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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Tanmay Badageri Bechir Hamdaoui Rami langar School of Electrical Engineering and Computer Science Oregon State University Corvallis Oregon USA Software and IT Engineering Department Ecole de Technologie Superieure (ETS) Montreal Canada CNRS LIGM Univ. Gustave Eiffel Marne-la-Vallee France

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Software-Defined Networking (SDN) provides the flexibility to dynamically manage network paths, facilitating efficient traffic flow and mutipath routing, thereby improving network resiliency to congestion. This study investigates three distinct SDN-enabled routing strategies: shortest path routing, Equal-Cost Multi-Path (ECMP) routing, and bandwidth-based multipath routing. The comparative result analysis across these three routing strategies revealed that bandwidth-based routing consistently outperforms Shortest Path and ECMP routing across key performance metrics. The study found that Bandwidth-based routing maintains lower delay and jitter, indicating its superior ability to manage network traffic efficiently even as data flow increases. Furthermore, it demonstrates a more modest increase in packet loss, underscoring its effective congestion management. These findings suggest that Bandwidth-based routing provides a more reliable and efficient network performance, particularly in high-traffic conditions, making it a preferable solution for SDN implementations seeking to optimize data flow and network stability.

关键词： Wireless communication Packet loss Telecommunication traffic Routing Reliability engineering Stability analysis Software defined networking

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A New Combined PV Output Power Forecasting Model Based on Optimized LSTM Network

A New Combined PV Output Power Forecasting Model Based on Op...

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Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： Azim Heydari Farshid Keynia Afef Fekih Lina Bertling Tjernberg Grazia Barchi David Moser Institute for Renewable Energy Eurac Research Bolzano Italy Department of Energy Management and Optimization Institute of Science and High Technology and Environmental Sciences Graduate University of Advanced Technology Kerman Iran Department of Electrical and Computer Engineering University of Louisiana at Lafayette Lafayette LA USA School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

One of the most challenging problems in designing and implementing effective management strategies and demand responses in renewable-rich grids is the uncertainty associated with the power output (PO) of solar photovoltaic (PV) systems. The exact and trustworthy prediction of PV power can provide substantial decision support for planning and operating power systems. This paper develops an intelligent PV output power (PV-OP) forecasting model. The proposed PV-OP forecasting model consists of an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) signal decomposition model to decompose the original PV output power signal to different frequencies, LSTM neural network as a main forecaster engine, and multi-objective NSGA-II optimization algorithm as a hyperparameters optimizer. The developed PV power forecasting model was validated using the PV power datasets of an off-grid village located in Chalokwa, Zambia. The obtained results confirmed the performance and accuracy of the proposed PV output power forecasting model.

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Adversarial Models Towards Data Availability and Integrity of Distributed State Estimation for Industrial IoT-Based Smart Grid

arXiv

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arXiv 2022年

作者： Haftu, Tasew Reda Mahmood, Abdun Anwar, Adnan Chilamkurti, Naveen Department of Computer Science and IT La Trobe University Plenty Rd BundooraVIC3086 Australia School of IT Deakin University 75 Pigdons Rd Waurn Ponds IC 3216 Australia

Security issue of distributed state estimation (DSE) is an important prospect for the rapidly growing smart grid ecosystem. Any coordinated cyberattack targeting the distributed system of state estimators can cause unrestrained estimation errors and can lead to a myriad of security risks, including failure of power system operation. This article explores the security threats of a smart grid arising from the exploitation of DSE vulnerabilities. To this aim, novel adversarial strategies based on two-stage data availability and integrity attacks are proposed towards a distributed industrial Internet of Things-based smart grid. The former's attack goal is to prevent boundary data exchange among distributed control centers, while the latter's attack goal is to inject a falsified data to cause local and global system unobservability. The proposed framework is evaluated on IEEE standard 14-bus system and benchmarked against the state-of-the-art research. Experimental results show that the proposed two-stage cyberattack results in an estimated error of approximately 34.74% compared to an error of the order of 10−3 under normal operating conditions. © 2022, CC BY-NC-ND.

