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Network and systems Medicine 2020年第1期3卷 36-56页

作者： Stalidzans, Egils Zanin, Massimiliano Tieri, Paolo Castiglione, Filippo Polster, Annikka Scheiner, Stefan Pahle, Jürgen Stres, Blaž List, Markus Baumbach, Jan Lautizi, Manuela Van Steen, Kristel Schmidt, Harald H.H.W. Computational Systems Biology Group University of Latvia Riga Latvia Latvian Biomedical Reasearch and Study Centre Riga Latvia Centro de Tecnología Biomédica Universidad Politécnica de Madrid Pozuelo de Alarcón Spain CNR National Research Council IAC Institute for Applied Computing Rome Italy Oslo Norway Institute for Mechanics of Materials and Structures Vienna University of Technology Vienna Austria BioQuant Heidelberg University Heidelberg Germany Department of Animal Science University of Ljubljana Ljubljana Slovenia Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana Slovenia Department of Automation Biocybernetics and Robotics Jozef Stefan Institute Ljubljana Slovenia Big Data in BioMedicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Computational Systems Medicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany BIO-Systems Genetics GIGA-R University of Liège Liège Belgium BIO3 - Systems Medicine Department of Human Genetics KU Leuven Leuven Belgium Department of Pharmacology and Personalised Medicine Faculty of Health Medicine and Life Science Maastricht University Maastricht Netherlands

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body. © Egils Stalidzans et al. 2020 Published b

关键词： Iterative methods

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Intelligent reflecting surface backscatter-enabled physical layer security enhancement via deep reinforcement learning

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PeerJ Computer Science 2025年 11卷 e2902-e2902页

作者： Manzoor Ahmed Touseef Hussain Muhammad Shahwar Feroz Khan Muhammad Sheraz Wali Ullah Khan Teong Chee Chuah It Ee Lee Artificial Intelligence Industrial Technology Research Institute Hubei Engineering University Xiaogan City China Hubei Engineering University Xiaogan City China School of Electronic Science and Technology Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Technology Qingdao University Qingdao China Centre for Smart Systems and Automation CoE for Robotics and Sensing Technologies Faculty of Artificial Intelligence and Engineering Multimedia University Persiaran Multimedia Cyberjaya Selangor Malaysia Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg

This article introduces a novel strategy for wireless communication security utilizing intelligent reflecting surfaces (IRS). The IRS is strategically deployed to mitigate jamming attacks and eavesdropper threats while improving signal reception for legitimate users (LUs) by redirecting jamming signals toward desired communication signals leveraging physical layer security (PLS). By integrating the IRS into the backscatter communication system, we enhance the overall secrecy rate of LU, by dynamically adjusting IRS reflection coefficients and active beamforming at the base station (BS). A design problem is formulated to jointly optimize IRS reflecting beamforming and BS active beamforming, considering time-varying channel conditions and desired secrecy rate requirements. We propose a novel approach based on deep reinforcement learning (DRL) named Deep-PLS. This approach aims to determine an optimal beamforming policy capable of thwarting eavesdroppers in evolving environmental conditions. Extensive simulation studies validate the efficacy of our proposed strategy, demonstrating superior performance compared to traditional IRS approaches, IRS backscattering-based anti-eavesdropping methods, and other benchmark strategies in terms of secrecy performance.

关键词： Backscatter communication Joint-beamforming Deep-PLS Eavesdropper Malicious jammer Secrecy rate Deep reinforcement learning Deep deterministic policy gradient

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Sensitivity analysis of the kinematic parameters during the industrial polishing process of ceramic floor tiles

Digital Engineering

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Digital Engineering 2025年 6卷

作者： Fábio Sousa Marcel Wagner Rennáh Gonçalves Ricardo Halla Matthias Klar Jan Aurich Federal University of Rio Grande do Norte – UFRN Brazil School of Sciences and Technology – ECT Campus Universitário Lagoa Nova 59078-970 Natal-RN Brazil Institute for Manufacturing Technology and Production Systems RPTU Kaiserslautern-Landau 67663 Kaiserslautern Germany Federal University of Uberlandia – UFU Brazil Laboratory for Automation and Robotics Av. João Naves de Ávila - 2121 - Bloco 1M - Térreo - Bairro Santa Mônica 38400-902 Uberlândia MG Brazil

