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Nonlinear Interference-Aware Routing,Wavelength and Power Allocation in C+L+S Multi-Band Optical Networks

China Communications 2025年第4期22卷 129-142页

作者： Zhang Xu Xie Wang Feng Chuan Zeng Hankun Zhou Shanshan Zhang Fan Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China

Multi-band optical networks are a potential technology for increasing network ***,the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck restricting the transmission capacity of multi-band optical *** overcome these challenges,it is particularly important to implement optical power optimization targeting wavelength ***,based on the generalized Gaussian noise model,we first formulate an optimization model for the problems of routing,modulation format,wavelength,and power allocation in C+L+S multi-band optical *** objective function is to maximize the average link capacity of the network while ensuring that the Optical Signal-to-Noise(OSNR)threshold of the service request is not ***,we propose a NonLinear Interferenceaware(NLI-aware)routing,modulation format,wavelength,and power allocation ***,we conduct simulations under different test *** simulation results indicate that our algorithm can effectively reduce the blocking probability by 23.5%and improve the average link capacity by 3.78%in C+L+S multi-band optical networks.

关键词： multiband optical communications multiband optical networks power allocation wavelength assignment

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ER-MRL: Emotion Recognition based on Multimodal Representation Learning 12

ER-MRL: Emotion Recognition based on Multimodal Representati...

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12th International Conference on Information Science and Technology, ICIST 2022

作者： Guo, Xiaoding Wang, Yadi Miao, Zhijun Yang, Xiaojin Guo, Jinkai Hou, Xianhong Zao, Feifei School of Computer and Information Engineering Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

In recent years, emotion recognition technology has been widely used in emotion change perception and mental illness diagnosis. Previous methods are mainly based on single-task learning strategies, which are unable to fuse multimodal features and remove redundant information. This paper proposes an emotion recognition model ER-MRL, which is based on multimodal representation learning. ER-MRL vectorizes the multimodal emotion data through encoders based on neural networks. The gate mechanism is used for multimodal feature selection. On this basis, ER-MRL calculates the modality specific and modality invariant representation for each emotion category. The Transformer model and multihead self-attention layer are applied to multimodal feature fusion. ER-MRL figures out the prediction result through the tower layer based on fully connected neural networks. Experimental results on the CMU-MOSI dataset show that ER-MRL has better performance on emotion recognition than previous methods. © 2022 IEEE.

关键词： Emotion Recognition

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Impacts of aggregation method on the simulation of multispectral broadband reflectance 4

Impacts of aggregation method on the simulation of multispec...

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4th International Conference on Geology, Mapping, and Remote Sensing, ICGMRS 2023

作者： Chen, Feng Yu, Guanghao Gao, Zhibin Song, Yuejun College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen361024 China Jiangxi Key Laboratory of Soil Erosion and Prevention Jiangxi Academy of Water Science and Engineering Nanchang330029 China

ISBN: (纸本)9781510672741

Simulation of multispectral broadband data from hyperspectral profiles is applicable for many purposes, mainly including sensor assessment and cross-calibration. For the simulation process, several aggregation methods have been proposed. In this paper, three aggregation methods were implemented, to investigate the impacts associated with method on the simulation of multispectral broadband. Comparative analyses were conducted in terms of channel reflectance and several derived vegetation indices, while mainly taking the wide field of view (WFV) sensor onboard Gaofen-6 for case study. Furthermore, impacts on the between-sensor comparability with the Landsat 8 Operational Land Imager (OLI) were discussed. Differences among aggregation methods in reflectance of the Gaofen-6 WFV varied along with channels, and obvious differences were observed in channels over near-infrared (770-890 nm) and red-edge (690-730 nm). Between-sensor difference relative to the Landsat 8 OLI was observed mainly over near-infrared, while the variations among aggregation methods were about 0.4%-2.0%. © 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Reflection

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Two drifting paths of Sargassum bloom in the Yellow Sea and East China Sea during 2019-2020

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Acta Oceanologica Sinica 2022年第6期41卷 78-87页

作者： Chao Yuan Jie Xiao Xuelei Zhang Mingzhu Fu Zongling Wang The MNR Key Laboratory of Marine Eco-Environmental Science and Technology First Institute of OceanographyMinistry of Natural ResourcesQingdao 266061China Laboratory of Marine Ecology and Environmental Science Pilot National Laboratory for Marine Science and Technology(Qingdao)Qingdao 266237China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an 710129China

