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检索条件"主题词=filtering algorithms"

共 10349 条记录，以下是431-440 订阅

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Advanced filtering Techniques for Enhanced Flood Mapping Using Multispectral Imaging

Advanced Filtering Techniques for Enhanced Flood Mapping Usi...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Thilagavathi K Harei Sharan S H Kaavya K Muvin M Ambarish R Akash K Amrith M Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The study investigates the utilisation of multispectral image processing techniques for flood mapping in the Goalpara District of Assam, India. The region experiences recurrent flooding during the monsoon season, impacting the Brahmaputra River and its surroundings. Dual-polarized Sentinel-1 Synthetic Aperture Radar (SAR) data, encompassing Vertical-Horizontal (VH) and Vertical-Vertical (VV) polarisation methods, were employed to assess flood conditions before, during, and after events occurring between April and June 2022. Various parameters, including the computation of the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), were calculated using the Otsu Algorithm. filtering methods such as Refined Lee, Focal Median, and Gamma Map were applied to enhance flood mapping accuracy. The study underscores the efficiency of multispectral image processing, particularly the Refined Lee filter, in accurately delineating flood extent compared to other filters. Flood mapping was facilitated through thresholding techniques applied to SAR data. Furthermore, the study identifies severe bank erosion in the Brahmaputra channel, signifying significant implications for river morphology and sediment transport dynamics. Leveraging remote sensing techniques, this research provides essential insights for proactive flood management, facilitating informed decision-making and mitigation strategies in the region. The study achieved an overall accuracy of 90% and a Kappa coefficient of 0.43, indicating high-quality flood map and ground truth data.

关键词： filtering Normalized difference water index Prevention and mitigation Decision making Sentinel-1 filtering algorithms Rivers Floods Synthetic aperture radar Remote sensing

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A Novel Hybrid Adaptive filtering Algorithm Controlled STATCOM for LVRT Capability Enhancement of DFIG

A Novel Hybrid Adaptive Filtering Algorithm Controlled STATC...

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE International Conference on

作者： Arindam Chakraborty Tanmoy Maity Nilanjan Senroy Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Department of Electrical Engineering Indian Institute of Technology (ISM) Dhanbad Dhanbad Jharkhand India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

This work focuses on enhancing transient stability or low voltage ride-through (LVRT) in a doubly fed induction generator (DFIG) by dynamically tuning all the PI controllers of the STATCOM using a novel approach. The hybrid enhanced block sphere Bayesian’ algorithm (EBSBA) and ‘least mean square root of exponential’ (LMSRE) algorithms (EBSBA- LMSRE) is proposed as a solution, offering key advantages such as high convergence speed, low computational complexity, and a straightforward design. The main goal of this work is to address voltage fluctuations and restore stable conditions in the DFIG. In this work, the cascaded structure-based proportional integral (PI) controllers are implemented on the voltage source converter (VSC) of the static synchronous compensator (STATCOM). During the transient, the gain of the PI controllers is automatically adjusted by the proposed hybrid EBSBA-LMSRE algorithm. The EBSBA-LMSRE-PI controlled STATCOM demonstrates superior response and damping compared to the LMSRE-PI and EBESBA-PI controllers, especially under normal and abnormal grid disturbances.

关键词： Damping PI control Voltage fluctuations Fluctuations Heuristic algorithms Doubly fed induction generators Power system stability filtering algorithms Automatic voltage control Transient analysis

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Efficiency Evaluation of a Combining Compact 1D CNN and Signal filtering Prototype Algorithm for Vibration Signal Classification

Efficiency Evaluation of a Combining Compact 1D CNN and Sign...

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First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP)

作者： Thosapon Krinsukont Taweedej Sirithanapipat National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Department of Mechanical Engineering Kasetsart University Bangkok Thailand

ISBN: (数字)9798331505400

ISBN: (纸本)9798331505417

This paper presents an experiment and results of the modified CNN algorithm, it was developed by combining a compact 1D convolution neural network with a tuned signal filter (low-pass filter in this experiment). The algorithm was specifically designed for signal shape detection, prediction, and classification. An experiment aimed to evaluate the created algorithm's structure and signal filter to ensure its suitability for vibration signal data analysis. A testbed was built using an inverter motor equipped with various unbalanced conditions by increasing screws. Four-class unbalanced conditions were studied compared with the normal condition. Each conditions produced the distinct signal shape, with slight differences, making them suitable for evaluating the proposed algorithm's performance. The results demonstrate the satisfied performance of the algorithm, which effectively classifies vibration signals unbalance-3 (precision 54%), unbalance-4 (precision 77 % ) and a group of normal, unbalance-1, and unbalance-2. The group of normal, unbalance-1, and unbalance-2 can't be easily classified due to the tiny differences in the signal shapes, caused by small mass variations of the unbalanced screw unbalance-1, and unbalance-2. In conclusion, the algorithm proved satisfied effectiveness for its intended purpose of vibration signal classification.

