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arXiv 2024年

作者： Yan, Yuling Wainwright, Martin J. Institute for Data Systems and Society Massachusetts Institute of Technology United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Sciences Massachusetts Institute of Technology United States Department of Mathematics Massachusetts Institute of Technology United States

Longitudinal or panel data can be represented as a matrix with rows indexed by units and columns indexed by time. We consider inferential questions associated with the missing data version of panel data induced by staggered adoption. We propose a computationally efficient procedure for estimation, involving only simple matrix algebra and singular value decomposition, and prove non-asymptotic and high-probability bounds on its error in estimating each missing entry. By controlling proximity to a suitably scaled Gaussian variable, we develop and analyze a data-driven procedure for constructing entrywise confidence intervals with pre-specified coverage. Despite its simplicity, our procedure turns out to be instance-optimal: we prove that the width of our confidence intervals match a non-asymptotic instance-wise lower bound derived via a Bayesian Cramér-Rao argument. We illustrate the sharpness of our theoretical characterization on a variety of numerical examples. Our analysis is based on a general inferential toolbox for SVD-based algorithm applied to the matrix denoising model, which might be of independent interest. Copyright © 2024, The Authors. All rights reserved.

关键词： Singular value decomposition

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Manipulating the Posterior Support of a Discrete Bayesian Estimator Under Full Sensor Control

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IFAC-PapersOnLine 2023年第2期56卷 252-257页

作者： Daniel Selvaratnam Farhad Farokhi Iman Shames Henrik Sandberg Decision and Control Systems School of Electrical Engineering & Computer Science KTH Royal Institute of Technology 114 28 Stockholm Sweden Department of Electrical and Electronic Engineering The University of Melbourne Parkville VIC 3010 Australia College of Engineering & Computer Science Australian National University Canberra ACT 2601 Australia

The asymptotic implausibility problem is introduced from the perspective of an adversary that seeks to drive the belief of a recursive Bayesian estimator away from a particular set of parameter values. It is assumed that the adversary controls all sensors informing the estimator, and can transmit false measurements stochastically according to a fixed distribution of its choice. First, we outline a method for verifying whether a given distribution solves the problem. We then consider the class of spoofing attacks, and show that the asymptotic implausibility problem has a solution if and only if it can be solved by a spoofing attack. Attention is restricted to finite parameter and observation spaces.

关键词： Network Security Bayesian Estimation Cyber-Physical systems Network systems Resilient Control systems Signal Processing

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Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication

arXiv

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arXiv 2023年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. The Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA02215 United States Hong Kong The Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia1678 Cyprus The Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland The Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden

In this paper, we study unconstrained distributed optimization strongly convex problems, in which the exchange of information in the network is captured by a directed graph topology over digital channels that have limited capacity (and hence information should be quantized). Distributed methods in which nodes use quantized communication yield a solution at the proximity of the optimal solution, hence reaching an error floor that depends on the quantization level used;the finer the quantization the lower the error floor. However, it is not possible to determine in advance the optimal quantization level that ensures specific performance guarantees (such as achieving an error floor below a predefined threshold). Choosing a very small quantization level that would guarantee the desired performance, requires information packets of very large size, which is not desirable (could increase the probability of packet losses, increase delays, etc) and often not feasible due to the limited capacity of the channels available. In order to obtain a communication-efficient distributed solution and a sufficiently close proximity to the optimal solution, we propose a quantized distributed optimization algorithm that converges in a finite number of steps and is able to adjust the quantization level accordingly. The proposed solution uses a finite-time distributed optimization protocol to find a solution to the problem for a given quantization level in a finite number of steps and keeps refining the quantization level until the difference in the solution between two successive solutions with different quantization levels is below a certain pre-specified threshold. Therefore, the proposed algorithm progressively refines the quantization level, thus eventually achieving low error floor with a reduced communication burden. The performance gains of the proposed algorithm are demonstrated via illustrative examples. © 2023, CC BY.

关键词： Directed graphs

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State of Charge Estimation for Zinc-Bromine Flow Batteries by Improved Long Short-Term Memory Network and Kalman Filter

State of Charge Estimation for Zinc-Bromine Flow Batteries b...

