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arXiv 2024年

作者： Wei, Shiqing Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Department of Electrical and Computer Engineering NYU Tandon School of Engineering 5 Metrotech Center BrooklynNY11201 United States

Designing control inputs that satisfy safety requirements is crucial in safety-critical nonlinear control, and this task becomes particularly challenging when full-state measurements are unavailable. In this work, we address the problem of synthesizing safe and stable control for control-affine systems via output feedback (using an observer) while reducing the estimation error of the observer. To achieve this, we adapt control Lyapunov function (CLF) and control barrier function (CBF) techniques to the output feedback setting. Building upon the existing CLF-CBF-QP (Quadratic Program) and CBF-QP frameworks, we formulate two confidence-aware optimization problems and establish the Lipschitz continuity of the obtained solutions. To validate our approach, we conduct simulation studies on two illustrative examples. The simulation studies indicate both improvements in the observer’s estimation accuracy and the fulfillment of safety and control requirements. © 2024, CC BY.

关键词： Lyapunov functions

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Effect of Ignition Voltage Amplitude on Breakdown Delay Time of Ring Laser Discharge Gap

Effect of Ignition Voltage Amplitude on Breakdown Delay Time...

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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Anton O. Sinelnikov Ilya A. Smetanin Bogdan A. Basov Egor A. Smetanin Uliana F. Bykanova Computer science and control systems department Laboratory of Technologies of Polymer Ferroelectrics Laboratory of Microelectronics and Nanotechnology Mechanical engineering technologies department Bauman Moscow State Technical University Moscow Russian Federation Department of Nanotechnology and Microsystems Engineering Department of Mechanics and Control Processes RUDN University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The modern space industry requires autonomous and high-precision control systems for scientific and commercial missions. Strapdown inertial navigation systems based on laser gyroscopes play a key role in spacecraft control, especially in environments where satellite navigation is unavailable. In this paper, ring lasers with an active medium made of a helium-neon mixture, their design and operating modes are investigated. The influence of voltage on the ignition electrodes (4.2-5 kV) on the breakdown time of the active medium has been analyzed. It was found that increasing the voltage reduces the breakdown time, providing reliable activation of gyroscopes. The experimental results allowed to determine the optimal ignition parameters that increase operation reliability and reduce production losses. This contributes to the development of highly efficient spacecraft control systems, opening up new prospects for deep space exploration and control of satellite systems.

关键词： Space vehicles Breakdown voltage Electric breakdown Power lasers Ignition Ring lasers Laser modes control systems Gyroscopes Reliability

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Evolution and Effectiveness of Loss Functions in Generative Adversarial Networks

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China Communications 2021年第10期18卷 45-76页

作者： Ali Syed Saqlain Fang Fang Tanvir Ahmad Liyun Wang Zain-ul Abidin School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Computer Science Portland State UniversityPortland 97207U.S. School of Information and Communication Engineering South West Jiaotong UniversityChengdu 610031China

Recently,the evolution of Generative Adversarial Networks(GANs)has embarked on a journey of revolutionizing the field of artificial and computational *** improve the generating ability of GANs,various loss functions are introduced to measure the degree of similarity between the samples generated by the generator and the real data samples,and the effectiveness of the loss functions in improving the generating ability of *** this paper,we present a detailed survey for the loss functions used in GANs,and provide a critical analysis on the pros and cons of these loss ***,the basic theory of GANs along with the training mechanism are ***,the most commonly used loss functions in GANs are introduced and ***,the experimental analyses and comparison of these loss functions are presented in different GAN ***,several suggestions on choosing suitable loss functions for image synthesis tasks are given.

