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Data Imputation Techniques Using the Bag of Functions: Addressing Variable Input Lengths and Missing Data in Time Series Decomposition 26

Data Imputation Techniques Using the Bag of Functions: Addre...

引用

26th International Conference on Industrial technology, ICIT 2025

作者： Torres, David Orlando Salazar Altinses, Diyar Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and learning systems Soest Germany

ISBN: (纸本)9798331521950

In time series analysis, the ability to effectively handle data with varying input lengths and missing data is crucial for accurate modeling. This paper presents the Bag-of-Functions-Driven Imputation framework, which leverages sequence-length independent techniques to decompose time series data while accommodating inputs of differing sizes. Unlike traditional methods that require uniform input lengths, the Padding-BoF framework employs a flexible encoding approach, allowing for the integration of variable-length time series and missing elements in the data. Through a series of experiments, we demonstrate that the BoF framework not only ensures precise reconstruction of the original data but also enhances data imputation capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling and disparate operational cycles, making it a valuable tool for applications in fields such as finance, healthcare, and industrial monitoring. © 2025 IEEE.

关键词： Data reduction

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Node Reservation Based Incremental learning Network for Object Detection 25

Node Reservation Based Incremental Learning Network for Obje...

引用

25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Ibrahim, M. Tahasanul Limaye, Nikhil Schwung, Andreas South Westphalia University Of Applied Sciences Department Of Automation Technology And Learning Systems Soest Germany

ISBN: (纸本)9798350340266

Object categorization is a crucial element in AI-driven computer vision systems, with its influence spanning from advanced surveillance technologies to basic projects. This field faces a key challenge in strategically incorporating new objects into existing networks, balancing between efficient training and precise object detection. Our paper introduces an innovative two-step method to fluidly integrate new objects into current networks, maintaining the accuracy of existing object detection accuracy and the integrity of prior knowledge. This method relies on the network's foresight in predicting upcoming class additions during the initial training, allocating specific layers for future expansions. These layers are then selectively activated when new classes are added, simplifying retraining processes and preserving pre-existing knowledge. This approach is particularly beneficial for time-sensitive and resource-limited systems, offering proactive resource allocation and adaptability to new class introductions. © 2024 IEEE.

关键词： Object detection

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Sim-to-Real Transfer for Robotics Using Model-Free Curriculum Reinforcement learning 25

Sim-to-Real Transfer for Robotics Using Model-Free Curriculu...

引用

25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Diprasetya, Mochammad Rizky Pullani, Ali Nafih Schwung, Andreas South Westphalia University Of Applied Sciences Department Of Automation Technology And Learning Systems Soest Germany

ISBN: (纸本)9798350340266

In this paper we propose a novel approach for transfer of model-free Reinforcement learning (RL) methods from simulation to a real-world model of a six-link robotic arm for industrial tasks. We develop an alternative approach to training robots for industrial tasks solely using a simulation of the system instead of the physical model itself as direct training on the physical robot models is a time and energy-consuming endeavor. The use of simulations not only ensures safe and controlled training implementation but also permits parallelization of the environment for improved training speed. As actions learned through the reinforcement learning approach in simulation often underperform in the real world because of the considerable gap in modeling real-world physical effects involved in robot actuation we therefore focuses on developing a iterative learning scheme using increasingly more high-fidelity models of the robot and the subsequent transfer. The experimental results prove the effectiveness of the proposed approach where combining the fine-tuned RL algorithm with the high-fidelity simulation of the robot resulted in a hundred percent task completion rate in simulation and subsequently in the real world. © 2024 IEEE.

关键词： Reinforcement learning

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Flexible Activation Bag: learning Activation Functions in Autoencoder Networks

Flexible Activation Bag: Learning Activation Functions in Au...

