Industry 4.0 aims to optimize the manufacturing environment by leveraging new technological advances, such as new sensing capabilities and artificial intelligence. The DRAEM technique has shown state-of-the-art performance for unsupervised classification. The ability to create anomaly maps highlighting areas where defects probably lie can be leveraged to provide cues to supervised classification models and enhance their performance. Our research shows that the best performance is achieved when training a defect detection model by providing an image and the corresponding anomaly map as input. Furthermore, such a setting provides consistent performance when framing the defect detection as a binary or multiclass classification problem and is not affected by class balancing policies. We performed the experiments on three datasets with real-world data provided by Philips Consumer Lifestyle BV.
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Quality control is a crucial activity performed by manufacturing companies to ensure their products conform to the requirements and specifications. The introduction of artificial intelligence models enables to automate the visual quality inspection, speeding up the inspection process and ensuring all products are evaluated under the same criteria. In this research, we compare supervised and unsupervised defect detection techniques and explore data augmentation techniques to mitigate the data imbalance in the context of automated visual inspection. Furthermore, we use Generative Adversarial Networks for data augmentation to enhance the classifiers' discriminative performance. Our results show that state-of-the-art unsupervised defect detection does not match the performance of supervised models but can be used to reduce the labeling workload by more than 50%. Furthermore, the best classification performance was achieved considering GAN-based data generation with AUC ROC scores equal to or higher than 0,9898, even when increasing the dataset imbalance by leaving only 25\% of the images denoting defective products. We performed the research with real-world data provided by Philips Consumer Lifestyle BV.
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Machine learning (ML) algorithms are remarkably good at approximating complex non-linear relationships. Most ML training processes, however, are designed to deliver ML tools with good average performance, but do not offer any guarantees about their worst-case estimation error. For safety-critical systems such as power systems, this places a major barrier for their adoption. So far, approaches could determine the worst-case violations of only trained ML algorithms. To the best of our knowledge, this is the first paper to introduce a neural network training procedure designed to achieve both a good average performance and minimum worst-case violations. Using the Optimal Power Flow (OPF) problem as a guiding application, our approach (i) introduces a framework that reduces the worst-case generation constraint violations during training, incorporating them as a differentiable optimization layer; and (ii) presents a neural network sequential learning architecture to significantly accelerate it. We demonstrate the proposed architecture on four different test systems ranging from 39 buses to 162 buses, for both AC-OPF and DC-OPF applications.
