power allocation strategy to be *** results show that the larger the imperfect SIC coefficient,the worse the outage performance of weak *** with pure OFDMA and NOMA,OFDMA-NOMA-RA always maintains an advantage when the imperfect SIC coefficient is less than a specific value.

关键词： Orthogonal frequency division multiple access(OFDMA) non-orthogonal multiple access(NOMA) random access(RA) imperfect successive interference cancellation(Imperfect SIC) outage probability

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Adaptive spatial-temporal graph attention network for traffic speed prediction

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High technology Letters 2024年第3期30卷 221-230页

作者： ZHANG Xijun ZHANG Baoqi ZHANG Hong NIE Shengyuan ZHANG Xianli School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050P.R.China

Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial ***,the spatial features are input into BiGRU to extract the time series ***,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the *** experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.

关键词： traffic speed prediction spatial-temporal correlation self-adaptive adjacency ma-trix graph attention network(GAT) bidirectional gated recurrent unit(BiGRU)

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Photonic generation of multi-format and reconfigurable microwave signals based on cascaded external modulation

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Optical and Quantum Electronics 2025年第3期57卷 1-14页

作者： Chen, Mo Chen, Hongyao Zhang, HongYu Li, ZhuoYang Wang, Jianping Feng, LiFang Lu, HuiMin School of Computer and Communication Engineering University of Science and Technology Beijing Beijing10083 China

This paper presents a photonic scheme for generating multi-format, multi-band, and reconfigurable microwave photonic signals through cascaded external modulation. The proposed system utilize dual-parallel Mach–Zehnder modulators (DP-MZM) and Mach–Zehnder modulators (MZM) to produce high-order optical sidebands, enabling flexible frequency multiplication factors switching and pulse waveform reconstruction. Additionally, using a polarization control structure allows for spurious suppression and encoded signal loading. The system can generate various signal formats, including rectangular optical frequency combs, frequency-multiplied signals, dual-frequency encoded signals, and multiple pulse waveforms. Simulation results demonstrate the successful generation of 16 GHz frequency-doubled signal and 32 GHz frequency-quadrupled signal, with spurious suppression ratios of 37.9 dB and 44.8 dB, respectively. It can also generate four-line rectangular optical frequency combs with frequency multiplication factors of 2 and 4, achieving flatness values of 0.24 dB and 0.16 dB. Furthermore, the system can generate symmetric triangular pulses and Sinc-Nyquist pulses with adjustable repetition frequencies, as well as encoded signals with good pulse compression performance, having PCR values close to the theoretical value of 13. This work demonstrates a versatile and reconfigurable approach to microwave photonic signal generation, offering promising applications in advanced radar systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Microwave photonics

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Identifying influential spreaders in social networks: A two-stage quantum-behaved particle swarm optimization with Lévy flight

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Chinese Physics B 2024年第1期33卷 743-754页

作者：卢鹏丽揽继茂唐建新张莉宋仕辉朱虹羽 Wenzhou Engineering Institute of Pump&Valve Lanzhou University of TechnologyWenzhou 325100China School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.

关键词： social networks influence maximization metaheuristic optimization quantum-behaved particle swarm optimization Lévy flight

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CPPABK: conditional privacy-preserving authentication scheme for VANETs based on the key derivation algorithm

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International Journal of Security and Networks 2023年第4期18卷 199-212页

作者： Wang, Yixuan Guo, Xian School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China

Vehicular ad hoc networks (VANETs) hold great potential for enhancing traffic management and driver safety. Consequently, ensuring the precision and reliability of information exchanged within VANETs is paramount. This paper introduces a distributed privacy-preserving authentication scheme explicitly designed for VANETs. Our scheme integrates blockchain technology, batch authentication, and the key derivation algorithm to achieve exceptional efficiency and scalability. Initially, the authentication process utilises fuzzy biometric extraction technology as the fundamental module to authenticate the vehicle owner’s identity. Subsequently, the scheme enables vehicles to authenticate the received message by retrieving data (anonymous public key) from the blockchain, thereby mitigating the adverse effects of the blockchain’s low throughput and high latency and eliminating the key escrow problem. A comprehensive security analysis confirms that the proposed scheme satisfies the security requirements of VANETs. Furthermore, performance analysis reveals significant improvements across various aspects. Specifically, our scheme achieves reductions in computation cost of up to 66.74% in the signature generation, up to 59.59% in single message verification time, and up to 78.97% in communication cost compared to the comparison schemes. © 2023 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Blockchain

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Emotion classification in internet memes utilizing enhanced ConvNeXt and tensor fusion

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International Journal of Information technology (Singapore) 2025年 1-12页

作者： Gao, Weijun Zhao, Xiaoxuan School of Computer and Communication Lanzhou University of Technology Lanzhou 730000 China

Prior to the development of multimodal approaches, sentiment analysis of internet memes predominantly relied on single-modality techniques. With advancements in deep learning, it is now feasible to extract and integrate features from both text and image modalities, enabling more accurate sentiment prediction and classification for memes. This study presents a novel sentiment classification framework for internet memes, leveraging an enhanced ConvNeXt model combined with tensor product fusion. The ConvNeXt model is optimized through a re-parameterization structure to improve visual feature extraction from memes, while the BERT model is utilized to obtain textual features. These features are subsequently fused using a tensor fusion module to enhance sentiment prediction accuracy. Experiments are conducted on the Memotion, Memotion2, and Memotion3 datasets, with performance comparisons made against image-only, text-only, and multimodal fusion models. Experimental findings indicate that the proposed method achieves notable improvements in overall performance and classification accuracy. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Internet memes Re-parameterized ConvNeXt Sentiment classification Tensor fusion

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A deep decentralized privacy-preservation framework for online social networks

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Blockchain(Research and Applications) 2024年第4期5卷 163-174页

作者： Samuel Akwasi Frimpong Mu Han Emmanuel Kwame Effah Joseph Kwame Adjei Isaac Hanson Percy Brown School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212013China Department of Computer Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana Department of Electrical&Electronic Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana Department of Computer Science and Information Systems Ashesi UniversityAccra3042Ghana Department of Telecommunication Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana

This paper addresses the critical challenge of privacy in Online Social Networks(OSNs),where centralized designs compromise user *** propose a novel privacy-preservation framework that integrates blockchain technology with deep learning to overcome these *** methodology employs a two-tier architecture:the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm(ePSOGSA)for optimizing feature selection,while the second tier employs an enhanced Non-symmetric Deep Autoencoder(e-NDAE)for anomaly ***,a blockchain network secures users’data via smart contracts,ensuring robust data *** tested on the NSL-KDD dataset,our framework achieves 98.79%accuracy,a 10%false alarm rate,and a 98.99%detection rate,surpassing existing *** integration of blockchain and deep learning not only enhances privacy protection in OSNs but also offers a scalable model for other applications requiring robust security measures.

关键词： Preprocessing Privacy-preservation Blockchain Deep learning Online social network

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