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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 777 条记录，以下是21-30 订阅

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Effectiveness of ML Algorithms for Prognostics of Bearings in Industry 4.0 5th

Effectiveness of ML Algorithms for Prognostics of Bearings i...

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5th international conference of international-Federation-for-the-Promotion-of-Mechanism-and-machine-Science Italy (IFToMM Italy)

作者： Arpa, Luca Battarra, Mattia Mucchi, Emiliano Univ Ferrara Dept Engn Via G Saragat 1 I-44124 Ferrara Italy

ISBN: (纸本)9783031645716;9783031645693;9783031645686

the purpose of this article is to investigate the effectiveness of different machine learning regression algorithms for predicting remaining useful life (RUL) of rolling element bearings. A dataset obtained from experimental activities conducted by the engineering department of the University of Ferrara was used. the dataset consists of six run-to-failure vibration signals from self - aligning double row ball bearings. the procedure used in this study consists in four steps: health indicator construction from RMS of signal, health status determination, feature engineering and RUL prediction. RMSE, MAE and MAPE are used as performance metrics. Ensemble modeling is implemented to improve robustness of the prediction. Regardless of the machine learning model employed, the importance of correct data acquisition and pre-processing to obtain good predictions is demonstrated.

关键词： machine learning Remaining useful life Bearing Prognostics Industry 4.0 Data driven prognostics

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Image Classification Based on Quantum machine learning 5

Image Classification Based on Quantum Machine Learning

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5th international conference on Intelligent Control, Measurement and signal processing, ICMSP 2023

作者： Xiong, Haocheng Duan, Xinyuan Yu, Yue Zhang, Junhan Yin, Hao School of Software Sichuan University Sichuan Chengdu China

ISBN: (纸本)9798350336030

Nowadays, using machine learning for image classification is very common. However, due to the increasing demand for data processing and fast computing, the idea of enhancing machine learning with quantum computing has been proposed, known as quantum machine learning (QML). Quantum machine learning has the advantages of higher efficiency and accuracy. Quantum computing uses quantum bits (qubits) for data storage and computing, where a qubit can represent quantum states |0and |1simultaneously, enabling the processing of information for two states simultaneously, which is unparalleled in classical computing. Moreover, quantum machine learning can handle more complex data and process data faster. In classical machine learning, the processing of large-scale data and complex problems often faces problems of high computational complexity and low algorithm efficiency. Quantum computing can handle multiple computing tasks simultaneously, achieving faster computing. therefore, in some scenarios that require efficient computing, quantum machine learning may be the best choice. In this study, we simulated quantum circuits using Qiskit and built a hybrid quantum-classical neural network model using VQNet to classify MNIST handwritten digits and CIFAR-10 datasets. the experiments showed that quantum machine learning has the advantages of efficiency, accuracy, and security over classical machine learning, which may be an improvement over classical machine learning. this research proposes a machine learning algorithm based on quantum computing, which promotes the development of quantum computing and quantum technology. At the same time, it provides a new solution and idea for image classification, enabling people to pursue faster and more accurate quantum machine learning instead of being limited to classical machine learning. © 2023 IEEE.

关键词： Complex networks

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TinyML based Classification of Fetal Heart Rate using Mother's Abdominal ECG signal 5

TinyML based Classification of Fetal Heart Rate using Mother...

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5th international conference on Multimedia, signal processing and Communication Technologies (IMPACT)

作者： Rahman, Shanur Khan, Yusuf Ahmed Singh, Yash Pratap Ali, Sayyed Arif Wajid, Mohd Aligarh Muslim Univ Dept Comp Engn ZHCET Aligarh India Aligarh Muslim Univ Dept Elect Engn ZHCET Aligarh India

ISBN: (纸本)9781665476478

ECG plays a vital role in the field of medical science. the proposed machine learning model is able to classify the heart rate of the fetus through ECG signals that are collected from the mother's womb. the noise and interference have been reduced in the ECG signal by preprocessing. the proposed Recurrent neural network (RNN) architecture is made on edge impulse. the potential efficiency of the designed model is studied by varying parameters such as kernel size, learning rate, epochs, etc using the abdominal and direct fetal ECG Database from the physionet. During training, the classification of heart rate using ECG signals has achieved 75% accuracy. the beauty of the model is that it is designed with tinyML for implementation on embedded systems.

