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In Situ Monitoring and Recognition of Printing Quality in Electrohydrodynamic Inkjet Printing via machine Learning

JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2024年第11期146卷 110901页

作者： Jiang, Liangkui Wolf, Rayne Alharbi, Khawlah Qin, Hantang Univ WisconsinMadison Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI 53706 USA Univ WisconsinMadison Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI 53706 USA Univ WisconsinMadison Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI 53706 USA Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI 53706 USA

Electrohydrodynamic (EHD) printing is an additive manufacturing technique capable of microscale and nanoscale structures for biomedical, aerospace, and electronic applications. To realize stable printing at its full resolution, the monitoring of jetting behavior while printing and optimization of the printing process are necessary. Various machine vision control schemes have been developed for EHD printing. However, in-line machine vision systems are currently limited because only limited information can be captured in situ toward quality assurance and process optimization. In this article, we presented a machine learning-embedded machine vision control scheme that is able to characterize jetting and recognize the printing quality by using only low-resolution observations of the Taylor Cone. An innovative approach was introduced to identify and measure cone-jet behavior using low-fidelity image data at various applied voltage levels, stand-off distances, and printing speeds. The scaling law between voltages and the line widths enables quality prediction of final printed patterns. A voting ensemble composed of k-nearest neighbor (KNN), classification and regression tree (CART), random forest, logistic regression, gradient boost classifier, and bagging models was employed with optimized hyperparameters to classify the jets to their corresponding applied voltages, achieving an 88.43% accuracy on new experimental data. These findings demonstrate that it is possible to analyze jetting status and predict high-resolution pattern dimensions by using low-fidelity data. The voltage analysis based on the in situ data will provide additional insights for system stability, and it can be used to establish the error functions for future advanced control schemes.

关键词： electrohydrodynamic inkjet printing Taylor cone machine learning high-speed imaging inspection and quality control micro- and nano-machining and processing sensing monitoring and diagnostics

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High precision banana variety identification using vision transformer based feature extraction and support vector machine

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SCIENTIFIC REPORTS 2025年第1期15卷 1-16页

作者： Ergun, Ebru Recep Tayyip Erdogan Univ Fac Engn & Architecture Dept Elect & Elect Engn Rize Turkiye

Bananas, renowned for their delightful flavor, exceptional nutritional value, and digestibility, are among the most widely consumed fruits globally. The advent of advanced image processing, computer vision, and deep learning (DL) techniques has revolutionized agricultural diagnostics, offering innovative and automated solutions for detecting and classifying fruit varieties. Despite significant progress in DL, the accurate classification of banana varieties remains challenging, particularly due to the difficulty in identifying subtle features at early developmental stages. To address these challenges, this study presents a novel hybrid framework that integrates the vision Transformer (ViT) model for global semantic feature representation with the robust classification capabilities of Support Vector machines. The proposed framework was rigorously evaluated on two datasets: the four-class BananaimageBD and the six-class BananaSet. To mitigate data imbalance issues, a robust evaluation strategy was employed, resulting in a remarkable classification accuracy rate (CAR) of 99.86%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\pm\:$$\end{document}0.099 for BananaSet and 99.70%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\pm\:$$\end{document}0.17 for BananaimageBD, surpassing traditional methods by a margin of 1.77%. The ViT model, leveraging self-supervised and semi-supervised learning mechanisms, demonstrated exceptional promise in extracting nuanced features critical for agricultural applications. By combining ViT features with cutting-edge machine learning classifiers, the proposed system establishes a ne

关键词： Agricultural diagnostics Banana classification Deep learning machine learning Precision agriculture vision transformer

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Design of Multi View Natural Language Query System on machine vision 4

Design of Multi View Natural Language Query System on Machin...

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4th IEEE International Conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Fang, Quanrong Investment Co. Ltd Beijing China

ISBN: (纸本)9798350359589

In various fields such as medical imaging, object detection, and video surveillance, multi view natural language query systems utilize image data to provide a more comprehensive perspective, allowing users to intuitively query and obtain information. Due to the lack of a deep understanding of natural language in the hard coded matching rule method, the query results do not match the user's intentions and are difficult to meet practical application needs. Therefore, this article introduces machine vision algorithms for optimization and improvement. This article first discusses the system architecture of four modules: data input and preprocessing, visual feature extraction, natural language understanding and matching, and result generation and feedback. Then, the application of machine vision technology in the system was analyzed using two calculation formulas: grayscale conversion and binarization, and natural language processing technology was briefly discussed. Subsequently, a context understanding module was added to construct a multi view natural language query system based on machine vision. Finally, two sets of simulation experiments were conducted to draw the following conclusion: compared with traditional methods, the overall average improvement in image recognition accuracy indicators is about 14.3%, while the overall average improvement in response speed indicators is about 26.5%. This research system can effectively process images from different perspectives and match them with natural language queries. © 2024 IEEE.

