财产数据的可用性是化学过程开发中的主要瓶颈之一，通常需要耗时且昂贵的实验或将设计空间限制为少数已知分子。这种瓶颈一直是预测性财产模型持续发展的动机。对于新分子的性质预测，群体贡献方法一直在开创性。最近，机器学习加入了更具成熟的财产预测模型。但是，即使取得了最近的成功，将物理约束集成到机器学习模型中仍然具有挑战性。物理约束对于许多热力学特性，例如吉布斯 - 杜纳姆（Gibbs-Dunham）关系至关重要，它将额外的复杂性层引入预测中。在这里，我们介绍了SPT-NRTL，这是一种机器学习模型，以预测热力学一致的活动系数并提供NRTL参数，以便于过程模拟。结果表明，SPT-NRTL在所有官能团的活性系数预测中的精度高于UNIFAC，并且能够以几乎实验的精度预测许多蒸气 - 液位均衡性，如示例性混合物所示。 N-己烷。为了简化SPT-NRTL的应用，用SPT-NRTL计算了100 000 000的NRTL参数，并在线提供。

对混合物相位平衡的知识在性质和技术化学本质上至关重要。混合物的相位平衡计算需要活性系数。但是，由于实验成本高，有关活性系数的实验数据通常受到限制。为了准确有效地预测活性系数，最近已经开发了机器学习方法。然而，对于未知分子的活性系数，当前的机器学习方法仍然很差。在这项工作中，我们介绍了一个自然语言处理网络的微笑到properties-Transformer（SPT），以预测微笑代码的二进制限制活动系数。为了克服可用实验数据的局限性，我们最初是在从COSMO-RS（1000万个数据点）采样的大型合成数据数据集上训练我们的网络，然后对实验数据（20 870个数据点）进行微调。该训练策略使SPT能够准确预测限制活动系数，即使对于未知分子，与最新的活动系数预测（例如COSMO-RS，UNIFAC）相比，将平均预测误差一半切成一半，并改善了最近的机器学习方法。

Profile extrusion is a continuous production process for manufacturing plastic profiles from molten polymer. Especially interesting is the design of the die, through which the melt is pressed to attain the desired shape. However, due to an inhomogeneous velocity distribution at the die exit or residual stresses inside the extrudate, the final shape of the manufactured part often deviates from the desired one. To avoid these deviations, the shape of the die can be computationally optimized, which has already been investigated in the literature using classical optimization approaches. A new approach in the field of shape optimization is the utilization of Reinforcement Learning (RL) as a learning-based optimization algorithm. RL is based on trial-and-error interactions of an agent with an environment. For each action, the agent is rewarded and informed about the subsequent state of the environment. While not necessarily superior to classical, e.g., gradient-based or evolutionary, optimization algorithms for one single problem, RL techniques are expected to perform especially well when similar optimization tasks are repeated since the agent learns a more general strategy for generating optimal shapes instead of concentrating on just one single problem. In this work, we investigate this approach by applying it to two 2D test cases. The flow-channel geometry can be modified by the RL agent using so-called Free-Form Deformation, a method where the computational mesh is embedded into a transformation spline, which is then manipulated based on the control-point positions. In particular, we investigate the impact of utilizing different agents on the training progress and the potential of wall time saving by utilizing multiple environments during training.

Modeling perception sensors is key for simulation based testing of automated driving functions. Beyond weather conditions themselves, sensors are also subjected to object dependent environmental influences like tire spray caused by vehicles moving on wet pavement. In this work, a novel modeling approach for spray in lidar data is introduced. The model conforms to the Open Simulation Interface (OSI) standard and is based on the formation of detection clusters within a spray plume. The detections are rendered with a simple custom ray casting algorithm without the need of a fluid dynamics simulation or physics engine. The model is subsequently used to generate training data for object detection algorithms. It is shown that the model helps to improve detection in real-world spray scenarios significantly. Furthermore, a systematic real-world data set is recorded and published for analysis, model calibration and validation of spray effects in active perception sensors. Experiments are conducted on a test track by driving over artificially watered pavement with varying vehicle speeds, vehicle types and levels of pavement wetness. All models and data of this work are available open source.

