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92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2022

作者： Tebbens, Jurjen Duintjer Lanzendörfer, Martin Matonoha, Ctirad Papáček, Štěpán Department of Computational Mathematics Institute of Computer Science of the Czech Academy of Sciences Pod Vodárenskou věží 271/2 Praha-Libeň 182 07 Czech Republic Department of Biophysics and Physical Chemistry Faculty of Pharmacy in Hradec Kralove Charles University Heyrovského 1203 Hradec Králové500 05 Czech Republic Institute of Hydrogeology Engi neering Geology and Applied Geophysics Faculty of Science Charles University Albertov 6 Praha 2 128 43 Czech Republic Department of Control Theory Institute of Information Theory and Automation of the Czech Academy of Sciences Pod Vodárenskou věží 4 Praha-Libeň 182 00 Czech Republic

We address efficient modeling of the intracellular action of a drug binding to the nuclear pregnane X receptor. The binded complex enters the cell nucleus and acts on DNA, resulting in enhanced production of an enzyme which metabolizes, among others, co-administered drugs. Pharmacodynamic modeling is traditionally based on a compartmental approach, which simplifies the complex processes in the human body through the definition of a small number of compartments representing organs, tissues, cells, cytoplasm or abstract units. Inside a compartment, the concentrations of all substances are assumed to be distributed homogeneously, i.e. there is no spatial dependence. They lead to a system of ODEs for the time-dependence of the concentrations of the active substances. We will present an extension of a model from the literature for the action of the tuberculose-drug Rifampicin. The extension consists of a first attempt to add spatial resolution for substances that are active in the cytoplasm. Spatial resolution can be benecifial to analyze important issues like, among others, local exceeding of toxic drug levels, delay of transport and drug-drug interactions. We address efficient solution of the linear systems arising when numerically integrating the resulting PDE’s and consider some preconditioning techniques based on properties of the underlying biochemical network. © 2023 Wiley-VCH GmbH.

关键词： Linear systems

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Modeling and fuzzy control of permanent magnet synchronous motors 10

Modeling and fuzzy control of permanent magnet synchronous m...

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10th International Conference on Power Electronics, Machines and Drives, PEMD 2020

作者： Juki, Domagoj-Kreimir Varga, Toni Beni, Tin Štil, Vedrana Jerkovi Baruki, Marinko Automation and Control Institute TU Wien Vienna Austria Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia

ISBN: (纸本)9781839535635

This paper deals with Permanent Magnet Synchronous Motor modeling and control. The derived nonlinear dq model is a combination of Finite Element Method simulation data with the classical dq model. The so derived model keeps the precision of the Finite Element Method simulations and also the fast execution times of the dq model. The Finite Element Method data is included in the dq model using Artificial Neural Networks. The derived model is also used for controller design. The chosen controller is a PI like Fuzzy Logic controller, which is tuned using Genetic Algorithm optimization. The Permanent Magnet Synchronous Motor with Fuzzy Logic controller operation is tested using Real-Time (Model-in-the-Loop) simulations. © PEMD *** right reserved.

关键词： Permanent magnets

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Output consensus control for linear port-Hamiltonian systems

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IFAC-PapersOnLine 2022年第30期55卷 230-235页

作者： Shuai Feng Yu Kawano Michele Cucuzzella Jacquelien M.A. Scherpen School of Automation Nanjing University of Science and Technology Nanjing 210094 China Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima 739-8527 Japan Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia PV Italy Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen 9747 AG Groningen the Netherlands

In this paper, we study output consensus of coupled linear port-Hamiltonian systems on graphs in the presence of constant disturbances, where couplings are allowed to be both static and dynamic. Utilizing port-Hamiltonian structures, we present dynamic controllers achieving output consensus where the consensus values are determined by the disturbances. Finally, the utility of the proposed controller is illustrated by applying it to current sharing of DC microgrids.

关键词： port-Hamiltonian systems networked systems output consensus

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Synergy Analysis of Industrial, Research and University Training Centers Collaboration: A Case Study in Thailand's Eastern Economic Corridor

Synergy Analysis of Industrial, Research and University Trai...

