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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

共 47854 条记录，以下是1311-1320 订阅

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An Integrated Secure Rail System For Accident Prevention and Anti-Hijacking Measures

An Integrated Secure Rail System For Accident Prevention and...

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2024 international conference on advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Pradeep, Ganesula Kiran, Samineni Uday Sathyadurga, V. Hindustan Institute Of Technology & Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

India has one of the world's longest rail systems, covering an amazing distance. However, Indian railways fall short of international norms in terms of dependability and passenger safety. In order to solve this, an improved interface solution this paper has been started to improve railway safety and prevent collisions. This method uses Arduino microcontrollers in conjunction with fingerprint sensor modules. In order to maintain safe and controlled train operations, trains fitted with these specialised fingerprint devices won't start their engines until after authenticating the fingerprints of authorised users. And also the system recognises when two trains are about to collide on the same track when fingerprint modules are activated simultaneously. The GSM module quickly sends the latitude and longitude coordinates to a specified web site after detecting something. The position and seriousness of the collision risk may be quickly determined thanks to this data interchange, which is essential for further processing and analysis. In addition, the apparatus features an LCD screen that offers instantaneous visual notifications to users on potential collision scenarios. By enabling proactive steps for collision prevention and easy connection with external monitoring systems, this novel solution strengthens overall railway safety rules. It makes use of web-based connectivity and cutting-edge sensor technology to do this. © 2024 IEEE.

关键词： Liquid crystal displays

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Extracting, Transforming, and Analyzing Olympic Data Using Azure Services: A Case Study

Extracting, Transforming, and Analyzing Olympic Data Using A...

引用

2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Samanta, Debajit Roy, Subhodeep Sarkar, Pratima Misra, Bitan Techno International New Town Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350388800

A field of investigation and the foundation of cloud computing are employed to administer a common resource pool. Analytics techniques are required in educational mining because of the massive amount of data needed to obtain relevant knowledge. As digital data repositories grow and dispersion, sophisticated data analysis techniques are required to quickly extract relevant knowledge from them. A cloud platform created as a service pipeline for creating and implementing distributed data analytics applications. In this scenario, we make use of learning models, data sets, analysis instruments, and data mining techniques that can be deployed as stand-alone services that can run on clouds. In this study the central focus lies in establishing a robust data pipeline utilizing Azure services, specifically targeting the Tokyo Olympic dataset obtained from Kaggle. The initial phase involves the extraction of relevant information from the Kaggle dataset using Azure Data Factory, a cloud-based integration service. Following this, Azure Databricks, featuring Apache Spark functionality, is employed for the transformation of the Tokyo Olympic data. The primary objective remains to enable advanced analytics, focusing on the Tokyo Olympic dataset. The utilization of Azure Synapse Analytics empowers organizations to perform intricate SQL queries on the Tokyo Olympic data, revealing latent trends and correlations. The outcomes can be visualized through SQL-based approaches in conjunction with Azure Synapse Analytics, providing a comprehensive understanding of the Tokyo Olympic dataset for well- informed decision-making. © 2024 IEEE.

关键词： Cloud platforms

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Speech Recognition Using Quantum Convolutional Neural Network 8

Speech Recognition Using Quantum Convolutional Neural Networ...

引用

8th IEEE international conference on science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Thejha, B. Yogeswari, S. Vishalli, A. Jeyalakshmi, J. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347791

Quantum machine learning (QML) has emerged as an exciting new technology that explores challenging machine learning issues by relying on advances in quantum computing. A convolutional neural network is primarily used in the classical portion of the QNN-based Speech Recognition System, which is made up of both classical and quantum components. The quantum component is based on the equation's quantum circuits with a certain learnable parameter. Speech recognition technology is one of the quickly evolving technologies, along with Siri on the iPhone, chatbots, Alexa on the Amazon Echo, etc... The research uses a quantum convolutional neural network (QCNN) to identify voice and convert it to text. The use of Quantum filters will reduce the input in to qubits and then it will be further moving in to the feature extraction and classification process. QCNN gives more accurate results and will increase the scalability by adding more layers in to the model. © 2023 IEEE.

