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检索条件"主题词=Filtering algorithms"

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SchurVINS: Schur Complement-Based Lightweight Visual Inertial Navigation System

SchurVINS: Schur Complement-Based Lightweight Visual Inertia...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yunfei Fan Tianyu Zhao Guidong Wang PICO ByteDance

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Accuracy and computational efficiency are the most important metrics to Visual Inertial Navigation System (VINS). The existing VINS algorithms with either high accuracy or low computational complexity, are difficult to provide the high precision localization in resource-constrained devices. To this end, we propose a novel filter-based VINS framework named SchurVINS (SV), which could guarantee both high accuracy by building a complete residual model and low computational complexity with Schur complement. Technically, we first formulate the full residual model where Gradient, Hessian and observation covariance are explicitly modeled. Then Schur complement is employed to decompose the full model into ego-motion residual model and landmark residual model. Finally, Extended Kalman Filter (EKF) update is implemented in these two models with high efficiency. Experiments on EuRoC and TUM-VI datasets show that our method notably outperforms state-of-the-art (SOTA) methods in both accuracy and computational complexity. The experimental code of SchurVINS is available at https://***/bytedance/SchurVINS.

关键词： Measurement Location awareness Visualization Accuracy Computational modeling Inertial navigation filtering algorithms

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ECG Signal Denoising and Feature Learning for Accurate Arrhythmia Classification

ECG Signal Denoising and Feature Learning for Accurate Arrhy...

引用

Conference on Information and Communication Technology (CICT)

作者： Mohammad Shahnawaz Pramod Kumar Singh Hemant Singh Manish Kumar Department of Information Technology IIIT Allahabad Prayagraj India

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

Proactive diagnosis of diseases with artificial intel-ligence and associated technologies has emerged as a potential area in recent years. Cardiovascular diseases require continuous monitoring and are a key focus area in medical care. Arrhythmia, a cardiovascular disease, is typically diagnosed by physicians using Electrocardiography, which monitors the electrical activity and rhythm of the heart. Neural networks have been used extensively recently to identify anomalies in electrocardiogram (ECG) data. Previous research has shown that using a denoised ECG signal rather than raw data significantly enhances the likelihood of detecting arrhythmia. This paper evaluates four filters applied to ECG signals to improve classification accuracy. Further, we have developed a custom deep-learning technique for ECG data analysis. We conducted extensive experiments using these four filters and the proposed deep neural network technique. The comparative analysis demonstrates that the pro-posed method outperforms existing methods across multiple performance metrics.

关键词： Accuracy Filters Arrhythmia Medical services Electrocardiography filtering algorithms Information filters Signal denoising Monitoring Long short term memory

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IoT-Based Electronic System for Monitoring a Vehicle’s Diesel Particulate Filter

IoT-Based Electronic System for Monitoring a Vehicle’s Dies...

引用

Communications, Information, Electronic and Energy Systems (CIEES), International Conference on

作者： Stiliyan Okishelov Boris Evstatiev Seher Kadirova Dept. of Automation and Electronics University of Ruse "Angel Kanchev" Ruse Bulgaria

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

In this paper, an IoT-based system for DPF filter monitoring is presented. The system consists of a DPF monitoring device and an ODB2 Dongle communicating via Bluetooth interface. The article presents the block diagram and algorithm of the system, as well as a practical implementation of the prototype. An experiment was carried out with a car that had a problem with the particulate filter, and the results obtained showed the performance of the system. The aim of the paper is to develop, implement and test an IoT based DPF monitoring system of a full combustion diesel engine vehicle. The developed DPF Monitoring IoT system is suitable for car diagnostics by service workers and also for monitoring the condition of cars owned by private individuals or companies. It can be expanded, by adding additional functionality, allowing controlling additional car parameters, which is the subject of future research.

关键词： Performance evaluation Bluetooth Diesel engines Prototypes Companies filtering algorithms Three-dimensional printing Automobiles Printers Monitoring

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A Full-ANN Control Method of Three-Phase Grid-Connected Inverter

A Full-ANN Control Method of Three-Phase Grid-Connected Inve...

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Energy Storage and Conversion (ISESC), CPSS & IEEE International Symposium on

作者： Kaizhe Nie Feng Gao Hanzhi Wang Tao Xu School of Control Science and Engineering Shandong University Jinan China Shandong University Electric Power Technology Co. Ltd Jinan China

ISBN: (数字)9798350380514

ISBN: (纸本)9798350380521

To break free from the confines of the d-q control framework and traditional control techniques, such as phase-locked loop, as well as proportional-integral/proportional-resonant controllers, this paper proposes a three-phase inverter grid-connected control method based on a full-artificial neural network (FANN-TPIGCC), which leverages the nonlinear approximation capabilities of artificial neural networks to directly obtain the modulation wave required for inverter control. First, the inverter circuit principle is analyzed and two sets of auxiliary variables are chosen. Subsequently, the first and second sets of auxiliary variables are separately input into two echo state networks based on the least square algorithm. These networks respectively output the reference modulation wave and the amplitude ratio along with the phase difference. Finally, utilizing the amplitude ratio and phase difference, a modulation transformation method is devised to fine-tune both amplitude and phase of the reference modulation wave, resulting in the ultimate modulation wave utilized for inverter grid-tied control. Simulink simulation results show that the FANN-TPIGCC method can achieve flexible switching between different reference current under both strong and weak grid conditions. It exhibits lower total harmonic distortion and ideal steady-state performance, meeting the control requirements of three-phase inverters.

