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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Hybrid prognosis of drill-bits based on direct inspection 5

Hybrid prognosis of drill-bits based on direct inspection

引用

5th International conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Bernini, Luca Malguzzi, Ugo Albertelli, Paolo Monno, Michele Politecn Milan Dept Mech Engn Via Masa 1 I-20156 Milan Lombardy Italy MUSP Macchine Utensili Sistemi Prod Str Torre Razza I-29122 Piacenza Emilia Romagna Italy

Within the paradigm of industry 5.0, manufacturing systems are seeking for human-centred production, where the operator finds high-level supervision tasks. In this context, low-level decision making should be performed by machines themselves. In this paper, a hybrid prognosis algorithm is developed to automatically inspect the cutting edges of drill-bits and to predict their Remaining Useful Life (RUL) and the associated probability density function. The solution relies on the automatic measurement of flank wear through convolutional filtering and edge detection. Prognosis exploits particle filter, which updates multi-layer perceptron with online data, to adaptively predict drill-bits RUL. The solution reduces the experimental preliminary run-to-failures needed for training standard machine learning algorithms, exploiting them in a real-time adaptive scenario, while predicting tool RUL under untested and variable cutting process operations. The algorithm uses direct wear observations, taken during set- up times (e.g., tool changes, workpiece change), thus not interfering with the process. (c) 2023 The Authors. Published by ELSEVIER B.V.

关键词： Hybrid prognostics Direct drill-bit inspection image processing Remaining Useful Life prediction

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Remote Sensing image Target Detection Based on Lightweight and Multi-modality

Remote Sensing Image Target Detection Based on Lightweight a...

引用

Natural Language processing (ICNLP), International conference on

作者： Yudi Yang Haibo Ge Zihan Xue Yu An Mengyang Cheng Shiao xin School of Electrical Engineering Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In order to cope with the problems of high target complexity, scale diversity, different resolutions and limited hardware resources in remote sensing image target detection, a lightweight and multi-modal remote sensing image target detection algorithm is proposed. The traditional convolutional layer (Conv) in the YOLOv8 network is replaced with GhostNet V2, which ensures the detection accuracy while achieving a lightweight network model; a Dual-Modal Fusion module (DMF) is built in the backbone network to integrate pixel-level RGB and IR modes to extract complementary information to enrich network feature information; the lightweight Efficient Channel Attention Mechanism (ECA) is introduced in the DFM module to ensure the lightweight of the model while alleviating channel information imbalance and improving the performance of target detection. The improved algorithm YOLOv8-LD achieved 89.5% mAP50, 16.3M Params, and 34.3G FLOPs on the DIOR data set. Experimental results on DOTA, NWPU and DIOR data sets show that compared with other algorithms, the proposed YOLOv8-LD algorithm achieves lightweight while improving detection accuracy.

关键词： Accuracy image resolution Object detection Feature extraction Hardware Natural language processing Complexity theory Sensors Data mining Remote sensing

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Vision-based Human Identification with Face and Nametape Recognition in Aerial Casualty Monitoring System 32

Vision-based Human Identification with Face and Nametape Rec...

引用

32nd IEEE International conference on Robot and Human Interactive Communication (RO-MAN)

作者： Lee, Jaeyeon Quist, Ethan Chambers, Jonathan Peel, Justin Roman, Kelly Fisher, Nathan TATRC Ft Detrick MD 21702 USA Arete Arlington VA USA

ISBN: (纸本)9798350336702

In emergency rescue scenarios, rapid identification of human casualties is a critical first step in enhancing emergency medical response. This task can be limited by the physical and cognitive capacity of rescue personnel, who are exposed to significant risk. The use of small unmanned aerial systems (sUAS) equipped with autonomous casualty assessment abilities can reduce these limitations and risks by enabling remote casualty detection, identification, and vitals assessment, providing standoff protection, and eliminating the need for human personnel to access the potentially hazardous scene. This paper presents a vision-based casualty assessment framework and specifically discusses our casualty identification software, which is designed to recognize the faces of casualties and identify their nametapes in images captured by sUAS under realistic conditions. Our approach addresses the limitations of the sUAS-captured long-distance images to enable accurate identification in challenging casualty monitoring situations. The face and nametape recognition algorithms will be integrated into the larger casualty perception framework and embedded into sUAS platforms to assist with emergency rescue operations. The total casualty perception system will detect, identify, and evaluate the condition of casualties from a remote location, providing standoff protection to first responders and rapid information to inform a suitable medical treatment plan.

关键词： Aerial image processing Neural network Face and text recognitions Synthetic text data Human detection identification monitoring Unmanned Aerial systems

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Benchmarking and Analyzing 3D-aware image Synthesis with a Modularized Codebase 37

Benchmarking and Analyzing 3D-aware Image Synthesis with a M...

