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63rd IEEE Conference on Decision and control, CDC 2024

作者： Zhang, Jiaojiao Zhu, Linglingzhi Johansson, Mikael Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Hong Kong

ISBN: (纸本)9798350316339

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 (, δ) 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. © 2024 IEEE.

关键词： Federated learning

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Computing Riemann zeros with light scattering

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Physical Review B 2024年第22期110卷 L220201-L220201页

作者： Sunkyu Yu Xianji Piao Namkyoo Park Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul 08826 Korea Wave Engineering Laboratory School of Electrical and Computer Engineering University of Seoul Seoul 02504 Korea Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul 08826 Korea

Finding hidden order within disorder is a common interest in material science, wave physics, and mathematics. The Riemann hypothesis, stating the locations of nontrivial zeros of the Riemann zeta function, tentatively characterizes statistical order in the seemingly random distribution of prime numbers. This famous conjecture has inspired various connections with different branches of physics, recently with non-Hermitian physics, quantum field theory, trapped-ion qubits, and hyperuniformity. Here we develop the computing platform for the Riemann zeta function by employing classical scattering of light. We show that the Riemann hypothesis suggests the landscape of semi-infinite optical scatterers for the perfect reflectionless condition under the Born approximation. To examine the validity of the scattering-based computation, we investigate the asymptotic behaviors of suppressed reflections with the increasing number of scatterers and the emergence of multiple scattering. The result provides another bridge between classical physics and the Riemann zeros, exhibiting the design of wave devices inspired by number theory.

关键词： Light scattering

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Programmable Photonic Time Circuits for Highly Scalable Universal Unitaries

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Physical Review Letters 2024年第10期132卷 103801-103801页

作者： Xianji Piao Sunkyu Yu Namkyoo Park Wave Engineering Laboratory School of Electrical and Computer Engineering University of Seoul Seoul 02504 Korea Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul 08826 Korea Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul 08826 Korea

Programmable photonic circuits (PPCs) have garnered substantial interest for their potential in facilitating deep learning accelerations and universal quantum computations. Although photonic computation using PPCs offers ultrafast operation, energy-efficient matrix calculations, and room-temperature quantum states, its poor scalability hinders integration. This challenge arises from the temporally one-shot operation of propagating light in conventional PPCs, resulting in a light-speed increase in device footprints. Here we propose the concept of programmable photonic time circuits, utilizing time-cycle-based computations analogous to gate cycling in the von Neumann architecture and quantum computation. Our building block is a reconfigurable SU(2) time gate, consisting of two resonators with tunable resonances, and coupled via time-coded dual-channel gauge fields. We demonstrate universal U(N) operations with high fidelity using an assembly of the SU(2) time gates, substantially improving scalability from O(N2) to O(N) in terms of both the footprint and the number of gates. This result paves the way for PPC implementation in very large-scale integration.

关键词： Integrated optics Photonics Quantum computation

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Employing Feature engineering for River Stage Forecasting to Improve Hybrid Model Performance 32

Employing Feature Engineering for River Stage Forecasting to...

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Mihel, Anna Maria Pecnik, Spela Vrbancic, Grega Lerga, Jonatan Krvavica, Nino University of Rijeka Faculty of Engineering Department of Computer Engineering Rijeka Croatia University of Maribor Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science Maribor Slovenia University of Rijeka Faculty of Civil Engineering Department of Hydrotechnics and Geotechnics Rijeka Croatia

ISBN: (纸本)9789532901382

Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (downstream) and discharge (upstream), making the modeling of river processes extremely complex. Data-driven methods have proven helpful for predicting the river stage parameter. This contrasts with existing numerical approaches, which suffer from several limitations. The aim of this study was to predict the river stage of the upstream part of the tidal river, where the influence of salinity is present during the summer months. Using the CNN-LSTM model trained on historical records of the discharge (station of the variable to be predicted), the river stage of three downstream stations, and the point of interest, the prediction was carried out up to 24 hours in advance. By incorporating feature engineering, we achieved an improvement in model results for the longest horizon, confirmed by standard performance metrics, and considered it efficient and effective for risk mitigation. Introducing feature engineering into the data preprocessing resulted in a predictive performance improvement of 0.98% for the Nash-Sutcliffe Efficiency (NSE) metric, 7.0% for the root mean squared error (RMSE) metric, and 7.25% for the mean absolute error (MAE) metric over the second and third-best scenarios using a spectrogram and time-series data. © 2024 University of Split, FESB.

