检索结果-南通市图书馆

When AI meets sustainable 6G

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(information Sciences) 2025年第1期68卷 4-22页

作者： Xiaohu YOU Yongming HUANG Cheng ZHANG Jiaheng WANG Hao YIN Hequan WU National Mobile communications Research Laboratory Southeast University Purple Mountain Laboratories Chinese Academy of Military Science China information Communication Technologies Group

Sixth-generation(6G) networks are anticipated to achieve transformative advancements, characterized by extreme connectivity, deep integration with artificial intelligence(AI) and sensing, and airground integration. The evolution of 6G exhibits two major trends: ubiquitous intelligence and sustainability. The former aims to embed state-of-the-art AI technology into the 6G network, from the physical layers to applications, while the latter emphasizes reducing energy consumption while enhancing network performance to address environmental concerns. Despite the amazing progress in recent years, AI advancements come with substantial increases in data and computational overhead, posing critical challenges for integrating AI into sustainable 6G networks. First, high energy consumption from large datasets and heavyweight AI models contradicts6G's green goals. Second, the precise collection of large datasets, message delivery latency, and inference delays in AI models pose challenges for real-time tasks in 6G. Third, the uninterpretability and unpredictability of AI models complicate meeting the stringent requirements for controllable transmission in dynamic wireless environments. Addressing these challenges and achieving sustainable 6G with ubiquitous intelligence calls for a revolutionary design of 6G architecture and AI frameworks. To this end, this paper introduces a novel and practical methodology for green, real-time, and controllable 6G native intelligence,starting with knowledge graph(KG) analysis to extract small but critical datasets, followed by the development of distributed lightweight AI models, and the use of digital twins(DTs) to create precise replicas of physical 6G networks. This leads to a pervasive multi-level(PML)-AI framework supported by a task-centric, three-layer 6G architecture. The AI framework operates through non-real-time and real-time cycles, leveraging three key technologies: wireless data KGs for efficient data management,lightweight AI mode

关键词： artificial intelligence sustainable 6G green real-time controllable

Text-Image Feature Fine-Grained Learning for Joint Multimodal Aspect-Based Sentiment Analysis

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2025年第1期82卷 279-305页

作者： Tianzhi Zhang Gang Zhou Shuang Zhang Shunhang Li Yepeng Sun Qiankun Pi Shuo Liu School of Data and Target Engineering Information Engineering UniversityZhengzhou450001China information Engineering Department Liaoning Provincial College of CommunicationsShenyang110122China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou450000China

Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal *** this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for ***,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect ***,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature ***,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and *** on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.

关键词： Multimodal sentiment analysis aspect-based sentiment analysis feature fine-grained learning graph convolutional network adjective-noun pairs

Using Box-Jenkins Models to Forecast Mobile Cellular Subscription

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Open Journal of Statistics 2016年第2期6卷 303-309页

作者： Ian Siluyele Stanley Jere zambia information and communications technology authority (ZICTA) Lusaka Zambia Department of Mathematics and Statistics Mulungushi University Kabwe Zambia

In this paper, the Box-Jenkins modelling procedure is used to determine an ARIMA model and go further to forecasting. The mobile cellular subscription data for the study were taken from the administrative data submitted to the zambia information and communications technology authority (ZICTA) as quarterly returns by all three mobile network operators Airtel zambia, MTN zambia and Zamtel. The time series of annual figures for mobile cellular subscription for all mobile network operators is from 2000 to 2014 and has a total of 15 observations. Results show that the ARIMA (1, 2, 1) is an adequate model which best fits the mobile cellular subscription time series and is therefore suitable for forecasting subscription. The model predicts a gradual rise in mobile cellular subscription in the next 5 years, culminating to about 9.0% cumulative increase in 2019.

关键词： Mobile Cellular Subscription Box-Jenkins Methodology ARIMA Model Autocorrelation Function Partial Autocorrelation Function

Evaluation of Rural Broadband Network Based on Broadband Universal Service Management System

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

China communications 2019年第8期16卷 51-57页

作者： Zejue Wang Meimei Dang China Academy of information and communications technology

Carrying out pilot project to provide broadband universal service nationwide, especially in rural impoverished areas, is a major policy decision in China. To accelerate implementation and ensure quality of the constructed network, it is of great significance to conduct comprehensive and scientific evaluation of the network status. In this paper, we present the evaluation of the broadband network constructed in rural China with several key indicators. It shows that with steppedup efforts of the telecom industry, broadband networks have been introduced into more and more villages. The average network speed reaches 60 Mbps, which is far exceeding 12 Mbps’ obligation.

关键词： network evaluation data analysis broadband service rural area

维普期刊数据库

SINR Driven Joint Network Selection Policy in the Heterogeneous Internet of Things

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2017年第4期26卷 842-848页

作者： LIU Xinyi JIANG Jian School of information Engineering Chang'an University China Academy of information and communications technology

In the heterogeneous Internet of things(Io T), the Signal to interference plus noise ratio(SINR)and delay constraint are two important factors that influence the throughput of Io T and the performance of *** recently, most network selection policy researches were based on either the Shannon theory or the signal strength, while the combined influence of the delay constraint and the SINR, which has a significant impact on resource utilization, is hardly considered. We therefore propose an SINR driven joint network selection policy, which incorporates the delay constraint and the signal strength into the SINR. This policy permits Io T users to access the network with the maximum of SINR from all the available networks under the delay and signal strength *** analysis and the simulation results show that the joint network selection policy can obtain the higher throughput of Io T and average SINR comparing with other polices.