This work is focused on the industrial polishing process of ceramic floor tiles. This study aims to evaluate how small deviations in three key operational parameters affect the glossiness of polished ceramic surfaces. These operating parameters are the most important kinematic variables that can be selected in industrial systems to control the movement of the grinding tools. Computational simulations were used to determine abrasive trajectories and quantify polishing uniformity. The simulated values enabled quantitative measurement of polishing quality sensitivity to kinematic deviations. These results are intended to support the daily tasks of operators and further research on process optimization.

关键词： Sensitivity analysis Computational simulation Floor tile polishing Industrial polishing process

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Multivariate Time Series Forecast in Industrial Process Based on XGBoost and GRU

Multivariate Time Series Forecast in Industrial Process Base...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Naiju Zhai Peifu Yao Xiaofeng Zhou Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Copper Co.LTD Kunming China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

In this paper, a time series prediction model that merges eXtreme Gradient Boosting (XGBoost) and Gate Recurrent Unit (GRU), XGB-GRU model, is proposed for multivariate time series prediction in industry. The XGB-GRU model uses XGBoost's strong feature extraction capabilities to extract the hidden information of multiple control variables in industrial data. Next, the model uses GRU's unique gating unit to extract the timing information in the industrial data. Finally, the importance of XGBoost output variables to guide actual production and solve the problem of inexplicability of neural networks. Predicting the temperature of the heating furnace verifies that the proposed XGB-GRU is better than a single XGBoost and GRU model. And the model has a good fit to the predicted value.

关键词： Heating systems Furnaces Time series analysis Predictive models Feature extraction Timing Data mining

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Learning data-adaptive non-parametric kernels

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Spotlight-based 3D instrument guidance for retinal surgery

arXiv

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arXiv 2020年

作者： Zhou, Mingchuan Wu, Jiahao Ebrahimi, Ali Patel, Niravkumar He, Changyan Gehlbach, Peter Taylor, Russell H. Knoll, Alois Nasseri, M. Ali Iordachita, Iulian Department of Mechanical Engineering and Laboratoryfor Computational Sensing and Robotics The Johns Hopkins University BaltimoreMD21218 United States Chair of Robotics Artificial Intelligence and Real-time Systems Technische Universität München München85748 Germany T Stone Robotics Institute The Department of Mechanical and Automation Engineering The Chinese University of HongKong Hong Kong The Wilmer Eye Institute Johns Hopkins Hospital BaltimoreMD21287 United States Augenklinik und Poliklinik Klinikum rechts der Isar der Technische Universität München München81675 Germany

Retinal surgery is a complex activity that can be challenging for a surgeon to perform effectively and safely. Image guided robot-assisted surgery is one of the promising solutions that bring significant surgical enhancement in treatment outcome and reduce the physical limitations of human surgeons. In this paper, we demonstrate a novel method for 3D guidance of the instrument based on the projection of spotlight in the single microscope images. The spotlight projection mechanism is firstly analyzed and modeled with a projection on both a plane and a sphere surface. To test the feasibility of the proposed method, a light fiber is integrated into the instrument which is driven by the Steady-Hand Eye Robot (SHER). The spot of light is segmented and tracked on a phantom retina using the proposed algorithm. The static calibration and dynamic test results both show that the proposed method can easily archive 0.5 mm of tip-to-surface distance which is within the clinically acceptable accuracy for intraocular visual guidance. © 2020, CC BY.

关键词： Ophthalmology

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Distributed Hierarchical Fault Diagnosis Based on Sparse Auto-Encoder and Random Forest 4th

Distributed Hierarchical Fault Diagnosis Based on Sparse Aut...

