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检索条件"主题词=Signal Processing Algorithms"

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Research on Anomaly Detection Method for Virtual Power Plant Measurement Data Based on Isolation Forest Algorithm

Research on Anomaly Detection Method for Virtual Power Plant...

引用

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

作者： Xiaoyi Zhang Jun Wu Xuan Xuan Jun Wang Xuliang Zhao Jing Yang Marketing Service Center State Grid Liaoning Electric Power Co. Ltd. (Metering Center) Shenyang City China Smart Energy Efficiency Division Wasion Group Co. Ltd. Changsha City China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Due to the complexity and diversity of virtual power plants, there are often outliers in the measurement data, which may have a negative impact on the state estimation, optimization scheduling, and decision support of the power system. Therefore, this paper proposes an anomaly detection method based on the Isolation Forest algorithm. Clean and normalize the measurement data of the virtual power plant. Introducing the Isolation Forest algorithm, combined with the characteristics and requirements of virtual power plant measurement data, the dataset is randomly divided to construct multiple isolation trees, and the degree of data anomaly is determined based on the path length from data points to the root node. The experimental results show that this method can accurately identify most outliers while maintaining good recognition ability for normal data.

关键词： Power measurement signal processing algorithms Forestry Virtual power plants signal processing Reliability engineering Scheduling State estimation Anomaly detection Optimization

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Enhancing Multimodal Sentiment Analysis with Deep Learning Techniques to Foster Emotional Intelligence

Enhancing Multimodal Sentiment Analysis with Deep Learning T...

引用

International Conference on Communications and signal processing

作者： N Kousika N Nanda Kumar Elango Baskaran M S Abbenaya G Arthika M Ashwanth Department of CSE Sri Krishna College of Engineering and Technology Coimbatore India Department of CSE Excel Engineering College Kumarapalayam India Department of ECE Adhiparasakthi Engineering College Melmaruvathur India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Sentiment analysis (SA) is a critical process in understanding the emotions associated with data on social networking sites. Building upon this foundational concept, this paper introduces a sophisticated web application aimed at enabling users to submit diverse forms of content, including text, images, videos, and audio, for hate content assessment. This platform employs BERT CNN and VGG16 CNN algorithms for performing a thorough analysis of provided information in various formats. Emphasizing the identification of emotions in text and the detection of hate speech, the methodology extends to incorporate facial emotion detection for images. Through meticulous preprocessing, feature extraction, and ensemble learning, the proposed solution provides a robust framework for accurately identifying and mitigating hate content across various modalities. Experimental tests employing varied datasets validated the usefulness of the strategy, contributing to the advancement of hate content detection systems and fostering a safer online environment.

关键词： Performance evaluation Sentiment analysis Social networking (online) Hate speech Refining signal processing algorithms signal processing

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Dictionary Learning Based Near-Field Channel Estimation for Wideband XL-MIMO Systems

Dictionary Learning Based Near-Field Channel Estimation for ...

引用

IEEE Workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Peicong Zheng Xuantao Lyu Ye Wang Yi Gong Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

Extremely large-scale MIMO (XL-MIMO) is pivotal for enabling future 6G networks. However, accurate channel estimation for XL-MIMO faces a new challenge known as the near-field beam split effect. To capture the characteristics of the channel, this paper proposes a dictionary learning based channel estimation algorithm for wideband XL-MIMO systems. The channel estimation problem is formulated as a bilevel optimization task, where the upper-level aims to learn the optimal dictionary while the lower-level aims to find sparse channel representations based on the learned dictionary. To efficiently solve this bilevel optimization problem, we unfold the iterative shrinkage-thresholding algorithm into a deep neural network architecture. Learnable network blocks are employed to represent the dictionary, thus enabling adaptation to the characteristics of near-field wideband channel in XL-MIMO. The unfolded network is trained in an unsupervised manner by minimizing the reconstruction error via backpropagation and stochastic gradient descent. This eliminates the need for explicit gradient calculations associated with the bilevel optimization. Simulation results demonstrate that the proposed algorithm achieves good channel estimation performance, while offering lower computational complexity than the conventional compressed sensing based estimators.

关键词： Wireless communication Dictionaries Simulation Channel estimation signal processing algorithms Machine learning signal processing Iterative algorithms Optimization Wideband

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Partitioned Successive-Cancellation List Flip Decoding of Polar Codes

Partitioned Successive-Cancellation List Flip Decoding of Po...