关键词： Internet of things(IoT) Heterogeneous network Joint network selection policy Signal to interfer ence plus noise ratio(SINR) Delay constraint

Energy consumption optimization for edge computing-supported cellular networks based on optimal transport theory

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Science China(information Sciences) 2024年第2期67卷 201-218页

作者： Xiangyu LV Xiaohu GE Yi ZHONG Qiang LI Yong XIAO School of Electronic information and communications Huazhong University of Science and Technology

With advancements in mobile communication technologies, there have been multiple user device(UD) connections to cellular networks. Because of changes in the spatial distribution of UDs and application requirements, the traditional offloading mechanism based on the nearest distance will result in heavy loads for some base stations(BSs). Because there is a high-order relationship between the energy consumption of the BS processing tasks and the number of computing tasks, the traditional offloading mechanism will result in high energy consumption, and UD's offloading decision must be dynamically adjusted. The offloading decision of UDs is optimized based on detailed information about various parameters associated with a network from the standpoint of distribution. This information includes details regarding the spatial distribution of UDs,application requirements, and the offloading period of computing tasks. Based on optimal transport theory,an energy consumption optimization algorithm is suggested to lower the total amount of energy consumed by the offloading process of UDs' computing tasks by reasonably planning the offloading BSs of the UDs in the networks. The simulation results show that the proposed offloading mechanism based on energy consumption optimization reduces the total energy consumption of the offloading process by 28.09% when compared to the traditional offloading mechanism, and the traffic managed by each BS is balanced.

关键词： optimal transport theory edge computing energy consumption reduction dynamic voltage frequency scaling

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Journal of Systems Science & Complexity 2019年第3期32卷 737-758页

作者： BERAHMAND Kamal BOUYER Asgarali Department of information technology and communications Azarbaijan Shahid Madani University

Community structure is one of the most best-known proper ties of complex networks. Finding communities help us analyze networks from a mesoscopic viewpoints instead of microscopic or macroscopic one. It helps to understand behavior grouping. Various community detection algorithms have been proposed with some shortcomings in time and space complexity, accuracy, or stability. Label Propagation Algorithm (LPA) is a popular method used for finding communities in an almost-linear time-consuming process. However, its performance is not satisfactory in some metrics such as accuracy and stability. In this paper, a new modified version of LPA is proposed to improve the stability and accuracy of the LPA by defining two concepts -nodes and link strength based on semi-local similarity-, while preserving its simplicity. In the proposed method a new initial node selection strategy, namely the tiebreak strategy, updating order and rule update are presented to solve the random behavior problem of original LPA. The proposed algorithm is evaluated on artificial and real networks. The experiments show that the proposed algorithm is close to linear time complexity with better accuracy than the original LPA and other compared met hods. Furthermore, the proposed algorithm has the robustness and stability advantages while the original LPA does not have these features.

关键词： Community detection complex networks LPA similarity measure

维普期刊数据库

Learning to focus: cascaded feature matching network for few-shot image recognition

在线全文

学校读者我要写书评

暂无评论

Science China(information Sciences) 2021年第9期64卷 90-102页

作者： Mengting CHEN Xinggang WANG Heng LUO Yifeng GENG Wenyu LIU School of Electronic information and communications Huazhong University of Science and Technology

Generally, deep networks learn to recognize a category of objects by training on a large number of annotated images accurately. However, a meta-learning problem known as a low-shot image recognition task occurs when a few images with annotations are available for learning a recognition model for a single category. Consequently, the objects in testing/query and training/support image datasets are likely to vary in terms of size, location, style, and so on. In this paper, we propose a method, cascaded feature matching network(CFMN), to solve this problem. We train the meta-learner to learn a more fine-grained and adaptive deep distance metric using feature matching block, which aligns associated features together and naturally ignores non-discriminative features. By applying the proposed feature matching block in different layers of the network, multi-scale information among the compared images is incorporated into the final cascaded matching feature, which boosts the recognition performance and generalizes better by learning on relationships. Moreover, the experiments for few-shot learning(FSL) using two standard datasets:miniImageNet and Omniglot, confirm the effectiveness of our proposed method. Besides, the multi-label fewshot task is first studied on a new data split of the COCO dataset, which further shows the superiority of the proposed feature matching network when performing the FSL in complex images.

关键词： few-shot learning image recognition feature matching self-attention

Deep graph cut network for weakly-supervised semantic segmentation

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Science China(information Sciences) 2021年第3期64卷 57-68页

作者： Jiapei FENG Xinggang WANG Wenyu LIU School of Electronic information and communications Huazhong University of Science and Technology

The scarcity of fully-annotated data becomes the biggest obstacle that prevents many deep learning approaches from widely applied. Weakly-supervised visual learning which can utilize inexact annotations is developed rapidly to remedy such a situation. In this paper, we study the weakly-supervised task achieving pixel-level semantic segmentation only with image-level labels as supervision. Different from other methods,our approach tries to transform the weakly-supervised visual learning problem into a semi-supervised visual learning problem and then utilizes semi-supervised learning methods to solve it. Utilizing this transformation,we can adopt effective semi-supervised methods to perform transductive learning with context *** the semi-supervised learning module, we propose to use the graph cut algorithm to label more supervision from the activation seeds generated from a classification network. The generated labels can provide the segmentation model with effective supervision information; moreover, the graph cut module can benefit from features extracted by the segmentation model. Then, each of them updates and optimizes the other iteratively until convergence. Experiment results on PASCAL VOC and COCO benchmarks demonstrate the effectiveness of the proposed deep graph cut algorithm for weakly-supervised semantic segmentation.

关键词： semantic segmentation weakly-supervised learning semi-supervised learning graph cut