513 research outputs found

    Health prognosis of bearings based on transferable autoregressive recurrent adaptation with few-shot learning

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    Data-driven prognostic and health management technologies are instrumental in accurately monitoring the health of mechanical systems. However, the availability of few-shot source data under varying operating conditions limits their ability to predict health. Also, the global feature extraction process is susceptible to temporal semantic loss, resulting in reduced generalization of extracted degradation features. To address these challenges, a transferable autoregressive recurrent adaptation method is proposed for bearing health prognosis. In the enhancement of few-shot data, a novel sample generation module with attribute-assisted learning, combined with adversarial generation, is introduced to mine data that better matches the source sample distribution. Additionally, a deep autoregressive recurrent model is designed, incorporating a statistical mode to consider the degradation processes more comprehensively. To complement the semantic loss, a semantic attention module is developed, embedded into the basic model of meta learning. To validate the effectiveness of this approach, extensive bearing prognostics are conducted across six tasks. The results demonstrate the clear advantages of this proposed method in bearing prognosis, especially when dealing with limited bearing data

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics

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    It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Immunizing Backdoored PRGs

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    A backdoored Pseudorandom Generator (PRG) is a PRG which looks pseudorandom to the outside world, but a saboteur can break PRG security by planting a backdoor into a seemingly honest choice of public parameters, pkpk, for the system. Backdoored PRGs became increasingly important due to revelations about NIST’s backdoored Dual EC PRG, and later results about its practical exploitability. Motivated by this, at Eurocrypt\u2715 Dodis et al. [21] initiated the question of immunizing backdoored PRGs. A kk-immunization scheme repeatedly applies a post-processing function to the output of kk backdoored PRGs, to render any (unknown) backdoors provably useless. For k=1k=1, [21] showed that no deterministic immunization is possible, but then constructed seeded 11-immunizer either in the random oracle model, or under strong non-falsifiable assumptions. As our first result, we show that no seeded 11-immunization scheme can be black-box reduced to any efficiently falsifiable assumption. This motivates studying kk-immunizers for k≥2k\ge 2, which have an additional advantage of being deterministic (i.e., seedless ). Indeed, prior work at CCS\u2717 [37] and CRYPTO\u2718 [7] gave supporting evidence that simple kk-immunizers might exist, albeit in slightly different settings. Unfortunately, we show that simple standard model proposals of [37, 7] (including the XOR function [7]) provably do not work in our setting. On a positive, we confirm the intuition of [37] that a (seedless) random oracle is a provably secure 22-immunizer. On a negative, no (seedless) 22-immunization scheme can be black-box reduced to any efficiently falsifiable assumption, at least for a large class of natural 22-immunizers which includes all cryptographic hash functions. In summary, our results show that kk-immunizers occupy a peculiar place in the cryptographic world. While they likely exist, and can be made practical and efficient, it is unlikely one can reduce their security to a clean standard-model assumption

    Instance-Wise Hardness Versus Randomness Tradeoffs for Arthur-Merlin Protocols

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    Commitments from Quantum One-Wayness

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    One-way functions are central to classical cryptography. They are both necessary for the existence of non-trivial classical cryptosystems, and sufficient to realize meaningful primitives including commitments, pseudorandom generators and digital signatures. At the same time, a mounting body of evidence suggests that assumptions even weaker than one-way functions may suffice for many cryptographic tasks of interest in a quantum world, including bit commitments and secure multi-party computation. This work studies one-way state generators [Morimae-Yamakawa, CRYPTO 2022], a natural quantum relaxation of one-way functions. Given a secret key, a one-way state generator outputs a hard to invert quantum state. A fundamental question is whether this type of quantum one-wayness suffices to realize quantum cryptography. We obtain an affirmative answer to this question by proving that one-way state generators with pure state outputs imply quantum bit commitments and secure multiparty computation. Along the way, we build an intermediate primitive with classical outputs, which we call a (quantum) one-way puzzle. Our main technical contribution is a proof that one-way puzzles imply quantum bit commitments

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum
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