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Iranian Conference of Biomedical Engineering (ICBME)

作者： Mojtaba Zarei Kimia Javadi Ahmad Kalhor Cyber-Physical Systems Lab (CPSL) Departments of Electrical and Computer Engineering Duke University NC USA Faculty of Computer and Electrical Engineering K.N.Toosi University of Technology Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781538679531;9781538679524

This paper aims at the estimation of the Domain of Attraction (DoA) of the free tumor equilibrium point of perturbed tumor immunotherapy model via the Arc-Length Function (ALF). The ALFs are categorized among the maximal Lyapunov functions which are able to provide a more accurate estimation of the DoA in comparison to their other counterparts such as Rational Lyapunov Functions (RLFs), Sum Of Square (SOS) polynomial Lyapunov functions, and Optimal Quadratic Lyapunov Functions (OQLFs). There is no analytical method to construct the ALFs, however, some numerical methods have been proposed in the literature. Based on the existing method, one can approximate the ALF with a certain degree of a polynomial function. That the system under study has a polynomial structure was the main basis of the previously proposed method to estimate the DoA via the ALFs. However, the intended model in this paper describing the tumor-immune system competition dynamics contains non-polynomial terms. To cope with the aforementioned problem, the Taylor expansion of the non-polynomial terms are considered and by solving an optimization problem, one can calculate the corresponding lower boundary of the level set with the approximated ALF as an estimation of the DoA. In order to represent the performance of the employed method, the obtained result is compared with the reported result in the literature.

关键词： Tumors Direction-of-arrival estimation Lyapunov methods Estimation Immune system Mathematical model Cancer

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Patient specific model based induction of hypnosis using fractional order control

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IFAC-PapersOnLine 2017年第1期50卷 15097-15102页

作者： Copot, Dana Muresan, Cristina De Keyser, Robin Ionescu, Clara Ghent University Research Group on Dynamical Systems and Control Technologiepark 914 GhentB9052 Belgium Technical University of Cluj-Napoca Faculty of Automation and Computer Science G. Baritiu 26-28 Cluj Napoca400027 Romania

Optimal and safe control of drug delivery systems with continuous infusion protocol is of key importance to avoid over- or under-dosing of the patient. By implementing close-loops one is able to optimize the amount of drug given to the patient. In this paper a robust control methodology is presented. Emerging tools from fractional calculus have been considered and a fractional order PI controller for drug dosing during hypnosis has been designed. In this paper a robust fractional order control of hypnosis is proposed. The controller has been evaluated on an artificial data set of 24 patients and the results indicate that such a control strategy is robust to uncertainty stemming from the inter- and intra-patient variability. © 2017

关键词： controlled drug delivery Calculations control engineering controllers Robust control Robustness (control systems) Targeted drug delivery Uncertainty analysis Continuous infusions control methodology Drug delivery system Fractional order control hypnosis Patient specific model Personalized healthcare uncertainty

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Front Cover: Water-in-Water Droplets Selectively Uptake Self-Assembled DNA Nano/Microstructures: a Versatile Method for Purification in DNA Nanotechnology (ChemBioChem 17/2022)

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ChemBioChem 2022年第17期23卷

作者： Dr. Marcos K. Masukawa Prof. Dr. Yusuke Sato Dr. Fujio Yu Prof. Dr. Kanta Tsumoto Prof. Dr. Kenichi Yoshikawa Prof. Dr. Masahiro Takinoue Department of Computer Science Tokyo Institute of Technology 4259 Nagatsuta-cho Midori-ku Yokohama Kanagawa 226-8502 Japan Frontier Research Institute for Interdisciplinary Sciences Tohoku University 6-3 Aramaki-Aza Aoba Aoba-ku Sendai Miyagi 980–8578 (Japan) Department of Intelligent and Control Systems Kyushu Institute of Technology 680-4 Kawazu Iizuka Fukuoka 820-8502 Japan Division of Chemistry for Materials Graduate School of Engineering Mie University 1577 Kurimamachiya-cho Tsu Mie 514-8507 Japan Center for Integrative Medicine and Physics Institute for Advanced Study Kyoto University Yoshida Ushinomiya-cho Sakyo-Ku Kyoto 606-8501 Japan Faculty of Life and Medical Sciences Doshisha University 1-3 Tataramiyakodani Kyotanabe Kyoto 610-0394 Japan

