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International Workshop on Knowledge Discovery and Data Mining (WKDD)

作者： Zhi-Dan Zhao Ming-sheng Shang School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China

ISBN: (纸本)9781424453979

Collaborative filtering (CF) algorithms are widely used in a lot of recommender systems, however, the computational complexity of CF is high thus hinder their use in large scale systems. In this paper, we implement user-based CF algorithm on a cloud computing platform, namely Hadoop, to solve the scalability problem of CF. Experimental results show that a simple method that partition users into groups according to two basic principles, i.e., tidy arrangement of mapper number to overcome the initiation of mapper and partition task equally such that all processors finish task at the same time, can achieve linear speedup.

关键词： Collaboration filtering algorithms Collaborative work Cloud computing Recommender systems Partitioning algorithms Scalability Computer science Data engineering Knowledge engineering

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Weighted Average Consensus filtering for Continuous-Time Linear Systems With Asynchronous Sensor Measurements

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IEEE SENSORS JOURNAL 2025年第4期25卷 6881-6893页

作者： Hu, Yanyan Lin, Xufeng Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Key Lab Intelligent Bion Unmanned Syst Minist Educ Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Intelligence Sci & Technol Beijing 100083 Peoples R China

In practical sensor networks, especially heterogeneous sensor networks, sensor nodes may have distinct sampling periods and initial sampling time instants, probably their observations are also nonuniform. However, the distributed consensus state estimation problem for continuous-time linear systems in such asynchronous sensor networks has not been addressed in the literature. To solve this problem, this article proposes a consensus filtering algorithm for distributed state estimation over asynchronous sensor networks based on the weighted average consensus strategy. First, asynchronous measurements at each sensor node within a given filtering interval are transformed to the consensus filtering time instant according to the continuous-time system dynamics. Statistical characteristics of converted measurement noises are carefully exploited as well as their cross-correlations induced by the synchronization procedure. It is also discovered that the converted measurement noises are one-step correlated with the discretized process noise. Second, measurements after synchronization are used to update local estimates at sensor nodes with the above correlations taken into account and weighted average consensus iterations are performed based on information interactions of sensor nodes with their neighbors. Finally, the estimation error of the proposed asynchronous consensus filtering algorithm is proved to be exponential mean-square bounded, and its effectiveness is evaluated by a simulation example.

关键词： Sensors Synchronization filtering State estimation Sensor systems Noise measurement Noise Time measurement filtering algorithms Sensor phenomena and characterization Asynchronous sensor networks consensus filtering measurement synchronization weighted average consensus

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Enhanced Detection of Narrow-Band Leakage Gas Infrared Image Based on Bilateral filtering and U-Net

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IEEE ACCESS 2025年 13卷 55559-55575页

作者： Dong, Kangjun Jin, Weiqi Wang, Minghe Wang, Xia Xu, Shiwei Beijing Inst Technol Sch Opt & Photon MOE Key Lab Optoelect Imaging Technol & Syst Beijing 100081 Peoples R China

Uncooled infrared image detection methods with a narrow-band filter have the advantages of low cost and can efficiently distinguish gas types;however, they also introduce nonuniform patterns to the image and reduce the target radiation flux and signal-to-noise ratio (SNR), resulting in low gas extraction accuracy. In this study, first, a temperature control device is designed in a part containing a filter and a detector array to effectively suppress dark current noise, which is positively correlated with temperature and eliminates nonuniform patterns, such as the lid effect, using the two-point correction method. Second, according to the infrared image characteristics of the leaked gas, a leakage gas infrared image enhancement algorithm based on bilateral filtering is developed. This algorithm considerably reduces the time frame noise of image sequences, improves the contrast between the gas plume region and background, and improves the SNR. Next, based on an improved fuzzy Gaussian background model, U-Net is used to separate the gas and non-gas foreground. Experimental results demonstrate that the proposed method exhibits high gas plume detection accuracy.

关键词： Image enhancement Dynamic range Feature extraction Signal to noise ratio Low-pass filters Information filters Histograms Optical filters filtering algorithms Visualization Bilateral filter gas leak detection image enhancement infrared imaging narrow band filter

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Improvement of Deep Learning Models Using Retinal Filter: A Systematic Evaluation of the Effect of Gaussian filtering With a Focus on Industrial Inspection Data

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IEEE ACCESS 2025年 13卷 43201-43217页

作者： Buettner, Ricardo Mai, Christopher Penava, Pascal Univ Fed Armed Forces Hamburg Helmut Schmidt Univ Chair Hybrid Intelligence D-22043 Hamburg Germany

