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Community microgrid:an approach towards positive energy community in an urban area of Dhaka,Bangladesh

Clean Energy 2023年第4期7卷 926-939页

作者： Mohammad Zeyad S.M.Masum Ahmed Sayeed Hasan Dewan Mahnaaz Mahmud Energy and Technology Research Division Advanced BioinformaticsComputational Biology and Data Science LaboratoryBangladesh(ABCD LaboratoryBangladesh)House:24Road:01Block:BHalishaharChattogram 4226Bangladesh School of Engineering and Physical Sciences(EPS) Heriot-Watt UniversityCampus The AvenueEdinburghScotland EH144ASUK Department of Energy Engineering University of the Basque Country(UPV/EHU)Ingeniero Torres Quevedo Plaza1BilbaoBiscay 48013Spain Department of Electrical Electronic and Control EngineeringSchool of Industrial EngineeringUniversity of ExtremaduraCampus UniversitarioAv.de Elvass/nBadajozBadajoz 06006Spain Centre for Research on Geography ResourcesEnvironmentEnergy and Networks(Green)Bocconi UniversityVia Roentgen 1MilanoLombardy 20136Italy Control and Robotics(CORO) Ecole Centrale De Nantes1 Rue de la No?44300 NantesFrance

The pollution and toxic greenhouse gases produced by fossil fuel combustion are troubling as global energy demand continues to *** mitigate the consequences of global warming,a transition to sustainable energy sources is *** manuscript presents a feasible community microgrid design in Hazaribagh,Dhaka based on meteorological data that leads to photovoltaic installation on the rooftop of a local community *** study shows a microgrid design of a system with the lowest cost of energy and a large renewable fraction,which is analysed using the HOMER Pro *** real-time data,analysis of the system cost,cost of energy,renewable fraction,unmet load,energy purchased and energy sold is discussed.A suitable case for electrification is also identified and presented for the selected *** proposed case yields a cost of energy of$0.0357/kWh,which is 52%less than the current tariff rate,with a 70%renewable *** study will provide people in this community with more green energy at a lower cost;in addition,this designed microgrid sells additional energy to the grid to avoid possible power *** potential for a positive energy community is also investigated in terms of energy consumption and renewable output of the planned microgrid.

关键词： cost of energy microgrid positive energy community renewable fraction

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Non-adaptive and two-stage coding over the Z-channel

arXiv

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arXiv 2022年

作者： Lebedev, Alexey Lebedev, Vladimir Vorobyev, Ilya Deppe, Christian Kharkevich Institute for Information Transmission Problems Russian Academy of Sciences Moscow Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia Institute for Communications Engineering Department of Electrical and Computer Engineering Technical University of Munich Munich Germany

In this paper, we developed new coding strategies for the Z-channel. In particular, we look at the case with two-stage encoding. In this case, the encoder uses noiseless feedback once and adjusts the further encoding strategy based on the previous partial output of the channel. Nevertheless, the developed codes improve the known results with full feedback for small length and 1 error. A tool for the two-stage strategy is the development of a new optimality condition for non-adaptive codes. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal encoding

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Blockchain-Based Solution to Prevent Plastic Pipes Fraud* 7

Blockchain-Based Solution to Prevent Plastic Pipes Fraud*

引用

7th International Conference on Software Defined Systems, SDS 2020

作者： Kostyuk, Pavel Kudryashov, Sergey Madhwal, Yash Maslov, Ivan Tkachenko, Vladislav Yanovich, Yury Bitfury Russia Bitfury Moscow123112 Russia Center for Computational and Data-Intensive Science and Engineering Moscow121205 Russia Moscow119530 Russia

ISBN: (数字)9781728172194

ISBN: (纸本)9781728172194

Use of counterfeit plastic pipes has caused considerable financial damage to the states and companies. This article proposes a blockchain-based supply chain management system for its market. It can make pipes' use and life cycles transparent, thus guarantee the invariability of pipes mass produced and used. The article provides a high-level technical description, containing ERP-blockchain connection and application for project-specific labels printing, mobile applications for manufactures and pipes installers. © 2020 IEEE.

