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共 729 条记录，以下是61-70 订阅

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sEMG signal processing and Recognition Based on CNN 3rd

sEMG Signal Processing and Recognition Based on CNN

引用

3rd international conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Tian, Zimo Xu, Guangjie Cai, Senwu He, Xuansen Guangzhou College of Commerce Guangzhou511363 China College of Information Science and Engineering Hunan University Changsha410082 China

ISBN: (纸本)9789819624676

Extraction and processing of surface electromyography (sEMG) signals play an important role in medical rehabilitation, artificial limb control and other applications. This paper proposes a new method for sEMG signal processing based on convolutional neural networks (CNN). Firstly, the time-domain feature extraction method is used to extract the features of sEMG signals. Then, a CNN model is constructed for identifying and processing sEMG signals. The parameters of the model are calibrated based on the sEMG dataset to find the most suitable model parameters for the objective of this paper, achieving optimization of the model. The constructed CNN network is applied to NinanPro dataset for simulation and experiment, and the results show that the optimized CNN model proposed in this paper has a high accuracy rate for sEMG signal recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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An ultra wideband (UWB) localization system for automatic guided vehicles (agvs) in smart warehouses 3

An ultra wideband (UWB) localization system for automatic gu...

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3rd international conference on signal processing and machine learning, CONF-spml 2023

作者： Huang, Hao Huang, Ziyu Wang, Dexin Wang, Jiarui Xu, Xiaotian Yao, Peixin Shenzhen College of International Education Shenzhen518043 China The University of Edinburgh EdinburghEH9 3JU United Kingdom New York University Abu Dhabi P.O. Box 129188 Abu Dhabi United Arab Emirates Pacific Academy Irvine Irvine92064 United States Nanjing University of Information Science and Technology Nanjing210044 China

As the main component of indoor positioning technology, UWB technology plays an important role in promoting the development of indoor and outdoor seamless positioning technology. This paper presents a proposed localization system of a propagation channel measurement campaign performed in a smart warehouse environment whose dimension is 100 meters ∗ 100 meters and provides an extensive model for this environment. Three different arrangements of basestations are simulated to analyze the effects of basestation constellations and virtual racks are set up to determine the approximate line-of-sight (LOS) probability. At the same time, by considering the influences of varying transmit power, using weighting and using two-way ranging, the distribution of errors and the reliability threshold are drawn and the required bandwidths are determined. Finally, according to the simulation results, the optimal layout and modulation parameters are recommended, and improvements in channel models and algorithms for future work are concluded. © Published under licence by IOP Publishing Ltd.

关键词： Automatic guided vehicles

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VAG signal Classification Using Time Domain Statistical Features and machine learning 3

VAG Signal Classification Using Time Domain Statistical Feat...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Patankar, Sanika Durge, Gaurav Joshi, Ashutosh Jaid, Atharva Kalambe, Khomesh Dhale, Hrishikesh Vishwakarma Institute of Technology Department of Instrumentation and Control Engineering Maharashtra Pune India

ISBN: (纸本)9798350317022

This research paper presents a novel approach for the classification of vibroarthrographic (VAG) signals to aid in the early diagnosis of knee arthritis, a disease whose prevalence is increasing worldwide. VAG signals are non-invasive and cost-effective, making them a promising tool for screening and detection. In this study, a time-domain statistical feature approach is employed to extract relevant features from VAG signals. Analytical measures like Kurtosis, skewness, Standard Deviation have been used in this work. These features are then fed into machine learning algorithms for the automatic detection of knee arthritis. The classification accuracy of 2 algorithms has been compared in this work, namely SVM and Random Forest. The latter resulted in an overall accuracy of 89% and an AUROC of. 90. The results indicate the potential of this approach to provide an accurate and reliable means for early knee arthritis detection, which would allow the patient to be treated ahead of time. © 2023 IEEE.

关键词： Classification Knee arthritis machine learning signal processing Time-domain statistical features Vibroarthrography

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3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on machine Vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis;deep learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI Images Using Deep learning;software Maintenance Prediction Using Stack Ensemble Deep learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic Applications;shaft Rotation Monitoring Using Radar signal processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus Images Using Pyramid Real Image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition System Using Hybrid Deep learning Feature;Classification of Copy and Move Image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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3rd international conference on Optimization, learning Algorithms and Applications, OL2A 2023

3rd International Conference on Optimization, Learning Algor...

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3rd international conference on Optimization, learning Algorithms and Applications, OL2A 2023

ISBN: (纸本)9783031530357

The proceedings contain 26 papers. The special focus in this conference is on Optimization, learning Algorithms and Applications. The topics include: Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models;Using LiDAR Data as Image for AI to Recognize Objects in the Mobile Robot Operational Environment;an Evaluation of Image Preprocessing in Skin Lesions Detection;an Artificial Intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Mills;vehicle Industry Big Data Analysis Using Clustering Approaches;enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data;An Efficient GPU Parallelization of the Jaya Optimization Algorithm and Its Application for Solving Large Systems of Nonlinear Equations;Multi-objective Optimal Sizing of an AC/DC Grid Connected Microgrid System;sub-system Integration and Health Dashboard for Autonomous Mobile Robots;movement Pattern Recognition in Boxing Using Raw Inertial Measurements;optimization Models for Hydrokinetic Energy Generated Downstream of Hydropower Plants;deep Conditional Measure Quantization;fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods;Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation;deep learning and machine learning Techniques Applied to Speaker Identification on Small Datasets;performance of Heuristics for Classifying Leftovers from Cutting Stock Problem;Deep learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approach;identification of Late Blight in Potato Leaves Using Image processing and machine learning;adaptive Convolutional Neural Network for Predicting Steering Angle and Acceleration on Autonomous Driving Scenario;Impact of EMG signal Filters on machine learning Model Training: A Comparison with Clustering on Raw signal;assessing the 3D Position of a Car with a Single 2D Camera Usin

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3rd international conference on Optimization, learning Algorithms and Applications, OL2A 2023

3rd International Conference on Optimization, Learning Algor...

