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Estimating Risk-aware Flexibility Areas for Electric Vehicle Charging Pools via AC Stochastic Optimal Power Flow

Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1247-1256页

作者： Juan S.Giraldo Nataly Bañol Arias Pedro P.Vergara Maria Vlasiou Gerwin Hoogsteen Johann L.Hurink Energy Transition Studies Group Netherlands Organisation for Applied Scientific Research(TNO)Amsterdam1043 NTThe Netherlands Department of Electrical Engineering Mathematics and Computer ScienceUniversity of TwenteEnschede7522 NBThe Netherlands Intelligent Electrical Power Grids Group Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2628 CDThe Netherlands

This paper introduces an AC stochastic optimal power flow(SOPF)for the flexibility management of electric vehicle(EV)charging pools in distribution networks under *** AC SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging *** application of the AC SOPF is described where a distribution system operator(DSO)requires flexibility to each charging pool in a day-ahead time frame,minimizing the cost for flexibility while guaranteeing technical *** areas are defined for each charging pool and calculated as a function of a risk parameter involving the uncertainty of the *** show that all players can benefit from this approach,i.e.,the DSO obtains a riskaware solution,while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.

关键词： Electric vehicle flexibility management stochastic optimal power flow(SOPF) risk awareness compensation mechanism

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Detecting Abnormal Communication Patterns in IoT Networks Using Graph Neural Networks 13th

Detecting Abnormal Communication Patterns in IoT Networks U...

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13th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2023

作者： Carletti, Vincenzo Foggia, Pasquale Vento, Mario Department of Information Engineering Electrical Engineering and Applied Mathematics University of Salerno Fisciano Italy

ISBN: (纸本)9783031427947

Nowadays, millions of Internet of Things (IoT) devices communicate over the Internet, thus becoming potential targets for cyberattacks. Due to the limited hardware capabilities of these devices, host-based countermeasures are unlikely to be deployed on them, making network traffic analysis the only reasonable way to detect malicious activities. In this paper, we face the problem of identifying abnormal communications in IoT networks using graph-based anomaly detection methods. Although anomaly detection has already been applied to graph-based data, most existing methods have been used for static graphs, with the aim of detecting anomalous nodes. In our case, the graphs represent snapshots of the network traffic, and change with time. In this paper we compare different graph-based methods, and different graph representations of the network traffic, using two large datasets of real IoT data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Fuzzy logic for situation awareness: a systematic review

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4419-4438页

作者： D’Aniello, Giuseppe Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Via Giovanni Paolo II 132 SA Fisciano84084 Italy

Situation awareness is the cognitive capability of human and artificial agents to perceive, understand and predict the status of the situation in an environment. Situation awareness systems aim at supporting the situation awareness of human and artificial agents using computational techniques, models, and approaches for supporting the assessment, tracking, and prediction of critical situations. Fuzzy logic formalisms have been extensively used in situation awareness systems thanks to their capability of dealing with uncertainties while providing agents with easily understandable models of situations and decisions. This paper proposes a systematic, unbiased, and updated review of the literature on fuzzy logic for situation awareness from 2010 to 2021, conducted using the PRISMA methodology, analyzing 139 articles. An in-depth discussion of the main open challenges and future research directions is provided. © 2023, The Author(s).

关键词： Computer circuits

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Certifying IoT Data with Verifiable Credentials 22

Certifying IoT Data with Verifiable Credentials

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22nd International Symposium on Network Computing and Applications, NCA 2024

作者： Mazzocca, Carlo Allevi, Stefano Montanari, Rebecca Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Salerno Italy Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (纸本)9798331510183

The Internet of Things (IoT) is a major contributor to the vast amount of data generated worldwide, significantly impacting the big data market. However, this data holds value only when utilized for insights and applications. Many organizations hesitate to use third-party data due to concerns about accuracy, reliability, and integrity. Enhancing trustworthiness in data is crucial to unlock their full potential, especially in critical domains such as healthcare where erroneous data can have severe consequences. The Identity of Things (IDoT) paradigm addresses this need by identifying trustworthy devices using Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), offering promising solutions for digital identification. Although DIDs and VCs have primarily been used for mutual trust and access control, their potential for data certification in IoT remains underexplored. This paper is the first to investigate the feasibility of IoT devices to use VCs for certifying data. We evaluated devices with varying capabilities, focusing on the latency, computing, and storage requirements for issuing VCs. Our findings demonstrate that IoT devices can issue VCs with up to 100 claims in less than 90 ms, with storage requirements growing linearly and remaining below 4 KB. Using VCs for data certification does not introduce significant computational overhead, suggesting its practicality for IoT environments. ©2024 IEEE.

