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检索条件"任意字段=Conference on Process Imaging for Automatic Control"

共 467 条记录，以下是1-10 订阅

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LIBS imaging as a process control tool in the cork industry 8

LIBS imaging as a process control tool in the cork industry

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conference on Optical Sensing and Detection VIII

作者： Ferreira, Miguel F. S. Oliveira, Rafaela Capela, Diana Lopes, Tomas Marrafa, Jose Meneses, Pedro Oliveira, Armindo Baptista, Carlos Gomes, Tiago Moutinho, Sergio Coelho, Jose da Silva, Raquel Nunes Guimaraes, Diana Silva, Nuno A. da Silva Jorge, Pedro A. INESC TEC Ctr Appl Photon Rua Campo Alegre 687 P-4169007 Porto Portugal Univ Porto Dept Fis & Astron Fac Ciencias Rua Campo Alegre 687 P-4169007 Porto Portugal EGITRON Rua Cent Vergada 1280 P-4535166 Mozelos Vfr Portugal Azevedos Ind Rua Santo Antonio 1Apartado 3 P-4536909 Lourosa Portugal Cork Technol Ctr Rua Amelia Camossa P-4535368 Santa Maria De Lamas Portugal

ISBN: (纸本)9781510673175;9781510673168

The application of surface treatments to cork stoppers is presently a common practice in the wine industry, designed to achieve maximum performance and optimal costumer experience of premium products. Unfortunately, current coating techniques lack efficient process control tools, often resulting in faulty products being detected too late, already in use, compromising performance, product quality and mining consumer confidence. In this work a fully automated system equipped with machine vision and automatic feeding of corks, was coupled with an imaging LIBS setup and used to perform a benchmarking against conventional quality control methods. Results clearly demonstrate the capability of the new LIBS system to effectively evaluate in real time the quality of silicone-based surface coatings in cork stoppers, effectively working as a tool for process control providing a route for effective optimization.

关键词： LIBS imaging Quality control cork stoppers Laser Plasma spectroscopy

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Aircraft Image Management based on AI Typical Mainland Zynq 2

Aircraft Image Management based on AI Typical Mainland Zynq

引用

2nd International conference on Machine Vision, Image processing and imaging Technology, MVIPIT 2024

作者： Shi, Dongqiang Gao, Jie Zhang, Ying Project Manage Department Beijing Aerospace Automatic Control Institute Beijing China Quality Manage Department Beijing Aerospace Automatic Control Institute Beijing China National Key Laboratory of Science and Technology on Aerospace Intelligent Beijing Aerospace Automatic Control Institute Peking University Beijing China

ISBN: (纸本)9798331543037

In the aircraft management system, the universality of aircraft target detection neural network model compression has been verified for many engineering application conditions such as full-range intelligent maneuvering target recognition in specific areas, long-distance maneuvering target detection, and deep space recognition of non-cooperative targets. A data set suitable for aerospace has been selected to complete variable-scale recognition without basically reducing the accuracy. The model is compressed to one-tenth, and the inference time is controlled at the millisecond level. Using neural network compression and quantization after pruning, a 90% compression rate SSD fixed-point model is achieved to control the resize process and effectively prevent precision dissipation, target loss of lock, and energy consumption expansion. Multi-acceleration IP core parallel computing is deployed on the mainland Zynq series single chip to achieve a peak performance of 1.2TOPS and an mAP value of 0.95, meeting the strict requirements of aerospace vehicles for single-time target recognition accuracy. ©2024 IEEE.

关键词： Aircraft detection

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Research on automatic alignment system of H-pillar assembly equipment of high-speed railway catenary based on visual guidance 9

Research on automatic alignment system of H-pillar assembly ...

