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Adaptive Uniform Performance Control of Strict-Feedback Nonlinear systems With Time-Varying Control Gain

IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 451-461页

作者： Kai Zhao Changyun Wen Yongduan Song Frank L.Lewis IEEE the Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned SystemsSchool of AutomationChongqing UniversityChongqing 400044China the UTA Research Institute University of Texas at ArlingtonFort WorthTX 76118 USA

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following ***,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control ***,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer ***,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is ***,simulation results verify the effectiveness and benefits of the approach.

关键词： Adaptive control enhanced Nussbaum function(ENF) strict-feedback systems unified prescribed performance

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Log Preservation in Custody Dual Blockchain With Energy Regime and Obfuscation Shuffle

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IEEE Transactions on Network Science and engineering 2024年第4期11卷 3495-3511页

作者： Chew, Chit-Jie Lee, Wei-Bin Chen, Tzu-Hao Lin, Iuon-Chang Lee, Jung-San Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan Hon Hai Research Institute Information Security Research Center Taipei236 Taiwan National Chung Hsing University Department of Management Information Systems Taichung402 Taiwan

The rise of cybercrime has led to an explosion of threats in recent years, resulting in the increasingly important of digital forensics. To achieve successful investigation and prosecution, the log preservation is a critical aspect of incident response. The blockchain technology is considered as one of the powerful log storage mechanism, which bringing data transparency and non-tempering property aside from taking off the hypothesis of a trusted third party. Nevertheless, the sensitivity of the data makes it vulnerable to malicious attacks, and the adoption of blockchain has significantly burdened participants regarding consensus time and storage. To address these challenges, we aim to develop a custody dual blockchain (CDB) to secure the confidentiality and integrity of log data. The design of reputation consensus attains that only attractive participants need to offer computation and storage for efficiency enhancing, while the energy regime constructs a positive feedback loop that invigorates users to contribute to CDB. Moreover, the obfuscation shuffle effectively obscures the explicit log plaintext to prevent illegal access. Thus, the confidential data on the CDB nodes can even be used as evidence of digital forensics. Simulation results have demonstrated that the ledger overhead can be significantly reduced, and CDB could pass the robust examination of two formal proof tools: Proverif (Blanchet et al. 2021) and AVISPA. © 2013 IEEE.

关键词： Blockchain

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multiphysical analysis and motor design optimization. This article proposes a new method for thermal steady state modeling and analysis based on transfer learning (TL), which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on TL is developed: the model is pretrained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then, the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R^{2} and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Volt-Var Control for Utility-scale Solar PV Plants to Replace SVCs 4

Volt-Var Control for Utility-scale Solar PV Plants to Replac...

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4th International Conference on High Voltage engineering and Power systems, ICHVEPS 2023

作者： Shimomukai, Ken Maeda, Haruka Fujita, Goro Shibaura Institute of Technology Functional Control Systems Graduate School of Engineering and Science Tokyo Japan Shibaura Institute of Technology Graduate School of Engineering and Science Department of Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350318678

One way to increase solar photovoltaic penetration in the grid is management of voltage fluctuations. This is because a photovoltaic plant cannot be interconnected to the grid if it causes voltage violations. Voltage violation is where voltage exceeds the acceptable range. Often, grid operators request photovoltaic plant owners to regulate voltage sufficiently with expensive and space-consuming static Var compensators. Unfortunately, this sometimes makes the project less feasible. This paper argues that there are better ways to regulate voltage. We ran a simulation with a 70 MWp photovoltaic plant as an addition to the grid. Without voltage regulation, voltage violations were found to be significant. This paper found that oversizing the inverter sufficiently would remove all voltage violations without deploying a static Var compensator. This is often a cheaper and space-saving solution for voltage management. This paper argues that economics and spatial efficiency of reactive power compensation devices is key to increasing photovoltaic penetration. © 2023 IEEE.

关键词： Reactive power

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Unpaired remote sensing image super-resolution with content-preserving weak supervision neural network

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Science China(Information Sciences) 2023年第1期66卷 324-325页

作者： Jie WU Runmin CONG Leyuan FANG Chunle GUO Bob ZHANG Pedram GHAMISI College of Electrical and Information Engineering Hunan University Institute of Information Science Beijing Jiaotong University College of Computer Science Nankai University Department of Computer and Information Science University of Macau Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Institute of Advanced Research in Artificial Intelligence (IARAI)

Dear editor,Image super-resolution(SR) uses image processing methods to increase the resolution of the image without increasing any hardware cost. Remote sensing images with high spatial resolution contain rich imagery information, which can be utilized for object detection, building segmentation, building extraction, change detection, and so on.

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Open Radio Access Network RIC Empowered Reconfigurable Intelligent Surface: A Physical-Layer Security Perspective

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2024年 2卷 615-625页

作者： Lien, Shao-Yu Tseng, Chih-Cheng Hung, Wei-Cheng Tsai, Cheng-You Liu, Ting-Yu Deng, Der-Jiunn Lin, Yuan-Chun Lin, Shih-Cheng Chang, Chia-Chan Chang, Sheng-Fuh Institute of Intelligent Systems National Yang Ming Chiao Tung University Tainan711010 Taiwan Department of Electrical Engineering National Ilan University Ilan260007 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi621301 Taiwan Department of Computer Science and Information Engineering National Changhua University of Education Changhua50074 Taiwan Department of Electrical Engineering National Chung Cheng University Chiayi621301 Taiwan

The reconfigurable intelligent surface (RIS) steering reflective beam directions toward a target mobile user equipment (UE) has been a promising technology for coverage enhancement and physical-layer (PHY) security to avoid signal eavesdroppers and jammers. However, tracking and predicting the moving trajectory of UE demand significant computing burdens unaffordable to the RIS. Performing beam steering computations on the radio access network (RAN) intelligent controller in the open RAN (O-RAN) architecture therefore emerges as a revolutionary paradigm. In this paper, we propose a PHY-secured beam steering design based on a pre-processing-based Kalman filter algorithm. Through a pre-processing phase to derive the radiation feature, the proposed design accurately tracks the position of the mobile UE and quickly recovers the position search while position tracking fails. Through practical field-try experiments, we justify the capability of O-RAN RIC-empowered RIS for PHY security. © 2013 IEEE.

