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2022 IEEE Power and Energy Conference at Illinois, PECI 2022

作者： Khatana, Vivek Chakraborty, Soham Saraswat, Govind Lundstorm, Blake Salapaka, Murti V. University of Minnesota Department of Electrical and Computer Engineering United States Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO80401 United States Enphase Energy AustinTX78758 United States

ISBN: (纸本)9781665402293

In the aftermath of increasingly frequent catastrophic events, a typical scenario is critical infrastructure (CI) units being supported by available backup sources with a weak power grid that can be intermittent or absent. Such a scenario is significantly challenging in the sense of providing reliable supply of power to CI units. In this article, an intelligent optimization scheme, termed a horizon of viability (HoV) engine, is developed to guarantee the viability of a sustained, reliable supply of power to the CI units over a time horizon. The proposed HoV engine generates a cost-optimal portfolio of the locally available generation sources and the loads over a time horizon using a mixed-integer convex programming problem. A controller-hardware-in-the-loop (CHIL) platform is developed to evaluate the control performance of the HoV engine. The experimental results corroborate the efficacy in maintaining the viability of the CI units after a grid interruption event. Further, the proposed HoV optimization scheme performs better than existing net-load management schemes in the literature. © 2022 IEEE.

关键词： Critical infrastructures

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A web-based Building Automation and Control Service 13

A web-based Building Automation and Control Service

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13th International Conference on Information, Intelligence, systems and Applications, IISA 2022

作者： Sarmas, Elissaios Dimitropoulos, Nikos Strompolas, Sofoklis Mylona, Zoi Marinakis, Vangelis Giannadakis, Athanasios Romaios, Alexandros Doukas, Haris National Technical University of Athens Decision Support Systems Laboratory School of Electrical and Computer Engineering Athens Greece Holistic Ike Athens Greece University of Peloponnese Mechanical Engineering Department Patra Greece Aeronautics University of Patras Department of Mechanical Engineering Patra Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

The reduction of the environmental impact in the building sector is necessary in achieving global sustainability. In this context, Building Automation and Control systems provide the opportunity for efficient monitoring and control facilities' subsystems, such as the heating and cooling system, the ventilation system, the hot water system, the lighting appliances among others, with the goal of improving thermal comfort as well as energy efficiency. This paper presents a Building Automation and Control system aiming at facilitating data-driven monitoring of complex, multi-storey facilities, by disagreggating total consumption of the different floors and rooms of the building and offering advanced insights and benchmarking indicators. The service is showcased with a use case application on a real building, where the benefits of the service for the energy manager are highlighted. © 2022 IEEE.

关键词： Energy efficiency

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Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework

Condition Monitoring with Incomplete Data: An Integrated Var...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Maryam Ahang Mostafa Abbasi Todd Charter Homayoun Najjaran Department of Electrical and Computer Engineering University of Victoria Victoria Canada Advanced Control and Intelligent Systems Laboratory (ACIS) University of Victoria Victoria Canada Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples. This paper introduces an innovative solution to this problem by proposing a new method for fault detection and condition monitoring for unseen data. Adopting an approach inspired by zero-shot learning, our method can identify faults and assign a relative health index to various operational conditions. Typically, we have plenty of data on normal operations, some data on compromised conditions, and very few (if any) samples of severe faults. We use a variational autoencoder to capture the probabilistic distribution of previously seen and new unseen conditions. The health status is determined by comparing each sample’s deviation from a normal operation reference distribution in the latent space. Faults are detected by establishing a threshold for the health indexes, allowing the model to identify severe, unseen faults with high accuracy, even amidst noise. We validate our approach using the run-to-failure IMS-bearing dataset and compare it with other methods. The health indexes generated by our model closely match the established descriptive model of bearing wear, attesting to the robustness and reliability of our method. These findings highlight the potential of our methodology in augmenting fault detection capabilities within industrial domains, thereby contributing to heightened safety protocols and optimized maintenance practices.

关键词： Condition monitoring Fault diagnosis Protocols Fault detection Zero shot learning Probabilistic logic Robustness Safety Maintenance Indexes

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UAS Traffic Management: Integrating Weather Information using System Adaptability

UAS Traffic Management: Integrating Weather Information usin...