关键词： Convolutional neural networks

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Adaptive Parameter Estimation of Robot with Actuator and Friction Dynamics 2

Adaptive Parameter Estimation of Robot with Actuator and Fri...

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2nd international conference on robotics, Artificial Intelligence and Intelligent Control, RAIIC 2023

作者： Fang, Yueming Deng, Hu Southwest University of Science and Technology School of Information Engineering Mianyang China Southwest University of Science and Technology Tianfu Institute of Research and Innovation Chengdu China

ISBN: (纸本)9798350328004

This article presents an adaptive parameter estimation framework for a robotic system, where actuator dynamics and friction characteristics are considered in the robotic model. By adopting integral operations and introducing the actuator dynamics, the system of robot can be constructed into a linear parameterized form without using acceleration and torque of joints, which possess implementation advantages. A novel adaptive parameter estimation is presented to ensure that the parameter estimation error converges exponentially to zero. The simulation of a selective compliance assembly robot arm (SCARA) confirms the effectiveness of the proposed model and algorithm. Meanwhile, it verifies that the dynamic continuously differentiable friction model can approximate the classic static friction model. © 2023 IEEE.

关键词： Actuators

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BEYOND THE PAPER: EXPLORING THE RELATIONSHIP BETWEEN THE PARTICIPATION OF WOMEN AND THE SOCIAL RELEVANCE OF RESEARCH TOPICS IN engineering DESIGN

BEYOND THE PAPER: EXPLORING THE RELATIONSHIP BETWEEN THE PAR...

引用

ASME 2024 international Design engineering Technical conferences and Computers and Information in engineering conference, IDETC-CIE 2024

作者： Running, Ivyann O. Parkinson, Bethany Magleby, Spencer Toh, Christine Brigham Young University ProvoUT United States University of Nebraska Omaha OmahaNE United States

ISBN: (纸本)9780791888407

The increase in participation of women in science, technology, engineering, and Mathematics (STEM) fields has not been uniform. In mechanical engineering, the proportion of women who participate in the profession at all levels is disproportionately low. One factor identified by education researchers that has the potential to draw higher numbers of women to the discipline is the presence of socially relevant and impactful applications of science concepts. To better understand this relationship, the gender distribution of researchers participating in sub-conferences within the mechanical engineering international Design engineering Technical conferences & Computer and Information in engineering (IDETC-CIE) was investigated. The social relevance of publications within two sub-conferences with differing levels of women participants, the "Design Theory and Methodology" (DTM) and "Mechanisms and robotics conference" (MR), was compared. Our results showed that both sub-conferences had comparable numbers of socially relevant papers, but differed in terms of which categories of social relevance were emphasized and if specific populations were explicitly mentioned in the studies. These results indicate that the relationship between the participation of women in engineering research communities and the focus on socially relevant applications is complex and multifaceted, potentially affected by not just if socially relevant concepts are studied, but also how these concepts are integrated into the shared values and norms of each community. Copyright © 2024 by ASME.

关键词： STEM (science, technology, engineering and mathematics)

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Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

引用

16th international conference on Social robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Research Progress in Cell Classification Detection Based on Deep Learning 14

Research Progress in Cell Classification Detection Based on ...

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14th IEEE international conference on Cyber technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Gai, Dongze Zhang, Chuang Zhang, Yiwei Guo, Jiaduo Zhang, Qi Qin, Hengshen Dang, Dan School of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China School of Mechanical Engineering Shenyang Jianzhu University Shenyang110168 China School of Science Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9798331506056

This paper reviews the application of deep learning in cell classification detection, highlights the importance of convolutional neural networks in image analysis, and discusses the application of image preprocessing, feature extraction and deep learning models. Despite the progress made by deep learning techniques in improving cell classification accuracy, challenges such as data quality, model generalization and interpretability remain. This article suggests future research directions, including combining medical knowledge with deep learning and developing multimodal learning to improve the accuracy and efficiency of cell detection. Meanwhile, this article also explores the potential of label-free cell classification methods combining optically induced optically-induced dielectrophoresis (ODEP) techniques with deep learning to achieve cell classification without labeling, providing a new perspective on cell analysis. optically induced. © 2024 IEEE.

