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A Fusion of CNN, MLP, and MediaPipe for Advanced Hand Gesture Recognition

A Fusion of CNN, MLP, and MediaPipe for Advanced Hand Gestur...

引用

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

作者： Priyanka, K. Rithik, R. Jaswanth, C. Rajesh, C. Nitte Meenakshi Institute of Technology Dept. of Ise Bangalore India

ISBN: (纸本)9798350388602

Hand gesture recognition has become evident as a critical component in human-centered computing systems. It provides users a natural intuitive way for users to interact with technology. The system introduced a concept of Gesture Fusion, a novel approach that leverages the complementary strengths of Convolutional Neural Networks (CNN), Multi-Layer Perceptron (MLP), and the MediaPipe framework to achieve robust hand gesture recognition. By integrating these advanced techniques, fusion of models surpasses the limitations of individual model, delivering improved accuracy and robustness in recognizing a diverse range of hand gestures. The proposed system begins by preprocessing input images using CNNs to extract informative spatial features. Simultaneously, it utilizes the MediaPipe framework to accurately detect and localize key hand landmarks. These landmarks are then processed by MLPs to capture intricate patterns and variations in hand gestures that may not be fully captured by CNNs alone. By combining the outputs of both CNN and MLP models through a weighted fusion mechanism, synthesizes a comprehensive understanding of hand gestures, resulting in more precise and reliable recognition outcomes. Through significant research and evaluation on benchmark datasets, Fusion model outperforms independent CNN and MLP models. The findings obtained demonstrates the improved accuracy, resilience, and adaptability in a variety of real-world circumstances, confirming its potential to revolutionize the field of hand gesture detection. It advances the pursuit for more natural and intuitive human-computer interaction paradigms by seamlessly combining cutting-edge technologies and approaches. © 2024 IEEE.

关键词： Convolutional neural networks

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DDPG-based Reactive Power Optimization Strategy For Active Distribution Network 7

DDPG-based Reactive Power Optimization Strategy For Active D...

引用

7th international conference on Intelligent robotics and Control engineering, IRCE 2024

作者： Liu, Xinghua Wang, Xiaoyu Fan, Bangji Li, Beibei Deng, Ruilong Kong, Minru Xi'an University of Technology School of Electrical Engineering Xi'an China Sichuan University Chengdu China Zhejiang University Hangzhou China State Grid Xi'an Power Supply Company Xi'an China

ISBN: (纸本)9798350352399

To tackle challenges such as voltage fluctuations and network losses arising from integrating distributed energy resources into the grid, this paper proposes a novel reactive power optimization approach for active distribution network based on deep reinforcement learning. Diverging from conventional methods, our approach models reactive power optimization as a constrained Markov decision process, encompassing the determination of reactive power outputs from photovoltaic sources and compensation devices. By employing a deep deterministic policy gradient algorithm, the agent learns to make daily decisions based on the state of the environment. The methodology incorporates distributed control by training the agent to formulate optimal coordination strategies for photovoltaic inverters and capacitors. The efficacy of our proposed method is demonstrated by a comprehensive analysis conducted on a modified IEEE 33-node distribution network. The results highlight the superior performance in reducing active losses and mitigating voltage offsets compared to conventional methods. © 2024 IEEE.

关键词： Power distribution networks

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Design and Control of a Spherical Robot Using Pendulum and Momentum Wheels 8

Design and Control of a Spherical Robot Using Pendulum and M...

引用

8th IEEE international conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Yuan, Zhicheng Chen, Xuechao Wu, Jiahao Ren, Siyu Yu, Zhangguo School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798350300178

Spherical robot has good potential for complex environment adaptation, but the single internal driving principle limits its multifaceted performance. In this paper, we present a composite driving principle which uses pendulum and momentum wheels. The abilities to change motion state and surmount obstacles have been improved. This article also contains the dynamic analysis and a controller of this driving principle. Finally, we implement necessary simulations and experiments to prove the performance of this driving principle in both aspects. © 2023 IEEE.

关键词： Pendulums

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Robust H∞Output Feedback Control for Vehicle Lateral Motion System with Quantization and Data Dropouts 9

Robust H∞Output Feedback Control for Vehicle Lateral Motion...