The macroalgal blooms of floating brown algae Sargassum horneri are increasing in the Yellow Sea and East China Sea during the past few ***,the annual pattern of Sargassum bloom is not well *** study the developing pattern and explore the impacts from hydro-meteorologic environment,high resolution satellite imageries were used to monitor the distribution,coverage and drifting of the pelagic Sargassum rafts in the Yellow Sea and East China Sea from September 2019 to August *** blooms were detected from October 2019 to June 2020 and presented two successive drifting paths that both initiated from around 37°*** first path spanned smaller spatial scale and shorter period,starting with a bloom of 3 km^(2) distribution area near the eastern tip of Shandong Peninsula in late October 2019 and drifted southwards,hit the Pyropia aquaculture area in early January 2020,then vanished in the northwest of East China Sea(ca.32°N)around end of *** second path began with a large distribution area of 23000 km^(2) east of 123°E in late January 2020,firstly moved southwards in the central Yellow Sea and northern East China Sea(north of 29°N)till late April,then turned northwards with monsoon wind and vanished from late June to *** mean sea surface temperature of 8℃ to 20℃ in the Sargassum bloom areas corresponded to in situ observed temperature range for vegetative growth and floating of *** was no observed floating Sargassum blooms during July through September in the Yellow Sea and East China *** results indicate that floating *** is unable to complete life cycle in the Yellow Sea and East China Sea,and provide insights to the future management of Sargassum *** studies are needed to validate the pattern and source of annual Sargassum bloom in the Yellow Sea and East China Sea.

关键词： Sargassum horneri macroalgal bloom high resolution remote sensing wind directions sea surface temperature Yellow Sea and East China Sea

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EFKAN: A KAN-Integrated Neural Operator For Efficient Magnetotelluric Forward Modeling

arXiv

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arXiv 2025年

作者： Wang, Feng Qiu, Hong Huang, Yingying Gu, Xiaozhe Wang, Renfang Yang, Bo College of Big Data and Software Engineering Zhejiang Wanli University Ningbo315200 China Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province School of Earth Sciences Zhejiang University Hangzhou310027 China

Magnetotelluric (MT) forward modeling is fundamental for improving the accuracy and efficiency of MT inversion. Neural operators (NOs) have been effectively used for rapid MT forward modeling, demonstrating their promising performance in solving the MT forward modeling-related partial differential equations (PDEs). Particularly, they can obtain the electromagnetic field at arbitrary locations and frequencies. In these NOs, the projection layers have been dominated by multi-layer perceptrons (MLPs), which may potentially reduce the accuracy of solution due to they usually suffer from the disadvantages of MLPs, such as lack of interpretability, overfitting, and so on. Therefore, to improve the accuracy of MT forward modeling with NOs and explore the potential alternatives to MLPs, we propose a novel neural operator by extending the Fourier neural operator (FNO) with Kolmogorov-Arnold network (EFKAN). Within the EFKAN framework, the FNO serves as the branch network to calculate the apparent resistivity and phase from the resistivity model in the frequency domain. Meanwhile, the KAN acts as the trunk network to project the resistivity and phase, determined by the FNO, to the desired locations and frequencies. Experimental results demonstrate that the proposed method not only achieves higher accuracy in obtaining apparent resistivity and phase compared to the NO equipped with MLPs at the desired frequencies and locations but also outperforms traditional numerical methods in terms of computational speed. Copyright © 2025, The Authors. All rights reserved.

关键词： Frequency domain analysis

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WOBTree:a write-optimized B+-tree for non-volatile memory

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Frontiers of Computer Science 2021年第5期15卷 37-51页

作者： Haitao WANG Zhanhuai LI Xiao ZHANG Xiaonan ZHAO Song JIANG School of Computer Science and Engineering Northwestern Polytechnical UniversityXi'An 710072China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityMinistry of Industry and Information TechnologyXi'An 710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’An 710072China Department of Computer Science and Engineering University of Texas at ArlingtonArlingtonTX 76010USA

The emergence of non-volatile memory(NVM)has introduced new opportunities for performance optimizations in existing storage *** better utilize its byte-addressability and near-DRAM performance,NVM can be attached on the memory bus and accessed via load/store memory instructions rather than the conventional block *** this scenario,a cache line(usually 64 bytes)becomes the data transfer unit between volatile and non-volatile ***,the failure-atomicity of write on NVM is the memory bit width(usually 8 bytes).This mismatch between the data transfer unit and the atomicity unit may introduce write amplification and compromise data consistency of node-based data structures such as B+-*** this paper,we propose WOBTree,a Write-Optimized B+-Tree for NVM to address the mismatch problem without expensive *** minimizes the update granularity from a tree node to a much smaller subnode and carefully arranges the write operations in it to ensure crash consistency and reduce write *** results show that compared with previous persistent B+-tree solutions,WOBTree reduces the write amplification by up to 86× and improves write performance by up to 61× while maintaining similar search performance.