关键词： Data integrity

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Physics-Constrained Adversarial Training for Neural Networks in Stochastic Power Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1121-1131页

作者： Li, Wenting Deka, Deepjyoti Wang, Ren Paternina, Mario R. Arrieta LANL Applied Mathematics and Plasmas Physics Division Los AlamosNM87544 United States Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States National Autonomous University of Mexico Department of Electrical Engineering Mexico City04510 Mexico

The growth in distributed energy resources (DERs) is an important step toward solving the global climate crisis. However, many DERs, such as wind and solar power, are random and intermittent, causing the data in power grids to be perturbed with high uncertainty. Such perturbations degrade the performance of data-driven algorithms introduced in power grids for sensing and control. Existing approaches can strengthen machine learning models against well-designed malicious attacks. However, such physics-agnostic efforts fail to ensure the robustness of natural perturbations in power grids, that occur due to varying loads and changes in control inputs. This article proposes a novel physics-constrained adversarial training method for robustifying neural networks for the crucial problem of locating edge-faults in power grids. Our approach relies on first deriving analytical physical laws that are satisfied by state perturbations in realistic grids and then using the descriptive sets during adversarial training. Compared to state-of-the-art methods, our perturbation-robust neural networks have higher robust accuracy as well as training efficiency. Also, we demonstrate that the proposed approach achieves a tighter upper bound of robust risks than traditional efforts. The numerical experimental results in the IEEE 68-bus benchmark system validate our adversarial training in two scenarios when loads and control inputs vary randomly. The proposed method shows superior performance in accuracy and efficiency for different perturbed datasets. In addition, we testify the influence of different inputs on the robustness. © 2023 IEEE.

关键词： Neural networks

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A New Pulse Skipping Control for Series Resonant Converter 3

A New Pulse Skipping Control for Series Resonant Converter

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Bhowmick, Supratik Chakraborty, Soham Maiti, Suman Indian Institute of Technology Kharagpur Department of Electrical Engineering Kharagpur721302 India Calcutta University UG Program of Electrical Engineering Department of Applied Physics Kolkata700009 India

ISBN: (纸本)9798331540319

This paper proposes a pulse skipping based combined frequency and duty ratio control for a series resonant converter which is targeted for voltage regulator applications. The proposed pulse skipping control combines duty ratio based phase shifted control for maintaining output voltage at its reference value, along with frequency based control which assists in development of the pulse skipping algorithm. This algorithm is structured so that pulses are skipped in generated gate pulses for upper switching devices present in the converter whenever the output voltage tries to be higher than its specified maximum value or be lower than its specified minimum value. These maximum and minimum voltages are determined based on its reference voltage and allowable voltage ripple within which the oscillations in output voltage are desired to be in. The converter system with the proposed control method is simulated in MATLAB/Simulink environment to validate its performance under transient conditions e.g., variation in load resistance and output voltage reference. The system performance with proposed method is also compared with conventional method where the proposed control demonstrates its superiority by significantly minimizing the output voltage ripple. © 2024 IEEE.

关键词： Resonant converters

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Performance Optimization of Series Resonant Converter by Combined Frequency and Duty Ratio Control 3

Performance Optimization of Series Resonant Converter by Com...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Chakraborty, Soham Bhowmick, Supratik Maiti, Suman Calcutta University UG Program of Electrical Engineering Department of Applied Physics Kolkata700009 India Indian Institute of Technology Kharagpur Department of Electrical Engineering Kharagpur721302 India

ISBN: (纸本)9798331540319

Resonant converters are widely used for voltage regulator applications. The basic converters i.e., Series resonant Converter (SRC) and Parallel Resonant Converter (PRC) are having some merits and limitations. These are addressed in multi element topologies at the cost of adding extra elements. This work takes up the SRC and reports a combined frequency and duty ratio control for performance optimization of the SRC. The key issue of SRC is that the voltage cannot be controlled at a low Q (i.e. Quality factor) operation. For example, at no load Q becomes zero making the converter inert to frequency variation. This however can be eliminated by pulse width modulation technique. Duty ratio can be properly controlled to achieve the output voltage at an extended load range. The work further takes up challenge to make efficiency optimized. For this a combined control of duty ratio and frequency are proposed. Efficiency Optimization Control (EOC) requires two voltage sensors (one at the input side and another at the output) and a current sensor. Following this, a new minimum rms current algorithm is developed that only requires a current and a voltage sensor. All the proposed techniques are simulated and verified using MATLAB/Simulink. © 2024 IEEE.