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9th international conference on Electrical engineering, Control and robotics, EECR 2023

作者： Ye, Zhuangwu Wuhan University of Science and Technology The School of Information Science and Engineering China

ISBN: (纸本)9781665491204

This study examines H∞ static output feedback control problem for discrete-time vehicle lateral dynamic system with quantization error and data packet dropout. The quadrilateral polytope model representes vehicle lateral dynamic system. The number of linear matrix inequalities is reduced by the polytope method based on the longitudinal velocity time varying parameters set. Additionally, this paper models data packet dropout by the Bernoulli random distribution approach, and quantizes the measured output signal by a dynamic quantizer. Such that the quantization error and data packet dropout are described in vehicle lateral dynamic system. The goal of this study is to construct a static output feedback controller that will ensure the system is stochastically stable and H∞ performance. Finally, the suggested controller design method is validated by using the co-simulation of Matlab/Simulink and Carsim. © 2023 IEEE.

关键词： Linear matrix inequalities

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Predicting Chip Failure Rates with Machine Learning in VLSI Design 9

Predicting Chip Failure Rates with Machine Learning in VLSI ...

引用

9th international conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Chennakesavulu, M. Banu, E. Afreen Sandhiya, R. Ramesh, D. Raja Devadas, M. Rathinasabapathi, G. Department of Electronics And Communication Engineering Andhra Pradesh Nandyal518501 India Department of Computer Science And Engineering Tamil Nadu Chennai602105 India Rmk College of Engineering And Technology Department of Mechanical Engineering Tamil Nadu Puduvoyal601206 India Department of Electronics And Communication Engineering Andhra Pradesh Vizianagaram535005 India Vaagdevi College of Engineering Department of Electronics And Communication Engineering Telangana Warangal506005 India Panimalar Engineering College Department of Mechanical Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

This exploration paper explores the integration of machineliteracyways toprognosticate chip failure rates in veritably Large Scale Integration(VLSI) design. As semiconductor technology advances, the complexity of VLSI circuits grows, posing challenges inicing theirtrust ability. Traditionalstyles forprognosticating failure ratesfrequently fallsuddenly in handling thecomplications ofultramodern chipinfrastructures. using machineliteracy algorithms, this aims to enhance thedelicacy andeffectiveness of failure rateprognostications byassaying a multitude of parameters, including design specifications, environmental factors, and manufacturing variables. Throughexpansive data analysis and model training, our approach seeks to uncover patterns and dependencesthat contribute to chip failures, therebyfurnishing aprecious tool for contrivers to proactively address implicittrust ability issues during the VLSI design phase. The findings of thisexploration offer a promising avenue forperfecting the robustness of VLSI circuits and advancing thetrust ability of electronic systems in the ever- evolvinggeography of semiconductor technology © 2024 IEEE.

关键词： Machine learning

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

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

引用

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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Multimodal Sentiment Analysis Model Based on Text-Guided and Cross-Modal Feature Fusion 6

Multimodal Sentiment Analysis Model Based on Text-Guided and...

引用

6th international conference on robotics and Computer Vision, ICRCV 2024

作者： Wang, Jiageng Zhao, Tongzhou Wang, Zihao Huang, Zhuo School of Computer Science and Engineering & Artificial Intelligent Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

Multimodal Sentiment Analysis (MSA) is a method that synthesizes information from different modalities to accurately identify expressed emotions. In MSA, textual modality usually carries more direct sentiment information, thus it occupies a key position in sentiment prediction. In this paper, we propose a text-guided and cross-modal feature fusion multimodal sentiment analysis model HTFF based on how to effectively fuse nonverbal and textual modalities while solving the problems of information redundancy and ambiguity of sentiment expression in nonverbal modalities. The model processes the modal features through the feature extraction embedding module and utilizes the collaborative attentiveness hyper-modality generator module to extract the visual, audio, and audio-visual under the guidance of language features. hyper-modality representations for mixed modalities. A complementary joint representation is formed by a Transformer-based cross-modal feature fusion module, which ultimately realizes emotion prediction. HTFF was evaluated on the CMU-MOSI and CH-SIMS datasets, results show that compared with other advanced models, HTFF has better overall performance. © 2024 IEEE.

关键词： Modal analysis

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Research on Intelligent Extraction Methods for Key Regions in Hyperspectral Images

Research on Intelligent Extraction Methods for Key Regions i...

