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Encoding-Decoding-Based Recursive filtering for Fractional-Order Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1103-1106页

作者： Bo Jiang Hongli Dong Yuxuan Shen Shujuan Mu Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318 Sanya Offshore Oil&Gas Research Institue Sanya 572025China IEEE

Dear editor,This letter focuses on the encoding-decoding-based recursive filtering problem for a class of fractional-order systems.For the purpose of protecting security of the wireless communication network,a dynamic-quantization-based encoding-decoding mechanism is introduced to encrypt the transmitted measurement.Specifically,the measurement outputs are first encoded by the encoder into codewords which are then transmitted over the wireless communication network.After received by the decoder,the codewords are decoded and then sent to the filter.In light of the mathematical properties of truncated Gaussian distribution,the variance of the encoding-decoding-induced error between the decoded measurement and the real measurement is derived.An upper bound on the filtering error covariance is first obtained based on the variance of the encoding-decoding-induced error.Then,the minimal upper bound is derived by choosing proper filter gain.Finally,the efficiency and superiority of the proposed algorithm are verified through a simulation example.

关键词： decoder filtering error

Distributed Dimensionality Reduction filtering for CPSs Under DoS Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1080-1082页

作者： Xiaoyuan Zheng Hao Zhang Xindi Yang Huaicheng Yan School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Department of Control Science and Engineering Tongji UniversityShanghai 200092China Key Laboratory of Smart Manufacturing in Energy Chemcical Process of Ministry of Education East China University of Science and TechnologyShanghai 200237 School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China

Dear Editor,This letter concerns about the distributed dimensionally reduction filtering for a class of cyber physical systems with denial of service(DoS)attacks.Considering each sensor exchanging measurements with ne... 详细信息

关键词： DoS service filtering

Self-adaptive label filtering learning for unsupervised domain adaptation

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Frontiers of Computer Science 2023年第1期17卷 225-227页

作者： Qing TIAN Heyang SUN Shun PENG Tinghuai MA School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China

1 Introduction As an emerging machine learning paradigm,unsupervised domain adaptation(UDA)aims to train an effective model for unlabeled target domain by leveraging knowledge from related but distribution-inconsistent source domain.Most of the existing UDA methods[2]align class-wise distributions resorting to target domain pseudo-labels,for which hard labels may be misguided by misclassifications while soft labels are confusing with trivial noises so that both of them tend to cause frustrating performance.To overcome such drawbacks,as shown in Fig.1,we propose to achieve UDA by performing self-adaptive label filtering learning(SALFL)from both the statistical and the geometrical perspectives,which filters out the misclassified pseudo-labels to reduce negative transfer.Specifically,the proposed SALFL firstly predicts labels for the target domain instances by graph-based random walking and then filters out those noise labels by self-adaptive learning strategy.

关键词： filtering resort overcome

Recursive filtering for Nonlinear Systems With Self-Interferences Over Full-Duplex Relay Networks

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2037-2040页

作者： Hailong Tan Bo Shen Qi Li Wei Qian the School of Mathematics-Physics and Finance Anhui Polytechnic UniversityWuhu 241000 the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 241000China IEEE the College of Information Science and Technology Donghua UniversityShanghai 201620 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the School of Information Science and Engineering Hangzhou Normal UniversityHangzhou 311121China the School of Electrical Engineering and Automation Henan Polytechnic UniversityJiaozuo 454000China

Dear Editor,In this paper,a recursive filtering problem(RRP)is addressed for nonlinear systems over full-duplex relay(FDR)networks.A FDR is adopted to forward measurements of the sensor to the filter.Because of concurrently transmitting and receiving,the FDR is interfered by the signals from itself,thereby exhibiting self-interference(SI).

关键词： thereby Nonlinear filtering

Distributed H∞ filtering of nonlinear systems with random topology by an event-triggered protocol

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Science China(Information Sciences) 2021年第10期64卷 206-220页

作者： Yun CHEN Mengze ZHU Renquan LU Anke XUE Key Lab for IoT and Information Fusion Technology of Zhejiang Hangzhou Dianzi University School of Automation Guangdong University of Technology

Applying an event-triggered protocol, this paper proposes a distributed Hfilter design for nonlinear perturbed systems under fading measurements with random topology. Nonlinearities in this system obey the one-sided Lipschitz constraint, which embraces the conventional Lipschitz condition as a special case.The sensor network allows random variations of the interconnection strengths between adjacent nodes, and the connection coefficient is determined as the product of a constant and a stochastic variable with a known probabilistic feature. To reduce the unnecessary data transmission and efficiently use the limited bandwidth,the transmissions are orchestrated by an event-triggered regulating strategy. A stochastic bounded real lemma is established for the resulting error dynamics. Based on the presented matrix decomposition, which removes the direct coupling between the statistical information of interconnection strengths and the filter gain, the distributed Hfilter gain can be explicitly expressed and easily solved. The usefulness of the theoretical method is demonstrated in a simulation study.

