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Dynamic Temporal Convolutional Networks for Fault Diagnosis of Rolling Bearings 7

Dynamic Temporal Convolutional Networks for Fault Diagnosis ...

引用

7th international conference on robotics, Control and Automation engineering, RCAE 2024

作者： Hu, Jingxiang Chen, Haojie Chen, Yongyi Zhang, Dan Zhejiang University of Technology Department of Automation Hangzhou310023 China

ISBN: (纸本)9798350355642

Since rolling bearing plays a crucial role in rotating machinery, the fault diagnosis research of it has been widely concerned in recent years. However, the collected fault signals often contain a lot of noise interference due to the complex and changeable operating environment. The anti noise capability of traditional deep learning methods is weak, resulting in poor feature extraction performance. Therefore, the dynamic temporal convolutional network (DyTCN) is presented for rolling bearing fault diagnosis in this article. Specifically, the causal dilation convolution is first designed to acquire fault feature of vibration signal by enlarging receptive field of the network. Then, the dynamically adjusted convolution kernel parameters are used to achieve adaptive acquisition of dynamic features and improve the robustness. Extensive experimental results on real dataset demonstrate that DyTCN has excellent accuracy in fault diagnosis. © 2024 IEEE.

关键词： Rotating machinery

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Autonomous Water Quality Monitoring Surface Vehicle 3

Autonomous Water Quality Monitoring Surface Vehicle

引用

3rd IEEE international conference on Recent advances in Systems science and engineering, RASSE 2023

作者： Nambiar, Manushri Saljith Ramakrishnan, Arjun Lakshman, Anirudh Abhiram, P.V. Girish, P. Toc H Institute of Science and Technology Dept. of Robotics and Automation India

ISBN: (纸本)9798350341676

Water pollution is the contamination of water sources by contaminants that render the water unfit for drinking, cooking, cleaning, swimming, and other uses. Pollutants include chemicals, waste, bacteria, and parasites. Traditional monitoring methods entail manually collecting water samples from vari-ous water resources, followed by testing and analysis in the laboratory. This procedure is frequently ineffective because itis time-consuming and does not produce results in real-Time. Water quality should be continuously monitored to ensure a safe supply of water to end users from any water resources or water bodies. Designing and creating a cost-effective system for real-Time monitoring of water quality utilising the Internet of Thingsis now required. A water vehicle is employed here to house the water quality monitoring system. The vehicle is self-contained, and set locations for measuring water quality can be provided. Using IoT devices and a Wi-Fi module, the proposed system aids in the continuous monitoring of water quality. The Wi-Fi moduleenables internet connectivity and sends sensor data to the Cloud. Various sensors are used to measure various factors in order to assess the water quality of water resources © 2023 IEEE.

关键词： Potable water

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Deep Learning Approaches for Cataract Detection Using Fundus and Real-Time Naked Eye Imaging

Deep Learning Approaches for Cataract Detection Using Fundus...

引用

2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Shetty, Chinmai Fernandes, Shainy Bagchi, Susama Malathi, S.Y. Nayak, Subrahmanya Rajesh Debnath, Sanjoy Kumar Department of Information Science and Engineering Nitte India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Kle Institute of Technology Dept of Computer Science and Engineering Hubballi India

ISBN: (纸本)9798350388602

Cataracts are the most common eye disease and the leading cause of blindness worldwide. The eye ailment known as cataracts results in clouded lenses, leading to vision that appears foggy or frosted. People with cataracts often struggle with tasks like reading, driving, or recognizing faces due to the impaired clarity of their vision. Effective treatment and prevention of vision loss depend on the early detection of cataracts. The proposed system is a deep learning-based approach to detect cataracts from the naked eye and fundus images that can help ophthalmologists diagnose cataracts earlier, more accurately, and efficiently. Both fundus and naked eye images are processed by the system to detect cataracts. The system offers a dependable and automated solution for cataract detection, enabling timely diagnosis and treatment. Its performance is on par with that of ophthalmologists, highlighting its potential as an effective and trustworthy tool for automated cataract detection. The proposed model employs two distinct deep learning approaches. For identifying cataracts using naked eye images, a standard CNN architecture was utilized, achieving an accuracy of 94.26%. For detecting cataracts in fundus eye images, a Deep Convolutional Neural Network (DCNN) with the VGG19 architecture was implemented, demonstrating a notable accuracy of 96.78%. This model holds substantial promise, offering a non-invasive and accessible method for cataract screening. It is particularly helpful in environments with limited resources, where access to expert eye care is frequently limited. © 2024 IEEE.

关键词： Convolutional neural networks

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Remote-Center-of-Motion Generation for Hands-on Robotic Surgery: A Constrained Optimization and Neural Network Approach

Remote-Center-of-Motion Generation for Hands-on Robotic Surg...

