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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

共 1333 条记录，以下是11-20 订阅

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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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FPGA-based image signal processing CEPST module design 2

FPGA-based image signal processing CEPST module design

引用

2nd international conference on Frontiers of Applied Optics and Computer Engineering, AOCE 2025

作者： Tsai, Lifen Chen, Chinta Yang, Chihchung Tang, Lijie Wang, Xiahui Xue, Wuyang Ma, Huan Zhaoqing University Guangdong China Zhaoqing University Guangdong China

ISBN: (纸本)9781510689404

CEPST algorithm is an important step in image processing, which is widely used in computer vision, pattern recognition, and machine learning. In order to improve the efficiency and performance of CEPST algorithm, more and more studies have implemented the task of CEPST algorithm on FPGA boards in recent years. FPGA boards offer parallel computing capabilities, low power consumption, and low latency, making them suitable for high-performance computing and real-time applications. In this paper, the Vivado tool is used to implement the method of CEPST algorithm. Vivado has a complete toolchain and debugging environment, supports a variety of hardware description languages, and provides a rich set of IP cores and acceleration libraries to facilitate FPGA design and development. This paper details the steps of CEPST algorithm using Vivado, from algorithm design to simulation. The CEPST algorithm was designed to improve the accuracy and speed, and further improve the performance of the overall system. © The Authors.

关键词： Image enhancement

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Improved deep residual network for chirp signal recovery in microwave photonic receiving systems 2

Improved deep residual network for chirp signal recovery in ...

引用

2nd international Academic conference on Optics and Photonics, IACOP 2024

作者： Wang, Chenlu Li, Yi Jiang, Jing Bai, Chaoyu Chen, Ye Wang, Li Chen, Shilin Nantong Branch of State Grid Corporation of China Nantong226006 China Computer Department Nanjing University of Science and Technology ZiJin College Nanjing210029 China Communication Department Nanjing Vocational College of Information Technology Nanjing210023 China College of Communications Engineering Army Engineering University of PLA Nanjing210007 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

We demonstrate an improved deep residual network model that can recover the distorted chirp signals of the microwave photonic receiving systems. The method can achieve high-precision recovery of distorted signals by learning the time-frequency characteristics of broadband chirp signals. The simulation results show that the peak-to-floor ratio (PFR) of signal correlation function is improved by about 20.3dB compared with that without deep learning processing. The residual network model can improve the performance of the microwave photonic receiving systems and is expected to reduce the hardware requirements of the receiving systems. © 2025 SPIE.

关键词： Chirp modulation

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2nd international conference on signal processing and Communication, ICSPC 2019 - proceedings

2nd International Conference on Signal Processing and Commun...

引用

2nd international conference on signal processing and Communication, ICSPC 2019

ISBN: (纸本)9781728118499

The proceedings contain 82 papers. The topics discussed include: comparative analysis of various machine learning techniques for intrusion detection system;influence of assorted back barriers on AlGaN/GaN HEMT for 5G K-band applications;a low-cost Arduino based automatic irrigation system using soil moisture sensor: design and analysis;sequential segmentation of EEG signals for epileptic seizure detection using machine learning;classification of lung images using deep convolutional neural network;a four grade brain tumor classification system using deep neural network;feature selection and classification for analysis of breast thermograms;multi-frame image super-resolution by interpolation and iterative backward projection;state of art of network on chip;and a hybrid secure and energy efficient cluster based intrusion detection system for wireless sensing environment.

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Enhancing defocus blur in three-dimensional scenes via high-speed optical axial scanning and deep learning 2

Enhancing defocus blur in three-dimensional scenes via high-...

引用

2nd international Academic conference on Optics and Photonics, IACOP 2024

作者： Li, Zilong Tang, Chenxi Dong, Jiaqing Liu, Xuan Lin, Jiabin Liu, Qiegen Song, Xianlin School of Information Engineering Nanchang University Nanchang330031 China Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications Nanchang University Nanchang330031 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

Mitigating image defocus blur induced by the limited depth of field in imaging systems and reconstructing the three-dimensional depth of scenes represents a significant hurdle. In this paper, a method for enhancing defocus blur in three-dimensional scenes via high-speed optical axial scanning and deep learning was proposed. The optical axial scanning system functions by synchronizing a liquid electronically tunable lens and a CCD camera to capture multi-focus image sequences. The system efficiently captures real-world images with a fast acquisition time of ~50 ms per image. The multi-focus image sequences are then fused to all-in-focus images. The multi-focus images and all-in-focus images (ground truth) serve as the dataset for the deep learning neural network. Defocused images can be reconstructed using the trained deep learning network. Furthermore, by establishing a direct relationship between the focal depth and the driving current applied to the liquid electronically tunable lens, the three-dimensional depth of the scene can be rapidly determined. Experimental results validate the feasibility and effectiveness of this method. It is expected to expand the applications of image defocus enhancement in fields such as medical imaging, 3D shape measurement, and augmented reality. © 2025 SPIE.

关键词： CCD cameras

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TalTech Systems for the PROCESS signal processing Grand Challenge

TalTech Systems for the PROCESS Signal Processing Grand Chal...

引用

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

作者： Erik Illaste Tanel Alumäe Tallinn University of Technology Estonia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The PROCESS Challenge aims to detect cognitive decline, including early stages like mild cognitive impairment, through spontaneous speech. This paper describes TalTech’s systems prepared for the challenge that applied machine learning models incorporating multimodal features to address both regression and classification tasks. For regression, the Lasso model achieved an RMSE of 2.54 on the test set, achieving 2nd place in the challenge. For classification, the XGBoost model achieved a macro F1 score of 0.61, placing 6th. These results demonstrate the potential of integrating diverse speech-based features and predictive modeling for scalable, early detection of cognitive decline.

