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检索条件"任意字段=International Conference on Computer and Automation Engineering"

共 58497 条记录，以下是1061-1070 订阅

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Comparative Analysis of MEMS Tuning Fork Gyroscope Designs with Microlever Amplification Mechanism for Enhanced Sensitivity and Robustness 6

Comparative Analysis of MEMS Tuning Fork Gyroscope Designs w...

引用

6th international conference on Robotics and automation in Industry, ICRAI 2024

作者： Bashir, Uzair Javaid, Faraz Ali, Osama Arshad, Adeel Rehman, Masood Ur Department of Electrical and Computer Engineering Islamabad Pakistan Department of Mechatronics Engineering Islamabad Pakistan Superior University Faculty of Engineering and Technology Lahore Pakistan

ISBN: (纸本)9798350355703

This paper presents a comparative analysis of two MEMS Tuning Fork Gyroscope (TFG) designs employing microlever amplification mechanisms for sensitivity amplification in the sense mode. An input frame added in microlever amplification frame to increase the robustness of MEMS gyroscope. Design A incorporates a non-anchored input frame in microlever, while Design B features an anchored input frame in microlever. Frequency response analysis and modal analysis were performed using CoventorMEMS+ to evaluate the impact of input frame of microlever on undesired mode suppression, mechanical sensitivity, and robustness. The results indicate that anchoring the microlever in Design B significantly reduces quadrature displacement arise because of environmental linear acceleration in the sense mode while maintaining comparable amplified sensitivity to Design A. Design B offers enhanced stability and robustness, making it a more suitable candidate for precision applications such as autonomous vehicle navigation systems. © 2024 IEEE.

关键词： Gyroscopes

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A single-transmit multi-receive magnetically coupled multi-frequency resonant radio energy transmission method 5

A single-transmit multi-receive magnetically coupled multi-f...

引用

5th international conference on Artificial Intelligence and Electromechanical automation, AIEA 2024

作者： Bin, Qunqun Ao, Chunyu Yang, Ru Cheng, Minghui Nie, Dazhi Wang, Liangying Tianjin University of Technology School of Integrated Circuit Science and Engineering Tianjin300384 China Tianjin University of Technology School of Computer Science and Engineering Tianjin300384 China

ISBN: (纸本)9798350366174

At present, the existing multi-load wireless Energy transmission (WPT) power supply system mainly adopts multiple single-transmit single-receive magnetic coupling power transmission structures based on single resonant frequency. There are some problems such as large transceiver circuit, high system cost and poor adaptability of wireless power supply type. In order to realize the effective transmission of radio energy under the condition of multi-frequency and multi-load power supply, a multi-frequency resonant radio energy transmission method based on single-emitter multi-receiver magnetic coupling structure is proposed in this paper. Finally, the system simulation model is established to verify the feasibility and effectiveness of the proposed multi-frequency harmonic radio energy transmission method. © 2024 IEEE.

关键词： Pulse width modulation

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Identification of Medicinal Herbs using Modified Xception Architecture 8

Identification of Medicinal Herbs using Modified Xception Ar...

引用

8th IEEE international conference on Computing, Communication, Control and automation, ICCUBEA 2024

作者： Manna, Asmtia Shinde, Rishi Supekar, Vishal Salvi, Mihir Yeole, Kunal Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350391770

Identifying medicinal herbs is important for traditional medicine, pharmacology, and protecting biodiversity. This paper proposes a novel way of identifying medicinal herbs from an image dataset. Different deep learning methods have been performed, including using pre-trained neural networks and creating custom models for herb recognition. A comprehensive analysis of convolutional neural network (CNN) models for medical picture recognition tasks, with an emphasis on architectures like VGG16, ResNet50, and several Xception versions has been done. After in-depth analysis, it can be concluded that the modified Xception architecture performs significantly better than the others, attaining the highest training and validation accuracies with the least amount of losses. The results provide important implications for healthcare applications by highlighting the crucial role that model design plays in improving the accuracy of medicinal image analysis. It also describes how AI-driven tools can help better identify medicinal herbs from images, leading to significant progress in the field of traditional medicines and pharmacology. © 2024 IEEE.

关键词： Convolutional neural networks

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Forecasting the Impact of Deep Learning on Data Science Practices: A Comprehensive Analysis 15

Forecasting the Impact of Deep Learning on Data Science Prac...

