A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in nonhomogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN). In total, combinations of fifteen feature groups and fifteen classical machine learning models, and eleven CNN models are considered and their performances explored. The results indicate that the combination of feature engineering and machine learning provides better performance than the direct use of CNN. Notably, feature engineering which is comprised of physics-guided transformation, signal representation-based feature extraction and Principal Component Analysis is found to be the most effective. Moreover, it is shown that when using the extracted features, the ensemble-based, light blender learning model offers the best performance with RMSE, RE, RRMSE and R values of 64.3, 0.017, 0.025 and 0.994, respectively. The proposed method, based on feature engineering and the light blender model, is capable of measuring nonuniform temperature distributions from low-resolution spectra, even when the species concentration distribution in the gas mixtures is unknown.

The current success of machine learning on image-based combustion monitoring is based on massive data, which is costly even impossible for industrial applications. To address this conflict, we introduce few-shot learning in order to achieve combustion monitoring and classification for the first time. Two algorithms, Siamese Network coupled with k Nearest Neighbors (SN-kNN) and Prototypical Network (PN), were tested. Rather than utilizing solely visible images as discussed in previous studies, we also used Infrared (IR) images. We analyzed the training process, test performance and inference speed of two algorithms on both image formats, and also used t-SNE to visualize learned features. The results demonstrated that both SN-kNN and PN were capable to distinguish flame states from learning with merely 20 images per flame state. The worst performance, which was realized by PN on IR images, still possessed precision, accuracy, recall, and F1-score above 0.95. We showed that visible images demonstrated more substantial differences between classes and presented more consistent patterns inside the class, which made the training speed and model performance better compared to IR images. In contrast, the relatively low quality of IR images made it difficult for PN to extract distinguishable prototypes, which caused relatively weak performance. With the entrire training set supporting classification, SN-kNN performed well with IR images. On the other hand, benefitting from the architecture design, PN has a much faster speed in training and inference than SN-kNN. The presented work analyzed the characteristics of both algorithms and image formats for the first time, thus providing guidance for their future utilization in combustion monitoring tasks.

地球天文台是一个不断增长的研究领域，可以在短时间预测（即现在的情况下）利用AI的力量。在这项工作中，我们使用视频变压器网络应对天气预报的挑战。视觉变压器体系结构已在各种应用中进行了探索，主要限制是注意力的计算复杂性和饥饿的培训。为了解决这些问题，我们建议使用视频Swin-Transformer，再加上专用的增强计划。此外，我们在编码器侧采用逐渐的空间减少，并在解码器上进行了交叉注意。在Weather4cast2021天气预报挑战数据中测试了建议的方法，该数据需要从每小时的天气产品序列预测未来的8小时（每小时4个小时）。将数据集归一化为0-1，以促进使用不同数据集的评估指标。该模型在提供训练数据时会导致MSE得分为0.4750，在不使用培训数据的情况下转移学习过程中为0.4420。

The two popular datasets ScanRefer [16] and ReferIt3D [3] connect natural language to real-world 3D data. In this paper, we curate a large-scale and complementary dataset extending both the aforementioned ones by associating all objects mentioned in a referential sentence to their underlying instances inside a 3D scene. Specifically, our Scan Entities in 3D (ScanEnts3D) dataset provides explicit correspondences between 369k objects across 84k natural referential sentences, covering 705 real-world scenes. Crucially, we show that by incorporating intuitive losses that enable learning from this novel dataset, we can significantly improve the performance of several recently introduced neural listening architectures, including improving the SoTA in both the Nr3D and ScanRefer benchmarks by 4.3% and 5.0%, respectively. Moreover, we experiment with competitive baselines and recent methods for the task of language generation and show that, as with neural listeners, 3D neural speakers can also noticeably benefit by training with ScanEnts3D, including improving the SoTA by 13.2 CIDEr points on the Nr3D benchmark. Overall, our carefully conducted experimental studies strongly support the conclusion that, by learning on ScanEnts3D, commonly used visio-linguistic 3D architectures can become more efficient and interpretable in their generalization without needing to provide these newly collected annotations at test time. The project's webpage is https://scanents3d.github.io/ .

Natural language interaction is a promising direction for democratizing 3D shape design. However, existing methods for text-driven 3D shape editing face challenges in producing decoupled, local edits to 3D shapes. We address this problem by learning disentangled latent representations that ground language in 3D geometry. To this end, we propose a complementary tool set including a novel network architecture, a disentanglement loss, and a new editing procedure. Additionally, to measure edit locality, we define a new metric that we call part-wise edit precision. We show that our method outperforms existing SOTA methods by 20% in terms of edit locality, and up to 6.6% in terms of language reference resolution accuracy. Our work suggests that by solely disentangling language representations, downstream 3D shape editing can become more local to relevant parts, even if the model was never given explicit part-based supervision.

