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Intelligent Preamble Allocation for Coexistence of mMTC/URLLC Devices:A Hierarchical Q-Learning Based Approach

China Communications 2023年第8期20卷 44-53页

作者： Jiadai Wang Chaochao Xing Jiajia Liu National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China

The emergence of various commercial and industrial Internet of Things(IoT)devices has brought great convenience to people’s life and *** low-power,massively connected mMTC devices(MDs)and highly reliable,low-latency URLLC devices(UDs)play an important role in different application ***,when dense MDs and UDs periodically initiate random access(RA)to connect the base station and send data,due to the limited preamble resources,preamble collisions are likely to occur,resulting in device access failure and data transmission *** the same time,due to the highreliability demands of UDs,which require smooth access and fast data transmission,it is necessary to reduce the failure rate of their RA *** this end,we propose an intelligent preamble allocation scheme,which uses hierarchical reinforcement learning to partition the UD exclusive preamble resource pool at the base station side and perform preamble selection within each RA slot at the device *** particular,considering the limited processing capacity and energy of IoT devices,we adopt the lightweight Qlearning algorithm on the device side and design simple states and actions for *** results show that the proposed intelligent scheme can significantly reduce the transmission failure rate of UDs and improve the overall access success rate of devices.

关键词： preamble allocation random access mMTC URLLC reinforcement learning

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Clause-level Relationship-aware Math Word Problems Solver

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Machine Intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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Adaptive disentangled target representation for unsupervised domain adaptation in remote sensing segmentation

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engineering applications of Artificial Intelligence 2025年 156卷

作者： Runuo Lu Shoubin Dong Jianxin Jia Xusheng Wang Kai Liu Jinsong Chen Shanxin Guo Xiaorou Zheng Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou 510641 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute Espoo FI-02150 Finland Center for Geo-Spatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application Shenzhen 518055 China

Due to the significant differences between the source and target domains, semantic segmentation models for remote sensing images trained on the source domain often struggle to generalize effectively to new target domains, leading to performance degradation. Most solutions disentangle features into domain-invariant features shared by both domains and domain-specific features unique to each domain, relying primarily on the domain-invariant features for model construction and inference. However, during this process, critical domain-specific features as essential information are often discarded as noise, leading to a reduction in segmentation accuracy. To address these issues, this study proposes an unsupervised domain adaptation, called ChameleonRS 1 . ChameleonRS separates the domain-invariant features and the domain-specific features by disentanglement learning, and introduces a multi-level feature alignment module to enhance the representation of domain-invariant features. More importantly, it aligns predictions based on domain-invariant features with those based on fused invariant and specific features using contrastive learning, effectively capturing target-specific features. This study explores two cross-domain semantic segmentation tasks: (I) Geographic location variation. (II) Geographic location and imaging mode variation. Results show that under setting (I), ChameleonRS outperforms the state-of-the-art method by 4.79% in Intersection over Union (IoU) and 6.51% in the F1 score. Under setting (II), it achieves improvements of 4.46% and 4.82% in IoU and F1 score. Results demonstrate the superiority and adaptability of ChameleonRS under varying geographical and imaging conditions, effectively mitigating the generalization issues caused by distribution differences, and thereby promoting the development and application of remote sensing technologies in practical scenarios.

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Inverse Markov Process Based Constrained Dynamic Graph Layout

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Journal of Computer Science & Technology 2021年第3期36卷 707-718页

作者： Shi-Ying Sheng Sheng-Tao Chen Xiao-Ju Dong Chun-Yuan Wu Xiao-Ru Yuan ParisTech Elite Institute of Technology Shanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Machine Perception(Ministry of Education) National Engineering Laboratory for Big Data Analysis and ApplicationPeking UniversityBeijing 100080China

In online dynamic graph drawing,constraints over nodes and node pairs help preserve a coherent mental map in a sequence of *** the constraints is challenging due to the requirements of both preserving mental map and satisfying the visual aesthetics of a graph *** existing algorithms basically depend on local changes but fail to do proper evaluations on the global propagation when setting *** solve this problem,we introduce a heuristic model derived from PageRank which simulates the node movement as an inverse Markov process hence to give a global analysis of the layout's change,according to which different constraints can be *** constraints,along with stress function,generate layouts maintaining spatial positions and shapes of relatively stable substructures between adjacent *** demonstrate that our method preserves both structure and position similarity to help users track graph changes visually.

