Gathering properly labelled, adequately rich, and case-specific data for successfully training a data-driven or hybrid model for structural health monitoring (SHM) applications is a challenging task. We posit that a Transfer Learning (TL) method that utilizes available data in any relevant source domain and directly applies to the target domain through domain adaptation can provide substantial remedies to address this issue. Accordingly, we present a novel TL method that differentiates between the source's no-damage and damage cases and utilizes a domain adaptation (DA) technique. The DA module transfers the accumulated knowledge in contrasting no-damage and damage cases in the source domain to the target domain, given only the target's no-damage case. High-dimensional features allow employing signal processing domain knowledge to devise a generalizable DA approach. The Generative Adversarial Network (GAN) architecture is adopted for learning since its optimization process accommodates high-dimensional inputs in a zero-shot setting. At the same time, its training objective conforms seamlessly with the case of no-damage and damage data in SHM since its discriminator network differentiates between real (no damage) and fake (possibly unseen damage) data. An extensive set of experimental results demonstrates the method's success in transferring knowledge on differences between no-damage and damage cases across three strongly heterogeneous independent target structures. The area under the Receiver Operating Characteristics curves (Area Under the Curve - AUC) is used to evaluate the differentiation between no-damage and damage cases in the target domain, reaching values as high as 0.95. With no-damage and damage cases discerned from each other, zero-shot structural damage detection is carried out. The mean F1 scores for all damages in the three independent datasets are 0.978, 0.992, and 0.975.

Using Structural Health Monitoring (SHM) systems with extensive sensing arrangements on every civil structure can be costly and impractical. Various concepts have been introduced to alleviate such difficulties, such as Population-based SHM (PBSHM). Nevertheless, the studies presented in the literature do not adequately address the challenge of accessing the information on different structural states (conditions) of dissimilar civil structures. The study herein introduces a novel framework named Structural State Translation (SST), which aims to estimate the response data of different civil structures based on the information obtained from a dissimilar structure. SST can be defined as Translating a state of one civil structure to another state after discovering and learning the domain-invariant representation in the source domains of a dissimilar civil structure. SST employs a Domain-Generalized Cycle-Generative (DGCG) model to learn the domain-invariant representation in the acceleration datasets obtained from a numeric bridge structure that is in two different structural conditions. In other words, the model is tested on three dissimilar numeric bridge models to translate their structural conditions. The evaluation results of SST via Mean Magnitude-Squared Coherence (MMSC) and modal identifiers showed that the translated bridge states (synthetic states) are significantly similar to the real ones. As such, the minimum and maximum average MMSC values of real and translated bridge states are 91.2% and 97.1%, the minimum and the maximum difference in natural frequencies are 5.71% and 0%, and the minimum and maximum Modal Assurance Criterion (MAC) values are 0.998 and 0.870. This study is critical for data scarcity and PBSHM, as it demonstrates that it is possible to obtain data from structures while the structure is actually in a different condition or state.

Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.

由于培训和测试数据分布之间的域移动，新的操作条件可能会导致故障诊断模型的大量性能下降。尽管已经提出了几种域的适应方法来克服此类域移位，但如果两个域中表示的故障类别不相同，则其应用是有限的。为了在两个不同的域之间启用训练有素的模型的更好可传递性，尤其是在两个域之间仅共享健康数据类别的设置中，我们提出了一个新的框架，以基于生成不同的故障签名的部分和开放式域适应一个瓦斯林甘。提出的框架的主要贡献是具有两个主要不同特征的受控合成断层数据生成。首先，所提出的方法使目标域中仅能访问目标域中的健康样品和源域中的样本错误，从而在目标域中生成未观察到的故障类型。其次，可以将故障产生控制以精确生成不同的故障类型和故障严重程度。所提出的方法特别适合于极端域的适应设置，这些设置在复杂和安全关键系统的背景下特别相关，其中两个域之间仅共享一个类。我们在两个轴承断层诊断案例研究上评估了部分和开放式域适应任务的拟议框架。我们在不同标签空间设置中进行的实验展示了提出的框架的多功能性。与给定较大域间隙的其他方法相比，提出的方法提供了优越的结果。