引用

2024 IEEE international conference on Control science and Systems engineering, ICCSSE 2024

作者： Wang, Yunsen Cui, Weiguang Yang, Haifeng Cao, Yangchuang Wang, Haoxuan Kang, Yang Shandong Institute of Space Electronic Technology China Academy of Space Technology Yantai China

ISBN: (纸本)9798331517199

In response to the problem of high complexity and inaccurate extraction of key areas in hyperspectral satellite images, this paper proposes an intelligent extraction method for key areas in hyperspectral images. Firstly, the feature layer fusion target spectral signal matching detection method is adopted, and the subspace projection method is used to suppress background clutter signals and enhance the contrast between the target and background clutter to extract the target area. In the absence of any prior information on key area targets, this method uses anomaly detection algorithms to perform coarse screening on abnormal areas. Finally, key area detection is achieved through the fusion of target spectral signal matching detection and anomaly detection decision level fusion in the feature layer. Experimental results show that the proposed method can effectively suppress clutter signals, enhance the distinguishability between targets and backgrounds, and handle situations whether or not the prior spectral information is known. Moreover, the algorithm has strong robustness and low processing complexity. © 2024 IEEE.

关键词： Radar clutter

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AODS: Adaptive Obstacle Detection System for Smart Wheelchairs

AODS: Adaptive Obstacle Detection System for Smart Wheelchai...

引用

2024 IEEE international conference on Control science and Systems engineering, ICCSSE 2024

作者： Li, Lucy Chen, Wenyu Chen, Keyu Electrical and Computer Engineering The Ohio State University ColumbusOH United States School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331517199

The rising global demand for smart mobility solutions underscores the critical need for advanced technologies in power wheelchairs to ensure the safety and independence of users with mobility challenges. This work introduces a novel segmentation-based pipeline for obstacle detection, specifically tailored for outdoor environments, to enhance navigational safety in smart wheelchairs. The segmentation model within the pipeline addresses prevalent issues in current technologies-such as variable lighting conditions, scale changes, and distance sensitivity-that significantly affect detection accuracy. The proposed model incorporates innovative components, including a Distance Augmentation Module (DAM), Scale Aware Module (SAM), and an Illumination Simulation Module (ISM), ensuring robust performance under diverse operational conditions. Validation on a specially curated dataset from urban settings in Chengdu, China, demonstrates a segmentation accuracy of 86.0%, out-performing existing solutions. The findings suggest substantial improvements in detecting and navigating around obstacles, thereby significantly enhancing the operational efficiency and safety of smart wheelchairs. This work not only contributes a sophisticated algorithmic approach and a tailored dataset to the field but also verifies the practical efficacy of the model through extensive experimental validation. © 2024 IEEE.

关键词： Obstacle detectors

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Static Modeling of A Soft Robotic Manipulator Using Neural Networks and Computer Vision Techniques 12

Static Modeling of A Soft Robotic Manipulator Using Neural N...

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12th RSI international conference on robotics and Mechatronics, ICRoM 2024

作者： Shojaeezadeh, Ali Rad, Pouya Firuzy Ghafarirad, Hamed Amirkabir University of Technology Tehran Polytechnic Mechanical Engineering Department Tehran Iran

ISBN: (纸本)9798331529734

This research explores the static modeling of a planar soft robotic arm composed of two parallel silicon-based actuators using neural networks. Soft robots, characterized by their pliable structure and pneumatic actuation, present challenges in traditional control and modeling due to their nonlinear behavior and material flexibility. Conventional methods often fail to accurately predict their motion, especially under varying internal pressures and structural imperfections. In contrast, neural network-based approaches offer robust solutions by effectively capturing these complexities through data-driven techniques. Utilizing a Levenberg-Marquardt algorithm for training, this study develops a neural network model that predicts the 2D spatial position of the robotic arm's end-effector based on input pressure variables. Experimental data, obtained via image processing techniques for precise position tracking, informs the model's training and validation phases. Results demonstrate high accuracy in predicting end-effector positions, validated by low mean squared errors and high coefficients of determination. This approach not only advances static modeling capabilities for soft robotic systems but also lays groundwork for future dynamic modeling and real-time control applications. © 2024 IEEE.

关键词： End effectors

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