关键词： Electrocardiogram Recurrent Neural Network Fetal Heart Rate signal processing machine learning

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Interpretability analysis of flight delay prediction based on KernelSHAP 5

Interpretability analysis of flight delay prediction based o...

引用

5th international conference on signal processing and Computer Science, SPCS 2024

作者： Li, Hancheng Qu, Jingyi Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In order to enhance the interpretability of deep learning models and improve the credibility of model predictions, we propose an interpretability analysis method for flight delay prediction based on KernelSHAP. Delay prediction uses flight and weather data, and the ATMAP algorithm is used to generate weather condition scores that are strongly correlated with flight delay conditions to enrich features. the deep learning model NR-DenseNet is selected for delay prediction. KernelSHAP is combined to analyze the input features and the decision-making process of the model from two perspectives: feature analysis of overall samples and feature analysis of single sample. the results show that the addition of KernelSHAP enhances the interpretability of the model, effectively breaks the black box characteristics of the model, and can provide professionals with more reliable decision-making guidance. © 2025 SPIE.

关键词： Prediction models

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ACTIVATION COMPRESSION OF GRAPH NEURAL NETWORKS USING BLOCK-WISE QUANTIZATION WIth IMPROVED VARIANCE MINIMIZATION 49

ACTIVATION COMPRESSION OF GRAPH NEURAL NETWORKS USING BLOCK-...

引用

49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Eliasson, Sebastian Selvan, Raghavendra Univ Copenhagen Dept Comp Sci Copenhagen Denmark

ISBN: (纸本)9798350344868;9798350344851

Efficient training of large-scale graph neural networks (GNNs) has been studied with a specific focus on reducing their memory consumption. Work by Liu et al. (2022) proposed extreme activation compression (EXACT) which demonstrated drastic reduction in memory consumption by performing quantization of the intermediate activation maps down to using INT2 precision. they showed little to no reduction in performance while achieving large reductions in GPU memory consumption. In this work, we present an improvement to the EXACT strategy by using block-wise quantization of the intermediate activations. We experimentally analyze different block sizes and show further reduction in memory consumption (> 15%), and runtime speedup per epoch ( approximate to 5%) even when performing extreme extents of quantization with similar performance trade-offs as with the original EXACT. Further, we present a correction to the assumptions on the distribution of intermediate activation maps in EXACT (assumed to be uniform) and show improved variance estimations of the quantization and dequantization steps.

关键词： graph neural networks quantization activation compression efficient machine learning deep learning

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Research on fault prediction optimization of CNC machine tools based on ensemble learning 5

Research on fault prediction optimization of CNC machine too...

引用

5th international conference on signal processing and Computer Science, SPCS 2024

作者： Yao, Li Wan, Yuwen Damjanovic, Srdjan Xin, Zhengfei Shanghai Polytechnic University 2360 Jinhai Road Pudong Shanghai China University of East Sarajevo Faculty of Business Economics Bijeljina Sarajevo Bosnia and Herzegovina

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

As the manufacturing industry develops towards high precision and intelligence, CNC machine tools play an important role in production. the occurrence of failures not only reduces production efficiency but also increases costs. In order to improve the accuracy and efficiency of fault prediction, this paper establishes an integrated learning model for CNC machine tool fault prediction by stacking ensemble learning algorithms and combining decision trees, support vector machine (SVM), random forests and other algorithms. the fault-related features are optimized through data preprocessing and feature engineering, and the results are finally obtained. the experimental results show that the ensemble learning model is superior to the single model in result indicators, especially recall rate and F1 score, reaching 0.6393 and 0.7027. this verifies that the ensemble learning model proposed in this paper has better performance in improving the performance of CNC machine tool fault prediction and can better solve the problems related to prediction in CNC machine tool faults. © 2025 SPIE.

