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Next Generation Memristor Reservoir Computing 3

Next Generation Memristor Reservoir Computing

3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Nikiruy, K. Ivanov, T. Ziegler, M. Rossetti, D. Corinto, F. Ascoli, A. Tetzlaff, R. Demirkol, A.S. Schmitt, N. Micro-and Nanoelectronic Systems Tu Ilmenau Department of Electrical Engineering and Information Technology Ilmenau Germany Turin Italy Institute of Circuits and Systems Tu Dresden Faculty of Electrical and Computer Engineering Dresden Germany

ISBN: (纸本)9798350378009

The nonlinear transformation used in reservoir computing can be effectively replaced by nonlinear vector autoregression (NVAR) for data prediction. In such a method, also known as next generation reservoir computing (NGRC), the input signal consists of a linear part, including several previous data points, and their nonlinear combinations. Here we show that the application of this method to a network with memristive weights (memristors) can be used to predict signals, depending on the nature of the nonlinear functions and the number of memristors. The network allows an accurate prediction of chaotic time series of Mackey-Glass and Duffing oscillators. © 2024 IEEE.

关键词： Memristors

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Comparison of Machine Learning Algorithms For Predicting CO2Emissions in the maritime domain 14

Comparison of Machine Learning Algorithms For Predicting CO2...

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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Michalakopoulos, Vasilis Ilias, Loukas Kapsalis, Panagiotis Mouzakitis, Spiros Askounis, Dimitris School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350318067

The maritime industry is a significant source of global carbon dioxide (CO2) emissions, and the accurate prediction of emissions in this domain is of paramount importance. In this paper, we conduct a comparative analysis of machine learning algorithms for predicting CO2 emissions in the maritime domain. Using a unique dataset, comprising historical maritime data for different vessel voyages, including vessel type, voyage information and environmental factors, we employ decision trees, a number of regression, and artificial neural network algorithms. Performance evaluation is conducted using established metrics such as R2 (%), Mean Absolute Error, Normalized Root Mean Squared Error and Symmetric Mean Biased Error. Our findings reveal that the Extra Trees Regressor and Multi-layer Perceptron regressor algorithms outperform the other methods in terms of prediction accuracy demonstrating the least amount of error. Our research contributes to the existing literature by highlighting the potential of machine learning in predicting maritime CO2 emissions and provides insights for further research, such as exploring alternative algorithms and incorporating real-time data Integration. © 2023 IEEE.

关键词： Carbon dioxide

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Language Models as Knowledge Bases for Visual Word Sense Disambiguation 1

Language Models as Knowledge Bases for Visual Word Sense Dis...

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1st Workshop on Knowledge Base Construction from Pre-Trained Language Models and the 2nd Challenge on Language Models for Knowledge Base Construction, KBC-LM + LM-KBC 2023

作者： Kritharoula, Anastasia Lymperaiou, Maria Stamou, Giorgos Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

Visual Word Sense Disambiguation (VWSD) is a novel challenging task that lies between linguistic sense disambiguation and fine-grained multimodal retrieval. The recent advancements in the development of visiolinguistic (VL) transformers suggest some off-the-self implementations with encouraging results, which however we argue that can be further improved. To this end, we propose some knowledge-enhancement techniques towards improving the retrieval performance of VL transformers via the usage of Large Language Models (LLMs) as Knowledge Bases. More specifically, knowledge stored in LLMs is retrieved with the help of appropriate prompts in a zero-shot manner, achieving performance advancements. Moreover, we convert VWSD to a purely textual question-answering (QA) problem by considering generated image captions as multiple-choice candidate answers. Zero-shot and few-shot prompting strategies are leveraged to explore the potential of such a transformation, while Chain-of-Thought (CoT) prompting in the zero-shot setting is able to reveal the internal reasoning steps an LLM follows to select the appropriate candidate. In total, our presented approach is the first one to analyze the merits of exploiting knowledge stored in LLMs in different ways to solve WVSD. © 2023 CEUR-WS. All rights reserved.