关键词： Radio access networks

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Extending OpenStack Monasca for Predictive Elasticity Control

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Big Data Mining and Analytics 2024年第2期7卷 315-339页

作者： Giacomo Lanciano Filippo Galli Tommaso Cucinotta Davide Bacciu Andrea Passarella Scuola Normale Superiore Pisa 56126Italy the Real-Time Systems Laboratory(RETIS) TelecommunicationsComputer Engineeringand Photonics Institute(TeCIP)Scuola Superiore Sant’AnnaPisa 56127Italy Department of Computer Science University of PisaPisa 56127Italy National Research Council Pisa56127Italy

Traditional auto-scaling approaches are conceived as reactive automations,typically triggered when predefined thresholds are breached by resource consumption *** such rules at scale is cumbersome,especially when resources require non-negligible time to be *** paper introduces an architecture for predictive cloud operations,which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the *** this way,they can anticipate load peaks and trigger appropriate scaling actions in advance,such that new resources are available when *** proposed architecture is implemented in OpenStack,extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard *** use our architecture to implement predictive scaling policies leveraging on linear regression,autoregressive integrated moving average,feed-forward,and recurrent neural networks(RNN).Then,we evaluate their performance on a synthetic workload,comparing them to those of a traditional *** assess the ability of the different models to generalize to unseen patterns,we also evaluate them on traces from a real content delivery network(CDN)*** particular,the RNN model exhibites the best overall performance in terms of prediction error,observed client-side response latency,and forecasting *** implementation of our architecture is open-source.

关键词： OpenStack monitoring elasticity control auto-scaling predictive operations Monasca

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A Dynamical Equation Approach For Quasi-Periodic Gaussian Processes

A Dynamical Equation Approach For Quasi-Periodic Gaussian Pr...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Nigam, Unnati Srivastava, Radhendushka Burke, Michael Marzbanrad, Faezeh IITB-Monash Research Academy India Department of Mathematics IIT Bombay Mumbai India Department of Electrical and Computer Systems Engineering Monash University Clayton Australia

ISBN: (纸本)9798350368741

Quasi (pseudo/approximate) periodic signals often occur in natural settings, particularly when a periodic signal is recorded with noise. In this work, we develop a new dynamical equation system to construct a family of Quasi-periodic Gaussian Processes (QPGP). We provide a computationally inexpensive algorithm based on a newly derived likelihood function for parameter estimation. Our approach also simplifies the signal forecasting. We illustrate via a simulation study that the proposed QPGP estimation strategy is faster as well and more accurate than existing constructions. Unlike these existing models, the proposed approach extends to multiple families of kernels, which we illustrate by modeling sunspot data with both periodic Matérn and MacKay's covariance kernel. This shows the exclusive advantage of the new QPGP family proposed in this work. © 2025 IEEE.

关键词： Covariance kernels Dynamical system Forecasting Likelihood Quasi-Periodic Gaussian Process

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Multistream Gaze Estimation With Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4232-4246页

作者： Mahmud, Zunayed Hungler, Paul Etemad, Ali Queen's University Department of Electrical and Computer Engineering Ingenuity Labs Research Institute KingstonONK7L 3N6 Canada Queen's University Ingenuity Labs Research Institute KingstonONK7L 3N6 Canada

We propose a novel neural pipeline, MSGazeNet, that learns gaze representations by taking advantage of the eye anatomy information through a multistream framework. Our proposed solution comprises two components, first a network for isolating anatomical eye regions, and a second network for multistream gaze estimation. The eye region isolation is performed with a U-Net style network which we train using a synthetic dataset that contains eye region masks for the visible eyeball and the iris region. The synthetic dataset used in this stage is procured using the UnityEyes simulator and consists of 80 000 eye images. Successive to training, the eye region isolation network is then transferred to the real domain for generating masks for the real-world eye images. In order to successfully make the transfer, we exploit domain randomization in the training process, which allows for the synthetic images to benefit from a larger variance with the help of augmentations that resemble artifacts. The generated eye region masks along with the raw eye images are then used together as a multistream input to our gaze estimation network, which consists of wide residual blocks. The output embeddings from these encoders are fused in the channel dimension before feeding into the gaze regression layers. We evaluate our framework on three gaze estimation datasets and achieve strong performances. Our method surpasses the state of the art by 7.57% and 1.85% on two datasets and obtains competitive results on the other. We also study the robustness of our method with respect to the noise in the data and demonstrate that our model is less sensitive to noisy data. Last, we perform a variety of experiments including ablation studies to evaluate the contribution of different components and design choices in our solution. © 2020 IEEE.

关键词： Deep neural networks

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Low-Latency and Energy-Efficient Task Scheduling for End-Edge-Cloud Collaborative Computing

Low-Latency and Energy-Efficient Task Scheduling for End-Edg...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Li, Jingyao Yuan, Haitao Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios. © 2024 IEEE.

关键词： Mobile edge computing

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