关键词： Smart power grids

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Adversarial Models Towards Data Availability and Integrity of Distributed State Estimation for Industrial Iot-Based Smart Grid

SSRN

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SSRN 2022年

作者： Reda, Haftu Tasew Mahmood, Abdun Anwar, Adnan Chilamkurti, Naveen Department of Computer Science and IT La Trobe University Plenty Rd BundooraVIC3086 Australia School of IT Deakin University 75 Pigdons Rd Waurn Ponds IC 3216 Australia

Security issue of distributed state estimation (DSE) is an important prospect for the rapidly growing smart grid ecosystem. Any coordinated cyberattack targeting the distributed system of state estimators can cause unrestrained estimation errors and can lead to a myriad of security risks, including failure of power system operation. This article explores the security threats of a smart grid arising from the exploitation of DSE vulnerabilities. To this aim, novel adversarial strategies based on two-stage data availability and integrity attacks are proposed towards a distributed industrial Internet of Things-based smart grid. The former’s attack goal is to prevent boundary data exchange among distributed control centers, while the latter’s attack goal is to inject a falsified data to cause local and global system unobservability. The proposed framework is evaluated on IEEE standard 14-bus system and benchmarked against the state-of-the-art research. Experimental results show that the proposed two-stage cyberattack results in an estimated error of approximately 34.74% compared to an error of the order of 10−3 under normal operating conditions. © 2022, The Authors. All rights reserved.

关键词： Smart power grids

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On the Dielectric Characterization of Biological Samples for Microwave Imaging Reconstruction

On the Dielectric Characterization of Biological Samples for...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2022

作者： Samaddar, Poulami Gaddam, Sunil Khan, Muhammad Roy, Sayan Mitra, Dipankar Arunachalam, Shivaram P. Division of Gastroenterology and Hepatology Department of Medicine RochesterMN United States South Dakota Mines Dept. of Electrical Engineering Rapid CitySD United States University of Wisconsin-La Crosse Dept. of Computer Science La CrosseWI United States Mayo Clinic Department of Radiology RochesterMN United States

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

Dielectric characteristics of tissues can be useful to develop new therapies and diagnostics through microwave imaging reconstruction. For accurate measurements, dielectric testing probe needs to be placed inside the tissue. This maximizes the contact between the probe-end and the tissue for better results. In practice, tissue samples may not always have enough depth for probe placement. This paper presents a study using the finite integration technique (FIT)-based 3D electromagnetic full-wave simulations to investigate the feasibility of a slim-form probe placed on top of the tissue (zero insertion depth). Complex permittivity of the biological tissue samples was calculated using the simulation results to match the tissue's characteristics based on a previously proposed mathematical model. Overall, very good agreement was observed between the analytical and calculated dielectric characteristics of biological tissues up to 8 GHz frequency. © 2022 IEEE.

关键词： Tissue

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Rapid Prediction of Grain Boundary Network Evolution in Nanomaterials Utilizing a Generative Machine Learning Approach

SSRN

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SSRN 2024年

作者： Wang, Yuheng Kazemi, Amirreza Jing, Taotao Ding, Zhengming Li, Like Yang, Shengfeng School of Mechanical Engineering Purdue University in Indianapolis IndianapolisIN46202 United States School of Mechanical Engineering Purdue University West LafayetteIN47907 United States Department of Computer Science Tulane University New OrleansLA70118 United States Department of Mechanical and Aerospace Engineering University of Central Florida OrlandoFL32816 United States

Predicting the behavior of nanomaterials under various conditions presents a significant challenge due to their complex microstructures. While high-fidelity modeling techniques, such as molecular dynamics (MD) simulations, are effective, they are also computationally demanding. Machine learning (ML) models have opened new avenues for the rapid exploration of design spaces. In this work, we developed a deep learning framework based on a conditional generative adversarial network (cGAN) to predict the evolution of grain boundary (GB) networks in nanocrystalline materials under mechanical loads, incorporating both morphological and atomic details. We conducted MD simulations on nanocrystalline tungsten and used the resulting ground-truth data to train our cGAN model. We assessed the performance of our cGAN model by comparing it to a Convolutional Autoencoder (ConvAE) model and examining the impact of changes in geometric morphology and loading conditions on the model's performance. Our cGAN model demonstrated high accuracy in predicting GB network evolution under a variety of conditions. This developed framework shows potential for predicting various materials' behaviors across a wide range of nanomaterials. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Towards Non-Invasive Mapping of Blood Flow Velocity using Microwaves

Towards Non-Invasive Mapping of Blood Flow Velocity using Mi...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2022

