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检索条件"任意字段=3rd International Conference on Automation, Robotics and Computer Engineering, ICARCE 2024"

共 803 条记录，以下是31-40 订阅

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One-Handed Gesture Recognition Based on Improved ST-GCN and CA Attention Mechanism 3

One-Handed Gesture Recognition Based on Improved ST-GCN and ...

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3rd international conference on automation, robotics and computer engineering, icarce 2024

作者： Wang, Guixin School of Big Data and Computer GuangDong BaiYun University Department of Software Engineering Guangzhou China

ISBN: (纸本)9798331529505

The article takes one-handed gesture video as the research object, and mainly focuses on the recognition process and method of 'isolated words' and static gesture mixtures. Firstly, the algorithm of extracting the key point positions of frame pictures is proposed by grouping rules of frame sequences of different gestures. This algorithm solves the problem of invalid gesture images affecting the recognition due to the redundancy of gesture images when extracting gestures from traditional gesture videos. Finally, a new network (ST-GCN-CA) is proposed, which improves the traditional spatio-temporal graph convolutional network (ST-GCN) and adds the CA attention mechanism. The method and network in this paper are applied to the Chinese isolated word sign language dataset (CSL-500) single-handed video, and the experimental results show that the recognition accuracies in the isolated word gesture video and the hybrid video reach 94.3% and 94.1%, respectively, which improves the recognition accuracies in comparison with other methods in the public domain. © 2024 IEEE.

关键词： Gesture recognition

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MiniBert24: A Lightweight Transformer-Based Model for Stock Market Movement Prediction 3

MiniBert24: A Lightweight Transformer-Based Model for Stock ...

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

作者： Rodrigues, Gourab Nicholas Nazmul Hossain Mir, Md Mahmud Bhuiyan, Md Shahriar Al Rafi, Md Fuad, K.M. Nafiur Rahman Shahedul Islam, Md. Nur, Kamruddin Washington University of Science and Technology 2900 Eisenhower ave AlexandriaVA22314 United States Bangladesh University of Business and Technology Dhaka Bangladesh Stamford University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331534400

The rapid growth of financial markets has necessitated developing more precise and efficient frameworks to predict stock price movements. This paper introduces MiniBert24, a lightweight, transformer-based architecture specifically designed for stock market movement prediction by leveraging news related to finance and past stock price information. The model is tailored for high computational efficiency, combining a reduced number of transformer layers with fewer attention heads while retaining strong predictive power. We utilize a unique dataset incorporating historical headlines from the Reddit WorldNews channel and Dow Jones Industrial Average (DJIA) data, creating a binary classification task to forecast daily stock price movement. MiniBert24 obtains an excellent AUC of 96.7% and an F1-score of 96.43%, outperforming standard transformer models while reducing computational overhead. These results demonstrate the potential of MiniBert24 to serve as a robust tool for market prediction in real-time trading environments. The model's ability to fuse textual data with stock market indicators provides a compelling solution for financial analysts and algorithmic traders seeking efficient, high-performing predictive models. © 2024 IEEE.

关键词： Prediction models

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Research on Unity3D Virtual Laboratory for Product Structure Cognition 2

Research on Unity3D Virtual Laboratory for Product Structure...

引用

2nd international conference on automation, robotics and computer engineering, icarce 2023

作者： Yan, Han-Sheng Zhao, Juan Yi, Huimin Zeng, Feng Long, Yuhui School of Mechanical and Electrical Engineering Guangdong College of Industry and Commerce Guangzhou China

ISBN: (纸本)9798350308341

Aiming at the insufficient of product structure cognition model for mechanics experiment teaching in colleges and universities, based on Unity3D platform, using Rhino and Creo for three-dimensional modeling, an internet virtual lab for product cognition is developed by selection of several common physical products as a case, combining C# for interactive function and integration of HTML pages. Relying on the industry design provincial training base in the Guangdong College of Industry and Commerce, integrated a variety of advanced technology, it is realized that virtual disassembling products can operate on PC and mobile terminal platform. The students' interest and training effect are significantly improved. Teaching structure models could be custom-made according to course demand for teachers, significantly saving spend on experimental instruments and equipment, and increasing the freedom of the students' experiments in time and space. As a result, the new method has a good research value and application prospect. © 2023 IEEE.

关键词： Product design

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Automated Knitting Instruction Generation from Fabric Images Using Deep Learning 3

Automated Knitting Instruction Generation from Fabric Images...