关键词： Mean square error

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Converter-Fed Induction Motor Efficiency Measurements - IEC 60034-2-3

Converter-Fed Induction Motor Efficiency Measurements - IEC ...

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2024 International Conference on electrical Machines, ICEM 2024

作者： Tiihonen, Matias Karkkainen, Hannu Aarniovuori, Lassi Niemela, Markku Liu, Dong Barta, Jan Laboratory of Electrical Drives Technology LUT University Lappeenranta Finland Laboratory of Electrical Drives Technology LUT University Lahti Finland LUT University Laboratory of control engineering and digital systems Lappeenranta Finland Brno University of Technology Department of Power Electrical and Electronic Engineering Brno Czech Republic

ISBN: (纸本)9798350370607

Induction motors consume huge amount of energy, so their energy efficiency is an important topic. The efficiency determination methods of direct-on-line motors are mature and during the last decade efforts have been made to create a standardized way to also determine efficiency of converter-fed motors. Here the efficiency determination methods given in IEC 60034-2-3 are utilized using a modern induction motor with the rated power of 7.5 kW and compared with the sinusoidal supply results. In addition, the high frequency harmonic power is extracted from the measured active power and its behavior as a function of the frequency and torque is illustrated. © 2024 IEEE.

关键词： comparison of standard methods energy efficiency frequency converter Induction motor measurements standardization

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Adaptive-Optimal control for Reconfigurable Robots

Adaptive-Optimal Control for Reconfigurable Robots

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2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Maheshwari, Adya Rautela, Aaditya Rayguru, Madan Mohan Valluru, Sudarshan K. Delhi Technological University Department of Electrical Engineering New Delhi India Delhi Technological University Control of Dynamical Systems and Computation Laboratory Department of Electrical Engineering New Delhi India

ISBN: (纸本)9781665474924

Reconfigurable robots refer to the robotic systems which have the ability to transform their morphological structure to adapt to changes in their surroundings. A specialized control system based upon the dynamic characteristics is required to be modelled so that a vast range of operations can be performed by the reconfigurable robots. Reinforcement learning based technique is therefore considered as a suitable mechanism for carrying out adaptive optimal control for the robotic systems. In this paper, an attempt has been made to model the adaptive optimal control mechanism for a single-link manipulator: a reconfigurable robotic system. The system has been modelled for a dynamic environment. The loss and reward plots are obtained from the simulation of the robotic system by deploying reinforcement learning based techniques with the given controlled input © 2023 IEEE.

关键词： Reinforcement learning

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Topological Photonic Molecule with Time-Varying Modulations

Topological Photonic Molecule with Time-Varying Modulations

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2023 Photonic Networks and Devices, Networks 2023 in Advanced Photonics Congress - Part of Advanced Photonics Congress 2023

作者： Suh, Joseph Kim, Gyunghun Park, Hyungchul Yu, Sunkyu Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

We propose dynamic modulations of a photonic molecule to achieve topological properties of light. We investigate the Hall transport in synthetic dimensions and the system modulation strategy to demonstrate the pumping... 详细信息

ISBN: (纸本)9781957171265

关键词： Molecules

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Data-driven finite-time adaptive attitude tracking control for spacecraft

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Advances in Space Research 2025年第12期75卷 8780-8791页

作者： Wang, Jianhui He, Guangping Bian, Guibin Geng, Shixiong College of Mechanical & Energy Engineering Beijing University Of Technology Beijing100124 China Department of Mechanical and Electrical Engineering North China University of Technology Beijing100144 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

