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检索条件"任意字段=IEEE Conference on Automation Science and Engineering"

共 60788 条记录，以下是4971-4980 订阅

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A Real Time Hand Gesture Recognition for Indian Sign Language using Advanced Neural Networks 2

A Real Time Hand Gesture Recognition for Indian Sign Languag...

引用

2nd International conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Pradeep, S. Poonguzhali, R. Amshavalli, R.S. Nelson, Leema Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Chitkara University Institute of Engineering & technology Chitkara University Punjab India

ISBN: (纸本)9798350340235

One of the most important means of survival in a group is verbal exchange. In addition, according to the World Health Organization, over 5% of people worldwide have speech impairments as of 2021. This means that they lack a fundamental requirement for the survival of the human species. The "Sign Language" that these people use in their communities contains meanings, grammar, and vocabulary that everyone else may not understand. Our suggested approach focuses on developing a frame-based system that leverages deep learning, an advanced machine learning technology, to translate sign language into legible speech or text on an embedded device. Our algorithm processes the gathered samples by using an integrated webcam to record hand gestures in real-time. For improved performance, the model uses a dataset including one million images for each move. A 9:1 ratio has been established between the training and test sets of data. © 2023 ieee.

关键词： Background Subtraction Convolutional Neural Network Deep Learning Hand gesture recognition Indian Sign Language

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Temperature Prediction for Ultra-Low Temperature Measurement System Based on Improved Extreme Learning Machine Algorithm 3

Temperature Prediction for Ultra-Low Temperature Measurement...

引用

3rd International conference on Electronic Information engineering and Computer Communication, EIECC 2023

作者： Zhang, Zhenyu Dong, Xiang Xue, Yufei Jiang, Man Cao, Huibin School of Electrical Engineering and Automation Anhui University Anhui Hefei230601 China Institutes of Physical Science and Information Technology Anhui University Anhui Hefei230601 China Hefei Institutes of Physical Science Chinese Academy of Sciences Anhui Hefei230601 China

ISBN: (纸本)9798350359961

Temperature monitoring is essential to ensure the operational safety of fusion devices in the ultra-low temperature liquid helium region. However, the unavoidable errors caused by environmental effects severely hinder the accuracy of its temperature measurement. Considering that traditional methods are not accurate enough to predict the temperature at low temperatures, for the first time, this work proposes an algorithm based on the SSA-PSO-ELM network for constructing a prediction model of the cryogenic measurement system. By this method, the initial number of hidden layer nodes is first randomly generated, then the network is trained with weight thresholds, and finally a quadratic optimization search is performed to determine the best network prediction model. Compared with the traditional algorithm, our algorithm effectively reduces the prediction error, improve the accuracy, and realize more accurate temperature monitoring in ultra-low temperature environment. © 2023 ieee.

关键词： Extreme Learning Machine Particle swarm arithmetic Sparrow algorithm Temperature prediction Ultra-low temperature measurement system

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Robust Intelligent Control of Second Order Nonlinear System with Application to MEMS Gyroscopes 8

Robust Intelligent Control of Second Order Nonlinear System ...

引用

8th ieee International conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Zhang, Rui Wang, Hai Zuo, Fangze Xu, Bin School of Automation Southeast University Nanjing210096 China Centre of Water Energy and Waste Murdoch University Discipline of Engineering and Energy PerthWA6150 Australia Changsha Technology Research Institute of Beidou Industry Safety Changsha410205 China School of Automation Northwestern Polytechnical University Xi'an710072 China

ISBN: (纸本)9798350300178

For the second order nonlinear system, a robust intelligent tracking control scheme is addressed in the presence of system uncertainties. Considering the dynamics with system uncertainties, the robust neural control is designed to obtain robust tracking performance, where a switching mechanism is employed to achieve the coordination between robust design and composite neural learning. To reduce the sliding mode chattering of terminal sliding mode controller (TSMC), the adaptive recursive integral TSMC (ARTSMC) is proposed, where the parameters of ARTSMC are online estimated by updating laws. Furthermore, the proposed method is applied to the dynamics of MEMS gyroscopes and simulations results are presented to verify that more accurate system tracking can be obtained. © 2023 ieee.

