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American Control Conference (ACC)

作者： Zheng, Tongjia Han, Qing Lin, Hai Univ Notre Dame Dept Elect Engn Notre Dame IN 46556 USA Univ Notre Dame Dept Math Notre Dame IN 46556 USA

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

Swarm robotic systems have foreseeable applications in the near future. Recently, there has been an increasing amount of literature that employs mean-field partial differential equations (PDEs) to model the time-evolution of the probability density of swarm robotic systems and uses density feedback to design stabilizing control laws that act on individuals such that their density converges to a target profile. However, it remains largely unexplored considering problems of how to estimate the mean-field density, how the density estimation algorithms affect the control performance, and whether the estimation performance in turn depends on the control algorithms. In this work, we focus on studying the interplay of these algorithms. Specifically, we propose new density control laws which use the mean-field density and its gradient as feedback, and prove that they are globally input-to-state stable (ISS) with respect to estimation errors. Then, we design filtering algorithms to estimate the density and its gradient separately, and prove that these estimates are convergent assuming the control laws are known. Finally, we show that the feedback interconnection of these estimation and control algorithms is still globally ISS, which is attributed to the bilinearity of the PDE system. An agent-based simulation is included to verify the stability of these algorithms and their feedback interconnection.

关键词： Estimation error Partial differential equations Estimation Swarm robotics filtering algorithms Stability analysis Mathematical models

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TS-RePSO: A Three-Stage Feature Selection Method Combing ReliefF and PSO in Bioinformatics

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 640-652页

作者： Bin Pu Haining Wang Zhaozhao Xu Fangyuan Yang Xiangqiong Wu Jianguo Chen College of Computer Science and Electronic Engineering Hunan University Changsha China School of Software South China University of Technology GuangZhou China School of Computer Science and Technology Henan Polytechnic University Jiaozuo China Department of Gynecologic Oncology The First Affiliated Hospital of Henan Polytechnic University Jiaozuo China School of Software Engineering Sun Yat-Sen University Guangzhou China

The inherent characteristics of high-dimensional feature redundancy of biomedical data lead to the “curse of dimensionality” in bioinformatics, which brings new challenges to feature selection problems. Recently, the two-stage approach combining the filter and wrapper methods has become popular for feature selection tasks. However, these two-stage or previous one-stage algorithms suffer from blindness in the setting of thresholds, and the search methods tend to fall into local optimum solutions. To this end, we propose a three-stage feature selection method that combines ReliefF and Particle swarm optimization as a specific case, called TS-RePSO, including the filter stage, grouping stage, and wrapper stage. Specifically, in the filter stage, ReliefF is utilized to compute the weights of the features and sort them in descending order. In the grouping stage, the ranked features are grouped based on the density equalization strategy so that the weight of groups in all groups is equal. In the wrapper stage, the proposed grouping PSO is employed to search for the grouped features and select them according to the in-group and out-group evaluation strategies. Extensive experiments are conducted on 5 benchmark datasets and 6 real-world datasets, and experiment results show that the proposed method achieves the best performance.

关键词： filtering algorithms Classification algorithms Bioinformatics Particle swarm optimization Feature extraction Diseases Prediction algorithms Information filters Genetic algorithms Diversity reception

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Probabilistic Approach to Online Stiffness Estimation for Robotic Tasks

Probabilistic Approach to Online Stiffness Estimation for Ro...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Tsuji, Toshiaki Kusakabe, Tsukasa Saitama Univ Grad Sch Sci & Engn 255 Shimo Okubo Saitama 3388570 Japan

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Information about environmental stiffness is useful for robotic tasks involving interactions with unstructured and unknown environments. However, online estimation remains a challenge. Owing to the nature of its calculation algorithm, a large amount of noise may be generated, depending on the response value of the force and position. In this study, we propose a variable gain filter that predicts the degree of such noise using a probabilistic approach and reflects only reliable data in the estimation. We show experimentally that the proposed method improves the accuracy of the stiffness estimation without degrading the estimation time constant.

关键词： Force Estimation filtering algorithms Robot sensing systems Probabilistic logic Probability distribution Sensors

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An Improved Multi-reference Frame Loop Filter Algorithm Based on Transformer for VVC

An Improved Multi-reference Frame Loop Filter Algorithm Base...

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Data Compression Conference (DCC)

作者： Liu, Zhi Duan, Yunpeng Zhang, Mengmeng North China Univ Technol Beijing Peoples R China

ISBN: (纸本)9781665478939

Deep learning methods have been achieving good results in the loop filtering in Versatile Video Coding(VVC). Multi-Frame In-Loop Filter of HEVC (MIF) algorithm is one of networks that effectively utilizes the multiple reference frames to enhance the quality of reconstruction. However, it has the disadvantages of low efficiency of the reference frame selection and its quality enhancement network does not fully utilize the inter-frame correlations of video sequence and has large redundancy. To address these problem, an improved multi-reference frame loop filter algorithm based on transformer optimization (T -MIF) is proposed.

