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Demand Prediction Based Slice Reconfiguration Using Dueling Deep Q-Network

China communications 2022年第5期19卷 267-285页

作者： Wanqing Guan Haijun Zhang School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China

To satisfy diversified service demands of vertical industries,network slicing enables efficient resource allocation of a common infrastructure by creating isolated logical ***,uncertainty and dynamics of service demands will cause performance *** to operation costs and resource constraints,it is challenging to maintain high quality of user experience while obtaining high revenue for service providers(SPs).This paper develops an optimal and fast slice reconfiguration(OFSR)framework based on reinforcement learning,where a novel scheme is proposed to offer optimal decisions for reconfiguring diverse slices.A demand prediction model is proposed to capture changes in resource requirements,based on which the OFSR scheme is triggered to determine whether to perform slice *** the large state and action spaces generated from uncertain service time and resource requirements,deep dueling architecture is adopted to improve the convergence *** simulations validate the effectiveness of the proposed framework in achieving higher long-term revenue for SPs.

关键词： network slicing slice reconfiguration reinforcement learning resource allocation

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Parallel Intelligence-enabled IoV Information Management System based on Blockchain 3

Parallel Intelligence-enabled IoV Information Management Sys...

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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Han, Shuangshuang Bai, Yongqiang Chen, Yueyun Tellambura, Chintha University of Science and Technology Beijing School of Computer & Communication Engineering Beijing China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350318470

The current Internet of Vehicles (IoV) data are facing challenges such as data silos, security and privacy concerns, data quality issues, and collaboration barriers. This paper proposes an IoV Information Management System (IIMS) based on parallel intelligence and blockchain to address these challenges. By leveraging the decentralized, immutable, and traceable nature of blockchain, combined with the incentive mechanisms and collaborative models of decentralized autonomous organizations (DAOs), a secure and trustworthy data sharing platform is established for collaborative analysis, business cooperation, and innovative applications of IoV data. The proposed approach utilizes the decentralized nature and data backup capabilities of blockchain to mitigate the single point of failure issues associated with centralized servers. Additionally, the anonymity, immutability, and traceability features of blockchain ensure the privacy, security, and integrity of data sharing. Moreover, the incentive mechanisms and DAO solutions within the blockchain promote data sharing among vehicles while ensuring data quality. © 2023 IEEE.

关键词： Blockchain

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Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

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Digital communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

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TROI: Cross-Subject Pretraining with Sparse Voxel Selection for Enhanced fMRI Visual Decoding

TROI: Cross-Subject Pretraining with Sparse Voxel Selection ...

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

作者： Wang, Ziyu Pan, Tengyu Li, Zhenyu Wu, Ji Li, Xiuxing Wang, Jianyong Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350368741

fMRI (functional Magnetic Resonance Imaging) visual decoding involves decoding the original image from brain signals elicited by visual stimuli. This often relies on manually labeled ROIs (Regions of Interest) to select brain voxels. However, these ROIs can contain redundant information and noise, reducing decoding performance. Additionally, the lack of automated ROI labeling methods hinders the practical application of fMRI visual decoding technology, especially for new subjects. This work presents TROI (Trainable Region of Interest), a novel two-stage, data-driven ROI labeling method for cross-subject fMRI decoding tasks, particularly when subject samples are limited. TROI leverages labeled ROIs in the dataset to pretrain an image decoding backbone on a cross-subject dataset, enabling efficient optimization of the input layer for new subjects without retraining the entire model from scratch. In the first stage, we introduce a voxel selection method that combines sparse mask training and low-pass filtering to quickly generate the voxel mask and determine input layer dimensions. In the second stage, we apply a learning rate rewinding strategy to fine-tune the input layer for downstream tasks. Experimental results on the same small sample dataset as the baseline method for brain visual retrieval and reconstruction tasks show that our voxel selection method surpasses the state-of-the-art method MindEye2 with an annotated ROI mask. © 2025 IEEE.

