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Enhancing Power Quality Event Classification with AI Transformer Models

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Enhancing Power Quality Event Classification with AI Transfo...

IEEE General Meeting Power& Energy Society

作者： Ahmad M. Saber Amr Youssef Davor Svetinovic Hatem Zeineldin Deepa Kundur Ehab El-Saadany EECS Department Advanced Power and Energy Center Khalifa University Abu Dhabi UAE Department of Electrical and Computer Engineering University of Toronto Toronto Canada Concordia Institute for Information Systems Engineering Montreal Canada

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal’s amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%–91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency.

关键词： Voltage measurement Noise Power quality Transformers Harmonic analysis Frequency measurement Numerical models Noise measurement Transient analysis Signal to noise ratio

CONSTRUCTION PROGRESS MONITORING IN SMART CITIES USING DEEP ALEXNET

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CONSTRUCTION PROGRESS MONITORING IN SMART CITIES USING DEEP ...

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Rajavel, A. Karkuzhali, S. Sharma, Sarvdaman Singh, Sitesh Kumar Senthilkumar, P. Ida, S. Jhansi Electronics and Instrumentation Engineering Kamaraj College of Engineering and Technology Virudhnagar India Department of Electrical and Electronics Engineering R.M.D. Engineering College Chennai India Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Nekemte Oromia Ethiopia KL School of Architecture Koneru Lakshmaiah Education Foundation Vaddeswaram Greenfield AP Guntur India Department of Computer Science and Business Systems R.M.K Engineering College India

ISBN: (纸本)9798350305708

Construction progress monitoring plays a crucial role in ensuring the timely and efficient completion of infrastructure projects within smart cities. This paper proposes a novel approach utilizing the Deep AlexNet architecture for construction progress assessment in smart cities. The Deep AlexNet model is trained on a diverse dataset of images captured from construction sites, enabling it to accurately analyze the visual cues indicative of construction advancement. By leveraging deep learning techniques, our system extracts and processes features from images, enabling automated and real-time progress evaluation. The proposed method contributes to enhancing construction management practices by providing stakeholders with a reliable tool to monitor and manage construction projects efficiently. Experimental results demonstrate the effectiveness and robustness of the Deep AlexNet-based approach in accurately assessing construction progress, thereby facilitating informed decision-making throughout the construction lifecycle. © 2023 IEEE.

关键词： Construction progress monitoring Deep AlexNet Deep learning Infrastructure projects Smart cities

A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning

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arXiv 2023年

作者： Valdeira, Pedro Chi, Yuejie Soares, Cláudia Xavier, João CMU IST United States CMU United States NOVA IST Department of Electrical and Computer Engineering Carnegie Mellon University United States Institute for Systems and Robotics Instituto Superior Técnico Portugal Department of Computer Science NOVA School of Science and Technology Portugal

Communication efficiency is a major challenge in federated learning (FL). In client-server schemes, the server constitutes a bottleneck, and while decentralized setups spread communications, they do not necessarily reduce them due to slower convergence. We propose Multi-Token Coordinate Descent (MTCD), a communication-efficient algorithm for semi-decentralized vertical federated learning, exploiting both client-server and client-client communications when each client holds a small subset of features. Our multi-token method can be seen as a parallel Markov chain (block) coordinate descent algorithm and it subsumes the client-server and decentralized setups as special cases. We obtain a convergence rate of O(1/T) for nonconvex objectives when tokens roam over disjoint subsets of clients and for convex objectives when they roam over possibly overlapping subsets. Numerical results show that MTCD improves the state-of-the-art communication efficiency and allows for a tunable amount of parallel communications. © 2023, CC BY.

关键词： Efficiency

NYCTALE: NEURO-EVIDENCE TRANSFORMER FOR ADAPTIVE AND PERSONALIZED LUNG NODULE INVASIVENESS PREDICTION

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arXiv 2024年

作者： Khademi, Sadaf Oikonomou, Anastasia Plataniotis, Konstantinos N. Mohammadi, Arash Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Medical Imaging Sunnybrook Health Sciences Centre Toronto Canada Department of Electrical and Computer Engineering University of Toronto Toronto Canada

Drawing inspiration from the primate brain’s intriguing evidence accumulation process, and guided by models from cognitive psychology and neuroscience, the paper introduces the NYCTALE framework, a neuro-inspired and evidence accumulation-based Transformer architecture. The proposed neuro-inspired NYCTALE offers a novel pathway in the domain of Personalized Medicine (PM) for lung cancer diagnosis. In nature, Nyctales are small owls known for their nocturnal behavior, hunting primarily during the darkness of night. The NYCTALE operates in a similarly vigilant manner, i.e., processing data in an evidence-based fashion and making predictions dynamically/adaptively. Distinct from conventional Computed Tomography (CT)-based Deep Learning (DL) models, the NYCTALE performs predictions only when sufficient amount of evidence is accumulated. In other words, instead of processing all or a pre-defined subset of CT slices, for each person, slices are provided one at a time. The NYCTALE framework then computes an evidence vector associated with contribution of each new CT image. A decision is made once the total accumulated evidence surpasses a specific threshold. Preliminary experimental analyses conducted using a challenging in-house dataset comprising 114 subjects. The results are noteworthy, suggesting that NYCTALE outperforms the benchmark accuracy even with approximately 60% less training data on this demanding and small dataset. © 2024, CC BY.