引用

IEEE Workshop on signal processing Systems (SIPS)

作者： Charles Pillet Ilshat Sagitov Grégoire Domer Pascal Giard Department of Electrical Engineering École de technologie supérieure Montréal Québec Canada Department of Electrical Engineering Enseirb-Matmeca Bordeaux INP Bordeaux France

ISBN: (数字)9798350373752

ISBN: (纸本)9798350373769

The recently proposed Successive-Cancellation List Flip (SCLF) decoding algorithm for polar codes improves the error-correcting performance of state-of-the-art SC List (SCL) decoding. However, it comes at the cost of a higher complexity. In this paper, we propose the Partitioned SCLF (PSCLF) decoding algorithm, an algorithm that divides a word in partitions and applies SCLF decoding to each partition separately. Compared to SCLF, PSCLF allows early termination but is more susceptible to cyclic-redundancy check (CRC) collisions. In order to maximize the coding gain, a new partition design tailored to PSCLF is proposed as well as the possibility to support different CRC lengths. Numerical results show that the proposed PSCLF algorithm has an error-correction performance gain of up to 0.15 dB with respect to SCLF. Moreover, the proposed CRC structure permits to mitigate the error-correction loss at low frame-error rate (FER) due to CRC collisions, showing a gain of 0.2 dB at a FER of 10– 4 with respect to the regular CRC structure. The average execution time of PSCLF is shown to be 1.5 times lower than that of SCLF, and matches the latency of SCL at FER = 4.10 −3 and lower.

关键词： Measurement Codes signal processing algorithms signal processing Performance gain Encoding Decoding Partitioning algorithms Polar codes Gain

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A Real-Time Clustering Method Based on Improved Fuzzy C-Means

A Real-Time Clustering Method Based on Improved Fuzzy C-Mean...

引用

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

作者： Fan Wang Bing Luo School of Electronics and Communications Guangdong Mechanical & Electrical College Guangzhou Guangdong China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Traditional FCM algorithms encounters problems such as low efficiency, poor performance, and slow response when dealing with large-scale real-time data streams, as they require multiple iterations to achieve optimization of clustering effects. To solve this problem, this paper proposes a real-time clustering method based on improved fuzzy C-means. Firstly, optimize the objective function and determine a new clustering center update formula; Secondly, receive the data stream and determine the category of data stream; Finally,according to the data category, use an real-time clustering method based on improved fuzzy C-means for clustering. Experimental results show that the algorithm can automatically and dynamically adjust the number of clustering categories while maintaining clustering performance, significantly reducing computational complexity and running time.

关键词： Runtime Clustering methods Heuristic algorithms signal processing algorithms Clustering algorithms signal processing Real-time systems Computational complexity Streams Optimization

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Wideband Modeling and Beamforming for Beyond Diagonal Reconfigurable Intelligent Surfaces

Wideband Modeling and Beamforming for Beyond Diagonal Reconf...

引用

IEEE Workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Hongyu Li Matteo Nerini Shanpu Shen Bruno Clerckx Department of Electrical and Electronic Engineering Imperial College London London U.K. Department of Electrical Engineering and Electronics University of Liverpool Liverpool U.K.

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

This work studies the wideband modeling and beam-forming design of beyond diagonal reconfigurable intelligent surface (BD-RIS), which generalizes and goes beyond conventional RIS with diagonal phase shift matrices to achieve enhanced channel gain. Specifically, we investigate the response of BD-RIS in wideband systems by going back to its hardware circuit realizations. We propose a novel wideband model which has simple expressions while capturing the response variations of BD-RIS for signals with different frequencies. With this wideband model, we propose a BD-RIS design algorithm for an orthogonal frequency division multiplexing system to maximize the average rate over all subcarriers. Finally, we provide simulation results to evaluate the performance of the proposed design and show the importance of wideband modeling for BD-RIS.

关键词： Wireless communication Array signal processing Simulation OFDM signal processing algorithms Reconfigurable intelligent surfaces Admittance Integrated circuit modeling Wideband Surface treatment

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Research on the Resource Allocation Optimization Model of Automobile Based on Cloud Computing Resource Scheduling Algorithm

Research on the Resource Allocation Optimization Model of Au...

引用

signal processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Kang Kai Zhu Peipei Zhang Fan China Automotive Technology and Research Center Co. Ltd. Tianjin China China Auto Information Technology (Tianjin) Co. Ltd. Tianjin China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

This paper proposes a global resource allocation optimization model based on cloud computing, aiming to improve the global resource management efficiency of automobile enterprises through cloud computing resource scheduling algorithm. The system makes full use of the flexibility and scalability of cloud computing, and designs a set of advanced resource scheduling algorithms, which can dynamically adjust the allocation of computing resources according to the changes in demand in different markets and regions around the world, ensuring that resources can be optimally configured at different times and regions. The simulation results show that the model has significantly improved the resource allocation efficiency compared with traditional methods, which not only greatly improves the resource utilization rate, but also effectively reduces the operating costs of enterprises. The cloud computing resource scheduling algorithm shows higher computing efficiency and economic benefits when dealing with complex resource allocation problems. Compared with the traditional static scheduling method, the algorithm proposed in this paper shows its excellent adaptability when dealing with fluctuations in global resource demand, and provides a new technical path and practical basis for the resource allocation optimization of automobile enterprises in the context of globalization.