关键词： Sliding mode control

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Deep Learning for Malware Detection and Classification 4

Deep Learning for Malware Detection and Classification

引用

4th International conference on Computation, automation and Knowledge Management, ICCAKM 2023

作者： Avhad, Parimal Kolse, Shivam Pangavhane, Parth Gadekar, Tushar Sangale, Abhimanyu Sanjivani College of Engineering Kopargaon Affiliated to Savitribai Phule Pune University Electronics and Computer Engineering Maharashtra Pune India Sanjivani College of Engineering Kopargaon Affiliated to Savitribai Phule Pune University Computer Science Engineering Maharashtra Pune India

ISBN: (纸本)9798350393248

Malicious software, or malware, poses a persistent and evolving threat to cybersecurity. Traditional detection methods based on signatures and heuristics are challenged by the rapid proliferation of polymorphic and zero-day attacks. This research paper explores the application of deep learning, specifically Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), in the realm of malware detection and classification. CNNs excel in capturing spatial patterns in binary data, treating it akin to images, while RNNs are adept at handling sequential information. Through rigorous experimentation and evaluation, this study provides a comparative analysis of the performance of these deep learning architectures against traditional methods. Furthermore, the paper delves into the interpretability of deep learning models, shedding light on their decision-making process in the context of malware detection. The findings of this research not only demonstrate the efficacy of deep learning in bolstering cybersecurity measures but also contribute valuable insights into the evolving landscape of malware defense strategies. © 2023 ieee.

关键词： Recurrent neural networks

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The Detection of engineering Vehicle on Remote Sensing Image Using Improved YOLOv5 5

The Detection of Engineering Vehicle on Remote Sensing Image...

引用

5th International conference on Big Data and Artificial Intelligence and Software engineering, ICBASE 2024

作者： Chen, Jinwang Chen, Shaoxin Shi, Mingxing Lin, Shu Huang, Mengying Liu, Xinyu State Grid Xiamen Electric Power Supply Company Xiamen361004 China Fuzhou University School of Electrical Engineering and Automation Fuzhou350108 China

ISBN: (纸本)9798331506612

Construction vehicles play an important role on construction sites. Real-time and accurate monitoring of these vehicles can help oversee site activities, improve work efficiency, and enhance safety. A novel engineering vehicle object detection algorithm based on an enhanced YOLOv5 is proposed since the challenges faced in object detection of engineering vehicles in remote sensing images such as complex backgrounds and low detection accuracy. To improve the detection accuracy of the model, three modules are introduced. First, the Backbone's CSP Bottleneck with 3 convolutions (C3) modules are replaced with the CSP Bottleneck with 2 convolutions (C2f) modules to enhance the accuracy and robustness of the improved model. Next, Global Attention Mechanism (GAM) is added after the fourth C2f in the Backbone to precisely capture crucial features. Finally, the Neck's fixed convolutions are substituted with the efficient and lightweight Group Shuffle Convolution (GSConv), which ensures a more lightweight improved model without compromising accuracy. Experimental results demonstrate that compared to the YOLOv5 and some other benchmark frameworks, our enhanced algorithm shows improvements in precision, recall and F1-score, which indicates superior performance in engineering vehicle detection. © 2024 ieee.

关键词： Benchmarking

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Chandrayaan-3: Challenges and Opportunities of using AI to Detect and Identify Exoplanets

Chandrayaan-3: Challenges and Opportunities of using AI to D...

引用

2023 International conference on New Frontiers in Communication, automation, Management and Security, ICCAMS 2023

作者： Benisha, M. Gopan, Gautham Sathiyanarayanan, Mithileysh Jeppiaar Institute of Technology Department of Electronics and Communication Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science Engineering Ramapuram Campus Chennai India Research & Innovation Mit Square Services Private Limited London United Kingdom

ISBN: (纸本)9798350317060

Exoplanets (planets beyond our solar system) have become a focal point in the field of astronomy and space exploration due to their potential to provide insights into planetary formation, habitability, and the possibility of extraterrestrial life. Though there are many prediction methods and models to detect exoplanets in the galaxy, but we need effective techniques to predict, detect and classify new exoplanets. The successful launch of the Chandrayaan-3, by the Indian Space Research Organisation (ISRO), has given confidence to the astronomic scientists that exoplanets can be effectively detected, identified, and analysed using the Artificial Intelligence (AI)-based spacecraft. In the Chandrayaan-3 project, AI has played a very important role for the spacecraft in launching, navigating, predicting, anomaly detecting, optimising mission operations, data processing and communication. This article presents a brief survey and overview on the opportunities and challenges of using AI to detect and identify exoplanets. © 2023 ieee.

