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检索条件"主题词=Signal processing algorithms"

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Deep Mean-Kernel Learning for Bayesian Optimization in IoT-Enabled Data Center

Deep Mean-Kernel Learning for Bayesian Optimization in IoT-E...

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Jinhui Dou Bo Cheng Jinan Li Yu Sun Gaoxiang Jiang Haibo Zhou School of Electronic Science and Engineering Nanjing University Nanjing China Guangdong Branch China Unicom Group Co. Ltd. Guangzhou China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In efforts to mitigate energy consumption in large data centers, numerous optimization strategies have been explored. However, these methods often encounter challenges including prolonged optimization times and settling on local optima. Among diverse algorithms, Bayesian optimization (BO) demonstrates exceptional performance. Due to the slow convergence and limited scalability of existing Bayesian methods in handling high-dimensional data, we explore the influence of the surrogate model on BO, and introduce an innovative optimization approach deep mean-kernel learning, which can converge rapidly to the global optimal solution in high dimensional space and is specifically designed for the refrigeration systems of data centers. In our approach, a neural network is employed to approximate the conventional mean function, while an infinite-width neural network serves as the kernel function, modifying the Gaussian process. Utilizing two prevalent acquisition functions, we conduct comparative experiments of our algorithm against existing optimization methods in the context of data center scenarios. Our findings demonstrate that our approach adeptly manages high-dimensional data and exhibits rapid convergence towards the global optima.

关键词： Wireless communication Data centers Energy consumption High dimensional data Neural networks signal processing algorithms Bayes methods Kernel Optimization Convergence

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Single-Channel Multiuser Transponder System Implementation on a Low-Power Platform

Single-Channel Multiuser Transponder System Implementation o...

引用

IEEE/OES China Ocean Acoustics (COA)

作者： Liu Zhaofeng Zhou Feng Zhao Jingxu Cao Chi Ji Xiaofeng National Key Laboratory of Underwater Acoustic Technology Harbin Engineering University Harbin China Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University) Ministry of Industry and Information Technology Harbin China College of Underwater Acoustic Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798350340297

ISBN: (纸本)9798350340303

The underwater acoustic position system is the current mainstream underwater navigation system. A long-baseline underwater acoustic system can perform navigation and positioning for an underwater cooperative target, and the transponder is one of the most important components of this system. This system is highly accurate, small, and convenient to use. However, its battery power restricts its application in ocean exploitation. This paper presents a low-power platform (STM32L4)-based single-channel multiuser underwater transponder system. The system can support up to four cooperative targets for range acquisition. Furthermore, a robust and fast signal processing algorithm based on copy correlation is applied. The sea experiment shows that the system can achieve reliable underwater acoustic ranging in a shallow-water region.

关键词： Correlation Oceans signal processing algorithms Distance measurement Underwater navigation Transponders Reliability Underwater acoustics System implementation signal detection

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Daily behavior recognition of cattle in complex environments based on improved YOLOv8s

Daily behavior recognition of cattle in complex environments...

引用

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

作者： Lei Tong Jiandong Fang Xiuling Wang Yudong Zhao College of Information Engineering Inner Mongolia University of Technology Innovation Center Hohhot China Inner Mongolia Key Laboratory of Perceptive Technology and Intelligent Systems Innovation Center Hohhot China Inner Mongolia Autonomous Region Intelligent Agriculture and Animal Husbandry Perceptual Technology Collaborative Innovation Center Hohhot China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Aiming at the problems of inconsistent target scales in the task of daily behavior recognition of cattle in farms due to the complex environment of farms, large changes in illumination, and the uneven distribution of cattle, a method based on the improved YOLOv8s is proposed for the recognition of daily behavior of cattle. By testing on the self-constructed dataset, the average accuracy mean mAP of the improved ELID-YOLOv8s network reaches 85.3%, which is 3.0% higher than the original network model YOLOv8s. Therefore, the method in this paper can significantly improve the recognition accuracy of cattle behavior in different environments, different lighting and multi-scale conditions.

关键词： Accuracy Attention mechanisms Target recognition Lighting signal processing algorithms Object detection Cows signal processing Feature extraction Intelligent control

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Optimization of Two-dimensional Path Optimization Algorithm Based on Dijkstra Ant Colony Optimization Algorithm

Optimization of Two-dimensional Path Optimization Algorithm ...

引用

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

作者： Heng Guo Qingquan Liu Zhihui Dang School of Equipment Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

To address the limitations of the traditional Dijkstra algorithm in two-dimensional path optimization problems, this article uses ant colony algorithm to optimize the Dijkstra algorithm, solving the limitations of the Dijkstra algorithm that requires path optimization in finite node graphs and is difficult to traverse the best path that matches actual situations. Generate initial path through Dijkstra algorithm and allocate pheromones to the initial path through ant colony algorithm for iterative calculation, ultimately converging to the optimal path. Through MATLAB software simulation, the optimized Dijkstra-ant algorithm was compared with the original algorithm, optimized algorithm resulted in a better path.

