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检索条件"主题词=Signal Processing Algorithms"

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SCNet: A Stress Calculation Neural Network in Digital Photoelastic Technique

SCNet: A Stress Calculation Neural Network in Digital Photoe...

引用

International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Xuejiao Zhang Niannian Chen Ling Wu Yong Fan Haoxing He Chuan Qiu School of Computer Science and Technology Southwest University of Science and Technology Mianyang Sichuan Province China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

In digital photoelastic analysis, accurately calculating stress components is crucial. Typically, this is achieved by solving equilibrium differential equations after determining isochromatic and isobaric parameters. However, reducing noise-induced errors and improving accuracy remains a pressing issue. To address this, a stress calculation method based on deep learning is proposed. The method employs neural networks to learn the entire process from phase unwrapping to stress separation, simplifying the otherwise complex inverse process. In addition, the method not only successfully avoids the cumbersome phase unwrapping process, but also omits the complicated image post-processing steps, which further improves the efficiency and accuracy of the calculation. Through the simulation and experimental verification of the radially compressed disk, we find that the method has excellent noise resistance and can effectively reduce the noise-induced stress calculation errors.

关键词： Deep learning Accuracy Computational modeling Biological system modeling Neural networks Noise signal processing algorithms Robustness Stress Testing

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Design of IIR Lowpass Differentiators with Nearly Linear Phases Using Sequential Partial Optimization

Design of IIR Lowpass Differentiators with Nearly Linear Pha...

引用

6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Mengqi Yang Hailong Meng Qilu University of Technology (Shandong Academy of Sciences) School of Mathematics and Statistics Jinan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Infinite Impulse Response (IIR) lowpass digital differentiators with nearly linear phases are extensively utilized. This paper introduces a Sequential Partial Optimization (SPO) methodology for crafting stable IIR lowpass differentiators that closely approximate linear phases. The proposed method minimizes the maximum phase deviation within the passband from an ideal linear phase while adhering to the specified maximum relative magnitude error in the passband and the prescribed mean squared amplitude response in the stopband. Stability is assured by incorporating stability-triangle constraints into the design. Through the Gauss-Newton strategy, the intricate nonconvex optimization problem is systematically deconstructed into a series of iterative convex subproblems, with each iteration refining only one second-order denominator factor. Design examples illustrate that differentiators designed using the proposed method exhibit smaller maximum passband phase errors compared to those obtained by several differentiators designed using competing methods, all while adhering to the same passband relative magnitude error and stopband amplitude response constraints. Moreover, the proposed method demonstrates the capability to obtaining differentiators with excellent performance starting from trivial initial points.

关键词： Refining signal processing algorithms signal processing Passband Iterative methods Optimization

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Enhancing Cybersecurity through Genetic Algorithm-Based Phishing Website Detection

Enhancing Cybersecurity through Genetic Algorithm-Based Phis...

引用

IEEE International Conference on signal processing, Information, Communication & Systems (SPICSCON)

作者： Md. Badiuzzaman Biplob Mohammad Shamsul Arefin Ahmed Wasif Reza Computer Science and Engineering Department Chittagong University of Engineering and Technology Chattogram Bangladesh Computer Science and Engineering Department East West University Dhaka Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

Cybercriminals design false webpages in an attempt to trick people into disclosing private information, such as passwords and credit card numbers. Strong cybersecurity measures are desperately needed, as phishing attacks are becoming more and more sophisticated. To improve cybersecurity, this study investigates the use of genetic algorithms for phishing website detection. We have used three real-world phishing website datasets to implement our work. Additionally, a thorough methodology that includes feature selection, data preprocessing, and the iterative process of the genetic algorithm is employed. This work proposes a hybrid-based method for phishing identification based on the website's URL that combines genetic and machine learning methods. The algorithm's effectiveness is demonstrated by the results, which also show its adaptability, relative advantages over conventional methods, and promising generalizability. By shedding light on the useful applications of genetic algorithms for phishing detection, the study advances the field of cybersecurity. The final model proposed in this work has a 99.99% accuracy rate on the test set.