无监督的域适应（UDA）显示出近年来工作条件下的轴承故障诊断的显着结果。但是，大多数UDA方法都不考虑数据的几何结构。此外，通常应用全局域适应技术，这忽略了子域之间的关系。本文通过呈现新的深亚域适应图卷积神经网络（DSAGCN）来解决提到的挑战，具有两个关键特性：首先，采用图形卷积神经网络（GCNN）来模拟数据结构。二，对抗域适应和局部最大平均差异（LMMD）方法同时应用，以对准子域的分布并降低相关子域和全局域之间的结构差异。 CWRU和Paderborn轴承数据集用于验证DSAGCN方法的比较模型之间的效率和优越性。实验结果表明，将结构化子域与域适应方法对准，以获得无监督故障诊断的准确数据驱动模型。

最近，面部生物识别是对传统认证系统的方便替代的巨大关注。因此，检测恶意尝试已经发现具有重要意义，导致面部抗欺骗〜（FAS），即面部呈现攻击检测。与手工制作的功能相反，深度特色学习和技术已经承诺急剧增加FAS系统的准确性，解决了实现这种系统的真实应用的关键挑战。因此，处理更广泛的发展以及准确的模型的新研究区越来越多地引起了研究界和行业的关注。在本文中，我们为自2017年以来对与基于深度特征的FAS方法相关的文献综合调查。在这一主题上阐明，基于各种特征和学习方法的语义分类。此外，我们以时间顺序排列，其进化进展和评估标准（数据集内集和数据集互联集合中集）覆盖了FAS的主要公共数据集。最后，我们讨论了开放的研究挑战和未来方向。

In recent years, applying deep learning (DL) to assess structural damages has gained growing popularity in vision-based structural health monitoring (SHM). However, both data deficiency and class-imbalance hinder the wide adoption of DL in practical applications of SHM. Common mitigation strategies include transfer learning, over-sampling, and under-sampling, yet these ad-hoc methods only provide limited performance boost that varies from one case to another. In this work, we introduce one variant of the Generative Adversarial Network (GAN), named the balanced semi-supervised GAN (BSS-GAN). It adopts the semi-supervised learning concept and applies balanced-batch sampling in training to resolve low-data and imbalanced-class problems. A series of computer experiments on concrete cracking and spalling classification were conducted under the low-data imbalanced-class regime with limited computing power. The results show that the BSS-GAN is able to achieve better damage detection in terms of recall and $F_\beta$ score than other conventional methods, indicating its state-of-the-art performance.

由于技术成本的降低和卫星发射的增加，卫星图像变得越来越流行和更容易获得。除了提供仁慈的目的外，还可以出于恶意原因（例如错误信息）使用卫星数据。事实上，可以依靠一般图像编辑工具来轻松操纵卫星图像。此外，随着深层神经网络（DNN）的激增，可以生成属于各种领域的现实合成图像，与合成生成的卫星图像的扩散有关的其他威胁正在出现。在本文中，我们回顾了关于卫星图像的产生和操纵的最新技术（SOTA）。特别是，我们既关注从头开始的合成卫星图像的产生，又要通过图像转移技术对卫星图像进行语义操纵，包括从一种类型的传感器到另一种传感器获得的图像的转换。我们还描述了迄今已研究的法医检测技术，以对合成图像伪造进行分类和检测。虽然我们主要集中在法医技术上明确定制的，该技术是针对AI生成的合成内容物的检测，但我们还审查了一些用于一般剪接检测的方法，这些方法原则上也可以用于发现AI操纵图像

在过去的几十年中，数据科学领域已经存在着激烈的进展，而其他学科则不断受益于此。结构健康监测（SHM）是使用人工智能（AI）的那些领域之一，例如机器学习（ML）和深度学习（DL）算法，用于基于所收集的数据的民用结构的条件评估。 ML和DL方法需要大量的培训程序数据;但是，在SHM中，来自民间结构的数据收集非常详尽;特别是获得有用的数据（相关数据损坏）可能非常具有挑战性。本文使用1-D Wasserstein深卷积生成的对抗网络，使用梯度惩罚（1-D WDCGAN-GP）进行合成标记的振动数据生成。然后，通过使用1-D深卷积神经网络（1-D DCNN）来实现在不同级别的合成增强振动数据集的结构损伤检测。损伤检测结果表明，1-D WDCAN-GP可以成功地利用以解决基于振动的民用结构的损伤诊断数据稀缺。关键词：结构健康监测（SHM），结构损伤诊断，结构损伤检测，1-D深卷积神经网络（1-D DCNN），1-D生成对抗网络（1-D GAN），深卷积生成的对抗网络（ DCGAN），Wassersein生成的对抗性网络具有梯度惩罚（WAN-GP）