关键词： Prediction models

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5th international conference on Soft Computing and signal processing, ICSCSP 2022

5th International Conference on Soft Computing and Signal Pr...

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5th international conference on Soft Computing and signal processing, ICSCSP 2022

ISBN: (纸本)9789811986680

the proceedings contain 60 papers. the special focus in this conference is on Soft Computing and signal processing. the topics include: A Hybrid Approach on Lexical Indian Sign Language Recognition;defining the Convergence of Artificial Intelligence and Cyber Security with a Case Study of Email Spam Detection;determining the Attribute Priority Among the Maintenance and Support Phases in Software Development Phases Using Intuitionistic Fuzzy Analytical Hierarchy Process;Impact of COVID-19 on Energy Generation in India;decentralized Payment Architecture for E-Commerce and Utility Transactions with Government Verified Identities;a machine learning-Based Approach for Enhancing Student learning Experiences;book Genre Classification System Using machine learning Approach: A Survey;a New Method for Imbalanced Data Reduction Using Data Based Under Sampling;Resource-Based Prediction in Cloud Computing Using LSTM with Autoencoders;Arduino UNO-Based COVID Smart Parking System;low-Cost Smart Plant Irrigation Control System Using Temperature and Distance Sensors;a Novel Approach to Universal Egg Incubator Using Proteus Design Tool and Application of IoT;dynamic Resource Allocation Framework in Cloud Computing;Region-Wise COVID-19 Vaccination Distribution Modelling in Tamil Nadu Using machine learning;detection of Phishing Websites Using machine learning;detection of Phishing Website Using Intelligent machine learning Classifiers;computational learning Model for Prediction of Parkinson’s Disease Using machine learning;eliminating Environmental Context for Fall Detection Based on Movement Traces;performance Analysis of Regression Models in Stock Price Prediction;review on the Image Encryption with Hyper-Chaotic Systems;Object Detection Using Mask R-CNN on a Custom Dataset of Tumbling Satellite;decentralized Blockchain-Based Infrastructure for Numerous IoT Setup;Sentiment Analysis Toward COVID-19 Vaccination Based on Twitter Posts.

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machine learning Algorithm for Epileptic Seizure Prediction from Scalp EEG Records 1

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5th international conference on Biomedical and Health Informatics (ICBHI)

作者： Aviles, Esteban Britto, Frank Villaseca, David Zegarra, Carlos Reyes, Francis Univ Peruana Cayetano Heredia Biomed Engn Program Lima Peru

ISBN: (数字)9783031592164

ISBN: (纸本)9783031592157;9783031592164

Epileptic seizures need to be predicted with sufficient time to allow the patients to prevent clinical symptoms by taking their prescribed anti-epileptic medications or via neurostimulation. In this context, automatic seizure prediction techniques with high sensitivity and specificity are needed. In this work, we present a machine learning method to predict epileptic seizures by analyzing the pre-ictal stage of scalp EEG recordings. the TUH EEG Seizure Corpus files were used to train and test the technique. After filtering and channel normalization, statistical parameters from the Teager energy operator, the absolute band power, the Hjorth parameters, and the kurtosis of the signal were extracted from each channel. Four machine learning classifiers were implemented after a feature selection step based on inter-feature cross-correlation values: Random Forest, XGBoost, Support Vector machine, and K-nearest neighbors. the XGBoost model yielded the best performance metrics with the validation set: 99.84% in accuracy, 100% in precision, 99.6% in recall, 100% in specificity, and 0.998 in the area under the receiving operating characteristics curve (AUC). Ultimately, these methods can detect subtle changes in scalp EEG records that forecast an epileptic seizure.

