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

共 18720 条记录，以下是251-260 订阅

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Indirect Current control in Shunt Active Power Filters: Leveraging Downsampling Empirical Mode Decomposition for Improved Performance 3

Indirect Current Control in Shunt Active Power Filters: Leve...

引用

3rd IEEE international conference on Power Electronics, Intelligent control and Energy Systems, ICPEICES 2024

作者： Behuria, Ashutosh Das, Sonali Gadanayak, Debadatta Amaresh Behera, Pritish Priyanshu Bansal, Ramesh C. Mishra, Manohar Iter Soa University Department of Eee Bhubaneswar India University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates University of Pretoria Department of Electric Electronic and Computer Engineering Pretoria South Africa

ISBN: (纸本)9798350387193

The increased efficacy of shunt active power filters (SAPF) in harmonic compensation, attributed to the utilization of fast-switching devices and reduced losses, positions them as the preferred solution over traditional passive filter. This paper presents an innovative methodology to indirect current control for SAPF, employing downsampling empirical mode decomposition (DEMD). By leveraging this novel technique, the research aims to enhance the harmonic compensation capabilities of SAPF within distribution systems. The findings of the study demonstrate that the proposed control strategy significantly improves the efficiency of harmonic compensation without compromising the timely tracking of capacitor voltage references. The incorporation of DEMD in the control methodology contributes to the overall advancement of SAPF technology, ensuring better performance and adaptability in mitigating harmonics in distribution networks. This research highlights the potential of DEMD-based control techniques for optimizing SAPF operation, advancing effective and reliable harmonic compensation in power systems. © 2024 IEEE.

关键词： Empirical mode decomposition

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Automated Assessment of Pizza Quality Using computer vision and Deep Learning Techniques 3

Automated Assessment of Pizza Quality Using Computer Vision ...

引用

3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Arakeri, Megha Lakshmana Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Department of Computer Science and Engineering Manipal India Seamovation Labs Pvt. Ltd Bangalore India

ISBN: (纸本)9798331523893

Quality control in the pizza-making process is essential to meet consumer expectations. In the age of online delivery, verifying the delivered pizza is necessary. The pizza usually consists of a base, sauce, cheese, vegetables, and meat. Low-quality pizza delivery may lead to consumer dissatisfaction and hinder pizza production business growth. Hence, this paper proposes a method to evaluate sauce spread, identify toppings, and assess their distribution on cooked and uncooked pizzas to address this issue. The proposed methodology uses a feature pyramid network for background and foreground separation in the pizza image. Then, the sauce spread thickness is classified into 3 categories using the U-Net model. Finally, a multi-label CNN and color segmentation were applied to the image to evaluate the spread of toppings on the pizza. Experiments were carried out on a standard image dataset, and it was observed that the proposed model achieved 95% accuracy in pizza quality evaluation. © 2025 IEEE.

关键词： Damage detection

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A 3D Positioning Method for Electric Robots Based on Binocular vision 3

A 3D Positioning Method for Electric Robots Based on Binocul...

引用

3rd international conference on computer, control and Robotics, ICCCR 2023

作者： Zhang, Wancai Yang, Wenqing Sun, Zhen Zhang, Nan Information System Integration Branch of NARI Technology Co. Ltd Nanjing China

ISBN: (纸本)9781665492126

When employing monocular camera model detection to determine the 3D position of the operation target, which is common in the field of electric power robotics, the robot arm cannot be accurately guided to work. The approach initially aligns, corrects, and denoises the binocular image data in accordance with the calibration parameters of the binocular camera system. This work offers a 3D positioning method based on binocular vision. Following the separation of the apparent features of the operation target from the binocular image data, the 3D coordinates of the operation target are computed utilizing the geometric constraints of the binocular pair. Finally, the 3D coordinates of the operation target are matched with the pre-constructed 3D model by ICP to obtain the accurate 3D pose of the operation target. It has been demonstrated through studies that the method presented in this work greatly improves the 3D positional identification accuracy when compared to other existing charging port detection methods. It may also be applied to other sectors of electric robots. © 2023 IEEE.

关键词： Binocular vision

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Application of lightweight YOLOv8n networks for insulator defect detection 3

Application of lightweight YOLOv8n networks for insulator de...