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Portland International Conference on Management of Engineering and Technology (PICMET)

作者： Udom Lewlomphaisarl Ukrit Pungjitwisut Wassana Pullpol Kumpee Suksomboon Arnuphap Dowrueng Industrial IoT and Automation Research Group (IIARG) Sustainable Manufacturing Center (SMC) Eastern Economic Corridor of Innovation (EECi) Rayong Thailand EEC Automation Park Faculty of Engineering Burapha University Chonburi Thailand Intelligent SCADA Technology Research Team (IST) Advanced Control and Electronics Research Group (ACERG) National Electronics and Computer Technology Center (NECTEC) Pathum Thani Thailand National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand

The Royal Thai Government has outlined a 20-year strategy to achieve high-income status by 2036. “Thailand 4.0” is guided by the concept of “Industry 4.0”, which aims to transform the country into an innovative, value-driven industry, with a focus on 12 areas such as automation & Robotics, Aviation & Logistics, Biofuel & Biochemicals, and Digital. The development of Eastern Economic Corridor (EEC) lies at the heart of Thailand 4.0 scheme. The EEC is an area-based development initiative aimed at revitalizing the prestigious East Coast, where numerous business developers have experienced a rewarding investment journey and exceptional success over the past 30 *** paper introduces a synergy analysis of the 3-phase collaboration approach between the EEC automation Park located on the campus of Burapha University as the main university in the EEC and the Sustainable Manufacturing Center (SMC) at automation Robotics and Intelligent System (ARIPOLIS) in Eastern Economic Corridor of Innovation (EECi) with strong partners from industry e.g. Mitsubishi Electric Factory automation (Thailand) Limited. This 3-phase approach:}{1. Build an island demonstrator, 2. Link production demonstration lines and 3. Transfer to 100 factories in the EEC helps managers realize possible difficulties and systematically prepare solutions in advance.

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Optimizing Neural Network Image Classification with Fractional Order Gradient Methods

SSRN

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SSRN 2023年

作者： Zhao, Haiming Chen, Jiejie Jiang, Ping Zeng, Zhigang Fan, Rui Yang, Honggang School of Computer and Information Engineering Hubei Normal University Huangshi435000 China School of Automation Wuhan University of Technology Wuhan430000 China School of Computer Hubei PolyTechnic University Huangshi435000 China Faculty of Artificial Intelligence Education Central China Normal University Wuhan430000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430000 China

As deep learning technology increasingly permeates various fields, the significance of optimization algorithms in neural network training has become more prominent. Typically, the training of deep learning networks relies on gradient descent optimizers based on calculus computations. Traditional integer-order calculus optimizers often fall short when dealing with complex and subtle data. To overcome these limitations, we innovatively utilized the Grünwald-Letnikov and Caputo definitions of fractional calculus to develop two new optimization algorithms: Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam). These algorithms have been proven to converge to extremal points.A key advantage of our methods is their capability in handling complex, high-dimensional tasks, such as artistic painting image classification, where they exhibit exceptional performance in recognizing and processing subtle differences. The robustness and adaptability of these algorithms were further validated through experiments on the CIFAR-100 dataset. The results demonstrate the efficacy of fractional order optimizers in enhancing training efficiency and accuracy. © 2023, The Authors. All rights reserved.

关键词： Image classification

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Energy-Saving Practice of Variable Frequency Drive Technology in Data Center HVAC System

Energy-Saving Practice of Variable Frequency Drive Technolog...

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International Conference on Power and Energy Technology (ICPET)

作者： Chuanxian Ai Jinpeng Yang Rui Chen Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Branch The National Computer Network Emergency Response Technical Team/Coordination Center of China Kunming China Power Dispatch Control Center Guizhou Power Grid Co. Ltd. China Southern Power Grid Co. Ltd Guiyang China

The variable-frequency drive technology (VFD) can modify the motor speed or torque by varying the frequency and voltage of the AC motor input power according to the load demand as a way to save energy. To analyse the energy saving effect of variable-frequency drive technology applied to data centre heating, ventilation, and air conditioning (HVAC) systems, this paper applies VFD technology to data centre fluorine pump-compressor composite air conditioning systems, implements monitoring and analysis of the operational energy efficiency of this system before and after retrofit, and combines the actual application of this technology. The findings indicate that the VFD technology can substantially reduce the number of compressor starts and stops, reduce the compressor start-up inrush current, and improve the power system’s stability. Furthermore, utilising this technology, the HVAC system can effectively synchronise with the load and fluctuations in outdoor temperature and humidity, resulting in accurate temperature regulation and optimal utilisation of outdoor cooling resources. This approach can lead to significant energy conservation of up to 42.95% when compared to extreme hot and cold weather conditions. The findings of the study indicate that the utilisation of VFD technology exhibits commendable adaptability and yields favourable outcomes in regions with varying temperature ranges. The minimum PUE value recorded was 1.228.

