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Indirect adaptive observer control (I-AOC) design for truck–trailer model based on T–S fuzzy system with unknown nonlinear function

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Complex and Intelligent Systems 2024年第5期10卷 7311-7331页

作者： Aslam, Muhammad Shamrooz Bilal, Hazrat Chang, Wer-Jer Yahya, Abid Badruddin, Irfan Anjum Kamangar, Sarfaraz Hussien, Mohamed Artificial Intelligence Research Institute China University of Mining and Technology Department of Automation University of Science and Technology of China Department of Marine Engineering of National Taiwan Ocean University (NTOU) Department of Electrical Computer and Telecommunications Engineering Botswana International University of Science and Technology Department of Mechanical Engineering College of Engineering King Khalid University Department of Chemistry Faculty of Science King Khalid University

Tracking is a crucial problem for nonlinear systems as it ensures stability and enables the system to accurately follow a desired reference signal. Using Takagi–Sugeno (T–S) fuzzy models, this paper addresses the problem of fuzzy observer and control design for a class of nonlinear systems. The Takagi–Sugeno (T–S) fuzzy models can represent nonlinear systems because it is a universal approximation. Firstly, the T–S fuzzy modeling is applied to get the dynamics of an observational system in order to estimate the unmeasurable states of an unknown nonlinear system. There are various kinds of nonlinear systems that can be modeled using T–S fuzzy systems by combining the input state variables linearly. Secondly, the T–S fuzzy systems can handle unknown states as well as parameters known to the indirect adaptive fuzzy observer. A simple feedback method is used to implement the proposed controller. As a result, the feedback linearization method allows for solving the singularity problem without using any additional algorithms. A fuzzy model representation of the observation system comprises parameters and a feedback gain. The Lyapunov function and Lipschitz conditions are used in constructing the adaptive law. This method is then illustrated by an illustrative example to prove its effectiveness with different kinds of nonlinear functions. A well-designed controller is effective and its performance index minimizes network utilization—this factor is particularly significant when applied to wireless communication systems. © The Author(s) 2024.

关键词： Adaptive observer control Feedback control design Indirect method Lyapunov–Krasovskii candidate T–S fuzzy models

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Resampling methods for private statistical inference

arXiv

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arXiv 2024年

作者： Chadha, Karan Duchi, John C. Kuditipudi, Rohith Departments of Electrical Engineering Departments of Statistics Departments of Computer Science Stanford University United States

We propose two private variants of the non-parametric bootstrap for privately computing confidence sets. Each privately computes the median of results of multiple "little" bootstraps, yielding asymptotic bounds on the coverage error of the resulting confidence sets. For a fixed differential privacy parameter Ε, our methods enjoy the same error rates as the non-private bootstrap to within logarithmic factors in the sample size n. We empirically validate the performance of our methods for mean estimation, median estimation, and logistic regression, and our methods achieve similar coverage accuracy to existing methods (and non-private baselines) while providing notably shorter (about 10×) confidence intervals than previous approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Adapting Software-defined Information Centric Networks for IEC 61850-based Substations 13

Adapting Software-defined Information Centric Networks for I...

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13th IEEE Power and Energy Society: Innovative Smart Grid Technologies - Asia, ISGT Asia 2024

作者： Shivran, Kuldeep Singh Shrestha, Sharad Torres, George Das, Sarasij Misra, Satyajayant Indian Institute of Science Department of Electrical Engineering Bengaluru India New Mexico State University Computer Science Department Las Cruces United States New Mexico State University Computer Science and Ece Departments Las Cruces United States

ISBN: (纸本)9798350373929

A robust communication infrastructure is the backbone of power system efficiency and reliability advancements, enabling real-time data exchange and control. The smart grid, a complex network of digital devices, relies on their seamless communication to share information for monitoring, control, and protection. As the number of these devices and their communication needs increase, the role of software-defined and information-centric networking frameworks will become more pronounced for offering reliable and secure operation solutions. Given the time-sensitive nature and specific architecture of smart grid protocols, software-defined information centric networks (SD-ICN) can be appropriately designed to meet these requirements. SD-ICN can improve security by controlling access and authentication and efficient distribution of contents in the network. This paper explores recent SD-ICN structures and strategies to improve the reliability and security of communication in power systems substations. An IEC 61850-based substation networking architecture is presented to demonstrate the application of SD-ICN. Additionally, a mechanism to map the substation devices using Substation Configuration Language (SCL) to a routable Named Data Networking (NDN) name to route in the SD-ICN network is proposed. © 2024 IEEE.