关键词： graph drawing data stream dynamic graph layout

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Invisible damage classification of crispy pears based on the combination of hyperspectral and thermal imaging using COGhost_ShuffleNet 5

Invisible damage classification of crispy pears based on the...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Zheng, Ling Wu, Feihong Yang, Huiwen Chen, Qun Anhui University National Engineering Research Venter for Agro-Ecological Big Data Analysis & Application Hefei China

ISBN: (纸本)9798350373820

Identifying and classifying invisible damage in crispy pears is vital for ensuring high-quality grading. Despite the demonstrated potential of hyperspectral imaging (HSI) and infrared thermal imaging (IR) for fruit identification, the performance of damage recognition is constrained by the low resolution of HSI images and the spectral redundancy. Additionally, IR exhibits suboptimal performance in identifying multiple types of damage. In this study, we developed a crispy pear damage identification method using a combination of multiple data inputs and an improved ShuffleNetV2. Firstly, this paper registers HSI and IR using the radiation-insensitive feature transform (RIFT) algorithm, obtaining the damaged areas of crisp pears through registration. Secondly, it extracts the HSI spectra from the damaged areas and visualizes the spectra through Gramian Angular Fields (GAF) two-dimensional imaging. Next, it investigates the optimal combination obtained under the fusion of multiple data, aiming to achieve the highest accuracy under the most compatible ShuffleNetV2 model. The optimal combination identified is IR-Band1-Band2-Band3-GAF. Finally, facing the multiple data inputs, reducing network parameters and Flops is crucial for the rapid and efficient damage identification of crisp pears. Therefore, we propose optimizations for *** Ghost module was used to replace the 1 × 1 convolution in the two basic unit modules of ShuffleNetV2, and Ghost_ShuffleNet used convolution optimization (CO) to construct the COGhost_ShuffleNet classification model. We used the optimal data combination as input for identification and classification on this model. COGhost_ShuffleNet achieved the best recognition performance with an ACCP of 98.93%, 1.158M parameters, and 0.086G FLOPS, outperforming other classification methods and data input combinations. Overall, this method accurately and reliably identifies crispy pear damage and can be extended to analyze other quality indicators

关键词： Hyperspectral imaging

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Heterogeneous pigeon-inspired optimization

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Science China(Information Sciences) 2019年第7期62卷 64-72页

作者： Hao WANG Zhuxi ZHANG Zhen DAI Jun CHEN Xi ZHU Wenbo DU Xianbin CAO School of Electronic and Information Engineering Frontier Institute of Science and Technology InnovationBeihang University National Transportation Preparation Office School of Engineering and Materials Science Queen Mary University of London National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation

Pigeon-inspired optimization(PIO) is a swarm intelligence optimizer inspired by the homing behavior of pigeons. PIO consists of two optimization stages which employ the map and compass operator,and the landmark operator, respectively. In canonical PIO, these two operators treat every bird equally,which deviates from the fact that birds usually act heterogenous roles in nature. In this paper, we propose a new variant of PIO algorithm considering bird heterogeneity — HPIO. Both of the two operators are improved through dividing the birds into hub and non-hub roles. By dividing the birds into two groups, these two groups of birds are respectively assigned with different functions of "exploitation" and "exploration", so that they can closely interact with each other to locate the best promising solution. Extensive experimental studies illustrate that the bird heterogeneity produced by our algorithm can benefit the information exchange between birds so that the proposed PIO variant significantly outperforms the canonical PIO.

关键词： heuristic optimization pigeon-inspired optimization particle heterogeneity network-based topology scale-free network selective-informed learning

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Rapid distinguish of edible oil adulteration using a hyperspectral spectroradiometer

Rapid distinguish of edible oil adulteration using a hypersp...