关键词： Ant colony optimization Software algorithms signal processing algorithms signal processing Iterative algorithms Software Graph theory Optimization Convergence Matlab

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Algorithm for fire detection and 3D point cloud data fusion construction under deep learning

Algorithm for fire detection and 3D point cloud data fusion ...

引用

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

作者： Shuya Liu Zikang Wu Ruiran Liu Feiyang Li Southwest Petroleum University Nanchong China Sichuan Agricultural University Chengdu China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Fire in high-rise buildings is a major hidden danger that threatens people's lives, and in order to know the fire situation, this paper proposes to utilize the optimized YOLOv11 model to accurately identify the flames and smoke, and to use 3D LIDAR to construct point cloud maps of high-rise buildings in real time. Through the high-definition camera and 3D LiDAR to achieve the fire and environment map construction, and the fire information mapped to the 3D map to achieve the three-dimensional visualization of fire information. The experimental results show that the improved algorithm improves the accuracy and F1 score by 1 percentage point compared with the original, and the fused point cloud map has high imaging fineness and complete information.

关键词： Point cloud compression Solid modeling Three-dimensional displays Laser radar Accuracy Buildings signal processing algorithms Fires Real-time systems Data models

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Electric Vehicles (EVS) with AI Technology

Electric Vehicles (EVS) with AI Technology

引用

International Conference on Communications and signal processing

作者： Roop Kumar Yekollu Tejal Bhimraj Ghuge Sammip Sunil Biradar Shivkumar V Haldikar Omer Farook Mohideen Abdul Kader Engineering – Suzy Inc Sparta NJ USA Engineering – Suzy Inc North Providence RI USA Engineering – Suzy Inc Edison NJ USA Engineering – Suzy Inc Wilmington NC USA

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In the automobile sector, the combination of Artificial Intelligence (AI) technology and Electric Vehicles (EVs) has disruptive potential. Vehicle performance, safety, and user experience are all improved by this confluence. AI systems enhance energy management, increasing EV range and charging effectiveness. Predictive maintenance is made possible by real-time data analytics, which lowers downtime and increases overall reliability.

关键词： Electric potential Technological innovation signal processing algorithms signal processing Electric vehicles User experience Safety

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Epilepsy Detection: Neural Network With PSO-Based Feature Selection

Epilepsy Detection: Neural Network With PSO-Based Feature Se...

引用

Brain Computer Interface & Healthcare Technologies (iCon-BCIHT), International Conference on

作者： Avani P P Josline Elsa Joseph Athul S Dona Mathews Aswin Raj V Aswin Sunil Department of Biomedical Engineering TKM Institute Of Technology Ezhukone Kerala India Dept. of Electronics&Biomedical Engg. Adi Shankara Institute of Engineering and Technology Ernakulam India

ISBN: (数字)9798331540067

ISBN: (纸本)9798331540074

Epilepsy is a persistent neurological condition marked by recurring, spontaneous seizures caused by unusual electrical brain activity. The nature and intensity of these seizures can vary greatly, spanning from momentary distractions or muscle twitches to severe and extended convulsions. Early detection and diagnosis are crucial to manage epilepsy effectively, as timely intervention with medications and other therapeutic strategies can help control seizures and improve quality of life for those affected. Electroencephalogram (EEG) is widely used diagnostic tool for recognizing abnormal activities of brain and helps to identify epilepsy cases but it is very time consuming,prone to artifacts and vary with the interpreter's experience. This study aims to develop a robust and efficient algorithm to detect different stages of epilepsy from EEG data. The proposed approach integrates Particle Swarm Optimization (PSO) for feature selection with a deep learning neural network to improve the accuracy and reliability of seizure detection. The detection model uses the features extracted from complete ensemble empirical based decomposed EEG signals. Features are optimized by Particle Swarm Organization which is input into classifier employing the artificial neural network (ANN)mechanism. Employing these methods, pre-ictal, ictal and inter-ictal stages of epilepsy is identified in EEG. The proposed approach successfully classified these epileptic stages with an accuracy of 98.33%. This enhanced epilepsy detection system holds significant potential for improving patient outcomes through early and accurate detection of seizures, providing a robust tool for neurologists and healthcare providers in the management of epilepsy.

关键词： Accuracy Epilepsy signal processing algorithms Medical services Artificial neural networks Feature extraction Electroencephalography Reliability Particle swarm optimization Biological neural networks

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Accelerated Scaled Gradient Descent Algorithm For Low-Rank Matrix Factorization

Accelerated Scaled Gradient Descent Algorithm For Low-Rank M...