引用

37th conference on Neural Information processing systems, NeurIPS 2023

作者： Wang, Qiuyu Shi, Zifan Zheng, Kecheng Xu, Yinghao Peng, Sida Shen, Yujun Ant Group China HKUST Hong Kong CUHK Hong Kong ZJU China

ISBN: (纸本)9781713899921

Despite the rapid advance of 3D-aware image synthesis, existing studies usually adopt a mixture of techniques and tricks, leaving it unclear how each part contributes to the final performance in terms of generality. Following the most popular and effective paradigm in this field, which incorporates a neural radiance field (NeRF) into the generator of a generative adversarial network (GAN), we build a well-structured codebase, dubbed Carver, through modularizing the generation process. Such a design allows researchers to develop and replace each module independently, and hence offers an opportunity to fairly compare various approaches and recognize their contributions from the module perspective. The reproduction of a range of cutting-edge algorithms demonstrates the availability of our modularized codebase. We also perform a variety of in-depth analyses, such as the comparison across different types of point feature, the necessity of the tailing upsampler in the generator, the reliance on the camera pose prior, etc., which deepen our understanding of existing methods and point out some further directions of the research work. We release code and models here to facilitate the development and evaluation of this field. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Generative adversarial networks

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An Efficient Hybrid Technique for Automatic License Plate Recognitions 1

An Efficient Hybrid Technique for Automatic License Plate Re...

引用

1st International conference on Recent Developments in Electronics and Communication systems, RDECS 2022

作者： Devi, Terli V. Satyanarayana, V. Singh, Mahesh K. Department of ECE Aditya Engineering College Surampalem India Department of ECE Aditya Engineering College A.P. Surampalem India

ISBN: (纸本)9781643683607

Automatic License Plate Recognition (ALPR) is an embedded real-time technology that automatically recognizes a vehicle's license plate. There are numerous uses, ranging from complex security to shared spaces, parking to acceleration. The ALPR is used to control the traffic and to take hold for those who violate the traffic rules. Automatic recognition of vehicle license plate numbers has become critical in our daily lives due to the rapid expansion of autos and transportation systems, which makes it difficult for the general public to keep up with and monitor. A license plate identification system uses image processing theory and soft computing technology to identify cars based on their number plates. Using fewer and simpler algorithms can reduce both calculation time and execution time needed by most number plate localization approaches. This is because most of these techniques require several operations. License plate recognition is the most used approach in traffic-related applications. Various approaches, tactics, and algorithms have been developed for the identification and recognition of license plates. In the context of Indian license plate identification, only a few research have looked at the topic. Character segmentation and character recognition are two of the three key steps in identifying a license plate. © 2023 The authors and IOS Press.

关键词： image segmentation

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Optimal Target Interception using Foldable Quadrotor with Rotating Arms

Optimal Target Interception using Foldable Quadrotor with Ro...

引用

International conference on image and Signal processing and their Applications (ISPA)

作者： Zakaria Mehal Yasser Bouzid Mohammed Guiatni Saddam Hocine Derrouaoui Complex Systems Control and Simulators (CSCS) Laboratory Ecole Militaire Polytechnique Bordj el Bahri Algeria Ecole Superieure Ali Chabati Algires Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

This article addresses a critical problem in the field of target interception using a new foldable quadrotor with rotating arms. The primary challenge is maximizing linear acceleration to enhance the quadrotor’s ability to intercept targets effectively. To address this challenge, we introduce a novel optimization framework. Our study employs two distinct optimization algorithms, namely the genetic algorithms and the whales optimization algorithm, to ascertain the maximum attainable linear acceleration for the foldable quadrotor. The results obtained are confirmed through the use of our innovative foldable quadrotor.

关键词： Heuristic algorithms Signal processing algorithms Life estimation Signal processing Whale optimization algorithms Optimization Robots

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Unveiling the Depths: A Comprehensive Analysis of Natural Language processing and Generative Adversarial Neural Networks for Text Generation Models in Deep Learning 1

Unveiling the Depths: A Comprehensive Analysis of Natural La...

引用

1st International conference on Circuits, Power, and Intelligent systems, CCPIS 2023

作者： Agarwal, Rashi Agarwal, Himanshu Pandey, Senam Gehu India

ISBN: (纸本)9798350315769

Deep Learning comes under Machine Learning that accomplishes more power and flexibility by learning to present different concepts or relations of real world to simpler concepts. We use Deep learning fundaments in this paper because it has massive amount of data that helps in innovations. We include these neural networks of deep learning because it comes with a high accuracy rate with lower computations. Natural processing Language (NLP) and Generative Adversarial Network (GAN) are the methods that individually contribute to the text generation method. Although these are two different technologies giving the output for some common motive where Text generation plays a very important role in smart translations and dialogue systems. This review paper presents a model centered around text generation. This is done because combinedly we want to present what can be different approaches to look at a model like this. To solve the problem of unnecessarily used large texts, unsatisfactory feedback, NLP is used for text generation, GANN is used for text generation model, image generation etc. Finally, this is done to reduce time complexities, speed, efficiency in process because this is noticed that learning for a problem plays a vital role in education to enhance features. © 2023 IEEE.