引用

15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Acharjya, Kalyan Devaraj Verma, C. Umamaheswari, B. Shelke, Priya M. Kumar, Mukesh Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350370249

It comprehensively discusses the effect of deep mastering on data technology practices. Deep studying has advanced to become a powerful tool for information mining, pattern recognition, and feature engineering. It is miles Information scientists are increasingly using the accuracy and efficiency of information science initiatives. Furthermore, the era has stepped forward hastily to one of these excessive levels. Many leading facts scientists throughout numerous domains have begun to contain deep mastering into their facts technological know-how workflows. In this paper, we examine and forecast the modern-day and destiny impact of deep gaining knowledge of data technology practices. We start by analyzing the distinct advantages of deep learning, including progressed accuracy, reduced guide attempt, more suitable automation, and advanced scalability. We then become aware of extraordinary applications in which deep learning is being applied and evaluate its impact on the respective information science pipelines. Sooner or later, we provide a comprehensive forecast of the capability applications of deep getting to know within the records technology subject inside the near and far destiny. Notably, we recall the capacity of deep studying to force essential modifications in how information mining, analytical choice-making, and large records packages. Through our analysis, we will forecast the ability of deep studying to revolutionize statistics technological know-how practices in extraordinary ways. © 2024 IEEE.

关键词： automation Practices Processing Technology Unprecedented

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Fault Diagnosis of Bearings Based on the Fusion of 1D-CNN and Random Forest Algorithm 6

Fault Diagnosis of Bearings Based on the Fusion of 1D-CNN an...

引用

6th international conference on Communications, Information System and computer engineering, CISCE 2024

作者： Li, Weikang Yan, En School of Mechanical Engineering Liaoning Technical University Fuxin123100 China

ISBN: (纸本)9798350353174

As industrial automation continues to advance, bearing faults have become one of the significant causes leading to mechanical equipment failures, potentially resulting in substantial economic losses. Traditional fault diagnosis methods often struggle to effectively address increasingly complex fault patterns. This study proposes a method for bearing fault diagnosis based on the fusion of one-dimensional convolutional neural network (1D-CNN) and random forest algorithm. Our approach combines the ability of 1D-CNN to capture local patterns in time-series data and the high performance of random forest in classification tasks. We conducted experiments on the bearing dataset from the Case Western Reserve University. The experimental results demonstrate that our method outperforms other popular diagnostic methods across multiple evaluation metrics, particularly in terms of recall, where it significantly surpasses other methods. This provides an efficient and reliable tool for bearing fault diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Mastering Rocket League with Reinforcement Learning A Proximal Policy Optimization Approach 4

Mastering Rocket League with Reinforcement Learning A Proxim...

引用

4th international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Misaros, Marius Gota, Dan Loan Niste, Daniela Florentina Stan, Anca Ciobotaru, Alexandru Miclea, Liviu Technical University of Cluj-Napoca Automation Department Cluj-Napoca Romania Technical University of Cluj-Napoca Power Systems and Management Department Cluj-Napoca Romania Technical University of Cluj-Napoca Industrial Engineering Cluj-Napoca Romania

ISBN: (纸本)9798350391183

Reinforcement learning (RL) is a subfield of artificial intelligence that involves training an agent to make optimal decisions by interacting with an environment. The agent learns through trial and error, receiving rewards for optimal decisions and penalties for suboptimal ones. The objective of this project was to develop an AI agent capable of playing the game Rocket League. Specifically, the agent was trained to compete in one-on-one (1v1) matches with the intention of optimizing its ability to score goals and prevent the opposing agent from scoring. While the developed agent is technically capable of participating in matches with more players, such as two-on-two (2v2) or three-on-three (3v3), its strategies and behaviors are principally designed for one-on-one (1v1) play. Consequently, it may not perform optimally in these other settings. © 2024 IEEE.

关键词： Reinforcement learning

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HandEEGnet: A Kolmogorov-Arnold Network for Hand Movements Classification from Motor Imagery EEG 3

HandEEGnet: A Kolmogorov-Arnold Network for Hand Movements C...

引用

3rd IEEE international conference on Robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hasan, Tasnimul Hossain, A.B.M. Aowlad Biswas, Shuvagata Tazrin, Farhana Khulna University of Engineering & Technology Department of Biomedical Engineering Khulna9203 Bangladesh Khulna University of Engineering & Technology Department of Electronics and Communication Engineering Khulna9203 Bangladesh Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna9203 Bangladesh

ISBN: (纸本)9798331534400

Motor imagery-based BCI plays a vital role in controlling hand movements by processing and recognizing features from EEG signals associated with the imagination of those movements. In this paper, we present a Kolmogorov-Arnold Networks (KAN) based framework in order to classify the left and right-hand movements along with the absence of movement's intention from the electroencephalogram (EEG) signal. The emergence of KAN is expected to be a breakthrough concept in the artificial intelligence paradigm considering its learnable activation functions mechanism and interpretability opportunities. Preprocessing of the collected raw EEG data has been accomplished with band-pass filtering of EEG components of five well-matched channels and corresponding power spectral density feature extraction. We have used 15282 samples with annotated classes from a publicly available dataset to train, validate, and test the classifier. A comparative analysis of the performance of KAN and conventional Multi-Layer Perceptron (MLP) networks has been conducted in order to investigate the effectiveness of the KAN for this classification task. The proposed framework attains 96.14% classification accuracy for 3057 blind test samples, which is found to be better than the conventional MLP neural network approach (76.64% accuracy) and compatible with the existing techniques. The finding of this research might be useful in hand movement-related applications for robotics, prosthetics, and neuro-rehabilitation supports. © 2024 IEEE.

