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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

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GoLive A modular Mixed Reality Simulation for Semantic Plug and Play

GoLive A modular Mixed Reality Simulation for Semantic Plug ...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Constantin Wanninger Thomas Badem Martin Schörner Christian Eymueller Alexander Poeppel Wolfgang Reif Institute for Software and Systems Engineering University of Augsburg Augsburg Germany

In the field of mobile robotics, the interaction of robots with their environment plays a key role. Simulations serve to simulate a part of the environment so that the partly complex hardware is not damaged in extensive field tests. However, the transition from simulation to reality can be difficult. This paper presents a modular simulation and visualization with mixed reality mechanisms that can be reconfigured at runtime using a new concept called MR devices. A MR device is a physical device that registers with the simulation through self-description mechanisms and can interact with real objects. MR devices themselves can be used as tangible user interfaces in mixed reality visualization, e.g. to influence the composition of the environment. This concept enables the stepwise transition from simulation to reality. The advantages of these intermediate steps are presented by means of three scenarios, on which drones, sensors, infrastructure and also gas emitters are used. GoLive builds on the self-description mechanisms resulting from the own preliminary work: Semantic Plug and Play, in which capabilities and properties can be described by Semantic Web mechanisms and linked to the execution logic of the hardware at runtime.

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Enhancing Deep Reinforcement Learning with Scenario-Based Modeling

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SN Computer Science 2023年第2期4卷 1-13页

作者： Yerushalmi, Raz Amir, Guy Elyasaf, Achiya Harel, David Katz, Guy Marron, Assaf Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot 76100 Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem 91904 Israel Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva 8410501 Israel

Deep reinforcement learning agents have achieved unprecedented results when learning to generalize from unstructured data. However, the “black-box” nature of the trained DRL agents makes it difficult to ensure that they adhere to various requirements posed by engineers. In this work, we put forth a novel technique for enhancing the reinforcement learning training loop, and specifically—its reward function, in a way that allows engineers to directly inject their expert knowledge into the training process. This allows us to make the trained agent adhere to multiple constraints of interest. Moreover, using scenario-based modeling techniques, our method allows users to formulate the defined constraints using advanced, well-established, behavioral modeling methods. This combination of such modeling methods together with ML learning tools produces agents that are both high performing and more likely to adhere to prescribed constraints. Furthermore, the resulting agents are more transparent and hence more maintainable. We demonstrate our technique by evaluating it on a case study from the domain of internet congestion control, and present promising results. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep reinforcement learning Domain expertise Machine learning Rule-based specifications Scenario-based modeling

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SensorClouds: A Framework for Real-Time Processing of Multi-modal Sensor Data for Human-Robot-Collaboration

SensorClouds: A Framework for Real-Time Processing of Multi-...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Alexander Poeppel Christian Eymüller Wolfgang Reif Institute for Software and Systems Engineering University of Augsburg Augsburg Germany

Human-robot-collaboration (HRC) requires fast and reliable sensor data to ensure the safety of humans in the workspace. Current solutions for processing multi-modal sensor data in HRC are either highly performant in specific scenarios or offer more flexibility at the cost of decreased performance. Our GPU accelerated SensorClouds framework, however, combines both high flexibility and real-time performance. The architecture aids developers in quickly implementing complex HRC applications with multiple sensors by encapsulating all functionality into reusable modules. The resulting pipeline is optimized by the framework and executed in real-time.

关键词： Solid modeling Multimodal sensors Pipelines Graphics processing units Computer architecture Robot sensing systems Real-time systems

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Identifying Challenges for OSS Vulnerability Scanners - A Study & Test Suite (Short Summary)

Identifying Challenges for OSS Vulnerability Scanners - A St...

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Fachtagung des GI-Fachbereichs softwaretechnik, software engineering 2022 - Conference of the GI software engineering Section, software engineering 2022

作者： Dann, Andreas Plate, Henrik Hermann, Ben Ponta, Serena Elisa Bodden, Eric The Secure Software Engineering Group Heinz Nixdorf Institute Paderborn University Germany SAP Security Research Mougins France Technical University of Dortmund Germany Paderborn University Germany Software Engineering IT-Security at Fraunhofer IEM Paderborn Germany

ISBN: (纸本)9783885797142

This short paper5 presents a study investigating the impact of typical development practices, like re-compilation, re-bundling, on the performance of vulnerability scanners to detect known vulnerabilities in used open-source dependencies. In particular, the paper studies (i) types of modifications that affect the detection of vulnerable open-source dependencies and (ii) their impact on the performance of vulnerability scanners through an empirical study on 7024 Java projects developed at SAP. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Open source software

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Combining Simulation and Recurrent Neural Networks for Model-Based Condition Monitoring of Machines

Combining Simulation and Recurrent Neural Networks for Model...

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Simulation Winter Conference

作者： Alexander Wuttke Markus Rabe Joachim Hunker Jan-Philipp Diepenbrock Dept. IT in Production and Logistics TU Dortmund University Dortmund GERMANY Fraunhofer Institute for Software and Systems Engineering Dortmund GERMANY IVA Schmetz GmbH Menden GERMANY

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Maintenance is pivotal in industry, with condition-based maintenance emerging as a key strategy. This involves monitoring the machine condition through sensor data analysis. Model-based approaches compare observed data with expected values from models, which requires high-quality models. An established method is to use simulation models, which in many cases produce good results but may lack precision due to uncertainties. Alternatively, models created by machine learning can detect patterns directly from data. This paper proposes combining simulation models with machine learning models, leveraging the simulation's a-priori knowledge and machine learning's data patterns to enhance models for condition monitoring. Recurrent neural networks are suggested as the machine learning method. The paper outlines a systematic approach and demonstrates its application in an industrial use case, which investigates vacuum processes in industrial furnaces.