引用

2024 IEEE international conference on robotics and Biomimetics, ROBIO 2024

作者： Li, Weibing Rao, Ruiqi Guo, Dongsheng Pan, Yongping Huang, Kai School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China School of Information and Communication Engineering Hainan University Haikou570228 China School of Advanced Manufacturing Sun Yat-sen University Shenzhen518107 China

ISBN: (纸本)9781665481090

In robot-assisted minimally invasive surgery, the robot needs to hold a long instrument that is inserted through an incision in the patient's body, and the motion of the instrument should meet remote-center-of-motion (RCM) constraints. This paper presents a quadratic programming (QP) based control scheme for hands-on robotic surgery. The proposed scheme enables simultaneous generation of the RCM considering the insertion of tool shaft, robot passivity under admittance control, and joint-limit avoidance. Both simulative and experimental validations have been successfully conducted. A linear-variational-inequality based primal-dual neural network (LVI-PDNN) is applied to solve the QP problem, validating that the proposed QP-based scheme can meet RCM constraints when the robot is guided by an external force applied by a human operator while simultaneously considering the insertion limit and joint constraints. © 2024 IEEE.

关键词： Constrained optimization

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Leveraging Artificial Intelligence in Computer science to Manage Stress and Anxiety: Applications and Innovations 2

Leveraging Artificial Intelligence in Computer Science to Ma...

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2nd international conference on advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Mittal, Ketan Shikha Sharma, Ronak Attri, Jeevesh Kumar Taneja, Kritika Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Faridabad India Department of electrical and electronics engineering Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798331541217

This study explores the effectiveness, ethical considerations, and demographic influences of AI-based interventions in managing stress and anxiety. AI technologies, including Machine Learning, Natural Language Processing (NLP), and Biometric Monitoring, have shown promising results in reducing stress and anxiety across diverse populations. Machine Learning algorithms excel in predictive analytics, enabling early detection and intervention, while NLP tools offer real-time cognitive-behavioural therapy (CBT) and emotional support through AI-powered chatbots. Biometric Monitoring provides objective physiological data, further enhancing the precision of stress management strategies. However, the effectiveness of these interventions is influenced by demographic variables such as age, gender, socio-economic status, and cultural background, with younger, tech-savvy users and those of higher socio-economic status showing greater engagement and benefit. Also, the mobile application named "sahayya" has been discussed along with its merits. Discussing how technology can be put to better use. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Cheque Security: Forgery Detection Via Signature Recognition Using Optical Character Recognition and Deep Learning

Enhancing Cheque Security: Forgery Detection Via Signature R...

引用

2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Vidhya, S.G. Balarengadurai, C. Prasad, Boddu Ravi Vidyavardhaka College of Engineering Vtu Department of Computer Science & Engineering Mysuru India Marri Laxman Reddy Institute of Technology and Management Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350388602

Trustworthy verification of bank checks is crucial for modern financial transaction security and the prevention of fraudulent activities. A novel approach to the authentication and categorization of bank check signatures is introduced in this study. It employs a comprehensive architecture that integrates CNN, line sweep algorithms, and optical character recognition (OCR). The proposed method aims to improve the accuracy and efficacy of signature verification processes by harnessing the potential of state-of-the-art technology. First, the system uses optical character recognition to extract text from images of checks;this allows it to recognize crucial components including account data and text-based attributes. The line sweep method to spatial connections within images of checks also helps to emphasize key features like account numbers and signatures. The technique relies heavily on Convolutional Neural Networks. The system is able to differentiate between authentic and false samples based on their distinct patterns and characteristics by training convolutional neural networks (CNNs) using a big collection of signatures. With this wealth of information at its disposal, the system can confidently assess the signatures' authenticity, resulting in a more robust and accurate verification procedure. Next, they came up with an innovative way to identify signatures and validate bank checks using convolutional neural networks (CNNs), optical character recognition (OCR), and line sweep analysis. With the automation and improvement of verification procedures, monetary transactions will be more trustworthy, less prone to errors, and more secure. © 2024 IEEE.

关键词： Convolution

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Intelligent Monitoring technology for the Impact of Deep Foundation Pit Excavation on Adjacent Facilities 2

Intelligent Monitoring Technology for the Impact of Deep Fou...

引用

2nd international conference on Automation, robotics and Computer engineering, ICARCE 2023

作者： Zhang, Cheng Tao, Feng Wang, Yang School of Urban Construction and Transportation Hefei University Anhui Hefei230601 China

ISBN: (纸本)9798350308341

Selecting a typical archaeological construction field, based on water system and hydrogeololgy conditions on the site, monitoring technology and numerical calculation were used to simulate the foundation settlement and deformation values of existing buildings during the excavation construction process. This research concluded that the settlement differences did not exceed the standard limit. It also showed that the monitoring values are consistent with the numerical calculation values. The purpose is to detect construction safety and ensure the normal use of adjacent buildings after the completion of archaeological projects. The research can provide construction reference for the restoration of ancient city, moat dredging, relics excavation and urban infrastructure. © 2023 IEEE.