关键词： Vibrations Shape Convolution Neural networks Low-pass filters Pattern classification filtering algorithms Fasteners Prediction algorithms Classification algorithms

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Application of LMS Adaptive filtering Algorithm Based on Fractional Order Iterative Order in Marine Acoustic Signal Processing

Application of LMS Adaptive Filtering Algorithm Based on Fra...

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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Yonghao Zhou Zhengang Wan Jiangsu University of Science and Technology Zhenjiang China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

In this paper, an improved fractional order LMS filtering algorithm is proposed. By dynamically adjusting the step size and fractional order, the parameters are optimized in real time according to the errors, and the convergence speed and steady-state accuracy are improved. The forgetting factor is introduced to enhance the tracking ability of time-varying signal. The simulation results show that the improved algorithm performs well in the processing of complex Marine acoustic signals containing noise, and has a wide application prospect in the fields of Marine communication and detection.

关键词： Heuristic algorithms Simulation Signal processing algorithms filtering algorithms Acoustics Real-time systems Steady-state Smart grids Noise measurement Convergence

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An Adaptive filtering Algorithm for Low-Voltage Regulators in Flexible DC Transmission Systems

An Adaptive Filtering Algorithm for Low-Voltage Regulators i...

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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Zheqin Zhao Jing Chen Jianjun Wang Dan Fang Guoliang Zhang State Grid Shanghai Electric Power Company Shanghai China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

This paper presents an adaptive filtering algorithm for low-voltage regulators based on flexible DC technology, addressing the challenges of noise suppression and voltage stability in DC transmission systems. We enhance the traditional LMS algorithm by incorporating a variable step-size adjustment to dynamically optimize filter parameters in response to real-time conditions. The proposed approach is validated through simulation results, which demonstrate significant improvements in noise suppression and voltage quality compared to conventional fixed-parameter filters. The results underline the algorithm's effectiveness in handling complex, dynamic noise environments and enhancing system reliability.

关键词： Low voltage Regulators Adaptive systems Heuristic algorithms Simulation Noise reduction Noise filtering algorithms Power system stability Stability analysis

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Optimal Hyperparameters for Collaborative filtering: A Comparative Study

Optimal Hyperparameters for Collaborative Filtering: A Compa...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Basil Jose Aditya Bhatkande Siddharth Phadte Richa Kerkar Adnan Muzavor Department of Computer Engineering Agnel Institute of Technology and Design Assagao Goa

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

In recommendation systems, collaborative filtering algorithms play a vital role in offering personalized recommendations based on user-item interactions. This paper conducts a detailed comparative study of three collaborative filtering algorithms, including Alternating Least Squares (ALS), Singular Value Decomposition (SVD), and Neural Collaborative filtering (NCF). The paper’s objective is to provide the optimal hyperparameter configurations for the three algorithms, which would boost their predictive power, using the Movie Lens 25M dataset. Initially the algorithms are evaluated with default parameters whose results shows that SVD performs better in Mean Absolute Error (MAE) while ALS performs better in RMSE. However, after the hyperparameter tuning with multiple parameters, such as epoch, rank, maxIter, learning rate, and batch size, the most optimal hyperparameter configuration for the algorithms was defined. The results tells that each of the algorithm have the capacity to handle sparsity. According to the RMSE and MAE, ALS and SVD have a slight advantage over NCF. These results are essential for choosing the appropriate hyperparameters enhancing the accuracy of the algorithms and the recommendation system as a whole.

关键词： Collaborative filtering filtering algorithms Prediction algorithms Motion pictures Tuning Recommender systems Singular value decomposition

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Solution of Robust Stochastic Discrete filtering Problem Based on Minimax Functional Criterion

Solution of Robust Stochastic Discrete Filtering Problem Bas...

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International Russian Automation Conference (RusAutoCon)

作者： Irina V. Reshetnikova Daniil V. Marshakov Sergey V. Sokolov Research Department North Caucasus branch Moscow Technical University of Communications and Informatics Rostov-on-Don Russia Department of Information Security North Caucasus branch Moscow Technical University of Communications and Informatics Rostov-on-Don Russia Department of Informatics and Computer Engineering North Caucasus branch Moscow Technical University of Communications and Informatics Rostov-on-Don Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

Currently, the solution to the vast majority of problems of assessing the state of discrete stochastic systems operating in stochastic environments is carried out using algorithms developed on the basis of the discrete Kalman filter. The main disadvantage of such estimation schemes is the need for an accurate a priori description of the probabilistic characteristics of the noise of the object and the interference of its observer, the absence of which leads to divergence of the filtering process. All known modifications of the Kalman filtering scheme ensure the stability of the estimation process only for some special cases, leaving the problem of an a priori description of the operating conditions of real systems as a whole unresolved. In this regard, the paper considers an alternative approach to the synthesis of a stable (robust) stochastic estimation scheme based on the choice of a matrix filtering coefficient from the optimization condition of a minimax functional criterion determined by the most unfavorable class of probabilistic distributions noise of the object and its observer. An advantageous feature of the proposed filter, in addition to its robust properties, is a significantly smaller dimension compared to the Kalman scheme, equal to the dimension of the object, which sharply reduces the amount of computational costs in the practical implementation of the filter. The results of a numerical experiment illustrating the effectiveness of the considered approach are presented.

