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arXiv 2023年

作者： Gürbüzbalaban, Mert Department of Management Science and Information Systems Department of Electrical and Computer Engineering Department of Statistics Rutgers University NJ United States

We consider the problem of minimizing a strongly convex smooth function where the gradients are subject to additive worst-case deterministic errors that are square-summable. We study the trade-offs between the convergence rate and robustness to gradient errors when designing the parameters of a first-order algorithm. We focus on a general class of momentum methods (GMM) with constant stepsize and two momentum parameters which can recover gradient descent (GD), Nesterov’s accelerated gradient (NAG), the heavy-ball (HB) and the triple momentum methods (TMM) as special cases. We measure the robustness of an algorithm in terms of the cumulative suboptimality over the iterations normalized by the squared 2 norm of the gradient errors. This quantity can be interpreted as the (squared) 2 gain of a dynamical system that represents the GMM iterations where the input is the gradient error sequence and the output is a weighted distance to the optimum. For quadratic objectives, we compute the 2 gain explicitly leveraging its representation as the H∞ norm of the GMM system in the frequency domain and construct gradient errors that lead to worst-case performance explicitly. We also study the stability of GMM with respect to multiplicative errors by characterizing the structured real and stability radius of the GMM system through their connections to the H∞ norm. This allows us to compare GD, HB, NAG methods in terms of robustness, and argue that HB is not as robust as NAG despite being the fastest in terms of the rate. We then develop the robustly stable heavy ball method that can be faster than NAG while being at the best robustness level possible. We also propose the robustly stable gradient descent that is the fastest version of GD with constant stepsize while being at the best robustness level. Finally, we extend our framework to general strongly convex smooth objectives, providing non-asymptotic rate results for inexact GMM methods and bounds on the 2 gain where we can choos

关键词： Momentum

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Efficient displacement convex optimization with particle gradient descent

arXiv

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arXiv 2023年

作者： Daneshmand, Hadi Lee, Jason D. Jin, Chi Laboratory for Information and Decision Systems MIT United States Department of Electrical Engineering and Computer Science Princeton Universit United States

Particle gradient descent, which uses particles to represent a probability measure and performs gradient descent on particles in parallel, is widely used to optimize functions of probability measures. This paper considers particle gradient descent with a finite number of particles and establishes its theoretical guarantees to optimize functions that are displacement convex in measures. Concretely, for Lipschitz displacement convex functions defined on probability over Rd, we prove that O(1/ϵ2) particles and O(d/ϵ4) computations are sufficient to find the ϵ-optimal solutions. We further provide improved complexity bounds for optimizing smooth displacement convex functions. We demonstrate the application of our results for function approximation with specific neural architectures with two-dimensional inputs. © 2023, CC BY-NC-ND.

关键词： Convex optimization

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False Data Injection Attack Detection Based on Wavelet Packet Decomposition and Random Forest in Smart Grid 23

False Data Injection Attack Detection Based on Wavelet Packe...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Zhenyu Yuan, Shuai Wu, Longfei Guan, Zhitao Du, Xiaojiang School of Control and Computer Engineering North China Electric Power University China Brock University Department of Finance Operations and Information Systems ON Canada Fayetteville State University Department of Mathematics and Computer Science Fayetteville United States Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9781665494571

As one of the critical infrastructures, the safety and reliability of the smart grid are directly associated with the development and stability of society. However, studies have shown that the power grid is at risk when the parameters are manipulated and cyber-attacks are generated against the state estimation, i.e., under false data injection attack (FDIA). Currently, a rich body of literature has studied on the FDIA defense methods, but most of them focus on the direct current (DC) scenario. This paper proposes a novel detection model that combines the wavelet packet decomposition (WPD) technique with the random forest (RF) algorithm. The WPD is able to capture the deviation of parameters from the normal conditions, whereas the RF is developed to classify these features and effectively identify the malicious data. The proposed model is also evaluated using real-world data on IEEE 118-bus power system. The results demonstrate excellent performance on precision rate and recall rate under varying scenarios. © 2021 IEEE.