这项工作提出了一种基于形态重建和启发式方法的聚集算法，称为K-Morphological集合（K-MS）。在最坏情况下，K-MS比CPU并行K-均值快，并且可以增强数据集的可视化以及非常不同的聚类。它也比对密度和形状（例如有丝分裂和三升）敏感的类似聚类方法更快。另外，K-MS是确定性的，具有最大簇的内在含义，可以为给定的输入样本和输入参数创建，与K-均值和其他聚类算法不同。换句话说，给定恒定的k，一个结构元素和数据集，k-ms会在不使用随机/伪随机函数的情况下产生K或更少的簇。最后，所提出的算法还提供了一种简单的手段，可以从图像或数据集中删除噪声。

这项工作提出了使用遗传算法（GA）在追踪和识别使用计算机断层扫描（CT）图像的人心包轮廓的过程中。我们假设心包的每个切片都可以通过椭圆建模，椭圆形需要最佳地确定其参数。最佳椭圆将是紧随心包轮廓的紧密椭圆形，因此，将人心脏的心外膜和纵隔脂肪适当地分开。追踪和自动识别心包轮廓辅助药物的医学诊断。通常，由于所需的努力，此过程是手动完成或根本不完成的。此外，检测心包可能会改善先前提出的自动化方法，这些方法将与人心脏相关的两种类型的脂肪分开。这些脂肪的量化提供了重要的健康风险标记信息，因为它们与某些心血管病理的发展有关。最后，我们得出的结论是，GA在可行数量的处理时间内提供了令人满意的解决方案。

我们提出了一种方法，以使用回归算法来预测计算机断层扫描图像中心外膜和纵隔脂肪体积。获得的结果表明，可以高度相关性预测这些脂肪是可行的，从而减轻了两种脂肪体积的手动或自动分割的需求。取而代之的是，仅分割其中一个就足够了，而另一个则可以相当准确地预测。使用MLP回归器通过旋转森林算法获得的相关系数预测基于心外膜脂肪的纵隔脂肪的相关系数为0.9876，相对绝对误差为14.4％，根相对平方误差为15.7％。基于纵隔的心外膜脂肪预测中获得的最佳相关系数为0.9683，相对绝对误差为19.6％，相对平方误差为24.9％。此外，我们分析了使用线性回归器的可行性，该回归器提供了对基础近似值的直观解释。在这种情况下，根据心外膜预测纵隔脂肪的相关系数为0.9534，相对绝对误差为31.6％，根相对平方误差为30.1％。关于基于纵隔脂肪的心外膜脂肪的预测，相关系数为0.8531，相对绝对误差为50.43％，根相对平方误差为52.06％。总而言之，有可能加快一般医学分析以及通过使用这种预测方法在最新技术中采用的一些细分和量化方法，从而降低成本，因此可以实现预防治疗减少健康问题。

在本文中，我们提出了一个深度学习框架，该框架为人形机器人步行步态中的腿部接触率检测提供了统一的方法。我们的配方实现了准确，稳健地估计每条腿的接触状态概率（即稳定或滑动/无接触）。所提出的框架采用了仅本体感知感应，尽管它依赖于模拟的基础真相接触数据进行分类过程，但我们证明了它在不同的摩擦表面和不同的腿部机器人平台上概括，同时也很容易地从模拟转移到模拟转移到实践。该框架是通过使用地面真实接触数据在模拟中进行定量和定性评估的，并与ATLA，NAO和TALOS类人类机器人的现状与ART方法形成对比。此外，用真实的talos人类生物生物估计得出了其功效。为了加强进一步的研究努力，我们的实施是作为开源的ROS/Python软件包，即创建的腿部接触检测（LCD）。

数字双胞胎最近对工业控制系统（ICS）的模拟，优化和预测维护产生了重大兴趣。最近的研究讨论了在工业系统中使用数字双胞胎进行入侵检测的可能性。因此，这项研究为工业控制系统的基于数字双胞胎的安全框架做出了贡献，从而扩展了其模拟攻击和防御机制的能力。在独立的开源数字双胞胎上实施了四种类型的过程感知攻击方案：命令注入，网络拒绝服务（DOS），计算的测量修改和天真的测量修改。根据八种监督机器学习算法的离线评估，建议将堆叠的合奏分类器作为实时入侵检测。通过组合各种算法的预测，设计的堆叠模型就F1得分和准确性而言优于先前的方法，同时可以在接近实时（0.1秒）中检测和分类入侵。这项研究还讨论了拟议的基于数字双胞胎的安全框架的实用性和好处。