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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Evgenij S. Barbin Tamara G. Nesterenko Aleksej N. Koleda Ayan Myrzakhmetov Ivan V. Kulinich Denis M. Mokhovikov Laboratory of microsystems engineering The Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Radiophotonics V.E. Zuev Institute of Atmospheric Optics of SB RAS Tomsk Russia Laboratory of Radiophotonics The Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Photonic Integrated Circuits The Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Intelligent Computer Systems V.E. Zuev Institute of Atmospheric Optics of SB RAS Tomsk Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The article studies the efficiency of optical radiation input/output between a photonic integrated circuit (PIC) and an edge-coupled tapered silicon nitride waveguide with dimensions 350 nm x 850 nm at a wavelength of 1550 nm. The work determines the losses by radiation for transverse electric and transverse magnetic modes at different taper width and input mode diameter for direct (edge width more than 850 nm) and inverted tapers (edge width less than 850 nm). The results show that the admissible width of the inverted taper edge lies within 150–300 nm with losses by input/output of up to 2 dB and low sensitivity to optical polarization. For the direct taper, the highest input efficiency (losses amounted to 3-6 dB) belongs to the optical fibers with a beam waist diameter of 2–3 μm and taper edge width of 2–3 μm. For both types of edge couplers, the effect of the waist diameter of lensed fiber is more important: the spread should be within 150 nm.

关键词： Optical fiber losses Optical fiber polarization Optical losses Optical fiber sensors Optical polarization Sensitivity Optical fiber couplers

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Port-Hamiltonian Neural Networks with Output Error Noise Models

arXiv

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arXiv 2025年

作者： Moradi, Sarvin Beintema, Gerben I. Jaensson, Nick Tóth, Roland Schoukens, Maarten Control System group Eindhoven University of Technology Eindhoven Netherlands Eindhoven Artificial Intelligence Systems Institute Eindhoven Netherlands Processing and Performance of Materials Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

Hamiltonian neural networks (HNNs) represent a promising class of physics-informed deep learning methods that utilize Hamiltonian theory as foundational knowledge within neural networks. However, their direct application to engineering systems is often challenged by practical issues, including the presence of external inputs, dissipation, and noisy measurements. This paper introduces a novel framework that enhances the capabilities of HNNs to address these real-life factors. We integrate port-Hamiltonian theory into the neural network structure, allowing for the inclusion of external inputs and dissipation, while mitigating the impact of measurement noise through an output-error (OE) model structure. The resulting output error port-Hamiltonian neural networks (OE-pHNNs) can be adapted to tackle modeling complex engineering systems with noisy measurements. Furthermore, we propose the identification of OE-pHNNs based on the subspace encoder approach (SUBNET), which efficiently approximates the complete simulation loss using subsections of the data and uses an encoder function to predict initial states. By integrating SUBNET with OE-pHNNs, we achieve consistent models of complex engineering systems under noisy measurements. In addition, we perform a consistency analysis to ensure the reliability of the proposed data-driven model learning method. We demonstrate the effectiveness of our approach on system identification benchmarks, showing its potential as a powerful tool for modeling dynamic systems in real-world applications. © 2025, CC BY-SA.

关键词： Hamiltonians

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Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm

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CSEE Journal of Power and Energy systems 2022年第4期8卷 1145-1156页

作者： Levent Yavuz Ahmet Soran AhmetÖnen Xiangjun Li S.M.Muyeen Electrical and Computer Engineering Department in Abdullah Gül University Kayseri 38080Turkey Department of Electrical and Computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscat123Oman Department of Electrical Engineering Qatar UniversityDoha 2713Qatar State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems China Electric Power Rescarch InstituteBeijing 100192China

This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to *** than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML *** the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)*** this purpose,power system failures are simulated by using the PSCA D-Python *** of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored ***,the proposed technique will be able to work on different systems,topologies,or data *** proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.

关键词： Decision tree(DT) ensemble machine learning algorithm fault detection islanding operation k-Nearest Neighbor(kNN) linear discriminant analysis(LDA) logistic regression(LR) Naive Bayes(NB) self-healing algorithm

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control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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A New Mutimodel approach for modelling of fractional order interval transfer functions 20

A New Mutimodel approach for modelling of fractional order i...

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20th International Multi-Conference on systems, Signals and Devices, SSD 2023

作者： Talmoudi, Samia Ksouri, Moufida Lahmari Applied College Department of Computer Science University of Tabuk Tabuk Saudi Arabia University of Tunis El Manar National Engineering School of Tunis LR11ES20 Analysis Design and Control of Systems Laboratory Tunis1002 Tunisia

ISBN: (纸本)9798350332568

This paper is focused on uncertain fractional order systems. In this context, a new modelling approach of uncertain fractional order systems represented by an explicit fractional order interval transfer function is proposed. This approach is based, essentially, on multimodel approach. In fact, firstly, two new determination methods of models' library, inspired from the Kharitonov approach, are exposed. Then, we propose to compute the validity degree of each model of the obtained library, by optimizing a constrained least squares problem. The global model is, finally deduced by fusion of outputs of the different library' models. To prove its efficiency and its precision, the proposed approach is then compared to the approximation approach of Oustaloup through two simulation examples. © 2023 IEEE.