引用

9th International conference on Energy Materials and Electrical Engineering, ICEMEE 2023

作者： Zhang, Yaqiang Ding, Huanhuan Li, Junwei Cssc Haiwei Tech Co. Ltd. Henan Zhengzhou450000 China

ISBN: (纸本)9781510672765

In the traditional sense, the assembly and installation of OCS pillars is carried out by manual hoisting, which requires a lot of manpower to bind the pillars, manually assist the alignment, unbind the pillars and other installation operations. There are some problems in the operation process, such as poor safety, low efficiency, high labor cost, high risk of high-altitude operation and so on. This paper presents a system of automatic alignment guided by binocular vision, which improves the construction process of high-speed railway catenary, improves the work efficiency and reduces the operation risk. This system is equipped with a binocular vision camera and customized specific targets for position difference calibration and guidance, and motion control is achieved through high-precision, multi degree of freedom robotic arms, thereby achieving automatic alignment and installation function. On the one hand, it has improved the reliability and automation level of the installation of the H-shaped pillar assembly of the high-speed rail contact system, and on the other hand, it provides strong support for the construction of intelligent high-speed rail and high-quality high-speed rail, which has profound significance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Assembly equipment automatic alignment Binocular vision control systems Robotics Visualization Motion controllers Cameras Calibration imaging systems

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"Full" automatic TEM from imaging to Metrology 49

"Full" Automatic TEM from Imaging to Metrology

引用

49th International Symposium for Testing and Failure Analysis conference, ISTFA 2023

作者： Liu, Yong Carlton, Christopher Earl Zhang, Qi Samsung Austin Semiconductor LLC AustinTX United States

ISBN: (纸本)9781627084741

TEM automation is dedicated to providing high-volume, fast and precise TEM data to enable semiconductor manufacturers to develop and control fabrication processes. It automates TEM operation and measurement procedures, and minimizes the requirements of user training. Traditionally, recipes are required for each specific structure, and automatic TEM imaging and Metrology had individual recipes for each structure, respectively and separately. TEM professionals review structures and TEM images and manually assign imaging and metrology recipes one by one. After metrology, it is tedious to manually check mis-measurement of hundreds of TEM images and Critical Dimensions (CDs). In this study, An Intelligent TEM automation process was developed by machine learning process that could "fully" automatically conduct TEM Analysis from imaging to Metrology starting right after TEM holder insertion without human intervention. It is not limited to automatically prepared TEM samples and also works for manually prepared samples. TEM Auto Metrology is carried out automatically right after Auto imaging in the background. After that, Auto capturing of mis-measurements can be carried out automatically to catch problematic metrology images and CDs as an invaluable complement. As a result, the whole workflow was streamlined, with better efficiency and accuracy achieved. Copyright © 2023 ASM International® All rights reserved.

关键词： Failure analysis

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Use of Artificial Intelligence in optical microscope imaging 19

Use of Artificial Intelligence in optical microscope imaging

引用

19th IMEKO TC10 conference on MACRO meets NANO in Measurement for Diagnostics, Optimization and control

作者： Carratù, M. Gallo, V. Laino, V. Liguori, C. University of Salerno Via Giovanni Paolo II 132 SA Fisciano Italy

ISBN: (纸本)9781713884125

Identification and morphological analysis of microorganisms are of high interest in scientific research, especially in the medical field and food industry. Identification allows rapid functional characterization based on similarities with known related species enabling to confirm the identity of an isolate used, for example, in a trademarked industrial process. Monitoring of microorganisms within a given ecosystem and analysis of the morphological characteristics of the observed species enable quality control of the process under analysis. Such procedures are carried out manually in specialized laboratories by trained personnel using the appropriate optical equipment;therefore, it may be of great interest to use automatic measurement approaches that enable rapid and effective process analysis. Artificial intelligence techniques in computer vision and especially deep learning are well suited for this purpose. This article describes the realization of an automatic measurement system based on deep learning for the identification and measurement of morphological parameters of Saccharomyces cerevisiae microorganisms present in brewer's yeast by returning for each of the objects identified within the image the confidence score, the coordinates, and the dimensions of the corresponding ellipsoid-shaped cell. The metrological characteristics of the system have been defined through a calibration process by comparing measurements with a reference system. © 2023 19th IMEKO TC10 conference "MACRO meets NANO in Measurement for Diagnostics, Optimization and control".

关键词： Deep Learning Good Health Identification and Morphological Analysis Saccharomyces cerevisiae Well-being YOLOv5

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COMPUTATIONALLY EFFICIENT KALMAN FILTER FRAMEWORK FOR INTRA-FRAME IMAGE RECONSTRUCTION WITH A ROLLING SHUTTER CAMERA 31

COMPUTATIONALLY EFFICIENT KALMAN FILTER FRAMEWORK FOR INTRA-...

