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On the Use of MPC Techniques to Decide Intervention Policies against COVID-19

IFAC-PapersOnLine 2021年第14期54卷 476-481页

作者： Zonglin Liu Olaf Stursberg Control and System Theory Dept. of Electrical Engineering and Computer Science University of Kassel Germany.

This paper aims at demonstrating how and that model predictive control (MPC) strategies can be used to determine optimal intervention policies against the COVID-19 pandemic. Especially for the time after a first wave of infection and before a vaccine can be safely distributed to a sufficient extent, the intervention experience from the first outbreak can be utilized to guide the policy decision in this period. The MPC problem in this paper takes the pandemic in different regions of a country and its neighboring countries into account, while policies such as wearing masks or social distancing are selected as inputs to be optimized. This optimized policy balances the risk of a second outbreak and socio-economic costs, while considering that the measure should not be too severe to be rejected by the population. Effectiveness of this policy compared to standard intervention policies is compared through numerical simulations.

关键词： Biomedical Systems Networked Systems Nonlinear Model Predictive control

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Multi-label transfer learning for identifying lung diseases using chest X-rays 2

Multi-label transfer learning for identifying lung diseases ...

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： El-Fiky, Azza Shouman, Marwa Ahmed Hamada, Salwa El-Sayed, Ayman Karar, Mohamed Esmail Electronics Research Institute Department of Informatics Cairo Egypt Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Minuf Egypt Menoufia University Faculty of Electronic Engineering Dept. Industrial Electronics and Control Engineering Minuf Egypt

ISBN: (纸本)9781665418423

Chest radiography presents one of the main medical imaging modalities for diagnosing lung diseases. To assist radiologists during interventional procedures, this paper aims at proposing a transfer learning-based classifier to automatically identify 14 different thoracic diseases in Chest X-ray (CXR) images. The proposed method is relied on deep residual neural networks with 50 layers (ResNet-50) to accomplish the diagnostic task of many chest diseases. In this study, a public dataset of 112, 120 frontal radiograph images for Chest X-ray has been used for validating the proposed deep learning classifier. It achieved the best performance of multi-label classification of normal and 14 different lung diseases with an average area under curve (AUC) of 0.911 and F1-score of 0.66. This study demonstrated that the proposed ResNet-50 classifier as a transfer learning model outperforms other relevant methods in the previous studies for automatic multi-label classification of chest X-rays. © 2021 IEEE.

关键词： computer aided diagnosis

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Differential Co-expression Network Analysis to Investigate Sexual Dimorphism in Colon Cancer

Differential Co-expression Network Analysis to Investigate S...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Caterina Alfano Lorenzo Farina Manuela Petti PhD program in Network Oncology and Precision Medicine Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering PhD program in Network Oncology and Precision Medicine Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Colorectal cancer is the third most diagnosed cancer in the world, but it has a higher mortality rate in men compared to women. However, we are not close to understanding how and why sex influences the outcome of the disease. This study focuses on mRNA expression profiles of colon cancer patients to look for molecular differences in the development of colon cancer between men and women. We used paired expression data (i.e., data collected in pairs of normal and cancer cells, by taking samples from the same individual), we identified differentially expressed genes (cancer vs normal) and computed co-expression and differential co-expression gene networks (men vs women). Doing so, we inferred the main changes and alterations happening in cancer tissues, and specifically how these changes were different among men and women. We found that the co-expression networks of women and men affected by colon cancer are quite different and we reported the genes that show the most differences in this comparison, checking if they could also be associated to sexual dimorphism or sexual hormones. Among these genes we found a interesting presence of genes associated to the Wnt signaling pathway which has been found to be regulated by estrogen and whose activation is strongly linked with colon cancer.

关键词： Network analyzers Biochemistry Colon Bioinformatics Cancer

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Correlation between wearable inertial sensor data and standardised Parkinson’s disease axial impairment measures using machine learning

Correlation between wearable inertial sensor data and standa...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Luigi Borzi’ Alessandro Manoni Alessandro Zampogna Fernanda Irrera Antonio Suppa Gabriella Olmo Dept. Control and Computer Engineering Politecnico di Torino Turin Italy Dept. Information Engineering Electronics and Telecommunication Sapienza University Rome Italy Dept. Human Neurosciences Sapienza University Rome Italy IRCCS NEUROMED Institute Pozzilli Italy