关键词： Total harmonic distortion Phase modulation Software packages Simulation Switches filtering algorithms Control systems Inverters Steady-state Phase locked loops

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2D-LLFE-NVC: 2D Laser Line Feature Extraction Using Normal Vector Clustering

2D-LLFE-NVC: 2D Laser Line Feature Extraction Using Normal V...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Jinshun Ou Jiying Ren Wenguang Li Panling Huang Jun Zhou Jiehan Zhou Tao Zhang School of Mechanical Engineering Shandong University Jinan China School of Computer Science and Technology Shandong University of Science and Technology Qingdao China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland Qingdao Hanhe Cable Co. Ltd. Qingdao China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

This paper presents a novel 2D laser data line feature extraction algorithm based on normal vector clustering(2D-LLFE-NVC). The algorithm consists of normal vector calculation, normal normalization based on multi-scale operator normal difference and median filter, and line feature extraction based on normal vector clustering. Unlike existing methods, 2D-LLFE-NVC leverages the continuity of laser point clouds and detects breakpoints using multi-scale operators based on normal differences. The extracted line features are more accurate and correct on a global scale. To validate its performance, we conducted SLAM experiments in a 5000-square-meter indoor environment using both the proposed algorithm and existing methods. By comparing the line feature points extracted by these different approaches with manually annotated line feature points, and demonstrating the superior performance of the proposed method in terms of correctness and completeness.

关键词： Point cloud compression Simultaneous localization and mapping Mechatronics Lasers Clustering algorithms Object segmentation filtering algorithms Feature extraction Vectors Indoor environment

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A Test for the Systematic Detection of Bowtie Effect and Stripping on the Edges of MODIS Images

A Test for the Systematic Detection of Bowtie Effect and Str...

引用

IEEE Biennial Congress of Argentina (ARGENCON)

作者： Paula Verónica Romero Jure Sergio Masuellli Juan Bautista Cabral University of Leeds Leeds United Kingdom Laboratorio de Radar y Sensores Remotos FAMAF UNC Grupo de Física de la Atmósfera y Córdoba Argentina Observatorio Astronómico de Córdoba UNC CONAE - Córdoba Córdoba Argentina

ISBN: (数字)9798350365931

ISBN: (纸本)9798350365948

MODIS images are distorted at the edges due to the nature of the scanning array (whiskbroom scanner). This distortion, known as bowtie effect, is produced because the geometry of the acquisition and the sensor array design causes overlapping of pixels on the edges of the image. Those pixels are larger than the ones at the centre, causing the image have a bowtie shape. To tackle this issue, in this work we have produced a test to detect the bowtie effect and filter the MODIS images that are affected by it. We have applied the test to a subset of tiles of images from the CUMULO dataset, producing a filtered subset of images. We have then built a dataset with images filtered from a ran of the test and uploaded it into a public database, in Kaggle. The test developed can be used to detect bowtie and periodic stripping defects in any image, useful to filter datasets for machine learning training.

关键词： MODIS Image edge detection Distortion Tiles Cloud computing Satellites Radar imaging Landsat filtering algorithms Clouds

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Recurrent-Neural-Network-Based Random Drift Compensation Method for MEMS Accelerometers

Recurrent-Neural-Network-Based Random Drift Compensation Met...

引用

6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Yichen Zheng Hong Liu Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science & Technology University Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Random drift is the main factor affecting the accuracy of MEMS accelerometers. Aiming at the problem that previous methods are brutal to suppress the random drift of MEMS accelerometers effectively, this paper proposes a technique combining deep learning and neural networks to compensate for the random drift of MEMS accelerometers. A recurrent neural network (RNN) model was established, a mean filter was used to filter the sampled signals, and an optimization algorithm based on Adaptive Moment Estimation (ADAM) was used to train the RNN model. Experimentally, it was found that the RNN model used for processing time series can also be applied to random drift compensation of MEMS accelerometers while comparing it with traditional filtering algorithms. The experimental results show that the performance indexes such as acceleration spontaneous wander, zero-bias stability, and zero-bias instability are reduced by more than 20% using the method of this paper to compensate the MEMS accelerometers, and the suppression of accelerometer random drift is realized.

