1 - 10 of 2668
-
PublicationInformed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems( 2023)Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.
-
PublicationMotorFactory: A Blender Add-on for Large Dataset Generation of Small Electric Motors( 2022)To enable automatic disassembly of different product types with uncertain condition and degree of wear in remanufacturing, agile production systems that can adapt dynamically to changing requirements are needed. Machine learning algorithms can be employed due to their generalization capabilities of learning from various types and variants of products. However, in reality, datasets with a diversity of samples that can be used to train models are difficult to obtain in the initial period. This may cause bad performances when the system tries to adapt to new unseen input data in the future. In order to generate large datasets for different learning purposes, in our project, we present a Blender add-on named MotorFactory to generate customized mesh models of various motor instances. MotorFactory allows to create mesh models which, complemented with additional add-ons, can be further used to create synthetic RGB images, depth images, normal images, segmentation ground truth masks and 3D point cloud datasets with point-wise semantic labels. The created synthetic datasets may be used for various tasks including motor type classification, object detection for decentralized material transfer tasks, part segmentation for disassembly and handling tasks, or even reinforcement learning-based robotics control or view-planning.
-
PublicationFast and Lightweight Online Person Search for Large-Scale Surveillance Systems( 2022)The demand for methods for video analysis in the fieldof surveillance technology is rapidly growing due to theincreasing amount of surveillance footage available. Intelligent methods for surveillance software offer numerouspossibilities to support police investigations and crime prevention. This includes the integration of video processingpipelines for tasks such as detection of graffiti, suspiciousluggage, or intruders. Another important surveillance taskis the semi-automated search for specific persons-of-interestwithin a camera network. In this work, we identify the major obstacles for the development of person search systemsas the real-time processing capability on affordable hardware and the performance gap of person detection and reidentification methods on unseen target domain data. Inaddition, we demonstrate the current potential of intelligentonline person search by developing a real-world, largescale surveillance system. An extensive evaluation is provided for person detection, tracking, and re-identificationcomponents on affordable hardware setups, for which thewhole system achieves real-time processing up to 76 FPS.
-
PublicationData-Driven Fault Detection in Industrial Batch Processes Based on a Stochastic Hybrid Process Model( 2022)This paper presents a novel fault detection approach for industrial batch processes. The batch processes under consideration are characterized by the interaction between discrete system modes and non-stationary continuous dynamics. Therefore, a stochastic hybrid process model (SHPM) is introduced, where process variables are modeled as time-variant Gaussian distributions, which depend on hidden system modes. Transitions between the system modes are assumed to be either autonomous or to be triggered by observable events such as on/off signals. The model parameters are determined from training data using expectation-maximization techniques. A new fault detection algorithm is proposed, which assesses the likelihoods of sensor signals on the basis of the stochastic hybrid process model. Evaluation of the proposed fault detection system has been conducted for a penicillin production process, with the results showing a significant improvement over the existing baseline methods.
-
PublicationWhere are we with Human Pose Estimation in Real-World Surveillance?( 2022)The rapidly increasing number of surveillance cameras offers a variety of opportunities for intelligent video analytics to improve public safety. Among many others, the automatic recognition of suspicious and violent behavior poses a key task. To preserve personal privacy, prevent ethnic bias, and reduce complexity, most approaches first extract the pose or skeleton of persons and subsequently perform activity recognition. However, current literature mainly focuses on research datasets and does not consider real-world challenges and requirements of human pose estimation. We close this gap by analyzing these challenges, such as inadequate data and the need for real-time processing, and proposing a framework for human pose estimation in uncontrolled crowded surveillance scenarios. Our system integrates mitigation measures as well as a tracking component to incorporate temporal information. Finally, we provide a detailed quantitative and qualitative analysis on both a scientific and a real-world dataset to highlight improvements and remaining obstacles towards robust real-world human pose estimation in uncooperative scenarios.
-
PublicationImproving Semantic Image Segmentation via Label Fusion in Semantically Textured Meshes( 2022)Models for semantic segmentation require a large amount of hand-labeled training data which is costly and time-consuming to produce. For this purpose, we present a label fusion framework that is capable of improving semantic pixel labels of video sequences in an unsupervised manner. We make use of a 3D mesh representation of the environment and fuse the predictions of different frames into a consistent representation using semantic mesh textures. Rendering the semantic mesh using the original intrinsic and extrinsic camera parameters yields a set of improved semantic segmentation images. Due to our optimized CUDA implementation, we are able to exploit the entire c-dimensional probability distribution of annotations over c classes in an uncertainty-aware manner. We evaluate our method on the Scannet dataset where we improve annotations produced by the state-of-the-art segmentation network ESANet from 52.05% to 58.25% pixel accuracy. We publish the source code of our framework online to foster future research in this area (https://github.com/fferflo/semantic-meshes). To the best of our knowledge, this is the first publicly available label fusion framework for semantic image segmentation based on meshes with semantic textures.
-
PublicationModel-assisted DoE applied to microalgae processes( 2022)This study assesses the performance of the model-assisted Design of Experiment (mDoE) software toolbox for the design of two microalgae bioprocesses. The mDoE-toolbox was applied to maximize biomass growth for Desmodesmus pseudocommunis in a photobioreactor by varying the light intensity and pH and for Chlorella vulgaris in shake flasks, by varying the light intensity and duration. For both case studies, a mathematical mechanistic model was applied. In the first study only one experiment was necessary to adapt the mathematical model and identify a combination of light intensity and pH that improved biomass yield, as confirmed experimentally. In the second study, no well-established model was available for the specific experimental arrangement. On the basis of the literature, a mathematical model was constructed and a first cycle of mDoE was performed, thus identifying the desired factor combinations. Experiments confirmed the high biomass yield but revealed shortcomings of the model. The model was improved and a second cycle of mDoE was performed. The recommended factor combinations from both cycles were comparable. The mDoE was found to be a time-saving, cost-effective and useful method enabling the identification of factor combinations leading to high biomass production for the design of two different microalgae bioprocesses with low experimental effort.
-
PublicationMultiagent Self-Redundancy Identification and Tuned Greedy-Exploration( 2022)The constant development of sensing applications using innovative and affordable measurement devices has increased the amount of data transmitted through networks, carrying in many cases, redundant information that requires more time to be analyzed or larger storage centers. This redundancy is mainly present because the network nodes do not recognize environmental variations requiring exploration, which causes a repetitive data collection in a set of limited locations. In this work, we propose a multiagent learning framework that uses the Gaussian process regression (GPR) to allow the agents to predict the environmental behavior by means of the neighborhood measurements, and the rate distortion function to establish a border in which the environmental information is neither misunderstood nor redundant. We apply this framework to a mobile sensor network and demonstrate that the nodes can tune the parameter s of the Blahut-Arimoto algorithm in order to adjust the gathered environment information and to become more or less exploratory within a sensing area.
-
PublicationIntelligente Bild- und Videoauswertung für die Sicherheit( 2022)
-
PublicationConfocal fluorescence microscopy with high-NA diffractive lens arrays( 2022)Traditionally, there is a trade-off between the numerical aperture and field of view for a microscope objective. Diffractive lens arrays (DLAs) with overlapping apertures are used to overcome such a problem. A spot array with an NA up to 0.83 and a pitch of 75 m is produced by the proposed DLA at a wavelength of 488 nm. By measurement of the fluorescence beads, the DLA-based confocal setup shows the capability of high-resolution measurement over an area of 3mm 3mm with a 2.5 0.07 NA objective. Further, the proposed fluorescence microscope is insensitive to optical aberrations, which has been demonstrated by imaging with a simple doublet lens.