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3rd international conference on automation, robotics and computer engineering, icarce 2024

作者： Zheng, Xingyu Sheng, Haoliang Cai, Songpu Cheng Lau, Meng Zhao, Kaiyu Shanghai International Fashion Education Centre Fashion College Shanghai China Bharti School of Engineering and Computer Science Laurentian University Greater Sudbury Canada

ISBN: (纸本)9798331529505

This paper proposes a deep learning method to generate knitting instructions from fabric images, reversing the traditional process of manual pattern coding. The instructions are based on the five-layer structure derived from knitting logic, consisting of a front label recognizable from the front real image and a complete label corresponding to the knitting programming. The approach is divided into two phases: the Generation Phase, where neural networks produce front label instructions, and the Inference Phase, where mesh-based logic expands these instructions into complete labels. We also introduce an optimized labeling method for Kaspar's dataset, improving the accuracy, scalability, and knittability of the generated instructions. This method simplifies the knitting process by automating instruction generation directly from fabric images. © 2024 IEEE.

关键词： Knit fabrics

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Enhancing Trajectory Tracking of Quadrotor Using Feedback Linearization with MPC-LPV and LQI-LPV under Variable Disturbances 3

Enhancing Trajectory Tracking of Quadrotor Using Feedback Li...

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

作者： Chowdhury, Raian Haider Imtiaz Ferdous, Ahmed Hossain, Amreen Rahman, Kazi Arafat Bangladesh University of Engineering and Technology Department of Mechanical Engineering Dhaka Bangladesh Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The deployment of Unmanned Aerial Vehicles (UAVs) has markedly improved industrial efficiency. Optimizing control algorithms for precise path tracking is essential for enhancing the reliability and performance of these systems. This paper presents a comparative analysis of Model Predictive Controller (MPC) and Linear Quadratic Integral (LQI) controller, combined with the Linear Parameter Varying (LPV) approach, for quadrotor trajectory tracking under variable disturbances. The quadrotor is modeled using Newton-Euler equations for a six-degrees-of-freedom system. The control architecture includes an outer loop position controller using Feedback Linearization (FL) and an inner loop attitude controller implemented as either MPC-LPV or LQI-LPV. Disturbances are modeled using a custom sinusoidal model. Numerical simulations are done to evaluate controller performance for six different trajectories. The results for the helical trajectory indicate that under variable disturbances, the MPC-LPV+FL outperforms the LQI-LPV+FL. The MPC-LPV+FL achieves a steady-state error of 0.1934 m compared to 0.6611 m for the LQI-LPV+FL. Additionally, the MPC-LPV+FL reaches the minimum error in 3.1 seconds, compared to 3.8 seconds for the LQI-LPV+FL, resulting in a 22.58% faster response. Therefore, MPC-LPV+FL is the superior choice based on accuracy, stability, and speed of response under adverse conditions. © 2024 IEEE.

关键词： Feedback linearization

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VOIS+: A Video Over Intercom Service with Home automation Features for Advanced Security and Safety 3

VOIS+: A Video Over Intercom Service with Home Automation Fe...

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

作者： Deb, Angkon Islam, Asif Nath, Arnab Haque, Mayesha Roy, Rudra Ul Islam, Md. Jawad Shahnaz, Celia UET Department of Electrical and Electronic Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798331534400

The advent of the Internet of Things (IoT) and the Web of Things (WoT) has greatly influenced the development of smart home systems, providing enhanced convenience, security, and automation. This paper introduces a novel solution that integrates video intercom systems with smart home automation features, addressing communication and safety concerns. In addition to traditional intercom capabilities, the proposed system incorporates fire hazard detection and electrical safety controls, automating household safety measures. A mobile and web-based interface allows remote monitoring and control, including smart door lock management, face recognition, and emergency contact alerts. Developed with affordability and accessibility in mind, the system - termed VOIS+, offers a comprehensive approach to home automation and safety, achieving high performance across multiple test scenarios, including face recognition accuracy and rapid emergency response times. This paper presents both the hardware architecture and software framework, demonstrating the system's potential to revolutionize smart home technology by merging security, automation, and artificial intelligence into a single, efficient platform. © 2024 IEEE.

关键词： Intercom systems

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Automated Wound Segmentation using Attention Mechanism based on Enhanced MobileNetV2 3

Automated Wound Segmentation using Attention Mechanism based...

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

作者： Muhtasim, Dewan Ahmed Pavel, Monirul Islam Paul, Arindrajit Shakib, Abdul Wadud Gazi, Sanji Sahlan Hasan, Md. Mahfujul Metropolitan University Dept. of Computer Science and Engineering Sylhet3104 Bangladesh University of South Australia STEM Mawson Lakes 5095 Australia University of Colorado Boulder Dept. of Computer Science Boulder80309-0430 United States

ISBN: (纸本)9798331534400

Wound healing is a morphogenetic reaction to injury that restores physiological and anatomical function. It is crucial in fields like obesity treatment, cancer, trauma, burns, and diabetic foot ulcer (DFU) care. Wound care experts rely on affordable, high-resolution cell phone cameras for image documentation;however, lack of information can hinder proper wound management, documentation, and diagnosis, highlighting the need for improved therapeutic techniques and better automated image segmentation methods. Recent deep learning approaches for automatic feature extraction from images face challenges due to limited training data and low neural networks depths, which hinder accuracy by failing to capture complex patterns. This research introduces an improved MobileNetV2 model which utilized transfer learning, two additional dense layers, and fusion with spatial attention mechanism to improve the accuracy of the wound segmentation task. The proposed approach achieved a dice score of 92.40% on the Foot Ulcer Dataset and 96.92% on the Medetec Wound Dataset. In comparison to existing state-of- the-art deep learning-based VGG16, SegNet, U-Net, and MobileNetV2+CCL techniques, the proposed attention-based MobileNetV2 technique obtained the highest dice score. © 2024 IEEE.