关键词： Resonant converters

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AV-Cloud: Spatial Audio Rendering Through Audio-Visual Cloud Splatting 38

AV-Cloud: Spatial Audio Rendering Through Audio-Visual Cloud...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Mingfei Shlizerman, Eli Department of Electrical & Computer Engineering University of Washington Seattle United States Department of Applied Mathematics University of Washington Seattle United States

We propose a novel approach for rendering high-quality spatial audio for 3D scenes that is in synchrony with the visual stream but does not rely or explicitly conditioned on the visual rendering. We demonstrate that such an approach enables the experience of immersive virtual tourism - performing a real-time dynamic navigation within the scene, experiencing both audio and visual content. Current audio-visual rendering approaches typically rely on visual cues, such as images, and thus visual artifacts could cause inconsistency in the audio quality. Furthermore, when such approaches are incorporated with visual rendering, audio generation at each viewpoint occurs after the rendering of the image of the viewpoint and thus could lead to audio lag that affects the integration of audio and visual streams. Our proposed approach, AV-Cloud, overcomes these challenges by learning the representation of the audio-visual scene based on a set of sparse AV anchor points, that constitute the Audio-Visual Cloud, and are derived from the camera calibration. The Audio-Visual Cloud serves as an audio-visual representation from which the generation of spatial audio for arbitrary listener location can be generated. In particular, we propose a novel module Audio-Visual Cloud Splatting which decodes AV anchor points into a spatial audio transfer function for the arbitrary viewpoint of the target listener. This function, applied through the Spatial Audio Render Head module, transforms monaural input into viewpoint-specific spatial audio. As a result, AV-Cloud efficiently renders the spatial audio aligned with any visual viewpoint and eliminates the need for pre-rendered images. We show that AV-Cloud surpasses current state-of-the-art accuracy on audio reconstruction, perceptive quality, and acoustic effects on two real-world datasets. AV-Cloud also outperforms previous methods when tested on scenes "in the wild". © 2024 Neural information processing systems foundation. All rights reserved.

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Unsupervised Anomaly Detection in Predictive Maintenance using Sound Data 31

Unsupervised Anomaly Detection in Predictive Maintenance usi...

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31st Symposium of Advanced Database Systems, SEBD 2023

作者： Ferraro, Antonino Galli, Antonio Gatta, Valerio La Moscato, Vincenzo Postiglione, Marco Sperlì, Giancarlo Moscato, Francesco Department of Electrical Engineering and Information Technology University of Naples Federico II Italy Department of Information Engineering Electrical Engineering and Applied Mathematics University of Salerno Italy

This paper represent an extended abstract of a recent proposal ([1]), in which the authors present a new methodology for unsupervised anomaly detection in predictive maintenance using sound data. In particular, the methodology leverages LSTM and CNN-based autoencoders to process continuous audio streams from different audio sources in real-world factories based on a customized sliding window. The novelties of the proposed approach include a general methodology for unsupervised anomaly detection, machine ID encoding using one-hot encoding, and conditioning an autoencoder by jointly analyzing the relationships between the mel-spectrogram and the machine ID to compute an anomaly score. The methodology achieves good performances in terms of effectiveness and in addition low inference time and memory requirements. © 2023 CEUR-WS. All rights reserved.

关键词： Anomaly detection

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Tell What You Hear From What You See - Video to Audio Generation Through Text 38

Tell What You Hear From What You See - Video to Audio Genera...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Xiulong Su, Kun Shlizerman, Eli Department of Electrical & Computer Engineering University of Washington Seattle United States Department of Applied Mathematics University of Washington Seattle United States

The content of visual and audio scenes is multi-faceted such that a video stream can be paired with various audio streams and vice-versa. Thereby, in video-to-audio generation task, it is imperative to introduce steering approaches for controlling the generated audio. While Video-to-Audio generation is a well-established generative task, existing methods lack such controllability. In this work, we propose VATT, a multi-modal generative framework that takes a video and an optional text prompt as input, and generates audio and optional textual description (caption) of the audio. Such a framework has two unique advantages: i) Video-to-Audio generation process can be refined and controlled via text which complements the context of the visual information, and ii) The model can suggest what audio to generate for the video by generating audio captions. VATT consists of two key modules: VATT Converter, which is an LLM that has been fine-tuned for instructions and includes a projection layer that maps video features to the LLM vector space, and VATT Audio, a bi-directional transformer that generates audio tokens from visual frames and from optional text prompt using iterative parallel decoding. The audio tokens and the text prompt are used by a pretrained neural codec to convert them into a waveform. Our experiments show that when VATT is compared to existing video-to-audio generation methods in objective metrics, such as VGGSound audiovisual dataset, it achieves competitive performance when the audio caption is not provided. When the audio caption is provided as a prompt, VATT achieves even more refined performance (with lowest KLD score of 1.41). Furthermore, subjective studies asking participants to choose the most compatible generated audio for a given silent video, show that VATT Audio has been chosen on average as a preferred generated audio than the audio generated by existing methods. VATT enables controllable video-to-audio generation through text as well as suggest

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