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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

共 2642 条记录，以下是361-370 订阅

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Fault detection of induction machine using artificial neural networks

Fault detection of induction machine using artificial neural...

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

作者： Moutaz Bellah Bentrad Adel Ghoggal Tahar Bahi Seif Eddine Bouziane Electrical Department LGEB Mohamed Khider University Annaba Algeria Electrical Department LGEB Mohamed Khider University Biskra Algeria Electrical Department LASA Badji Mokhtar University Annaba Algeria Intelligent Systems Engineering Department National School of Artificial Intelligence Algiers Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

The diagnosis and monitoring of industrial machinery faults is crucial to the Industrial Revolution but is frequently difficult and labor-intensive. Due to its quick computation, accurate prediction and robustness in performance, the use of artificial intelligence techniques has become a crucial component of condition monitoring of rotating machinery. This paper aims to use the artificial intelligence (neural network technique) to be able to detect and identify air gap eccentricity (static and mixed) and its influence on the stator current signature, the aim is to demonstrate the reliability and accuracy of these techniques to help us make the decision, The achieved simulation results allow distinguishing the types of eccentricity based on the Motor Current Signature Analysis technique data.

关键词： Simulation Fault detection artificial neural networks Stators Signal processing Motors Robustness

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A Physics-informed Low-rank Deep neural Network for Blind and Universal Lens Aberration Correction

A Physics-informed Low-rank Deep Neural Network for Blind an...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Gong, Jin Yang, Runzhao Zhang, Weihang Suo, Jinli Dai, Qionghai Tsinghua Univ Dept Automat Beijing Peoples R China Tsinghua Univ Inst Brain & Cognit Sci Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

High-end lenses, although offering high-quality images, suffer from both insufficient affordability and bulky design, which hamper their applications in low-budget scenarios or on low-payload platforms. A flexible scheme is to tackle the optical aberration of low-end lenses computationally. However, it is highly demanded but quite challenging to build a general model capable of handling non-stationary aberrations and covering diverse lenses, especially in a blind manner. To address this issue, we propose a universal solution by extensively utilizing the physical properties of camera lenses: (i) reducing the complexity of lens aberrations, i.e., lens-specific non-stationary blur, by warping annual-ring-shaped sub-images into rectangular stripes to transform non-uniform degenerations into a uniform one, (ii) building a low-dimensional non-negative orthogonal representation of lens blur kernels to cover diverse lenses;(iii) designing a decoupling network to decompose the input low-quality image into several components degenerated by above kernel bases, and applying corresponding pre-trained deconvolution networks to reverse the degeneration. Benefiting from the proper incorporation of lenses' physical properties and unique network design, the proposed method achieves superb imaging quality, wide applicability for various lenses, high running efficiency, and is totally free of kernel calibration. These advantages bring great potential for scenarios requiring lightweight high-quality photography.

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Using LiDAR Data as image for AI to Recognize Objects in the Mobile Robot Operational Environment 3rd

Using LiDAR Data as Image for AI to Recognize Objects in the...

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3rd International conference on Optimization, Learning Algorithms and applications (OL2A)

作者： Nowakowski, Marek Kurylo, Jakub Braun, Joao Berger, Guido S. Mendes, Joao Lima, Jose Mil Inst Armoured & Automot Technol Okuniewska 1 PL-05070 Sulejowek Poland Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia Braganca Portugal INESC Technol & Sci Porto Portugal Inst Politecn Braganca Lab Sustainabil & Technol Mt Reg SusTEC Braganca Portugal Univ Tras os Montes & Alto Douro UTAD Sch Sci & Technol Dept Engn P-5000801 Vila Real Portugal Bialystok Tech Univ 45A Wiejska St PL-15351 Bialystok Poland