关键词： machine vision technology multi view processing natural language query system design

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Sora for Social vision With Parallel Intelligence: Social Interaction in Intelligent Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4240-4243页

作者： Yu, Hui Liang, Wei Fan, Lili Wang, Yutong Wang, Fei-Yue Univ Portsmouth Sch Creat Technol Portsmouth PO1 2DJ England Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100811 Peoples R China

Artificial technologies have made rapid progress and achieved various superior tasks in the past few years, including but not limited to classification, detection, image generation and data processing. Particularly, the very recent emerging Sora has demonstrated the exceptional ability of text-to-video generation lasting for 1 minute long with impressive quality. It provides a huge potential for many new applications across industries, especially social interaction in intelligent vehicles. The emergence of innovative intelligence vehicle applications has given rise to novel requirements for social and human-vehicle interaction within the associated contexts, where Sora and social vision could play an important role. In this perspective, we present a new Social Interaction framework based on Sora and parallel intelligence in intelligent vehicles and provide a novel perspective for conducting new social and human-vehicle interaction in the context of intelligent vehicles.

关键词： Intelligent vehicles Computational modeling Transformers Computer vision Visualization Human-vehicle systems Human computer interaction Sora parallel intelligence social vision social interaction intelligent Vehicles diffusion model human-machine interaction

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Unifying image processing as Visual Prompting Question Answering 41

Unifying Image Processing as Visual Prompting Question Answe...

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41st International Conference on machine Learning, ICML 2024

作者： Liu, Yihao Chen, Xiangyu Ma, Xianzheng Wang, Xintao Zhou, Jiantao Qiao, Yu Dong, Chao Shanghai Artificial Intelligence Laboratory China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Macau China Kuaishou Technology China

image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally, task-specific models are developed for individual tasks and designing such models requires distinct expertise. Building upon the success of large language models (LLMs) in natural language processing (NLP), there is a similar trend in computer vision, which focuses on developing large-scale models through pretraining and in-context learning. This paradigm shift reduces the reliance on task-specific models, yielding a powerful unified model to deal with various tasks. However, these advances have predominantly concentrated on high-level vision tasks, with less attention paid to low-level vision tasks. To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc. Our proposed framework, named PromptGIP, unifies these diverse image processing tasks within a universal framework. Inspired by NLP question answering (QA) techniques, we employ a visual prompting question answering paradigm. Specifically, we treat the input-output image pair as a structured question-answer sentence, thereby reprogramming the image processing task as a prompting QA problem. PromptGIP can undertake diverse cross-domain tasks using provided visual prompts, eliminating the need for task-specific finetuning. Capable of handling up to 15 different image processing tasks, PromptGIP represents a versatile and adaptive approach to general image processing. Codes will be available at https://***/lyh-18/PromptGIP. Copyright 2024 by the author(s)

关键词： image enhancement

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A Robust Cross-Weighted Thresholding Method for Object Extraction in Complex Scenes

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CIRCUITS SYSTEMS AND SIGNAL processing 2024年第9期43卷 5964-5988页

作者： Yu, Yue Tang, Jun Xiao, Min Zhang, Xuyang Xiamen Univ Technol Sch Optoelect & Commun Engn Xiamen 361024 Fujian Peoples R China Xiamen Univ Technol Detect & Intelligent Percept Grp Xiamen 361024 Fujian Peoples R China

Traditional thresholding methods are widely used to extract objects of interest from image backgrounds in various practical applications. However, these methods often face challenges in complex scenes due to poor uniformity, noise, and low contrast. To overcome these limitations, this paper proposes a peak-weaken Otsu method (PWOTSU) that improves the segmentation performance of the Otsu method for automatically extracting objects in complex scenes. The proposed approach uses a set of cross parameters as weights for the Otsu criterion function to adaptively weaken the between-class variance at the peak of the histogram. This ensures that an appropriate threshold value is always obtained for images with different types of histogram distribution. The improved criterion function has the advantage of obtaining a more accurate threshold value without the need for additional parameters, making it easily applicable to various practical applications. Experimental results demonstrate that the proposed method effectively improves the segmentation accuracy and robustness compared to the standard Otsu method and its modifications, as evidenced by qualitative and quantitative evaluations.

关键词： image segmentation Otsu criterion Complex scene Object extraction machine vision

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A Review of Pre-processing Techniques for Weed-Plant Detection and Classification in Precision Agriculture 4th

A Review of Pre-processing Techniques for Weed-Plant Detecti...