关键词： Radio frequency Training data Interference Power system stability Feature extraction Data models Wavelet packets

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Efficient Drinking Water Quality Analysis using Machine Learning Model with Hyper-Parameter Tuning 7

Efficient Drinking Water Quality Analysis using Machine Lear...

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7th International Conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Maheswari, M Uma Sudharsanan, R. Arthy, M. Jenefer, Anne Oormila, L. Samuthira Pandi, V. RMD Engineering College Department of Electrical and Electronics Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Information Technology Tamil Nadu Ramapuram India SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu India Agni College of Technology Department of Computer Science Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Electronics Ad Communication Engineering Tamilnadu Ramapuram India

ISBN: (纸本)9798350397253

The conventional approaches to determining the quality of water include costly and lengthy statistical and laboratory testing;hence, the concept of real-time monitoring is no longer applicable in the modern day. The disastrous implications of poor water quality demand that there be a remedy that is both more expedient and more realistic. This research study examines a variety of supervised machine learning algorithms in order to identify the quality of the water. A variety of variables, such as the pH value, the hardness, the solids, the chloramines, the sulfates, the conductivity, the organic carbon, the trihalomethanes, the turbidity, and the potability of the water, are essential for determining the quality of the water. Random Forest (RF) and Decision Tree (DT) are used in order to ascertain the caliber of the water that is suitable for human consumption. The standard laboratory approach for testing the quality of water is one that takes a lot of time and may be somewhat expensive at times. In a very short length of time, the algorithms that are proposed in this study are able to provide an estimation of the quality of drinking water. DT has a height accuracy F1 score of 99%, whereas RF has a score of 87.86% and a precision of 82.36%. The difference in these two scores is due to the fact that DT's precision is lower. The suggested method demonstrates its potential for usage in real-time water quality monitoring systems by achieving appropriate accuracy with a minimal set of parameters. To prove the usefulness of this program, this is essential. © 2023 IEEE.

关键词： Water quality

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Image-based Cutting Tool Health Diagnosis in Multi-machine Settings

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Procedia computer Science 2025年 253卷 1412-1421页

作者： Debasish Mishra Krishna R. Pattipati George M. Bollas Data Science and Information Systems Krea University 5655 Central Expressway Sri City 517646 India Electrical and Computer Engineering University of Connecticut 371 Fairfield Way Storrs 06269 USA Chemical and Biomolecular Engineering University of Connecticut 159 Discovery Drive Storrs 06269 USA

Manufacturers are increasingly focused on evaluating the condition of cutting tools during machining processes to ensure high-quality production. This study introduces a method for accurately and cost-effectively diagnosing the health of cutting tools. The method employs image similarity indices–such as Bhattacharyya distance, Fourier transform, and Cosine similarity–to compute indicators of tool health from wavelet coefficient maps of cutting signals. These indicators are then used in an unsupervised machine learning (ML) algorithm, agglomerative clustering, to categorize the tool health state. To validate the method, run-to-failure tests of the tool were conducted on two different machines with varying specifications and machining parameters. The results demonstrate a strong association between the computed indicators and the tool health, regardless of the differences between the two machines and their cutting settings. This is due to the reliance of the method on the physics of machining processes to derive the health indicators. The ML model effectively categorizes the progressive health of cutting tools and provides easily interpretable results on the health of the tool due to the explainability of the indicators.

关键词： Tool condition monitoring Precision machining Similarity Machine Learning Unsupervised Clustering

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Joint Optimization for Cooperative Service-Caching, Computation-Offloading, and Resource-Allocations Over EH/MEC-Based Ultra-Dense Mobile Networks

Joint Optimization for Cooperative Service-Caching, Computat...

