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Partial discharge based recognition of water droplets location in high voltage insulator using convolutional neural network - Bacterial foraging algorithm based optimized machine learning classifier

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MEASUREMENT 2023年第1期221卷

作者： Kalaivani, L. Maheswari, R. V. Vigneshwaran, B. Karthick, Alagar Kathirvelu, Murugan Marquez, Fausto Pedro Garcia Natl Engn Coll Dept Elect & Elect Engn Kovilpatti 628503 Tamil Nadu India KPR Inst Engn & Technol Dept Elect & Elect Engn Renewable Energy Lab Coimbatore 641407 Tamil Nadu India KPR Inst Engn & Technol Dept Elect & Commun Engn Coimbatore 641407 Tamil Nadu India Univ Castilla La Mancha Ingneium Res Grp Ciudad Real 13071 Spain Univ Cordoba Dept Quim Organ Cordoba Spain

Measurement and analysis of Partial Discharge (PD) patterns have appeared as an emerging field in assessing insulation failure in High Voltage apparatus. This paper uses a PD signal combined with the deep convolution -optimized learning machine classifier (DC-OLMC) to predict the location of water droplets in 11 kV polymer insulators subjected to alternating currents. There are two major confront when applying the proposed algorithm: i) Contamination is a significant issue in PD signal measurement, which causes a reduction in recognition rate (RR), and ii) with minimal computing time, high-level feature extraction and recognition. Traditional condition monitoring methods of insulators concentrated on extracting fewer priority features from the input patterns. In the current work, to address this problem, an Alexnet with Bacterial Foraging Algorithm (BFO) based optimized kernel parameter classifier and Translation Invariant Wavelet Transform (TIWT) is employed to remove interference from PD signals. The analysis demonstrates that the suggested technique, with an identification rate of 99.17%, is considered a valuable tool for locating water droplets in high-voltage insulators.

关键词： High voltage insulators image processing techniques Deep neural network Bacterial Foraging Optimization Kernel function

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Galaxy morphology classification based on Convolutional vision Transformer (CvT)

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ASTRONOMY & ASTROPHYSICS 2024年 683卷

作者： Cao, Jie Xu, Tingting Deng, Yuhe Deng, Linhua Yang, Mingcun Liu, Zhijing Zhou, Weihong Yunnan Minzu Univ Sch Math & Comp Sci Kunming 650504 Yunnan Peoples R China Chinese Acad Sci Key Lab Struct & Evolut Celestial Objects Kunming 650011 Yunnan Peoples R China

Context. The classification of galaxy morphology is among the most active fields in astronomical research today. With the development of artificial intelligence technology, deep learning is a useful tool in the classification of the morphology of galaxies and significant progress has been made in this domain. However, there is still some room for improvement in terms of classification accuracy, automation, and related issues. Aims. Convolutional vision Transformer (CvT) is an improved version of the Vision Transformer (ViT) model. It improves the performance of the ViT model by introducing a convolutional neural network (CNN). This study explores the performance of the CvT model in the area of galaxy morphology classification. methods. In this work, the CvT model was applied, for the first time, in a five-class classification task of galaxy morphology. We added different types and degrees of noise to the original galaxy images to verify that the CvT model achieves good classification performance, even in galaxy images with low signal-to-noise ratios (S/Ns). Then, we also validated the classification performance of the CvT model for galaxy images at different redshifts based on the low-redshift dataset GZ2 and the high-redshift dataset Galaxy Zoo CANDELS. In addition, we visualized and analyzed the classification results of the CvT model based on the t-distributed stochastic neighborhood -embedding (t-SNE) algorithm. Results. We find that (1) compared with other five-class classification models of galaxy morphology based on CNN models, the average accuracy, precision, recall, and F1_score evaluation metrics of the CvT classification model are all higher than 98%, which is an improvement of at least 1% compared with those based on CNNs;(2) the classification visualization results show that different categories of galaxies are separated from each other in multi-dimensional space. Conclusions. The application of the CvT model to the classification study of galaxy morpho

