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A Material Identification Approach Based on Wi-Fi Signal

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computers, Materials & Continua 2021年第12期69卷 3383-3397页

作者： Chao Li Fan Li Wei Du Lihua Yin Bin Wang Chonghua Wang Tianjie Luo Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510700China PCL Research Center of Cyberspace Security Peng Cheng LaboratoryShenzhen518052China Department of Computer Science and Computer Engineering University of ArkansasFayetteville72701USA College of Electrical Engineering Zhejiang UniversityHangzhou310058China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Material identification is a technology that can help to identify the type of target *** approaches depend on expensive instruments,complicated pre-treatments and professional *** is difficult to find a substantial yet effective material identification method to meet the daily use *** this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level *** practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi *** that,the amplitude ratios and phase differences can be more stably utilized to classify the *** implement our system and evaluate its ability to identify materials in indoor *** result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.

关键词： Internet of Things Wi-Fi signal channel state information material identification noise elimination

Multi-Objective Transmission Expansion: An Offshore Wind Power Integration Case Study

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arXiv 2023年

作者： Khanal, Saroj Graf, Christoph Liang, Zhirui Dvorkin, Yury Ünel, Burçin The Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States The Departments of Electrical and Computer Engineering Department and Civil and Systems Engineering Johns Hopkins University BaltimoreMD21218 United States The Institute for Policy Integrity New York University School of Law New YorkNY10012 United States

Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind and other clean-energy resources in onshore grids, highlights the urgent need for a coordinated planning framework. Our paper describes a multi-objective, multistage generation, storage and transmission expansion planning model to facilitate efficient and resilient large-scale adoption of offshore wind power. Recognizing regulatory emphasis and, in some cases, requirements to consider externalities, this model explicitly accounts for negative externalities: greenhouse gas emissions and local emission-induced air pollution. Utilizing an 8-zone ISO-NE test system and a 9-zone PJM test system, we explore grid expansion sensitivities such as impacts of optimizing Points of Interconnection (POIs) versus fixed POIs, negative externalities, and consideration of extreme operational scenarios resulting from offshore wind integration. Our results indicate that accounting for negative externalities necessitates greater upfront investment in clean generation and storage (balanced by lower expected operational costs). Optimizing POIs could significantly reshape offshore topology or POIs, and lower total cost. Finally, accounting for extreme operational scenarios typically results in greater operational costs and sometimes may alter onshore line investment. Copyright © 2023, The Authors. All rights reserved.

关键词： Investments

Learning to control and coordinate mixed traffic through robot vehicles at complex and unsignalized intersections

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arXiv 2023年

作者： Wang, Dawei Li, Weizi Zhu, Lei Pan, Jia Department of Computer Science The University of Hong Kong Hong Kong Department of Electrical Engineering and Computer Science The University of Tennessee KnoxvilleTN United States Department of Industrial and Systems Engineering The University of North Carolina at Charlotte CharlotteNC United States

Intersections are essential road infrastructures for traffic in modern metropolises. However, they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence of traffic coordination mechanisms such as traffic lights. Recently, various control and coordination mechanisms that are beyond traditional control methods have been proposed to improve the efficiency of intersection traffic by leveraging the ability of autonomous vehicles. Among these methods, the control of foreseeable mixed traffic that consists of human-driven vehicles (HVs) and robot vehicles (RVs) has emerged. We propose a decentralized multi-agent reinforcement learning approach for the control and coordination of mixed traffic by RVs at real-world, complex intersections—an open challenge to date. We design comprehensive experiments to evaluate the effectiveness, robustness, generalizablility, and adaptability of our approach. In particular, our method can prevent congestion formation via merely 5% RVs under a real-world traffic demand of 700 vehicles per hour. In contrast, without RVs, congestion will form when the traffic demand reaches as low as 200 vehicles per hour. Moreover, when the RV penetration rate exceeds 60%, our method starts to outperform traffic signal control in terms of the average waiting time of all vehicles. Our method is not only robust against blackout events, sudden RV percentage drops, and V2V communication error, but also enjoys excellent generalizablility, evidenced by its successful deployment in five unseen intersections. Lastly, our method performs well under various traffic rules, demonstrating its adaptability to diverse scenarios. Videos and code of our work are available at https://***/view/mixedtrafficcontrol. © 2023, CC BY.

