检索结果-内蒙古大学图书馆

Finite Dimensional Estimation Algebra for Time-Varying filtering System and Optimal Transport Particle Filter: A Tangent Flow Point of View

引用

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第6期59卷 8005-8021页

作者： Kang, Jiayi Jiao, Xiaopei Yau, Stephen S. -T. Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China Yanqi Lake Beijing Inst Math Sci & Applicat Huairou 101400 Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China

Ever since the technique of the Kalman filter was popularized, there has been a lot of research interest in finding more classes of finite-dimensional recursive filters. In past research, the estimation algebra method could only be used for time-invariant systems. In this article, we extend the estimation algebra method so that it applies to a general class of time-varying filtering systems. Then, the Wei-Norman method can be used to derive the explicit solution of the posterior distribution of state estimation. As a special control law, tangent flow is derived for the nonlinear filtering system based on the Monge-Ampere equation in optimal transport. As a result, we propose an optimal transportation filter by applying stochastic tangent flow to Yau filtering systems. The numerical experiments demonstrate the higher efficacy and accuracy of the proposed optimal transportation filter compared to common traditional algorithms such as the extended Kalman filter and feedback particle filter.

关键词： Algebra filtering Estimation filtering algorithms Maximum likelihood detection Mathematical models Nonlinear filters Estimation algebra filtering optimal transportation

来源：评论

学校读者我要写书评

暂无评论

Recursive Constrained Adaptive filtering Algorithm Based on Arctangent Framework

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第4期70卷 1650-1654页

作者： Jia, Wenyi Feng, Zefan Cai, Tianfu Li, Mingyu Shi, Weimin Dai, Zhijiang Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China

In this brief, a robust constrained filtering algorithm is proposed by introducing a novel cost function framework into the constrained adaptive algorithm. The proposed algorithm is called the recursive constrained least arctangent (RCLA) adaptive algorithm. Thanks to the robustness of arctangent function, the proposed RCLA algorithm shows superior convergence performance and better steady-state behavior against impulsive noises compared to other existing recursive methods. The mean square convergence analysis and theoretical transient mean square deviation (MSD) are derived in detail. Besides, to validate the theoretical analysis, the computer simulations are conducted to demonstrate the consistency between theoretical and simulated MSD results. Simulation results under non-Gaussian environments verify the superior behavior of the proposed RCLA algorithm compared to known algorithms.

关键词： Optimized production technology Cost function Convergence Adaptive algorithms Robustness filtering algorithms Transient analysis Recursive constrained adaptive algorithm arctangent function non-Gaussian noise convergence analysis

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Dynamic State Estimation Framework Using a Koopman Operator-Based Linear Predictor

引用

IEEE ACCESS 2025年 13卷 31660-31670页

作者： Yang, Deyou Gao, Han Chen, Zhe Lv, Yanling Wang, Lixin Harbin Univ Sci & Technol Sch Elect & Elect Engn Harbin 150080 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China

Dynamic state estimation (DSE) is a fundamental task in many fields, including control systems, robotics, and signal processing. Traditional DSE methods, which rely on mathematical models to describe system dynamics, are often limited in their applicability to real-world scenarios due to inaccuracies and assumptions. In this paper, we propose a purely data-driven DSE framework based on a Koopman operator-based linear predictor. The Koopman operator is a powerful tool in dynamical systems theory that allows us to analyze and predict the behavior of nonlinear systems. By leveraging the Koopman operator extracted solely from measured input-output data through the extended dynamic mode decomposition with control (EDMDc) method, a linear predictor that can accurately estimate the state variables of a dynamic system is developed. Introducing the extended Kalman filter (EKF) as an estimation method, the learned Koopman operator-based linear predictor is then used to estimate the current state of the system given only the past and present input-output measurements. To evaluate the effectiveness of the proposed framework, we conduct experiments on both simulated and real-world datasets of complex power systems. The results demonstrate that the proposed data-driven approach outperforms traditional model-based approaches in terms of accuracy and robustness. Moreover, the proposed framework is capable of handling nonlinear and time-varying systems, making it applicable to a wide range of practical cases.

