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Flexible Safety systems for Smart Manufacturing

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Flexible Safety Systems for Smart Manufacturing

International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Dieter Etz Thomas Frühwirth Wolfgang Kastner Institute of Computer Engineering Automation Systems Group TU Wien Research Department Austrian Center of Digital Production Wien

ISBN: (数字)9781728189567

ISBN: (纸本)9781728189574

Smart manufacturing is realizing the idea and potential of Industry 4.0 in reality. An essential part of smart manufacturing are production facilities that dynamically adapt to changing production needs. This brings completely new challenges to functional safety systems, which are mandatory for the protection of man, machine, and environment. Currently, functional safety systems are designed and certified in a static way. The safety design and the configuration are derived from the risk assessment which is performed during the design of a machine. Once the system is put into operation, the safety configuration is not changed anymore. This approach constitutes an impediment to flexibility which smart manufacturing production facilities require *** paper proposes the design of a self-organizing safety system with the objective to assist an engineer who operates a smart manufacturing facility by discovering all safety-related devices and generating automatically a suitable safety configuration. This configuration will be deployed to the system automatically after adaptation and validation by the safety engineer. The proposed self-organizing safety system, simplifies the safety configuration in a dynamically changing environment. Consequently, it would reduce engineering efforts and decrease machine downtime which improves profitability.

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Application of a Microfabricated Microwave Resonator in a Co-Pd–Based Magnetic Hydrogen-Gas Sensor

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Physical Review Applied 2022年第2期18卷 024015-024015页

作者： Thomas A. Schefer Ryszard Narkowicz Kilian Lenz Fabian Ganss Malcolm P. Roberts Olav Hellwig Mariusz Martyniuk Jürgen Lindner Mikhail Kostylev Department of Physics and Astrophysics M013 The University of Western Australia Crawley Western Australia 6009 Australia Institute of Ion Beam Physics and Materials Research Helmholtz-Zentrum Dresden-Rossendorf 01328 Dresden Germany Institute of Physics Chemnitz University of Technology 09126 Chemnitz Germany Centre for Microscopy Characterisation and Analysis The University of Western Australia Crawley Western Australia 6009 Australia ARC Centre for Transformative Meta-Optical Systems Department of Electrical Electronic and Computer Engineering The University of Western Australia Crawley Western Australia 6009 Australia

We investigate the ferromagnetic resonance (FMR) response of microfabricated microwave resonators loaded with small Co16Pd84 alloy rectangles. A major increase in the FMR signal-to-noise ratio is achieved by employing the microwave-resonator structure. A FMR peak shift similar to that of Co16Pd84 continuous films is measured in the presence of hydrogen gas in the sample environment. We show that the very high sensitivity of the FMR signal of the Co16Pd84 alloy rectangle to hydrogen exposure can be used to measure relatively small hydrogen-concentration steps near 100% H2. Additionally, we also demonstrate that this structure can measure hydrogen over a concentration range from 3% to 100% H2 in N2. In time-dependent FMR measurements, we discover a temperature dependence of the FMR signal, which we relate to intrinsic temperature-dependent changes in saturation magnetization and the magnetic anisotropy of the Co-Pd alloy.

关键词： Analytical chemistry Magnetic anisotropy Metrology Surface & interfacial phenomena Molecules Solid-state detectors Transition metal alloys Ferromagnetic resonance

Energy systems Test Case Discovery Enabled by Test Case Profile and Repository

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arXiv 2023年

作者： Raussi, Petra Kamsamrong, Jirapa Paspatis, Alexandros Heussen, Kai Zerihun, Tesfaye Amare Widl, Edmund Andrén, Filip Pröstl Kazmi, Jawad H. Strasser, Thomas I. Castro, Felipe Pellegrino, Luigi Carbon Neutral Solutions VTT Techn Research Centre of Finland Espoo Finland Energy Division OFFIS e.V. Oldenburg Germany School of Electrical and Computer Eng National Technical University of Athens Athens Greece Wind and Energy Systems Technical University of Denmark Roskilde Denmark Energy Systems SINTEF Energy AS Trondheim Norway Center for Energy AIT Austrian Institute of Technology Vienna Austria Engineering Services SMA Solar Technology AG Kassel Germany Materials and Generation Technology Ricerca sul Sistema Energetico S.p.A. Milan Italy