引用

2024 International conference on Image processing

作者： Kanagasingham, Sabeethan Mills, Andrew R. Kadirkamanathan, Visakan Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

ISBN: (纸本)9798350349405;9798350349399

This paper addresses the problem of reconstructing image sequences from a rolling shutter camera-based thermal image acquisition system that integrates the image field over an exposure time. It proposes a novel approach to extend the distributed Kalman filtering framework for intra-frame reconstruction. This accounts for the exposure effect through a state-augmentation model, while respecting the row-byrow image acquisition process. Additionally, two alternative rolling shutter scan strategies: interlaced-x and random, are explored to mitigate delays in observing abrupt changes inherent in sequential rolling shutter scans. Simulation results demonstrate that the proposed approach effectively accommodates exposure and achieves reliable intra-frame reconstruction quality. The interlaced-x scan strategy, with x equal to the size of the image partition block, emerges as the preferred choice, highlighting improved performance in recovering from sudden events. The augmented distributed Kalman filter offers a scalable solution to enhance temporal resolution and overall reliability of thermal imaging of dynamic thermal processes.

关键词： Temporal resolution distributed Kalman filter rolling shutter readout infrared thermography exposure handling

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imaging Histopathology of Breast Cancer for Detection 4

Imaging Histopathology of Breast Cancer for Detection

引用

4th IEEE International Maghreb Meeting of the conference on Sciences and Techniques of automatic control and Computer Engineering, MI-STA 2024

作者： Thabet, M. Maghfiroh, H. University of Tripoli Electrical and Electronics Engineering Tripoli Libya Universitas Sebelas Maret Department of Electrical Engineering Surakarta Indonesia

ISBN: (纸本)9798350372632

Breast cancer is the most common type of cancer seen in women globally, accounting for the majority of cancer-related fatalities in this population. Breast cancer is a serious global health problem. The most common subtype of breast cancer is invasive ductal carcinoma (IDC), which accounts for around 80% of all instances of the disease that are detected. Efficient and accurate diagnosis of this illness is essential for choosing the best course of therapy and improving the prognosis of those who are afflicted. In this study, we present a reliable diagnosis tool based on Convolutional Neural Networks (CNN s) to improve patient survival rates by increasing the number of patients diagnosed early. The process of doing this usually involves the reuse of pre-trained CNN model pieces in a new CNN model through a process known as transfer learning. This approach is employed because a pre-trained CNN already knows how to extract useful and insightful features from a large collection of pictures, and fine-tuning a CNN via transfer learning is frequently quicker and simpler than building and training a new CNN network from the ground up. In this study, we re-trained three pre-trained CNNs (EffecientNet V2, Inception, and Xception) using a fresh dataset of IDC and Non-IDC pictures. By employing the EffecientNet V2 model, we were able to achieve a prediction accuracy of up to 99.56%. © 2024 IEEE.

关键词： Diseases

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Radar-Based Recognition of Static Hand Gestures in American Sign Language

Radar-Based Recognition of Static Hand Gestures in American ...

引用

IEEE Radar conference (RadarConf)

作者： Schuessler, Christian Zhang, Wenxuan Braunig, Johanna Hoffman, Marcel Stelzig, Michael Vossiek, Martin Inst Microwaves & Photon Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Erlangen Germany

ISBN: (纸本)9798350329216;9798350329209

In the fast-paced field of human-computer interaction (HCI) and virtual reality (VR), automatic gesture recognition has become increasingly essential. This is particularly true for the recognition of hand signs, providing an intuitive way to effortlessly navigate and control VR and HCI applications. Considering increased privacy requirements, radar sensors emerge as a compelling alternative to cameras. They operate effectively in low-light conditions without capturing identifiable human details, thanks to their lower resolution and distinct wavelength compared to visible light. While previous works predominantly deploy radar sensors for dynamic hand gesture recognition based on Doppler information, our approach prioritizes classification using an imaging radar that operates on spatial information, e.g. image-like data. However, generating large training datasets required for neural networks (NN) is a time-consuming and challenging process, often falling short of covering all potential scenarios. Acknowledging these challenges, this study explores the efficacy of synthetic data generated by an advanced radar ray-tracing simulator. This simulator employs an intuitive material model that can be adjusted to introduce data diversity. Despite exclusively training the NN on synthetic data, it demonstrates promising performance when put to the test with real measurement data. This emphasizes the practicality of our methodology in overcoming data scarcity challenges and advancing the field of automatic gesture recognition in VR and HCI applications.

