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SSRN 2024年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

Incidence of surge within axial compressors profoundly influences efficacy, reliability of aero-engines. Conventional methodologies in engine design have predominantly concentrated on precise ,efficient forecasting critical characteristics during such occurrences. paper presents novel approach for predicting surge phenomenon in axial compressors. Surge is a major issue in operation axial compressors, causing reduced performance, increased wear, tear, complete machine failure. proposed approach utilizes NN model based on compressor's design data to forecast behavior of compressor's working map under off-design conditions. model is verified using real data collected over five years during compressor operation, including opening angle (IGV), important parameter often ignored or assumed to be fixed in previous studies. results show NN-based approach effectively predicts compressor's behavior under different operating conditions, providing valuable insights into onset of surge , offering real-time surge prediction ,control ,ability to predict compressor's working map enables optimizing its performance , controlling instability. model was validated by comparing its predictions with real operating conditions observed over five years across various compressor operating modes. As result, predictive model enables safe operation of axial compressors by mitigating instability risks during operation. study's findings highlight model's effectiveness, as evidenced by minimal error observed between predicted and actual data collected across various compressor operating modes. model exhibits strong performance, characterized by high regression factor of 0.92667 , low mean squared error of 3.04465 x 10^-3. numerical values underscore reliability , precision proposed approach in predicting operation of axial compressor under diverse conditions, ultimately contributing to enhanced stability , safety during compressor operations. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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A Globally Convergent Method for Computing B-stationary Points of Mathematical Programs with Equilibrium Constraints

arXiv

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arXiv 2025年

作者： Nurkanovic, Armin Leyffer, Sven Systems Control and Optimization Laboratory Department of Microsystems Engineering University of Freiburg Germany Mathematics and Computer Science Division Argonne National Laboratory United States

This paper introduces a method that globally converges to Bstationary points of mathematical programs with equilibrium constraints (MPECs) in a finite number of iterations. B-stationarity is necessary for optimality and means that no feasible first-order direction improves the objective. Given a feasible point of an MPEC, B-stationarity can be certified by solving a linear program with equilibrium constraints (LPEC) constructed at this point. The proposed method solves a sequence of LPECs, which either certify B-stationarity or provide an active-set estimate for the complementarity constraints, and nonlinear programs (NLPs) - referred to as branch NLPs (BNLPs) - obtained by fixing the active set in the MPEC. A BNLP is more regular than the MPEC, easier to solve, and with the correct active set, its solution coincides with the solution of the MPEC. The method has two phases: the first phase identifies a feasible BNLP or certifies local infeasibility, and the second phase solves a finite sequence of BNLPs until a B-stationary point of the MPECis found. The paper provides a detailed convergence analysis and discusses implementation details. In addition, extensive numerical experiments and an open-source software implementation are provided. The experiments demonstrate that the proposed method is more robust and faster than relaxation-based methods, while also providing a certificate of B-stationarity without requiring the usual too-restrictive *** Codes 90C30, 90C33, 49M37, 65K10, 90C11 © 2025, CC BY.

关键词： Nonlinear programming

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A Fuzzy System for Improving the Electromagnetic Compatibility of an Arc Furnace and its Study 17

A Fuzzy System for Improving the Electromagnetic Compatibili...

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2024

作者： Paranchuk, Roman Paranchuk, Yaroslav Kuznyetsov, Oleksiy Khai, Mykhailo Ivan Franko National University of Lviv Electronics and Computer Technologies Department Lviv Ukraine Lviv Polytechnic National University Lviv Ukraine Hetman Petro Sahaidachnyi National Army Academy Lviv Ukraine Institute of Power Engineering and Control Systems Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9798331520564

Based on the principles of fuzzy control, a number of structural and system engineering solutions have been developed and researched for the double-loop structure of the electric mode control system of the arc steel furnace (AF) in order to comprehensively improve electromagnetic compatibility indicators. A method of operative real-time synthesis of artificial external characteristics of the arc furnace was created for the implementation of optimal control with the criterion of minimizing fluctuations and deviation of the power grid voltage at the AF connection point. The proposed solutions are aimed at the implementation of the strategy of comprehensive improvement of indicators of electromagnetic compatibility and energy efficiency of the arc furnace and the power grid (PG). A Simulink model of the developed double-loop automatic control system (ACS) of the arc furnace electric modes (EM) and the power grid was created and computer studies were carried out. The obtained research results showed a comprehensive improvement of AF and PG electromagnetic compatibility indicators. © 2024 IEEE.