关键词： Mathematical models Power system dynamics Heuristic algorithms Nonlinear dynamical systems Kalman filters Accuracy Estimation filtering algorithms filtering Covariance matrices Koopman operator data-driven linear predictor extended dynamic mode decomposition with control (EDMDc) extended Kalman filter (EKF) dynamic state estimation (DSE)

来源：评论

学校读者我要写书评

暂无评论

Statistical Analysis of Maximum Correntropy Criterion Subband Adaptive filtering Algorithm

引用

IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 2025-2029页

作者： Xu, Wenjing Zhao, Haiquan Lv, Shaohui Minist Educ Key Lab Magnet Suspens Technol & Maglev Vehicle Beijing 100816 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

The correntropy contains all the even-order information of data and is well suited for dealing with non-Gaussian noise. The subband adaptive filtering algorithm combined with the maximum correntropy criterion (MCC-SAF) has been proposed to efficiently handle colored input signals and impulsive noise. Compared with other similar algorithms, the MCC-SAF algorithm exhibits faster convergence speed and lower steady-state error in non-Gaussian noise environments. However, to the best of our knowledge, the transient and steady-state statistical performance of this algorithm is still missing. In this letter, the mean-square performance of the MCC-SAF is analyzed under Gaussian, binary, and uniform noises using vectorization method, in which the mathematical expectation of the exponential term is computed by means of the moment generating function (MGF), and finally the theoretical prediction models for mean square deviation (MSD) are derived and verified by computer simulations.

关键词： Signal processing algorithms Adaptive filters filtering algorithms Convergence Prediction algorithms filtering theory Attenuation Transient analysis Steady-state Gaussian noise Maximum correntropy criterion non-Gaussian noise performance analysis moment generating function (MGF)

来源：评论

学校读者我要写书评

暂无评论

Longest-First Search Using Bloom Filter: Algorithm and FPGA Implementation

引用

IEEE ACCESS 2025年 13卷 49354-49361页

作者： Lee, Jinsol Lim, Hyesook Ewha Womans Univ Dept Elect & Elect Engn Seoul 03760 South Korea

Due to the surge in Internet traffic and the rapid increase in forwarding table entries, achieving wire-speed packet forwarding in Internet routers demands both algorithmic enhancements and hardware innovations. In this paper, we propose the longest-first search algorithm with a Bloom filter that stores prefixes in a leaf-pushing trie. Our approach utilizes an on-chip Bloom filter to indicate the presence of prefixes within the trie, while an off-chip hash table stores the corresponding output port information for each prefix. For each incoming IP address, the Bloom filter query begins from the longest length, reducing the queried number of bits by one for each negative result. The off-chip hash table is only accessed when the query to the Bloom filter produces a positive result. Therefore, access to the slower off-chip memory is minimized to once, given a reasonable Bloom filter size. The proposed approach is simulated using C++ and constructed with Verilog for field programmable gate array (FPGA) implementation. The experimental results indicate that the proposed approach achieves the throughput of 0.8 million packets per second at a clock frequency of 100MHz.

关键词： filtering algorithms IP networks Information filters filtering theory Field programmable gate arrays Matched filters Lenses Internet System-on-chip Hash functions Binary trie Bloom filter FPGA IP address lookup leaf-pushing Verilog HDL

来源：评论

学校读者我要写书评

暂无评论

Research on An Improved Time Scale Algorithm of Time-keeping Atomic Clocks

引用

IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2025年第1期28卷 64-70页

作者： Jiang, Meng Zhang, Yu Xian Polytech Univ Sch Elect & Informat Xian Peoples R China Xian Univ Sci & Technol Coll Mech Engn Xian Peoples R China Zhejiang Weiying Intelligent Technol Co Ltd Zhuji Peoples R China Chinese Acad Sci Natl Time Serv Ctr Xian Peoples R China