关键词： Excavation

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Intrusion Detection in WSN with Optimal Trained Hybrid Model: Introduction to Self-Improved Beluga Whale Optimization 4

Intrusion Detection in WSN with Optimal Trained Hybrid Model...

引用

4th international conference on advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Chandan, Madhavarapu Santhi, S.G. Rao, T. Srinivasa Annamalai University Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Annamalainagar India Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Annamalainagar608002 India Gudlavalleru Engineering College Department of Computer Science and Engineering Andhra Pradesh Gudlavalleru India

ISBN: (纸本)9798350343670

WSN security is a current area of research for many scientists. One of the best security tools for defending the network from malicious assaults or the unauthenticated access is IDS. In this article, we suggest an optimized hybrid classification model-based IDS with certain steps to follow. In preprocessing, the issue of class imbalance data is solved using SMOTE. A collection of features, including raw features, statistical features, and entropy features are extracted in the feature extraction step. Optimal feature selection is done based on the extracted feature set via proposed SI-BWO (Self Improved Beluga Whale Optimization) algorithm. Attack detection is the next stage, in which hybrid classification will be takes place by combining the CNN and DBN model. The same SI-BWO algorithm is used to perform the hybrid model's optimum training. Once an attack has been detected, the attack mitigation procedure is then carried out. © 2024 IEEE.

关键词： CNN IDS SI-BWO SMOTE WSN

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Mechanism design and performance analysis of tracked pipe robot 6

Mechanism design and performance analysis of tracked pipe ro...

引用

6th international conference on robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Wang, Shuhai Zhang, Nanfeng Jin, Biao Li, Qingan College of Mechanical and Electrical engineering Guangdong University of Science & Technology Guangdong Dongguan523083 China

ISBN: (纸本)9798331541699

Pipeline robot is a kind of system that can automatically walk along the pipeline, with one or more sensors and operation machinery (such as manipulator, spray gun, etc.), under the remote control or automatic control of the operator, can carry out a series of pipeline operations machine-electric-instrument integrated system. It is often used in petroleum, chemical industry, nuclear industry, urban construction and other engineering pipeline quality inspection, welding, maintenance and so on. Pipeline robot is also a combination of multiple disciplines, involving mechanical engineering, automatic control, computers, sensors, artificial intelligence and electronic technology. At present, the development of pipeline robot mainly focuses on the innovation and development of mechanism, drive, sensors, intelligent control, miniaturization, power supply and communication. Based on the scientific research achievements at home and abroad, this paper systematically studied the driving principle and mode, movement principle and mechanical mechanism of the pipeline robot. Main work for the institutional research design of pipeline robots. Based on the comprehensive consideration of various factors, the pipeline robot mobile mechanism is determined, and points out the main problems that should be considered when designing the robot. © 2024 IEEE.

关键词： Nuclear industry

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Flexible Hybrid Electronics for Printing of Nano and Chip Scale Structures 1

Flexible Hybrid Electronics for Printing of Nano and Chip Sc...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Singh, Neelima Gupta, Radha Singh, Rakesh Kumar Shruthi, T.S. Gupta, Parbhat Singhal, Abhinav Uttaranchal University Department of Computer Science and Engineering Dehradun India Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India IIMT College of Engineering U.P. Greater Noida India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad Modinagar India Tula's Institute Department of Computer Science and Engineering Dehradun248197 India

ISBN: (纸本)9798331518578

Adaptable sensors, circuits, and substrates are joined with unbending electronic parts to make adaptable flexible hybrid electronics (FHE). The printing of nanomaterials has benefits over the costly, multi-step, and blunder inclined cleanroom-based nano-microfabrication process, like expense adequacy, high-throughput, steadfastness, and adaptability. FHE usually consists of devices that range in size from nano to chip, and it might be difficult to integrate them using a standard procedure. The mix of ultrathin chips and nanowires (NWs) based electronic layers on a solitary substrate is shown here utilizing printed gadgets. The performance of the transistors on UTCs (ultrathin chips) is thoroughly studied in order to optimise the printing process. The adequacy of the printing system is affirmed by electrical information accumulated from 14 semiconductors on 3 unmistakable chips, which uncover next to no adjustment of execution following exchange printing. Building UV photodetectors utilizing DIW printing further exhibits the unrivaled grade nature of ZnO-NWs-put together electronic layers delivered with respect to a similar substrate. © 2025 IEEE.

关键词： Substrates

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