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Energy Storage and Conversion (ISESC), CPSS & IEEE International Symposium on

作者： Kaiwen Peng Bichen Chen Liguo Wang Denis Sidorov Aliona Dreglea Zongjie Wang School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Northeast Agricultural University Harbin China Melentiev Energy Systems Institute Siberian Branch of the Russian Academy of Sciences Irkutsk Russia Department of Electrical and Computer Engineering University of Connecticut Mansfield USA

ISBN: (数字)9798350380514

ISBN: (纸本)9798350380521

In order to improve the accuracy of estimating the state of charge (SOC) of zinc-bromine flow batteries (ZBFB) in the discharge stage and overcome the problems caused by sudden voltage changes, this paper proposes an algorithm for estimating and correcting the SOC. The core contribution is the proposal of an improved Long Short-Term Memory (LSTM) neural network algorithm, which considers the effect of the circulating pump speed on the battery voltage during the discharge stage, and establishes a neural network model for the battery. The estimation result of the improved LSTM network has a mean square error of 3.322, representing a 12% improvement in estimation accuracy compared to the traditional LSTM network. Additionally, Kalman filtering is employed for error mitigation to further enhance estimation accuracy. After applying the proposed method, the maximum error between the estimated SOC and the actual SOC is 1.91%, and the average error is 0.30%. This indicates that the proposed method improves the accuracy of SOC estimation through enhanced real-time LSTM network estimation and Kalman filtering error cancellation, particularly during voltage sudden change in the discharge of ZBFB.

关键词： Accuracy Filtering Neural networks Estimation Voltage Discharges (electric) Batteries State of charge Kalman filters Long short term memory

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Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis

arXiv

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arXiv 2024年

作者： Lanzino, Romeo Fontana, Federico Cinque, Luigi Scarcello, Francesco Maki, Atsuto Sapienza University of Rome Computer Science Department Rome00198 Italy University of Calabria Department of Computer Engineering Modeling Electronics and Systems Rende87030 Italy KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper introduces the Neural Transcoding Vision Transformer (NT-ViT), a generative model designed to estimate high-resolution functional Magnetic Resonance Imaging (fMRI) samples from simultaneous Electroencephalography (EEG) data. A key feature of NT-ViT is its Domain Matching (DM) sub-module which effectively aligns the latent EEG representations with those of fMRI volumes, enhancing the model’s accuracy and reliability. Unlike previous methods that tend to struggle with fidelity and reproducibility of images, NT-ViT addresses these challenges by ensuring methodological integrity and higher-quality reconstructions which we showcase through extensive evaluation on two benchmark datasets;NT-ViT outperforms the current state-of-the-art by a significant margin in both cases, e.g. achieving a 10× reduction in RMSE and a 3.14× increase in SSIM on the Oddball dataset. An ablation study also provides insights into the contribution of each component to the model’s overall effectiveness. This development is critical in offering a new approach to lessen the time and financial constraints typically linked with high-resolution brain imaging, thereby aiding in the swift and precise diagnosis of neurological disorders. Although it is not a replacement for actual fMRI but rather a step towards making such imaging more accessible, we believe that it represents a pivotal advancement in clinical practice and neuroscience research. Code is available at https://***/rom42pla/ntvit. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Skeleton-Based Multi-Stream Adaptive Graph Convolutional Network for Indoor Scene Action Recognition

Skeleton-Based Multi-Stream Adaptive Graph Convolutional Net...

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2023 China Automation Congress, CAC 2023

作者： Li, Jiazhuo Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru The School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland The Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey The Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798350303759

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results. © 2023 IEEE.

关键词： Convolution

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An Encoder-Sequencer-Decoder Network for Lane Detection to Facilitate Autonomous Driving 23

An Encoder-Sequencer-Decoder Network for Lane Detection to F...

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23rd International Conference on Control, Automation and systems, ICCAS 2023

作者： Hussain, Muhammad Ishfaq Rafique, Muhammad Aasim Ko, Yeongmin Khan, Zafran Olimov, Farrukh Naz, Zubia Kim, Jeongbae Jeon, Moongu School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Korea Republic of College of Computer Sciences and Information Technology King Faisal University Department of Information Systems Al Ahsa31982 Saudi Arabia Monitorapp Threat Intelligence Team Seoul Korea Republic of Pusan National University Pusan Korea Republic of

