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Decoding Emotions: How Temporal modelling Enhances Recognition Accuracy 12

Decoding Emotions: How Temporal Modelling Enhances Recogniti...

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Chandrasekharan, S. Arabian, H. Moeller, K. Hsch Furtwangen D-78054 Villingen Schwenningen Germany Inst Tech Med ITeM D-78054 Villingen Schwenningen Germany

Facial emotion recognition (FER) is a crucial component in the field of human-computer interaction, it can be strategically utilized for the therapeutic intervention of individuals who have been clinically diagnosed with autism spectrum disorder (ASD). The application of deep learning techniques for FER has the potential to enhance therapeutic intervention for this population. However, achieving high accuracy and rapid response times is essential for the practical implementation of emotion recognition systems. This paper introduces two models that incorporate temporal modelling, an approach that employs the time domain aspect, to enhance the accuracy of recognition. The results demonstrate that the integration of the time domain approach significantly improved the prediction accuracy, with an average increase of 8% compared to the convolution neural network (CNN) model. This research underscores the potential of long short-term memory (LSTM) in advancing the field of FER, leading to more efficient human-computer interaction systems. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： deep learning emotion recognition facial expression analysis GoogLeNet OULU-CASIA temporal modeling

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How to Model Human Robot Collaborative Logistics systems: Systematic Literature Review and Future Perspectives 18

How to Model Human Robot Collaborative Logistics Systems: Sy...

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18th ifac symposium on Information control Problems in Manufacturing (INCOM)

作者： Zhang, Minqi Grosse, Eric H. Saarland Univ Chair Digital Transformat Operat Management Fac Human & Business Sci Saarbrucken Germany

Thanks to rapid technological developments in robotics, various automation technologies have been introduced into different logistics systems. A human robot collaborative logistics system can contain different technologies. Until now, most studies have focused on technologies that could only partially automate the logistics process. In contrast, some market-ready products exist with which the entire logistics process could be automated. An example is the hybrid order picking system, which utilizes autonomous mobile robots to conduct picking and transporting tasks and can be integrated into manual warehouses. In this case, human workers and robots can be modeled as heterogeneous agents with strengths and weaknesses. To date, the issue of how to model these logistics systems has remained largely unaddressed. Inspired by this system, this work intends to contribute to the discussion of possible (analytical) modeling approaches to leverage the strengths of both humans and robots to achieve better outcomes. To this end, a systematic literature review was conducted to find usable approaches in adjacent disciplines. We identified modelling approaches in the field of last-mile delivery, in-house transportation, assembly, and robotics, which considered a (human-robot) heterogenous team in the task allocation. Efforts are then made to construct a framework for possible design variables for such logistics systems to offer promising insights and perspectives for future research. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Logistics Human robot collaboration Order picking Mathematical modelling Systematic literature review

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Stochastic Data-Driven Predictive control: Regularization, Estimation, and Constraint Tightening 20

Stochastic Data-Driven Predictive Control: Regularization, E...

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20th ifac symposium on System Identification (SYSID)

作者： Yin, Mingzhou Iannelli, Andrea Smith, Roy S. Swiss Fed Inst Technol Automat Control Lab Zurich Switzerland Univ Stuttgart Inst Syst Theory & Automat Control Stuttgart Germany

Data-driven predictive control methods based on the Willems' fundamental lemma have shown great success in recent years. These approaches use receding horizon predictive control with nonparametric data-driven predictors instead of model-based predictors. This study addresses three problems of applying such algorithms under unbounded stochastic uncertainties: 1) tuning-free regularizer design, 2) initial condition estimation, and 3) reliable constraint satisfaction, by using stochastic prediction error quantification. The regularizer is designed by leveraging the expected output cost. An initial condition estimator is proposed by filtering the measurements with the one-step-ahead stochastic data-driven prediction. A novel constraint-tightening method, using second-order cone constraints, is presented to ensure high-probability chance constraint satisfaction. Numerical results demonstrate that the proposed methods lead to satisfactory control performance in terms of both control cost and constraint satisfaction, with significantly improved initial condition estimation. Copyright (c) 2024 The Authors.