引用

3rd international conference on Robotics, Artificial Intelligence and Intelligent control (RAIIC)

作者： Ma, Fulin Gao, Zhengzhong Chai, Xinbin Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China Wenshang Yiqiao Coal Mine Co Ltd Jining Peoples R China

ISBN: (纸本)9798350352634;9798350352627

To solve the problems of small insulator defect targets and complex background information in transmission lines, as well as the difficulty of edge-end devices to meet real-time detection requirements, a lightweight insulator defect detection algorithm based on YOLOv8n is proposed. The backbone network of YOLOv8n is reconstructed by introducing a lightweight bottleneck structure, GhostNetV2 BottleNeck, which reduces the number of network parameters and improves the detection speed of the model, and the CBAM attention mechanism is embedded in the backbone network, which improves the ability of the network to extract the target features, and thus improves the detection accuracy of the model. By validating the improved algorithmic model on the insulator dataset, the results show that the mean average accuracy of the improved algorithmic model reaches 85.7%, and the detection speed reaches 171.4 frames/s, which verifies the effectiveness of the improved algorithmic model for the detection of insulators and their defects.

关键词： machine vision deep learning insulator defect detection YOLOv8n

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Pneumonia Detection in Chest X-rays using CNN 3

Pneumonia Detection in Chest X-rays using CNN

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Chothani, Aaryan Khemani, Bharti Malave, Sachin Jain, Mit Gupta, Jaya A.P. Shah Institute of Technology Department of Computer Engineering Thane India Thane India

ISBN: (纸本)9798350381931

Pneumonia is a life-threatening condition which affects people of all ages but is mostly found in infants and older adults. It is caused by a variety of factors including bacteria, viruses and fungi and causes infections in one lung or both. The diagnosis of Pneumonia is usually facilitated using chest X-rays. This diagnosis becomes difficult in cases where access to expert radiologists or doctors to discover the disease is unavailable. Hence, using a system which makes use of powerful Machine Learning and Deep Learning techniques in order to make the diagnosis of Pneumonia in chest X-rays easier is more feasible. This problem has seen tremendous advancements in recent times with the use of various computer vision and ML methodologies. In this work, we have showcased the use of multiple such methods in order to create a complete system based on the purpose of detecting Pneumonia in chest X-rays. The integral component of the system is a Convolutional Neural Network (CNN) model, trained on a pre-processed dataset of chest X-rays. CNN has become the most used Deep Learning algorithm for computer vision problems due to its strength of dealing with images. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient vision transformer for dynamic embedding of multiscale features 3

Efficient vision transformer for dynamic embedding of multis...

引用

3rd international conference on Machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Zhang, Mingrui Wang, Ronggui Yang, Juan Xue, Lixia School of Computer and Information Hefei University of Technology Anhui230601 China

ISBN: (纸本)9781510681880

vision Transformer (ViT) fully demonstrates the potential of the transformer architecture in the field of computer vision. However, the computational complexity is proportional to the length of the input sequence, thus limiting the application of transformers to high-resolution images. In order to improve the overall performance of vision Transformer, this paper proposes an efficient vision Transformer (MLVT) with dynamic embedding of multi-scale features, adopting the pyramid architecture, replacing the self-attention operation with linear self-attention, proposing a local attention enhancement module to address the problem of the dispersal of linear self-attention scores that ignores local correlation, and supplementing the local attention with the convolution of the self-attention-like computation. operation of self-attention-like computation is utilized to supplement the local attention. Aiming at the increase of feature dimension in pyramid architecture, the bottleneck of linear self-attention computation is changed from sequence length to feature dimension, and the linear self-attention with compressed feature dimension is proposed. In addition, since multi-scale inputs are crucial for processing image information, this paper proposes a flexible and learnable dynamic multi-scale feature embedding module, which dynamically adjusts the weights of different scale features according to the input image for fusion. A large number of experiments on image classification and target detection tasks show that competitive results are achieved while reducing the computational effort. © 2024 SPIE.

关键词： Image fusion

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Machine Learning-Powered Real-Time Motion Detection System: A Review 3

Machine Learning-Powered Real-Time Motion Detection System: ...

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Khurana, Naman Bansal, Madhavi Thakur, Ananya Varshitha, Velma Sai Tannu Kaur, Er. Kirat Chandigarh University Computer Science & Engineering Punjab India

ISBN: (纸本)9798350343632

Motion detection, a fundamental technology in computer vision, is crucial for a wide array of applications, including security,surveillance, gaming and robotics. OpenCV, the open-source computer vision Library, has emerged as a versatile tool for developing robust and efficient motion detection systems. The article offers insights into the intricacies of motion detection using OpenCV, combining existing research findings with a unique perspective on this critical subject. It explores traditional and advanced techniques, highlights real-world applications and addresses persistent challenges in the field such as handling background clutter, adapting to changing lighting conditions, and achieving real-time processing. It presents a fundamental approach to motion detection, encompassing frame acquisition, frame differencing, thresholding, noise reduction, object tracking, and more, with a focus on the tools and technologies that can enhance these processes. The primary problem addressed in the article is the development of accurate, reliable, and real-time motion detection systems considering various challenges and objectives across different application domains. The provided references encompass a wide range of research in the field, contributing to a comprehensive understanding of motion detection. The article can serve as a valuable resource for individuals interested in motion detection and its integration with OpenCV, offering a foundation for readers to embark on their motion detection projects and foster innovation in this dynamic field. © 2023 IEEE.