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3rd international conference on Optimization, learning Algorithms and Applications, OL2A 2023

ISBN: (纸本)9783031530241

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Analysis of the Relationship Between COVID-19 and Diet Based on machine learning Algorithm 3

Analysis of the Relationship Between COVID-19 and Diet Based...

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3rd international signal processing, Communications and Engineering Management conference, ISPCEM 2023

作者： Jie, Hao Yan, Li Yong, Liu School of Electrical Engineering Northwest Minzu University Gansu Engineering Research Center for Eco-Environmental Intelligent Networking Lanzhou China

ISBN: (纸本)9798350307023

In this paper, the data set of food and nutrition intake from the Food and Agriculture Organization of the United Nations (FAO) and COVID-19 case counts from the World Health Organization (WHO) are used to analyze the relationship between diet and COVID-19 infections in different countries. Different machine learning algorithms, like regression, clustering, and classification, are used to process and analyze the data set. In addition, an improved random forest method for feature selection is proposed. This paper has studied the relationship between food intake and obesity or undernourished, the association between the amount of food consumed and the amount of fat, protein, and calories consumed through that food, and the impact on the confirmed, deaths and recovery rate of COVID-19. The purpose is trying to find some foods that can prevent or assist in the treatment of COVID-19, and achieve a non-pharmaceutical intervention for COVID-19 through a healthy diet. © 2023 IEEE.

关键词： COVID-19

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Optimized SVM based on improved whale algorithm for EEG signal pattern classification 3

Optimized SVM based on improved whale algorithm for EEG sign...

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3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Peng, Junqiang Liu, Lijun Tiangong University Tianjin China

ISBN: (纸本)9781665472784

In order to improve the classification accuracy of moving image electroencephalogram ( EEG ) signals, aiming at the problem that the traditional support vector machine (SVM ) parameter value is not accurate, which leads to poor classification model, this study introduces Sobol sequence, reverse learning strategy and piecewise nonlinear time-varying factor. In order to improve the random layout strategy of boundary processing, Levy flight strategy improves the traditional whale algorithm, combines the improved whale algorithm with SVM, and compares the combined SVM classifier with other classifiers for EEG signal classification. The results show that the accuracy of the optimization algorithm is 2 ∼ 4 percentage points. © 2023 IEEE.

关键词： Electroencephalography

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A Comparative Study on machine learning Models for a Crop Recommendation System 3

A Comparative Study on Machine Learning Models for a Crop Re...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Pongen, Sako T Shakila, B. Prakash, M. Nit Nagaland Department of Electrical and Electronics Engineering Chumukedima797 103 India

ISBN: (纸本)9798350361186

Agriculture contributes significantly in shaping the GDP of a nation. Farming is a crucial aspect in agricultural nations such as that of India. While numerous technological innovations have emerged in agriculture, the current need of the hour is the continual enhancement and improvement of the current technology at hand. Due to the diverse climatic conditions, an efficient technique becomes crucial for enabling better crop cultivation and supporting farmers in their production and management. Offering a recommendation system to farmers based on historic data can significantly assist in their crop cultivation. In this paper, analysis of crop dataset and crop recommendation is done using a variety of machine learning algorithms. The models used include traditional models such as Decision Tree Classifier, k-Nearest Neighbor and Naïve-Bayes Classifier. Ensemble learning models such as Random Forest Classifier, a deep learning model, Multi-layer Perceptron Classifier as well as a hybrid model which combines the Decision Tree Classifier, Random Forest Classifier, Naïve-Bayes Classifier, Multi-Layer Perceptron Classifier and a Support Vector Classifier are also utilized in this paper. The models are evaluated, and their performance are assessed based on various evaluation metrics. The evaluation metrics include accuracy, precision, and recall, and F1 score of the models. © 2024 IEEE.

关键词： Crops

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Research on Intelligent Auxiliary Review of Power Grid 3D Design Based on BP Neural Network 3

Research on Intelligent Auxiliary Review of Power Grid 3D De...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Fan, Dongmei Yan, Wendou Liang, Yi Cheng, Zhi Guangzhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Guangzhou102200 China

ISBN: (纸本)9798350319125

BP neural network, or back propagation neural network, is a machine learning algorithm widely used in pattern recognition, function approximation, signal processing and other fields. In recent years, with the expansion of power grid scale and the increase of complexity, BP neural network plays an increasingly important role in the three-dimensional processing of power grid. This paper mainly discusses the application of BP neural network in intelligent assistance, evaluation and optimization of power grid. © 2023 IEEE.

关键词： 3D model based on BP neural network computer Grid design power system

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