Smart energy systems comprise multiple domains like power, thermal, control, information, and communication technology, which increases the complexity of research and development studies. This expansion also requires larger and ever so complex experimental pilot environments driving the demand for geographically distributed multi-research infrastructure tests. The Holistic Test Description approach supports the design of multi-domain and multi-research infrastructure tests by organizing the test cases into comprehensive segments, ensuring all relevant items for testing are covered. These test cases eventually form a pool, which to understand holistically would require studying and reading all the descriptions. This work proposes therefore the concept of Test Case Profiles to improve test case discovery and the structured creation of them. Test Case Profiles add further structure to the indexing in test case repositories. Along with the proposed indexing method, four different use cases are introduced to motivate additional applications of the proposed concept. Copyright © 2023, The Authors. All rights reserved.

关键词： Indexing (of information)

Multidimensional Central Limit Theorem of the Multiclass M/M/1/1 Retrial Queue 23rd

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Multidimensional Central Limit Theorem of the Multiclass M/M...

23rd International Conference on Distributed computer and Communication Networks, DCCN 2020

作者： Nazarov, Anatoly Phung-Duc, Tuan Izmailova, Yana Institute of Applied Mathematics and Computer Science National Research Tomsk State University 36 Lenina ave. Tomsk634050 Russia Faculty of Engineering Information and Systems University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8573 Japan

ISBN: (纸本)9783030664701

In this paper, we consider the multiclass M/M/1/1 retrial queueing system. Customers of each class arrive from outside the system according to a Poisson process. The service times of customers are assumed to be exponentially distributed with the parameter corresponding to the type of the customer. If the server is busy incoming customers join the orbit according to their type and make a delay for an exponentially distributed time. Equations for the characteristic function of the multi-dimensional probability distribution of the numbers of customers in the orbits are obtained. These equations are investigated by method of asymptotic analysis under the long delay condition of customers in the orbits. It is shown that the probability distribution can be approximated by a multi-dimensional Gaussian distribution. Equations are obtained for finding the parameters of this probability distribution. © 2020, Springer Nature Switzerland AG.

关键词： Asymptotic analysis

Dynamical causality under invisible confounders

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arXiv 2024年

作者： Yan, Jinling Zhang, Shao-Wu Zhang, Chihao Huang, Weitian Shi, Jifan Chen, Luonan MOE Key Laboratory of Information Fusion Technology School of Automation Northwestern Polytechnical University Xi’an710072 China Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai200031 China NCMIS CEMS RCSDS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematics Sciences University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Guangdong Institute of Intelligence Science and Technology Hengqin Guangdong Zhuhai519031 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai200032 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Chinese Academy of Sciences Hangzhou310024 China

Causality inference is prone to spurious causal interactions, due to the substantial confounders in a complex system. While many existing methods based on the statistical methods or dynamical methods attempt to address misidentification challenges, there remains a notable lack of effective methods to infer causality, in particular in the presence of invisible/unobservable confounders. As a result, accurately inferring causation with invisible confounders remains a largely unexplored and outstanding issue in data science and AI fields. In this work, we propose a method to overcome such challenges to infer dynamical causality under invisible confounders (CIC method) and further reconstruct the invisible confounders from time-series data by developing an orthogonal decomposition theorem in a delay embedding space. The core of our CIC method lies in its ability to decompose the observed variables not in their original space but in their delay embedding space into the common and private subspaces respectively, thereby quantifying causality between those variables both theoretically and computationally. This theoretical foundation ensures the causal detection for any high-dimensional system even with only two observed variables under many invisible confounders, which is actually a long-standing problem in the field. In addition to the invisible confounder problem, such a decomposition actually makes the intertwined variables separable in the embedding space, thus also solving the non-separability problem of causal inference. Extensive validation of the CIC method is carried out using various real datasets, and the experimental results demonstrates its effectiveness to reconstruct real biological networks even with unobserved confounders. © 2024, CC BY-NC-ND.