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Annual IEEE systems Conference

作者： William Brooks V. Chandrasekar Rob Cifelli Systems Engineering Department Colorado State University Ft. Collins CO Electrical and Computer Engineering Department Colorado State University Ft. Collins CO NOAA Physical Sciences Laboratory Boulder CO

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Unmanned Aerial Vehicles (UAVs), or drones, are becoming increasingly prevalent in the commercial and defense sectors, operating throughout the continental United States airspace. UAVs have wing sizes ranging from 0.25 centimeters to 61 meters, and they perform a myriad of roles from job site surveillance to 3D mapping of agricultural fields. However, a UAV is not a standalone system, but joins a ground control system, pilot and communications network to form an Unmanned Aircraft System (UAS) platform. With the various UAS platforms of different UAV sizes and missions, the Federal Aviation Administration (FAA) is developing a UAS Traffic Management (UTM) system to safely integrate them into the airspace. UAS ground systems require ingestion, processing, compilation and dissemination of weather data specific to a local area. The NOAA Advanced Quantitative Precipitation Information (AQPI) system has proven to reliably perform these functions. Expanding the mission for a system changes the requirements originally used to design the system. The ability of a system to adjust is the foundation of System Adaptability. Using the concept of System Adaptability and leveraging the existing Digital Twin of the Bay Area AQPI, this paper presents the theoretical application of adapting the San Francisco Bay Area AQPI system to provide meteorological information to UAS platforms operating under the Bay Area UTM.

关键词： Meters Three-dimensional displays Precipitation Surveillance FAA Autonomous aerial vehicles systems engineering and theory Reliability engineering Digital twins National Oceanic and Atmospheric Administration

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Distributed Model-Predictive Energy Management Strategy for Shipboard Power systems Considering Battery Degradation

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IFAC-PapersOnLine 2024年第28期58卷 360-365页

作者： Satish Vedula Seyyed Shaho Alaviani Olugbenga Moses Anubi Department of Electrical and Computer Engineering FAMU-FSU College of Engineering Tallahassee FL 32310 USA Center for Advanced Power Systems (CAPS) Florida State University and National High Magnetic Field Laboratory (MagLab) Tallahassee FL 32310 USA

With the integration of loads such as pulse power loads, a new control challenge is presented in meeting their high ramp rate requirements. Existing onboard generators are ramp rate limited. The inability to meet the load power due to ramp rate limitation may lead to instability. The addition of energy storage elements in addition to the existing generators proves a viable solution in addressing the control challenges presented by high ramp rate loads. A distributed energy management strategy maximizing generator efficiency and minimizing energy storage degradation is developed that facilitates an optimal adaptive power split between generators and energy storage elements. The complex structure of the energy storage degradation model makes it tough for its direct integration into the optimization problem and is not practical for real-time implementation. A degradation heuristic to minimize absolute power extracted from the energy storage elements is proposed as a degradation heuristic measure. The designed strategy is tested through a numerical case study of a consolidated shipboard power system model consisting of a single generator, energy storage element, and load model. The results show the impact of the designed energy management strategy in effectively managing energy storage health.

关键词： Battery degradation model predictive control energy management

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A Multi-layer Non-Terrestrial Networks Architecture for 6G and Beyond under Realistic Conditions and with Practical Limitations

arXiv

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arXiv 2025年

作者： Khennoufa, Faical Abdellatif, Khelil Yanikomeroglu, Halim Ozturk, Metin Elganimi, Taissir Kara, Ferdi Rabie, Khaled LGEERE Laboratory Department of Electrical Engineering Echahid Hamma Lakhdar University El-Oued Algeria Laboratory Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Electrical and Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey The Department of Electrical and Electronic Engineering University of Tripoli Libya The Department of Computer Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey Division of Communications Systems KTH Royal Institute of Technology Sweden The Department of Computer Engineering King Fahd University of Petroleum and Minerals Saudi Arabia

In order to bolster the next generation of wireless networks, there has been a great deal of interest in non-terrestrial networks (NTN), including satellites, high altitude platform stations (HAPS), and uncrewed aerial vehicles (UAV). To unlock their full potential, these platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and next-generation multiple access (NGMA). However, in practical applications, transceivers often suffer from radio frequency (RF) impairments, which limit system performance. In this regard, this paper explores the potential of multi-layer NTN architecture to mitigate path propagation loss and improve network performance under hardware impairment limitations. First, we present current research activities in the NTN framework, including RIS, multiple access technologies, and hardware impairments. Next, we introduce a multi-layer NTN architecture with hardware limitations. This architecture includes HAPS super-macro base stations (HAPS-SMBS), UAVs–equipped with passive or active transmissive RIS–, and NGMA techniques, like non-orthogonal multiple access (NOMA), as the multiple access techniques to serve terrestrial devices. Additionally, we present and discuss potential use cases of the proposed multi-layer architecture considering hardware impairments. The multi-layer NTN architecture combined with advanced technologies, such as RIS and NGMA, demonstrates promising results;however, the performance degradation is attributed to RF impairments. Finally, we identify future research directions, including RF impairment mitigation, UAV power management, and antenna designs. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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IMPS: Informative Map Point Selection for Visual-Inertial SLAM