ISBN: (纸本)9783031530357;9783031530364

Nowadays, there has been a growing interest in the use of mobile robots for various applications, where the analysis of the operational environment is a crucial component to conduct our special tasks or missions. The main aim of this work was to implement artificial intelligence (AI) for object detection and distance estimation navigating the developed unmanned platform in unknown environments. Conventional approaches are based on vision systems analysis using neural networks for object detection, classification, and distance estimation. Unfortunately, in the case of precise operation, the used algorithms do not provide accurate data required by platforms operators as well as autonomy subsystems. To overcome this limitation, the authors propose a novel approach using the spatial data from laser scanners supplementing the acquisition of precise information about the detected object distance in the operational environment. In this article, we introduced the application of pretrained neural network models, typically used for vision systems, in analysing flat distributions of LiDAR point cloud surfaces. To achieve our goal, we have developed software that fuses detection algorithm (based on YOLO network) to detect objects and estimate their distances using the MiDaS depth model. Initially, the accuracy of distance estimation was evaluated through video stream testing in various scenarios. Furthermore, we have incorporated data from a laser scanner into the software, enabling precise distance measurements of the detected objects. The paper provides discussion on conducted experiments, obtained results, and implementation to improve performance of the described modular mobile platform.

关键词： Convolutional neural Network Depth Estimation Point Clouds

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Deep Learning for Cardiotocography Analysis: Challenges and Promising Advances 19th

Deep Learning for Cardiotocography Analysis: Challenges and ...

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19th International conference on Advanced Intelligent Computing Technology and applications (ICIC)

作者： Chen, Cang Xie, Weifang Cai, Zhiqi Lu, Yu Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Fujian Business Sch Dept Informat Fuzhou Peoples R China

ISBN: (纸本)9789819947416;9789819947423

Given the recent emergence of deep neural networks and their remarkable ability to handle complex tasks across various "elds and modalities, it is expected that fetal monitoring systems employing artificial intelligence (AI) will soon assist in detecting abnormalities in cardiotocography (CTG). It is vital to comprehend the current state of AI in obstetrics and gynecology, as well as its impact on clinical applications. This paper presents a concise introduction to the fundamentals of computerized cardiotocography and related technologies, followed by an in-depth analysis of contemporary machine learning (ML) and deep learning (DL) advancements in cardiotocography research over the past decades. The review also encompasses other fetal heart monitoring tools, such as the fetal cardiogram. Furthermore, the paper offers recommendations for future research topics when implementing AI in real-world settings, potential ML solutions to address these issues, and how they will catalyze a paradigm shift in automated antepartum and intrapartum monitoring.

关键词： Cardiotocography Machine learning Deep learning Signal processing Explainable AI

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An Innovative Approach for Long ECG Synthesis with Wasserstein GAN Model 24th

An Innovative Approach for Long ECG Synthesis with Wasserste...

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24th International conference on Computational Science and Its applications (ICCSA)

作者： Tran, Thi Diem Dang, Thi Thu Khiet Tran, Ngoc Quoc Univ Informat Technol VNU HCM Ho Chi Minh City 70000 Vietnam Univ Int VNU HCM Ho Chi Minh City 70000 Vietnam Univ Sci John Von Neumann Inst VNU HCM Ho Chi Minh City 70000 Vietnam

ISBN: (纸本)9783031646072;9783031646089

Deep neural networks (DNNs) have set new standards in identifying and classifying irregular patterns in ECG (electrocardiogram) signals, surpassing previous methods. Despite the easy access and affordability of ECG sensors, a critical bottleneck remains the limited availability of reliable data for complex heart rhythms like second and third-degree atrioventricular block, ventricular tachycardia, and supraventricular tachycardia. This shortage has been a significant obstacle to improving DNN algorithms. Recent studies have turned to Generative Adversarial networks (GANs) to create synthetic ECG data, enhancing the diversity of training datasets. However, much of this research has only managed to produce basic ECG components, missing the intricate details found in real patient data that includes multiple heartbeats. Our research has taken a groundbreaking approach by converting ECG signals into a two-dimensional format, allowing us to utilize advanced GAN models originally developed for image processing. This method has enabled us to generate extended, realistic ECG sequences closely mimicking those from actual patients. We have tested and refined our model using two databases, Physionet and Chapman, and have successfully produced 10-second ECG sequences showcasing a variety of heart rhythms previously unachieved in other studies. Our innovative technique not only surpasses existing methods in generating high-quality, realistic ECG data but also sets a new benchmark in ECG synthesis.

