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检索条件"主题词=Filtering algorithms"

共 10354 条记录，以下是811-820 订阅

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Advanced Parametric Modeling of the Four-Pole Waveguide Filter Using Decomposed Sub-ANN-models

Advanced Parametric Modeling of the Four-Pole Waveguide Filt...

引用

UK, Europe, China Millimeter Waves and THz Technology Workshop (UCMMT)

作者： Hui Zong Kaige Qu Wei Zhang Tong Zhai Shuting Jin Zhehao Yan Zhiguo Zhang CETC Metrology & Test & Certification (Beijing) Company Limited Beijing China Beihang University Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331530228

ISBN: (纸本)9798331530235

This paper presents an innovative technique for developing wide-range parametric models for microwave components by utilizing the decomposition method. In this method, a systematic algorithm is proposed to decompose the overall ranges of geometrical parameters into multiple small sub-ranges. We can build very accurate sub-model by good training for each sub-range. However, discontinuity issues may arise at the boundaries of adjacent sub-models. To address this, a new algorithm has been proposed that resolves these discontinuities and integrates all sub-models into a single, comprehensive model. By employing parallel computation, the model development process is accelerated. This proposed method not only improves accuracy but also reduces modeling time compared to methods that do not use decomposition. This technique is validated through the example of a four-pole waveguide filter.

关键词： Training Microwave technology Accuracy Systematics Computational modeling Terahertz materials filtering algorithms Data models Parametric statistics Microwave filters

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Distributed fusion estimator for networked multi-rate multi-sensor systems with unknown inputs

Distributed fusion estimator for networked multi-rate multi-...

引用

Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Qiang Liu Honglei Lin Department of Automation Heilongjiang University Harbin China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper concerns the state estimation problem for multi-rate multi-sensor systems with unknown inputs, time delays, fading measurements, and multiplicative noise in the state equation. Firstly, the measurement delay models are converted into measurement delay free models. Subsequently, Lagrange multiplier method is used to compute the gain of the optimal local unbiased minimum variance filter which is independent of unknown input information. Based on the local filter, the estimation error cross-covariance matrices between any two estimators are derived. A distributed optimal fusion estimator is obtained by using the matrix-weighted fusion estimation algorithm. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed estimation algorithms.

关键词： Fading channels Estimation error Noise filtering algorithms Information filters filtering theory Mathematical models

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A Passive $\frac{dv}{dt}$ Filter for Integrated Induction Motor Drive Fed from 3-Phase 2-Level GaN Based Inverter

A Passive $\frac{dv}{dt}$ Filter for Integrated Induction Mo...

引用

International Conference on Power Electronic Drives

作者： Shambhunath Dutta Apurv Kumar Yadav Department of Electrical Engineering IIT Roorkee Roorkee India

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

In recent times, the Si-IGBT switches are being replaced by the WBG devices (SiC, GaN) in the next gen EV drive train as the WBG devices exhibit inherent advantages like higher switching frequency, better efficeincy as compared to Si-IGBTs. On the other hand, between SiC MOSFETs and GaN, the GaN exhibits superior performance when compared with SiC in terms of efficiency. Moreover, GaN is found to be smaller size as compared to SiC. Because of these advantages, GaN is being used widely in next gen EV drive drain as the switchingdevice of inverter. However, this GaN devices exhibit higher $\frac{d v}{d t}$ at the inverter terminal (approximately 40 V/ns) and because of that uneven distribution of voltage occurs in the motor windings. As a result, the life span of motor winding is degraded. To overcome this problem, passive $\frac{dv}{dt}$ filter is used. On the other hand, the loading effect of motor impedance is not considered while designing the passive $\frac{dv}{dt}$ filter. As a result, the accuracy in the $\frac{dv}{dt}$ at the motor terminal is missed. In this paper, a novel passive LC based $\frac{dv}{dt}$ filter designing algorithm is proposed considering the loading effect of motor impedance. The $\frac{dv}{dt}$ at the motor terminal is maintained at 0.5 V/ns. The propose-passive $\frac{dv}{dt}$ filter is applied on a 120 V, 2.2 kW induction motor. The simulation and experimental results illustrate the accuracy in designing LC based $\frac{dv}{dt}$ filter.

关键词： Hands Accuracy Silicon carbide Passive filters Loading Windings filtering algorithms Motors Inverters Impedance

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A Proportionate Fast NLMS with Competent Individual Activation Factors

A Proportionate Fast NLMS with Competent Individual Activati...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Lynda Fedlaoui Ahmed Benallal Mountassar Maamoun DIC Laboratory University of Blida Blida Algeria LATSI Laboratory University of Blida Blida Algeria LATSI Laboratory l University of Blida Blida Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

This study presents the CIAF-PFNLMS algorithm, an abbreviation for Proportionate Fast Normalized Least Mean Square with Competent Individual Activation Factors. This algorithm provides a competent method for calculating individual activation factors. This guarantees that the gain remains proportional to its associated coefficient even when the coefficient is inactive, in accordance with the concept of proportionality. This proves very beneficial in scenarios involving sparse impulse responses, leading to significant performance improvements. Additionally, the algorithm smoothly integrates the Fast Normalized Least Mean Square (FNLMS) adaptation gain into its framework, making it effective in environments with dispersive impulse responses and capable of accurately tracking echo path variations. In our study, we performed simulations to validate the capabilities of the proposed algorithm. We evaluated its performance across a range of scenarios, including sparse and dispersive environments, non-stationary situations, and compared it with established algorithms.