ISBN: (数字)9781665442800

ISBN: (纸本)9781665442817

Wearable sensors represent a valuable means for monitoring motion signs and symptoms of Parkinson’s disease (PD). In this paper, we explore the potential of a single inertial sensor to yield information correlated to the patient’s subjective perception of axial motion impairment during daily activities. This latter is expressed using as a relevant metric the sum of MDS-UPDRS items 2.11-2.13. Methods: thirty-one patients with PD were enrolled in this study, and asked to perform a timed-up-and-go test while wearing an inertial sensor on their thigh. Several time- and frequency-domain features were extracted from the inertial signals. They were fed to a random forest regression model for the prediction of the axial impairment metric. The model was optimized using 10-fold cross-validation and performance were assessed using leave-one-subject-out test. Results: Pearson correlation coefficient with the addressed metric of 0.76 (0.86) and mean absolute error of 1.70 (1.52) were obtained in patients under (not under) dopaminergic therapy. Moreover, moderate to strong correlations were found between the predicted score and some important disease progression, axial impairment, and motor performance metrics. Conclusion: a single wearable inertial sensor may be used for assessing motor disabilities of patients with PD.

关键词： Measurement Correlation Inertial sensors Thigh Predictive models Feature extraction Biomedical monitoring

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I Can Read Your Mind: control Mechanism Secrecy of Networked Dynamical Systems under Inference Attacks

arXiv

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arXiv 2022年

作者： He, Jianping Li, Yushan Cai, Lin Guan, Xinping The Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The Dept. of Electrical and Computer Engineering University of Victoria BC Canada

Recent years have witnessed the fast advance of security research for networked dynamical system (NDS). Considering the latest inference attacks that enable stealthy and precise attacks into NDSs with observation-based learning, this article focuses on a new security aspect, i.e., how to protect control mechanism secrets from inference attacks, including state information, interaction structure and control laws. We call this security property as control mechanism secrecy, which provides protection of the vulnerabilities in the control process and fills the defense gap that traditional cyber security cannot handle. Since the knowledge of control mechanism defines the capabilities to implement attacks, ensuring control mechanism secrecy needs to go beyond the conventional data privacy to cover both transmissible data and intrinsic models in NDSs. The prime goal of this article is to summarize recent results of both inference attacks on control mechanism secrets and countermeasures. We first introduce the basic inference attack methods on the state and structure of NDSs, respectively, along with their inference performance bounds. Then, the corresponding countermeasures and performance metrics are given to illustrate how to preserve the control mechanism secrecy. Necessary conditions are derived to guide the secrecy design. Finally, thorough discussions on the control laws and open issues are presented, beckoning future investigation on reliable countermeasure design and tradeoffs between the secrecy and control performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Cybersecurity

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency

Intelligent Operational Decision Support System for Sugar Fa...

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International Conference on Modern Electrical and Energy Systems (MEES)

作者： Nataliia Lutska Nataliia Zaiets Lidiia Vlasenko Stefan Junge Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Packaging Technology Berlin University of Applied Sciences Berlin Germany

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%.

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Live Demo: E2P-Events to Polarization Reconstruction from PDAVIS Events

Live Demo: E2P-Events to Polarization Reconstruction from PD...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Delbruck, Tobi Wang, Zuowen Mei, Haiyang Haessig, Germain Joubert, Damien Haque, Justin Chen, Yingkai Milde, Moritz B. Gruev, Viktor Univ. of Zurich and ETH Zurich Sensors Group Institute of Neuroinformatics Switzerland Center for Vision Automation & Control High-Performance Vision Systems AIT Austrian Institute of Technology Vienna Austria Western Sydney University Intl. Centre for Neuromorphic Systems The MARCS Institute Sydney Australia University of Illinois at Urbana-Champaign Dept. of Electrical and Computer Engineering UrbanaIL United States

This demonstration shows live operation of of PDAVIS polarization event camera reconstruction by the E2P DNN reported in the main CVPR conference paper Deep Polarization Reconstruction with PDAVIS Events (paper 9149 [... 详细信息

ISBN: (纸本)9798350302493

关键词： Polarization

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Cooperation and mental states neurophysiological assessment for pilots’ training and expertise evaluation

Cooperation and mental states neurophysiological assessment ...