关键词： loss functions deep learning machine learning unsupervised learning generative adversarial networks(GANs) image synthesis

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Operating Cost and Peak Load Driven Day-Ahead Multivariate Load Forecasting For Integrated Coal Mine Energy Systems

Operating Cost and Peak Load Driven Day-Ahead Multivariate L...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaoxuan Xing Xiaoyan Sun Xianming Ye School of Information and Control Engineering China University of Mining and Technology Xuzhou China Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria South Africa

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Day-ahead multivariate load forecasting is the foundation of establishing scheduling strategies for integrated coal mine energy systems. However, conventional evaluation metrics cannot reliably indicate whether the forecasting model can reduce the system’s operating costs, and peak load plays a significant role in the day-ahead load forecasting errors, which is crucial for the system’s stable and profitable operation. This paper proposes an operating cost and peak load driven day-ahead multivariate load forecasting method for integrated coal mine energy systems. First, we use an improved temporal convolutional network and a long short-term memory network to extract and merge deep load features so as to get the preliminary prediction of dayahead multivariate loads driven by operating costs. Following that, we predict the multivariate loads’ peak using the peak attention mechanism. Finally, based on the results of the peak load prediction, the day-ahead multivariate load preliminary prediction is corrected to obtain the day-ahead multivariate load prediction driven by peak load and operating costs. The proposed method is applied to an integrated coal mine energy system and contrasted with previous methods. The experimental results show that the proposed method can reduce the system’s operating costs while increasing the multivariate loads’ prediction accuracy.

关键词： Costs Accuracy Load forecasting Systems operation Predictive models Feature extraction Coal mining

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Coarse-to-Fine Detection of Multiple Seams for Robotic Welding

arXiv

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arXiv 2024年

作者： Wei, Pengkun Cheng, Shuo Li, Dayou Song, Ran Zhang, Yipeng Zhang, Wei School of Control Science and Engineering Shandong University Jinan250061 China Department of Electrical and Computer Engineering University of California Los AngelesCA United States

Efficiently detecting target weld seams while ensuring sub-millimeter accuracy has always been an important challenge in autonomous welding, which has significant application in industrial practice. Previous works mostly focused on recognizing and localizing welding seams one by one, leading to inferior efficiency in modeling the workpiece. This paper proposes a novel framework capable of multiple weld seams extraction using both RGB images and 3D point clouds. The RGB image is used to obtain the region of interest by approximately localizing the weld seams, and the point cloud is used to achieve the fine-edge extraction of the weld seams within the region of interest using region growth. Our method is further accelerated by using a pre-trained deep learning model to ensure both efficiency and generalization ability. The performance of the proposed method has been comprehensively tested on various workpieces featuring both linear and curved weld seams and in physical experiment systems. The results showcase considerable potential for real-world industrial applications, emphasizing the method’s efficiency and effectiveness. Videos of the real-world experiments can be found at https://***/pq162HSP2D4. © 2024, CC BY.

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Reinforcement Learning-Based Output Stabilization control for Nonlinear Systems With Generalized Disturbances

Reinforcement Learning-Based Output Stabilization Control fo...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Zezhi Tang J Anthony Rossiter Yi Dong George Panoutsos Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Department of Electronics and Computer Science University of Southampton Southampton United Kingdom

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

This paper proposes a new disturbance observer (DO)-based reinforcement learning (RL) control approach for nonlinear systems with unmatched (generalized) disturbances. While a nonlinear disturbance observer (NDO) is utilized to measure the plant uncertainties, disturbances can exist in the plant via distinct channels from those of the control signals; so-called mismatched disturbances are theoretically difficult to attenuate within the channel of the system's states. A generalized disturbance observer-based compensator is implemented to address the uncertainty cancellation problem by removing the influence of uncertainties from the output channels. Con-currently, a composite actor-critic RL scheme is utilized for approximating the optimal control policy as well as the ideal value function pertaining to the compensated system by solving a Hamilton-Jacobi-Bellman (HJB) equation for both online and offline iterations simultaneously. Stability analysis verifies the convergence of the proposed framework. Simulation results are included to illustrate the effectiveness of the proposed scheme.

关键词： Uncertainty Simulation Measurement uncertainty Optimal control Estimation Reinforcement learning Disturbance observers

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Nonlinear Auto-Regressive Moving Average (NARMA-L2) controller Design for Two CSTR

Nonlinear Auto-Regressive Moving Average (NARMA-L2) Controll...