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Robust medical instrument segmentation challenge 2019

arXiv

引用

arXiv 2020年

作者： Roβ, Tobias Reinke, Annika Full, Peter M. Wagner, Martin Kenngott, Hannes Apitz, Martin Hempe, Hellena Filimon, Diana Mindroc Scholz, Patrick Tran, Thuy Nuong Bruno, Pierangela Arbelaéz, Pablo Bian, Gui-Bin Bodenstedt, Sebastian Bolmgren, Jon Lindström Bravo-Sánchez, Laura Chen, Hua-Bin González, Cristina Guo, Dong Halvorsen, Pål Heng, Pheng-Ann Hosgor, Enes Hou, Zeng-Guang Isensee, Fabian Jha, Debesh Jiang, Tingting Kirtac, Kadir Jin, Yueming Kletz, Sabrina Leger, Stefan Li, Zhixuan Maier-Hein, Klaus H. Ni, Zhen-Liang Riegler, Michael A. Schoeffmann, Klaus Shi, Ruohua Speidel, Stefanie Stenze, Michael Twick, Isabell Wang, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Zhang, Yujie Zhou, Yan-Jie Zhu, Lei Wiesenfarth, Manuel Kopp-Schneider, Annette Müller-Stich, Beat P. Maier-Hein, Lena Heidelberg Germany University of Heidelberg Germany Heidelberg Germany Department of General Heidelberg University Hospital Visceral and Transplant Surgery Heidelberg Germany caresyntax Berlin Germany University of Chinese Academy Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China SimulaMet Oslo Norway Tromsø Norway Oslo Norway School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Partner Site Dresden Heidelberg Germany Faculty of Medicine and University Hospital Carl Gustav Carus Technische Universität Dresden Dresden Germany Dresden Germany Universidad de Los Andes Bogotá Colombia Peking University Peking China Department of Computer Science School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong German Cancer Research Center Division of Biostatistics Heidelberg Germany Institute of Information Technology Klagenfurt University Austria

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and roboticassisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization;algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts). Copyright © 2020, The Authors. All rights reserved.

关键词： Endoscopy

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A comparison of nonsmooth, nonconvex, constrained optimization solvers for the design of time-delay compensators

arXiv

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arXiv 2018年

作者： Kungurtsev, Vyacheslav Mitchell, Tim Vyhlídal, Tomáš Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague

We present a detailed set of performance comparisons of two state-of-the-art solvers for the application of designing time-delay compensators, an important problem in the field of robust control. Formulating such robust control mechanics as constrained optimization problems often involves objective and constraint functions that are both nonconvex and nonsmooth, both of which present significant challenges to many solvers and their end-users hoping to obtain good solutions to these problems. In our particular engineering task, the main difficulty in the optimization arises in a nonsmooth and nonconvex stability constraint, which states that the infinite spectrum of zeros of the so-called shaper should remain in the open left half-plane. To perform our evaluation, we make use β-relative minimization profiles, recently introduced visualization tools that are particularly suited for benchmarking solvers on nonsmooth, nonconvex, constrained optimization problems. Furthermore, we also introduce new visualization tools, called Global-Local Profiles, which for a given problem and a fixed computational budget, assess the tradeoffs of distributing the budget over few or many starting points, with the former getting more budget per point and latter less. Copyright © 2018, The Authors. All rights reserved.

关键词： Robust control

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Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison

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Scientific reports 2025年第1期15卷 19073页

作者： Farag M A Altalbawy Ahmad Alkhayyat Ramdevsinh Jhala Anupam Yadav T Ramachandran Aman Shankhyan A Karthikeyan Dhirendra Nath Thatoi Vladimir Vladimirovich Sinitsin Department of Chemistry University College of Duba University of Tabuk Tabuk Saudi Arabia. f_altalbawy@***. Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq. Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq. Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Babylon Babylon Iraq. Marwadi University Research Center Department of Mechanical Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat 360003 India. Department of Computer Engineering and Application GLA University Mathura 281406 India. Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India. Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India. Department of Mechanical Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India. Department of Mechanical Engineering Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India. Research Laboratory of Technical Self-Diagnostics and Self-Control of Devices and Systems Department of Scientific and Innovative Activities Research Institute of Mechanical Engineering South Ural State University Lenin prospect 76 Chelyabinsk 454080 Russian Federation.

This paper introduces an approach to temperature prediction by employing three distinct machine learning models: K-Nearest Neighbors (KNN), Gaussian Process Regression (GPR), and Multi-layer Perceptron (MLP) which are integrated into Computational Fluid Dynamics (CFD). The dataset consists of inputs, represented by the features x and y, and the corresponding output, which is the temperature. The case study is fluid flow of nanofluid through a pipe and the nanofluid contains CuO particles. To enhance model efficacy, the Political Optimizer (PO) algorithm was utilized for fine-tuning purposes. The findings substantiate the capability of the optimized frameworks in delivering precise temperature estimations. GPR yielded a notable R value of 0.998, reflecting a strong concordance between the estimated and actual measurements. In a comparable manner, the KNN approach exhibited outstanding predictive performance, also attaining an R value of 0.998. MLP, although slightly lower in performance compared to the other models, still proves to be a reliable predictor of temperature values, with an R-squared score of 0.984. Overall, the combination of the PO algorithm and the machine learning models showcases promising results in temperature prediction. The study's findings offer valuable insights for decision-making processes in a variety of domains that rely on accurate temperature forecasting.

关键词： Gaussian process regression Heat transfer K-Nearest neighbors Multi-layer perceptron Nanofluid

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Opening more data a new privacy risk scoring model for open data 7

Opening more data a new privacy risk scoring model for open ...