关键词： Speech analysis machine learning signal processing Predictive models Feature extraction Acoustics Speech processing Dementia

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High-performance UV detector based on freestanding AlGaN film by electrochemical etching technology 2

High-performance UV detector based on freestanding AlGaN fil...

引用

2nd international conference on Optical Communication and Optical Information processing, OCOIP 2024

作者： Yang, Yuqing Wang, Xingfu Zhang, Lu Wu, Shuanghong School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China No.4 Section 2 North Jianshe Road Chengdu610054 China Faculty of Engineering South China Normal University Taoyuan East Road Nanhai District Foshan528225 China

ISBN: (数字)9781510688995

ISBN: (纸本)9781510688988

Ultraviolet (UV) photodetector (PD) is a device that converts optical signal into electrical signal, serving as a critical component in optoelectronic systems. Due to the leakage effect caused by defects of substrate and buffer layer, the traditional III-nitride semiconductors material UV PDs usually have a large dark current, which seriously restricts the performance of the UV PDs. Herein, freestanding Al0.1Ga0.9N UV PD was prepared by electrochemical (EC) etching technology. Based on the freestanding Al0.1Ga0.9N UV detector, the photoelectric performance was charactered. The freestanding Al0.1Ga0.9N UV PD shows a low dark current below of 1 nA owing to the removing of redundant buffer layer, and exhibits a high responsivity of 0.61 A/W, a high detectivity of 4.1 × 1012 Jones. The time response demonstrates that the rise time and decay time of the Al0.1Ga0.9N UV PD are merely 0.291 and 0.007 s. The Al0.1Ga0.9N PD demonstrates a stable and rapid response time at 325 nm under a low bias of 2 V. This research offers a method for the fabrication and transfer of Al0.1Ga0.9N UV PD, facilitating the development of novel functional devices, including AlGaN heterogeneously integrated devices and flexible devices. © 2025 SPIE.

关键词： Electrochemical etching

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2nd international conference on machine Intelligence and signal processing, MISP 2019

2nd International Conference on Machine Intelligence and Sig...

引用

2nd international conference on machine Intelligence and signal processing, MISP 2019

ISBN: (纸本)9789811513657

The proceedings contain 36 papers. The special focus in this conference is on machine Intelligence and signal processing. The topics include: Real-time RADAR and LIDAR sensor fusion for automated driving;generalizing streaming pipeline design for big data;Adaptive fast composite splitting algorithm for MR image reconstruction;extraction of technical and non-technical skills for optimal project-team allocation;modified flower pollination algorithm for optimal power flow in transmission congestion;Intelligent condition monitoring of a CI engine using machine learning and artificial neural networks;bacterial foraging optimization in non-identical parallel batch processing machines;healthcare information retrieval based on neutrosophic logic;Convolutional neural network long short-term memory (CNN + LSTM) for histopathology cancer image classification;a novel approach for music recommendation system using matrix factorization technique;forecasting with multivariate fuzzy time series: A statistical approach;nature-inspired algorithm-based feature optimization for epilepsy detection;a combined machine-learning approach for accurate screening and early detection of chronic kidney disease;backpropagation and self-organizing map neural network methods for identifying types of eggplant fruit;head pose prediction while tracking lost in a head-mounted display;recommendation to group of users using the relevance concept;ACA: Attention-based context-aware answer selection system;dense and partial correspondence in non-parametric scene parsing;audio surveillance system;mopsa: Multiple output prediction for scalability and accuracy;Generation of image captions using VGG and resnet CNN models cascaded with RNN approach;impact of cluster sampling on the classification of landsat 8 remote sensing imagery;deep neural networks for out-of-sample classification of nonlinear manifolds;FPGA implementation of LDPC decoder.

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proceedings of 2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

Proceedings of 2nd International Conference on Innovations i...

引用

2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

ISBN: (纸本)9781728107448

The proceedings contain 60 papers. The topics discussed include: modeling a H-H neuron based spiking neural network incorporating multiple pre-synaptic inputs;design of memristor - CMOS based logic gates and logic circuits;voltage controlled stimulator for nerve conduction study;a review on artifacts removal techniques for electroencephalogram signals;a non-intrusive opto-mechanical technique for the measurement of liquid level;and improved shuf?ed frog leaping algorithm for path planning of multiple mobile-robot.

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Optimizing DDoS Attack Detection Using machine learning

Optimizing DDoS Attack Detection Using Machine Learning

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Saransh Aggarwal Bhagrajyoti Behera Murari Kumar Singh Ajeet Kumar Sharma School of Computing Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The increase in the Distributed Denial of Service attack (DDoS) leads to a significant threat to the network security. Inability to timely and accurately detect DDoS attacks disrupts services offered by companies and the government causing financial losses. Additionally, prolonged DDoS attacks can spoil user experience and degrade company reputation. The recent literature reveals various techniques for detecting DDoS attacks, including pre-trained machine learning and deep learning algorithms. This research paper addresses this challenge by tweaking random forest algorithms for maximum accuracy using CICDDoS2019 dataset. The methodology includes collecting of dataset, data processing, feature scaling and training multiple machine learning algorithms to identify maximum accuracy for detecting DDoS attacks. The result shows improvement of accuracy and precision of machine learning models used. Future work may include training the model for other types of attacks, real time implementation of the model developed, developing a hybrid approach for detection and protection from DDoS attack.

关键词： Training machine learning algorithms Accuracy Computational modeling Companies Network security Denial-of-service attack User experience Computer crime Protection

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