关键词： Learning algorithms

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The K-DPBFT Algorithm based on Byzantine Fault Tolerance Algorithm

The K-DPBFT Algorithm based on Byzantine Fault Tolerance Alg...

引用

Natural Language processing (ICNLP), International conference on

作者： Yan Li Jiulun Fan Gang Han National Engineering Research Center for Secured Wireless Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

The PBFT algorithm, a classic Byzantine Fault Tolerance consensus mechanism, is designed to mitigate node failures and malicious behavior within distributed systems. However, its effectiveness is hindered when confronted with large-scale networks and heightened latency. In response to these challenges, we proposed the K-DPBFT algorithm, a novel approach based on Byzantine Fault Tolerance principles, aimed at overcoming the aforementioned *** dataset is initially partitioned through multiple iterations, merging clustering results from different partitions. Subsequently, the data is clustered using the DBSCAN algorithm. Finally, two rounds of the PBFT consensus algorithm are conducted to achieve node consensus, aiming to strike a balance between efficiency and performance. Experimental results demonstrate that, in a consensus process involving a significant number of participating nodes, the K-DPBFT algorithm, while possessing Byzantine Fault Tolerance capability, reduces communication interactions by over tenfold compared to the traditional PBFT algorithm. Moreover, the consensus duration is shortened by more than 20%, significantly enhancing consensus efficiency.

关键词： Fault tolerance Fault tolerant systems Merging Clustering algorithms Consensus algorithm Natural language processing Partitioning algorithms Consensus protocol

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C-ESRGAN: Synthesis of super-resolution images by image classification 5

C-ESRGAN: Synthesis of super-resolution images by image clas...

引用

5th IEEE International conference on image processing Applications and systems (IPAS)

作者： Liu, Jingan Chandrasiri, Naiwala P. Kogakuin Univ Intelligent Media Lab Tokyo Japan

ISBN: (纸本)9781665462198

With the development of deep learning, super-resolution image synthesis techniques for enhancing low-resolution images have advanced remarkably. However, mainstream algorithms focus on improving the quality of the entire image on average and this may result in blurring. In this paper, we propose three key components for synthesizing super-resolution images that can reflect the fine details of an image. We synthesize super-resolution images by image classification. First, the neural network weights learned using the images in the same image category were utilized in synthesizing super-resolution images. For this purpose, image classification was performed using a transfer-trained ResNet. Second, SENet was applied to the generators in our proposed method to obtain detailed information about the images. Finally, the feature extraction network was changed from VGG to ResNet in order to get more important features. As a result, we achieved better image evaluation values (PSNR, NIQE) for the super-resolution images of dogs and cats compared to the previous studies. Furthermore, the images were generated more naturally on the benchmark dataset.

关键词： Super-Resolution Generative Adversarial Networks (GANs) SENet image Classification ResNet

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A Fault Detection Method for Power Transmission Lines Using Aerial images

A Fault Detection Method for Power Transmission Lines Using ...

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International conference on Unmanned Aircraft systems (ICUAS)

作者： Yulong Zhang Shaowei Cao Lingxia Mu xianghong Xue Jing xin Youmin Zhang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing School of Automation and Information Engineering Xi'an University of Technology Xi'an Shaanxi China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Quebec Canada

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

It is necessary to regularly detect faults to maintain the safety and stability of power lines. Insulators are one of the important electrical components in high-voltage transmission lines. It is extremely necessary to check the working status of insulators regularly. Traditional manual inspection is inefficient because it requires a significant amount of labor costs. In this paper, a method for detecting insulators' missing defect based on aerial images is proposed to address the issue by unmanned aerial vehicle (UAV). Firstly, the improved Faster R-CNN (region-based convolutional neural network) is used to identify and locate insulators in aerial images. Secondly, the U-Net image segmentation network segments insulators from the images. The adaptive threshold segmentation method completely separates the insulator from the background. Then the binary image of the insulator is obtained. Finally, the binary image is converted into a fault curve which is used for determining the missing insulators based on the distribution of the fault curve. By using collected insulator datasets on a 330kV overhead transmission line using a DJI M300 UAV platform and an onboard H20T camera/sensor, the detection accuracy of glass insulators is as high as 0.98 with the proposed algorithm. The positioning accuracy of the proposed algorithm is also higher than other algorithms. This method has high detection accuracy for missing defects in insulators. The experimental results show that compared with similar algorithms, this method has higher accuracy and efficiency.

关键词： image segmentation Accuracy Power transmission lines Glass Manuals Power system stability Insulators

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