关键词： Traffic congestion

Fault Isolation of a Class of Uncertain Nonlinear Parabolic PDE systems

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IFAC-PapersOnLine 2022年第37期55卷 13-18页

作者： Jingting Zhang Chengzhi Yuan Paolo Stegagno Wei Zeng Department of Mechanical Industrial and Systems Engineering University of Rhode Island Kingston RI 02881 USA Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI 02881 USA School of Mechanical and Electrical Engineering Longyan University Longyan 364012 China

This paper proposes a novel fault isolation (FI) scheme for distributed parameter systems modeled by a class of parabolic partial differential equations (PDEs) with nonlinear uncertain dynamics. A key feature of the proposed FI scheme is its capability of dealing with the effects of system uncertainties for accurate FI. Specifically, an approximate ordinary differential equation (ODE) system is first derived to capture the dominant dynamics of the original PDE system. An adaptive dynamics identification approach using radial basis function neural network is then proposed based on this ODE system, to achieve locally-accurate identification of the uncertain system dynamics under faulty modes. A bank of FI estimators with associated adaptive thresholds are finally designed for real-time FI decision making. Rigorous analysis on the fault isolatability is provided. Simulation study on a representative transport-reaction process is conducted to demonstrate the effectiveness of the proposed approach.

关键词： Partial differential equations fault isolation adaptive dynamics identification neural network distributed parameter systems

New Equivalences Between Interpolation and SVMs: Kernels and Structured Features

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arXiv 2023年

作者： Kaushik, Chiraag McRae, Andrew D. Davenport, Mark A. Muthukumar, Vidya School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Institute of Mathematics EPFL Lausanne Switzerland School of Industrial & Systems Engineering Georgia Institute of Technology AtlantaGA United States

The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick. Recent work has demonstrated that in certain sufficiently overparameterized settings, the SVM decision function coincides exactly with the minimum-norm label interpolant. This phenomenon of support vector proliferation (SVP) is especially interesting because it allows us to understand SVM performance by leveraging recent analyses of harmless interpolation in linear and kernel models. However, previous work on SVP has made restrictive assumptions on the data/feature distribution and spectrum. In this paper, we present a new and flexible analysis framework for proving SVP in an arbitrary reproducing kernel Hilbert space with a flexible class of generative models for the labels. We present conditions for SVP for features in the families of general bounded orthonormal systems (e.g. Fourier features) and independent sub-Gaussian features. In both cases, we show that SVP occurs in many interesting settings not covered by prior work, and we leverage these results to prove novel generalization results for kernel SVM *** Codes 62H30, 62J07, 68Q32, 46E22 Copyright © 2023, The Authors. All rights reserved.

关键词： Support vector machines

Faster margin maximization rates for generic optimization methods 23

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Faster margin maximization rates for generic optimization me...

Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Guanghui Wang Zihao Hu Vidya Muthukumar Jacob Abernethy College of Computing Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology and School of Industrial and Systems Engineering Georgia Institute of Technology College of Computing Georgia Institute of Technology and Google Research Atalanta

First-order optimization methods tend to inherently favor certain solutions over others when minimizing a given training objective with multiple local optima. This phenomenon, known as implicit bias, plays a critical role in understanding the generalization capabilities of optimization algorithms. Recent research has revealed that gradient-descent-based methods exhibit an implicit bias for the ℓ2-maximal margin classifier in the context of separable binary classification. In contrast, generic optimization methods, such as mirror descent and steepest descent, have been shown to converge to maximal margin classifiers defined by alternative geometries. However, while gradient-descent-based algorithms demonstrate fast implicit bias rates, the implicit bias rates of generic optimization methods have been relatively slow. To address this limitation, in this paper, we present a series of state-of-the-art implicit bias rates for mirror descent and steepest descent algorithms. Our primary technique involves transforming a generic optimization algorithm into an online learning dynamic that solves a regularized bilinear game, providing a unified framework for analyzing the implicit bias of various optimization methods. The accelerated rates are derived leveraging the regret bounds of online learning algorithms within this game framework.

