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European control Conference (ECC)

作者： Lionel Tondji Dirk A. Lorenz Ion Necoara TU Braunschweig Institute of Analysis and Algebra Braunschweig Germany Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest Romania

In this paper we propose a randomized accelerated method for the minimization of a strongly convex function under linear constraints. The method is of Kaczmarz-type, i.e. it only uses a single linear equation in each iteration. To obtain acceleration we build on the fact that the Kaczmarz method is dual to a coordinate descent method. We use a recently proposed acceleration method for the randomized coordinate descent and transfer it to the primal space. This method inherits many of the attractive features of the accelerated coordinate descent method, including its worst-case convergence rates. Theoretical analysis of the convergence of the proposed method is given. Numerical experiments show that the proposed method is more efficient and faster than the existing methods for solving the same problem

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A reliable spacecraft power supply subsystem based on discrete water cycle multi-objective optimization

Franklin Open

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Franklin Open 2024年 7卷

作者： Mokhtar, Ahmed Ibrahim, Mohamed Hanafy, Mohamed E. ElTohamy, Fawzy H. Amer Murtada, Wael A. Elhalwagy, Yehia Z. Aircraft Electric Equipment Department Military Technical College Cairo Egypt Automatic Control Systems Department Military Technical College Cairo Egypt National Authority for Remote Sensing & Space Sciences Cairo Egypt Electrical and Computer Engineering Military Technical College Cairo Egypt

The successful execution of spacecraft missions relies heavily on the performance and reliability of their power supply subsystems. Achieving optimal component size and configuration is crucial for efficient power generation, distribution, and storage. Designing an efficient power supply subsystem for spacecraft missions is a complex and multi-objective optimization problem. Traditional optimization techniques, such as genetic algorithm and particle swarm optimization, have been employed, but their limitations in handling discrete variables and achieving global optima necessitate the exploration of alternative methods. In this context, this research paper explores the application of discrete water cycle algorithm (DWCA) optimization as a novel approach for optimizing the component size and configuration of spacecraft power supply subsystems. By emulating the dynamic flow and distribution of water, DWCA seeks to efficiently explore the design space and identify optimal solutions. Through comprehensive experimentation and comparative analysis, this research demonstrates that the proposed DWCA outperforms/surpasses other traditional optimization techniques in optimizing component size and configuration for spacecraft power supply subsystems. It consistently determines designs that are more efficient in power generation and storage. Moreover, DWCA enhances the efficiency and reliability of spacecraft power supply systems, especially in mission-critical scenarios. The successful optimization of spacecraft power supply subsystems is paramount to the overall success of space missions. Therefore, this advancement contributes significantly to the ongoing efforts to ensure the success of future space missions. © 2024 The Author(s)

关键词： Collaborative optimization Component-based system analysis Design performance Discrete Water Cycle Algorithm (DWCA) Multi-objective design Multidisciplinary design optimization Space electric power system Trade-off analysis

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Photodegradation of psychotropic medications: Impact on efficacy, safety, and drug properties

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Computers in Biology and Medicine 2025年 191卷 110115-110115页

作者： Udrea, Ana-Maria Buiu, Catalin Staicu, Angela Dabu, Aurelia Nicoleta Avram, Speranta Laser Department National Institute for Laser Plasma and Radiation Physics Magurele Ilfov Romania Department of Anatomy Animal Biology Animal Physiology and Biophysics Faculty of Biology University of Bucharest Bucharest Romania Earth Environmental and Life Sciences Section Research Institute of the University of Bucharest University of Bucharest Romania Department of Automatic Control and Systems Engineering Politehnica University of Bucharest Bucharest Romania Department of Neurosurgery The University Emergency Hospital of Bucharest Bucharest Romania

Antipsychotics and antidepressants are essential psychotropic medications used for treating various mental health conditions such as depression, schizophrenia, and bipolar disorder. However, when exposed to light, these compounds are susceptible to photodegradation, potentially changing their biological activity and safety profiles. This study evaluates the pharmacokinetic and pharmacodynamic properties of several photoproducts derived from 13 psychotropic drugs. We used computational methods to predict the biological activity, toxicity, and drug-like properties of the photoproducts. Our results indicate that photoproducts such as amisulpride_TP166, TP246, quetiapine_D4, and quetiapine_PH1 show enhanced biological affinity and ADME-Tox profiles similar to their parent compounds, suggesting possible therapeutic advantages in their interaction with targeted receptors. However, some of the photocompounds exhibit lower predicted binding affinities when interacting with those receptors compared to their parent compounds, indicating a possible loss of function. These findings emphasize the need for further investigation into the effects and safety of drug photoproducts, particularly in the context of long-term pharmacotherapy. © 2025 The Author(s)

关键词： Bioactivity

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Research of Electromagnetic and Electromechanical Processes in High-Voltage Soft Start Devices with Gate Turn-Off Thyristors

Research of Electromagnetic and Electromechanical Processes ...