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A Unified Formulation of Geometry-aware Discrete Dynamic Movement Primitives

arXiv

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arXiv 2022年

作者： Abu-Dakka, Fares J. Saveriano, Matteo Kyrki, Ville Electronic and Computer Science Department Faculty of Engineering Mondragon Unibertsitatea Arrasate20500 Spain Automatic Control Lab Department of Industrial Engineering University of Trento Trento Italy Intelligent Robotics Group Department of Electrical Engineering and Automation Aalto University Aalto Finland

Learning from demonstration (LfD) is considered as an efficient way to transfer skills from humans to robots. Traditionally, LfD has been used to transfer Cartesian and joint positions and forces from human demonstrations. The traditional approach works well for some robotic tasks, but for many tasks of interest, it is necessary to learn skills such as orientation, impedance, and/or manipulability that have specific geometric characteristics. An effective encoding of such skills can be only achieved if the underlying geometric structure of the skill manifold is considered and the constrains arising from this structure are fulfilled during both learning and execution. However, typical learned skill models such as dynamic movement primitives (DMPs) are limited to Euclidean data and fail in correctly embedding quantities with geometric constraints. In this paper, we propose a novel and mathematically principled framework that uses concepts from Riemannian geometry to allow DMPs to properly embed geometric constrains. The resulting DMP formulation can deal with data sampled from any Riemannian manifold including, but not limited to, unit quaternions and symmetric and positive definite matrices. The proposed approach has been extensively evaluated both on simulated data and real robot experiments. The performed evaluation demonstrates that beneficial properties of DMPs, such as convergence to a given goal and the possibility to change the goal during operation, apply also to the proposed formulation. © 2022, CC BY-NC-ND.

关键词： Demonstrations

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A Global Asymptotically Stable Nonlinear Robust Bias Observer for MEMS Gyros

TechRxiv

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TechRxiv 2022年

作者： Tang, Ning Chang, Jieling Wang, Lingling Fu, Li Neusypin, Konstantin A. Selezneva, Maria S. Peng, Linping The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Faculty of Computer Science and Control Systems Bauman Moscow State Technical University Moscow105005 Russia Experimental Teaching Demonstration Center of Mechanical and Control Engineering Beihang University Beijing100191 China The School of Mathematics and System Sciences Beihang University Beijing100191 China

In practical applications, commercial-grade Micro-Electro-Mechanical System (MEMS) gyros tend to exhibit dynamics sensitivities which are resistant to modeling by linear drift. To address the problem, a new nonlinear robust bias observer (NRBO) is proposed in this paper. Unlike the existing attitude and gyro bias nonlinear observers, we propose a dynamics-sensitive gyro bias decomposition method to tackle unknown nonlinear dynamic models of MEMS gyro bias. We expose the potential advantages of the proposed method: the global asymptotic stability of the NBRO and the robustness to the bias instability of MEMS gyros can be guaranteed through a rational design of the attitude-angular rate nonlinear dynamic coupling (AARNDC) term and the attitude-linear coupling (ALC) term. In addition, the attitude estimation with bias correction is given in the framework of NBRO. The experiment of a cable-parallel robot demonstrate the robustness to the bias instability and the accuracy improvement of MEMS gyros. © 2022, CC BY.

关键词： Gyroscopes

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Grasp-Anything: Large-scale Grasp Dataset from Foundation Models

arXiv

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arXiv 2023年

作者： Vuong, An Dinh Vu, Minh Nhat Le, Hieu Huang, Baoru Huynh, Binh T.T. Vo, Thieu Kugi, Andreas Nguyen, Anh FPT Software AI Center Viet Nam Automation & Control Institute TU Wien Vienna Austria AIT Austrian Institute of Technology Vienna Austria Imperial College London United Kingdom Hanoi University of Science and Technology Viet Nam Faculty of Mathematics and Statistics Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science University of Liverpool United Kingdom

Foundation models such as ChatGPT have made significant strides in robotic tasks due to their universal representation of real-world domains. In this paper, we leverage foundation models to tackle grasp detection, a persistent challenge in robotics with broad industrial applications. Despite numerous grasp datasets, their object diversity remains limited compared to real-world figures. Fortunately, foundation models possess an extensive repository of real-world knowledge, including objects we encounter in our daily lives. As a consequence, a promising solution to the limited representation in previous grasp datasets is to harness the universal knowledge embedded in these foundation models. We present Grasp-Anything, a new large-scale grasp dataset synthesized from foundation models to implement this solution. Grasp-Anything excels in diversity and magnitude, boasting 1M samples with text descriptions and more than 3M objects, surpassing prior datasets. Empirically, we show that Grasp-Anything successfully facilitates zero-shot grasp detection on vision-based tasks and real-world robotic experiments. Our dataset and code are available at https://***. © 2023, CC BY-NC-SA.

关键词： Robotics

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