关键词： IEC 61850 Based Substation Information Centric Networks Named Data Networking Smart Grid Software Defined Networks

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Optimum Estimation of Coastal Currents Using Moving Vehicles

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Journal of Atmospheric and oceanic Technology 2023年第12期40卷 1431-1441页

作者： Chen, Kuanyu Huang, Chen-Fen Zheng, Zhe-Wen Lin, Sheng-Fong Liu, Jin-Yuan Guo, Jenhwa Institute of Oceanography National Taiwan University Taipei Taiwan Department of Power Vehicle and Systems Engineering National Defense University Taoyuan Taiwan Department of Earth Science National Taiwan Normal University Taipei Taiwan Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Electrical and Computer Engineering Tamkang University New Taipei City Taiwan Department of Engineering Science and Ocean Engineering National Taiwan University Taipei Taiwan

ocean acoustic tomography (OAT) deploys most moored stations on the periphery of the tomographic region to sense the solenoidal current field. Moving vehicle tomography (MVT), an advancement of OAT, not only samples the region from various angles for improving the resolution of mapped currents but also acquires information about the ir-rotational flow due to the sampling points inside the region. To reconstruct a complete two-dimensional current field, the spatial modes derived from the open-boundary modal analysis (OMA) are preferable to the conventional truncated Fourier series since the OMA technique describes the solenoidal and irrotational flows efficiently in which all modes satisfy the coastline and open boundary conditions. Comparisons of the reconstructions are presented using three different representations of currents. The first two representations explain only the solenoidal flow: the truncated Fourier series and the OMA Dirichlet modes. The third representation, accounting for the solenoidal and irrotational flows, uses all the OMA modes. For reconstructing the solenoidal flow, the OMA representation with the Dirichlet modes performs better than the Fourier series. A large difference appears near the bay mouth, where the OMA-Dirichlet reconstruction shows a better fit to the uniform currents. However, considerable uncertainty exists outside the bay mouth where the irrotational currents dominate. This can be improved by the third representation with the inclusion of the Neumann and boundary modes. The reconstruction results using field data were validated against the acoustic Doppler current profiler (ADCP) measurements. Additionally, incorporating constraints from ADCP measurements enhances the accuracy of the reconstruction. © 2023 American Meteorological Society.

关键词： Modal analysis

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Robust Conformal Prediction Using Privileged Information

arXiv

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arXiv 2024年

作者： Feldman, Shai Romano, Yaniv Department of Computer Science Technion Israel Departments of Electrical and Computer Engineering and of Computer Science Technion Israel

We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data, such as missing or noisy variables. Our approach builds on conformal prediction, a powerful framework to construct prediction sets that are valid under the i.i.d assumption. Importantly, naively applying conformal prediction does not provide reliable predictions in this setting, due to the distribution shift induced by the corruptions. To account for the distribution shift, we assume access to privileged information (PI). The PI is formulated as additional features that explain the distribution shift, however, they are only available during training and absent at test time. We approach this problem by introducing a novel generalization of weighted conformal prediction and support our method with theoretical coverage guarantees. Empirical experiments on both real and synthetic datasets indicate that our approach achieves a valid coverage rate and constructs more informative predictions compared to existing methods, which are not supported by theoretical guarantees. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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Accelerated gradient methods for nonconvex optimization: Escape trajectories from strict saddle points and convergence to local minima

arXiv

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arXiv 2023年

作者： Dixit, Rishabh Gürbüzbalaban, Mert Bajwa, Waheed U. Department of Electrical and Computer Engineering Departments of Electrical & Computer Engineering Management Science and Information Systems and Statistics United States Departments of Electrical & Computer Engineering and Statistics

