TY - GEN A1 - Schneidereit, Slavomira A1 - Mansouri Yarahmadi, Ashkan A1 - Schneidereit, Toni A1 - Breuß, Michael A1 - Gebauer, Marc T1 - YOLO- based Object detection in industry 4.0 Fischertechnik Model Environment T2 - Computer Science > Computer Vision and Pattern Recognition, Intelligent Systems Conference 2023 (IntelliSys 2023) N2 - In this paper we extensively explore the suitability of YOLO architectures to monitor the process flow across a Fischertechnik industry 4.0 application. Specifically, different YOLO architectures in terms of size and complexity design along with different prior-shapes assignment strategies are adopted. To simulate the real world factory environment, we prepared a rich dataset augmented with different distortions that highly enhance and in some cases degrade our image qualities. The degradation is performed to account for environmental variations and enhancements opt to compensate the color correlations that we face while preparing our dataset. The analysis of our conducted experiments shows the effectiveness of the presented approach evaluated using different measures along with the training and validation strategies that we tailored to tackle the unavoidable color correlations that the problem at hand inherits by nature. Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2301.12827 ER - TY - GEN A1 - Schneidereit, Slavomira A1 - Yarahmadi, Ashkan Mansouri A1 - Schneidereit, Toni A1 - Breuß, Michael A1 - Gebauer, Marc T1 - YOLO-Based Object Detection in Industry 4.0 Fischertechnik Model Environment T2 - Lecture Notes in Networks and Systems N2 - In this paper we extensively explore the suitability of YOLO architectures to monitor the process flow across a Fischertechnik Industry 4.0 application. Specifically, different YOLO architectures in terms of size and complexity design along with different prior-shapes assignment strategies are adopted. To simulate the real world factory environment, we prepared a rich dataset augmented with different distortions that highly enhance and in some cases degrade our image qualities. The degradation is performed to account for environmental variations and enhancements opt to compensate the color correlations that we face while preparing our dataset. The analysis of our conducted experiments shows the effectiveness of the presented approach evaluated using different measures along with the training and validation strategies that we tailored to tackle the unavoidable color correlations that the problem at hand inherits by nature. KW - Object detection KW - Image augmentation KW - Classification KW - YOLO KW - Fischertechnik industry KW - Industry 4.0 Y1 - 2024 SN - 9783031477232 U6 - https://doi.org/10.1007/978-3-031-47724-9_1 SN - 2367-3370 VL - 823 SP - 1 EP - 20 PB - Springer Nature Switzerland CY - Cham ER - TY - GEN A1 - Schneidereit, Toni A1 - Breuß, Michael T1 - Solving Ordinary Differential Equations using Artificial Neural Networks - A study on the solution variance T2 - Proceedings of the Conference Algoritmy 2020 N2 - Solving differential equations can be realised with simple artificial neural network architectures. Several methods make use of trial solutions with different construction approaches and can provide reliable results. However, many parameters, different optimisation methods and random weight initialisation result in a non constant variance to the exact solution. To our knowledge, this variance has not been studied yet. We investigate several parameters and constant versus random weight initialisation for two solution methods to determine their reliability with the use of backpropagation and ADAM optimisation. Y1 - 2020 UR - http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/1547/811 SP - 21 EP - 30 PB - Open Journal Systems , Public Knowledge Project ER - TY - GEN A1 - Schneidereit, Toni A1 - Breuß, Michael T1 - Polynomial Neural Forms Using Feedforward Neural Networks for Solving Differential Equations T2 - Artificial Intelligence and Soft Computing N2 - Several neural network approaches for solving differential equations employ trial solutions with a feedforward neural network. There are different means to incorporate the trial solution in the construction, for instance one may include them directly in the cost function. Used within the corresponding neural network, the trial solutions define the so-called neural form. Such neural forms represent general, flexible tools by which one may solve various differential equations. In this article we consider time-dependent initial value problems, which requires to set up the trial solution framework adequately. The neural forms presented up to now in the literature for such a setting can be considered as first order polynomials. In this work we propose to extend the polynomial order of the neural forms. The novel construction includes several feedforward neural networks, one for each order. The feedforward neural networks are optimised using a stochastic gradient descent method (ADAM). As a baseline model problem we consider a simple yet stiff ordinary differential equation. In experiments we illuminate some interesting properties of the proposed approach. KW - Feedforward neural networks / Initial value problem / Trial solution /Differential equations Y1 - 2021 SN - 978-3-030-87985-3 U6 - https://doi.org/10.1007/978-3-030-87986-0_21 SN - 978-3-030-87986-0 SP - 236 EP - 245 PB - Springer CY - Cham ER - TY - GEN A1 - Schneidereit, Toni A1 - Breuß, Michael T1 - Collocation polynomial neural forms and domain fragmentation for solving initial value problems T2 - Neural Computing and Applications N2 - Several neural network approaches for solving