关键词： EEG Epilepsy Seizures Random Forest XGBoost SVM KNN machine learning

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CSGNet: A network with attention mechanism for automatic modulation recognition 5

CSGNet: A network with attention mechanism for automatic mod...

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5th international conference on signal processing and Computer Science, SPCS 2024

作者： Li, Xianyi School of Control Science and Engineering Shandong University Shandong Jinan250002 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In this paper, a new network called CSGNet is introduced for automatic modulation recognition, designed to improve recognition accuracy in complex electromagnetic environments. this innovative network leverages the strengths of both Convolutional Neural Networks (CNN) and Gate Recurrent Units (GRU) while mitigating their inherent weaknesses. Additionally, it incorporates a Self-Attention mechanism to further enhance its perceptual abilities. the primary advantage of this method is its superior recognition accuracy under low SNR conditions compared to traditional methods. the process starts by extracting basic I/Q data from the RadioML 2018.01a dataset as input. Subsequently, the CSGNet model performs an end-to-end modulation recognition task. the results demonstrate that this method excels at multiple SNR levels, highlighting the significant potential of deep learning in solving automatic modulation recognition challenges. this study underscores the effectiveness of integrating CNN, GRU, and Self-Attention mechanisms in enhancing the robustness and accuracy of modulation recognition systems. Furthermore, it demonstrates how deep learning techniques can be effectively applied to complex signal processing tasks. the findings suggest that CSGNet could be a valuable tool for improving communication systems, especially in challenging environments with low SNR. © 2025 SPIE.

关键词： Modulation signal to noise ratio Feature extraction Deep learning Matrices Data modeling machine learning Data processing Neural networks Quadrature amplitude modulation

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A Smart Deep learning Based Self Driving Product Delivery Car 5

A Smart Deep Learning Based Self Driving Product Delivery Ca...

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5th international conference on Artificial Intelligence, Robotics and Control (AIRC)

作者： Saeedi, Mohammed A. Alhindi, Ahmed H. Kamel, Mohammed A. Muttawiffy Hujjaj South Ssia Co Dept IT Mecca Saudi Arabia Umm Al Qura Univ Dept Comp Sci Mecca Saudi Arabia Umm Al Qura Univ Dept IT Mecca Saudi Arabia

ISBN: (纸本)9798350385083;9798350385076

Today, Tesla, Google, Uber, and GM are all trying to create their own self-driving cars that can run on real-world roads and Dominos Pizza, Amazon and Walmart decided to create them own proprietary delivery vehicles for commercial use. Intelligent logistics services using self-driving technology, in contrast with the traditional last-mile logistics services, provide a viable alternative for lowering delivery costs and improving quality. Many analysts predict that within the next 5 years, we will start to have fully autonomous cars running in our cities, and within 30 years, nearly ALL cars will be fully autonomous [1]. Wouldn't it be cool to build your very own self-driving car using some of the same techniques the big guys use? I have built my own prototype for a self-driving autonomous car from scratch that uses Deep Neural Network. I built enable the car to detect and follow lanes, recognize, and respond to signs also, it can detect post code box for commercial use. I also used Computer Vision and Deep learning software needed to recognize the signs, post code box and road lanes. I used python as main tool and second tool is OpenCV powerful computer vision package. To construct a self-driving car, this project proposes an interesting methodology that combines machine learning, Image processing, and IoT concepts. With the help of image processing, the input image is prepared. In order to process the photos, two major models were used. In the first model, I used image processing to recognize road lanes, causing the automobile to move to the right or left to stay within the same lane. the photos of the side of the road right after the lane line, on the other hand, are the region of interest in the second model. the picture of the region of interest is sent into the Convolutional Neural Network in this model, which processes the image. the output of the Neural Network assists in making targeted judgments. After that, the controller just sends the proper signal, and t

关键词： Self Driving car AI machine learning

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