This paper considers the problem of understanding the behavior of a general class of accelerated gradient methods on smooth nonconvex functions. Motivated by some recent works that have proposed effective algorithms, based on Polyak’s heavy ball method and the Nesterov accelerated gradient method, to achieve convergence to a local minimum of nonconvex functions, this work proposes a broad class of Nesterov-type accelerated methods and puts forth a rigorous study of these methods encompassing the escape from saddle-points and convergence to local minima through a both asymptotic and a nonasymptotic analysis. In the asymptotic regime, this paper answers an open question of whether Nesterov’s accelerated gradient method (NAG) with variable momentum parameter avoids strict saddle points almost surely. This work also develops two metrics of asymptotic rate of convergence and divergence, and evaluates these two metrics for several popular standard accelerated methods such as the NAG, and Nesterov’s accelerated gradient with constant momentum (NCM) near strict saddle points. In the local regime, this work provides an analysis that leads to the "linear" exit time estimates from strict saddle neighborhoods for trajectories of these accelerated methods as well the necessary conditions for the existence of such trajectories. Finally, this work studies a sub-class of accelerated methods that can converge in convex neighborhoods of nonconvex functions with a near optimal rate to a local minima and at the same time this sub-class offers superior saddle-escape behavior compared to that of NAG. Copyright © 2023, The Authors. All rights reserved.

关键词： Gradient methods

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Universally Instance-Optimal Mechanisms for Private Statistical Estimation 37

Universally Instance-Optimal Mechanisms for Private Statisti...

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37th Annual Conference on Learning Theory, COLT 2024

作者： Asi, Hilal Duchi, John C. Haque, Saminul Li, Zewei Ruan, Feng Apple United States Departments of Statistics and Electrical Engineering Stanford University United States Department of Computer Science Stanford University United States Department of Statistics and Data Science Northwestern University United States

We consider the problem of instance-optimal statistical estimation under the constraint of differential privacy where mechanisms must adapt to the difficulty of the input dataset. We prove a new instance specific lower bound using a new divergence and show it characterizes the local minimax optimal rates for private statistical estimation. We propose two new mechanisms that are universally instance-optimal for general estimation problems up to logarithmic factors. Our first mechanism, the total variation mechanism, builds on the exponential mechanism with stable approximations of the total variation distance, and is universally instance-optimal in the high privacy regime Ε ≤ 1/√n. Our second mechanism, the T-mechanism, is based on the T-estimator framework (Birgé, 2006) using the clipped log likelihood ratio as a stable test: it attains instance-optimal rates for any Ε ≤ 1 up to logarithmic factors. Finally, we study the implications of our results to robust statistical estimation, and show that our algorithms are universally optimal for this problem, characterizing the optimal minimax rates for robust statistical estimation. © 2024 H. Asi, J.C. Duchi, S. Haque, Z. Li & F. Ruan.

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Label noise robustness of conformal prediction

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期25卷 16026-16091页

作者： Bat-Sheva Einbinder Shai Feldman Stephen Bates Anastasios N. Angelopoulos Asaf Gendler Yaniv Romano Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Department of Computer Science Technion - Israel Institute of Technology Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science University of California Berkeley Departments of Electrical and Computer Engineering and of Computer Science Technion--Israel Institute of Technology

We study the robustness of conformal prediction, a powerful tool for uncertainty quanti _cation, to label noise. Our analysis tackles both regression and classification problems, characterizing when and how it is possible to construct uncertainty sets that correctly cover the unobserved noiseless ground truth labels. We further extend our theory and formulate the requirements for correctly controlling a general loss function, such as the false negative proportion, with noisy labels. Our theory and experiments suggest that conformal prediction and risk-controlling techniques with noisy labels attain conservative risk over the clean ground truth labels whenever the noise is dispersive and increases variability. In other adversarial cases, we can also correct for noise of bounded size in the conformal prediction algorithm in order to ensure achieving the correct risk of the ground truth labels without score or data regularity.