本文提出了一种新的劣化和损坏识别程序（DIP）并应用于建筑模型。与这些类型的结构的应用相关的挑战与响应的强相关性有关，这在应对具有高噪声水平的真实环境振动时进一步复杂化。因此，利用低成本环境振动设计了DIP，以分析使用股票变换（ST）来产生谱图的加速响应。随后，ST输出成为建立的两系列卷积神经网络（CNNS）的输入，用于识别建筑模型的恶化和损坏。据我们所知，这是第一次通过高精度的ST和CNN组合在建筑模型中评估损坏和恶化。

Deep domain adaptation has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature subspaces or reuse important source instances with shallow representations, deep domain adaptation methods leverage deep networks to learn more transferable representations by embedding domain adaptation in the pipeline of deep learning. There have been comprehensive surveys for shallow domain adaptation, but few timely reviews the emerging deep learning based methods. In this paper, we provide a comprehensive survey of deep domain adaptation methods for computer vision applications with four major contributions. First, we present a taxonomy of different deep domain adaptation scenarios according to the properties of data that define how two domains are diverged. Second, we summarize deep domain adaptation approaches into several categories based on training loss, and analyze and compare briefly the state-of-the-art methods under these categories. Third, we overview the computer vision applications that go beyond image classification, such as face recognition, semantic segmentation and object detection. Fourth, some potential deficiencies of current methods and several future directions are highlighted.

随着深度学习生成模型的最新进展，它在时间序列领域的出色表现并没有花费很长时间。用于与时间序列合作的深度神经网络在很大程度上取决于培训中使用的数据集的广度和一致性。这些类型的特征通常在现实世界中不丰富，在现实世界中，它们通常受到限制，并且通常具有必须保证的隐私限制。因此，一种有效的方法是通过添加噪声或排列并生成新的合成数据来使用\ gls {da}技术增加数据数。它正在系统地审查该领域的当前最新技术，以概述所有可用的算法，并提出对最相关研究的分类法。将评估不同变体的效率；作为过程的重要组成部分，将分析评估性能的不同指标以及有关每个模型的主要问题。这项研究的最终目的是摘要摘要，这些领域的进化和性能会产生更好的结果，以指导该领域的未来研究人员。

机器学习模型通常会遇到与训练分布不同的样本。无法识别分布（OOD）样本，因此将该样本分配给课堂标签会显着损害模​​型的可靠性。由于其对在开放世界中的安全部署模型的重要性，该问题引起了重大关注。由于对所有可能的未知分布进行建模的棘手性，检测OOD样品是具有挑战性的。迄今为止，一些研究领域解决了检测陌生样本的问题，包括异常检测，新颖性检测，一级学习，开放式识别识别和分布外检测。尽管有相似和共同的概念，但分别分布，开放式检测和异常检测已被独立研究。因此，这些研究途径尚未交叉授粉，创造了研究障碍。尽管某些调查打算概述这些方法，但它们似乎仅关注特定领域，而无需检查不同领域之间的关系。这项调查旨在在确定其共同点的同时，对各个领域的众多著名作品进行跨域和全面的审查。研究人员可以从不同领域的研究进展概述中受益，并协同发展未来的方法。此外，据我们所知，虽然进行异常检测或单级学习进行了调查，但没有关于分布外检测的全面或最新的调查，我们的调查可广泛涵盖。最后，有了统一的跨域视角，我们讨论并阐明了未来的研究线，打算将这些领域更加紧密地融为一体。

最近的智能故障诊断（IFD）的进展大大依赖于深度代表学习和大量标记数据。然而，机器通常以各种工作条件操作，或者目标任务具有不同的分布，其中包含用于训练的收集数据（域移位问题）。此外，目标域中的新收集的测试数据通常是未标记的，导致基于无监督的深度转移学习（基于UDTL为基础的）IFD问题。虽然它已经实现了巨大的发展，但标准和开放的源代码框架以及基于UDTL的IFD的比较研究尚未建立。在本文中，我们根据不同的任务，构建新的分类系统并对基于UDTL的IFD进行全面审查。对一些典型方法和数据集的比较分析显示了基于UDTL的IFD中的一些开放和基本问题，这很少研究，包括特征，骨干，负转移，物理前导等的可转移性，强调UDTL的重要性和再现性 - 基于IFD，整个测试框架将发布给研究界以促进未来的研究。总之，发布的框架和比较研究可以作为扩展界面和基本结果，以便对基于UDTL的IFD进行新的研究。代码框架可用于\ url {https:/github.com/zhaozhibin/udtl}。