关键词： methods: data analysis techniques: image processing Galaxy: general

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Radio-astronomical image Reconstruction with Conditional Denoising Diffusion Model

arXiv

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arXiv 2024年

作者： Drozdova, Mariia Kinakh, Vitaliy Bait, Omkar Taran, Olga Lastufka, Erica Dessauges-Zavadsky, Miroslava Holotyak, Taras Schaerer, Daniel Voloshynovskiy, Slava Department of Computer Science University of Geneva 7 route de Drize Carouge1227 Switzerland Geneva Observatory University of Geneva 51 Chemin Pegasi Versoix1290 Switzerland

Reconstructing sky models from dirty radio images for accurate source extraction, e.g. source localisation and flux estimation, is a complex yet critical task. It has important applications in galaxy evolution studies at high redshift, particularly in deep extragalactic fields using for example the Atacama Large Millimetre Array (ALMA). With the development of large-scale projects like the Square Kilometre Array (SKA), we anticipate the need for more advanced source extraction methods. Existing techniques, such as CLEAN and PyBDSF, currently struggle to effectively extract faint sources, highlighting the necessity for the development of more precise and robust methods. The success of source extraction task critically depends on the quality and accuracy of image reconstruction. Since the imaging process represents an information-lossy operator, the reconstruction is characterized by uncertainty. The current study proposes the application of stochastic neural networks for the direct reconstruction of sky models from dirty images. This approach allows to localize radio sources and to determine their fluxes with the corresponding uncertainties, providing a potential advancement in the field of radio source characterization. We utilize a dataset of 10164 images simulated with the CASA tool simalma based on the ALMA Cycle 5.3 antenna configuration. We apply conditional Denoising Diffusion Probabilistic Models (DDPMs) to directly reconstruct sky models from these dirty images, followed by processing through Photutils to extract the coordinates and fluxes of the sources. To test the robustness of the proposed model which was trained on a fixed water vapour value, we examine its performance under varying levels of water vapor. The proposed approach demonstrates state-of-the-art in source localisation, achieving over 90% completeness at a signal-to-noise ratio (SNR) as low as 2. Additionally, the described method offers an inherent measure of prediction reliability thanks to

关键词： image reconstruction

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Deep neural Decision Forest for Acoustic Scene Classification

Deep Neural Decision Forest for Acoustic Scene Classificatio...

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European signal processing Conference (EUSIPCO)

作者： Jianyuan Sun Xubo Liu Xinhao Mei Jinzheng Zhao Mark D. Plumbley Volkan Kılıç Wenwu Wang Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey UK College of Computer Science and Technology Qingdao University China Department of Electrical and Electronics Engineering Izmir Katip Celebi University Turkey

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

Acoustic scene classification (ASC) aims to classify an audio clip based on the characteristic of the recording environment. In this regard, deep learning based approaches have emerged as a useful tool for ASC problems. Conventional approaches to improving the classification accuracy include integrating auxiliary methods such as attention mechanism, pre-trained models and ensemble multiple sub-networks. However, due to the complexity of audio clips captured from different environments, it is difficult to distinguish their categories without using any auxiliary methods for existing deep learning models using only a single classifier. In this paper, we propose a novel approach for ASC using deep neural decision forest (DNDF). DNDF combines a fixed number of convolutional layers and a decision forest as the final classifier. The decision forest consists of a fixed number of decision tree classifiers, which have been shown to offer better classification performance than a single classifier in some datasets. In particular, the decision forest differs substantially from traditional random forests as it is stochastic, differentiable, and capable of using the back-propagation to update and learn feature representations in neural network. Experimental results on the DCASE2019 and ESC-50 datasets demonstrate that our proposed DNDF method improves the ASC performance in terms of classification accuracy and shows competitive performance as compared with state-of-the-art baselines.