In this article, quadratic exponential smoothing combined with VC filter, a new time scale data fusion algorithm is proposed. The frequency drifts of hydrogen masers are predicted by the use of the minimum deviation method, so that the time difference prediction becomes more accurate. The time scale is constructed by the modified quadratic exponential smoothing method based on the data of hydrogen masers, and the cesium atomic clocks time scale is established by the weighted mean method, and the VC filter is designed to combine the advantages of long-short-term stability of the two time scales to improve the comprehensive time scale performance. The experimental results demonstrate that the proposed approach produces a comprehensive time scale has a stability of 1.60e-15 for one hour and 3e-15 for one day, and it is superior to the performance of time scale generated by three classic methods: ALGOS, AT1, and Kalman filtering.

关键词： Time-frequency analysis Smoothing methods filtering Hydrogen Stability criteria Masers filtering algorithms Prediction algorithms Kalman filters Atomic clocks

来源：评论

学校读者我要写书评

暂无评论

Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated Framework

引用

IEEE ACCESS 2025年 13卷 5660-5678页

作者： Maghsoudi, Mehrdad Valikhani, Mohammad Hossein Zohdi, Mohammad Hossein Shahid Beheshti Univ Fac Management & Accounting Dept Ind & Informat Management Tehran *** Iran Iran Univ Sci & technol Dept Management Econ & Progress Engn Tehran *** Iran

The proliferation of video-on-demand (VOD) services has led to a paradox of choice, overwhelming users with vast content libraries and revealing limitations in current recommender systems. This research introduces a novel approach by combining implicit user data, such as viewing percentages, with social network analysis to enhance personalization in VOD platforms. The methodology constructs user-item interaction graphs based on viewing patterns and applies centrality measures (degree, closeness, and betweenness) to identify important videos. Modularity-based clustering groups related content, enabling personalized recommendations. The system was evaluated on a documentary-focused VOD platform with 328 users over four months. Results showed significant improvements: a 63% increase in click-through rate (CTR), a 24% increase in view completion rate, and a 17% improvement in user satisfaction. The approach outperformed traditional methods like Naive Bayes and SVM. Future research should explore advanced techniques, such as matrix factorization models, graph neural networks, and hybrid approaches combining content-based and collaborative filtering. Additionally, incorporating temporal models and addressing scalability challenges for large-scale platforms are essential next steps. This study contributes to the state of the art by introducing modularity-based clustering and ego-centric ranking methods to enhance personalization in video recommendations. The findings suggest that integrating network-based features and implicit feedback can significantly improve user engagement, offering a cost-effective solution for VOD platforms to enhance recommendation quality.

关键词： Social networking (online) Recommender systems Streaming media Measurement filtering Collaborative filtering Accuracy filtering algorithms Entertainment industry Prediction algorithms Video recommendation social network analysis implicit feedback modularity clustering ego-centric ranking

来源：评论

学校读者我要写书评

暂无评论

Pseudolite Self-Interference Cancellation Without Additional GNSS Signal Distortion

引用

IEEE ACCESS 2025年 13卷 55927-55938页

作者： Gao, Lichao He, Jin Wu, Yuanxin Yu, Wenxian Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Key Lab Intelligent Sensing & Recognit Shanghai 200240 Peoples R China

The goal of this paper is to solve the low-orbit pseudolite self-interference problem. The low-orbit pseudolite navigation signals will interference satellite navigation as they broadcast at the same frequency and same time. However, the low-orbit pseudolite also needs satellite navigation to get continuous and effective high-precision positioning. So, while effectively filtering the self-interference, the GNSS navigation signals need low distortion. In this paper, we propose an anti-self-interference method which ensures that GNSS navigation signals are not distorted. Specifically, the method uses the known characteristics of self-interference signals to establish an optimization problem. By solving this problem, self-interference filtering can be achieved without estimating the direction of satellite navigation signal and without the assistance of the receiver channel amplitude-frequency and phase-frequency characteristics. The simulation test results show that the algorithm can filter self-interference without additional distortion of the satellite navigation signals. The application test results show that, the algorithm can filter self-interference with high-precision carrier observation of the satellite navigation signal which statistical standard deviation is only 0.01 times of the carrier wavelength. The method could help in achieving high-precision satellite navigation and positioning of pseudolite terminals and providing high-precision position information for low-orbit pseudolites.