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IEEE International Conference on Communications (ICC)

作者： Zhian Chen Fei Wang Xi Zhang College of Electronic and Information Engineering Southwest University Chongqing China Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

Mobile edge-computing (MEC) enabled ultra-dense networks (UDNs), which merges edge-computing with UDNs, can provide enormous benefits, e.g., ultra-low latency. However, due to the ultra-dense deployment of small-cell base stations (SBSs), it becomes infeasible to just depend on the grid power for energy providing, and also it is challenging to jointly optimize service-caching, computation-offloading, and resource-allocation. Integrating energy-harvesting (EH) techniques into MEC-enabled UDNs, we investigate the joint optimization for cooperative service-caching, computation-offloading, and resource-allocation. In our considered UDNs, there exist a large number of EH-based mobile users (MUs) and a mixture of on-grid SBSs, powered by electric grid, and off-grid SBSs, powered by solar, radio frequency (RF) energy, etc. We formulate an energy minimization problem to minimize the sum of weighted energy consumption of all MUs and off-grid SBSs. Also, we develop a two-timescale based joint cooperative service-caching, computation-offloading, and resource-allocation scheme based on the hierarchical multiagent deep reinforcement learning (HMDRL). Using HMDRL, we first derive SBSs' cooperative service-caching policies which are updated in each time frame consisting of multiple time slots. Then, we derive MUs' and SBSs' computation-offloading policies and SBSs' computation resource-allocation policies, which are updated in each time slot. Finally, we validate and evaluate the performances of our proposed schemes through simulations.

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System-Level Study on Radar-Based Human Vocal Sound Estimation

System-Level Study on Radar-Based Human Vocal Sound Estimati...

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IEEE International Conference on Radar

作者： Yu Rong Kawon Han Adarsh A. Venkataramani Isabella Lenz Visar Berisha Daniel W. Bliss Center for Wireless Information Systems and Computational Architectures (WISCA) Arizona State University Tempe AZ USA The Department of Electronic and Electrical Engineering University College London London U.K. The Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM College of Health Solutions and School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA

ISBN: (数字)9798331539566

ISBN: (纸本)9798331539573

This paper explores the use of mmWave radar for estimating human vocal sounds, focusing on its capability to capture vocal fold vibrations during speech. The key contributions include a domain transformation method enabling comparison between radar and acoustic sensors in the same measurement domain, a noise performance analysis demonstrating radar's superiority over tactical throat microphones in noisy environments, and a comparative analysis of radar performance for estimating pitch frequency and harmonics across various radar configurations. The results highlight the potential of mmWave radar as a robust tool for speech sensing, outperforming traditional methods in challenging acoustic conditions while maintaining high accuracy in capturing pitch-related metrics at short distances.

关键词： Vibrations Radar measurements Radar Acoustic measurements Radar signal processing Acoustics Filtering theory Noise measurement Millimeter wave communication Microphones

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Scaling Distributed Collaboration in Mixed Reality

Scaling Distributed Collaboration in Mixed Reality

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2025 CHI Conference on Human Factors in Computing systems, CHI EA 2025

作者： Genay, Adélaïde Syiem, Brandon Victor Wong, Emily Feuchtner, Tiare Knibbe, Jarrod Grønbæk, Jens Emil Sloth Velloso, Eduardo School of Computer Science University of Sydney SydneyNSW Australia School of Computing and Information Systems The University of Melbourne MelbourneVIC Australia HCI Group University of Konstanz Konstanz Germany Department of Computer Science Aarhus University Aarhus Denmark School of Electrical Engineering and Computer Science The University of Queensland St. LuciaQLD Australia

ISBN: (纸本)9798400713958

Distributed collaboration in Mixed Reality (MR) promises to revolutionise how people connect across different physical environments, offering experiences akin to face-to-face interactions. However, previous work has mostly focused on enabling this vision in overly simplified settings such as with only two users interacting in identical distributed environments. Scaling current systems to work with large groups and for common real-life scenarios is a persistent challenge that requires addressing multiple tensions. We identified six challenges: 1) supporting locally congruent actions from heterogeneous remote spaces, 2) communicating accurate user behaviours through virtual representation instead of physical bodies, 3) facilitating organic group interactions within limited physical space, 4) maintaining conversational dynamics even in asynchronous exchanges, 5) providing equal access to physical objects for all participants, and 6) enabling efficient task switching within a complex ecology of applications, devices, and accessibility needs. This workshop aims to gather researchers and practitioners to explore actionable strategies for resolving these challenges. Through a mix of presentations, hands-on activities, and group discussions, participants will generate new ideas and develop a research agenda to articulate the future of MR collaboration systems. The workshop outcomes will include a list of concrete next steps for the community to bring distributed MR collaboration at scale. © 2025 Copyright held by the owner/author(s).