关键词： Supply chain management

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An Adaptive Explainable AI Framework for Securing Consumer Electronics-Based IoT Applications in Fog-Cloud Infrastructure

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Tripathy, Subhranshu Sekhar Guduri, Manisha Chakraborty, Chinmay Bebortta, Sujit Pani, Subhendu Kumar Mukhopadhyay, Sabyasachi School of Computer Engineering KIIT Deemed to be University Bhubaneswar India University of Louisiana at Lafayette USA Department of Electronics and Communication Engineering Birla Institute of Technology Jharkhand India Department of Computer Science Ravenshaw University Cuttack Odisha India Kausalya Ganga Bhubaneswar Odisha India Centre For Computational And Data Sciences Indian Institute of Technology Kharagpur Kharagpur West Bengal India

A prominent use case of consumer electronics-based Internet of Things (IoT) applications, focused on smart cities, is connected devices that enable cities to optimize their operations via access to high volumes of sensitive data. Yet, these devices commonly utilize public channels for data access and sharing, requiring consistent communication protocols and an Intrusion Detection System (IDS) with the aid of AI. However, most of them involve high computation and communication costs. They are not fully reliable, either. Also, AI-based IDS solutions are viewed as black boxes because they cannot justify their decisions. To resolve these issues, we have proposed a framework based on explainable artificial intelligence (XAI) for securing consumer IoT applications in smart cities. At the beginning of the protocol execution, the participants exchange authenticated data through the blockchain-based AKA procedure. Meanwhile, we adopt the Python-based Shapley Additive Explanation (SHAP) framework to explain and interpret the core features guiding decision-making. The working model of this framework depicts its validation with recent benchmark methods. IEEE

关键词： Energy efficiency

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Iceland wind farm assessment case study and development: An empirical data from wind and wind turbine

引用

Cleaner Energy Systems 2023年 4卷

作者： Hassanian, Reza Helgadóttir, Ásdís Riedel, Morris The Faculty of Industrial Engineering Mechanical Engineering and Computer Science University of Iceland Reykjavík 102 Iceland Computational Fluid Dynamics Simulation and Data Lab National Competence Centre of Iceland University of Iceland Reykjavík 102 Iceland Juelich Supercomputing Centre Jülich 52428 Germany

This study aimed to apply empirical data to assess wind energy production at the Búrfell site in Iceland based on the E44 turbine model. The empirical data are 5 years of recordings at the site location by the Iceland Metrological office. The wind speed data are measured at a 10 m height from 2017 to 2021. There are two E44 wind turbines test installed on the site. In the previous studies, the wind farm capacity and Levelized cost of energy (LCOE) were reported without investigating the wake loss model and its impacts on LCOE and have an estimation applied. The previous research was based on the two installed wind turbines at the site, which are located in a straight line and perpendicular to the prevailing wind speed. This study applies the Jensen-Katic model to investigate wake loss. Downwind and crosswind ten-rotor diameters and five-rotor diameters are calculated respectively as the best options. Afterward, an appropriate number of wind turbines is suggested for 80MW production. In addition, this study's optimum capacity factor (CF) is 26.08%, which was reported at 37.9% - 38.38% before. On average, the turbines produce less than 30% of their rated power, which has been reported at 38.15% in prior studies. This study presents the LCOE as equal to 0.0659 USD/kWh, which is less than 0.0703 USD/kWh in the previous studies and the LCOE reported by the 2020 LCOE European report. The obtained LCOE in this study is based on the weighted average cost of capital in the energy project by Landsvirkjun, the national power company of Iceland. The obtained result from the model used, which matched the empirical measurements, displays Iceland's best rank for wind energy LCOE metric among European countries. The proposed method provides a vision to use the wake loss model output in deep learning training to predict power production, leading to a sustainable and reliable power grid. © 2023 The Author(s)