关键词： Adaptation models Signal processing algorithms Adaptive filters filtering algorithms Acoustics Dispersion Convergence

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Data-Driven Approximation of Stationary Nonlinear Filters with Optimal Transport Maps

Data-Driven Approximation of Stationary Nonlinear Filters wi...

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IEEE Conference on Decision and Control

作者： Mohammad Al-Jarrah Bamdad Hosseini Amirhossein Taghvaei Department of Aeronautics & Astronautics University of Washington Seattle Department of Applied Mathematics University of Washington Seattle

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The nonlinear filtering problem is concerned with finding the conditional probability distribution (posterior) of the state of a stochastic dynamical system, given a history of partial and noisy observations. This paper presents a data-driven nonlinear filtering algorithm for the case when the state and observation processes are stationary. The posterior is approximated as the push-forward of an optimal transport (OT) map from a given distribution, that is easy to sample from, to the posterior conditioned on a truncated observation window. The OT map is obtained as the solution to a stochastic optimization problem that is solved offline using recorded trajectory data from the state and observations. An error analysis of the algorithm is presented under the stationarity and filter stability assumptions, which decomposes the error into two parts related to the truncation window during training and the error due to the optimization procedure. The performance of the proposed method, referred to as optimal transport data-driven filter (OT-DDF), is evaluated for several numerical examples, highlighting its significant computational efficiency during the online stage while maintaining the flexibility and accuracy of OT methods in nonlinear filtering.

关键词： Training filtering Nonlinear filters filtering algorithms Stability analysis Probability distribution Trajectory Noise measurement Optimization Numerical stability

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Lossy Compression of Multi-Channel EEG and PPG Signals Based on Golomb-Rice Coding with Parameter Estimation

Lossy Compression of Multi-Channel EEG and PPG Signals Based...

引用

International Conference on Advanced Technologies for Communications, ATC

作者： Senura Hansaja Wanasekara Han Huy Dung Ngoc Hung Nguyen Van-Dinh Nguyen College of Engineering and Computer Science & Center for Environmental Intelligence VinUniversity Hanoi Vietnam School of Electrical and Electronic Engineering Hanoi University of Science and Technology

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

The integration of electroencephalography (EEG) and photoplethysmography (PPG) in consumer-grade wearable devices is revolutionizing healthcare by enabling continuous health monitoring. However, these devices face significant challenges in terms of power consumption, computational complexity, and data storage, necessitating efficient data compression techniques. This paper proposes a modified Golomb-Rice coding method tailored for lossy compression of multi-channel EEG and PPG signals. By leveraging the statistical properties of filtered data and employing an optimized parameter estimation strategy, the proposed method achieves high compression ratios while minimizing computational overhead and preserving critical signal information. The proposed algorithm shifts data to handle negative values, scales and converts it into an appropriate format for encoding, and uses a novel parameter estimation technique to optimize the Golomb-Rice parameter. Extensive simulations on EEG and PPG datasets demonstrate that this parameter estimation method yields significantly better performance, improving both efficiency and processing speed. Results underscore the importance of preprocessing in data compression and provide a robust framework for future advancements in biomedical signal processing.

关键词： Parameter estimation Signal processing algorithms Data compression filtering algorithms Signal processing Brain modeling Electroencephalography Encoding Wearable devices Monitoring

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The Harmonic Optimization of High-Power Converter Based on The LLCL Filter

The Harmonic Optimization of High-Power Converter Based on T...

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IEEE International Conference on Mechatronics and Automation

作者： Rui Dong Qiang Fu Chunjie Wang Lin Cui School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin University of Technology National Ocean Technology Center Ministry of Natural Resources Tianjin China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

A T-type three-level bidirectional converter based on the LLCL filter was constructed using transformer integrated filtering technology. The dual closed loop control is designed using the grid voltage vector-oriented control strategy, and the principle of controller parameter design based on the LLCL filter model is discussed. Low-order harmonic amplification occurs due to the presence of the ripple current and deadband. The transmission relations of harmonic components in the control loop are analyzed to find out the paths to suppress the lower order harmonics of grid side currents. Quasi-proportional resonance (Q-PR) controller and the second-order bandstop filter were used to remove the introduction of harmonic content in the control loop, effectively filtering out the pulsating voltage of the DC-side voltage without affecting the dynamic performance of the control system and improving the quality of transmitted power.