关键词： Electric arc furnaces

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Differentially Private Online Federated Learning with Correlated Noise

Differentially Private Online Federated Learning with Correl...

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IEEE Conference on Decision and control

作者： Jiaojiao Zhang Linglingzhi Zhu Mikael Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Systems Engineering and Engineering Management Chinese University of Hong Kong

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an $(\epsilon, \delta)$ DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm.

关键词： Privacy Federated learning Heuristic algorithms Noise Signal processing algorithms Servers

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Optimization of classification model for electric vehicle charging station placement using dynamic graylag goose algorithm

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Frontiers in Energy Research 2024年 12卷

作者： Alhussan, Amel Ali Khafaga, Doaa Sami El-kenawy, El-Sayed M. Eid, Marwa M. Ibrahim, Abdelhameed Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia MEU Research Unit Middle East University Amman Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences. Copyright © 2024 Alhussan, Khafaga, El-kenawy, Eid and Ibrahim.

关键词： Charging stations

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Domain adaptive FL for edge-enabled privacy-preserving MRI analysis

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Alexandria engineering Journal 2025年 128卷 324-339页

作者： Iqbal, Saeed Choudhry, Imran Arshad Ullah, Inam Kaur, Kuljeet Choi, Bong Jun Hassan, Mohammad Mehedi College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan Department of Computer Engineering Gachon University Seongnam13120 Korea Republic of Electrical Engineering Department École de technologie supérieure MontréalQCH3C1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India School of Computer Science and Engineering Soongsil University Seoul Korea Republic of Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Data heterogeneity, privacy leakage challenges, the ineffectiveness of conventional collaborative learning techniques, and unresolved managing non-IID data distributions are some of the major obstacles to implementing artificial intelligence (AI) in healthcare. Federated learning (FL) frameworks frequently have trouble distinguishing between privacy protection and model accuracy, especially when used for delicate medical imaging applications. This study presents a novel framework that synergizes federated learning (FL) with edge computing to address these issues while safeguarding patient privacy. Our proposed Domain Adaptive Federated (DAD) learning approach effectively mitigates both inter-client and intra-client data heterogeneity, enabling collaborative model training across diverse medical imaging modalities (MRI, CT, PET) through cross-domain adaptation. Experimental evaluations on MRI brain segmentation datasets demonstrate the superior performance of DAD compared to traditional FL methods, as evidenced by significant improvements in F1-score (96.3), sensitivity (96.0), specificity (97.1), and AUC (96.7). This enhanced accuracy and robustness in handling heterogeneous and privacy-sensitive data render DAD an ideal candidate for privacy-preserving AI in consumer healthcare. By pioneering innovative strategies for collaborative model training and data privacy, this research contributes to the emerging field of edge intelligence, paving the way for improved patient outcomes while adhering to stringent confidentiality and ethical mandates. © 2025

关键词： Federated learning

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Platoon-Local Dynamic Map: Micro cloud support for platooning cooperative perception

Platoon-Local Dynamic Map: Micro cloud support for platoonin...

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Carlos Mateo Risma Carletti Claudio Casetti Jérôme Härri Fulvio Risso Department of Control and Computer Engineering Politecnico di Torino Turin Italy Communication Systems Department EURECOM Sophia-Antipolis France

Platooning is a popular vehicular application for autonomous driving on which the Platoon Leader (PL) manages all maneuvers using context information from Vehicle-to-Vehicle (V2V) messages. However, redundant context information from nearby vehicles in the platoon can increase computational costs for the PL. To solve this issue, vehicular micro-clouds can be formed to enable collective data processing and aggregation, thus reducing the PL’s perception workload. The proposed solution, called Platoon Local Dynamic Map (P-LDM), creates a single database of context information, distributing the data aggregation load among all members of the platoon. Simulation results evaluate the effectiveness of the proposed solution and compare it to typical Cooperative Perception mechanisms.