关键词： Deep learning image analysis neural networks stochastic processes Training data Forestry Acoustics

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SCW-SGD: stochasticALLY CONFIDENCE-WEIGHTED SGD

SCW-SGD: STOCHASTICALLY CONFIDENCE-WEIGHTED SGD

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IEEE International Conference on image processing (ICIP)

作者： Kobayashi, Takumi Natl Inst Adv Ind Sci & Technol 1-1-1 Umezono Tsukuba Ibaraki Japan

ISBN: (纸本)9781728163956

As the field of deep learning has been rapidly growing, the optimization methods (optimizers) gain keen attention for efficiently training neural networks. While SGD exhibits practically favorable performance on various tasks, adaptive methods, such as ADAM, are also formulated to equip the gradient-based updating with adaptive scaling in a sophisticated way. In this paper, we propose a novel optimizer to integrate those two approaches of the adaptive method and SGD through assigning stochastic confidence weights to the gradient-based updating. We define statistical uncertainty of the gradients which is implicitly embedded in the adaptive scaling of ADAM, and then based on the uncertainty, naturally incorporate stochasticity into the optimizer as a bridge between SGD and ADAM. Thereby, the proposed optimizer, SCWSGD, endows the parameter updating with two types of stochasticity regarding multiplicative scaling for the gradient and mini-batch sampling to compute the gradient, for improving generalization performance. In the experiments on image classification using various CNNs, the proposed optimizer produces favorable performance in comparison to the other optimizers.

关键词： neural Network Optimization SGD stochastic weighting

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From CNNs to Adaptive Filter Design for Digital image Denoising Using Reinforcement Q-Learning

From CNNs to Adaptive Filter Design for Digital Image Denois...

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IEEEE Southeast Conference (SoutheastCon)

作者： Alolaiwy, Muhammad Tanik, Murat Jololian, Leon Univ Alabama Birmingham Elect & Comp Engn Dept Birmingham AL 35294 USA

ISBN: (纸本)9781665403795

Multi-modal image acquisition techniques have allowed digital images to penetrate domains from micro-scale medical imaging to mega-scale satellite imaging. For post-processing, deep learning techniques have widely been used for image denoising and artifact suppression. However, far little work has been done to summarize their effectiveness concerning adaptive filter design, e.g., salt and pepper noise, stochastic Poisson, or additive white noise. Because different images, natural or urban, structured or unstructured scenes, and objects produce different types of noise, from the modality as well as from the imaging medium, devising a single method for all noise types is impractical. This paper proposes to use reinforcement learning (Q-learning) to adaptively design filters of a convolutional neural network (CNN). In contrast to the popular state of the art methods that use filter designs based on the noise model, CNN filters lack the power to do so. We have attempted to address this limitation of CNN by introducing a new modality of reinforcement learning for adaptive filter design. The qualitative and quantitative analysis of the proposed method is done and its efficacy is demonstrated using the following evaluation metrics: Peak signal to Noise Ratio (PSNR), Contrast to Noise Ratio (CNR), and Structure Similarity Index (SSIM).

关键词： Reinforcement learning Q-learning Digital image denoising

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Comparison of parametric and non-parametric Bayesian inference for fusing sensory estimates in physiological time-series analysis

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HEALTHCARE TECHNOLOGY LETTERS 2021年第2期8卷 25-30页

作者： Zhu, Tingting Javed, Hamza Clifton, David A. Univ Oxford Dept Engn Inst Biomed Engn Oxford England

The rapid proliferation of wearable devices for medical applications has necessitated the need for automated algorithms to provide labelling of physiological time-series data to identify abnormal morphology. However, such algorithms are less reliable than gold-standard human expert labels (where the latter are typically difficult and expensive to obtain), due to their large inter- and intra-subject variabilities. Actions taken in response to these algorithms can therefore result in sub-optimal patient care. In a typical scenario where only unevenly sampled continuous or numeric estimates are provided, without access to the ground truth, it is challenging to choose which algorithms to trust and which to ignore, or even how to merge the outputs from multiple algorithms to form a more precise final estimate for individual patients. In this work, the novel application of two previously proposed parametric fully-Bayesian graphical models is demonstrated for fusing labels from (i) independent and (ii) potentially correlated algorithms, validated on two publicly available datasets for the task of respiratory rate (RR) estimation. These unsupervised models aggregate RR labels and estimate jointly the assumed bias and precision of each algorithm. Fusing estimates in this way may then be used to infer the underlying ground truth for individual patients. It is shown that modelling the latent correlations between algorithms improves the RR estimates, when compared to commonly employed strategies in the literature. Finally, it is demonstrated that the adoption of a strongly Bayesian approach to inference using Gibbs sampling results in improved estimation over the current state-of-the-art (e.g. hierarchical Gaussian processes) in physiological time-series modelling.