关键词： conformal prediction risk control uncertainty quantification label noise distribution shift

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An Efficient Spatiotemporal Representation Learning Model for Quantitative Precipitation Estimation 9

An Efficient Spatiotemporal Representation Learning Model fo...

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9th IEEE Smart World Congress, SWC 2023

作者： Li, Wenyuan Wang, Xinyi Chen, Haonan Han, Lei Ge, Yurong Ocean University of China Faculty of Information Science and Engineering Qingdao266100 China Colorado State University Department of Electrical and Computer Engineering Fort CollinsCO80523 United States

ISBN: (纸本)9798350319804

Real-time and accurate rainfall estimation, also called quantitative precipitation estimation (QPE), is critical for environmental protection and water resources management. Weather radars are the main instruments for QPE tasks in many nations. However, how to represent the complex nonlinear relationship between radar images and real precipitation intensities, as well as to explore the spatiotemporal dependencies of precipitation for more accurate precipitation estimations remains a challenging problem. This study develops an efficient spatiotemporal representation (ESTR) learning network that uses multi-head attention to capture global information and a Gated Recurrent Unit (GRU) to extract temporal features after residual blocks. In particular, the dual-polarization radar images are utilized as inputs, and point-wise rain gauge measurements as ground truths. The performance of the ESTR network is evaluated using radar images collected from U.S. Weather Surveillance Radar - 1988 Doppler (WSR-88D) during 2016-2019 in Melbourne, Florida. The experimental results show that the ESTR models exhibit superior performance than conventional QPE methods, which achieves the best evaluation metrics, with a mean absolute error (MAE) of 0.89mm, root mean squared error (RMSE) of 1.83mm, normalized standard error (NSE) of 29% and correlation coefficient (CC) of 0.93. These results suggest that the ESTR learning network has great potential for regional rainfall estimation and mapping. © 2023 IEEE.

关键词： deep learning dual-polarization radar images efficient spatiotemporal representation (ESTR) network Environmental protection rainfall estimation

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Development of Phylum Chordata Sound Recognition System using Machine Learning and Deep Learning Techniques: A Case Study of Catfish

Development of Phylum Chordata Sound Recognition System usin...

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2023 IEEE International Conference on electrical, computer and Energy Technologies, ICECET 2023

作者： Akinyemi, Lateef Adesola Oshinuga, Olamide Peter Eshilokun, Oluwafikunmi Adegbenga Oladejo, Sunday Oladayo Ekwe, Stephen Obono Cset University of South Africa Departments of Computer Science and Electrical Engineering Johannesburg South Africa Lagos State University Department of Electronic and Computer Engineering Lagos Nigeria School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Cape Peninsula University of Technology Department of Electrical Electronic and Computer Engineering Cape Town South Africa

ISBN: (纸本)9798350327816

Sound recognition refers to the technology or process of identifying and classifying different sounds or audio signals. This study aims to develop a sound recognition system using machine learning (ML) and deep learning (DL) techniques to classify and identify the gender of catfish based on their speech patterns. The study explores a range of supervised ML and DL algorithms, such as the Artificial Neural Network (ANN), Naïve Bayes (NB), and others, to predict the gender of catfish. The extracted features from the audio signals of the catfish serve as inputs to these algorithms, which produce binary predictions classifying the catfish samples as either male or female. This study further explores data analysis of the audio signals from the male and female catfish. The automated sound recognition system proves to be a more efficient and accurate approach compared to traditional methods, which rely on time-consuming visual observations. Evaluation metrics such as Accuracy, Sensitivity, and more, were employed to gauge the effectiveness of the algorithms in predicting the gender of catfish based on speech patterns. Among the employed algorithms, ANN and NB demonstrate the highest accuracy, achieving a score of 89.47%. © 2023 IEEE.

关键词： Catfish Deep Learning Gender Prediction Machine Learning Audio Data Analysis Sound Recognition

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