引用

Computer Engineering and Intelligent Control (ICCEIC) International Conference on

作者： Xiaojing Jia Xiangchu Feng Hua Wu School of mathematics and statistics Xidian University Xi'an China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

Low-rank matrix factorization is a particularly effective method which finds numerous applications in signal processing, machine learning and imaging science. Recent literature has proposed that scaled gradient descent (ScaledGD) algorithm. It achieves linear convergence without depending on the condition number, while maintaining the low per-iteration cost. The task of this paper is to propose an accelerated ScaledGD algorithmic approach called scaled momentum algorithm, which using a method similar to momentum to accelerate ScaledGD. Momentum is a commonly used optimization algorithm in deep learning that helps accelerate gradient descent in the relevant direction and dampens oscillations. The new scaled momentum algorithm has a benign convergence guarantee similar to the ScaledGD that is independent of the number of conditions, while prominently improve the rate of convergence than ScaledGD.

关键词： Deep learning Machine learning algorithms signal processing algorithms Imaging signal processing Sensors Optimization Oscillators Convergence Intelligent control

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Random Forest-Based Enhanced Detection of Lumpy Skin Disease Under Two Data Types

Random Forest-Based Enhanced Detection of Lumpy Skin Disease...

引用

International Conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Madhumita Pal Ganapati Panda Rajiv Lochan Bal Dept. of Electrical Engineering Government College of Engineering Keonjhar Odisha India Dept. of Electronics & Telecommunication Engineering C.V.Raman Global University Bhubaneswar India Dept. of Mining Engineering Orissa School of Mining Engg. Keonjhar India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

The deadly and extremely contagious Lumpy Skin Lesion Disease (LSLD) frequently affects cattle. Tough raised nodules that form on the cattle's body, high fever, sterility, and a marked reduction in milk output by nursing cattle are typical symptoms of LSLD. Drooling eyes, increased nasal and salivary secretions, genital and limb problems, and a reluctance to move or eat are among the symptoms. Furthermore, predicting the course of the disease in advance can significantly lessen the economic burden, even though the mortality rate among cattle infected with LSLD is low. A random forest (RF) machine learning (ML) model was used in this work to ascertain whether or not cattle have lumpy skin disease. The paper's main goal is to use an RF model to discover and classify lumpy skin lesions in cattle as normal, mild, or severe. Veterinarians can save many cattle's lives by detecting the sickness early. To evaluate the effectiveness of the random forest model, we also looked at two aspects of the dataset: balanced and imbalanced. The random forest model with an unbalanced dataset yielded the following results: 0.74, 0.76, 0.89, 0.82, 0.63&0.75 for normal skin, 0.74, 0.52, 0.35, 0.42, 0.63, and 0.33 for mild lumpy skin lesions, and 0.74,0.50,0.22,0.31,0.60, and 0.18 for severe lumpy skin lesions. These findings were derived for the precision-recall curve (P-R), accuracy (Arc), precision (Pn), recall (RI), Fl-score, and the area under the curve (AUC). Accuracy (Arc) 97.13, precision (Pn) 0.96, recall (RI) 0.91, F1-score 0.93, the area under the curve (AUC) 0.94, and P-R curve value 0.90 are the metrics for categorizing normal skin for a balanced dataset. The following values were obtained to classify minor lumpy skin lesions: Arc 97.13, Pn 0.92, RI 0.94, F1-score 0.93, AUC 0.95, and P-R 0.88. An Arc of 97.13, pn of 0.96, Rl of 0.99, F1-score of 0.97, AUC value of 0.98, and P-R value of 0.95 are the results for severe lumpy skin lesions. Various training schemes can also evalua

关键词： Radio frequency Training Accuracy signal processing algorithms Cows signal processing Skin Lesions Random forests Diseases

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Design and Analysis of IPFC-Based Damping Controller

Design and Analysis of IPFC-Based Damping Controller

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signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International Conference on

作者： Rajendra Ku. Khadanga Sidhartha Panda Preeti Ranjan Sahu Sasmita Padhy Department of Electrical and Electronics Engineering Centurion University of Technology and Management Bhubaneswar India Department of Electrical Engineering Veer Surendra Sai University of Technology Burla India Department of Electrical and Electronics Engineering NIST University of Science and Technology Berhampur India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

This work presents a novel design methodology for creating a supplementary damping controller based on an Interline Power flow controller (IPFC). This strategy appears to be quite effective and yields positive results. Low-frequency oscillation (LFO) is a significant problem in every power system. This is mainly due to a minor disturbance in a power system. This proposed study considers an IPFC installed inside an SMIB power system. A damping controller is used to lower the low frequency of oscillation. An optimization difficulty is determining the controller parameter. This study discovers the damping controller's parameters by applying the Gravitational Search Algorithm (GSA). The simulation-based results are displayed and contrasted with previously published results derived from the Phase Compensation Technique (PCT).

关键词： Damping Simulation signal processing algorithms signal processing Control systems Power systems Oscillators

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