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Hourly solar irradiance forecasting based on statistical methods and a stochastic modeling approach for residual error compensation

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stochastic ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2023年第12期37卷 4857-4892页

作者： Nikseresht, Ali Amindavar, Hamidreza Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA Univ Washington Dept Elect Engn Seattle WA 98195 USA

By reducing fossil fuel use, renewable energy improves the economy, quality of life, and environment. These impacts make renewable energy forecasting crucial for lowering fossil fuel utilization. This paper aims to mathematically improve time series forecasting literature by focusing on solar irradiance applications in Los Angeles, Denver, and Hawaii solar irradiance sites. A three-phased time series forecasting hybrid method is devised for this endeavor. The ARFIMA is used to forecast the original solar irradiance time series in phase I. Next, the dataset's residuals, are retrieved by subtracting the phase I results from the observed time series to prepare the scenario for the following phase. A novel enhanced fractional Brownian motion is used for residual forecasting in phase ii. The parameter estimation in phase ii is implemented adaptively to capture the dynamic statistical characteristics of the time series efficiently. Finally, the phases I and ii results are numerically conglomerated to form the final forecasting results in phase iiI. The residual forecasting part, in phase ii, reveals a substantial superiority. Also, when comparing the proposed hybrid algorithm results to other existing cutting-edge algorithms applied to the same solar irradiance applications, the output demonstrates that the suggested algorithm has a significantly improved performance.

关键词： Solar energy Solar irradiance forecasting statistical signal processing Algorithmic information theory Fractional stochastic differential equations Residual forecasting

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Modeling a stochastic Computing Nonscaling Adder and its Application in image Sharpening

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS ii-EXPRESS BRIEFS 2022年第5期69卷 2543-2547页

作者： Temenos, Nikos Sotiriadis, Paul P. Natl Tech Univ Athens Dept Elect & Comp Engn Athens 15780 Greece

Non-scaling stochastic Computing adder and subtracter architectures are introduced. They are modeled using Markov Chains to obtain important statistical properties enabling their design optimization. To demonstrate their efficacy, they are used to realize a stochastic computing-based image sharpening filter which is simulated in MATLAB and Synopsys. The filter's computational efficiency is showcased with standard image processing metrics while its hardware resources are compared to those of the standard binary filter, highlighting the advantages of the proposed approach.

关键词： Adders Standards Computer architecture Mathematical models Markov processes Computational modeling Registers stochastic computing stochastic non-scaling adder stochastic image sharpening filter

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Riemannian Generalized Gaussian Distributions on the Space of SPD Matrices for image Classification

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IEEE ACCESS 2024年 12卷 26096-26109页

作者： Abbad, Zakariae El Maliani, Ahmed Drissi El Hassouni, Mohammed Abbassi, Mohamed Tahar Kadaoui Bombrun, Lionel Berthoumieu, Yannick Mohammed V Univ Rabat ENSIAS Rabat 10000 Morocco Mohammed V Univ Rabat Fac Sci LRIT Rabat IT Ctr Rabat 10000 Morocco Mohammed V Univ Rabat FLSH Rabat 10000 Morocco Sidi Mohamed Ben Abdellah Univ Fac Sci Dhar El Mahraz Lab Math Sci & Applicat Fes 30000 Morocco Univ Bordeaux CNRS Bordeaux INP IMSUMR 5218 F-33400 Talence France Bordeaux Sci Agro F-33175 Gradignan France

The space of symmetric positive definite (SPD) matrices, denoted as $P_{m}$ , plays a crucial role in various domains, including computer vision, medical imaging, and signal processing. Its significance lies in its capacity to represent the underlying structure in nonlinear data using its Riemannian geometry. Nevertheless, a notable gap exists in the absence of statistical distributions capable of characterizing the statistical properties of data within this space. This paper proposes a new Riemannian Generalized Gaussian distribution (RGGD) on that space. The major contributions of this paper are, first of all, providing the exact expression of the probability density function (PDF) of the RGGD model, as well as an exact expression of the normalizing factor. Furthermore, an estimation of parameters is given using the maximum likelihood of this distribution. The second contribution involves exploiting the second-order statistics of feature maps derived from the first layers of deep convolutional neural networks (DCNNs) through the RGGD stochastic model in an image classification framework. Experiments were carried out on four well-known datasets, and the results demonstrate the efficiency and competitiveness of the proposed model.