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7th International Symposium on Business Modeling and Software Design, BMSD 2017

作者： Ali-Eldin, Amr M. T. Zuiderwijk, Anneke Janssen, Marijn Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands TBM Delft University of Technology Jaffalaan 5 Delft2628 BX Netherlands Computer and Control Systems Dept. Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9789897582387

While the opening of data has become a common practice for both governments and companies, many datasets are still not published since they might violate privacy regulations. The risk on privacy violations is a factor that often blocks the publication of data and results in a reserved attitude of governments and companies. Additionally, even published data, which might seem privacy compliant, can violate user privacy due to the leakage of real user identities. This paper proposes a privacy risk scoring model for open data architectures to analyse and reduce the risks associated with the opening of data. The key elements consist of a new set of open data attributes reflecting privacy risks versus benefits trades-offs. Further, these attributes are evaluated using a decision engine and a scoring matrix intro a privacy risk indicator (PRI) and a privacy risk mitigation measure (PRMM). Privacy Risk Indicator (PRI) represents the predicted value of privacy risks associated with opening such data and privacy risk mitigation measures represent the measurements need to be applied on the data to avoid the expected privacy risks. The model is exemplified through five real use cases concerning open datasets.

关键词： Data mining

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ON THE EXISTENCE AND UNIQUENESS OF THE SOLUTION OF A PARABOLIC OPTIMAL control PROBLEM WITH UNCERTAIN INPUTS

arXiv

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arXiv 2018年

作者： Benner, Peter Onwunta, Akwum Stoll, Martin Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstrasse 1 Magdeburg39106 Germany Department of Computer Science University of Maryland College ParkMD20742 United States Technische Universität Chemnitz Faculty of Mathematics Professorship Scientific Computing Chemnitz09107 Germany

In this note, we consider the existence and uniqueness of the solution of a time-dependent optimal control problem constrained by a partial differential equation with uncertain inputs. Relying on the Lions’ Lemma for deterministic problems, we furthermore characterize the optimal control of the stochastic problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Optimal control systems

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Dynamic modeling using system identification and attitude control design of GaneFly flapping wings micro aerial vehicle (FWMAV) 7

Dynamic modeling using system identification and attitude co...

引用

7th IEEE International Conference on System Engineering and Technology, ICSET 2017

作者： Benyamin, R. S. Brian Nugroho, Muhammad Aldo Aditiya Sepri, A. Yosua Trilaksono, Bambang Riyanto Moelyadi, Agoes Laboratory of Control Systems and Computer School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Aeronautics and Astronautics Faculty of Mechanical and Aerospace Engineering Bandung Institute of Technology Bandung40132 Indonesia

ISBN: (纸本)9781538603833

Flapping Wings MAV (FWMAV) is the result of UAV technology development with the mechanism of flapping wings and with the dimensions of the object as small as possible. Modeling of FWMAV dynamics is needed to predict future conditions based on information from current conditions and designed control actions. Modeling of the FWMAV system is done by system identification, based on measurable data from IMU sensors. Modeling is done each with a black-box approach using the N4SID estimation, while the grey-box approach is used structure parametric, using SSEST estimation. Then a fit model of these two models is compared by plotting the Paparazzi log with the output of the system obtained in MATLAB for the same input. Then this model will be simulated on SIMULINK. The automated PID Tuning is perform to obtain PID constants to be implemented into Paparazzi software for Pitch Loop and Roll Loop. © 2017 IEEE.

关键词： Simulink

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A New Approach to Robust MPC Design for LPV systems in Input-Output Form

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IFAC-PapersOnLine 2018年第26期51卷 112-117页

作者： Abbas, Hossam S. Hanema, Jurre Tóth, Roland Mohammadpour, Javad Meskin, Nader Institute for Elec. Eng. in Medicine Univeristy of Lübeck Lübeck23558 Germany Control Systems Group Dept. of Elec. Eng. Eindhoven University of Technology P.O. Box 513 EindhovenMB5600 Netherlands School of Elec. and Computer Eng. College of Eng. University of Georgia AthensGA30602 United States Dept. of Elec. Eng. College of Eng. Qatar University PO Box 2713 Doha Qatar Elec. Eng. Dept. Faculty of Eng. Assiut University Assiut71515 Egypt

In this paper, a robust model predictive control (MPC) technique is introduced to control MIMO linear parameter-varying (LPV) systems subject to input-output constraints. The LPV system is represented in input-output form, which is a common form obtained through LPV system identification. The method guarantees asymptotic stability of the closed-loop system and provides integral action for a given piecewise constant reference trajectory based on past measurements of the inputs and outputs. The technique offers computationally more efficient and less conservative design approach compared to previous works. A simulation example is given to demonstrate the effectiveness of the proposed technique. © 2018

关键词： System stability Asymptotic stability Closed loop systems Convex optimization Model predictive control Predictive control systems Robust control Conservative designs Input output constraints Linear parameter varying systems Performance Piece wise constants Reference trajectories Robust model predictive control Robust stability

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