关键词： Insulated gate bipolar transistors Mechatronics filtering Simulation filtering algorithms Harmonic analysis Transformers

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Adaptive LMS Algorithm for Effective Noise Suppression

Adaptive LMS Algorithm for Effective Noise Suppression

引用

North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Rajani S. Pujar Chayalakshmi C. L. Mahabaleshwar S. K. Department of Electronics and Communication Engineering Basaveshwar Engineering College Bagalkote Karnataka India Department of Electrical and Electronics Engineering Basaveshwar Engineering College Bagalkote Karnataka India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Noise cancellation aims to reduce or eliminate unwanted signals from the original signals, such as audio signals, thereby improving signal quality and clarity. A popular method for active noise cancellation is the adaptive filter, which uses an algorithm to dynamically adjust the filter parameters based on the input signal to effectively remove noise. An adaptive filter, a type of digital filter, adjusts its coefficients to reach an optimal state without needing prior information. The Least Mean Squares (LMS) algorithm is a well-known adaptive filtering technique utilized in signal processing and communication systems. This algorithm is a form of stochastic gradient descent used to minimize the mean square error between the desired signal and the filter's output. To assess the performance of the proposed LMS-based noise cancellation algorithm, both subjective and objective analyses were conducted. The subjective evaluation involved Mean Opinion Score (MOS) tests to gauge the perceived quality of the speech signal under various Signal-to-Noise Ratio (SNR) conditions in noisy environments. Objective evaluation was performed using Segmental SNR. The results from the listening tests showed the following MOS scores for the processed speech "The burse can lead on a smooth plank": 3.3, 3.5, 4.3, 4.0, and 4.3 for SNR values of 0, 5, 10, 15, and 20 dB, respectively. In contrast, the MOS scores for unprocessed speech were significantly lower: 1.0, 1.5, 2.0, 2.0, and 2.8 for the same SNR values. Additionally, Segmental SNR values for the processed speech improved to 4.5712, 4.0248, 3.5847, 3.0045, and 2.547 for the corresponding SNR values. These findings indicate a noticeable improvement in speech quality under different SNR conditions due to the LMS algorithm. Furthermore, similar subjective and objective analyses were performed for various speech signals in the presence of car noise and babble noise, demonstrating the algorithm's effectiveness across different noise environ

关键词： Noise reduction Signal processing algorithms Adaptive filters filtering algorithms Speech enhancement Lead Noise cancellation Noise measurement Digital filters Signal to noise ratio

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A DVCC Based Realization of Optimized Fractional Step Low Pass Filter

A DVCC Based Realization of Optimized Fractional Step Low Pa...

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Emerging Trends in Engineering and Medical Sciences (ICETEMS), International Conference on

作者： Shibendu Mahata Soumen Mallick Rahul Kumar Tanti Balaram Halder Piyush Dutta Subhajit Hati Dept. of Electrical Engineering Dr. B. C. Roy Engineering College Durgapur India

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Realization of fractional-step low-pass Butterworth filter (FSLBF) using a differential voltage current conveyor (DVCC) is presented. To approximate the FSLBF characteristic, the coefficients of three different filter transfer functions are optimally determined using the Craziness-based Particle Swarm Optimization (CRPSO) algorithm. Minimum absolute pole angle, −3 dB frequency response, squared magnitude error, etc., of the FSLBFs are evaluated. Simulations verify the efficacy of DVCC-based realizations for the designed frequency filters.

关键词： Matched filters Histograms Sensitivity Monte Carlo methods Circuits Voltage filtering algorithms SPICE Robustness Particle swarm optimization

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Research on weight prediction method based on IJSOA-BPNN for cigarette filter rod

Research on weight prediction method based on IJSOA-BPNN for...

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Chinese Control and Decision Conference, CCDC

作者： Yunhe Luo Xiao Qiu Yuanfa Zhang Guanglin Li Qingna Duan Sirui Xue Shuxian Ye Wei Yang Changsheng Zhang Hongyun Honghe Tobacco(Group)Co Ltd. Kunming Cigarette Factory Kunming China Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The weight of filter rods is an important indicator for monitoring the production quality of cigarette filter rods. This paper proposes a prediction method for filter rod weight, which is based on IJSOA-BPNN. The improved jumping spider optimization algorithm (IJSOA) is used to adjust the threshold value of the BP neural network to compensate for its susceptibility to local optimum. The trained BP is applied to the prediction model to predict the weight of the next moment of the filter rod forming machine. The results show that the proposed algorithm achieves the specifications including RMSE of 1.6159, MAPE of 0.0018, and R 2 of 93.35%, which are superior to BPNN and JSO-BPNN in terms of prediction accuracy and stability.

关键词： Accuracy Neural networks Fitting Production filtering algorithms Predictive models Prediction algorithms

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