关键词： Symmetric positive definite matrices generalized Gaussian distribution texture Riemannian geometry Rao's distance Riemannian metric

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An adaptive enhancement method based on stochastic parallel gradient descent of glioma image

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IET image processing 2023年第14期17卷 3976-3985页

作者： Wang, Hongfei Peng, Xinhao Ma, ShiQing Wang, Shuai Xu, Chuan Yang, Ping Chinese Acad Sci Key Lab Adapt Opt Chengdu Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China Univ Elect Sci & Technol China Sch Med Chengdu Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Sichuan Acad Med Sci Dept Oncol Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Canc Inst Sichuan Acad Med Sci Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Dept Lab Med Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Sichuan Prov Key Lab Human Dis Gene Study Chengdu Sichuan Peoples R China

Brain tumour diagnosis is significant for both physicians and patients, but the low contrast and the artefacts of MRI glioma images always affect the diagnostic accuracy. The existing mainstream image enhancement methods are insufficient in improving contrast and suppressing artefacts simultaneously. To enrich the field of glioma image enhancement, this research proposed a glioma image enhancement method based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm. Firstly, this method modifies the cumulative distribution function on the image histogram and performs gamma correction on the image according to the modified histogram to obtain a contrast-enhanced image. Then, the method suppresses the artefacts of glioma images by total variational and wavelet denoising algorithm. To get better enhancement images, the optimal parameters in the proposed method are searched by the SPGD algorithm. The statistical studies performed on 580 real glioma images demonstrate that the authors' approach can outperform the existing mainstream image enhancement methods. The results show that the proposed method increases the discrete entropy of the image by 8.9% and the contrast by 2.8% compared to original images. The enhanced images are produced by the proposed method with a natural appearance, appealing contrast, less degradation, and reasonable detail preservation. To enrich the field of glioma image enhancement, this paper proposed a glioma image enhancement framework based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm. The results show that the proposed framework has superior performance compared to several existing glioma image enhancement methods. image

关键词： image denoising image enhancement parallel algorithms artefacts suppression contrast improvement histogram modification stochastic parallel gradient descent

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Development of control charts to monitor image data using the contourlet transform method

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第2期40卷 876-898页

作者： Khodadadi, Zahra Owlia, Mohammad Saleh Amiri, Amirhossein Fallahnezhad, Mohammad Saber Yazd Univ Dept Ind Engn Yazd Iran Shahed Univ Dept Ind Engn Tehran Iran

In recent years, researchers and practitioners have been exploring new methods for quality control, including image processing. The effective use of high-volume image data can significantly improve the monitoring of production and service systems in terms of speed, accuracy, and cost. Adopting an image-based approach is better than relying on operator-based solutions, and it offers new perspectives for process monitoring. image processing can involve extracting features to identify, classify, detect, and cluster. Although there are several transformations to extract features from images, the Fourier method cannot consider the concurrency of frequency and time data, and the wavelet method only considers two specific directions. In multidimensional transforms, the optimal method can provide more information using fewer coefficients. The contourlet transform has advantages such as multiresolution, localization, critical sampling, directionality, and anisotropy. This research investigates the advantages of applying the contourlet transform to images and using data in a generalized likelihood ratio control chart. The results show that this method is more accurate than others because it can examine various directions in images. The proposed methodology algorithm is also presented in this study.

关键词： average run length contourlet transform feature extraction generalized likelihood ratio image data Phase ii statistical process monitoring

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Brain tumour detection in magnetic resonance imaging using Levenberg-Marquardt backpropagation neural network

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IET image processing 2023年第1期17卷 88-103页

作者： Ghahramani, Marzieh Shiri, Nabiollah Islamic Azad Univ Shiraz Branch Dept Elect Engn Shiraz *** Iran