关键词： data-driven control model predictive control estimation and filtering stochastic optimal control stochastic system identification

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Data-Driven Predictive control and MPC: Do we achieve optimality? 20

Data-Driven Predictive Control and MPC: Do we achieve optima...

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20th ifac symposium on System Identification (SYSID)

作者： Anand, A. S. Sawant, S. Reinhardt, D. Gros, S. Norwegian Univ Sci & Technol NTNU Dept Engn Cybernet Trondheim Norway

In this paper, we explore the interplay between Predictive control and closed-loop optimality, spanning from Model Predictive control to Data-Driven Predictive control. Predictive control in general relies on some form of prediction scheme on the real system trajectories. However, these predictions may not accurately capture the real system dynamics, for e.g., due to stochasticity, resulting in sub-optimal control policies. This lack of optimality is a critical issue in case of problems with economic objectives. We address this by providing sufficient conditions on the underlying prediction scheme such that a Predictive controller can achieve closed-loop optimality. However, these conditions do not readily extend to Data-Driven Predictive control. In this context of closed-loop optimality, we conclude that the factor distinguishing the approaches within Data-Driven Predictive control is if they can be cast as a sequential decision-making process or not, rather than the dichotomy of model-based vs. model-free. Furthermore, we show that the conventional approach of improving the prediction accuracy from data may not guarantee optimality. Copyright (c) 2024 The Authors.

关键词： Prediction models

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Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear systems 20

Learning Reduced-Order Linear Parameter-Varying Models of No...

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20th ifac symposium on System Identification (SYSID)

作者： Koelewijn, Patrick J. W. Singh, Rajiv Seiler, Peter Toth, Roland Sioux Technol BV Eindhoven Netherlands Eindhoven Univ Technol Control Syst Grp Eindhoven Netherlands MathWorks Inc Natick MA USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA HUN REN Inst Comp Sci & Control Syst & Control Lab Budapest Hungary

In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step procedure. In the first step, we learn a projection to a lower dimensional state-space. In step two, an LPV model is learned on the reduced-order state-space using a novel, efficient parameterization in terms of neural networks. The improved modeling accuracy of the method compared to an existing method is demonstrated by simulation examples. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Reduced-order modeling Neural Networks Linear Parameter-Varying Models

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An Efficient Implementation for Regularized Frequency Response Function and Transient Estimation 20

An Efficient Implementation for Regularized Frequency Respon...

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20th ifac symposium on System Identification (SYSID)

作者： Xu, Yu Fang, Xiaozhu Mu, Biqiang Chen, Tianshi Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Peoples R China Chinese Acad Sci Key Lab Syst & Control Inst Syst Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

In this paper, we propose an efficient implementation for the kernel-based regularized frequency response function (FRF) and transient estimation. In particular, we show that when the diagonal correlated (DC) kernel is used, the output kernel matrix in the frequency domain has a hierarchically of-diagonal low-rank (HODLR) structure, and by exploring this structure, the FRF and transient estimates can be computed very efficiently The overall computational complexity is O(gamma(2) N-b log(2) N-b), where N-b is the number of data in the interested frequency band and gamma is the HODLR rank of the output kernel matrix that is often much smaller than N-b. The efficacy of the proposed implementation is demonstrated by numerical simulations. Copyright (c) 2024 The Authors.

关键词： Kernel-based regularization method Efficient implementation HODLR matrix

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Deep Learning of Dynamic systems using System Identification Toolbox™ 20

Deep Learning of Dynamic Systems using System Identification...