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5595-5605页

作者： Changxiang Liu Hongshan Yu Qiang Fu Xieyuanli Chen Naveed Akhtar Zhi-Hong Mao College of Electrical and Information Engineering School of Robotics Quanzhou Institute of Industrial Design and Machine Intelligence Innovation Hunan University Changsha China National University of Defense Technology Changsha China School of Computing and Information Systems University of Melbourne Melbourne VIC Australia Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA USA

Visual-inertial SLAM (VINS) stands at the forefront of advancements in computer vision, robotics, and autonomous driving, revolutionizing the way we perceive and navigate the world. The typical approach in optimization-based methods for VINS is to estimate camera poses by minimizing the reprojection errors of all corresponding map points. However, not all map points are suitable for accurate pose estimation. Some map points introduce noise and can compromise the accuracy of the estimation. Current methods primarily focus on removing 2D noise points, which proves to be ineffective since triangulation can increase the uncertainty of map points. To address this problem, we propose a new method for Informative Map Point Selection (IMPS) in VINS systems. IMPS identifies the most informative map points by utilizing mutual and geometric information as the optimization target. We integrate IMPS into a VINS system and evaluate its performance using publicly available EuRoC, TUM and M2DGR datasets, as well as our own data. Experimental results demonstrate that our method surpasses existing VINS methods and achieves state-of-the-art pose estimation performance. Importantly, IMPS functions as an independent module with strong generalization capabilities, allowing for easy integration into other VINS systems and enhancing pose estimation performance. This integration not only elevates pose estimation accuracy but also holds great promise for advancing applications in intelligent driving and unmanned systems.

关键词： Simultaneous localization and mapping Optimization Cameras Pose estimation Robots Mutual information Intelligent vehicles

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth IoanninaGR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory HeraklionGR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering HeraklionGR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep learning

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Spontaneous Emergence of Multitasking Robotic Swarms

Spontaneous Emergence of Multitasking Robotic Swarms

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhang, Ji Liang, Yiming Zhu, Shiqiang Xiang, Tian Zhu, Hongwei Gu, Jason Song, Wei Li, Tiefeng Zhejiang Lab Hangzhou310027 China Dalhousie Universit Department of Electrical and Computer Engineering Halifax Canada State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665481090

Robot swarms promise to replace humans in complex scenarios like resource exploration, environmental monitoring, and military missions. These complex scenarios require robots to be able to complete multiple tasks at the same time adaptively. From the point of view of statistical mechanics, the phenomenon of the multiply states of a given system is a kind of partial synchronization. Inspired by biological groups such as flocks of birds and schools of fish, we model the robots as self-propelled particles with Kuramoto-Sakaguchi like interactions. We uncover the state of the robot swarm can be manipulated using the phase lag of the Kuramoto-Sakaguchi like interactions. The system is partially synchronized at the edge between order and disorder, and the robots present two distinguished motion patterns, one is completely periodic movement, and the other one is chaos. This study provides new insights into the collaboration of robot swarms. © 2022 IEEE.

关键词： Statistical mechanics

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Insect Identification in Pulsed Lidar Images Using Changepoint Detection Algorithms

Insect Identification in Pulsed Lidar Images Using Changepoi...

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Intermountain engineering, Technology and computing (IETC)

作者： Nathaniel Sweeney Caroline Xu Joseph A. Shaw Toby D. Hocking Bradley M. Whitaker Electrical and Computer Engineering Department Montana State University USA Electrical Engineering and Computer Science Department University of Michigan USA Optical Technology Center Montana State University USA School of Informatics Computing and Cyber Systems Northern Arizona University USA

Noninvasive entomological insect monitoring often utilizes a variety of tools such as LiDAR to gather information without interfering with the insects in their habitat. These collection methods often result in large amounts of data that can be te-dious and lengthy to interpret and analyze. Machine learning has been previously used in the past in order to analyze Li-DAR images to detect insects, but often suffers from pitfalls such as long training times and large computational power requirements. In an attempt to offer an alternative that takes little to no training on the data and much less computational power, this paper looks at the use of changepoint detection algorithms to analyze LiDAR images containing insects. By analyzing the rows or columns of a LiDAR image, the algorithms should be able to detect abrupt changes in the image that would represent the insects. While not as accurate, the changepoint detection algorithms give comparable results to a machine learning algorithm tested on the same dataset without the need for supervised training.

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