关键词： Forecasting

σ-ZERO: GRADIENT-BASED OPTIMIZATION OF 0-NORM ADVERSARIAL EXAMPLES

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arXiv 2024年

作者： Cinà, Antonio Emanuele Villani, Francesco Pintor, Maura Schönherr, Lea Biggio, Battista Pelillo, Marcello Department of Computer Science Bioengineering Robotics and Systems University of Genoa Italy Department of Electrical and Electronic Engineering University of Cagliari Italy CISPA Helmholtz Center for Information Security Germany Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Italy

Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging. While most attacks consider 2- and ∞-norm constraints to craft input perturbations, only a few investigate sparse 1- and 0-norm attacks. In particular, 0-norm attacks remain the least studied due to the inherent complexity of optimizing over a non-convex and non-differentiable constraint. However, evaluating adversarial robustness under these attacks could reveal weaknesses otherwise left untested with more conventional 2- and ∞-norm attacks. In this work, we propose a novel 0-norm attack, called σ-zero, which leverages a differentiable approximation of the 0 norm to facilitate gradient-based optimization, and an adaptive projection operator to dynamically adjust the trade-off between loss minimization and perturbation sparsity. Extensive evaluations using MNIST, CIFAR10, and ImageNet datasets, involving robust and non-robust models, show that σ-zero finds minimum 0-norm adversarial examples without requiring any time-consuming hyperparameter tuning, and that it outperforms all competing sparse attacks in terms of success rate, perturbation size, and efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

A Multi-Layer Software Architecture for Aerial Cognitive Multi-Robot systems in Power Line Inspection Tasks

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arXiv 2023年

作者： Silano, Giuseppe Bednar, Jan Nascimento, Tiago Capitan, Jesus Saska, Martin Ollero, Anibal The Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Department of Computer Systems Universidade Federal da Paraiba Brazil The GRVC Robotics Laboratory University of Seville Spain

This paper presents a multi-layer software architecture to perform cooperative missions with a fleet of quad-rotors providing support in electrical power line inspection operations. The proposed software framework guarantees the compliance with safety requirements between drones and human workers while ensuring that the mission is carried out successfully. Besides, cognitive capabilities are integrated in the multi-vehicle system in order to reply to unforeseen events and external disturbances. The feasibility and effectiveness of the proposed architecture are demonstrated by means of realistic simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Software architecture

Role of RPA & AI in Optimizing Network Field Services 7

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Role of RPA & AI in Optimizing Network Field Services

7th IEEE International Conference on Computational systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. University of the Cumberlands Computer Science United States Jawaharlal Technological University Ece Hyderbad India University of the Central Missouri Computer Information Systems Warrensburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Cumberlands Information Technology Williamsburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350343144

The paper investigates incorporating and implementing RPA and AI technologies within NFS to improve efficiency and boost service quality. Robotic Process Automation enables the streamlining of repetitive processes. It simplifies work processes and releases personnel for more critical projects. On the contrary, AI strengthens NFS providers via data-informed decision-making. Additionally, it facilitates anticipatory maintenance and preemptive network administration. The document explores particular RPA and cognitive computing instances, including AI-powered network issue resolution and AI-driven preventive maintenance for the network hardware. Furthermore, it emphasizes the advantages and constraints of adopting robotic process automation and artificial intelligence within the NFS framework. It considers aspects including data handling, growth potential, and moral implications. Prospects for the future related to RPA and AI within the NFS domain are also analyzed. It foresees self-governing network management, sophisticated predictive analytics, and merging with IoT and edge computing technologies. The article highlights the revolutionary capability of robotic process automation and artificial intelligence in transforming the field service sector, aiming for increased efficiency, reliability, and a customer-centric approach. It emphasizes embracing these technologies to keep up in the dynamic business sector. © 2023 IEEE.

关键词： Decision making

PrivShape: Extracting Shapes in Time Series Under User-Level Local Differential Privacy

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PrivShape: Extracting Shapes in Time Series Under User-Level...

International Conference on Data engineering

作者： Yulian Mao Qingqing Ye Haibo Hu Qi Wang Kai Huang Department of Computer Science and Engineering Southern University of Science and Technology Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology School of Computer Science and Engineering Macau University of Science and Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ω-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes.

关键词： Privacy Differential privacy Shape Smart cities Frequency-domain analysis Time series analysis Transforms

Slow Inter-area Electro-mechanical Oscillations Revisited: Structural Property of Complex Multi-area Electric Power systems

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Slow Inter-area Electro-mechanical Oscillations Revisited: S...

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Hiya Akhil Gada Marija D. Ilic Laboratory for Information and Decision Systems (LIDS) of Massachusetts Institute of Technology Cambridge MA USA Department of Carnegie Electrical and Computer Engineering (ECE) Mellon University Pittsburgh PA

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper introduces a physically-intuitive notion of inter-area dynamics in systems comprising multiple interconnected energy conversion modules. The idea builds on an earlier general approach of setting their structural properties by modeling internal dynamics in stand-alone modules (components, areas) using the fundamental conservation laws between energy stored and generated, and then constraining explicitly their Tellegen’s quantities (power and rate of change of power). In this paper we derive, by following the same principles, a transformed state-space model for a general nonlinear system. Using this model we show the existence of an area-level interaction variable, intVar, whose rate of change depends solely on the area internal power imbalance and is independent of the model complexity used for representing individual module dynamics in the area. Given these structural properties of stand-alone modules, we define in this paper for the first time an inter-area variable as the difference of power wave incident to tie-line from Area I and the power reflected into tie-lie from Area II. Notably, these power waves represent the interaction variables associated with the two respective interconnected areas. We illustrate these notions using a linearized case of two lossless inter-connected areas, and show the existence of a new inter-area mode when the areas get connected. We suggest that lessons learned in this paper open possibilities for computationally-efficient modeling and control of inter-area oscillations, and offer further the basis for modeling and control of dynamics in changing systems comprising faster energy conversion processes.

关键词： Computational modeling Power system dynamics Process control Energy conversion Nonlinear dynamical systems State-space methods Complexity theory Information technology Oscillators