关键词： Probability theory, stochastic processes, and statistics Optical, image and video signal processing Computer vision and image processing techniques Other topics in statistics Other topics in statistics Patient diagnostic methods and instrumentation Biology and medical computing

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DEEP PRODUCT QUANTIZATION MODULE FOR EFFICIENT image RETRIEVAL

DEEP PRODUCT QUANTIZATION MODULE FOR EFFICIENT IMAGE RETRIEV...

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IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Liu, Meihan Dai, Yongxing Bai, Yan Duan, Ling-Yu Peking Univ Inst Digital Media Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Peking Univ SECE Shenzhen Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9781509066315

Product Quantization (PQ) is one of the most popular Approximate Nearest Neighbor (ANN) methods for large-scale image retrieval, bringing better performance than hashing based methods. In recent years, several works extend the hard quantization to soft quantization with specially designed deep neural architectures. We propose a simple but effective deep Product Quantization Module (PQM) to jointly learn discriminative codebook and precise hard assignment in an end-to-end manner. In this work, we use the straight-through estimator to make it feasible to directly optimize the discrete binary representations in deep neural networks with stochastic gradient descent. Different from previous deep vector quantization methods, PQM is a plug-and-play module which can be adaptive to various base networks in the scenarios of image search or compression. Besides, we propose a reconstruction loss to minimize the domain gap between the original embedding features and codebook. Experimental results show that PQM outperforms state-of-the-art deep supervised hashing and quantization methods on several image retrieval benchmarks.

关键词： Product Quantization Hashing Deep Learning

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EDITORIAL SPECIAL ISSUE: PART IV-iiI-ii-I SERIES - FRACTALS-FRACTIONAL AI-BASED ANALYSES AND APPLICATIONS TO COMPLEX SYSTEMS finestre of creativity, complexity and universality…

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2023年第10期31卷 2302005-2302005页

作者： Karaca, Yeliz Baleanu, Dumitru Moonis, Majaz Zhang, Yu-dong Gervasi, Osvaldo Univ Massachusetts Chan Med Sch 55 Lake Ave North Worcester MA 01655 USA Lebanese Amer Univ Beirut 11022 Lebanon Inst Space Sci Bucharest 077125 Romania Univ Leicester Univ Rd Leicester LE1 7RH England Univ Perugia I-06123 Perugia Italy

Complex systems, as interwoven miscellaneous interacting entities that emerge and evolve through self-organization in a myriad of spiraling contexts, exhibit subtleties on global scale besides steering the way to understand complexity which has been under evolutionary processes with unfolding cumulative nature wherein order is viewed as the unifying framework. Indicating the striking feature of non-separability in components, a complex system cannot be understood in terms of the individual isolated constituents' properties per se, it can rather be comprehended as a way to multilevel approach systems behavior with systems whose emergent behavior and pattern transcend the characteristics of ubiquitous units composing the system itself. This observation specifies a change of scientific paradigm, presenting that a reductionist perspective does not by any means imply a constructionist view;and in that vein, complex systems science, associated with multiscale problems, is regarded as ascendancy of emergence over reductionism and level of mechanistic insight evolving into complex system. While evolvability being related to the species and humans owing their existence to their ancestors' capability with regards to adapting, emerging and evolving besides the relation between complexity of models, designs, visualization and optimality, a horizon that can take into account the subtleties making their own means of solutions applicable is to be entailed by complexity. Such views attach their germane importance to the future science of complexity which may probably be best regarded as a minimal history congruent with observable variations, namely the most parallelizable or symmetric process which can turn random inputs into regular outputs. Interestingly enough, chaos and nonlinear systems come into this picture as cousins of complexity which with tons of its components are involved in a hectic interaction with one another in a nonlinear fashion amongst the other related systems