关键词： Micromechanical devices Accelerometers Adaptation models Recurrent neural networks Time series analysis Signal processing algorithms filtering algorithms Stability analysis Performance analysis Optimization

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Enhanced Failure Detection in Induction Motors Using Supervised Learning and Data Smoothing Techniques

Enhanced Failure Detection in Induction Motors Using Supervi...

引用

Artificial Intelligence (SLAAI-ICAI), SLAAI International Conference on

作者： W.C. Nirmal H.K.I.S. Lakmal S.A.K. Dhananjaya Department of Mechatronic & Industrial Engineering Faculty of Engineering NSBM Green University Homagama Sri Lanka Department of Electrical Electronic & Systems Engineering Faculty of Engineering NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331509170

ISBN: (纸本)9798331509187

Production machinery failures often cause significant financial losses, and traditional maintenance methods may struggle to mitigate these issues effectively. Predictive maintenance offers a modern solution by forecasting failures before they occur. This study applies supervised learning algorithms to pre-collected vibration data from a single-phase induction motor under normal and simulated abnormal working conditions. Data smoothing techniques such as Moving Average Filter, Kalman Filter, and FFT were employed to enhance data quality. Supervised algorithms including Logistic Regression, Decision Tree, Random Forest, SVM, Naive Bayes, and kNN were implemented and evaluated. Performance metrics such as accuracy, precision, recall, and F1 score were used to compare these algorithms. Results indicate that data smoothing significantly improves model performance, with the Moving Average Filter achieving the highest accuracy for Random Forest at $\mathbf{9 9. 7 7 \%}$. These findings highlight the potential of advanced machine learning techniques combined with data preprocessing for predictive maintenance in industrial settings. By reducing downtime and maintenance costs, these models enhance operational reliability and efficiency. This study contributes to the growing knowledge on applying machine learning for predictive maintenance, offering practical insights for improving the sustainability and productivity of manufacturing processes.

关键词： Logistic regression Smoothing methods Accuracy Induction motors Fast Fourier transforms filtering algorithms Maintenance Kalman filters Random forests Predictive maintenance

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UWB Localization Algorithm Based on Kalman Filter and IPSO Algorithm

UWB Localization Algorithm Based on Kalman Filter and IPSO A...

引用

Information Systems and Computing Technology (ISCTech), International Conference on

作者： Yiran Hu Xu Zhao School of Automation BISTU Beijing China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379877

Aiming at the problem that Ultra Wide Band (UWB)devices are susceptible to environmental interference and localization errors, this paper firstly uses Kalman filter to process the ranging values to eliminate the erroneous ranging values caused by noise interference in the environment, and then uses Gaussian filter to process the ranging values to improve the ranging accuracy, and then solves the initial coordinates by Chan algorithm, and then uses the coordinates as the initial value of searching in the improved particle swarm algorithm to further solve the more accurate coordinate values, and measures the positioning accuracy by the mean square error (RMSE) of the algorithm. The coordinates are then used as the search initial values for the improved particle swarm algorithm, which is further solved to obtain more accurate coordinate values, and the localization accuracy of the algorithm is measured by the mean square error (RMSE). Through the experiments, the range error of this algorithm in the presence of signal interference is reduced by 63.4% compared with Chan algorithm, 26.7% compared with Chan-Kalman algorithm, and its range error is in the centimeter level, which can be concluded that this algorithm can effectively solve the problem of improving the accuracy of UWB indoor positioning in the presence of interference.

关键词： Location awareness Accuracy Measurement uncertainty Interference Mean square error methods filtering algorithms Particle measurements Distance measurement Kalman filters Particle swarm optimization

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Filter Synthesis for Robust Feedback Active Noise Control Using Non-Uniformly Discretized Fourier Spectra

Filter Synthesis for Robust Feedback Active Noise Control Us...

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European Signal Processing Conference (EUSIPCO)

作者： Willem Alexander Klatt Michael Bürger Rainer Martin Henning Puder Institute of Communication Acoustics Ruhr-Universität Bochum Bochum Germany WS Audiology Erlangen Germany Signal Processing Group Technische Universität Darmstadt Darmstadt Germany

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In this contribution, we present a novel approach to formulate spectral weightings and frequency response data that are used to synthesize the time-invariant control filter of a robust feedback active noise control system. The new method adopts a frequency discretization approach, which utilizes a non-uniform sampling procedure. We present the underlying mathematical descriptions that lead to the non-uniformly discretized spectra and discuss how this new approach affects the numerical optimization procedure in terms of complexity and degrees of freedom. Additionally, we analyze how the proposed filter synthesis method affects the achievable stability and noise attenuation capability of active noise control designs. The main advantage of this approach is the reduced complexity of the filter optimization problem and the improved noise attenuation performance.

关键词： Robust stability Working environment noise Noise Signal processing algorithms filtering algorithms Attenuation Stability analysis Complexity theory Optimization Frequency control

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