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Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In this work, we create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale with the use of segmentation deep learning models. Particularly, we present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers (climate, vegetation, oceanic indices, human-related variables), as well as the historical burned areas and wildfire emissions for 2001-2021. We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time. Our work motivates the use of deep learning for global burned area forecasting and paves the way towards improved anticipation of global wildfire patterns.
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在排放限制下优化的气体网络规划优化优先考虑最少$ _2 $强度的天然气供应。由于此问题包括复杂的气流物理定律，因此标准优化求解器无法保证融合与可行解决方案。为了解决这个问题，我们开发了一个输入 - 控制神经网络（ICNN）辅助优化例程，该程序结合了一组训练有素的ICNN，以高精度近似于气流方程。比利时气体网络上的数值测试表明，ICNN辅助优化主导了非凸和基于弛豫的求解器，其最佳增长较大，与更严格的发射目标有关。此外，每当非凸线求解器失败时，ICNN ADED优化为网络计划提供了可行的解决方案。
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新兴的非挥发记忆设备的备忘录在神经形态硬件设计中显示出有希望的潜力，尤其是在尖峰神经网络（SNN）硬件实现方面。基于Memristor的SNN已成功应用于各种应用程序，包括图像分类和模式识别。但是，在文本分类中实施基于备忘录的SNN仍在探索中。主要原因之一是，培训基于备忘录的SNN用于文本分类是由于缺乏有效的学习规则和不理想性的不存在。为了解决这些问题，并加快了在文本分类应用程序中探索基于备忘录的尖峰神经网络的研究，我们使用经验的Memristor模型开发了使用虚拟备忘录阵列的仿真框架。我们使用此框架来演示IMDB电影评论数据集中的情感分析任务。我们采用两种方法，通过将预训练的人工神经网络（ANN）转换为基于Memristor的SNN或2），通过直接训练基于Memristor的SNN，以获取训练有素的尖峰神经网络：1）通过将预训练的人工神经网络（ANN）转换为基于Memristor的SNN。这两种方法可以在两种情况下应用：离线分类和在线培训。鉴于等效ANN的基线训练精度为86.02％，我们通过将预训练的ANN转换为基于Memristor的SNN的ANN通过将预培训的ANN转换为基于Memristor的SNN的85.88％的分类准确性为85.88％。我们得出的结论是，可以在从ANN到SNN以及从非同步突触到数据驱动的Memristive突触的模拟中实现类似的分类精度。我们还研究了诸如Spike火车长度，读取噪声和重量更新停止条件之类的全局参数如何影响两种方法的神经网络。
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接受注释较弱的对象探测器是全面监督者的负担得起的替代方案。但是，它们之间仍然存在显着的性能差距。我们建议通过微调预先训练的弱监督检测器来缩小这一差距，并使用``Box-In-box''（bib'（bib）自动从训练集中自动选择了一些完全注销的样品，这是一种新颖的活跃学习专门针对弱势监督探测器的据可查的失败模式而设计的策略。 VOC07和可可基准的实验表明，围嘴表现优于其他活跃的学习技术，并显着改善了基本的弱监督探测器的性能，而每个类别仅几个完全宣布的图像。围嘴达到了完全监督的快速RCNN的97％，在VOC07上仅10％的全已通量图像。在可可（COCO）上，平均每类使用10张全面通量的图像，或同等的训练集的1％，还减少了弱监督检测器和完全监督的快速RCN之间的性能差距（In AP）以上超过70％ ，在性能和数据效率之间表现出良好的权衡。我们的代码可在https://github.com/huyvvo/bib上公开获取。
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数字双胞胎（DT）本质上是动态数据驱动的模型，可作为现实世界系统的实时共生“虚拟副本”。 DT可以利用动态数据驱动的应用系统（DDDAS）双向共生感应反馈循环的基本面来进行连续更新。因此，传感循环可以操纵测量，分析和重新配置，旨在在DT中进行更准确的建模和分析。重新配置决策可以是自主的或互动的，可以保持人类在循环中。这些决定的可信赖性可能会因理由的解释性不足而阻碍，并在实施替代方案之间对给定情况的决定中获得了实用性。此外，不同的决策算法和模型具有不同的复杂性，质量，并可能导致模型获得不同的效用。解释性的不足可能会限制人类可以评估决策的程度，通常会导致更新，这些更新不适合给定的情况，错误，损害了模型的整体准确性。本文的新颖贡献是一种利用人类界DDDA和DT系统中解释性的方法，利用双向共生感应反馈。该方法利用可解释的机器学习和目标建模来解释性，并考虑了获得的实用程序的权衡分析。我们使用智能仓储中的示例来演示这种方法。
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备忘录显示了增强神经形态计算概念和AI硬件加速器的有希望的功能。在本文中，我们提出了一个用户友好的软件基础架构，该基础架构允许使用Memristor模型模拟各种神经形态架构。该工具赋予了将备忘录用于在线学习和在线分类任务的研究，从而预测了培训过程中的备忘录抵抗状态的变化。该工具的多功能性是通过功能来展示的，以供用户自定义所使用的Memristor和Neuronal模型中的参数以及所采用的学习规则。这进一步允许用户在广泛的参数中验证概念及其灵敏度。我们通过MNIST分类任务演示了该工具的使用。最后，我们展示了如何使用该工具通过与市售的特征工具进行适当的接口来模拟与实用的回忆设备中研究的概念。
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乘车共享因其方便和乘客的便利性和成本效率而获得了全球知名度，以及其强大的潜力有助于实施联合国可持续发展目标。结果，近年来，目睹了RSODP的研究兴趣（用于乘车共享的原产地预测）问题，目的是预测未来的乘车共享请求并提前为车辆提供时间表。大多数现有的预测模型都利用深度学习，但是它们无法有效地考虑空间和时间动态。在本文中，提出了基准的门控注意复发网络（BGARN），该网络（BGARN）使用具有多头门的图形卷积来提取空间特征，以提取时间特征的复发模块以及基线转移层来计算最终结果。该模型是使用Pytorch和DGL（Deep Graph库）实施的，并使用纽约出租车需求数据集对实验进行了评估。结果表明，BGARN在预测准确性方面优于所有其他现有模型。
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