关键词： filtering Noise Stability criteria Estimation Interference Observers filtering algorithms Probabilistic logic Kalman filters Numerical stability

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RSSI Indoor Positioning Algorithm Based on Kalman filtering

RSSI Indoor Positioning Algorithm Based on Kalman Filtering

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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Yinqing Xie Li Jiang School of Electronics and Automation City Institute Dalian University of Technology Dalian China Engineering Practice Center City Institute Dalian University of Technology Dalian China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

In the context of the increasingly deep construction of smart home, indoor localization using wireless network has become a research hotspot. However, in practical applications, the training samples obtained by most indoor localization algorithms are not exactly the same as the detection samples, and the external environmental changes and signal drift also lead to differences in the probability distributions of the two. The indoor positioning technology based on wireless signals has become a research hotspot due to its low cost and easy deployment. Among them, RSSI (Received Signal Strength Indication), as one of the commonly used indoor positioning techniques, its accuracy and stability directly affect the performance of the positioning system. Traditional methods are difficult to ensure accurate positioning in multi-source heterogeneous environments, resulting in decreased positioning accuracy or even unusable. A RSSI indoor positioning algorithm based on Kalman filtering is proposed to address this challenge. Experiments show that the accuracy of this algorithm reaches more than 0.95 under the number of training samples of 300,400,900. The algorithm can effectively suppress the influence of external environment and improve the localization accuracy.

关键词： Location awareness Training Accuracy Wireless networks Smart homes filtering algorithms Indoor environment

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Distributed Recursive filtering with Bit Rate Constraints and Cross-Correlated Noises over Sensor Networks

Distributed Recursive Filtering with Bit Rate Constraints an...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Pengyu Wen Hanbo Wang Fan Yang Hongli Dong National Key Laboratory of Continental Shale Oil Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development Ministry of Education Northeast Petroleum University Daqing China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper focuses on the distributed filtering issue for a type of discrete-time systems with cross-correlated measurement noises under constrained bit rate. The system under consideration is subject to cross-correlated measurement noises, that is, for different sensor nodes, the measurement noises are a-step cross-correlated. For digital communication, the binary encoding scheme is employed to encode the signals into binary bit strings, in which the random bit errors induced by the channel noise may occur during the propagation of the binary bit string via the memoryless binary symmetric channel. To characterize the occurrence of the bit errors, a set of Bernoulli distributed random variables is adopted. Furthermore, to better reflect the practice, the time delays in the coding-decoding process is incorporated in the filter design. Considering the impacts of the cross-correlated noises, constrained bit rates and bit errors, an upper bound of the filtering error covariance matrix is obtained and the filter parameters are acquired by minimizing the upper bound. Finally, a simulation example is conducted to demonstrate the effectiveness of the proposed algorithm.

关键词： Discrete-time systems Upper bound filtering Delay effects Noise Bit rate filtering algorithms

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The Effect of the filtering Algorithm on Absolute Stress Measurement of the Steel Member Based on the LCR Wave

The Effect of the Filtering Algorithm on Absolute Stress Mea...

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SPAWDA Symposium on Piezoelectricity, Acoustic Waves, and Device Applications

作者： Yongzhi Liu Hongquan Li Zhili Long Junjie Mo Shuyuan Ye Heng Zhao Department of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China

ISBN: (数字)9798331523732

ISBN: (纸本)9798331523749

The absolute stress of the steel member is a vital indicator for evaluating the performance of steel structure buildings. However, further research is required to investigate the effect of the filtering algorithms on the accuracy and speed of the absolute stress measurement. We explore median filtering, Savitzky-Golay (SG) filtering, Finite Impulse Response (FIR) filtering and wavelet threshold filtering algorithms and establish a model between the stress and the ultrasound time-of-flight (TOF) theoretically. The stress measurement platform is constructed and the experiment is conducted. There are good linear relationships between the stresses and the TOF differences for different filtering algorithms, and their fitting determination coefficients (R 2 ) are all greater than 0.9995. With different filtering algorithms, the stress measurement errors are less than 9.98 MPa. The experimental results indicate that the filtering algorithms have limited effect in detecting the absolute stress of the steel member utilizing the Longitudinal Critically Refracted (LCR) wave.

关键词： Ultrasonic imaging Accuracy filtering Finite impulse response filters Noise reduction filtering algorithms Calibration Steel Stress Stress measurement

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