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Technologies for Defense and Security (TechDefense), IEEE International Workshop on

作者： Gianluca Borghini Andrea Giorgi Vincenzo Ronca Lorenzo Mezzadri Rossella Capotorto Pietro Aricò Gianluca Di Flumeri Fabio Babiloni Dept. of Molecular Medicine Sapienza University of Rome Rome Italy Dept. of Anatomical Histological Forensic and Orthopaedic Sciences Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Rome Italy Flight Training School Urbe Aero ATO Rome Italy

The aim of the study was to validate a methodology for professional pilots’ neurophysiological assessment to improve training program tailoring and management. In particular, the assessment focused on quantifying (i) mental workload, (ii) stress level (iii) and the cooperation degree between the two members of the crews. Two groups of pilots were involved in the experiments: Experienced and Unexperienced. Additionally, the Instructor was responsible to provide subjective evaluation about the pilots’ mental states and cooperation. During the entire flight simulations, the pilots’ brain activity was acquired through the Electroencephalography (EEG). The results demonstrated that it is possible to quantify operators’ mental states and degree of cooperation while dealing with flight simulations under realistic settings. Pilots’ mental workload and stress behavioral levels resulted to be positively and significantly correlated with the corresponding neurophysiological measurements (all R > 0.6, all p < 0.05), while cooperation was observed to be higher (p < 0.05) for the Experienced than the Unexperienced. Although the results are preliminary, they show how the capability of quantifying pilots’ mental states and cooperation while dealing with tasks will provide the instructors with additional and objective information. In particular, this information would allow for tailoring the training sessions based on crew’s behavior.

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Pursuit-Evasion on a Sphere and When It Can Be Considered Flat

arXiv

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arXiv 2024年

作者： Milutinović, Dejan Von Moll, Alexander Manyam, Satyanarayana G. Casbeer, David W. Weintraub, Isaac E. Pachter, Meir Electrical and Computer Engineering Dept. University of California Santa Cruz Santa CruzCA United States The Control Science Center Aerospace Systems Directorate Air Force Research Laboratory DaytonOH45435 United States DCS Corporation DaytonOH United States Dept. of Electrical Engineering Air Force Institute of Technology Wright-Patterson AFB OH United States

In classical works on a planar differential pursuit-evasion game with a faster pursuer, the intercept point resulting from the equilibrium strategies lies on the Apollonius circle. This property was exploited for the construction of the equilibrium strategies for two faster pursuers against one evader. Extensions for planar multiple-pursuer single-evader scenarios have been considered. We study a pursuit-evasion game on a sphere and the relation of the equilibrium intercept point to the Apollonius domain on the sphere. The domain is a generalization of the planar Apollonius circle set. We find a condition resulting in the intercept point belonging to the Apollonius domain, which is the characteristic of the planar game solution. Finally, we use this characteristic to discuss pursuit and evasion strategies in the context of two pursuers and a single slower evader on the sphere and illustrate it using numerical *** Codes 58E10, 86A30, 37D40, 91A23, 49L12, 49Q10 © 2024, CC BY-NC-ND.

关键词： Spheres

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Behavioural Cloning for Serious Games in Support of Pediatric Neurorehabilitation

Behavioural Cloning for Serious Games in Support of Pediatri...

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Mediterranean Conference on control and Automation (MED)

作者： Federico Baldisseri Edoardo Montecchiani Arturo Maiani Danilo Menegatti Alessandro Giuseppi Antonio Pietrabissa Vincenzo Fogliati Francesco Delli Priscoli Dept. of Computer Control and Management Engineering (DIAG) Sapienza University of Rome Italy 4You – Association for Social Advancement Rome Italy Consortium for the Research in Automation and Telecommunication (CRAT) Rome Italy

Behavioural Cloning is a Machine Learning method concerning how a machine attempts to autonomously mimic the actions of a human, or in general a complex controller, performing a given task. This work innovatively exploits Behavioural Cloning in support of Pediatric Neurorehabilitation. In particular, an Artificial Neural Network Classifier has been implemented to autonomously adapt the difficulty, through a set of tunable parameters, of a Serious Game that was specifically developed to stimulate some relevant cognitive capabilities of the patient. Data augmentation via Behavioural Cloning allows such autonomous difficulty adaptation system to improve its classification performances and, thus, to enforce a control logic that, in turn, improves the effectiveness of the cognitive training. The system is validated through an experimental assessment on a Serious Game that trains motor coordination: experimental results of children gameplay are analyzed and discussed.

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