Federated Learning (FL) is a promising distributed learning framework designed for privacy-aware applications. FL trains models on client devices without sharing the client’s data and generates a global model on a se...
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We demonstrate a CMOS electronic-photonic photon-pair source with integrated feedback-controlled frequency locking, >80 dB on-chip pump rejection, and signal/idler demultiplexing, achieving a CAR of 5 at 40 ccps pa...
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The power transformer is a crucial asset and a fundamental component of the power grid. Assets undergo aging due to the stresses present in insulation materials. Partial discharges (PDs) are the most common fault sour...
The power transformer is a crucial asset and a fundamental component of the power grid. Assets undergo aging due to the stresses present in insulation materials. Partial discharges (PDs) are the most common fault source in power transformers and an excellent indicator of aging. The detection, classification, and localization of PD activities in power transformers are persisting challenges, while techniques utilizing machine learning (ML) are widely sought to deal with those challenges. Existing ML techniques show promising results with an elevated level of accuracy and precision. However, there is a lack of conventional ML-based real-time monitoring capability. Therefore, this paper presents a comprehensive review of the application of ML techniques for online PD activity detection, classification, and localization in power transformers, focusing on supervised, unsupervised, semi-supervised, and reinforcement learning techniques. In addition, this paper explores the challenges, future trends, perspectives, and outlook of machine learning for online transformer fault analysis.
Recent advances in IoT, machine learning, and edge computing have driven transformative paradigms like smart cities, grids, healthcare, and transportation systems, providing efficient solutions. This has led to a perv...
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ISBN:
(数字)9798350351255
ISBN:
(纸本)9798350351262
Recent advances in IoT, machine learning, and edge computing have driven transformative paradigms like smart cities, grids, healthcare, and transportation systems, providing efficient solutions. This has led to a pervasive proliferation of connected devices, ranging from high-power computers to low-power sensors. Yet, the complex IoT architecture poses numerous vulnerabilities, demanding robust security measures. Existing firmware attestation techniques often encounter obstacles due to proprietary constraints, necessitating access to the device’s authentic firmware. To address this challenge, this paper proposes a novel software-based attestation framework that utilizes RAM traces from IoT devices for remote verification. By employing deep learning models trained in a representation learning paradigm, our framework empowers the remote verifier to authenticate the internal state of IoT devices. Leveraging data collected from real-world prototype devices, our approach achieves an impressive 100% detection rate for critical attacks on IoT devices with a false positive rate of 10 −3 . Remarkably, our framework preserves device availability and maintains low authentication latency, highlighting its efficacy and practicality for securing IoT ecosystems.
We present a photon subtraction scheme designed to deterministically extract single photons from multiphoton states within arbitrary input pulses of light using single-photon Raman interaction (SPRINT) [1]. The propos...
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ISBN:
(数字)9798350366365
ISBN:
(纸本)9798350366372
We present a photon subtraction scheme designed to deterministically extract single photons from multiphoton states within arbitrary input pulses of light using single-photon Raman interaction (SPRINT) [1]. The proposed system comprises two cascaded Lambda-type atoms with transitions selectively coupled to distinct modes of a single chiral waveguide. Through numerical simulations, we evaluate the device's performance in a potential application involving photon-number-splitting (PNS) attacks against quantum key distribution (QKD).
We study a status update system with a source, a sampler, a transmitter, and a monitor. The source governs a stochastic process that the monitor wants to observe in a timely manner. To achieve this, the sampler sample...
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The main focus of the work is to improve the smart traffic systems for the Internet of Things (IoT) such as traditional signals and safe driving via tracking the congestion and monitoring traffic slowly. Depending on ...
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Counterfeit products have become a major concern especially because they are easy to produce and their impacts are far reaching. The purpose of this study is to survey the use of artificial intelligence techniques tow...
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Rarity is known to be a factor in the price of non-fungible tokens (NFTs). Most investors make their purchasing decisions based on the rarity score or rarity rank of NFTs. However, not all rare NFTs are associated wit...
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We study a class of systems termed Markov Machines (MM) which process job requests with exponential service times. Assuming a Poison job arrival process, these MMs oscillate between two states, free and busy. We consi...
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