关键词： Condition monitoring Industries Recurrent neural networks Vacuum systems Uncertainty Systematics Machine learning Data models Maintenance Monitoring

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Architecture for Emergency Control of Autonomous UAV Ensembles∗∗This work is partly funded by the German Research Foundation (DFG) under the COMBO grant 3

Architecture for Emergency Control of Autonomous UAV Ensembl...

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3rd IEEE/ACM International Workshop on Robotics software engineering, RoSE 2021

作者： Schorner, Martin Wanninger, Constantin Hoffmann, Alwin Kosak, Oliver Reif, Wolfgang Institute for Software and Systems Engineering University of Augsburg Germany

ISBN: (纸本)9781665444743

Applying unmanned aerial vehicles (UAV) has benefits for many different use-cases. Existing implementations of ground control stations (GCS) to manage UAVs in such scenarios already provide some support for the operation of multi-unit systems, i.e., ensembles. However, since they are usually designed for the operation of only one copter at once, this is often not sufficient to react quickly in dangerous situations, e.g., search and rescue scenarios. To address this problem, we propose an approach for easy observation and control of complete autonomous UAV ensembles: The Intention of our approach is to greatly reduce the number of personnel required for the operation of an UAV ensemble. Thereby, we generate the possibility for rapid intervention in potentially dangerous situations in order to prevent damage to the UAVs and the environment. In this paper, we present a software architecture for this safety-critical multi UAV ground control station including a fully implemented prototype which we also tested in a realistic environment. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Artificial Intelligence Applications for Resilience in Manufacturing — A Systematic Literature Review

Artificial Intelligence Applications for Resilience in Manuf...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Florian A. Maier Sivaphani Puppala Michael Oberle Software Defined Energy-Cell Manufacturing Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Center for Digitalized Battery Cell Manufacturing Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Centre for Digital Tools in Manufacturing Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany

This review provides a structured literature analysis of Artificial Intelligence (AI) applications in enhancing manufacturing resilience. The research is guided by three primary questions addressing the use cases, technologies, and benefits of AI across the five resilience phases: Prepare, Prevent, Protect, Respond, and Recover. Findings from 78 papers reveal that AI significantly contributes to predictive maintenance, risk mitigation, and quality control, with machine learning and deep learning being the predominant technologies. The study highlights the pivotal role of AI in advancing manufacturing towards proactive, resilient, and adaptable operations. The insights gleaned offer a roadmap for future research and practical AI integration in manufacturing, underscoring the value of AI in driving industrial innovation and efficiency.

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Sea Level Anomalies Prediction Via Two-Stage Spatiotemporal Transformer 21

Sea Level Anomalies Prediction Via Two-Stage Spatiotemporal ...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Fang, Shaoteng Zhao, Yuehao Wang, Kefan Tian, Chenyu Kang, Qi Tongji University Department of Control Science and Engineering Shanghai China The School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Sydney Australia Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Software Technology Dalian University of Technology Dalian China

ISBN: (纸本)9798350365221

This study successfully applies the spatiotemporal transformer-based model to sea surface height anomaly prediction task. An improved ResNet autoencoder, named SW-GD Autoencoder, is proposed based on Sliced-Wasserstein distance and Gradient Difference loss functions to extract spatial feature information. This autoencoder demonstrates a reduced complexity and training difficulty compared to GAN-based architectures while maintaining high data generation quality. Additionally, the VidHRFormer model is employed for inference, introducing a large receptive field spatial feed-forward network module that enhances information transmission between adjacent patches in the feature map, thereby improving the observability of prediction results. To simplify training, we used a two-stage approach: first training the autoencoder separately, then combining it with VidHRFormer for final training. Compared to state-of-the-art ConvLSTM models, our method yields superior results. © 2024 IEEE.

关键词： Sea level

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***-Processor-Towards Development of AI-enhanced End-To-End BCIs

***-Processor-Towards Development of AI-enhanced End-To-End ...

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作者： Buron, Leo Erbslöh, Andreas Seidl, Karsten Schiele, Gregor Seidl, Karsten Ur-Rehman, Zia Klaes, Christian University of Duisburg-Essen Faculty of Engineering Duisburg Germany Fraunhofer Institute of Microelectronic Circuit and Systems Business Unit Health Duisburg Germany Ruhruniversity Bochum Faculty of Medicine Bochum Germany

This paper presents a software-based Python framework for developing future AI-enhanced end-To-end Brain-Computer-Interfaces (BCI). This framework contains modules from the emulated analogue front-end and from neural signal pre-processing for invasive neural applications. These modules can be assembled into several pipeline versions for evaluation and benchmarking. The aim of this framework is to accelerate the development of BCIs due to system-wide optimizations in order to set the requirements for hardware development without prior knowledge on the basis of accuracy (recall and precision) and latency. In the next step, the pipeline can be optimised for on-chip and embedded execution. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Pipelines

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