关键词： Grid computing

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Orthogonal matrix of polarization combinations:concept and application to multichannel holographic recording

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Opto-Electronic Advances 2024年第10期7卷 4-13页

作者： Shujun Zheng Jiaren Tan Hongjie Liu Xiao Lin Yusuke Saita Takanori Nomura Xiaodi Tan Information Photonics Research Center College of Photonic and Electronic EngineeringFujian Normal UniversityFuzhou 350117China Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA College of Photonic and Electronic Engineering Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of EducationFujian Provincial Key Laboratory of Photonics TechnologyFujian Provincial Engineering Technology Research Center of Photoelectric Sensing ApplicationFujian Normal UniversityFuzhou 350117China Faculty of Systems Engineering Wakayama University930 SakaedaniWakayama640-8510Japan.

Orthogonal matrices have become a vital means for coding and signal processing owing to their unique distributional *** orthogonal matrices based on amplitude or phase combinations have been extensively explored,the orthogonal matrix of polarization combinations(OMPC)is a novel,relatively unexplored ***,we propose a method for constructing OMPCs of any dimension encompassing 4n(where n is 1,2,4,8,…)mutually orthogonal 2ncomponent polarization *** the field of holography,the integration of polarization multiplexing techniques with polarization-sensitive materials is expected to emerge as a groundbreaking approach for multichannel hologram multiplexing,offering considerable enhancements in data storage capacity and security.A multidimensional OMPC enables the realization of multichannel multiplexing and dynamical modulation of information in polarization holographic *** consolidating all information into a single position within the material,we effectively avoided extraneous crosstalk during the reconstruction *** results show that achieving four distinct holographic images individually and simultaneously depends on the polarization combination represented by the incident *** discovery opens up a new avenue for achieving highly holographic information storage and dynamically displayed information,harnessing the potential of OMPC to expand the heretofore limited dimensionality of orthogonal polarization.

关键词： orthogonal matrix of polarization combinations polarization-modulated multiplexing multichannel recording polarization holography dynamical information modulation

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Integration of SAP and Intelligent Robotic Process Automation

Integration of SAP and Intelligent Robotic Process Automatio...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Yendluri, Dileep Kumar Tatikonda, Reshmi Thatikonda, Ramya Ponnala, Jainath Kempanna, M. Bhuvanesh, A. University of the Cumberlands Department of Computer Science WilliamsburgMO United States University of the Central Missouri Department of Computer Information Systems WarrensburgMO United States University of the Cumberlands Department of Information Technology WilliamsburgMO United States Jawaharlal Technological University Department of ECE Hyderbad India Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350319088

This article explores how implementing IRPA in enterprises could have a revolutionary effect. Businesses looking to maximize efficiency can find a partnership between IRPA and SAP systems to be a wise way to achieve their goals. This study investigates how several company operations including payroll processing, human administration, financial and accounting processes, and logistics are impacted by integration. This talk explores the benefits, challenges, and best practices for integrating IRPA with SAP, offering specific instances from real-world projects that highlight the effectiveness of this relationship. The future scope of the IRPA-SAP integration looks at enhanced analytics, hyper-automation, cutting-edge cognitive features, broader industry applications, and the growth of human-robot cooperation. A thorough analysis highlights tactics that businesses may use to use SAP's and IRPA's combined power to accelerate digital transformation and achieve peak performance. © 2023 IEEE.

关键词： Integration

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