关键词： Cloud computing Scheduling algorithms Optimization models Simulation Globalization signal processing algorithms Scheduling Resource management Automobiles Optimization

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EVM/DD-Domain-Filtering/PAPR Joint Modification Method with Adjustable Parameters for OTFS

EVM/DD-Domain-Filtering/PAPR Joint Modification Method with ...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Shuai Han Yu Wang Shiji Wang Weixiao Meng Abderrahim Benslimane Communication Research Center Harbin Institute of Technology Harbin China Beijing Aerospace Measurement & Control Technology CO. LTD Beijing China LIA/CERI Avignon University Avignon France

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This paper focuses on the RF index optimization of orthogonal time-frequency space (OTFS). Firstly, an optimization problem is designed to cover the three elements of EVM/DD domain filtering /PAPR. The analysis of this problem shows that an iterative optimization scheme can be designed to solve this problem. Like many traditional researches, the proposed problem can be solved by optimization methods like ADMM. However, traditional methods are opaque and difficult to control which leads to difficulties in deployment, this paper proposes a joint adjustment method of EVM/DD-domain-filtering/PAPR with adjustable joint parameters for practical use, which can adjust the indexes adaptively according to the demand. The simulation results show the system’s effectiveness using parameter adjustment to achieve index modification.

关键词： Radio frequency Time-frequency analysis Power demand Filtering Simulation signal processing algorithms Optimization methods Peak to average power ratio signal processing Indexes

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Development and Execution for Inertial Wheels in Satellite Stabilization

Development and Execution for Inertial Wheels in Satellite S...

引用

IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Cong Zhang Yue Wang Fenming Wang Yuming Zhang Hao Wu Qizhi He Intelligent Planning and Control Team on Unmanned Systems Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing Zhejiang China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

The aim is to craft a inertial wheel for attitude stabilization, showcasing its practicality and efficacy. To achieve this, a torque-mode framework is erected, and a digital control scheme is concocted, leveraging DSP (Digital signal Processor) and FPGA (Field-Programmable Gate Array) technologies. Initially, the analytical dissection of the brushless DC motor's mathematical model, integral to the inertial wheel, is undertaken. Subsequently, a current closed-loop system is orchestrated, utilizing a PWM (Pulse Width Modulation) style buck converter, through the electrical module library, followed by model experimentation. The culmination is the assembly of a prototype. By exemplifying the electromotive force, the response curves of the inertial wheel prototype's torque, and current are scrutinized. The prototype demonstrates an average output torque accuracy exceeding 5%. The experimental outcomes from the prototype align with the simulation outcomes, corroborating the theoretical analysis and affirming the rationality and potency of the model and design. Moreover, the model and prototype serve as a test bed for an array of sophisticated control algorithms, offering innovative approaches to the conception and debugging.

关键词： Torque Digital signal processors Wheels Prototypes signal processing algorithms Debugging Pulse width modulation Prediction algorithms Mathematical models Field programmable gate arrays

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Machine Learning-Assisted Mobile Network Speed Prediction: A Real-World Case Application

Machine Learning-Assisted Mobile Network Speed Prediction: A...

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IEEE signal processing and Communications Applications (SIU)

作者： Emrecan Özkan İbrahim Yazıcı Semih Engin Elektrik-Elektronik Mühendisliği Bölümü İstinye Üniversites İstanbul Türkiye ArGe Operasyonları Müdürlüğü Türk Telekom İstanbul Türkiye Bilgisayar Mühendisliği Bölümü̈ Gazi Üniversitesi İstanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

In recent times, as the use of mobile devices has significantly increased, there is a growing expectation for uninterrupted and high-quality wireless mobile network services in all areas. In line with this expectation, ongoing performance measurements and analyses are conducted. However, conducting such studies in an area where no measurements have been taken would result in a loss of both cost, increased workload, and time. To prevent such losses, machine learning was developed to predict the speed test results based on data collected previously with traditional measurement devices. This machine learning model has been designed to anticipate the speed test outcomes in an area where no measurements have yet been taken, relying on the similarity to previously measured parameters. In this study, a variety of data from different sources has been used. Various parameters such as different cities, residential areas, mobile device brands, and various generations of mobile networks were utilized as inputs in the machine learning modeling. The performance metric of the mobile network speed test is used as output. Various models were applied to this dataset. The achieved results were compared, leading to the selection of the algorithm that provided the most suitable results for this study. The results overall suggest that the Random Forest Regression model generally performed better in predicting mobile network performance in these cities, while AdaBoost and XGBoost had varying degrees of accuracy and predictive power in different regions. The study can be used in service quality prediction for internet providers by integrating relevant machine learning models into real systems with application programming interfaces (API).

关键词： Wireless communication Area measurement Urban areas signal processing algorithms Predictive models signal processing Loss measurement

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