Magnetic resonance imaging (MRI) is a high-quality medical image that is used to detect brain tumours in a complex and time-consuming manner. In this study, a back propagation neural network (BPNN) along with the Levenberg-Marquardt algorithm (LMA) is proposed to classify MRIs and diagnose brain tumours in a simple and fast process. The BPNN has 10 neurons in the hidden layer, and the default function of the feedforward feeds is mean squared error (MSE). The LMA is optimized as a multivariable adaptive approach and considerably decreases the MSE of the BPNN, so the errors of the tumour classification are diminished. The proposed method follows four steps including preprocessing, skull removal, feature extraction, and classification. The input MRIs are converted to greyscale, resized, and thresholding is performed in the preprocessing step and followed by skull removal. Morphological operations of closing, opening, and dilation are used to segment abnormal areas in the MRIs, and the opening operator recognizes the tumour more accurately. Using statistical analysis and a grey-level co-occurrence matrix (GLCM) 12 features are extracted from the MRIs and used as the inputs of the BPNN. To evaluate the proposed method, 670 normal and 670 abnormal brain MRIs are used as input data, and the classification is performed in 0.494 s. The accuracy, sensitivity, specificity, precision, dice, recall, and MSE are 98.7%, 97.61%, 99.7%, 97.61%, 98.6%, 97.61%, and 0.005, respectively. The approach is accurate and fast for medical images classification.

关键词： medical image classification image segmentation feature extraction tumour classification default function Biology and medical computing input MRIs high-quality medical image image classification biomedical MRI Medical magnetic resonance imaging and spectroscopy multivariable adaptive approach brain tumour detection Biophysics of neurophysiological processes brain tumour diagnosis Neural nets backpropagation mean squared error opening operator greyscale Computer vision and image processing techniques Biomedical magnetic resonance imaging and spectroscopy statistical analysis grey-level cooccurrence matrix neural nets skull removal hidden layer preprocessing step image recognition MSE preprocessing removal Levenberg-Marquardt algorithm magnetic resonance imaging image thresholding abnormal brain MRIs BPNN morphological operations brain LMA Other topics in statistics Patient diagnostic methods and instrumentation tumours Levenberg-Marquardt backpropagation neural network medical image processing Probability theory, stochastic processes, and statistics

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Localized Binarization of Document images Based on Suprathreshold stochastic Resonance 4

Localized Binarization of Document Images Based on Suprathre...

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4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Yan, Xiaoyue Mu, Dazhong School of Information Engineering Beijing Institute Of Graphic Communication Beijing China

ISBN: (纸本)9798331528850

When processing text images with traditional binarization methods, the image background noise often causes the results to become blurred or leads to the loss of edge details. To solve this problem, this paper proposes an image binarization method based on stochastic resonance theory. First, we divide the image into sub-blocks and set a binarization threshold based on the statistical properties of the pixels in each sub-block. Next, the image signal is converted into a one-dimensional time series signal using Hilbert scanning. The processed signals are input into a threshold array system, which amplifies the weak edge information in the input signals through the stochastic resonance phenomenon. Subsequently, we performed modulation and inverse scanning on the output signals of the system to generate the binary image for each sub-block. Finally, all sub-block binary images were combined to complete the binarization of the overall image. Experimental results show that the method proposed in this paper can effectively retain the detailed information of document images and significantly outperforms the traditional binarization method regarding image quality. © 2024 IEEE.

关键词： stochastic systems

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Carbon fibre detection in polymer composites reinforced by chopped carbon fibres through digital image processing and machine learning

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JOURNAL OF COMPOSITE MATERIALS 2024年第21期58卷 2379-2395页

作者： Geier, Norbert Magyar, Gergely Giner, Jakob Lukacs, Tamas Poka, Gyorgy Budapest Univ Technol & Econ Fac Mech Engn Dept Mfg Sci & Engn 1111-HU BudapestMuegyetem rakpart 3 H-1111 Budapest Hungary Fraunhofer Austria Res GmbH Div Factory Planning & Prod Management Vienna Austria

Mechanical and thermodynamical properties and thus machinability of carbon fibre reinforced polymer composites significantly depend on the fibre orientation relative to the load direction. However, the orientations of the fibre groups in polymer composites reinforced by chopped carbon fibres are stochastic;therefore, the properties and machinability of such composites are challenging to plan, predict and optimise. We developed four different and novel approaches for fibre detection in polymer composites reinforced by chopped carbon fibres: (i) detecting the fibres through naked eye supported manual drawing, (ii) digital image processing of optical images, (iii) machine learning-based fibre detection, and (iv) rectangle fitting on the outputs of the automated processes using the Chaudhuri and Samal method. The applicability of the novel approaches was tested through optically captured images of polymer composites reinforced by chopped carbon fibres. The developed methods are each capable of detecting fibre groups at the top and bottom of the composite plate with certain limitations. The rectangle fitting approaches performed the best from the point of view of correctly identifying of fibre groups, followed by the machine learning-based and the conventional digital image processed, respectively. As a result of this study, the machining process planning and condition monitoring of polymer composites reinforced by chopped carbon fibres is more deeply supported.