关键词： non-volatile memory B+-tree atomic granularity mismatch write amplification performance optimization

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The Processes-Based Attributes of Four Major Surface Melting Events over the Antarctic Ross Ice Shelf

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Advances in Atmospheric Sciences 2023年第9期40卷 1662-1670页

作者： Wenyi LI Yuting WU Xiaoming HU School of Atmospheric Sciences Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519000China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China Chongqing Research Institute of Big Data Peking UniversityChongqing 401333China Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen UniversityZhuhai 519000China

The Ross-Amundsen sector is experiencing an accelerating warming trend and a more intensive advective influx of marine air *** a result,massive surface melting events of the ice shelf are occurring more frequently,which puts the West Antarctica Ice Sheet at greater risk of *** study shows the connection between surface melting and the prominent intrusion of warm and humid air flows from lower *** applying the Climate Feedback-Response Analysis Method(CFRAM),the temporal surge of the downward longwave(LW)fluxes over the surface of the Ross Ice Shelf(RIS)and adjacent regions are identified for four historically massive RIS surface melting *** melting events are decomposed to identify which physical mechanisms are the main *** found that intrusions of warm and humid airflow from lower latitudes are conducive to warm air temperature and water vapor anomalies,as well as cloud *** changes exert a combined impact on the abnormal enhancement of the downward LW surface radiative fluxes,significantly contributing to surface warming and the resultant massive melting of ice.

关键词： Ross Ice Shelf(RIS) surface melting warm and humid air advection downward longwave radiation Climate Feedback-Response Analysis Method(CFRAM)

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Multi-Label classification with Missing Labels by Preserving Feature-Label Space Consistency

Multi-Label classification with Missing Labels by Preserving...

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IEEE International Conference on big knowledge (ICBK)

作者： Zan Zhang Depeng Zhang Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Multi-label classification deals with the problem where an instance is associated with multiple labels simultaneously. Most existing multi-label classification algorithms assume that the labels of the training data are complete. However, we can obtain only a partial label set of each instance in some real applications since labelling data is difficult or costly. Some existing works on multi-label classification with missing labels focus on exploiting label correlations to complete the original label space and simultaneously build a multi-label learning model using label specific features. However, these methods may be suboptimal since they do not preserve feature-label space consistency. In this paper, we propose a Space Consistency-based Multi-Label classification algorithm named SCML to address this issue. First, label correlation in label space is learned to augment the incomplete original label matrix to a new supplementary label matrix, and the multi-label classifier is constructed simultaneously based on the new supplementary label matrix. Then, correlation information in feature space is learned based on the probabilistic neighborhood similarities to preserve feature-label space consistency. Moreover, the proposed algorithm has an effective mechanism for learning label-specific features to improve the multi-label classification with missing labels. Extensive experiments on twelve benchmark data sets validate the effectiveness of the proposed approach for improving the generalization performance of state-of-the-art algorithms of multi-label learning with missing labels.

关键词：

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Multi-objective multi-population Genetic Programming for feature selection and classification to high-dimensional data

Multi-objective multi-population Genetic Programming for fea...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Li, Qiaoman Gao, Xiaoying Meng, Wenyang Ma, Jianbin Hebei Agricultural University College of Information Science and Technology Baoding China Victoria University of Wellington School of Engineering and Computer Science Wellington New Zealand College of Information Science and Technology Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural University Baoding China

ISBN: (纸本)9798400704956

Classification for high-dimensional data is a challenging task due to a great number of redundant and irrelevant features. Genetic Programming (GP) has its built-in feature selection characteristics and is suitable for processing high-dimensional data. However, if the number of features in GP individual is not restricted, the bloat phenomenon will occur, which affects the generalization of the training model. Moreover, GP is easy to fall into local search in the evolutionary process. In this paper, a multi-objective multi-population GP classifier construction method is proposed, which uses a multipopulation co-evolution strategy to divide the evolving population into the main and auxiliary populations with different evolutionary strategies and different evaluation criteria, and employs a multiobjective strategy to ensure classification performance and restrict the number of features at the same time. Experiments on seven high-dimensional datasets show that our proposed multi-population co-evolution strategy and multi-objective strategy are all effective to improve the classification performance of GP classifiers. Comparisons with three state-of-the-art GP classifier construction methods show that our proposed methods achieve better or comparable classification performance on most cases. © 2024 held by the owner/author(s).

关键词： Spatio-temporal data

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Malicious URL Recognition Based on Multi-feature Fusion and Machine Learning 41

Malicious URL Recognition Based on Multi-feature Fusion and ...

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第41届中国控制会议

作者： Changyou Ma Aimin Wu Wenzhuo Ma Ke Chen Yun Liu Xiaoning Liang Data Recovery Key Laboratory of Sichuan Province Neijiang Normal University College of Big Data & Intelligent Engineering Chongqing College of International Business and Economics College of Artificial Intelligence Southwest University XLY Salvationdata Technology INC.

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the rapid development of the Internet,malicious web pages including phishing,spamming and malware are gradually increasing,resulting in endangering network *** address this challenging issue,a novel malicious URL recognition method based on multi-feature fusion and machine learning is *** specifically,three classic features(URL,HTML and JavaScript code features) and two presented effective features(HTTP request and Text features) are first extracted from URL ***,several machine learning models are adopted to train binary classification model with the selected discriminant ***,we use ten-fold cross-validation accuracy to evaluate the performance of different classification *** results verify that the selected features are conductive for malicious URLs detection and the random forest model trained using the presented hybrid feature set performs better than other classification techniques.

关键词： Malicious URL detection multi-feature fusion machine learning features

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