关键词： Transfer functions

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Optimal Sensor Placement for Full-Set TDOA Localization Accounting for Sensor Location Errors

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhang, Chengjie Han, Xinyang Kong, He Ho, K.C. Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO65211 United States

The positioning accuracy of a signal source is influenced by where the sensors are deployed. Studies in the literature for the optimal sensor placement (OSP) of localization often ignore the presence of sensor position errors. In practice, however, it is impossible to place the sensors precisely at the desired locations. This work designs OSP by accounting for the presence of Gaussian sensor position errors that are independent and identically distributed (IID), with the full set of Time Difference of Arrival measurements corrupted by Gaussian IID noise. The criterion is the A-optimality equal to the trace of the source position Cramer-Rao Bound (CRB). We show that the presence of sensor position errors introduces a multiplication factor to the same criterion for the case without sensor position errors. The latter factor is dependent on the ratio of the sensor position error power to the range difference noise power but independent of the sensor positions, implying that the A-optimality OSP in the presence of sensor position errors is the same as in the absence. To our knowledge, this is the first time such results have been derived. Simulation studies validate the derived OSP, show the minimal OSP performance gap with sensor positioning error and without, and illustrate the better source localization accuracy by OSP. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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Application of Petri Nets for Modeling Ore Flows to Create Dynamic Management and Quality control System in Mineral Resource Complexes

Application of Petri Nets for Modeling Ore Flows to Create D...

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2024 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024

作者： Larichev, Pavel Tynchenko, Vadim Nekrasov, Ivan Institute Of Computer Science And Telecommunications Reshetnev Siberian State University Of Science And Technology Information-Control Systems Department Krasnoyarsk Russia Bauman Moscow State Technical University Artificial Intelligence Technology Scientific And Education Center Moscow Russia Federal Univercity Laboratory Of Biofuel Composition Siberian Krasnoyarsk Russia

ISBN: (纸本)9798350395013

The article discusses the issue of creating a dynamic system for managing and monitoring the quality of ore flow in mineral resource complexes using simulation modeling. The purpose of this study is to adapt the mathematical apparatus of Petri nets to such modeling. The article describes the choice and justification of the simulation modeling apparatus and provides a conceptual representation of the ore flow model in the form of an adaptation of a continuous Petri net. An architectural solution is proposed that covers a number of processes, such as geological exploration, mining, moving loads in the business processes of enterprises, but also to support the rock mass. The proposed architecture is being coordinated with information systems and software and hardware of mining enterprises, which makes it possible to identify data needs for the formation of technical specifications for the correction of business processes. The proposed control system will make it possible to implement simulation modeling both in model and real time for timely and adequate assessment of the progress of work being carried out and clarifying the reasons for the lag behind planned schedules using a Petri net. © 2024 IEEE.

关键词： Petri nets

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Estimating the Sensitivity of Microoptoelectromechanical Micro-g Accelerometer 23

Estimating the Sensitivity of Microoptoelectromechanical Mic...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Barbin, Evgenij S. Nesterenko, Tamara G. Koleda, Aleksej N. Shesterikov, Evgeniy V. Kulinich, Ivan V. Sheyerman, Fedor I. The Tomsk State University of Control Systems and Radioelectronics Laboratory of Radiophotonics Tomsk Russia Institute of Optics and Atmosphere Siberian Branch of the Russian Academy of Sciences Laboratory of Intelligent Computer Systems Tomsk Russia National Research Tomsk Polytechnic University Division for Electronic Engineering Tomsk Russia

ISBN: (纸本)9781665498043

The article studies a MOEMS-accelerometer with an optical measuring element based on an optical tunneling effect intended for measuring micro-g accelerations. The work estimates the sensitivity threshold of the accelerometer based on the Noise Equivalent Acceleration, which is stipulated by thermal Brownian motion of molecules, noises of a laser and photodiode. Shot noise has the largest effect on the MOEMS-accelerometer noise threshold and depends on its mechanical properties. The general sensitivity of the MOEMS-accelerometer equals 0.938, 3, or 6.255 A/g, depending on a chosen operational point. The sensitivity threshold in terms of photodiode dark current equals 107 × 10-9 g, 33.3 × 10-9 g, 16 × 10-9 g. The dynamic range for the photodiode equals ± 3.2 × 10-3 g, while for the measuring transducer it is ±0.32g, ±0.1, or ±0.05g. To decrease the noise down to 0.1 µ g, the inertial mass should amount at least to 65 × 10-7 kg, and the average laser noise power should be below 1 %. © 2022 IEEE.

关键词： Accelerometers

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Attitude Takeover control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning

arXiv

引用

arXiv 2023年

作者： Liu, Yuhan Wang, Pengyu Lee, Chang-Hun Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task. To address this issue, a recursive online sparse Gaussian Process (GP)-based learning strategy for attitude takeover control of noncooperative targets with maneuverability is proposed, where the unknown dynamics are online compensated based on the learnt GP model in a semi-feedforward manner. The method enables the continuous use of on-orbit data to successively improve the learnt model during online operation and has reduced computational load compared to standard GP regression. Next to the GP-based feedforward, a feedback controller is proposed that varies its gains based on the predicted model confidence, ensuring robustness of the overall scheme. Moreover, rigorous theoretical proofs of Lyapunov stability and boundedness guarantees of the proposed method-driven closed-loop system are provided in the probabilistic sense. A simulation study based on a high-fidelity simulator is used to show the effectiveness of the proposed strategy and demonstrate its high performance. © 2023, CC BY-NC-SA.

关键词： Maneuverability

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection 30

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Cheng, Long He, Haoyang Gu, Yan Liu, Qingzhi Zhao, Zhiming Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Wageningen University and Research Information Technology Group Netherlands University of Amsterdam Research Group of Multi-Scale Networked Systems Netherlands

ISBN: (纸本)9798331515966

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of 53.18% and costs by 61.98% compared to basic techniques, and achieving 20.26% and 25.71% improvements over the latest advanced methods, respectively. © 2024 IEEE.

关键词： Deep reinforcement learning

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