关键词： Hand Gesture Recognition Microwave imaging Machine Learning Radar Simulation Ray Tracing

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Role of redundancy in boosting the robustness in Image analysis in the automatic Alignment System at the National ignition Facility 18

Role of redundancy in boosting the robustness in Image analy...

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conference on Optics and Photonics for Information processing XVIII

作者： Awwal, Abdul A. S. Lawrence Livermore Natl Lab Computat Engn Div Natl Ignit Facil Integrated Comp Control Syst Livermore CA 94551 USA

ISBN: (纸本)9781510679320;9781510679337

The first demonstration of indirect drive fusion in the National Ignition Facility (NIF) has ushered in a new era of laser-based fusion work. Before every experiment in NIF all 192 laser beams passing through various laser sub-sections must be aligned to the target chamber center within 30 minutes with a 50-micron accuracy at the target chamber center. NIF uses a CCD camera-based imaging system to identify the beam location;the laser beams are then aligned using motorized mirrors within a feedback control system. The success of the automatic alignment (AA) is contingent upon calculating the exact location of the laser beam in the CCD image of the beam. When an algorithm fails due to poor imaging, gradient illumination, or high noise content, the automated operation for the affected alignment loop stops, and the alignment enters a manual mode with operator intervention. The operator/shot director must decide if the imaging condition needs to be improved to successfully pass alignment or if the beam has to be dropped from participating in the shot thus reducing the total energy delivered to the target. To minimize such failures where a legitimate but challenging beam image is present, additional alternate algorithms are added to the original algorithm. When the first algorithm fails, this alternate algorithm is executed to process the image even though it may have a slightly higher uncertainty. Although the second algorithm may report a higher uncertainty, this system design allows the loop to continue to align the laser without operator intervention. The implication of this approach may be far-reaching, for example, in the application of driver-less cars, where the original algorithm developed by training on a large set of data, may sometimes fail to account for the presence of a pedestrian and lead to a fatal accident. Here we hypothesize that a back-up algorithm based on a second approach will minimize such risks in the real-world driver-less driving scenarios.

关键词： algorithm feature based recognition robustness redundancy laser beam alignment image processing National Ignition Facility

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Res-U2Net: Augmenting 2D/3D Image Reconstruction through Untrained Deep Learning Models for Phase Retrieval Enhancement 20

Res-U2Net: Augmenting 2D/3D Image Reconstruction through Unt...

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20th International conference on Electrical Engineering, Computing Science and automatic control, CCE 2023

作者： Quero, C. Osorio Martinez, J. A. Cisneros Ramos-Garcia, R. Electronics Department Tonantzintla Puebla72840 Mexico Optics Department Tonantzintla Puebla72840 Mexico

ISBN: (纸本)9798350306767

In the field of computational imaging (CI), supervised training methods have long been the dominant approach for neural networks in optics. These methods heavily rely on large amounts of labeled data to adjust network weights and biases effectively. However, obtaining a substantial number of ground-truth images for training poses significant challenges in real-world scenarios like phase retrieval or 2D/3D imaging applications. To overcome this limitation, we propose an innovative approach that merges principles from physics with deep neural networks. Our objective is to reduce the dependency on extensive labeled data by incorporating a comprehensive physical model that accurately represents the image formation process. This unique approach allows us to achieve 3D imaging through phase retrieval, utilizing techniques such as Gerchberg-Saxton (GS) and Fourier-Rytov (FR), in combination with deep learning architectures to extract intricate information from the phase. Consequently, this information enables us to detect changes in an object's surface and generate a mesh representation of its 2D/3D structure. In our proposal, we introduce Res-U2Net, a novel untrained neural network designed to estimate the 3D structure of objects. By adopting a unified method for object analysis, this approach presents a new paradigm for neural network design, seamlessly integrating physical models. Furthermore, this framework can be extended to address a wide range of other computational imaging challenges © 2023 IEEE.

关键词： Computational imaging

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