作者： Gaddam, Sunil Samaddar, Poulami Khan, Muhammad Damani, Devanshi Shivaram, Suganti Roy, Sayan Mitra, Dipankar Dey, Shuvashis Arunachalam, Shivaram P. Microwave Engineering & Imaging Lab Division of Gastroenterology and Hepatology Dept. of Medicine Mayo Clinic RochesterMN United States Department of Cardiology Department of Pharmacology Mayo Clinic RochesterMN United States Dept. of Electrical Engineering South Dakota Mines Rapid CitySD United States Dept. of Computer Science University of Wisconsin-La Crosse La CrosseWI United States Dept. of Electrical & Computer Engineering North Dakota State University FargoND United States Department of Radiology Mayo Clinic RochesterMN United States

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

Hemodynamic assessment is typically indicated for patients with a critical illness. Unstable hemodynamic conditions can lead to organ dysfunction and eventually death unless treated immediately. Vascular blood pressure and central venous pressure are vital parameters estimated with hemodynamic assessment. Non-invasive methods like ultrasound imaging measure blood velocity and derive pressure gradients. This paper presents an approach to assess the blood flow velocity fields using microwaves non-invasively. For this study, a through-plane blood velocity profile from a left ventricle is used in a 2D electromagnetic simulation at a frequency of 915 MHz. Dielectric properties of blood are assumed to vary linearly by 10% over a velocity range of 0 m/s to 0.2 m/s. Point sources placed around a blood-filled ventricle model act as transmitters and receivers. Distorted Born Iterative Method (DBIM) was used to reconstruct the dielectric parameters. Mapping these parameters back to velocities gives us a visual of the velocity field. The mapped blood flow velocity field from the reconstructed dielectric parameters resembles the velocity fields estimated using Computational Fluid Dynamics (CFD). This study demonstrates the conceptual feasibility of using microwaves for the non-invasive assessment of blood velocities. © 2022 IEEE.

关键词： Hemodynamics

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia computer science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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Robust Blind Color Deconvolution and Blood Detection on Histological Images Using Bayesian K-Svd

SSRN

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SSRN 2023年

作者： Pérez-Bueno, Fernando Engan, Kersti Molina, Rafael Dpto. Ciencias de la Computación e Inteligencia Artificial Universidad de Granada Spain Spain Department of Electrical Engineering and Computer Science University of Stavanger Norway

Hematoxylin and Eosin (H&E) color variation among histological images from different laboratories can significantly degrade the performance of computer-Aided Diagnosis systems. The staining procedure is the primary factor responsible for color variation, and consequently, the methods designed to reduce such variations are designed in concordance with this procedure. In particular, Blind Color Deconvolution (BCD) methods aim to identify the true underlying colors in the image and to separate the tissue structure from the color information. Unfortunately, BCD methods often assume that images are stained solely with pure staining colors (e.g., blue and pink for H&E). This assumption does not hold true when common artifacts such as blood are present, requiring an additional color component to represent them. This is a challenge for color standardization algorithms, which are unable to correctly identify the stains in the image, leading to unexpected results. In this work, we propose a Blood-Robust Bayesian K-Singular Value Decomposition model designed to simultaneously detect blood and extract color from histological images while preserving structural details. We evaluate our method using both synthetic and real images, which contain varying amounts of blood pixels. © 2023, The Authors. All rights reserved.

关键词： Color

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Collisionless Pattern Discovery in Robot Swarms Using Deep Reinforcement Learning

arXiv

引用

arXiv 2022年

作者： Sharma, Nelson Ghosh, Aswini Misra, Rajiv Mukhopadhyay, Supratik Sharma, Gokarna Department of Computer Science and Engineering Indian Institute of Technology Patna India Department of Environmental Sciences Louisiana State University Baton RougeLA United States Department of Computer Science Kent State University KentOH United States

We present a deep reinforcement learning-based framework for automatically discovering patterns available in any given initial configuration of fat robot swarms. In particular, we model the problem of collision-less gathering and mutual visibility in fat robot swarms and discover patterns for solving them using our framework. We show that by shaping reward signals based on certain constraints like mutual visibility and safe proximity, the robots can discover collision-less trajectories leading to "well-formed" gathering and visibility patterns. © 2022, CC BY.

关键词： Reinforcement learning

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