关键词： Time series

Event-triggered feedback control for signal temporal logic tasks

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arXiv 2020年

作者： Lindemann, Lars Maity, Dipankar Baras, John S. Dimarogonas, Dimos V. Stockholm100 44 Sweden Department of Electrical and Computer Engineering Institute for Systems Research at University of Maryland United States

A framework for the event-triggered control synthesis under signal temporal logic (STL) tasks is proposed. In our previous work, a continuous-time feedback control law was designed, using the prescribed performance control technique, to satisfy STL tasks. We replace this continuous-time feedback control law by an event-triggered controller. The event-triggering mechanism is based on a maximum triggering interval and on a norm bound on the difference between the value of the current state and the value of the state at the last triggering instance. Simulations of a multi-agent system quantitatively show the efficacy of using an event-triggered controller to reduce communication and computation efforts. Copyright © 2020, The Authors. All rights reserved.

关键词： Continuous time systems

Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey

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arXiv 2020年

作者： Zhang, Xiaoqing Hu, Yan Xiao, Zunjie Fang, Jiansheng Higashita, Risa Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Cataracts are the leading cause of visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading, aiming to prevent cataracts early and improve clinicians' diagnosis efficiency. This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning methods and deep learning methods. This survey also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research. © 2020, CC BY.

关键词： Surveys

Safe reinforcement learning for probabilistic reachability and safety specifications: A lyapunov-based approach

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arXiv 2020年

作者： Huh, Subin Yang, Insoon Department of Electrical and Computer Engineering Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of

Emerging applications in robotic and autonomous systems, such as autonomous driving and robotic surgery, often involve critical safety constraints that must be satisfied even when information about system models is limited. In this regard, we propose a model-free safety specification method that learns the maximal probability of safe operation by carefully combining probabilistic reachability analysis and safe reinforcement learning (RL). Our approach constructs a Lyapunov function with respect to a safe policy to restrain each policy improvement stage. As a result, it yields a sequence of safe policies that determine the range of safe operation, called the safe set, which monotonically expands and gradually converges. We also develop an efficient safe exploration scheme that accelerates the process of identifying the safety of unexamined states. Exploiting the Lyapunov shieding, our method regulates the exploratory policy to avoid dangerous states with high confidence. To handle high-dimensional systems, we further extend our approach to deep RL by introducing a Lagrangian relaxation technique to establish a tractable actorcritic algorithm. The empirical performance of our method is demonstrated through continuous control benchmark problems, such as a reaching task on a planar robot arm. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

Learning-based distributionally robust motion control with Gaussian processes

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arXiv 2020年

作者： Hakobyan, Astghik Yang, Insoon Department of Electrical and Computer Engineering Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of

— Safety is a critical issue in learning-based robotic and autonomous systems as learned information about their environments is often unreliable and inaccurate. In this paper, we propose a risk-aware motion control tool that is robust against errors in learned distributional information about obstacles moving with unknown dynamics. The salient feature of our model predictive control (MPC) method is its capability of limiting the risk of unsafety even when the true distribution deviates from the distribution estimated by Gaussian process (GP) regression, within an ambiguity set. Unfortunately, the distributionally robust MPC problem with GP is intractable because the worst-case risk constraint involves an infinite-dimensional optimization problem over the ambiguity set. To remove the infinite-dimensionality issue, we develop a systematic reformulation approach exploiting modern distributionally robust optimization techniques. The performance and utility of our method are demonstrated through simulations using a nonlinear car-like vehicle model for autonomous driving. Copyright © 2020, The Authors. All rights reserved.

关键词： Predictive control systems