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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

共 18716 条记录，以下是161-170 订阅

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Deep Learning and computer vision Based Warning System for Animal Disruption in Farming Environments 3

Deep Learning and Computer Vision Based Warning System for A...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohanavel, Dhivya Muthu Ishwarya, A. Vellore Institute of Technology School of Advanced Sciences Department of Mathematics Chennai Campus Tamilnadu Chennai600 127 India

ISBN: (纸本)9798350372120

The quarrel between humans and animals due to disruption is cited as a human-wildlife conflict (HWC). These types of incidents frequently leads to a loss of crops, farm animals and humans. In addition to that, it also endangers wildlife species casualty. The local populace in areas where wildlife borders, parks and reserves are close to their dawn have a major dilemma. In this case, animal punishment is not a deterrent. Technology-based security patches must be set into practice. The proposed study suggests installation of an animal detection warning system in the form of an alarm set up in the control room to lessen the harm that untamed animals cause to the surroundings and the humans who are employees around. Animal detection can be adapted using deep learning and computer vision (AI) techniques which will be straightforward to locate and identify any creatures that attempt to attack or enter any agricultural field, or farm. The animals are further deterred by alarm systems that provide sound and signals to the field owners and forest officials. In this analysis, with deep learning as a more reliable object identification strategy, YOLO method is used to examine figures incredibly and quickly. Further, the Artificial intelligence (AI field)'s of computer vision enables machines to gather valuable data and to behave appropriately or offer suggestions following the input. These techniques can effectively handle localization, multi-classification, and target that recurs numerously and a danger signal will notify the forest ranger if an animal is spotted or a warning bell will ring at a certain point indicating field owners that animals are close to them. © 2024 IEEE.

关键词： Animals

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Deep Learning Based Automated Lip Reading for Deaf 3

Deep Learning Based Automated Lip Reading for Deaf

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Prathyakshini Prathwini Pratheeksha Hegde, N. Vaishali Rashmi, N. Kumar, Archana Praveen NMAM Institute of Technology Department of Information Science and Engineering Karkala Nitte India NMAM Institute of Technology Department of Master of Computer Applications Karkala Nitte India NMAM Institute of Technology Department of Computer Science and Engineering Karkala Nitte India Manipal Academy of Education Manipal Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350354171

Speech recognition systems play an integral role in numerous applications, from virtual assistants to accessibility tools. This paper offers a new viewpoint to speech recognition utilizing computer vision and deep learning techniques. The proposed system is trained on a sizable dataset comprising 700 video clips of individuals uttering predefined words, add up to approximately 3 GB of data. Leveraging TensorFlow and Keras, model architecture is designed incorporating convolutional and dense layers. The training process yielded promising results, with a training accuracy of 95.7% and a validation accuracy of 98.5%, indicative of robust classification performance. The integration of computer vision enriches the system's ability to extract meaningful features from audio-visual inputs, enhancing its overall recognition accuracy. Proposed method demonstrates significant potential for real-time speech recognition applications in different areas, like human-computer interaction and assistive technologies. © 2024 IEEE.

关键词： Assistive technology

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SLRNet: Shallow Convolutional Neural Network for Efficient Sign Language Recognition 3

SLRNet: Shallow Convolutional Neural Network for Efficient S...

引用

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

作者： Mostafa, Lamia Brishti, Faria Jerin Akther, Eishita Sultana Khaliluzzaman, M.D. Hoque, MD Jiabul Department of Computer Science and Engineering Chattogram4318 Bangladesh Department of Computer and Communication Engineering Chattogram4318 Bangladesh

ISBN: (纸本)9798331534400

In recent times, recognizing hand gestures from a sign image has become a vital research field in the areas of computer vision and human-computer interaction. Many researchers have taken advantage of various features and deep learning architectures (DLA) to update the performance of existing sign recognition systems. In small datasets such as ASL, shallow DLAs such as convolutional neural networks (CNN) work well. However, the deeper deep learning architectures are overfitted on the small datasets. To overcome this problem, in this work, two shallow CNN models, SLRNet1 and SLRNet2, are proposed. The proposed models use four convolutional layers to extract the features from the sign image and two fully connected layers to recognize the sign. The first and second models gain 98.94% and 99.14% accuracy, respectively. Both models achieve significant improvements with respect to the present state of the art. Contribution - Two regularized hand gesture recognition models, SLRNet1 and SLRNet2, were developed to work efficiently on small datasets without overfitting and perform fine-tuning to achieve better accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Fuzzy Fractional Order PID controller Design for AVR System 3