differential equations employ trial solutions with a feedforward neural network. There are different means to incorporate the trial solution in the construction, for instance, one may include them directly in the cost function. Used within the corresponding neural network, the trial solutions define the so-called neural form. Such neural forms represent general, flexible tools by which one may solve various differential equations. In this article, we consider time-dependent initial value problems, which require to set up the neural form framework adequately. The neural forms presented up to now in the literature for such a setting can be considered as first-order polynomials. In this work, we propose to extend the polynomial order of the neural forms. The novel collocation-type construction includes several feedforward neural networks, one for each order. Additionally, we propose the fragmentation of the computational domain into subdomains. The neural forms are solved on each subdomain, whereas the interfacing grid points overlap in order to provide initial values over the whole fragmentation. We illustrate in experiments that the combination of collocation neural forms of higher order and the domain fragmentation allows to solve initial value problems over large domains with high accuracy and reliability. KW - Collocation neural forms KW - Polynomial neural forms KW - Trial solution KW - Initial value problems KW - Domain fragmentation Y1 - 2022 U6 - https://doi.org/10.1007/s00521-021-06860-4 SN - 1433-3058 SN - 0941-0643 VL - 34 IS - 9 SP - 7141 EP - 7156 ER - TY - GEN A1 - Schneidereit, Toni A1 - Breuß, Michael T1 - Computational characteristics of feedforward neural networks for solving a stiff differential equation T2 - Neural Computing and Applications N2 - Feedforward neural networks offer a possible approach for solving differential equations. However, the reliability and accuracy of the approximation still represent delicate issues that are not fully resolved in the current literature. Computational approaches are in general highly dependent on a variety of computational parameters as well as on the choice of optimisation methods, a point that has to be seen together with the structure of the cost function. The intention of this paper is to make a step towards resolving these open issues. To this end, we study here the solution of a simple but fundamental stiff ordinary differential equation modelling a damped system. We consider two computational approaches for solving differential equations by neural forms. These are the classic but still actual method of trial solutions defining the cost function, and a recent direct construction of the cost function related to the trial solution method. Let us note that the settings we study can easily be applied more generally, including solution of partial differential equations. By a very detailed computational study, we show that it is possible to identify preferable choices to be made for parameters and methods. We also illuminate some interesting effects that are observable in the neural network simulations. Overall we extend the current literature in the field by showing what can be done in order to obtain useful and accurate results by the neural network approach. By doing this we illustrate the importance of a careful choice of the computational setup. KW - Feedforward neural networks KW - Ordinary differential equations KW - Trial solution KW - Adam KW - Backpropagation Y1 - 2022 U6 - https://doi.org/10.1007/s00521-022-06901-6 VL - 34 IS - 10 SP - 7975 EP - 7989 ER - TY - GEN A1 - Mansouri Yarahmadi, Ashkan A1 - Breuß, Michael A1 - Hartmann, Carsten A1 - Schneidereit, Toni T1 - Unsupervised Optimization of Laser Beam Trajectories for Powder Bed Fusion Printing and Extension to Multiphase Nucleation Models T2 - Mathematical Methods for Objects Reconstruction : From 3D Vision to 3D Printing N2 - In laser powder bed fusion, it is known that the quality of printing results crucially depends on the temperature distribution and its gradient over the manufacturing plate. We propose a computational model for the motion of the laser beam and the simulation of the time-dependent heat evolution over the plate. For the optimization of the laser beam trajectory, we propose a cost function that minimizes the average thermal gradient and allows to steer the laser beam. The optimization is performed in an unsupervised way. Specifically, we propose an optimization heuristic that is inspired by the well-known traveling salesman problem and that employs simulated annealing to determine a nearly optimal pathway. By comparison of the heat transfer simulations of the derived trajectories with trajectory patterns from standard printing protocols we show that the method gives superior results in terms of the given cost functional. KW - Additive manufacturing Multiphase alloys Trajectory optimization Powder bed fusion printing Heat simulation Linear-quadratic control Y1 - 2023 SN - 978-981-99-0775-5 U6 - https://doi.org/10.1007/978-981-99-0776-2_6 SN - 978-981-99-0776-2 SP - 157 EP - 176 PB - Springer CY - Singapor ER - TY - GEN A1 - Schneidereit, Toni A1 - Breuß, Michael T1 - Adaptive neural-domain refinement for solving time-dependent differential equations T2 - Advances in Continuous and Discrete Models N2 - A classic approach for solving differential equations with neural networks builds upon neural forms, which employ the differential equation with a discretisation of the solution domain. Making use of neural forms for time-dependent differential equations, one can apply the recently developed method of domain segmentation. That is, the domain may be split