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2023 IEEE International Conference on Industrial technology, ICIT 2023

作者： Klopries, Hendrik Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and Learning Systems Soest Germany

ISBN: (纸本)9798350336504

An active area of research in the field of Machine learning is the optimization of network structures, including activation functions. However, selecting a suitable activation function is not that simple and usually requires an elaborate empirical process. Unfortunately, this process is very resource intensive and does not always lead to the best solution. This paper presents a novel approach to select, compose and generate adaptive activation functions without any supervision. The best-suited activation function is learned from a collection of function candidates while training the neural network. Standard activation functions are replaced by a sub-network that outputs an indi-vidually parameterized function. The work raises the question whether modern activation functions that are rectified linear unit shaped are beneficial in unsupervised models. We evaluate our approach in autoencoder structures on industrial data and show performance improvements compared to existing methods. The approach is generally applicable to any task and data domain. Finally, ablation studies on the corresponding parameters under-line the cardinality of the approach and performance impacts. © 2023 IEEE.

关键词： Machine learning

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Deep Multimodal Fusion with Corrupted Spatio-Temporal Data Using Fuzzy Regularization 49

Deep Multimodal Fusion with Corrupted Spatio-Temporal Data U...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Altinses, Diyar Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and Learning Systems Soest Germany

ISBN: (纸本)9798350331820

Deep networks have been successfully applied to unsupervised feature learning and supervised classification and regression for unimodal data (e.g., sensors, images, or audio). Multimodal data is often used to improve the performance of networks according to the slogan: the more, the better. Limited research is available to compensate for corrupted signals from multimodal approaches. In this work, we propose a novel regularization method for deep networks to learn features over multiple modalities designed to compensate for relative sensor weaknesses, such as sensor malfunction, inaccuracy, restricted spatial coverage, and uncertainty. We have used a special augmentation strategy for image and time series modalities to enhance the dataset of underrepresented industrial failure cases. The primary objective is to prevent these cases from negatively impacting the model's predictions. Our approach involves incorporating a fuzzy regularizer that can modify the intensity of activations depending on the quality of the signal, enabling disturbances from various modalities to be identified based on the activations. Our experiments on a simulated Universal Robots UR5 dataset demonstrate the effectiveness of our proposed regularization in increasing the model's stability, accuracy, and generalization to uncertainties and failures in the input signals. By incorporating fuzzy regularization in deep fusion models, their efficiency on complex tasks can be improved while reducing the complexity of the architectures. © 2023 IEEE.

关键词： Chemical activation

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Bag-of-Functions Denoising: Extracting main components in time series 32

Bag-of-Functions Denoising: Extracting main components in ti...

引用

32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Klopries, Hendrik Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and Learning Systems Soest Germany

ISBN: (纸本)9798350399714

Denoising sequences of time series is one of the elementary preprocessing steps in data mining. Current statistical methods work on the univariate input data stream and do not obtain long dependencies over the whole series. Due to advanced architectures and learning techniques, machine learning approaches are getting more effective. However, none of the existing methods propose a learnable denoising process to select interpretable components out of a given subset of time dependencies. Therefore, we propose a novel denoising method in the field of machine learning. In particular, we decompose multivariate time series data in a dense representation of selected functional relationships. The decomposition process is assembled as a residual stacking of autoencoders, while the last remainder denotes the extracted noise. We elaborate our approach on a comparison between statistical and machine learning methods, dealing with synthetic as well as industrial data. Beside the performance boost, the approach is capable to learn the main components of a time series as interpretable features in the latent space representation, even on the difficult blind denoising task. © 2023 IEEE.

关键词： Time series

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Multimodal Synthetic Dataset Balancing: a Framework for Realistic and Balanced Training Data Generation in Industrial Settings 49

Multimodal Synthetic Dataset Balancing: a Framework for Real...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Altinses, Diyar Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and Learning Systems Soest Germany

ISBN: (纸本)9798350331820

Deep networks have been successfully applied to industrial applications for clean unimodal data (e.g., sensors, images, or audio). Leveraging multimodal data is a common approach to enhance performance, guided by the principle that a larger quantity of data leads to improvement. However, performance may decline considerably if corruption in the data is present (e.g., noise, blur, failure). Although researchers have explored various data augmentation methods to improve the generalization capacity, these methods are not adapted for industrial settings. The primary distinction is that current augmentation methods are designed to enhance model generalization capabilities and not realistically simulate real-world industry scenarios. In this paper, we present industry-related augmentation methods for temporal and spatial data for multimodal fusion with deep neural networks. Our methods are specifically designed to encourage modality collaboration and reinforce generalization capability. The impact of the proposed data extension strategy to train multimodal fusion models is assessed on a synthetic dataset from an industrial UR5 robot with varying degrees of imbalance. In our study, we analyze different combinations of methods and evaluate their performance. Through these experiments, we are able to identify the challenges in multimodal fusion with deep learning models in an industrial setting. © 2023 IEEE.