关键词： Image segmentation

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Fabrication of a Revolutionary Pick-and-Place Robot with Omnidirectional Mobility 3

Fabrication of a Revolutionary Pick-and-Place Robot with Omn...

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3rd international conference on Image Processing and robotics, ICIPRoB 2024

作者： Rahman, Md Hasibur Haque, Samiul Monir, Md Fayed Al Uddin, Mohammad Faisal Alam, Saadia Binte Independent University Bangladesh Department of Computer Science Engineering Dhaka Bangladesh Independent University of Bangladesh RIoT Research Center Dhaka Bangladesh

ISBN: (纸本)9798350374766

The paper outlines the creation of an innovative robot capable of omnidirectional movement, enhancing its ability to navigate confined industrial spaces. This robot, uniquely designed for picking and placing tasks, underwent a comprehensive development process, including planning, design, fabrication, and programming. It represents a notable advancement in the field of industrial robotics, with potential applications in environments where traditional robots may be less effective. The discussion also covers possible future improvements and broader applications of this technology. © 2024 IEEE.

关键词： Pulse width modulation

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Detecting Polycystic Ovary Syndrome with Convolutional Neural Networks: Enhancements through Adversarial Training and Gradient-Weighted Class Activation Mapping (Grad-CAM) 3

Detecting Polycystic Ovary Syndrome with Convolutional Neura...

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

作者： Shikdar, Omar Faruq Sazeda, Ruba Islam, Sujana Islam, Ashraful Haque, Tanzia Mily, Arifa Sultana East West University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798331534400

Polycystic Ovary Syndrome (PCOS) is one of the most common causes of infertility for women around the world. It is a metabolic and hormonal condition that affects women of reproductive age. It is vital to detect this problem as soon as possible. Among all the possible diagnostic imaging ultrasound is the safest option as it has no risk of radiation. This research aims to create a system that will detect PCOS from ultrasound images. A dataset containing 3840 images of 2 classes which are infected and non-infected has been used in the research. Using techniques such as resizing, rescaling, center cropping the dataset has been preprocessed. A feed forward model using Convolutional Neural Network (CNN) architecture with 8 layers has been deployed for that task. The dataset was split into train, validation and test set. After training the CNN model with training data, the model has been trained further using adversarial training through Fast Gradient Sign Method (FGSM). Hyper parameter tuning was done for optimal result. The highest accuracy achieved by the model is 99.07% for epsilon=1.6 (adversarial training). The model's output prediction has been explained by using Gradient Weighted Class Activation Mapping (Grad-CAM). After 1000 iterations, the 99% confidence interval for the model is [0.985,0.996]. © 2024 IEEE.

关键词： Convolutional neural networks

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Robust Phishing URL Classification Using FastText Character Embeddings and Hybrid Deep Learning 3

Robust Phishing URL Classification Using FastText Character ...

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

作者： Al-Sakib, Abdullah Limon, Zishad Hossain Sakib, Anamul Pranto, Md Nuruzzaman Islam, Md Ariful Sultana, Shayma Ahmed, Md. Redwan Rahman, Shafiur Westcliff University Dept. of Information Technology CA92614 United States Westcliff University Dept. of Computer Science CA92614 United States International American University Dept. of Business Administration CA90010 United States International American University Dept. of Management Information System CA90010 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Phishing attacks are a major cybersecurity threat that resulted in over 1.2 million incidents in the first half of 2020. These attacks caused substantial financial losses and posed risks to individuals and organizations. Being able to identify fraudulent websites is crucial in order to effectively address these potential risks. This study introduces a novel method for detecting phishing URLs by using word and character embeddings to capture complex URL patterns. We used a dataset of 80,000 URLs, including 50,000 legitimate ones and 30,000 phishing instances, and applied thorough preprocessing techniques. We utilized word embeddings in FastText to handle unseen words, with the added advantage of n-gram representations. Additionally, we captured character-level features through dense character embeddings. We trained several machine learning and deep learning models, and one model, the Convolutional Bidirectional LSTM (CBiLSTM), stood out with an accuracy of 99.01% and an F1-score of 99.08%. Furthermore, we made a thorough comparison with the most advanced techniques available, and our findings demonstrated clear superiority over previous research. This study presents an effective approach for classifying phishing URLs, providing a valuable tool to combat fraud and protect against identity theft, thereby helping to minimize the financial and emotional harm experienced by victims. © 2024 IEEE.

关键词： Phishing

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