关键词： ECG generating Long ECG Signal to image GAN

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Layer-wise External Attention for Efficient Deep Anomaly Detection

Layer-wise External Attention for Efficient Deep Anomaly Det...

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3rd International conference on image processing and Vision Engineering, IMPROVE 2023

作者： Hayakawa, Tokihisa Nakanishi, Keiichi Katafuchi, Ryoya Tokunaga, Terumasa Kyushu Institute of Technology 680-4 Kawazu Iizuka-Shi Fukuoka Japan

ISBN: (纸本)9789897586422

Recently, the visual attention mechanism has become a promising way to improve the performance of Convolutional neural networks (CNNs) for many applications. In this paper, we propose a Layer-wise External Attention mechanism for efficient image anomaly detection. The core idea is the integration of unsupervised and supervised anomaly detectors via the visual attention mechanism. Our strategy is as follows: (i) prior knowledge about anomalies is represented as an anomaly map generated by the pre-trained network;(ii) the anomaly map is translated to an attention map via an external network. (iii) the attention map is then incorporated into intermediate layers of the anomaly detection network via visual attention. Notably, the proposed method can be applied to any CNN model in an end-to-end training manner. We also propose an example of a network with Layer-wise External Attention called Layer-wise External Attention Network (LEA-Net). Through extensive experiments using real-world datasets, we demonstrate that Layer-wise External Attention consistently boosts the anomaly detection performances of an existing CNN model, even on small and unbalanced data. Moreover, we show that Layer-wise External Attention works well with Self-Attention networks. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Convolutional neural networks

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Hollow n-grams Vectorizer for Natural Language processing Problems 13th

Hollow n-grams Vectorizer for Natural Language Processing P...

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13th International conference on image processing and Communications, IP and C 2023, 13th International conference on Computer Recognition Systems, CORES 2023

作者： Borek-Marciniec, Weronika Ksieniewicz, Pawel Department of Systems and Computer Networks Wroclaw University of Science and Technology Wyb. Wyspiańskiego 27 Wroclaw50370 Poland

ISBN: (纸本)9783031416293

Words don’t come easy, which fosters the use of generative artificial intelligence models in ongoing popularity of widely available applications such as ChatGPT. The result is an even greater flood of online content that takes time to process. It is where Natural Language processing tools for classification come in handy. Distinguishing fake news, event types, and other tasks can help process everyday information. In practice, such systems must work on data streams where fast prediction is needed. To achieve it, methods not based on neural networks can be used. Instead, they require feature extractions from the text to convert it to the model input. The primary methods used for this purpose are bag-of-words and n-grams, which allow converting the corpus of texts into a numerical format. This paper proposes a new strategy for creating n-grams – Hollow n-grams – which can be used to create classifiers ensembles with a higher generalization ability than models based on regular n-grams only. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Graph neural networks-Based Multilabel Classification of Citation Network 14th

Graph Neural Networks-Based Multilabel Classification of Cit...

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14th Asian conference on Intelligent Information and Database Systems (ACiiDS)

作者： Lachaud, Guillaume Conde-Cespedes, Patricia Trocan, Maria ISEP Inst Super Elect Paris 10 Rue Vanves F-92130 Issy Les Moulineaux France

ISBN: (纸本)9783031219665;9783031219672

There is an increasing number of applications where data can be represented as graphs. Besides, it is well-known that artificial intelligence approaches have become a very active and promising research field, mostly due to deep learning technologies. However popular deep learning architectures were designed to treat mostly image and text data. Graph neural Network is the branch of machine learning which builds neural networks for graph data. In this context, many authors have recently proposed to adapt existing approaches to graphs and networks. In this paper we train three models of Graph neural networks on an academic citation network of Computer Science papers, and we explore the advantages of turning the problem into a multilabel classification problem.