Fuzzy Fractional Order PID Controller Design for AVR System

引用

3rd IEEE international conference on control, Instrumentation, Energy and Communication, CIEC 2024

作者： Chakraborty, Susmit Mondal, Arindam Das, Chandan Brainware University Department of Computer Science and Engineering Kolkata India Dr. B. C. Roy Engineering College Department of Electrical Engineering Durgapur India Dr. B. C. Roy Engineering College Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350313703

An automated voltage regulator (AVR) is a feedback control system that examines the output voltage of generator, compares it to a predetermined value, and outputs an error signal that is used to modify excitation of the generator. In this manuscript, Fuzzy logic-based fractional order PID controller (Fuzzy-FOPID) is designed for the AVR problem. The parameters of the controller are tuned with a newly introduced metaheuristic optimization algorithm, known as Firebug Swarm optimization (FSO). When compared to other types of controllers described in the literature, the effectiveness of the FSO-tuned Fuzzy-FOPID controller performs the most effective control. The optimized gains of the proposed controller are evaluated on two different types of objective functions Integral Absolute Error (IAE) and Integral of time multiplied absolute error (ITAE). The output of the MATLAB simulation demonstrates the superiority of the suggested controller for the AVR system in terms of the per unit terminal voltage (TV) of the alternator. © 2024 IEEE.

关键词： Optimization

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control Model of Mechatronics Technology Based on Hybrid Particle Swarm Optimization 3

Control Model of Mechatronics Technology Based on Hybrid Par...

引用

3rd international conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Shi, Wei Jiangsu Automation Research Institute Lianyungang222000 China

ISBN: (纸本)9798331519032

People's demand for mechatronics technology control model is increasing gradually. It is necessary to improve the control model of mechatronics technology by studying, and hybrid particle swarm optimization is an effective analysis method. Aiming at the hybrid particle swarm optimization algorithm of electromechanical integration technology control model, the overall scheme demonstration and training demonstration test of portable electromechanical integration system are carried out to find a more suitable solution. In this paper, the control model of mechatronics technology and its development trend are briefly discussed, the existing models are studied, and the control model of mechatronics technology based on hybrid particle swarm optimization is verified through a series of experiments. At the same time, computer simulation and practical verification experiments are carried out. The final research results show that the accuracy of the control model of mechatronics technology can reach 98.1% when the number of operations is 77. The experimental data show that the accuracy of the control model of mechatronics technology has been kept at a relatively stable level, and the fluctuation range is between 98% and 99%. This shows that the accuracy of the control model of mechatronics technology does not fluctuate obviously with the change of operation times. © 2024 IEEE.

关键词： Mechatronics

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Estimation of Cholesterol for Multiple Food Dish on One Plate Using computer vision 3

Estimation of Cholesterol for Multiple Food Dish on One Plat...

引用

3rd international conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Hernawati Nurtanio, Ingrid Syafaruddin Hasanuddin University Department of Informatics Makassar Indonesia Hasanuddin University Department of Electrical Engineering Makassar Indonesia

ISBN: (纸本)9798350327762

High cholesterol is a dangerous condition that can lead to heart attacks, strokes, and even death. One of the causes is consuming foods that are high in cholesterol. Therefore, it is essential for us to know the cholesterol content of every food we consume daily. In an effort to help consumers manage their food intake, we conducted research to create an Android-based application that utilizes computer vision technology. In this research, we employed a deep learning approach to estimate the cholesterol content of various types of food served on a plate. We utilized the Single Shot Multibox Detector (SSD) method with the Mobilenet v2 architecture to detect food objects in images. Subsequently, the detected images were labeled with the respective cholesterol amounts for each food object, presented as bounding boxes. To implement this application in real-time on mobile devices, we used the Tensorflow object detection framework during the extraction process. Through our object detection testing, we achieved an accuracy rate of 93%. This result demonstrates that the computer vision approach using the SSD Mobilenet V2 architecture is capable of effectively detecting food objects. © 2023 IEEE.