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Electrical engineering, control and Robotics (EECR), International Conference on

作者： Husam E. Elfadel Haroon School of Control and Computer Engineering North China Electric Power University Beijing China Department of Electrical and Electronic Engineering Omdurman Islamic University Khartoum Sudan

ISBN: (数字)9798350370003

ISBN: (纸本)9798350370010

Continuous Stirred Tank Reactors (CSTRs) are important components in chemical processes. They are often interconnected to achieve desired reaction sequences. However, controlling the concentration in such interconnected systems presents significant challenges due to nonlinear dynamics and interactions between reactors. This paper proposed a Nonlinear Auto-Regressive Moving Average (NARMA-L2) controller developed to regulate the concentration for a series-connected two-CSTR system. The NARMA-L2 controller takes advantage of the inherent capabilities of neural networks to accurately model the nonlinear behavior of a two-CSTR system, and it is developed to regulate the concentration of two CSTRs that are connected in a series. The controller is designed, implemented, and trained using Simulink-MATLAB. The ability of the NARMA-L2 controller to monitor concentration target values and reject disturbances effectively is assessed through comparative analysis with the conventional Proportional-Integral-Derivative (PID) controller. The Outcomes demonstrated the exceptional performance of the NARMA-L2 controller in achieving precise concentration control in the series-connected two-CSTR system.

关键词： Neural networks Interconnected systems Nonlinear dynamical systems System identification Continuous-stirred tank reactor PD control Isothermal processes

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Flag Gadgets Based on Classical Codes

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PRX Quantum 2024年第4期5卷 040340-040340页

作者： Benjamin Anker Milad Marvian Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico 87106 USA Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87106 USA

Fault-tolerant syndrome extraction is a key ingredient in implementing fault-tolerant quantum computation. While conventional methods use a number of extra qubits that are linear in the weight of the syndrome, several improvements have been introduced using flag gadgets. In this work, we develop a framework to design flag gadgets using classical codes. Using this framework, we show how to perform fault-tolerant syndrome extraction for any stabilizer code with arbitrary distance using exponentially fewer qubits than conventional methods when qubit measurement and reset are relatively slow compared to a round of error correction. In particular, our method requires only (2t+1)t⌈log2 (w)⌉ flag qubits to fault-tolerantly measure a weight-w stabilizer. We further take advantage of the saving provided by our construction to fault-tolerantly measure multiple stabilizers using a single gadget and show that it maintains the same exponential advantage when it is used to fault-tolerantly extract the syndromes of quantum low-density parity-check codes. Using the developed framework, we perform computer-assisted search to find several small examples where our constructions reduce the number of qubits required. These small examples may be relevant to near-term experiments on small-scale quantum computers.

关键词： Qubits

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DPPL Hallway Tracker: Hospital Contact Tracing During the COVID-19 Pandemic 9

DPPL Hallway Tracker: Hospital Contact Tracing During the CO...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Marinoni, Christian Ponzi, Valerio Comminiello, Danilo Dpt. of Information Engineering Electronics and Telecommunications Sapienza University of Rome Via Eudossiana 18 Roma00184 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

During the COVID-19 pandemic, the use of a people tracking system could have been crucial, particularly in sensitive environments, such as hospitals. DPPL Hallway Tracker is a framework that uses security camera footage to determine which rooms in a corridor a person has entered. It generates a database containing all the people identified and allows quick identification of potential cases of infection based on the time spent in a room and its maximum capacity. DPPL Hallway Tracker is structured in two phases: detection and re-identification. In the first phase, it exploits Mask RCNN to identify people and room doors. In the second one, it uses the deep association metric model from DeepSORT to re-identify a person as he leaves a room. © 2023 Copyright for this paper by its authors.

关键词： COVID-19

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FUSE: a federated learning and U-shape split learning-based electricity theft detection framework

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Science China(Information Sciences) 2024年第4期67卷 339-340页

作者： Xuan LI Naiyu WANG Liehuang ZHU Shuai YUAN Zhitao GUAN School of Control and Computer Engineering North China Electric Power University School of Cyberspace Science and Technology Beijing Institute of Technology Department of Finance Operationsand Information Systems(FOIS)Brock University

In recent years, the data-driven electricity theft detection methods integrated with edge cloud computing [1, 2] have not only demonstrated superior detection accuracy but also improved efficiency, making them viable alternatives to indoor inspections. Energy service providers(ESPs) typically manage regions by dividing them into various transformer districts(TDs). The detection of electricity theft in a particular region is performed by the associated TD,

关键词： Sensitive data

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