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20th ifac symposium on System Identification (SYSID)

作者： Dai, Tianyu Aljanaideh, Khaled Chen, Rong Singh, Rajiv Stothert, Alec Ljung, Lennart MathWorks 1 Apple Hill Dr Natick MA 01760 USA Jordan Univ Sci & Technol Fac Engn Dept Aeronaut Engn Irbid Jordan Linkoping Univ Dept Elect Engn Div Automat Control Linkoping Sweden

MATLAB (R) releases over the last 3 years have witnessed a continuing growth in the dynamic modeling capabilities offered by the System Identification Toolbox (TM). The emphasis has been on integrating deep learning architectures and training techniques that facilitate the use of deep neural networks as building blocks of nonlinear models. The toolbox offers neural state-space models which can be extended with auto-encoding features that are particularly suited for reduced-order modeling of large systems. The toolbox contains several other enhancements that deepen its integration with the state-of-art machine learning techniques, leverage auto-differentiation features for state estimation, and enable a direct use of raw numeric matrices and timetables for training models. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licneses/by-nc-nd/4.0/)

关键词： System Identification Toolbox machine learning deep learning reduced order modeling

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On identifying the non-linear dynamics of a hovercraft using an end-to-end deep learning approach 20

On identifying the non-linear dynamics of a hovercraft using...

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20th ifac symposium on System Identification (SYSID)

作者： Schwan, R. Schmid, N. Chassaing, E. Samaha, K. Jones, C. N. Ecole Polytech Fed Lausanne Automat Control Lab Lausanne Switzerland Ecole Polytech Fed Lausanne Risk Analyt & Optimizat Chair Lausanne Switzerland

We present the identification of the non-linear dynamics of a novel hovercraft design, employing end-to-end deep learning techniques. Our experimental setup consists of a hovercraft propelled by racing drone propellers mounted on a lightweight foam base, allowing it to float and be controlled freely on an air hockey table. We learn parametrized physics-inspired non-linear models directly from data trajectories, leveraging gradient-based optimization techniques prevalent in machine learning research. The chosen model structure allows us to control the position of the hovercraft precisely on the air hockey table. We then analyze the prediction performance and demonstrate the closed-loop control performance on the real system. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Nonlinear system identification Learning for control Hovercraft Parametric modeling Air hockey Physics-inspired modeling

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A minimum Time-to-Target MPC approach for depth of hypnosis in total intravenous anesthesia 12

A minimum Time-to-Target MPC approach for depth of hypnosis ...

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Milanesi, Marco Consolini, Luca Di Credico, Giulia Laurini, Mattia Schiavo, Michele Visioli, Antonio Univ Brescia Dipartimento Ingn Meccan & Ind Brescia Italy Univ Parma Dipartimento Ingn & Architettura Parma Italy

In this paper we propose a new approach for the Depth-of-Hypnosys (DoH) control in Total Intravenous Anesthesia (TIVA), where the bispectral index (BIS) is the process variable and the infusion rate of propofol is the control variable. In particular, a Proportional-Integral-Derivative (PID) controller is employed when the BIS is inside the specified range from 60 to 40. Then, a model predictive control (MPC) technique is employed when the BIS is higher than 60 with the aim of minimizing the time to-target, thus reducing the risk of awareness for the patient during the maintenance phase. The same approach is also effective in the induction phase, reducing its duration. Simulation results show the effectiveness of the methodology. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Anesthesia control DoH control MPC PID control optimization

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Modeling and control of an Automated Hemolysis Test Bench 12

Modeling and Control of an Automated Hemolysis Test Bench

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Borchers, Patrick Leonhardt, Steffen Walter, Marian Rhein Westfal TH Aachen Chair Med Informat Technol D-52074 Aachen Germany

Hemolysis test benches are routinely used to evaluate the blood trauma caused by left ventricular assist devices. Nowadays, the desired LVAD operating condition within the test bench is achieved and maintained by manually adjusting the clamp position and the pump speed. This work proposes an automated hemolysis test bench to reduce the workload during hemolysis testing and allow for continuous data recording. Firstly, the controller designs of the actuators (left ventricular assist device and the automated tube clamp) are described. Subsequently, the hemodynamics of the test bench are modeled, where the Sputnik pump is used as left ventricular assist device. Finally, a gain-scheduled flow controller is presented that exhibits good reference tracking. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Hemolysis test bench Left ventricular assist device Modeling PI control Gain-scheduling

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