ISBN: (纸本)9788993215274

Lane detection in all weather conditions is a pressing necessity for autonomous driving. Accurate lane detection ensures the safe operation of autonomous vehicles, enabling advanced driver assistance systems to effectively track and maintain the vehicle within the lanes. Traditional lane detection techniques heavily rely on a single image frame captured by the camera, posing limitations. Moreover, these conventional methods demand a constant stream of pristine images for uninterrupted lane detection, resulting in degraded performance when faced with challenges such as low brightness, shadows, occlusions, and deteriorating environmental conditions. Recognizing that continuous sequence patterns on the road represent lanes, our approach leverages a sequential model to process multiple images for lane detection. In this study, we propose a deep neural network model to extract crucial lane information from a sequence of images. Our model adopts a convolutional neural network in an encoder/decoder architecture and incorporates an extended short-term memory model for sequential feature extraction. We evaluate the performance of our proposed model using the TuSimple and CuLane datasets, showcasing its superiority across various lane detection scenarios. Comparative analysis with state-of-the-art lane detection methods further substantiates our model's effectiveness. © 2023 ICROS.

关键词： Advanced driver assistance systems

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An Iterative Method for a Frequency Domain PID Controller Synthesis

An Iterative Method for a Frequency Domain PID Controller Sy...

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SICE Annual Conference

作者： Kenjiro Minamikawa Yasushi Kami Eitaku Nobuyama Atsushi Hosokawa Department of Mechanical and Electronic System Engineering Advanced Course of National Institute of Technology Akashi College Hyogo Japan Department of Electrical Systems Engineering Hiroshima Institute of Technology Hiroshima Japan Department of Intelligent and Control Systems Kyushu Institute of Technology Fukuoka Japan Department of Electrical and Computer Engineering National Institute of Technology Akashi College Hyogo Japan

This study is concerned with a control system design method with frequency response data named a finite number of frequency responses(an FNFR) model. In this paper a numerical descent direction is introduced and an iterative method for solving control problems formulated with the FNFR model is proposed with the descent direction. One feature of our approach is to update controller variables so that they approach to a feasible region of considering control problems from the outside. The efficiency of our method is shown by numerical examples.

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Live Demonstration: A fully embedded adaptive real-time hand gesture classifier leveraging HD-sEMG and deep learning

Live Demonstration: A fully embedded adaptive real-time hand...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Xavier Isabel Thomas Labbé Félix Chamberland Étienne Buteau Evan Campbell Ulysse Côté-Allard Simon Tam Paul Fortier Alexandre Campeau-Lecours Mounir Boukadoum Erik Scheme Benoit Gosselin Electrical Engineering and Computer Engineering Department Laval University Institute of Biomedical Engineering University of New-Brunswick Department off Technology Systems University of Oslo Mechanical Engineering Department Laval University Computer Science Department University of Quebec at Montreal

In a fully immersive virtual reality setting, visitors will actively participate in our demonstration, engaging in an interactive videogame utilizing our cutting-edge HD-sEMG Bracelet. Prior to this, they will undergo state-of-the-art training to personalize their machine learning gesture recognition model. The gameplay will be broadcast on a computer monitor, allowing onlookers to observe while the demonstrator explains the core concept of our work. By wearing the bracelet, the player gains control over their limb in a game inspired by Simon Says. Throughout the demonstration, visitors will gain firsthand experience and knowledge about the forthcoming advantages of HD-sEMG for control applications, such as prosthesis control. They will also discover how the EMaGer HD-sEMG Bracelet capitalizes on its uniform and high electrode density to enhance the reliability of hand gesture recognition models utilized in myoelectric prosthesis control.

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Ge2Sb2Se4Te Phase Change Material Monolithic Platform for Integrated Photonics

Ge2Sb2Se4Te Phase Change Material Monolithic Platform for In...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Acharjee, Niloy Sun, Hongyi Ghosh, Siddhartha Ríos Ocampo, Carlos A. Institute for Research in Electronics & Applied Physics University of Maryland College ParkMD20742 United States Department of Electrical & Computer Engineering University of Maryland College ParkMD20742 United States Department of Materials Science & Engineering University of Maryland College ParkMD20742 United States Space Systems - Northrop Grumman 1 Space Park Blvd Redondo BeachCA90278 United States

ISBN: (纸本)9781957171395

We demonstrate a monolithic Ge2Sb2Se4Te platform for tunable photonic integrated circuits. We fabricated and measured various on-chip components, including waveguides with preliminary 55.7±3.65 dB/cm propagation loss. Refractive index tuning is demonstrated via post-fabrication localized crystallization. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： System-on-chip

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