关键词： Contrastive Learning

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Skin Disease Classification using Transfer Learning with a Modified ResNet-50 Architecture

Skin Disease Classification using Transfer Learning with a M...

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2024 international conference on advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Priya Bhaminie, S. Yogesh, C. School Of Computer Science And Engineering Vellore Institue Of Technology-Chennai Chennai India

ISBN: (纸本)9798350364828

This paper presents an advanced approach in skin disease classification using a modified ResNet-50 architecture, applied to a specific subset from the ISIC 2019 Dataset focusing on Benign Keratosis, Basal Cell Carcinoma, and Melanoma. The study integrates deep learning and medical imaging to enhance diagnostic accuracy in dermatology. It leverages transfer learning, adapting the well-established ResNet-50 model, and modifies it by incorporating global average pooling and dense layers, thus tailoring it for dermatological use. This method is inspired by significant strides in skin cancer detection using ResNet and innovative transfer learning techniques in skin disease classification. A notable aspect of this research is the evaluation of data augmentation's impact on model generalization and robustness. The findings of this study contribute to the growing field of deep learning in skin disease classification, providing a refined and effective tool for potential applications in dermatology. © 2024 IEEE.

关键词： Dermatology

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Transforming Agriculture with AI: Automated Crop Insect Identification Using InceptionV3 Transfer Learning 1

Transforming Agriculture with AI: Automated Crop Insect Iden...

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1st international conference on advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Thyagharajan, K.K. Kalaiarasi, G. Nirmala, G. Saravanan, P. Vignesh, T. Makashkumar, V. R.M.D Engineering College Department of Electronics and Communication Engineering Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.D Engineering College Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350342796

Identifying crop insects and classifying them is a challenging task for agriculturalists. A transfer learning method is suggested in this paper to address this issue. The proposed approach uses InceptionV3 as a base deep learning model and it is given additional custom layers. The model is fine-tuned using a custom dataset made up of five classes with 1000 insects each. The insect and pest images available from the National Bureau of Agricultural Insect Resources datasets and the Xie1 dataset are used for testing the model. Preprocessing techniques are applied to resize the images, and data augmentation is employed to prevent overfitting. The model was analyzed on the validation dataset with Adam, Adagrad, and Nadam optimizers, and obtained recognition accuracies of 86.6%, 77.8%, and 88.56% respectively. The outcomes confirms that the model performs well with Nadam optimizer for insect identification. © 2023 IEEE.

关键词： Deep Learning InceptionV3 Insect Detection Smart Agriculture Transfer Learning

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Automated Wheat Leaf Disease Detection Using Deep Learning with TensorFlow and Keras

Automated Wheat Leaf Disease Detection Using Deep Learning w...

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

作者： Kumar, P. Senthil Pandi, S. Dineshkumar, Mithun Sheriff, Muqaddam Aaqil Rajalakshmi Engineering College Department of Cse Chennai India Northeastern University Khoury College of Computer Science Boston United States Brainvault Technologies Chennai India

ISBN: (纸本)9798350388602

Wheat leaf diseases seriously compromise world food security because of their impact on crop yields and quality. The quality and productivity of crops can be much affected by wheat leaf diseases. Good treatment of diseases depends on quick identification and accurate diagnosis. This work offers a creative approach employing TensorFlow and Keras, two powerful deep learning structures, for the evident proof of wheat leaf diseases. Convolutional neural network architecture was created to efficiently extract important features from photos of wheat leaves afflicted by different illnesses. To increase generalizability, the CNN model was taught on a collection of wheat leaf images using data augmentation methods. Images of wheat leaves stained with common illnesses including rust, blight, and mildew annotated on them train the system. The model's efficient classification and analysis of the photos lets faster intervention and identification possible. Including transfer learning and data augmentation methods helps the model to be more generalizable. By means of automation of disease identification, this technology offers farmers and agricultural experts a scalable and efficient tool for enhancing crop health management. Thanks to its capacity to properly categorize wheat leaf diseases, as shown by the experimental findings, farmers and other agricultural experts seeking for quick diagnosis and treatment of diseases may find the suggested method to be a useful tool. © 2024 IEEE.

关键词： Convolutional neural networks

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