关键词： Forecasting

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Differentiate Music Genre from an Audio File Using CNN

Differentiate Music Genre from an Audio File Using CNN

引用

2023 International conference on System, Computation, automation and Networking, ICSCAN 2023

作者： Raja Rajeswari, S. Dhanalakshmi, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Ctr. for Cyber Phys. Syst. Sch. of Comp. Science and Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798350315127

The complexity of music production has gradually decreased recently, causing a big number of individuals to produce music as a result and submitting it to streaming media. Because there are so many music streaming services, people are putting in a lot of effort looking for certain songs. The result says, it's critical to be able to swiftly classify musical genres in today's culture. As deep learning and machine learning technologies progress, Convolutional Neural Networks (CNN) are indeed being applied in a number of disciplines, and new CNN-based versions have appeared one after the other. Manually extracting features from time-series data is required for traditional music genre classification. Applying deep learning to time-series data has proven to be useful and efficient. To save time for the user when looking for different styles of genres. Librosa is used to convert into a corresponding Mel spectrum. Converted Mel send to a CNN model for training. © 2023 ieee.

关键词： Convolutional neural networks

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Integration of Lean Manufacturing in Industry 4.0: An overview of tools and applications 2

Integration of Lean Manufacturing in Industry 4.0: An overvi...

引用

2nd International conference on Innovative Research in Applied science, engineering and Technology (IRASET)

作者： Silvestri, Luca Gallo, Tommaso Silvestri, Cecilia Falcone, Domenico Univ Rome Niccolo Cusano Dept Engn Rome Italy Univ Tuscia Dept Econ Engn Soc & Management DEIM Viterbo Italy Univ Cassino & Southern Lazio Dept Civil & Mech Engn Cassino Italy

ISBN: (纸本)9781665422093

Industry 4.0 (I4.0) is an innovative advancement able to modify the production sector, maximize movement, flexibility, and production adaptability. As a result, companies become more flexible and improve their processes based on the evaluation of instant state data and, consequently, optimizing production. This feature is particularly required in the current era, where the demand of mass production of highly customized products results in increasingly complex products, requiring a higher flexibility of production systems. Lean Manufacturing (LM) and high automation are currently two strategies for addressing these issues. The aim of this research study is to provide an overview on the I4.0 tools that are currently implemented in LM by companies, presenting main achievements, strategies, and related advantages.

关键词： Industry 4.0 lean manufacturing lean production lean automation management

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CCNet: a segmentation network based on U-Net for calculating cell confluency

CCNet: a segmentation network based on U-Net for calculating...

引用

2024 International conference on Advanced Mechatronic Systems, ICAMechS 2024

作者： Sheng, Xiaoyu Li, Shaoyuan Bu, Ni Qingdao University of Science & Technology College of Automation and Electronic Engineering Qingdao266061 China

ISBN: (纸本)9798350366204

Cell confluency is a critical indicator for determining the optimal collection time of cultured stem cells in vitro. Currently, assessing cell confluency heavily relies on the subjective experience of researchers, which can introduce significant variability. To achieve a more accurate calculation of cell confluency, we proposes an image segmentation network - - cell confluency network (CCNet), based on the U-Net architecture. CCNet incorporates attention modules and a residual structure, thereby improving the network's learning capacity and accuracy. By calculating the pixel ratio (the ratio of cell pixels to total pixels) from the images processed by CCNet, the cell confluency value is obtained. Experiments indicate that the proposed CCNet provides more accurate segmentation results for stem cell images compared to the previous version of U-Net, thereby demonstrating the effectiveness of CCNet. © 2024 ieee.

关键词： Image segmentation

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A Bandwidth-Adaptive On-Chip Storage Network Architecture 9

A Bandwidth-Adaptive On-Chip Storage Network Architecture

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9th International conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Wang, Kou Yu, Ningmei Tian, Dian Yang, Liang Wang, Peng Xie, Minghui School of Automation and Information Engineering Xi'an University of Technology Xi'an China University of Alcalá Electronics Department Alcalá de Henares Madrid Spain

ISBN: (纸本)9798331509453

A scalable bandwidth-adaptive on-chip storage network architecture is proposed to address the severe data conflict and low bus parallelism in existing multi-level storage, Crossbar, and NoC architectures in edge accelerated computing. This architecture combines the advantages of centralized and distributed memory, allowing the memory hierarchy to be flexibly adapted based on current computational needs and memory access patterns to better accommodate multi-core processors and parallel computing requirements. This paper designs the result visualization method to visualize the stress of storing each node and bus. Furthermore, the full path test incentive is intended for the secondary sharing policy and router. The feasibility of this paper's architecture is verified through VCS simulation and visualization analysis. © 2024 ieee.

关键词： Parallel architectures

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