X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

在实践中，非常苛刻，有时无法收集足够大的标记数据数据集以成功培训机器学习模型，并且对此问题的一个可能解决方案是转移学习。本研究旨在评估如何可转让的时间序列数据和哪些条件下的不同域之间的特征。在训练期间，在模型的预测性能和收敛速度方面观察到转移学习的影响。在我们的实验中，我们使用1,500和9,000个数据实例的减少数据集来模仿现实世界的条件。使用相同的缩小数据集，我们培训了两组机器学习模型：那些随着转移学习的培训和从头开始培训的机器学习模型。使用四台机器学习模型进行实验。在相同的应用领域（地震学）以及相互不同的应用领域（地震，语音，医学，金融）之间进行知识转移。我们在训练期间遵守模型的预测性能和收敛速度。为了确认所获得的结果的有效性，我们重复了实验七次并应用了统计测试以确认结果的重要性。我们研究的一般性结论是转移学习可能会增加或不会对模型的预测性能或其收敛速度产生负面影响。在更多细节中分析收集的数据，以确定哪些源域和目标域兼容以用于传输知识。我们还分析了目标数据集大小的效果和模型的选择及其超参数对转移学习的影响。

Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.

脑电图（EEG）解码旨在识别基于非侵入性测量的脑活动的神经处理的感知，语义和认知含量。当应用于在静态，受控的实验室环境中获取的数据时，传统的EEG解码方法取得了适度的成功。然而，开放世界的环境是一个更现实的环境，在影响EEG录音的情况下，可以意外地出现，显着削弱了现有方法的鲁棒性。近年来，由于其在特征提取的卓越容量，深入学习（DL）被出现为潜在的解决方案。它克服了使用浅架构提取的“手工制作”功能或功能的限制，但通常需要大量的昂贵，专业标记的数据 - 并不总是可获得的。结合具有域特定知识的DL可能允许开发即使具有小样本数据，也可以开发用于解码大脑活动的鲁棒方法。虽然已经提出了各种DL方法来解决EEG解码中的一些挑战，但目前缺乏系统的教程概述，特别是对于开放世界应用程序。因此，本文为开放世界EEG解码提供了对DL方法的全面调查，并确定了有前途的研究方向，以激发现实世界应用中的脑电图解码的未来研究。

深度神经网络在人类分析中已经普遍存在，增强了应用的性能，例如生物识别识别，动作识别以及人重新识别。但是，此类网络的性能通过可用的培训数据缩放。在人类分析中，对大规模数据集的需求构成了严重的挑战，因为数据收集乏味，廉价，昂贵，并且必须遵守数据保护法。当前的研究研究了\ textit {合成数据}的生成，作为在现场收集真实数据的有效且具有隐私性的替代方案。这项调查介绍了基本定义和方法，在生成和采用合成数据进行人类分析时必不可少。我们进行了一项调查，总结了当前的最新方法以及使用合成数据的主要好处。我们还提供了公开可用的合成数据集和生成模型的概述。最后，我们讨论了该领域的局限性以及开放研究问题。这项调查旨在为人类分析领域的研究人员和从业人员提供。

鉴于无线频谱的有限性和对无线通信最近的技术突破产生的频谱使用不断增加的需求，干扰问题仍在继续持续存在。尽管最近解决干涉问题的进步，但干扰仍然呈现出有效使用频谱的挑战。这部分是由于Wi-Fi的无许可和管理共享乐队使用的升高，长期演进（LTE）未许可（LTE-U），LTE许可辅助访问（LAA），5G NR等机会主义频谱访问解决方案。因此，需要对干扰稳健的有效频谱使用方案的需求从未如此重要。在过去，通过使用避免技术以及非AI缓解方法（例如，自适应滤波器）来解决问题的大多数解决方案。非AI技术的关键缺陷是需要提取或开发信号特征的域专业知识，例如CycrationArity，带宽和干扰信号的调制。最近，研究人员已成功探索了AI / ML的物理（PHY）层技术，尤其是深度学习，可减少或补偿干扰信号，而不是简单地避免它。 ML基于ML的方法的潜在思想是学习来自数据的干扰或干扰特性，从而使需要对抑制干扰的域专业知识进行侧联。在本文中，我们审查了广泛的技术，这些技术已经深入了解抑制干扰。我们为干扰抑制中许多不同类型的深度学习技术提供比较和指导。此外，我们突出了在干扰抑制中成功采用深度学习的挑战和潜在的未来研究方向。