关键词： Graph neural networks Citation network Multilabel classification

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Early: An Importance-aware Early Firing and Exit for SNN Acceleration 42

Early: An Importance-aware Early Firing and Exit for SNN Acc...

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42nd International conference on Computer Design

作者： Zhang, Xuan Song, Zhuoran Zhou, Peng Li, Xing Liu, Xueyuan Lin, Xiaolong He, Zhezhi Jiang, Li Jing, Naifeng Liang, Xiaoyao Shanghai Jiao Tong Univ Shanghai Peoples R China LuxiTech Co Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350380415;9798350380408

Spiking neural networks (SNNs) have been promising applications in the image recognition domain, and their key component is the spiking neuron. SNNs mainly contain integration and firing processes, which are essentially weight accumulation and threshold comparison, respectively. However, spike trains of the neurons exhibit high sparsity and irregularity in both temporal and spatial domains, leading to inefficient memory access and computation. Therefore, designing an efficient accelerator for SNNs is urgent. This paper presents an elaborate accelerator Early in a software-hardware co-design way. At the software level: (i) Noticing the importance of weights, where larger weights disproportionately affect the membrane potential, we devise a weight importance-aware early firing solution for the firing neurons. It prioritizes the accumulation of these large weights, thereby accelerating the membrane potential's rise to surpass the threshold sooner. (ii) Meanwhile, given the observation that a large proportion of neurons do not eventually be fired even after experiencing a long delay of weight accumulation, we propose a weight importance-aware early exit mechanism. It preferentially accumulates large weights and compares the membrane potential with the predetermined threshold, which early halts the accumulation of neurons that are unlikely to be fired, enhancing efficiency. At the hardware level, we design a specialized processing element (PE) featuring the reorder engine for spikes and weights, tailored to realize the aforementioned strategies. Experimental results show that Early averagely achieves 20.3x, 6.5x, and 2.4x speedup compared to the state-of-the-art accelerators Spinalflow, PTB, and SATO. Meanwhile, it averagely achieves 25.2x, 7.4x, and 3.2x energy savings with respect to the three accelerators.

关键词： spiking neural networks accelerator weight importance-aware early firing and early exit

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DCT-SwinGAN: Leveraging DCT and Swin Transformer for Face Synthesis from Sketch and Thermal Domains 8th

DCT-SwinGAN: Leveraging DCT and Swin Transformer for Face Sy...

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8th International conference on Computer Vision and image processing (CVIP)

作者： Kotadiya, Haresh Kumar Singh, Satish Kumar Dubey, Shiv Ram Yadav, Nand Kumar Indian Inst Informat Technol Comp Vis & Biometr Lab CVBL Allahabad Prayagraj India

ISBN: (纸本)9783031581731;9783031581748

Face generation remains a crucial task owing to its applications in crime investigation, entertainment, etc. Sketch to face synthesis is an important task accomplished using Generative Adversarial Network (GAN) models. The generator of the GAN is usually designed using Convolutional neural networks (CNNs). GANs are able to deliver promising outcomes in some cases but fail in others. Owing to the local nature of CNN, it is unable to keep track of long range dependencies, limiting the model to local information and delivering underwhelming results. However, recently developed Transformers encode the global context having long range dependencies. Swin Transformer is designed to be suitable for images with promising performance on several vision tasks. We utilize the Swin Transformer along with Discrete Cosine Transform (DCT) in the GAN framework and propose DCT-SwinGAN for face synthesis from sketch and thermal domains. DCT-SwinGAN comprises a multi scale discriminator paired with a generator comprising an attention module, DCT ResNet Convolution, Deconv decoder, and Swin Transformer. The proposed model captures not only local but also global information to generate realistic face images. The generated model outperforms the existing state-of-the-art models on CUHK sketch-to-face and the WHU-iiP thermal-to-face datasets.

关键词： Face Synthesis Sketch Thermal GAN DCT Swin Transformer Multi Scale Discriminator image Translation

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