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Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds

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arXiv 2023年

作者： Xu, Zhenghao Ji, Xiang Chen, Minshuo Wang, Mengdi Zhao, Tuo H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Policy gradient methods equipped with deep neural networks have achieved great success in solving high-dimensional reinforcement learning (RL) problems. However, current analyses cannot explain why they are resistant to the curse of dimensionality. In this work, we study the sample complexity of the neural policy mirror descent (NPMD) algorithm with deep convolutional neural networks (CNN). Motivated by the empirical observation that many high-dimensional environments have state spaces possessing low-dimensional structures, such as those taking images as states, we consider the state space to be a d-dimensional manifold embedded in the D-dimensional Euclidean space with intrinsic dimension d D. We show that in each iteration of NPMD, both the value function and the policy can be well approximated by CNNs. The approximation errors are controlled by the size of the networks, and the smoothness of the previous networks can be inherited. As a result, by properly choosing the network size and hyperparameters, NPMD can find an ϵ-optimal policy with Oe(ϵ− α d −2) samples in expectation, where α ∈ (0, 1] indicates the smoothness of environment. Compared to previous work, our result exhibits that NPMD can leverage the low-dimensional structure of state space to escape from the curse of dimensionality, explaining the efficacy of deep policy gradient algorithms. © 2023, CC BY.

关键词： Reinforcement learning

A Method for Minimally-Invasive Injection of Wireless Microdevices into Brain Tissue

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A Method for Minimally-Invasive Injection of Wireless Microd...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Cassandra Acebal Gurleen Kainth Harry Villanueva Ava Hajjar Valery Fuhrer Paul Wanczuk Alexander S. Lim Shriya Srinivasan Kevin J. Otto Adam Khalifa Department of Biomedical Engineering J. Crayton Pruitt Family University of Florida USA Department of Psychology University of Florida USA Department of Biology University of Florida USA Department of Industrial and Systems Engineering University of Florida USA School of Engineering and Applied Sciences Harvard University USA Department of Electrical and Computer Engineering University of Florida USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

As the field advances with the introduction of a new generation of electrodes, the demand for refined injection methodologies becomes evident. These electrodes, frequently termed microdevices, are intricately microfabricated and require precise insertion into neural tissue. Despite the existence of conventional injection methods, there is a pressing need to formulate strategies aimed at minimizing the damage associated with the deployment of microdevices of varied sizes and configurations. In recognition of this need, the introduced technique can be applied for damage mitigation when injecting microdevices into brain tissue. We introduce a novel injection aid and describe an injection technique that results in reduced tissue damage. The damage of injecting into agarose and the brain, with and without the aid of a tool that creates microvibrations is evaluated. Cryosectioning data is presented to assess the damage caused by the injection, which was found to be 7.4% less when using microvibrations.

关键词： Electrodes Wireless communication Brain Tissue damage Prevention and mitigation Pressing Implants Reliability engineering engineering in medicine and biology Next generation networking

APECS: Adaptive Personalized Control System Architecture

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arXiv 2025年

作者： Juston, Marius F.R. Gisi, Alex Norris, William R. Nottage, Dustin Soylemezoglu, Ahmet The Grainger College of Engineering Industrial and Enterprise Systems Engineering Department University of Illinois Urbana-Champaign UrbanaIL61801-3080 United States The Grainger College of Engineering Electrical and Computer Engineering Department University of Illinois Urbana-Champaign UrbanaIL61801-3080 United States Construction Engineering Research Laboratory U.S. Army Corps of Engineers Engineering Research and Development Center IL61822 United States

This paper presents the Adaptive Personalized Control System (APECS) architecture, a novel framework for human-in-the-loop control. An architecture is developed which defines appropriate constraints for the system objectives. A method for enacting Lipschitz and sector bounds on the resulting controller is derived to ensure desirable control properties. An analysis of worst-case loss functions and the optimal loss function weighting is made to implement an effective training scheme. Finally, simulations are carried out to demonstrate the effectiveness of the proposed architecture. This architecture resulted in a 4.5% performance increase compared to the human operator and 9% to an unconstrained feedforward neural network trained in the same way. © 2025, CC BY.

关键词： Human robot interaction