关键词： computer vision Frame Differencing Motion Detection Object Tracking Thresholding

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Sentiment Analysis with LSTM Recurrent Neural Network Approach for Movie Reviews using Deep Learning 3

Sentiment Analysis with LSTM Recurrent Neural Network Approa...

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Hegde, Sachin J Madhunandana, H.M. Mohana RV College of Engineering® Computer Science and Engineering Bengaluru India Bengaluru India

ISBN: (纸本)9798350343632

In this research work, an integrated sentiment analysis system is designed specifically for movie reviews. Leveraging LSTM-based sentiment analysis, Proposed implementation simplifies model training using pre-trained LSTM models, resulting in robust sentiment prediction capabilities. The architecture emphasizes abstraction, separating frontend and backend components, optimizing data flow with JSON formatting and Pickle Files, and providing a user-friendly interface. By highlighting the role of abstraction in enhancing user experiences and system efficiency, the research reveals the successful synergy of DL techniques, architectural design, and abstraction principles. The LSTM model achieved testing accuracy of 87.53%. © 2023 IEEE.

关键词： Artificial Intelligence(AI) computer vision(CV) Convolutional Neural Network (CNN) Deep Learning (DL) Long Short-Term Model (LSTM) Model Natural Language Processing (NLP) Sentiment Analysis

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A Real-Time Virtual Yoga Assistant Using Machine Learning 3

A Real-Time Virtual Yoga Assistant Using Machine Learning

引用

3rd international conference on Smart Data Intelligence, ICSMDI 2023

作者： Chittineni, Aruna Kotagiri, Yaswanth Sai Kolli, Mohit Kollipara, Teja Modepalli, John Raju Namburi, Sravan Kumar Kkr & Ksr Institute of Technology and Sciences Computer Science & Engineering Guntur India

ISBN: (纸本)9781665464871

Yoga is a practice that aims to develop an all-around personality by synchronizing the mind, body, and spirit. However, incorrect postures or techniques can cause damage. In ancient times, yoga was performed under the supervision of a teacher, but it is difficult to find a competent guru in today's fast-paced world. The goal of this project is to develop an application that can track and evaluate physical exercise, specifically yoga, through the use of human pose estimation. This application, called 'A Real-Time Virtual Yoga Assistant,' uses machine learning methodologies to classify data on yoga positions in both pre-recorded and real-time videos. The research also examines various pose estimation and key point detection approaches and deep learning models used for posture classification. © 2023 IEEE.

关键词： computer vision

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Intelligent Visual Tracking Robots Using Cascade PID controllers 3

Intelligent Visual Tracking Robots Using Cascade PID Control...

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3rd IEEE international conference on Signal, control and Communication, SCC 2023

作者： Farkh, Rihem Oudinet, Ghislain Sananes, Florian Alzahrani, Sultan Alhuwaimel, Saad De Leruyelle, Thibaut Isen Toulon Toulon France Digital Health Institute Health Sector Kacst Riyadh Saudi Arabia King Abdulaziz City for Science and Technology Riyadh Saudi Arabia

ISBN: (纸本)9798350326390

A tracking system is useful for a variety of robotic applications, including obstacle detection, autonomous path planning and surveillance. One of the most difficult issues in computer vision is visual object tracking, which involves estimating the position of a target in an image series. Utilizing color information for visual tracking poses a formidable challenge due to variations in illuminant, shadows, shading, specular highlights, cameras, and object geometry. Consequently, color measurements exhibit significant fluctuations throughout an image series. A camera is frequently used for object tracking to acquire information on construction robot motion, which may subsequently be used in coordination controls. The paper presents a mobile robot vision system that allows the robot to recognize and track objects based on their color. A new approach is proposed for stable tracking of mobile robot using combined algorithm based on computer vision techniques and a cascade proportional integral derivative controllers (PID). Within a system designed for controlling the speed of a DC motor, the goal of a PID controller is to maintain speed at a specified reference value while dynamically accepting new set point values. The combination of feedback encoder and PID controllers can be employed to control motor speeds in the presence of noise and load disturbances © 2023 IEEE.

关键词： Three term control systems

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