关键词： Multilayer neural networks

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FSCM Quality Control using Blockchain & IOT 8

FSCM Quality Control using Blockchain & IOT

引用

8th IEEE international conference on Computing, Communication, Control and automation, ICCUBEA 2024

作者： Kadam, Amol Patil, Dhanashree V. Pawar, Vaibhav Mali, Mohan Chavan, Sandip Yadav, Prashant D. College of Engineering Pune India College of Engineering Pune India Bharati Vidyapeeth College of Engineering Department of Information Technology Navi Mumbai India Bharati Vidyapeeth Institute of Technology Department of Computer Technology Navi Mumbai India Bharati Vidyapeeth College of Engineering Department of Computer Engineering Navi Mumbai India College of Engineering Department of Mechanical Engineering Pune India

ISBN: (纸本)9798350391770

Globally, the issue of food safety is getting increasingly critical and alarming. In order to tackle this straining situation, people would require a reliable food traceability system that is capable of monitoring and supervising every stage of the food generation process, right from the planting and harvesting of raw food materials to the manufacturing, processing, transportation network & transit delivery, & enhanced storing facilities into the warehouse, as well as the selling procedure, as an effort to precisely solve the technical elements of food safety *** research stage aims to investigate food conditions and other numerous challenges experienced by transporters when selling fresh food by integrating the functioning of blockchain technology alongside the sensing processing of Internet of Things (IoT) devices with respect to the advancement of smart contracts, which are an example of the applications that blockchain offers to keep an eye on the record, as well as handling all stakeholder communications and transactions. All the transactions related to online technology can be verified with the use of IoT technology;whilst these activities are registered and subsequently stored into a centrally managed database structure. Faster food traceability is made possible using the advancement of Blockchain Technology, and this is anticipated for useful needs in complicated food supply chain management. These are some of the main significances of Blockchain technology to be adopted based on secured data processing of *** transactions related to stakeholders, IoT thus makes an affordable and safe FSCM system possible for Quality Food preservation. Stakeholders will have fresh options for implementing a more effective food safety management system given that the methods adopted within the information technologies, like data storage within centralized warehouses, IOT based communication combined with data science enhancement, like data mining, patte

关键词： Food storage

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Content-Based Galaxy Image Retrieval Using Convolutional Neural Networks 4

Content-Based Galaxy Image Retrieval Using Convolutional Neu...

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4th international conference on Consumer Electronics and computer engineering, ICCECE 2024

作者： Yu, Songtian Hu, Tao Gu, Minjuan Li, Tengyu Zhang, Dashu School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798350384062

The morphology of galaxies can reflect their own physical information and evolutionary processes. However, with the continuous improvement and application of astronomical observation equipment, the number of galaxy images has also grown exponentially. Therefore, accurately retrieving specific images from a large dataset of galaxy images is a challenging task. Using content-based image retrieval and deep learning in galaxy images can easily retrieve similar images, aiding astronomers in their analysis. In this paper, we propose a method based on ResNet-50 for accurate galaxy image retrieval. First, we preprocessed data from the Galaxy Zoo project and categorized it into six classes. Then, we conducted a set of comparative experiments, training the ResNet-50 model using both cross-entropy loss function and triplet loss function. Experimental results show that the ResNet-50 model trained with the triplet loss function performed the best. The model achieved a mAP of 0.9366 and mAP@5 of 0.9538, which enable accurate retrieval of specific galaxy images. © 2024 IEEE.

关键词： Galaxies

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Reach on Operation Principle and Electromagnetic Characteristics of the Partitioned-Stator Permanent-Magnet Vernier Motor 8

Reach on Operation Principle and Electromagnetic Characteris...

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8th international conference on Electrical, Mechanical and computer engineering, ICEMCE 2024

作者： Qiu, Ganggang Zhong, Xu He, Baigen Wang, Tao Jiangsu Institute of Automation Jari Lianyungang China

ISBN: (纸本)9798331506230

A new Partitioned-Stator Permanent-Magnet Vernier (PS-PMV) motor is proposed in this paper, in which the power density and torque density are further improved to meet the operational requirements of direct drive systems for low-speed and high-torque conditions. Besides, partitioned-stator and intermediate rotor structure are adopted, and the PM magnetic field can be modulated to effective harmonic magnetic field with few pole-pair number and high speed, then the field winging is designed according to the effective harmonic magnetic field, so the magnetic field excited by two sets of PMs is coupled of armature windings. Firstly, the topology of PS-PMV motor is introduced. Secondly, the operational principle of the motor is elaborated in detail, and an analysis of the air-gap flux density is conducted based on the equivalent magnetic circuit method. Finally, the performances of PS-PMV motor are calculated and simulated using the finite element analysis, thus confirming its suitability for direct drive systems with high power density and high torque density. © 2024 IEEE.

关键词： Permanent magnets

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