关键词： Computational linguistics

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Dynamic and Private Recommendation System

Dynamic and Private Recommendation System

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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Lo, Gee Yen Tan, Terence Peng Lian Curtin University Malaysia Computer Systems and Networking Miri Malaysia Curtin University Malaysia Department of Electrical & Computer Engineering Miri Malaysia

ISBN: (纸本)9798350310689

The amount of data that we retrieve and use every day is increasing rapidly. As data naturally exist in different data domains, the number of data domains that we retrieve and use every day has also increased as a result. As different recommendation systems are needed for different data domains, more and more recommendation systems will need to be built. Furthermore, users will lose their privacy as many recommendation systems collect information about their users. This paper aims to solve the aforementioned issues by creating a recommendation system that will work with any data domain and upholds the privacy of its users. The Dynamic Implementation Of Privacy Based Recommendation System Application is created in this paper. The objective of this thesis is achieved as the Dynamic Implementation Of Privacy Based Recommendation System Application developed in this paper upholds the privacy of its users as it only stores users' data in their device's local database and the application has the ability to adapt to different data domains. © 2023 IEEE.

关键词： Recommender systems

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MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid Operation

MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Acharya, Samrat Khan, Hafiz Anwar Ullah Karri, Ramesh Dvorkin, Yury Pacific Northwest National Laboratory RichlandWA99354 United States New York University Department of Electrical and Computer Engineering BrooklynNY11201 United States Johns Hopkins University Ralph o'Connor Sustainable Energy Institute Department of Electrical and Computer Engineering Department of Civil and Systems Engineering BaltimoreMD21218 United States

ISBN: (纸本)9798350313604

This paper examines the feasibility of demand-side cyberattacks on power grids launched via internet-connected high-power EV Charging Stations (EVCSs). By distorting power grid frequency and voltage, these attacks can trigger system-wide outages. Our case study focuses on Manhattan, New York, and reveals that such attacks will become feasible by 2030 with increased EV adoption. With a single EVCS company dominating Manhattan, compromising a single EVCS server raises serious power grid security concerns. These attacks can overload power lines and trip over-frequency (OF) protection relays, resulting in a power grid blackout. This study serves as a crucial resource for planning authorities and power grid operators involved in the EV charging infrastructure roll-out, highlighting potential cyberthreats to power grids stemming from high-power EVCSs. © 2024 IEEE.

关键词： Relay protection

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Feedback Optimization of Nonlinear Strict-Feedback systems

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Journal of systems Science & Complexity 2025年第2期38卷 717-738页

作者： LIU Tong LIU Tengfei JIANG Zhong-Ping Department of Electrical and Computer Engineering New York University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Feedback optimization aims at regulating the output of a dynamical system to a value that minimizes a cost function. This problem is beyond the reach of the traditional output regulation theory, because the desired value is generally unknown and the reference signal evolves according to a gradient flow using the system's real-time output. This paper complements the output regulation theory with the nonlinear small-gain theory to address this challenge. Specifically, the authors assume that the cost function is strongly convex and the nonlinear dynamical system is in lower triangular form and is subject to parametric uncertainties and a class of external disturbances. An internal model is used to compensate for the effects of the disturbances while the cyclic small-gain theorem is invoked to address the coupling between the reference signal, the compensators, and the physical system. The proposed solution can guarantee the boundedness of the closed-loop signals and regulate the output of the system towards the desired minimizer in a global sense. Two numerical examples illustrate the effectiveness of the proposed method.

关键词： Feedback optimization internal model output regulation small-gain theorem

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Integrated preparation and manipulation of high‑dimensional flying structured photons

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eLight 2024年第1期4卷 185-192页

作者： Haoqi Zhao Yichi Zhang Zihe Gao Jieun Yim Shuang Wu Natalia MLitchinitser Li Ge Liang Feng Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Materials Science and Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Electrical and Computer Engineering and Department of Physics Duke UniversityDurhamNC 27708USA Department of Engineering Science and Physics College of Staten IslandCUNYStaten IslandNY 10314USA The Graduate Center CUNYNew YorkNY 10016USA

The hope for a futuristic global quantum internet that provides robust and high-capacity quantum information transfer lies largely on qudits,the fundamental quantum information carriers prepared in high-dimensional superposition ***,preparing and manipulating N-dimensional flying qudits as well as subsequently establishing their entanglement are still challenging tasks,which require precise and simultaneous maneuver of 2(N-1)parameters across multiple degrees of ***,using an integrated approach,we explore the synergy from two degrees of freedom of light,spatial mode and polarization,to generate,encode,and manipulate flying structured photons and their formed qudits in a four-dimensional Hilbert space with high quantum fidelity,intrinsically enabling enhanced noise resilience and higher quantum data *** four eigen spin–orbit modes of our qudits possess identical spatial–temporal characteristics in terms of intensity distribution and group velocity,thereby preserving long-haul coherence within the entirety of the quantum data transmission *** leveraging the bi-photon entanglement,which is well preserved in the integrated manipulation process,we present versatile spin–orbit cluster states in an extensive dimensional Hilbert *** cluster states hold the promise for quantum error correction which can further bolster the channel robustness in long-range quantum communication.