关键词： Uniform resource locators Machine learning algorithms Phishing signal processing algorithms Optimization methods Machine learning Passwords signal processing Iterative algorithms Genetic algorithms

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Encryption With Private KEY for Data Security and Deduplication

Encryption With Private KEY for Data Security and Deduplicat...

引用

International Conference on Communications and signal processing

作者： Shayani Debbarma Nitturu Pavithra B. Prakash Dept.Of CTECH SRM Institute of Science and Technology KTR Chennai Department of Computing Technologies Faculty Engineering and technology SRM Institute of Science and technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

A new method of data compression called deduplication helps make better use of storage space and works better when handling duplicate data. Original, unique data copies can be uploaded to storage thanks to deduplication, and subsequent copies are given a linking pointer to the genuine stored copy. Data privacy is an important issue in cloud computing that needs to be taken into account. This study reviews and assesses the methods of the most common deduplication algorithms. The resulting survey is tabulated to highlight the precise nature of the deduplication’s evolution and varieties. This survey has shown us that deduplication compromises data confidentiality on a number of levels, which opens the door for more unique ideas.

关键词： Surveys Data privacy Cloud computing Reviews Data security signal processing algorithms Data compression

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Optimized DSP-FPGA Communication in CHB-STATCOM with Direct MPC

Optimized DSP-FPGA Communication in CHB-STATCOM with Direct ...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Francesco Simonetti Morteza Dezhbord Sobhan Mohamadian Alessandro D’Innocenzo Carlo Cecati Roberta Di Fonso Energy Department Aalborg University Aalborg Denmark DISIM University of L’Aquila L’Aquila Italy

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Digital signal processors (DSPs) and field programmable gate arrays (FPGAs) are extensively used in modular multilevel converters. Integrated analog to digital converters (ADCs) in DSPs implement high-speed samplings of measurements from sensors while FPGAs can handle complex algorithms computations. This work analyzes practical issues arising in DSP to FPGA data communication using serial peripheral interface protocol. A step by step communication strategy to address the delay is presented. The impact on the closed-loop performance is analyzed for a cascaded h-bridge static synchronous compensator controlled by direct model predictive control. Experimental results show that the proposed solution minimizes the communication delay.

关键词： Multilevel converters Protocols Digital signal processors signal processing algorithms Prediction algorithms Delays Sensors Data communication Field programmable gate arrays Predictive control

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Machine-learning-based predictive classifier for sand body types and reservoir properties using geological data

Machine-learning-based predictive classifier for sand body t...

引用

Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Jinxiang Shao Yujuan Meng Lingyan Zhao Jinzi Liu The Seventh Oil Production Plant Daqing Oilfield Limited Company Daqing China School of Mathematics and Statistics Northeast Petroleum University Daqing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

The classification and identification of reservoir sand body types has important research significance in oil-gas oilfield development plans. The traditional recognition method mainly relies on logging curves and manual recognition, which has the disadvantages of relying on expert experience and longtime consumption. This article is driven by geological data and compares various machine learning algorithms to select the intelligent algorithm suitable for solving this problem, achieving automatic recognition of reservoir sand body types and providing reference for reservoir engineering. The results show that the accuracy of the neural network algorithm reaches 97.4%, which can be used as an effective intelligent algorithm for reservoir sand body type classification and recognition.

关键词： Machine learning algorithms Geology Neural networks signal processing algorithms Manuals Machine learning signal processing Reservoirs Prediction algorithms Classification algorithms

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Experimental Study of Visible Light Localization Based on Machine Learning and Multiple Photo Diodes

Experimental Study of Visible Light Localization Based on Ma...