关键词： Satellite broadcasting Satellites Interference cancellation Global navigation satellite system Receiving antennas Aircraft navigation Accuracy filtering algorithms Calibration Full-duplex system Pseudolite navigation self-interference cancellation GNSS zero-distortion low-orbit

来源：评论

学校读者我要写书评

暂无评论

Kullback-Leibler Divergence-Based Observer Design Against Sensor Bias Injection Attacks in Single-Output Systems

引用

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 2763-2777页

作者： Tosun, Fatih Emre Teixeira, Andre M. H. Dong, Jingwei Ahlen, Anders Dey, Subhrakanti Uppsala Univ Dept Elect Engn S-75103 Uppsala Sweden Uppsala Univ Dept Informat Technol S-75103 Uppsala Sweden

This paper considers observer-based detection of sensor bias injection attacks (BIAs) on linear cyber-physical systems with single output driven by white Gaussian noise. Despite their simplicity, BIAs pose a severe risk to systems with integrators, which we refer to as integrator vulnerability. Specifically, the residual generated by any linear observer is indistinguishable under attack and normal operation at steady state, making BIAs detectable only during transients. To address this, we propose a principled method based on Kullback-Leibler divergence to design a residual generator that significantly increases the signal-to-noise ratio against BIAs. For systems without integrator vulnerability, our method also enables a trade-off between transient and steady-state detectability. The effectiveness of the proposed method is demonstrated through numerical comparisons with three state-of-the-art residual generators.

关键词： Security Generators Symmetric matrices Transient analysis Steady-state Linear systems Vectors Control theory filtering algorithms Anomaly detection Cyber-physical systems sensor deception attacks bias injection attacks observer-based anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Enhanced Knowledge Tracing With Learnable Filter

引用

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年第1期12卷 198-209页

作者： Qian, Fulan Hu, Yetong Li, Guangyao Chen, Jie Wang, Shijin Zhao, Shu Anhui Univ Artificial Intelligence Inst Hefei 230601 Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China IFLYTEK AI Res Hefei 230088 Peoples R China

The primary objective of knowledge tracing (KT) is to evaluate students' understanding and mastery of knowledge through their responses to exercises, which aids in predicting their future performance. Deep neural networks have been widely applied in the area of knowledge tracing and have demonstrated encouraging results. Nevertheless, in real-world scenarios, there is a substantial amount of noise in students' response records. These noises may amplify the inherent risk of overfitting in deep neural networks, leading to a decrease in model performance. To address these issues, we introduce a new model called filter knowledge tracing (FKT). This innovative model incorporates a learnable filter into KT to filter out noise information from students' exercise sequences. We redefine the input paradigm of the data, using learnable filters to perform filtering operations in its frequency domain representation space, effectively removing noise. Additionally, an attention module has been introduced in the FKT model to evaluate the impact of students' historical interactions on their current knowledge state. To validate our model, we conduct extensive experiments utilizing four publicly available datasets. The results demonstrate that FKT outperforms existing benchmarks, particularly on larger datasets, signifying an improvement in KT performance while effectively reducing the risk of overfitting.

关键词： Data models Noise Noise reduction Signal processing algorithms Knowledge engineering Prediction algorithms Noise measurement Heuristic algorithms Frequency-domain analysis filtering algorithms Attention mechanism denoising filtering algorithm knowledge tracing (KT)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：