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International Conference on Modern Electrical and Energy systems (MEES)

作者： Victor Busher Oleksii Chornyi Valerii Tytiuk Oksana Glazeva Anatoliy Shestaka Lubov Melnikova Dept. of Electrical Engineering and Electronics National University “Odessa Maritime Academy” Odessa Ukraine Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Kryvyi Rih National University Kryvyi Rih Ukraine Dept. Electromechanical Engineering Odessa State Polytechnic University Odessa Ukraine

The purpose of the work is to investigate the possibilities of soft-start devices with gate turn-off thyristors in electric drives of bow thrusters with adjustable propeller pitch to increase reliability, reduce mechanical shocks and currents, and improve electromagnetic compatibility with the ship’s electrical power plant. The characteristic features of these drives are operation on fan load and the need for a smooth start in a sufficiently short time to reduce impact torque in the mechanism. As a result of the analysis, a solution with a shunt capacitor is proposed, which provides high accuracy of regulation of the 1st harmonic of the voltage on the motor according to the given law. The minimum value of the capacitor capacity was found, which for the ABB/AMD710L8T motor with a capacity of 2.7 MW is 150-250 μF at switching frequencies from 500 to 1000 Hz. For the first time, the dependences of the GTO switching frequency on the capacitor capacity and the coefficient of harmonic distortion of the current in the stator phase of the selected motor were found. Frequencies are obtained that correspond to the actual physical characteristics of the GTO (500–1000 Hz). At such switching frequencies, the characteristics of the soft start device practically do not differ from the ideal regulator of the phase voltage of an asynchronous motor. This makes it possible to synthesize closed systems with stabilization of the main coordinates of soft starter electric drive, two-circuit systems with the formation of a given tachogram with torque limitation. All this makes it feasible to implement soft start devices based on gate turn-off thyristors.

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Mathematical Model of a Three-Phase Induction Motor with Asymmetrical Twelve-Zone Stator Windings 4

Mathematical Model of a Three-Phase Induction Motor with Asy...

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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Tytiuk, Valerii Chornyi, Oleksii Busher, Victor Siverskaya, Tatyana Baranovskaya, Mila Kuznetsov, Vitalii Njsc 'Karaganda Industrial University' Department 'Artificial Intelligence Technologies' Temirtau Kazakhstan Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine National University 'Odessa Maritime Academy' Dept. of Electrical Engineering and Electronics Odessa Ukraine Njsc 'Karaganda Industrial University' Department 'Power Engineering' Temirtau Kazakhstan Kryvyi Rih National University Department of Electromechanics Kryvyi Rih Ukraine Ukrainian State University of Science and Technologies Department of the Electrical Engineering Dnipro Ukraine

ISBN: (纸本)9798350346831

The paper proposes a mathematical model of a three-phase induction motor with an asymmetrical twelve-zone winding of stator with parallel connection of subwindings. The mathematical model was developed using the theory of generalized electromechanical converter and contains equations of electrical equilibrium of six stator windings and three rotor windings. The equations for calculating the electromagnetic moment and the equations of motion of the induction motor with an asymmetrical twelve-zone stator winding rotor were obtained. The developed mathematical model in the form of cell-matrix equations has been implemented in MATLAB/Simulink. © 2022 IEEE.

关键词： Induction motors

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On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing

On the Approximation of the Quotient of Two Gaussian Densiti...

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International Conference on Information Fusion

作者： Yi Liu Xi Li Le Yang Lyudmila Mihaylova Yanbo Xue Department of Electrical and Computer Engineering University of Canterbury Christchurch NZ State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems National University of Defense Technology Changsha China Department of Automatic Control and System Engineering University of Sheffield Sheffield UK Career Science Lab Beijing China

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target.