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Low-Complexity control for a Class of Uncertain MIMO Nonlinear Systems under Generalized Time-Varying Output Constraints (extended version)

arXiv

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arXiv 2024年

作者： Mehdifar, Farhad Lindemann, Lars Bechlioulis, Charalampos P. Dimarogonas, Dimos V. Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States Division of Systems and Control Department of Electrical and Computer Engineering University of Patras Patra Greece

This paper introduces a novel control framework to address the satisfaction of multiple time-varying output constraints in uncertain high-order MIMO nonlinear control systems. Unlike existing methods, which often assume that the constraints are always decoupled and feasible, our approach can handle coupled time-varying constraints even in the presence of potential infeasibilities. First, it is shown that satisfying multiple constraints essentially boils down to ensuring the positivity of a scalar variable, representing the signed distance from the boundary of the time-varying output-constrained set. To achieve this, a single consolidating constraint is designed that, when satisfied, guarantees convergence to and invariance of the time-varying output-constrained set within a user-defined finite time. Next, a novel robust and low-complexity feedback controller is proposed to ensure the satisfaction of the consolidating constraint. Additionally, we provide a mechanism for online modification of the consolidating constraint to find a least violating solution when the constraints become mutually infeasible for some time. Finally, simulation examples of trajectory and region tracking for a mobile robot validate the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Feedback control

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Insulation condition forewarning of form-wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network

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High Voltage 2021年第2期6卷 302-313页

作者： Yalin Wang Jiandong Wu Tao Han Kiruba Haran Yi Yin Department of Electrical Engineering Shanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignUrbanaIllinoisUSA Department of Instrument Science and Engineering Shanghai Jiao Tong UniversityShanghaiChina

Form-wound windings in electric machines designed for electric aircraft propulsion face reliability challenges due to the severe operating environment,such as high temperature and low *** study proposes a forewarning method for insulation condition monitoring of form-wound windings based on partial discharge(PD)and deep learning neural *** PD features are extracted from the PD profile,which provides information about physics-of-failure and reflects the degree of insulation *** algorithm fusion extracted from auto-encoder and long short-term recurrent neural network is proposed to synthesize one failure precursor from these three features and make multi-time-step prediction through historical data to provide *** electrical and thermal accelerated ageing test is performed on the form-wound windings at 0.2 atm to simulate working environment of electric *** proposed method is validated on the accelerated ageing dataset and shows better prediction accuracy than some existing time-series prediction methods,indicating the advantages of the proposed ***,an on-line hardware setup using a deep learning processor is rec-ommended to implement the forewarning *** proposed approach has the potential to be widely applied to other insulation systems and contribute to work on condition monitoring.

关键词： network processor neural

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Mixed-Integer Optimal control With Disjunctive Characteristic Time Constraints for Energy-Optimal Speed Planning

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IEEE Transactions on Transportation Electrification 2025年

作者： Dong, Shiying Chen, Hong Luo, Xi Meng, Xiangyu Huang, Yanjun Liu, Qifang Gao, Bingzhao Jilin University Department of Control Science and Engineering Changchun China Tongji University College of Automotive Studies Shanghai China Louisiana State University Division of Electrical and Computer Engineering Baton RougeLA70803 United States

This paper considers the energy optimization problem with multiple time window constraints, which is motivated by the eco-driving of connected and automated vehicles (CAVs) in urban traffic. Timely crossing of signalized intersections defines complex spatio-temporal constraints, and multiple available open time windows introduce the discontinuous feasible domains of crossing times. The CAVs should select an open time-window for each intersection, determine the crossing times, and optimize the speed profiles accordingly, which turns out to be a hard-to-solve optimization problem. To address this challenge, this paper proposes a unifying approach that transforms the energy optimization problem into a novel mixed-integer optimal control problem with characteristic time constraints (MIOCP-CT). This approach facilitates the simultaneous optimization of open time windows, crossing times, and speed profiles within a single optimization problem, which can be solved by our proposed novel numerical solution procedure. The application of the proposed method in the eco-driving of CAVs approaching multiple signalized intersections demonstrates its performance within real-world urban driving cycles. The experimental results of the hardware-in-the-loop (HiL) demonstrate a superior energy-saving performance of 5.83% compared to existing methods, while also achieving real-time feasibility within 100 milliseconds. © 2015 IEEE.

关键词： Mixed-integer linear programming

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