关键词： Deep learning Correlation filtering Video sequences Redundancy Data compression filtering algorithms

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A Novel Rolling Bearing Fault Diagnosis Method Based on Adaptive Feature Selection and Clustering

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IEEE ACCESS 2021年 9卷 99756-99767页

作者： Hou, Jingbao Wu, Yunxin Ahmad, Abdulrahaman Shuaibu Gong, Hai Liu, Lei Cent South Univ Light Alloy Res Inst Changsha 410083 Peoples R China Changsha Inst Min Res Co Ltd Changsha 410012 Peoples R China Cent South Univ State Key Lab High Performance Complex Mfg Changsha 410083 Peoples R China

Rolling bearing is an important part of mechanical equipment. Timely detection of rolling bearing fault is one of the important factors to ensure the safe operation of equipment. In order to diagnose rolling bearing fault accurately, a novel rolling bearing fault diagnosis method based on adaptive feature selection and clustering is proposed. Firstly, the vibration signal obtained from rolling bearing is decomposed by ensemble empirical mode decomposition(EEMD) to extract as much important information as possible. Feature extraction is performed for each intrinsic mode function(IMF) component and the original signal, and finally 240 features are obtained. And the Chi-square Test algorithm, Variance-Relief-F algorithm, and hierarchical clustering algorithm are used to filter all the features in layers to obtain the optimal features. Then the optimal features are input into fuzzy c-means(FCM) clustering to complete fault diagnosis. After the fault diagnosis analysis of four groups of vibration signal data, it is found that whether the characteristic number parameters are set based on engineering experience or adaptive feature selection, good fault diagnosis results are obtained. Furthermore, through comparative experiments, the fault diagnosis effect of the method based on adaptive parameter setting is better. The results indicate that the proposed adaptive parameter fault diagnosis method is feasible and effective for rolling bearing fault diagnosis.

关键词： Fault diagnosis Vibrations Fans Rolling bearings Clustering algorithms Hydraulic systems filtering algorithms Fault diagnosis rolling bearing adaptive feature selection Relief-F algorithm clustering

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Is Vulnerability Report Confidence Redundant? Pitfalls Using Temporal Risk Scores

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IEEE SECURITY & PRIVACY 2021年第4期19卷 44-53页

作者： Boechat, Francois Ribas, Gabriel Senos, Lucas Bicudo, Miguel Nogueira, Mateus Schulz de Aguiar, Leandro Pfleger Menasche, Daniel Sadoc Univ Fed Rio de Janeiro Rio De Janeiro Brazil Siemens Technol Princeton NJ USA

The Common Vulnerability Scoring System score is the de facto standard to assess risk of software vulnerabilities, with three temporal components: exploitability, remediation level, and report confidence. We discuss how the latter may be inferred from the first two, pointing practical and conceptual issues in the usage of temporal risk scores.

关键词： Program Diagnostics Risk Analysis Risk Management Security Of Data Temporal Risk Scores Common Vulnerability Scoring System Score Software Vulnerabilities Temporal Components Vulnerability Report Confidence Software Engineering Decision Trees Standards Security filtering algorithms Bayes Methods Uncertainty

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A Dual-Domain CNN-Based Network for CT Reconstruction

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IEEE ACCESS 2021年 9卷 71091-71103页

作者： Jiao, Fengyuan Gui, Zhiguo Li, Kunpeng Hong, Shangguang Wang, Yanling Liu, Yi Zhang, Pengcheng North Univ China Shanxi Prov Key Lab Biomed Imaging & Big Data Taiyuan 030051 Peoples R China Taiyuan Univ Sci & Technol Sch Elect Informat Engn Taiyuan 030024 Peoples R China Shanxi Univ Finance & Econ Sch Informat Management Taiyuan 030006 Peoples R China

Convolutional neural network (CNN)-based deep learning techniques have enjoyed many successful applications in the field of medical imaging. However, the complicated between-manifold projection from the projection domain to the spatial domain hinders the direct application of CNN techniques in computed tomography (CT) reconstruction. In this work, we proposed a novel CT reconstruction framework based on a CNN, i.e., an intelligent back-projection network (iBP-Net). The proposed iBP-Net method fused a pre-CNN, a back-projection layer, and a post-CNN into an end-to-end network. The pre-CNN adopted CNN techniques to model a filtering operation in the projection domain. In the back-projection layer, a back-projection operation was employed to perform between-manifold projection. Based on CNN techniques, the post-CNN worked together with the pre-CNN to recover reconstructed images from the outputs of the back-projection layer in the spatial domain while maintaining high visual sensitivity. The experimental results demonstrate the feasibility of the proposed iBP-Net framework for CT reconstruction.