关键词： cluster thereby preparing

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IoT Based Smart Refrigerator Using Machine Learning 2

IoT Based Smart Refrigerator Using Machine Learning

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Devanath, B. Dharunish, A.P. Mohana Bharathi, S. Suganth, R. Banupriya, V. Ramesh, M. M Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India M Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

The fridge is the most constantly employed menage/ kitchen electrical device encyclopedically for conserving food. The leading sectors of smart widgets include refrigerators in the kitchen. The Web of Effects (IoT) denotes the tools and systems employed to link the factors deduced from the detectors and selectors. Primarily this contrivance is employed for different purposes such as storing flora, fruits, etc. This design transforms any pre-existing fridge into an intelligent, budget-friendly outfit with exercise sensors. The proposed design is educated and worked grounded on the medium of learning machines, and it contrasts the condition of the aliment, for case, Mass, quantum, etc. The significance of this undertaking will involve barring food decay, dwindling affections, and promoting a more restorative way of life for contemporary mortal beings. The arrangement of dish recommendations grounded on the veggies being in the caddy is carried out through an abecedarian algorithm of machine literacy, which categorizes the vegetables grounded on the tinges, latterly suggesting a specific form. It'll be canny enough to warn the stoner IoT about the present condition of food papers through an Android operation on our cellular device. © 2024 IEEE.

关键词： Refrigerators

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A Comprehensive Approach to Real-Time and Batch Processing for Energy-Efficient IoT Homes: Leveraging Lambda Architecture and Data Lakes 15

A Comprehensive Approach to Real-Time and Batch Processing f...

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15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Serepas, Filippos Papias, Ioannis Bellos, Nikolaos Marinakis, Vangelis Holistic Ike Athens Greece School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350368833

The incorporation of IoT technology into energy-efficient home systems has resulted in a surge in data volume, prompting the need for sophisticated storage and processing solutions. This paper proposes a system that integrates the Lambda Architecture with data lakes to address real-time and batch processing needs in the context of energy-efficient homes. By leveraging technologies such as TimescaleDB for short-term storage and Apache Hudi for long-term storage, coupled with Kafka for data streaming, the system ensures efficient data management and analysis. Real-time insights are provided through GraphQL-powered visualizations, while batch processing facilitates advanced analytics and machine learning model training. The proposed system addresses the dual demands of end-users seeking real-time insights and data scientists requiring extensive datasets for analysis. © 2024 IEEE.

关键词： Smart homes

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Real-time binaural speech reproduction with deep neural networks and dynamic head tracking 156

Real-time binaural speech reproduction with deep neural netw...

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AES Madrid 2024: 156th Audio engineering Society Convention

作者： Papavasileiou, Ioannis Thoidis, Iordanis Sevastiadis, Christos Vryzas, Nikolaos Papanikolaou, George Laboratory of Electroacoustics and TV Systems School of Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki Thessaloniki Greece Laboratory of Electronic Media Department of Journalism and Mass Media Aristotle University of Thessaloniki Thessaloniki Greece

This study examines the efficacy of a deep learning-based binaural speech reproduction system via headphones that integrates real-time head tracking to simulate a stable speech source in a virtual environment. We developed a binaural speech reproduction system that operates in real time using a custom head-tracking device based on the Arduino platform and a pre-trained neural network model for binaural speech synthesis. The system dynamically adapts audio output based on the head orientation of the listener, operating with an overall latency of 70 ms on standard computing hardware. We conducted a subjective speech localization task with 15 young, normal-hearing participants. Initial results showed a 40% accuracy in localizing a static speech source, which significantly improved with dynamic head movements. Participants reported minimal dizziness and distortion, indicating good tolerability and sound quality. Despite some limitations, such as non-personalized settings and perceivable latency, the findings demonstrate the potential of deep learning approaches to enhance realism in virtual auditory environments. Further research is needed to refine these technologies for broader applications in immersive audio settings. © AES Madrid 2024: 156th Audio engineering Society Convention. All rights reserved.

关键词： Deep neural networks

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