关键词： Energy Experiments Levelized cost Turbine Wake loss model Wind

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Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction

arXiv

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arXiv 2024年

作者： Maldonado-Garcia, Cynthia Zakeri, Arezoo Frangi, Alejandro F. Ravikumar, Nishant Centre for Computational Imaging and Simulation Technologies in Biomedicine School of Computing University of Leeds Leeds United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom School of Computer Science Faculty of Science and Engineering University of Manchester Kilburn Building Manchester United Kingdom Christabel Pankhurst Institute University of Manchester Manchester United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom

Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients’ quality of life. This study demonstrates the potential of retinal optical coherence tomography (OCT) imaging in conjunction with fundus photographs for identifying future adverse cardiac events. We utilized data from 977 patients who experienced CVD within a 5-year interval post-image acquisition, alongside 1,877 control participants without CVD, totaling 2,854 subjects. We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients’ fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not. Our model, trained on both imaging modalities, achieved promising results (AUROC 0.78 ± 0.02, accuracy 0.68 ± 0.002, precision 0.74 ± 0.02, sensitivity 0.73 ± 0.02, and specificity 0.68 ± 0.01), demonstrating its efficacy in identifying patients at risk of future CVD events based on their retinal images. This study highlights the potential of retinal OCT imaging and fundus photographs as cost-effective, non-invasive alternatives for predicting cardiovascular disease risk. The widespread availability of these imaging techniques in optometry practices and hospitals further enhances their potential for large-scale CVD risk screening. Our findings contribute to the development of standardized, accessible methods for early CVD risk identification, potentially improving preventive care strategies and patient outcomes. © 2024, CC BY-NC-ND.

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Synthesising 3D Cardiac CINE-MR Images and Corresponding Segmentation Masks using a Latent Diffusion Model

Synthesising 3D Cardiac CINE-MR Images and Corresponding Seg...

引用

IEEE International Symposium on Biomedical Imaging

作者： Nina Cheng Zhengji Liu Yash Deo Haoran Dou Ning Bi Kun Wu Fengming Lin Zeike A Taylor Nishant Ravikumar Alejandro F Frangi CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Leeds School of Optometry The Hong Kong Polytechnic University NIHR Leeds Biomedical Research Centre Leeds UK Alan Turing Institute London UK Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester UK Cardiovascular Sciences Departments Medical Imaging Research Center (MIRC) Electrical Engineering KU Leuven Leuven Belgium

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

We propose a novel pipeline for the generation of synthetic full spatial cine cardiac magnetic resonance (CMR) images via a latent Denoising Diffusion Implicit Models (DDIMs). These synthetic images can be used as viable alternatives to real data in deep learning model training for downstream cardiac image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic CMR images along with their corresponding segmentation masks. We evaluated model performance using a variety of methods, including generated image fidelity, diversity and calculated the volumes of the generated segmentation masks and compare it with the real segmentation masks. The proposed pipeline has the potential to be widely applied to other tasks in various medical imaging modalities. Effective and efficient generation of 3D cine cardiac images with corresponding segmentation masks can supplement real patient datasets and help reduce the burden of manually annotating images.

关键词： Training Image segmentation Solid modeling Three-dimensional displays Biological system modeling Pipelines Noise reduction

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MultiCalib4DEB: A toolbox exploiting multimodal optimisation in Dynamic Energy Budget parameters calibration

arXiv

引用

arXiv 2023年

作者： Robles, Juan Francisco Chica, Manuel Filgueira, Ramón Agüera, Antonio Damas, Sergio Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Benthic resources Norwegian Institute of Marine Research BergenNO-5817 Norway Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Marine Affairs Program Life Sciences Centre Dalhousie University 1459 Oxford Street HalifaxNSB3H 4R2 Canada

1. Calibration is a crucial step for the validation of computational models and a challenging task to accomplish. 2. Dynamic Energy Budget (DEB) theory has experienced an exponential rise in the number of published papers, which in large part has been made possible by the DEBtool toolbox. Multimodal evolutionary optimisation could provide DEBtool with new capabilities, particularly relevant on the provisioning of equally optimal and diverse solutions. 3. In this paper we present MultiCalib4DEB, a MATLAB toolbox directly integrated into the existing DEBtool toolbox, which uses multimodal evolutionary optimisation algorithms to find multiple global and local optimal and diverse calibration solutions for DEB models. 4. MultiCalib4DEB adds powerful calibration mechanisms, statistical analysis, and visualisation methods to the DEBtool toolbox and provides a wide range of outputs, different calibration alternatives, and specific tools to strengthen the DEBtool calibration module and to aid DEBtool users to evaluate the performance of the calibration results. © 2023, CC BY-NC-ND.