The range of products manufactured in the industry is vast, as are the anomalies that may occur during the manufacturing process. Some companies have initiated the automation of the quality control process and the implementation of modern deep learning methodologies to promptly identify defective objects. In addition to architectural optimizations within the deep learning model, the utilization of pre-processing filters can also be employed to help improve recognition performance. The Gaussian filter serves as an illustrative example, exhibiting functionality that parallels aspects of the human visual processing system. Although some authors have successfully applied this filter, a comprehensive study assessing its actual impact on a large number of objects and the extent to which various performance indicators are affected remains pending, which is the primary objective of this study. To this end, a variety of architectural approaches are employed, including Xception, InceptionV3, ResNet50V2, VGG19, and VGG16. In addition to hyperparameter tuning, all architectures utilize transfer learning. The results demonstrate that the implementation of an innovative Gaussian filter approach enhances the balanced accuracy in at least one architecture for 13 out of 15 product categories. Furthermore, the filtering approach positively influences various performance indicators across the majority of categories. It can be concluded that the Gaussian filter is often an effective technique for enhancing model performance across various product categories, making it a valuable and efficient tool for industrial defect detection in quality control applications. This study provides an overview of the results achieved and other key performance indicators for all the models used.

关键词： Quality control Deep learning Accuracy Optical filters Defect detection Gaussian distribution filtering algorithms Visualization Retina Transfer learning Gaussian filter defect detection transfer learning pre-trained architectures

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Error-Mask-Adaptive Dynamic filtering for Image Inpainting

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IEEE ACCESS 2025年 13卷 18403-18417页

作者： Ko, Keunsoo Woo, Seunggyun Kim, Chang-Su Catholic Univ Korea Dept Artificial Intelligence Bucheon 14662 South Korea Korea Univ Sch Elect Engn Seoul 02841 South Korea

A novel error-mask-adaptive dynamic filtering (EMDF) algorithm is proposed in this paper, which uses a continuous error mask to inpaint an image adaptively and faithfully. In an EMDF layer, we determine a spatially varying filter adaptively according to an error mask and perform separable dynamic filtering. Meanwhile, we update the error mask by modeling the error propagation during the filtering. Through several EMDF layers, we predict an inpainting result. Finally, we refine it to reconstruct a more faithful image. Experimental results on diverse datasets show that the proposed EMDF algorithm outperforms existing inpainting algorithms significantly. The source codes are available at https://***/keunsoo-ko/EMDF.

关键词： filtering Convolution Image reconstruction Heuristic algorithms filtering algorithms Adaptive filters Adaptation models Prediction algorithms Image restoration Visualization Image inpainting dynamic filtering mask-adaptive filtering convolutional neural networks

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Distributed Consensus-Based filtering Against False Data Injection Attacks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第1期12卷 110-121页

作者： Yang, Yuhang Gao, Xiangzhou Song, Shenmin Li, Zhiqiang Harbin Inst Technol Ctr Control Theory & Guidance Technol Harbin 150001 Peoples R China China Elect Technol Grp Corp 32nd Res Inst Shanghai 201808 Peoples R China

Existing distributed state estimation algorithms usually show satisfactory performance when dealing with data bias caused by network-induced phenomena. However, the security characteristics of these algorithms are often significantly affected by more complex and severe network attacks. Specifically, due to the lack of dynamic adaptability and abnormal data detection ability of the estimator, the estimator may deteriorate significantly or even diverge, which poses a serious threat to the stability and reliability of the system. To remedy this issue, we propose a distributed estimation algorithm based on the classical Kalman consensus filter framework. The accuracy of the estimator is significantly improved by utilizing the innovation of neighbor nodes. Furthermore, we construct an adaptive weight allocation mechanism based on the principle of minimizing the estimation error variance according to the possible accuracy differences between different estimators. This mechanism can evaluate the data accuracy of each node, and dynamically adjust its weight accordingly. Subsenquently, an event-triggered detector with random thresholds is designed to enhance the anti-attack ability of the estimator. The detector can monitor the data flow in the network in real time, and identify the potential abnormal or attack behavior by setting dynamic thresholds. Once abnormal data is detected, the detector can immediately trigger corresponding countermeasures to block the propagation path of erroneous data and protect the safe and stable operation of the system. Simulation results are employed to validate the effectiveness of the proposed method.

关键词： State estimation Accuracy Technological innovation Kalman filters Distributed databases Heuristic algorithms Detectors Estimation error filtering algorithms Convergence False data injection attack wireless sensor networks Kalman consensus filter state estimation

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DGLG: A Novel Deep Generalized Legendre-Galerkin Approach to Optimal filtering Problem

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2584-2590页

作者： Shi, Ji Jiao, Xiaopei Yau, Stephen S. -T. Capital Normal Univ Acad Multidisciplinary Studies Beijing 100048 Peoples R China Univ Twente Dept Appl Math Enschede Netherlands Beijing Inst Math Sci & Applicat Beijing 101400 Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China

The optimal filtering problem for general nonlinear and continuous state-observation systems attracts lots of attention in the control theory. The essence of optimal filtering requires solving the Duncan-Mortensen-Zakai (DMZ) equation in a computationally feasible way. Under the pioneering work of Yau-Yau filtering, the DMZ equation is reduced to a pathwise computation of a forward Kolmogorov equation with time-varying initial conditions, which is very challenging. To overcome the computational difficulty, in this article, we proposed a new efficient filtering algorithm consisting of a forward Kolmogorov equation solver based on a physics-informed neural network and a probability density approximator based on generalized Legendre polynomials. By utilizing the advanced deep learning method and classical Galerkin approximation, our developed algorithm not only maintains the high accuracy of the spectral method but also removes massive computational loads in the offline part. Furthermore, the convergence of our method is proved. Numerical experiments have been carried out to verify the feasibility of the new method. Regarding accuracy and efficacy, the newly proposed deep generalized Legendre-Galerkin algorithm outperforms other popular suboptimal methods including the extended Kalman filter and particle filter.