关键词： Image reconstruction Computed tomography filtering algorithms filtering theory Matrices filtering Training Convolutional neural network deep learning between-manifold projection CT~reconstruction

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Technique to Adjust Adaptive Digital Filter Coefficients in Residue Number System Based Filters

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IEEE ACCESS 2021年 9卷 82402-82416页

作者： Kaplun, Dmitrii Voznesensky, Alexander Veligosha, Alexander V. Kalmykov, Igor A. Sarma, Kandarpa Kumar St Petersburg Electrotech Univ LETI Dept Automat & Control Proc St Petersburg 197376 Russia Mil Acad Strateg Missile Forces Dept 52 Serpukhov 142210 Russia North Caucasus Fed Univ Dept Informat Secur Automated Syst Stavropol 355009 Russia Gauhati Univ Dept Elect & Commun Engn Gauhati 781014 India

The paper discusses adaptive filtering using Least Mean Square (LMS) and Recursive Least Square (RLS) algorithms. An algorithm for adjusting the coefficients of an adaptive digital filter in the Residue Number System and a procedure of developed algorithm applying depending on filter length and signal length are proposed. Mathematical modeling of the considered algorithms is performed. Examples are presented to demonstrate how the proposed technique can help the designer in the adjustment of the filter coefficients without the need for extensive trial-and-error procedures. The analysis of the denoising quality and computational complexity is made. Synthetic and real data (earthquake recording) were used while testing. The proposed algorithm surpasses the existing ones like LMS and RLS, and their modifications in a number of parameters: adaptation (denoising) quality, ease of implementation, execution time. The main difference between the developed algorithm is the sequential adaptation of each coefficient with zero error. In the known algorithms, the entire vector of coefficients is iteratively adapted, with some specified accuracy. The iterations (steps) number is determined by the input signal length for all algorithms.

关键词： Digital filters Signal processing algorithms filtering algorithms Wiener filters Finite impulse response filters Electronic mail Approximation algorithms Adaptive digital filter residue number system least mean square algorithm recursive least square algorithm denoising quality computational complexity

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Real-Time Artifact Removal System for Surface EMG Processing During Ten-Fold Frequency Electrical Stimulation

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IEEE ACCESS 2021年 9卷 68320-68331页

作者： Wang, Hai-Peng Bi, Zheng-Yang Fan, Wen-Jie Zhou, Yi-Xin Zhou, Yu-Xuan Li, Fei Wang, Keping Lu, Xiao-Ying Wang, Zhi-Gong Sanjiang Univ Sch Elect & Informat Engn Nanjing 210012 Peoples R China Southeast Univ Inst RF & OE ICs Nanjing 210096 Peoples R China Southeast Univ State Key Lab Bioelect Nanjing 210096 Peoples R China Nanjing Med Univ Sch Biomed Engn & Informat Dept Biomed Engn Nanjing 211166 Peoples R China Nantong Univ Coinnovat Ctr Neuroregenerat Nantong 226001 Peoples R China

In this paper, three easily implemented hardware algorithms, including the adaptive prediction error filter based on the Gram-Schmidt algorithm (GS-APEF), the least mean square adaptive filter and the comb filter, are extensively investigated for artifact denoising on a constructed semi-simulated database with varied ten-fold frequency stimulation. By implementing the GS-APEF in the field-programmable gate array (FPGA) and using the edge noise mitigating technique, a stimulation artifact denoising system is designed to realize real-time stimulation artifact removal under varied ten-fold frequency functional electrical stimulation. Good performance of the artifact denoising is demonstrated in proof-of-concept experiments on able-bodied subjects with a mean correlation coefficient between the root mean square profile of denoised surface electromyography and volitional force of 0.94, verifying the validity of the proposed prototype.

关键词： Electromyography Iron Real-time systems Noise reduction filtering algorithms Blanking Field programmable gate arrays Functional electrical stimulation (FES) stimulus artifact removal (SAR) surface electromyography (sEMG) adaptive filter field-programmable gate array (FPGA)

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A Multidimensional Feature Extraction and Selection Method for ECG Arrhythmias Classification

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IEEE SENSORS JOURNAL 2021年第13期21卷 14180-14190页

作者： Yang, Jianyong Yan, Ruqiang Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710000 Peoples R China

This article presents a novel multidimensional feature extraction and selection method for electrocardiogram (ECG) arrhythmias classification. First, a novel multi-interval symmetrized dot pattern method (MSDP) is proposed to extract image shape features, which are combined with time-frequency features, morphological features and RR interval features to form a new feature set. Then a novel average weight based ReliefF method (WReliefF) is proposed to integrate with genetic algorithm (GA) and support vector machine (SVM) and form a feature selection method named as WReliefF-GA-SVM. The WReliefF-GA-SVM is a hybrid type feature selection method, and it can be divided into Filter stage and Wrapper stage to select features. In Filter stage, the algorithm uses WReliefF to sort the features according to the average weight. In Wrapper stage, GA and SVM are combined, and the classification performance of SVM is used as the fitness function to search the best feature subsets. Finally, the five types of ECG beats from the MIT-BIH arrhythmia database are classified by a one against all (OAA) SVM model. The results show that the proposed method achieves average sensitivity, specificity and accuracy of 99.42%, 99.83%, and 99.74%, respectively.

关键词： Support vector machines Time-frequency analysis Sensitivity Shape Heart beat Electrocardiography filtering algorithms Electrocardiogram (ECG) multi-interval symmetrized dot pattern (MSDP) S-transform average weight based ReliefF (WReliefF)

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