引用

Emerging Technology (INCET), International Conference for

作者： Xiaoyun Yang China Acknowledgments Jingchu University of Technology Jingmen City Hubei Province China

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In view of the limitations of the traditional Search for excellent algorithm in the research of System for locating visible light resources, a research scheme of localization experimental study based on machine learning together with multiple photo diodes was proposed. Firstly, the influencing factors is accurately located through signal processing theories, and the indicators is reasonably divided to reduce interference, and the machine learning together with multiple photo diodes is used to construct a research scheme for localization experimental study. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional Search for excellent algorithm in terms of research accuracy and processing time of influencing factors of localization experimental study, and has obvious advantages. The research of localization experimental study plays an extremely important role in visible light, which can accurately predict and optimize the growth characteristics and product generation of visible light. However, the traditional Search for excellent algorithm has some limitations in solving the problem of positioning experiment simulation, especially when dealing with complex problems. In this paper, this paper proposes a research scheme of localization experimental study based on machine learning together with multiple photo diodes to better solve this problem. The scheme accurately locates the influencing factors through signal processing theories, so as to determine the division of indicators, and uses the machine learning together with multiple photo diodes to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on machine learning together with multiple photo diodes in the research of System for locating visible light resources can bet

关键词： Location awareness Accuracy Machine learning algorithms signal processing algorithms Machine learning signal processing Prediction algorithms

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Enhanced Fourier Series for Precise signal Analysis

Enhanced Fourier Series for Precise Signal Analysis

引用

Progress in Electromagnetic Research Symposium (PIERS)

作者： Hsin-Jung Lee Cheng-Che Lee Yi-Min Yang Wei-Yu Lee Chieh-Hsiung Kuan Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei Taiwan

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

This study introduces an innovative enhancement to the Fourier series expansion by incorporating novel correction terms. We rigorously examine the relationship between the root-mean-square error (RMSE) and the number of expansion terms, considering various correction frequencies and bandwidths. Our method significantly improves convergence efficiency within a limited bandwidth, thereby optimizing the signal expansion process. When applied to electrocardiography (ECG), this enhanced algorithm demonstrates its capability in detecting noise and improving signal analysis accuracy. This advancement holds potential for refined analysis in various signal processing applications.

关键词： Electric potential Noise signal processing algorithms Bandwidth Electrocardiography Fourier series signal representation

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Research on rolling mill hydraulic fault diagnosis based on GA improved ANN algorithm

Research on rolling mill hydraulic fault diagnosis based on ...

引用

signal processing and Intelligent Computing (SPIC), International Conference on

作者： Jiaqi Xiao Ning Ye Bin Xia Chenglong Xu Control Engineering Department Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In view of the difficulties in fault feature extraction, nonlinear signal changes and large data volume in the current fault diagnosis process of rolling mill servo hydraulic cylinder, a hydraulic fault diagnosis study based on GA (Genetic Algorithm) improved ANN (Artificial Neural Network) algorithm was proposed. Taking advantage of the rapidity of artificial neural network fault diagnosis, the signal was normalized and put into ANN for training, and then the network parameters were optimized through back propagation learning to improve the diagnosis accuracy. According to the working principle of the rolling mill system, a rolling mill system simulation model was established to simulate the internal leakage fault condition of the rolling mill. The research results show that the use of GA improved ANN algorithm can effectively optimize the average error and data accuracy, and effectively reduce the number of iterations, indicating that the GA improved ANN method can be used to improve the performance of neural network calculations.

关键词： Fault diagnosis Training Accuracy signal processing algorithms Artificial neural networks Hydraulic systems Systems simulation Servomotors Genetic algorithms Convergence

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A D2D and RIS-Assisted Task Offloading Method Based on an Improved Fox Optimization Algorithm

A D2D and RIS-Assisted Task Offloading Method Based on an Im...

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International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Youbin Zhang Yuliang He Changxi Yuan School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

With the progress of industrial Internet of Things and communication technology, task offloading in edge computing has attracted more and more attention. Aiming at the impact of obstacles on task offloading performance in the process of device offloading in industrial scenarios and the problem of high communication costs of network edge devices, we propose a task offloading scheme assisted by combined Device to Device and Reconfigurable Intelligent Surface. By jointly optimizing computing resources, task offloading decisions, RIS phase shifting and industrial device transmission power, a balanced total energy consumption and latency cost optimization problem is established. We propose an approach using the Improved Fox Optimization Algorithm (IFOA) to maximize system revenue. Extensive simulation data demonstrates that compared with traditional optimization algorithms, it has better comprehensive performance.

关键词： Performance evaluation Costs signal processing algorithms Reconfigurable intelligent surfaces signal processing Device-to-device communication Particle swarm optimization Informatics Optimization Surface treatment

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