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4th International Conference on Machine Learning and Intelligent Communications, MLICOM 2019

作者： Li, Tong Song, Chunhe Liu, Yang Wang, Zhongfeng Yu, Shimao Su, Shanting Liaoning Electric Power Research Institute State Grid Liaoning Electric Power Co. Ltd. 110000 Shenyang China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9783030323875

For the diagnosis of large-scale local devices, the traditional centralized fault diagnosis systems are becoming incompetent to meet the requirement of real-time monitoring. This paper proposes the Distributed hierarchical Fault Diagnosis System (DFDS). Specifically, DFDS implements fault monitoring by an improved Sparse Auto-Encoder (SAE) on the monitor layer, classifies faults and identifies unknown faults by an improved random forest on the classification layer, learns new knowledge and updates the system on the decision layer. We apply DFDS in the laboratory data of Case Western Reserve University to verify the efficiency of the proposed system. The experimental results show that our method can accurately detect the fault and accurately identify the fault type. © 2019, ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Fault detection

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Distributed Functional Safety for Modular Process Plants

Distributed Functional Safety for Modular Process Plants

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Anselm Klose Florian Pelzer Mike Barth Rainer Drath Rainer Oehlert Silvia Vélez León Alexander Horch Christoph Kotsch Jochen Knab Bernhard Gut Hartmut Manske Leon Urbas Process Control Systems & Process Systems Engineering TU Dresden Dresden Germany Research Training Group 2323 TU Dresden Dresden Germany IoS³ - Institute of Smart Systems and Services Pforzheim University Pforzheim Germany Retired Process Engineering & Automation The Dow Chemical Company Stade Germany Dept. Global Consulting HIMA Paul Hildebrandt GmbH Brühl Germany R. D. & Product Management HIMA Paul Hildebrandt GmbH Brühl Germany Control Systems Technology BASF SE Ludwigshafen Germany Engineering Services Merck KGaA Darmstadt Germany Retired Electrical Engineering for Process Automation Endress+Hauser Germany Automation and Robotics Merck KGaA Darmstadt Germany

ISBN: (数字)9781728189567

ISBN: (纸本)9781728189574

Modular process plants enable the process industry to adapt to volatile and fluctuating markets. The preferred process technology concept for these market scenarios is realized through combining pre-engineered process equipment assemblies (PEA) from different manufacturers with standardized interfaces. While the flexibility gain for this type of plants is high, the design of the safety systems of these modular plants in terms of exchangeability and compatibility is challenging. One solution could be the development of a safety-interface for PEAs to create flexible and scalable safety systems. The principles of modular basic process control are analyzed and requirements with respect to safety design are discussed. The authors provide a differentiation of the intramodular safety concept of a single PEA from the intermodular safety concept of a complete modular plant. A concept for safe and modular interaction of PEAs in a modular plant is developed and additionally validated on a basic demonstrator. Finally, from these findings, standardization requirements are provided and aspects for further research are pointed out.

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Development and Future Challenges of Bio-Syncretic Robots

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Engineering 2018年第4期4卷 452-463页

作者： Chuang Zhang Wenxue Wang Ning Xi Yuechao Wang Lianqing Liu Stute Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China University of Chinese Academy of Sciences Beijing 100049 China Emerging Technologies Institute Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Pokfulam Hong Kong China

Bio-syncretic robots consisting of both living biological materials and non-living systems possess desirable attributes such as high energy efficiency, intrinsic safety, high sensitivity, and self-repairing capabilities. Compared with living biological materials or non-living traditional robots based on elec- tromechanical systems, the combined system of a bio-syncretic robot holds many advantages. Therefore, developing bio-syncretic robots has been a topic of great interest, and significant progress has been achieved in this area over the past decade. This review systematically summarizes the development of bio-syncretic robots. First, potential trends in the development of bio-syncretic robots are discussed. Next, the current performance of bio-syncretic robots, including simple movement and controllability of velocity and direction, is reviewed. The living biological materials and non-living materials that are used in bio-syncretic robots, and the corresponding fabrication methods, are then discussed. In addition, recently developed control methods for bio-syncretic robots, including physical and chemical control methods, are described. Finally, challenges in the development of bio-syncretic robots are discussed from multiple viewpoints, including sensing and intelligence, living and non-living materials, control approaches, and information technology.

关键词： Bio-syncretic robot Hybrid robot Bio-actuator Cardiomyocyte Muscle cells

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