into several subdomains, on which the optimisation problem is solved. In classic adaptive numerical methods, the mesh as well as the domain may be refined or decomposed, in order to improve the accuracy. Also, the degree of approximation accuracy may be adapted. Therefore, it is desirable to transfer such important and successful strategies to the field of neural-network-based solutions. In the presented work, we propose a novel adaptive neural approach to meet this aim for solving time-dependent problems. To this end, each subdomain is reduced in size until the optimisation is resolved up to a predefined training accuracy. In addition, while the neural networks employed are by default small, we propose a means to adjust also the number of neurons in an adaptive way. We introduce conditions to automatically confirm the solution reliability and optimise computational parameters whenever it is necessary. Results are provided for several initial-value problems that illustrate important computational properties of the method. KW - Neural forms KW - Adaptive neural refinement KW - Domain decomposition KW - Physics-informed neural networks KW - Differential equations Y1 - 2023 U6 - https://doi.org/10.1186/s13662-023-03789-x SN - 2731-4235 IS - 1 ER - TY - GEN A1 - Khan Mohammadi, Mohsen A1 - Schneidereit, Toni A1 - Mansouri Yarahmadi, Ashkan A1 - Breuß, Michael T1 - Investigating Training Datasets of Real and Synthetic Images for Outdoor Swimmer Localisation with YOLO T2 - AI N2 - In this study, we developed and explored a methodical image augmentation technique for swimmer localisation in northern German outdoor lake environments. When it comes to enhancing swimmer safety, a main issue we have to deal with is the lack of real-world training data of such outdoor environments. Natural lighting changes, dynamic water textures, and barely visible swimming persons are key issues to address. We account for these difficulties by adopting an effective background removal technique with available training data. This allows us to edit swimmers into natural environment backgrounds for use in subsequent image augmentation. We created 17 training datasets with real images, synthetic images, and a mixture of both to investigate different aspects and characteristics of the proposed approach. The datasets were used to train YOLO architectures for possible future applications in real-time detection. The trained frameworks were then tested and evaluated on outdoor environment imagery acquired using a safety drone to investigate and confirm their usefulness for outdoor swimmer localisation. Y1 - 2024 U6 - https://doi.org/10.3390/ai5020030 SN - 2673-2688 VL - 5 IS - 2 SP - 576 EP - 593 PB - MDPI AG CY - Basel ER - TY - GEN A1 - Khan Mohammadi, Mohsen A1 - Schneidereit, Toni A1 - Mansouri Yarahmadi, Ashkan A1 - Breuß, Michael T1 - Investigating training datasets of real and synthetic images for swimmer localisation with YOLO T2 - Preprints.org N2 - In this paper we develop and explore a methodical pipeline for swimmer localisation in outdoor environments. The developed framework is intended to be used for enhancing swimmer safety. A main issue we deal with by the proposed approach is the lack of real world training data in such outdoor environments. Natural lighting changes, dynamic water textures and possibly barely visible swimming persons are key elements to approach. We account for these difficulties by adopting an effective background removal technique with available training data. This allows us to edit swimmers into natural environment backgrounds for the use in subsequent image augmentation. We created 17 training datasets with real images, synthetic images and a mixture of both to investigate different aspects and characteristics of the proposed approach. The datasets are used to train a YOLO architecture for the possible future application in real-time detection. The trained framework is then tested and evaluated on outdoor environment imagery acquired by a safety drone to investigate and confirm the usefulness for outdoor swimmer localisation. Y1 - 2024 U6 - https://doi.org/10.20944/preprints202402.0446.v1 PB - MDPI AG ER - TY - GEN A1 - Schneidereit, Toni A1 - Gohrenz, Stefan A1 - Breuß, Michael T1 - Object detection characteristics in a learning factory environment using YOLOv8 N2 - AI-based object detection, and efforts to explain and investigate their characteristics, is a topic of high interest. The impact of, e.g., complex background structures with similar appearances as the objects of interest, on the detection accuracy and, beforehand, the necessary dataset composition are topics of ongoing research. In this paper, we present a systematic investigation of background influences and different features of the object to be detected. The latter includes various materials and surfaces, partially transparent and with shiny reflections in the context of an Industry 4.0 learning factory. Different YOLOv8 models have been trained for each of the materials on different sized datasets, where the appearance was the only changing parameter. In the end, similar characteristics tend to show different behaviours and sometimes unexpected results. While some background components tend to be detected, others with the same features are not part of the detection. Additionally, some more precise conclusions can be drawn from the results. Therefore, we contribute a challenging dataset with detailed investigations on 92 trained YOLO models, addressing some issues on the detection accuracy and possible overfitting. Y1 - 2025 U6 - https://doi.org/https://doi.org/10.48550/arXiv.2503.10356 VL - 2503.10356 SP - 1 EP - 18 ER -