关键词： A. carbon fibres a. polymer-matrix composites b. mechanical properties d. optical microscopy

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Non-invasive coronary artery disease identification through the iris and bio-demographic health profile features using stacking learning

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image AND VISION COMPUTING 2024年 146卷

作者： Ozbilgin, Ferdi Kurnaz, Cetin Aydin, Ertan Giresun Univ Dept Elect & Elect Engn Giresun Turkiye Ondokuz Mayis Univ Dept Elect & Elect Engn Samsun Turkiye Giresun Univ Fac Med Dept Cardiol Giresun Turkiye

This study proposes a non-invasive method for predicting Coronary Artery Disease (CAD) using iris analysis, patient data, and Machine Learning (ML), primarily with iris images. It involved 281 participants, comprising 155 CAD patients and 126 non -patient controls, with eye images and biodemographic data collected at a Cardiology outpatient clinic. The study explored three scenarios: Scenario -I focused on biodemographic data, Scenario -ii on iris features, and Scenario -iiI combined iris images and data. Iris processing included location determination, normalization, and heart region selection, with image enhancement via adaptive histogram equalization. Feature extraction through a 2 -level wavelet transform generated 272 attributes, including statistical, Gray Level Co -occurrence Matrix, and Gray Level Run Length Matrix features for eight subcomponents. Correlation -based selection identified the best features, and classification employed ML techniques and incorporated stacking learning to enhance the results. Scenario -I achieved the highest accuracy at 83.57% among all evaluated algorithms. In Scenario -ii, the proposed algorithm consistently outperformed others, achieving 94.88% accuracy and strong performance in other metrics, highlighting its effectiveness. In Scenario -iiI, the algorithm maintained superiority with 96.07% accuracy, specificity, recall, and area under the curve values. The proposed algorithm consistently outperforms other methods across scenarios, indicating its potential for CAD diagnosis, making it a promising choice for future CAD systems. The proposed algorithm presents a novel approach to the preliminary diagnosis of CAD, eliminating the necessity for electrocardiography, echocardiography, or effort tests. It also enables seamless integration into telemedicine systems, allowing for tele -diagnosis to conduct preliminary assessments before routine clinical practice.

关键词： Coronary artery disease iris, image processing Bio-demographic data Stacking machine learning

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Drone-towed electromagnetic and magnetic systems for subsurface characterization and archaeological prospecting

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NEAR SURFACE GEOPHYSICS 2024年第6期22卷 617-635页

作者： Vilhelmsen, Tobias B. Grayver, Alexander V. Dossing, Arne Tech Univ Denmark DTU Space Geomagnetisme & Geospace Centrifugevej 356 DK-2800 Kongens Lyngby Denmark Univ Cologne Inst Geophys & Meteorol Cologne Germany

Drone-based geophysical surveying is an emerging measuring platform. High time-cost efficiency and flexibility to survey over inaccessible areas make drones attractive or sometimes the only feasible option to carry geophysical measurements. This study presents a new drone-towed electromagnetic induction and magnetic gradient sensor system used for near-surface characterization and areal *** system uses various datasets to enhance processing and interpretation. The system includes;an electromagnetic induction instrument;magnetic sensors;GNSS-IMU system;photogrammetry;Lidar model data;and geoid model data. Robust data processing and stochastic inversion subsurface characterization for archaeological prospecting with drone-towed electromagnetic induction and magnetic gradient sensor systems. Robust statistical methods were used to process the *** conducted the fieldwork at one of the ancient Viking settlements in Denmark. The surveyed area was approximately 100x$\times$200 m. We then implemented and applied a one-dimensional laterally constrained non-linear stochastic inversion to image the subsurface electrical conductivity. The inversion results show a consistent conductive layer at 5-8 m depths, likely associated with the groundwater level. This conductive layer is disrupted under a prominent anomaly within a 2-4 m wide area. Our analysis showed that this conductivity disruption could be a flint mine extending 7 m deep. This anomaly also has a strong signature in magnetic gradient data.

关键词： airborne EM archaeogeophysics data processing magnetic modelling

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