关键词： Computational neuroscience multi-modal learning region of interest analysis small sample learning

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MULTI-FREQUENCY POLSAR IMAGE CLASSIFICATION BASED ON CROSS ATTENTION VIT

MULTI-FREQUENCY POLSAR IMAGE CLASSIFICATION BASED ON CROSS A...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Hongmiao Ren, Dawei Yin, Junjun Yang, Jian Departments of Electronic Engineering Tsinghua University Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781839539954

In the land cover classification of multi-frequency Polarimetric SAR (PolSAR) images, the classification performance highly depends on the effectiveness of feature fusion. Currently, linear fusion methods are widely employed in multi-frequency PolSAR land cover classification. However, linear fusion methods do not introduce extra interactions between the features from different bands, thus failing to fully exploit the advantages of fused features. To address this issue, a feature fusion method based on cross-attention is proposed. The proposed method is based on the Vision Transformer architecture. We enhance the model's capability to extract multi-frequency fusion features by introducing additional cross-frequency feature interactions within the self-attention module. Experimental results demonstrate that using the proposed cross-attention module significantly improves the model's performance in multi-frequency PolSAR image classification without introducing extra parameters or computational costs. © The Institution of engineering & technology 2023.

关键词： Polarimeters

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Optimized Convolutional Neural Network at the IoT edge for image detection using pruning and quantization

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Multimedia Tools and Applications 2025年第9期84卷 5435-5455页

作者： Naveen, Soumyalatha Kounte, Manjunath R Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India School of Electronics and Communication Engineering REVA University Bangalore India

Most real-time computer vision applications heavily rely on Convolutional Neural Network (CNN) based models, for image classification and recognition. Due to the computationally and memory-intensive nature of the CNN model, it’s challenging to deploy on resource-constrained Internet of Things (IoT) devices to enable Edge intelligence for real-time decision-making. Edge intelligence requires minimum inference latency, memory footprint, and energy-efficient model. This work aims to develop an energy-efficient deep learning accelerator using a 3-stage pipeline: Training, Weight-pruning, and Quantization to reduce the model size and optimize the resources. First, we employ YOLOv3, a CNN architecture to detect objects in an image on the trained data. In addition, a sparse network of YOLO has been created by using pruning, which helps to improve the network’s performance and efficiency by reducing the computational requirements. Finally, we utilize 8-bit quantization to reduce the precision of the weights and activations, in a neural network. The evaluation of our proposed model shows that combining pruning and 8-bit quantization improves the efficiency and performance of the model. While pruning shows a decline of 80.39% in model parameters. The combination of 8-bit quantization results in an improvement in inference latency by 22.72% compared to existing SQuantization approach and a reduction of energy consumption by 29.41%. © The Author(s) 2024.

关键词： Convolutional neural networks

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Incremental Weighted Ensemble for Data Streams With Concept Drift

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 92-103页

作者： Jiao, Botao Guo, Yinan Yang, Cuie Pu, Jiayang Zheng, Zhiji Gong, Dunwei School of Electromechanical and Information Engineering Beijing100083 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Victoria University of Wellington School of Engineering and Computer Wellington6140 New Zealand Electromechanical and Information Engineering Beijing100083 China Qingdao University of Science and Technology School of Information Science and Technology Qingdao266061 China

As a popular strategy to tackle concept drift, chunk-based ensemble method adapts a new concept by adjusting the weights of historical classifiers. However, most previous approaches normally evaluate the historical classifier based on an entire chunk newly arrived, which may cause delayed adaptation. To address the issue, two novel ensemble models, named incremental weighted ensemble (IWE) and incremental weighted ensemble for multi-classification (IWE-M), are proposed. At each time step, all base classifiers are incrementally updated on a newly arrived instance. Following that, the instance is collected into a cache array. Once a data chunk is formed, a new base classifier is created. More specially, a forgetting mechanism based on variable-size window is designed to adjust the weight of each base classifier in IWE in terms of its classification accuracy on the latest instances in an online manner. IWE-M, an extension of IWE, aims to solve multiclass problems with local concept drifts. In IWE-M, the weight of a base classifier is expanded to a weight vector. In this way, this ensemble model can retain specific historical information about nondrift regions from a local drift. Experimental results show that the proposed ensemble frameworks outperform six competitive approaches on accuracy and G-mean. © 2020 IEEE.