关键词： Applied Mathematics Computational Mathematics Pure Mathematics Mathematical Sciences Discrete Mathematics Mathematical Physics Theoretical Sciences Exact Science Computational methods Computational Intelligence Applied Sciences Computer Science Computational Science Data Science Engineering Electrical and Electronics Engineering Exact Sciences Natural Sciences Social Sciences Nonlinear Dynamics Nonlinear Systems Linear Systems Fractional Calculus AI Control Control Systems Optimization Complexity Chaos Uncertainty Causality Symmetry Oscillations Bifurcation Stability Sensitivity Quantum Medicine Biology Physics Chemistry Mathematical Bioengineering Mathematical Neuroscience Mathematical Biology Artificial Intelligence neural Networks Artificial neural Networks Data-Driven Models Encryption Soft Computing Coding Theory Machine Learning Deep Learning Intelligent Systems Probabilities and stochastic Processes Game Theory Solitons, Fractals, Multifractals Fractional Calculus Fractional Dynamics Fractional Mathematics Fractional Systems Fractional Models Fractals Multifractals Waves Wavelet Entropy Algebraic methods Computational (-Algorithmic) Complexity Fractional Order Differential Equations Fractional Differential Equations Fuzzy Fractional Differential Equations Special Functions Functional Analysis Error Estimations Fuzzy Calculus Differential/Integral Equations, stochastic Differential Equations, Fractal-Fractional Differential Equations Fractional-Order Controllers Fractional-Order Systems Intelligent Fractional Order Systems Variational Principles image and/or signal processing Mathematical Models Fractal AI Fractional AI Fractals-Fractional AI Quantum AI Dynamical systems Chaotic Systems Complex Systems

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NA-SODINN: A deep learning algorithm for exoplanet image detection based on residual noise regimes

arXiv

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arXiv 2023年

作者： Cantero, Carles Absil, Olivier Dahlqvist, Carl-Henrik Van Droogenbroeck, Marc Montefiore Institute Université de Liège Liège4000 Belgium STAR Institute Université de Liège Allée du Six Août 19C Liège4000 Belgium

Context. Supervised deep learning was recently introduced in high-contrast imaging (HCI) through the SODINN algorithm, a convolutional neural network designed for exoplanet detection in angular differential imaging (ADI) datasets. The benchmarking of HCI algorithms within the Exoplanet Imaging Data Challenge (EIDC) showed that (i) SODINN can produce a high number of false positives in the final detection maps, and (ii) algorithms processing images in a more local manner perform better. Aims. This work aims to improve the SODINN detection performance by introducing new local processing approaches and adapting its learning process accordingly. methods. We propose NA-SODINN, a new deep learning binary classifier based on a convolutional neural network (CNN) that better captures image noise correlations in ADI-processed frames by identifying noise regimes. The identification of these noise regimes is based on a novel technique, named PCA-pmaps, which allowed us to estimate the distance from the star in the image from which background noise started to dominate over residual speckle noise. NA-SODINN was also fed with local discriminators, such as signal-to-noise ratio (S/N) curves, which complement spatio-temporal feature maps during the model's training. Results. Our new approach was tested against its predecessor, as well as two SODINN-based hybrid models and a more standard annular-PCA approach, through local receiving operating characteristics (ROC) analysis of ADI sequences from the VLT/SPHERE and Keck/NIRC-2 instruments. Results show that NA-SODINN enhances SODINN in both sensitivity and specificity, especially in the speckle-dominated noise regime. NA-SODINN is also benchmarked against the complete set of submitted detection algorithms in EIDC, in which we show that its final detection score matches or outperforms the most powerful detection algorithms. Conclusions. Throughout the supervised machine learning case, this study illustrates and reinforces the importanc

关键词： signal to noise ratio

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