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Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning

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Neural Computing and Applications 2024年第30期36卷 18841-18862页

作者： Mamalakis, Michail Banerjee, Abhirup Ray, Surajit Wilkie, Craig Clayton, Richard H. Swift, Andrew J. Panoutsos, George Vorselaars, Bart Department of Psychiatry University of Cambridge Herchel Smith Building Robinson Way CambridgeCB2 0SZ United Kingdom Department of Computer Science University of Sheffield 211 Portobello SheffieldS1 4DP United Kingdom Insigneo Institute for in silico Medicine University of Sheffield The Pam Liversidge Building SheffieldS1 3JD United Kingdom School of Mathematics and Statistics University of Glasgow GlasgowG12 8QQ United Kingdom Department of Infection Immunity and Cardiovascular Disease University of Sheffield SheffieldS10 2HQ United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom School of Mathematics and Physics University of Lincoln Brayford Pool LincolnLN6 7TS United Kingdom Department of Engineering Science Institute of Biomedical Engineering University of Oxford OxfordOX3 7DQ United Kingdom Division of Cardiovascular Medicine Radcliffe Department of Medicine University of Oxford OxfordOX3 9DU United Kingdom

The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and demanding datasets. In our study, we have developed and evaluated a new training methodology named deep multi-metric training (DMMT) for enhanced training performance. The DMMT delivers a state of robust learning for deep networks using a new important criterion of multi-metric performance evaluation. We have tested the DMMT methodology in multi-class (three, four, and ten), multi-vendors (different X-ray imaging devices), and multi-size (large, medium, and small) datasets. The validity of the DMMT methodology has been tested in three different classification problems: (i) medical disease classification, (ii) environmental classification, and (iii) ecological classification. For disease classification, we have used two large COVID-19 chest X-rays datasets, namely the BIMCV COVID-19+ and Sheffield hospital datasets. The environmental application is related to the classification of weather images in cloudy, rainy, shine or sunrise conditions. The ecological classification task involves a classification of three animal species (cat, dog, wild) and a classification of ten animals and transportation vehicles categories (CIFAR-10). We have used state-of-the-art networks of DenseNet-121, ResNet-50, VGG-16, VGG-19, and DenResCov-19 (DenRes-131) to verify that our novel methodology is applicable in a variety of different deep learning networks. To the best of our knowledge, this is the first work that proposes a training methodology to deliver robust learning, over a variety of deep learning networks and multi-field classification problems. © Crown 2024.

关键词： COVID-19

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A lightweight pedestrian detection model for edge computing systems 17th

A lightweight pedestrian detection model for edge computing ...

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17th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2020

作者： Chen, Wen-Hui Kuo, Han-Yang Lin, Yu-Chen Tsai, Cheng-Han Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Mechanical and Mechatronics Systems Laboratories Industrial Technology Research Institute Hsinchu Taiwan

ISBN: (纸本)9783030530358

Most vision-based pedestrian detection systems adopted deep learning approaches with the framework of convolutional neural networks (CNNs) to reach the state-of-the-art detection accuracy. As CNN-based approaches are computationally intensive, the deployment of those systems to a resource-limited edge device is a challenging task, especially for a real-time application such as intelligent vehicles. In this study, we proposed a lightweight high-performance edge computing solution to achieve rapid and accurate performance in pedestrian detection. Experimental results showed that the proposed framework can effectively reduce the miss rate of the YOLO-tiny detection model from 48.8% to 26.2% while achieving an inference speed of 31 frames per second (FPS) tested on the Caltech pedestrian dataset. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Edge computing

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Secure state estimation over Markov wireless communication channels

Secure state estimation over Markov wireless communication c...

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IEEE Conference on Decision and control

作者： Anastasia Impicciatore Anastasios Tsiamis Yuriy Zacchia Lun Alessandro D’Innocenzo George J. Pappas Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Automatic Control Laboratory ETH Zürich Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA United States

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This note studies state estimation in wireless networked control systems with secrecy against eavesdropping. Specifically, a sensor transmits a system state information to the estimator over a legitimate user link, and an eavesdropper overhears these data over its link independent of the user link. Each connection may be affected by packet losses and is modeled by a finite-state Markov channel (FSMC), an abstraction widely used to design wireless communication systems. This paper presents a novel concept of optimal mean square expected secrecy over FSMCs and delineates the design of a secrecy parameter requiring the user mean square estimation error (MSE) to be bounded and eavesdropper MSE unbounded. We illustrate the developed results on an example of an inverted pendulum on a cart whose parameters are estimated remotely over a wireless link exposed to an eavesdropper.

关键词： Wireless communication Wireless sensor networks Estimation error Networked control systems Channel estimation Packet loss Markov processes

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Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach

Real-Time Object Detection and Robotic Manipulation for Agri...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Hongyu Zhao Zezhi Tang Zhenhong Li Yi Dong Yuancheng Si Mingyang Lu George Panoutsos The Department of Physics Imperial College London United Kingdom The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom The Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom The Department of Electronics and Computer Science University of Southampton Southampton United Kingdom The Department of Economics Fudan University Shanghai China The Center for Nondestructive Evaluation (CNDE) Iowa State University Ames IA United States

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

The optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The You Only Look Once(YOLO) algorithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation.

关键词： YOLO Visualization Crops Grasping Production Robustness Real-time systems

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