关键词： Classifiers

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POLSAR SHIP DETECTION BASED ON NEIGHBORHOOD POLARIMETRIC TENSOR

POLSAR SHIP DETECTION BASED ON NEIGHBORHOOD POLARIMETRIC TEN...

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IET International Radar Conference 2023, IRC 2023

作者： Ren, Dawei Han, Songli Yin, Junjun Yang, Jian Department of Electronic Engineering Tsinghua University Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781839539954

Using polarimetric Synthetic Aperture Radar (PolSAR) imagery for ship detection is a prominent research area within the realm of marine surveillance. The predominant approach in current practice encompasses the employment of super-pixel techniques and neighborhood matrix methodologies. Nonetheless, these methods may not fully capitalize on the spatial information. In this paper, the authors propose an innovative detection methodology by employing a neighborhood polarimetric tensor. This tensor encapsulates the entirety of polarization data within the pixel's neighborhood, with its constituent elements implicitly encoding spatial information. Furthermore, this research introduces the Kernelized Support Tensor Machine (KSTM) as the designated detection algorithm. KSTM, an extension of the conventional Support Vector Machine (SVM), unveils both polarization and spatial characteristics embedded within the neighborhood polarimetric tensor. Experiments were conducted utilizing PolSAR data acquired from Radarsat-2. The results demonstrate the effectiveness of the neighborhood polarimetric tensor in achieving precise ship detection. © The Institution of engineering & technology 2023.

关键词： Synthetic aperture radar

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Carbon materials:The burgeoning promise in electronics

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International Journal of Minerals,Metallurgy and Materials 2022年第3期29卷 404-423页

作者： Yuting Zheng Junjun Wei Jinlong Liu Liangxian Chen Kang An Xiaotong Zhang Haitao Ye Xiaoping Ouyang Chengming Li Institute for Advanced Materials and Technology(IAMT State Key Laboratory for Advanced Metals and MaterialUniversity of Science and Technology BeijingBeijing 100083China Shunde Graduate School University of Science and Technology BeijingFoshan 528399China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Engineering University of LeicesterLeicester LE17RHUnited Kingdom School of Materials Science and Engineering Xiangtan UniversityXiangtan 411105China

Current electronic technology based on silicon is approaching its physical and scientific limits. Carbon-based devices have numer- ous advantages for next generation electronics (e.g., fast speed, low power consumption and simple process), that when combined with the unique nature of the versatile allotropes of carbon elements, are creating an electronics revolution. Carbon electronics are greatly advancing with new preparations and sophisticated designs. In this perspective, representatives with various dimensions, e.g., carbon nanotubes, graphene, bulk diamond, and their extraordinary performance, are reviewed. The associated state-of-the-art devices and composite hybrid all-carbon structures are also emphasized to reveal their potential in the electronics field. Advances in commercial production have improved the cost effi-ciency, material quality, and device design, accelerating the promise of carbon materials.

关键词： carbon materials nanotube graphene diamond electronic devices

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Proactive Caching at the Wireless Edge:A Novel Predictive User Popularity-Aware Approach

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computer Modeling in engineering & sciences 2024年第8期140卷 1997-2017页

作者： Yunye Wan Peng Chen Yunni Xia Yong Ma Dongge Zhu Xu Wang Hui Liu Weiling Li Xianhua Niu Lei Xu Yumin Dong College of Computer Science Chongqing UniversityChongqing400044China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer and Information Engineering Jiangxi Normal UniversityNanchang330022China Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd.Yinchuan750002China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Computer Science and Technology Beijing Institute of TechnologyBeijing100083China School of Computer Science and Technology Dongguan University of TechnologyDongguan523808China School of Emergency Management Xihua UniversityChengdu610039China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as *** an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user ***,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of ***,techniques for caching content at the edge are becoming popular for addressing the above *** is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud ***,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity *** address this challenge,we introduce a novel method for content caching over MEC,i.e.,*** synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching *** results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

关键词： Mobile edge computing content caching system average cost deep reinforcement learning collaborative mechanism

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