关键词： sensor network distributed H ∞ filtering one-sided Lipschitz condition event-triggered protocol random topology

An efficient hybrid recommendation model based on collaborative filtering recommender systems

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CAAI Transactions on Intelligence Technology 2021年第4期6卷 480-492页

作者： Mohammed Fadhel Aljunid Manjaiah Doddaghatta Huchaiah Department of Computer Science Mangalore UniversityMangaloreIndia

In recent years,collaborative filtering(CF)techniques have become one of the most popularly used techniques for providing personalised services to users.CF techniques collect users9 previous information about items such as books,music,movies,ideas,and so on.Memory-based models are generally referred to as similarity-based CF models,which are one of the most widely agreeable approaches for providing service recommendations.The memory-based approach includes user-based CF(UCF)and item-based CF(ICF)algorithms.The UCF model recommends items by finding similar users,while the ICF model recommends items by finding similar items based on the user-item rating matrix.However,consequent to the ingrained sparsity of the user-item rating matrix,a large number of ratings are missing.This results in the availability of only a few ratings to make predictions for the unknown ratings.The result is the poor prediction quality of the CF model.A model to find the best algorithm is provided here,which gives the most accurate recommendation based on different similarity metrics.Here a hybrid recommendation model,namely rUICF,is proposed.The rUICF model integrates the UCF and ICF models with the T linear regression model to model the sparsity and scalability issue of the user-item rating matrix.Detailed experimentation on two different real-world datasets shows that the proposed model demonstrates substantial performance when compared with the existing methods.

关键词： services filtering similarity

Multi-model deep learning approach for collaborative filtering recommendation system

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CAAI Transactions on Intelligence Technology 2020年第4期5卷 268-275页

作者： Mohammed Fadhel Aljunid Manjaiah Doddaghatta Huchaiah Computer Science Mangalore UniversityMangaloreIndia

As a result of a huge volume of implicit feedback such as browsing and clicks,many researchers are involving in designing recommender systems(RSs)based on implicit feedback.Though implicit feedback is too challenging,it is highly applicable to use in building recommendation systems.Conventional collaborative filtering techniques such as matrix decomposition,which consider user preferences as a linear combination of user and item latent features,have limited learning capacities,hence suffer from a cold start and data sparsity problems.To tackle these problems,the research direction towards considering the integration of conventional collaborative filtering with deep neural networks to maps user and item features.Conversely,the scalability and the sparsity of the data affect the performance of the methods and limit the worthiness of the results of the recommendations.Therefore,the authors proposed a multimodel deep learning(MMDL)approach by integrating user and item functions to construct a hybrid RS and significant improvement.The MMDL approach combines deep autoencoder with a one-dimensional convolution neural network model that learns user and item features to predict user preferences.From detail experimentation on two real-world datasets,the proposed work exhibits substantial performance when compared to the existing methods.

关键词： filtering approach neural

Distributed filtering and control of complex networks and systems

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Frontiers of Information Technology & Electronic Engineering 2021年第1期22卷 1-4页

作者： Guanrong CHEN Sergej CELIKOVSKY Lei GUO Youmin ZHANG Tiancheng LI Depariment of Electrical Engineering City University of Hong KongHong KongChina The Czech Academy of Sciences Institute of Imnformation Theory and AutomationPrague 18200Czech Republic School of Automation Science and Electrical Engineering Beihang UniversityBejjing 100191China Department of Mechanical Industrial and Aerospace EngineeringConcordia UniversitMontrealQuebec H3G 1M8Canada MOE Key Laboratory of Information Pusion Technology School ofAutomationNorthwestern Polytechnical UniversiyXi'an 710072China

In the last two decades,research on distributed filtering and control(FAC)has burgeoned into large-scale complex networks and systems.A distributed networked system,typically composed of a number of cost-effective dynamical nodes(agents)with limited sensing,computing,and communication capabilities,is gaining considerable popularity due to its low power consumption,simple installation,high performance,and strong reliability,as compared with the centralized setting.As a result,various networkinduced FAC systems,techniques,and algorithms have emerged,including node registration and control,network consensus and synchronization,multi-sensor data clustering/fusion,network topological design and analysis,and the like.

关键词： networks filtering network

Sensitivity analysis of parameters and contrasting performance of ground filtering algorithms with UAV photogrammetry-based and LiDAR point clouds

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International Journal of Digital Earth 2020年第12期13卷 1672-1694页

作者： Petr Klápště Michal Fogl Vojtěch Barták Kateřina Gdulová Rudolf Urban Vítězslav Moudrý Department of Applied Geoinformatics and Spatial Planning Faculty of Environmental SciencesCzech University of Life Sciences PragueKamýcká129Praha-Suchdol16500Czech Republic Department of Special Geodesy Faculty of Civil EngineeringCzech Technical University in PraguePragueCzech Republic

Most ground filtering algorithms are primarily designed for airborne LiDAR point cloud processing and their successful use in identifying ground points from photogrammetric point clouds remains questionable.We compared six ground filtering algorithms implemented in Metashape,ArcGIS,CloudCompare,LAStools,and PDAL.We used UAV photogrammetry-based(acquired under leaf-off conditions)and airborne LiDAR(leaf-on)point clouds of the same area to:(i)compare accuracy of generated DTMs;(ii)evaluate the effect of vegetation density and terrain slope on filtering accuracy;and(iii)assess which algorithm parameters have the greatest effect on the filtering accuracy.Our results show that the performance of filtering algorithms was affected by the point cloud type,terrain slope and vegetation cover.The results were generally better for LiDAR(RMSE 0.13–0.19 m)than for photogrammetric(RMSE 0.19–0.23 m)point clouds.The behavior in varying vegetation and terrain conditions was consistent for LiDAR point clouds.However,when applied on photogrammetric point clouds,the algorithms’behavior was inconsistent,especially in areas of steep slope(except for the Progressive Triangulated Irregular Network in LAStools).Parameters related to the selection of the initial minimum elevation ground points were the most influential in all algorithms and point clouds.

关键词： DTM filtering LiDAR point cloud spoil heap UAV