关键词： Calibration

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Detection Of Counterfeit Images Through Deep Learning based Yolov3 Model

Detection Of Counterfeit Images Through Deep Learning based ...

引用

Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Jobez J. C Golden Nancy R Syed Rabiya Mohamed Ali Muthukumaran N Raghavan Sheeja Jasmine Gnana Malar A Department of AIML Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K. Engineering College Gummidipoondi Tamil Nadu India Department of ECE PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Image forgery is the procedure of analysing and detecting any fraudulent or unauthorised alterations made to digital images. The detection in images is difficult for achieving a high accuracy rate and the process is time-consuming. Researchers and forensic experts are developing tools for detecting fake images. The identification of computer-generated picture forgeries is a notable area of study, aiming to provide a comprehensive review of detection tools and techniques. In this work, a novel deep-learning model is proposed for detecting forgery in images. The images are pre-processed using an adaptive Gaussian bilateral (AGB) filter to enhance the quality of the images. The enhanced photos are fed into the Mobile Net for extracting the relevant features in the pictures. The modified Yolov3 is utilized for object detection in images and detecting the real and forgery images. According to the result, the proposed model attains 99.55% for forgery detection in images. The modified Yolov3 yields 99.55% accuracy as opposed to Mask RCNN, RCNN, and SSD's 0.68%, 0.65%, and 1.16% accuracy. CNN, ResNetV2 and DCNN are all outperformed the proposed model in terms of overall accuracy by 0.11%, 1.10%, and 5.55%, respectively.

关键词： YOLO Deep learning Adaptation models Accuracy Reviews computational modeling Forensics Digital images Feature extraction Forgery

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On the Particle Approximation of Lagged Feynman-Kac Formulae

arXiv

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arXiv 2024年

作者： Awadelkarim, Elsiddig Caffarel, Michel Moral, Pierre Del Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia University of Toulouse CNRS-Lab. de Chimie et Physique Quantiques Toulouse31062 France Centre de Recherche Inria Bordeaux Sud-Ouest Talence33405 France School of Data Science The Chinese University Hong Kong Shenzhen China

In this paper we examine the numerical approximation of the limiting invariant measure associated with Feynman-Kac formulae. These are expressed in a discrete time formulation and are associated with a Markov chain and a potential function. The typical application considered here is the computation of eigenvalues associated with non-negative operators as found, for example, in physics or particle simulation of rare-events. We focus on a novel lagged approximation of this invariant measure, based upon the introduction of a ratio of time-averaged Feynman-Kac marginals associated with a positive operator iterated l ∈ N times;a lagged Feynman-Kac formula. This estimator and its approximation using Diffusion Monte Carlo (DMC) have been extensively employed in the physics literature. In short, DMC is an iterative algorithm involving N ∈ N particles or walkers simulated in parallel, that undergo sampling and resampling operations. In this work, it is shown that for the DMC approximation of the lagged Feynman-Kac formula, one has an almost sure characterization of the L1-error as the time parameter (iteration) goes to infinity and this is at most of O(exp{−κl}/N), for_ κ > 0. In addition a non-asymptotic in time, and time uniform L1−bound is proved which is O(l/√N). We also prove a novel central limit theorem to give a characterization of the exact asymptotic in time variance. This analysis demonstrates that the strategy used in physics, namely, to run DMC with N and l small and, for long time enough, is mathematically justified. Our results also suggest how one should choose N and l in practice. We emphasize that these results are not restricted to physical applications;they have broad relevance to the general problem of particle simulation of the Feynman-Kac formula, which is utilized in a great variety of scientific and engineering fields. © 2024, CC BY.

关键词： Eigenvalues and eigenfunctions

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