Decades of progress in simulation-based surrogate-assisted optimization and unprecedented growth in computational power have enabled researchers and practitioners to optimize previously intractable complex engineering problems. This paper investigates the possible benefit of a concurrent utilization of multiple simulation-based surrogate models to solve complex discrete optimization problems. To fulfill this, the so-called Self-Adaptive Multi-surrogate Assisted Efficient Global Optimization algorithm (SAMA-DiEGO), which features a two-stage online model management strategy, is proposed and further benchmarked on fifteen binary-encoded combinatorial and fifteen ordinal problems against several state-of-the-art non-surrogate or single surrogate assisted optimization algorithms. Our findings indicate that SAMA-DiEGO can rapidly converge to better solutions on a majority of the test problems, which shows the feasibility and advantage of using multiple surrogate models in optimizing discrete problems.

We present a novel clustering algorithm, visClust, that is based on lower dimensional data representations and visual interpretation. Thereto, we design a transformation that allows the data to be represented by a binary integer array enabling the further use of image processing methods to select a partition. Qualitative and quantitative analyses show that the algorithm obtains high accuracy (measured with an adjusted one-sided Rand-Index) and requires low runtime and RAM. We compare the results to 6 state-of-the-art algorithms, confirming the quality of visClust by outperforming in most experiments. Moreover, the algorithm asks for just one obligatory input parameter while allowing optimization via optional parameters. The code is made available on GitHub.

我们提出了一种新的抽样策略，称为Smart Active Sapling，以在生产线之外进行质量检查。根据主动学习的原则，机器学习模型决定将哪些样品发送到质量检查。一方面，由于较早发现质量违规行为，这可以最大程度地减少废料零件的产生。另一方面，质量检查成本降低了，以进行平稳运行。

韵律在言语交流中起着至关重要的作用。韵律的声明已被广泛研究。但是，韵律特征不仅被视而不见，而且在视觉上是基于头部和面部运动的视觉上。本报告的目的是提出一种使用虚拟现实检查视听韵律的方法。我们表明，基于虚拟人的动画提供了与真正说话者视频录音相似的运动提示。虚拟现实的使用开辟了新的途径，以检查口头交流的多模式效应。我们讨论了研究人工耳蜗听众中韵律感知的框架中的方法。

本文介绍了一种用于检测变更点的算法，并鉴定了瞬态多元时间序列数据（MTSD）中相应的子序列。由于许多工业领域的可用性增加，对此类数据的分析变得越来越重要。用于基于训练条件的维护（CBM）模型的标签，排序或过滤高度瞬态测量数据很麻烦且容易出错。对于某些应用程序，可以通过简单阈值或基于平均值和变化的变化找到更改点来过滤测量值。但是，例如，组件组中组件的强大诊断，该组件在多个传感器值之间具有复杂的非线性相关性，简单的方法是不可行的。可以将CBM模型出现的有意义且相干的测量数据。因此，我们介绍了一种使用基于复发的神经网络（RNN）自动编码器（AE）的算法，该算法对传入数据进行了迭代训练。评分函数使用重建误差和潜在空间信息。保存了确定的子序列的模型，并用于识别重复子序列以及快速离线聚类。为了进行评估，我们提出了一种基于曲率的新相似性度量，以实现更直观的时间序列子序列聚类指标。与其他七种最先进的算法和八个数据集进行了比较，显示了我们算法对在线群集MTSD和与机电系统结合的群集MTSD的功能和性能的提高。

鉴于问题的复杂性，从各种传感器模式到高度纠缠的对象布局，再到多样化的项目属性和抓地力类型，因此对视觉驱动的机器人系统提出了重大挑战。现有方法通常从一个角度解决问题。各种项目和复杂的垃圾箱场景需要多种选择策略以及高级推理。因此，要构建可靠的机器学习算法来解决这项复杂的任务，需要大量的全面和高质量的数据。在现实世界中收集此类数据将太昂贵，时间过高，因此从可伸缩性角度来看。为了解决这个大型，多样化的数据问题，我们从最近的元素概念上的增长中获得了灵感，并引入了MetagraspNet，这是一种通过基于物理学的元合成构建的大规模的照片现实垃圾箱挑选数据集。所提出的数据集在82种不同的文章类型上包含217K RGBD图像，并具有完整的注释，可用于对象检测，Amodal感知，关键点检测，操纵顺序和平行jaw和真空吸尘器的Ambidextrous Grasp标签。我们还提供了一个真实的数据集，该数据集由超过2.3k全面注释的高质量RGBD图像组成，分为5个困难级别和一个看不见的对象，以评估不同的对象和布局属性。最后，我们进行了广泛的实验，表明我们提出的真空密封模型和合成数据集实现了最先进的性能，并将其推广到现实世界用例。