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4th International Conference on Data, Engineering, and applications, IDEA 2022

作者： Sonawane, Sandip Patil, Nitin N. R. C. Patel Institute of Technology Shirpur India

ISBN: (纸本)9789819700363

Precision agriculture has recently gained significant importance in computer vision technologies. Various processes as a part of agricultural production cycle from planting to harvesting can be carried out automatically and effectively by using computer vision. The lack of publicly available image datasets is a major obstacle to the rapid design and assessment of computer vision based applications and also to machine learning algorithms which support these applications. To reduce this bottleneck, numerous image dataset collections have been discovered and made publicly available since 2015. In spite of this development, there is still a need to focus on survey of these datasets. Two most important concerns—choosing the right dataset and knowing how to pre-process and prepare the images in datasets, are considerably challenging task in every application. This review paper gives a thorough analysis of the public image datasets and numerous pre-processing techniques carried out in the field of precision agriculture. This thorough study can lead to development of suitable methods for improved quality and productivity of the crop along with proper weed management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Are Natural Domain Foundation Models Useful for Medical image Classification?

Are Natural Domain Foundation Models Useful for Medical Imag...

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IEEE/CVF Winter Conference on applications of Computer vision (WACV)

作者： Huix, Joana Pales Soderberg, Magnus Ganeshan, Adithya Raju Matsoukas, Christos Haslum, Johan Fredin Smith, Kevin KTH Royal Inst Technol Stockholm Sweden Sci Life Lab Stockholm Sweden AstraZeneca Gothenburg Sweden

ISBN: (纸本)9798350318920;9798350318937

The deep learning field is converging towards the use of general foundation models that can be easily adapted for diverse tasks. While this paradigm shift has become common practice within the field of natural language processing, progress has been slower in computer vision. In this paper we attempt to address this issue by investigating the transferability of various state-of-the-art foundation models to medical image classification tasks. Specifically, we evaluate the performance of five foundation models, namely SAM, SEEM, DINOv2, BLIP, and OPENCLIP across four well-established medical imaging datasets. We explore different training settings to fully harness the potential of these models. Our study shows mixed results. DINOv2 consistently outperforms the standard practice of imageNET pretraining. However, other foundation models failed to consistently beat this established baseline indicating limitations in their transferability to medical image classification tasks.

关键词： Algorithms Algorithms and algorithms applications Biomedical / healthcare / medicine Datasets and evaluations formulations machine learning architectures

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When dual contrastive learning meets disentangled features for unpaired image deraining

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machine vision AND applications 2023年第5期34卷 1-12页

作者： Wang, Tianming Wang, Kaige Li, Qing Chinese Acad Sci Intelligent Mfg Elect Res Ctr Inst Microelect Beijing 100029 Peoples R China Univ Chinese Acad Sci Sch Integrated Circuits Beijing 100029 Peoples R China China Acad Aerosp Sci & Innovat Lab 2050 Beijing 100086 Peoples R China

As the basis work of image processing, rain removal from a single image has always been an important and challenging problem. Due to the lack of real rain images and corresponding clean images, most rain removal networks are trained by synthetic datasets, which makes the output images unsatisfactory in practical applications. In this work, we propose a new feature decoupling network for unsupervised image rain removal. Its purpose is to decompose the rain image into two distinguishable layers: clean image layer and rain layer. In order to fully decouple the features of different attributes, we use contrastive learning to constrain this process. Specifically, the image patch with similarity is pulled together as a positive sample, while the rain layer patch is pushed away as a negative sample. We not only make use of the inherent self-similarity within the sample, but also make use of the mutual exclusion between the two layers, so as to better distinguish the rain layer from the clean image. We implicitly constrain the embedding of different samples in the depth feature space to better promote rainline removal and image restoration. Our method achieves a PSNR of 25.80 on Test100, surpassing other unsupervised methods.

关键词： image processing Deraining Contrastive learning Unsupervised learning

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Advanced thermal vision techniques for enhanced fault diagnosis in electrical equipment: a review

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年第5期16卷 1914-1932页

作者： Sasithradevi, A. Persiya, J. Roomi, S. Mohamed Mansoor Perumal, D. Arumuga Prakash, P. Vijayalakshmi, M. Ebenezer, L. Brighty Vellore Inst Technol Ctr Adv Data Sci Chennai Tamil Nadu India Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India Thiagarajar Coll Engn Dept Elect & Commun Engn Madurai Tamilnadu India Natl Inst Technol Karnataka Dept Mech Engn Surathkal Mangalore India Anna Univ Dept Elect Engn MIT Campus Chennai India

Ensuring the reliability and safety of electrical equipment is essential for industrial and residential applications. Traditional fault diagnosis methods involving physical inspections are time-consuming and ineffective for early fault detection. Infrared (IR) thermography offers a non-invasive and efficient solution by identifying anomalies in temperature profiles. This review explores thermal vision-based fault diagnosis techniques, including region of interest (ROI) segmentation, image pre-processing, and fault diagnosis algorithms, with a focus on deep learning approaches. The study highlights the effectiveness of machine learning models in enhancing fault detection accuracy while identifying challenges such as environmental variations, data inconsistencies, and system integration issues. The review discusses the role of real-time applications, wireless technologies, and AI-based automation in improving fault detection. Research gaps are identified, and future directions are proposed to enhance efficiency, reliability, and industrial adoption.

关键词： Fault diagnosis Infrared thermography Electrical equipment machine learning Deep learning Segmentation

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