关键词： filtering algorithms filtering filtering theory Kalman filters Approximation algorithms Real-time systems Estimation Convergence Probability density function Polynomials filtering generalized Legendre polynomial (GLP) neural networks nonlinear systems

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Minimum Total Quaternion Error Entropy filtering With Fiducial Points Against Asymmetric Noise

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 306-310页

作者： Lin, Dongyuan Cai, Peng Chen, Xiaofeng Zheng, Yunfei Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Jiaotong Univ Dept Math Chongqing 400074 Peoples R China

Quaternion adaptive filters (QAFs) are extensively used in processing three- or four-dimensional signals effectively. However, their performance can significantly deteriorate or even diverge when system inputs and outputs are contaminated by complex noises. Therefore, this letter addresses the issue of parameter estimation in the quaternion errors-in-variables (QEIV) in asymmetric noise. First, a novel robust criterion, called improved quaternion minimum error entropy criterion with fiducial points (IQMEEF), is constructed. Then, a minimum total quaternion error entropy algorithm with fiducial points (MTQEEF) is proposed by integrating the IQMEEF criterion with the total least squares (TLS) method, leveraging stochastic gradient and quaternion generalized Hamilton-real (GHR) calculus theory. Finally, simulations validate the superior performance of MTQEEF in the QEIV model under asymmetric noise environments.

关键词： Quaternions Noise Entropy Signal processing algorithms Adaptation models Vectors Parameter estimation Kernel filtering algorithms Calculus Robust quaternion adaptive filters quaternion error entropy criterion with fiducial points quaternion errors-in-variables model asymmetric noise

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Application of Multidip Structure-Oriented filtering to Poststack Seismic Data

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Guo, Mengxin Chen, Siyuan Liu, Zuoshi Wang, Weihong Northeast Petr Univ Sch Earth Sci Heilongjiang Prov Key Lab Oil & Gas Geophys Explor Daqing 163318 Heilongjiang Peoples R China Northeast Petr Univ Natl Engn Res Ctr Offshore Oil & Gas Explorat Daqing 163318 Heilongjiang Peoples R China

In geophysics, structure-oriented filtering (SOF) has been extensively utilized as an effective algorithm to eliminate random noise. This type of filtering is mainly restricted by the local dip of the data and smoothed along the structural direction. The algorithm assumes the strata of a single local dip, and even if the seismic data contain strata and faults, the SOF remains smoothed with a single dip angle. This condition leads to the smoothing of faults or strata in the wrong direction, which results in the effective removal of reflected signals. Given this, we herein propose a multidip SOF (Mdip-SOF) algorithm for complex structure data, including faults. This investigation employs a guided filtering theory to pre-extract structural features of faults. Guided by this attribute, we construct an edge-preserving filter, where faults may exist, aiming to preserve faults and other structures. Other positions with a single dip are also adopted for conventional SOF. A poststack data denoising test is also conducted, and the results obtained reveal that the proposed multidip constrained SOF algorithm outperforms the existing SOF algorithm in terms of fault handling.

关键词： filtering Three-dimensional displays Noise reduction filtering algorithms Prediction algorithms Smoothing methods filtering theory Transforms Heuristic algorithms Image edge detection Guided filtering preserving faults random noise structure-oriented filtering (SOF)

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Interbeat Interval filtering

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 481-485页

作者： Bayram, Ilker WHOOP Boston MA 02215 USA

Several inhibitory and excitatory factors regulate the beating of the heart. Consequently, the interbeat intervals (IBIs) vary around a mean value. Various statistics have been proposed to capture heart rate variability (HRV) to give a glimpse into this balance. However, these statistics require accurate estimation of IBIs as a first step, which can be challenging especially for signals recorded in ambulatory conditions. We propose a lightweight state-space filter that models the IBIs as samples of an inverse Gaussian distribution with time-varying parameters. We make the filter robust against outliers by adapting the probabilistic data association filter to the setup. We demonstrate that the resulting filter can accurately identify outliers and the parameters of the tracked distribution can be used to compute a specific HRV statistic (standard deviation of normal-to-normal intervals, SDNN) without further analysis.

关键词： Heart rate variability filtering algorithms Gaussian distribution Adaptive filters Standards Random variables Heart beat Computational modeling Adaptation models Accuracy Interbeat interval heart rate variability state-space robust filter probabilistic data association

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