关键词： computer vision

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UAV images three-dimensional reconstruction based on neural radiance fields 3

UAV images three-dimensional reconstruction based on neural ...

引用

3rd international conference on Electronic Information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Wang, Shijie Meng, Jicheng School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9781510680449

This paper investigates the three-dimensional reconstruction technique of unmanned aerial vehicle (UAV) images under multiscale scenarios based on neural radiance fields. The approach combines the concept of residual networks and the convergence method of unbounded region functions, and its feasibility is demonstrated through theoretical analysis. Experimental studies are conducted on UAV image datasets at various scales, with a particular focus on unbounded regions with diverse camera poses. The proposed model achieves satisfactory results in handling the challenges posed by multiscale UAV image data. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： computer vision

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Smart Patrolling System Using Machine Learning 3

Smart Patrolling System Using Machine Learning

引用

3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Megala, P. Prabhu, R. Devi, V. Yamini, S. Sri Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore641202 India Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi642003 India Hindustan College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore641032 India Karpagam Institute of Technology Department of Computer Science and Engineering Coimbatore641021 India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

The implementation of Smart Patrolling System using IoT is to provide a safety to the people those who are present in the area by the help of fixing a Sound Sensor, Ultrasonic sensor, Raspberry pi, Node MCU, Night vision camera, GPS, GSM in street lights. The Smart Patrolling System using IoT continuously monitor the place and it gives the signal to the people those who are living in the place first then the control room. The proposed system can be used in Residential areas for giving better safety to the people. The proposed system gives a very most importance to Women and children safety. By the use of our proposed system several crime attacks can be found immediately and it can be avoided from crucial situations. © 2024 IEEE.

关键词： Sensor nodes

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An automated test tube identification and sorting system based on machine vision 3

An automated test tube identification and sorting system bas...

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3rd international conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Jing, Yanhao School of International Education Beijing University of Chemical Technology Changping102200 China

ISBN: (纸本)9781510681279

This research presents a machine vision system for automated test tube identification and sorting in medical laboratories, improving upon manual methods that are error-prone and inefficient. The system employs digital image processing to detect specimen presence via tube caps, enhancing accuracy and throughput. The setup includes a transparent test tube rack with CCD cameras capturing images against a black background. Images are processed from RGB to HSI format for better color differentiation and smoothed to reduce noise. Edge and circle detection algorithms pinpoint the locations of test tubes for robotic arm sorting. Experiments across various filling scenarios validate the system's effectiveness. The system promises to reduce human error, optimize lab workflows, and increase automation in healthcare diagnostics. © 2024 SPIE.

关键词： computer vision

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FPA-based Approximation and Realization of Analog Fractional-Order Integrator 3

FPA-based Approximation and Realization of Analog Fractional...

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3rd IEEE international conference on control, Instrumentation, Energy and Communication, CIEC 2024

作者： Mahata, Shibendu Ghosal, Soumya Suvra De Maity, Ritu Rani Dr. B. C. Roy Engineering College Dept. of Electrical Engineering Durgapur India University of Maryland Dept. of Computer Science College Park United States

ISBN: (纸本)9798350313703

This paper presents the approximation of the analog fractional-order integrator, commonly referred to as the s-α operator, where α∈ (0, 1), using a metaheuristic optimization strategy. The minimization of the frequency response error of the biquadratic approximant with respect to the targeted model is carried out using Flower Pollination Algorithm (FPA). Five different models are suggested by varying a tuning parameter in the cost function which controls the degree of effort individually expended by FPA on minimizing the magnitude and the phase errors. Comparisons with the current literature justify the improved approximation accuracy of the proposed designs. Circuit implementation for the FPA-based half-order integrator is validated using PSPICE. © 2024 IEEE.

关键词： Optimization

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