关键词： Deep neural networks

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A Model-Based Deep learning Approach for Self-learning in Smart Production systems 28

A Model-Based Deep Learning Approach for Self-Learning in Sm...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Yuwono, Steve Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and Learning Systems Soest Germany

ISBN: (纸本)9798350339918

In this research, we discuss the impact of combining model-based deep learning and game theory in dynamic games to develop a sample-efficient self-learning methodology for smart production systems. We propose a novel approach, namely Model-Based Game Theory (MBGT), by incorporating model-based deep learning into a successful self-learning strategy of State-based Potential Games. Most of the advanced self-learning approaches are established based on machine learning and artificial intelligence, including reinforcement learning and game-theoretical method. However, because of the iterative behaviour during the learning processes, the learning processes are almost impossible to be conducted directly in a real-world manufacturing environment. One potential approach is to conduct the learning processes using the digital representation of the systems, e.g. the digital twin or simulation. Nonetheless, such representations in industrial settings are not always available and developing a digital representation of a complex system is immensely challenging. Hence, this problem can be solved through model-based learning, in which deep learning models are trained to predict the dynamics of the systems with a small margin of error. Then, the trained models are deployed as virtual environments for the learning processes of the GT-based control policies, instead of learning in the real world. In this study, we implement MBGT to a bulk good system and thoroughly analyse the performances between training in the real system and virtual environment, which eventually turn out to be positive effects. © 2023 IEEE.

关键词： Game theory

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Stability-Guaranteed Control systems with Min-Max Constraints and Machine learning-Based Virtual Sensors

Stability-Guaranteed Control Systems with Min-Max Constraint...

引用

2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Hilgert, Eric Schwung, Andreas Graduate School for Applied Research in North Rhine-Westphalia Department of Technology and Systems Bochum Germany South Westphalia University of Applied Sciences Department of Automation Technology and Learning Systems Soest Germany

ISBN: (纸本)9798350337020

In this paper, we present a comprehensive approach for designing and analyzing control systems with minmax constraint controllers and machine learning-based virtual sensors. By leveraging the Standard Nonlinear Operator Form (SNOF), we establish the necessary conditions for global asymptotic stability and demonstrate their applicability through an illustrative example based on a modified plant model from the literature. The proposed methodology effectively handles non-linearities and constraints, ensuring stability while providing a systematic procedure for constructing a well-formed SNOF by integrating the plant, virtual sensor, and controller. The successful application of this method in the example highlights its potential for addressing complex control problems involving min-max constraints and virtual sensors in real-world scenarios. This paper contributes to the growing body of knowledge in this area and sets the stage for future advancements, including the exploration of transforming other machine learning architectures into the SNOF and extending the stability analysis to accommodate different types of nonlinearities and constraints. © 2023 IEEE.

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Neural Data Fusion Enhanced PD Control for Precision Drone Landing in Synthetic Environments

Neural Data Fusion Enhanced PD Control for Precision Drone L...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Altinses, Diyar Torres, David Orlando Salazar Lier, Stefan Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and learning systems Soest Germany South Westphalia University of Applied Sciences Department of Logistics and Supply Chain Management Meschede Germany

ISBN: (纸本)9798331533892

Unmanned aerial vehicles are increasingly used in applications like surveillance, mapping, and delivery, where precise and safe landings are crucial, especially on small, designated platforms in multimodal systems. However, achieving consistent landing accuracy is challenging due to sensor inaccuracies, environmental disturbances, and dynamic conditions. This paper introduces a synthetic drone simulation environment to test landing scenarios and control strategies. We propose a control framework combining a classical PD controller with a neural network-based data fusion model that corrects sensor noise, particularly in GPS and UWB data, to improve landing precision. Simulation results demonstrate that our approach enhances landing accuracy and robustness compared to traditional methods. © 2025 IEEE.

关键词： Drones

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