This paper proposes a novel data-driven finite-time adaptive control method for the spacecraft attitude tracking control problem with inertial uncertainty. Based on the dynamic regression extension technique, the disturbance unit matrix is introduced, and the parameter estimation law in the form of algebraic equations is presented, which realizes the arbitrarily small convergence of parameter estimation error. The convergence of parameter estimation errors is verified through stability analysis. Globally non-singular fast terminal sliding mode control is designed to achieve finite-time arrival of the sliding surface and convergence of the system state error. The stability analysis of the closed-loop system demonstrates the effectiveness of the proposed method. Furthermore, the effectiveness and superiority of the proposed method are verified through numerical simulations of spacecraft attitude tracking control. © 2025 COSPAR

关键词： Attitude control

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Data-Driven Digital Twins for Power Estimations of a Solar Photovoltaic Plant

Data-Driven Digital Twins for Power Estimations of a Solar P...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Walters, Michael Yonce, John Venayagamoorthy, Ganesh K. Clemson University Realtime Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson United States

ISBN: (纸本)9781665430654

Renewable energy generation sources (RESs) are gaining increased popularity due to global efforts to reduce carbon emissions and mitigate effects of climate change. Planning and managing increasing levels of RESs, specifically solar photovoltaic (PV) generation sources is becoming increasingly challenging. Estimations of solar PV power generations provide situational awareness in distribution system operations. A digital twin (DT) can replicate PV plant behaviors and characteristics in a virtual platform, providing realistic solar PV estimations. Furthermore, neural networks, a popular paradigm of artificial intelligence may be used to adequately learn and replicate the relationship between input and output variables for data-driven DTs (DD-DTs). In this paper, DD-DTs are developed for Clemson University's 1 MW solar PV plant located in South Carolina, USA to perform realistic solar PV power estimations. The DD-DTs are implemented utilizing multilayer perceptron (MLP) and Elman neural networks. Typical practical results for two DD-DT architectures are presented and validated. © 2023 IEEE.

关键词： Digital twin neural network power estimation solar photovoltaic

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Collaborative Routing and Charging/Discharging Scheduling of Electric Autonomous Vehicles in Coupled Power-Traffic Networks: A Multiobjective Approach

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IEEE Internet of Things Journal 2025年第11期12卷 17753-17764页

作者： Chu, Kai-Fung Chen, Tianlun Xie, Yue Lam, Albert Y. S. Song, Yue Iida, Fumiya University of Cambridge Department of Engineering CambridgeCB2 1PZ United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The University of Hong Kong Department of Electrical and Electronic Engineering Department of Geography Hong Kong Fano Labs Hong Kong Tongji University Department of Control Science and Engineering Shanghai201804 China Tongji University National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai200120 China

Autonomous vehicles (AVs) are vehicles that traverse on the road without active human intervention. With a coordinator, AVs can be connected to provide high-efficiency transport services, such as AV-based public transport networks. The controller can manage the network by coordinating the transport request assignment, traveling, and charging/discharging schedule. On the other hand, AVs are likely to be electric and benefit the smart grid via vehicle-to-grid technology. A well-designed mobility network connecting electric AVs (EAVs) and smart grid can substantially reduce unnecessary travel and energy costs. In this article, we aim to maximize utilities in the AV-based public transport network and the power distribution network for the vehicle network containing EAVs, charging stations, and distributed power generations. We formulate the assignment and scheduling problem as a multiobjective mixed-integer program (MIP). To solve the optimization problem, we develop a hybrid heuristic approach based on nondominated sorting genetic algorithm II (NSGA-II) and branch-and-bound (BnB) algorithms. Experiments are conducted on a modified 15-bus distribution system and a simulated traffic network. The results show that the proposed strategy effectively minimizes the total travel and energy purchase cost by 21%. This study provides valuable insights on vehicle coordination for multiple tasks, offering visionary guidance for stakeholders engaged in multifaceted transportation endeavors. © 2014 IEEE.

关键词： Vehicle-to-grid

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