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2019 International Conference on Electronic engineering and Informatics, EEI 2019

作者： Weng, Shizhuang Pan, Fangfang Yu, Shuan Guo, Bingqing National Engineering Research Center for Agro-Ecological Big Data Analysis &application Anhui University Hefei China

ISBN: (纸本)9781728140766

The adulteration of edible oil is one of the widely existing oil quality problems. In this study, a hyperspectral spectroradiometer was utilised to discriminate sesame and rapeseed oil adulteration rapidly using soybean oil. The reflectance spectra of 350-2500 nm were obtained from different adulterated oil samples using the hyperspectral spectroradiometer. Support Vector Machine (SVM) and random forest (RF) were used for spectral analysis to classify adulteration types. And RF achieved an accuracy of 100%. Considering the redundant information of reflectance spectra, the optimal wavelengths were selected by Principal Component analysis to increase processing speed and simplify the model for subsequent applications. With the spectra of eight selected wavelengths, the accuracy of SVM achieved 92.86%. These results indicated that a hyperspectral spectroradiometer can distinguish edible oil adulteration. © 2019 IEEE.

关键词： Support vector machines

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Multi-Attention Fusion Modeling for Sentiment analysis of Educational big data

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big data Mining and Analytics 2020年第4期3卷 311-319页

作者： Guanlin Zhai Yan Yang Heng Wang Shengdong Du the School of Information Science and Technology National Engineering Laboratory of Integrated Transportation Big Data Application TechnologySouthwest Jiaotong UniversityChengdu 611756China

As an important branch of natural language processing,sentiment analysis has received increasing *** teaching evaluation,sentiment analysis can help educators discover the true feelings of students about the course in a timely manner and adjust the teaching plan accurately and timely to improve the quality of education and *** at the inefficiency and heavy workload of college curriculum evaluation methods,a Multi-Attention Fusion Modeling(Multi-AFM)is proposed,which integrates global attention and local attention through gating unit control to generate a reasonable contextual representation and achieve improved classification *** results show that the Multi-AFM model performs better than the existing methods in the application of education and other fields.

关键词： educational big data sentiment analysis aspect-level attention

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Deep learning approaches in flow visualization

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Advances in Aerodynamics 2022年第1期4卷 327-340页

作者： Can Liu Ruike Jiang Datong Wei Changhe Yang Yanda Li Fang Wang Xiaoru Yuan Key Laboratory of Machine Perception(Ministry of Education) and School of AIPeking UniversityBeijingChina China Aerodynamics Research and Development Center MianyangChina National Engineering Laboratory for Big Data Analysis and Application Peking UniversityBeijingChina

With the development of deep learning(DL)techniques,many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL *** reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these *** also analyzed the prospects of the development of flow visualization with the help of deep learning.

关键词： Deep learning Flow visualization data management Feature extraction Particle tracing

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SketchConf: A Framework for Automatic Sketch Configuration 39

SketchConf: A Framework for Automatic Sketch Configuration

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Miao, Ruijie Dong, Fenghao Zhao, Yikai Zhao, Yiming Wu, Yuhan Yang, Kaicheng Yang, Tong Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350322279

Sketches have risen as promising solutions for frequency estimation, which is one of the most fundamental tasks in approximate data stream processing. In many scenarios, users have a strong demand to apply sketches under the expected error constraints. In this paper, we explore how to configure sketch parameters to satisfy user-defined error constraints. We propose SketchConf, an automatic sketch configuration framework, which efficiently generates memory-optimal configurations for the first time. We show that SketchConf can be applied to order-independent sketches, including CM, Count, Tower, and Nitro sketches. We further discuss how to deal with the unknown and changeable workloads when applying SketchConf to the real scenarios of streaming data processing. Experimental results show that SketchConf can be up to 715.51 times faster than the baseline algorithm, and the outputted configurations save up to 99.99% memory and achieve up to 27.44 times throughput, compared with the theory-based configurations. The code is open sourced at Github. © 2023 IEEE.

关键词： Frequency estimation

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