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Enhancing and Validating Simulator data Generation Integrity Through Frequency-Based Analysis 9

Enhancing and Validating Simulator Data Generation Integrity...

引用

9th ieee/ACIS international conference on Big data, Cloud Computing, and data science, BCD 2024

作者： Sharifirad, Iman Boudjadar, Jalil Larsen, Peter Gorm Aarhus University Department of Electrical and Computer Engineering Denmark

ISBN: (纸本)9798350394191

data seamlessly becomes a critical component for the state estimation and performance optimization of Cyber-Physical systems (CPSs). Ensuring timely access to CPS data could be challenging due to operation costs, limited accessibility, security and connectivity, etc. CPS simulators have been thoroughly used in industry to synthesize CPS data. However, a common drawback for CPS simulators is the low integrity and fidelity of the synthesized data to the actual system. In this paper, we propose an extension, calibration, and validation to enhance the data synthesis integrity of an industrial CPS simulator. Behavior patterns are inferred from actual data, using frequency analysis, and applied to the simulation data synthesis, where a calibration using cross-correlation is utilized to fine-tune the data synthesis. According to the analysis results, our enhanced simulation data synthesis demonstrates a correlation of 93.7% to the actual CPS data. © 2024 ieee.

关键词： Operating costs

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Refining Automation in Power Dispatching systems: A Cloud Optimization Investigation 7

Refining Automation in Power Dispatching Systems: A Cloud Op...

引用

7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Zhang, Chi Zeng, Ciling Zeng, Weiwei Changsha University of Science And Technology International Institute of Technology Changsha410114 China State Grid Hunan Electric Power Company Changsha410004 China Beijing Kedong Electric Power Control System Co.Ltd. Beijing100192 China

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

Efficiency in power dispatch systems is critical to ensure as it depends on the optimal resource utilization and maintaining grid stability. As power systems evolve into data-intensive environments, traditional centralized computing platforms are challenged to efficiently process information. To address this, the use of distributed computing and storage technologies is developed. In addition, the integration of advanced technologies such as cloud computing, artificial intelligence, and big data analytics can further enhance the performance and scalability of power dispatching systems. This study investigates the application of cloud optimization techniques in refining automation within power dispatching systems. Specifically, it explores the integration of cloud computing technology to improve data processing algorithms and efficiency challenges in power grid systems. Through a proposed methodology that incorporates a distributed data service bus, massive data storage systems, and dynamic load balancing mechanisms, the study aims to improve system reliability, real-time responsiveness, and accuracy. In addition, the study discusses the optimization of real-time load forecasting and dispatching strategies using time series analysis, statistical methods, and emerging machine learning techniques. The effectiveness of the proposed approach is demonstrated through simulation and verification, highlighting the improved system efficiency and reliability achieved through cloud optimization in power dispatching systems. © 2024 ieee.

关键词： Efficiency

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Cough Event Prediction based on Spectral Features and SVM and KNN Machine learning using Triaxial Accelerometer data from Multiple body positions 67

Cough Event Prediction based on Spectral Features and SVM an...

引用

67th ieee international Midwest Symposium on Circuits and systems (MWSCAS)

作者： Gnanasekar, Sudarsini Tekkam Doddabasappa, Kruthi Vyas, Rushi Univ Calgary Elect & Software Engn Calgary AB Canada

ISBN: (纸本)9798350387186;9798350387179

Cough is a major illness and needs to be tackled efficiently to manage respiratory distress and the long-term effects of respiratory problems. In this work an efficient method is proposed using the multi-band spectral features of triaxial accelerometer data which is recorded from different body positions is analyzed and this method reduces the computational complexity significantly. The measurements yield better results in the Y axis compared to the X and Z axes for chest and stomach positions whereas for the ear position it is observed that Z axis produced a higher feature score for cough activity prediction. The classification was performed using SVM and KNN and the best performance observed was in the ear position with a maximum accuracy of 99%.Unlike prior works that use computationally intensive methods such as CNN the proposed method uses Spectral features and SVM and KNN from worn Triaxial Accelerometer signals.

关键词： Cough Sensor Spectral Features Support Vector Machines k-Nearest Neighbors Body positions Frequency Domain

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Performance Analysis of Integrated data and Energy Transfer Assisted by Fluid Antenna systems 59

Performance Analysis of Integrated Data and Energy Transfer ...

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59th Annual ieee international conference on Communications (ieee ICC)

作者： Lin, Xiao Yang, Halvin Zhao, Yizhe Hu, Jie Wong, Kai-Kit Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China UCL Dept Elect & Elect Engn London England

ISBN: (纸本)9781728190549

Fluid antenna multiple access (FAMA) is capable of exploiting the high spatial diversity of wireless channels to mitigate multi-user interference via flexible port switching, which achieves a better performance than traditional multi-input-multi-output (MIMO) systems. Moreover, integrated data and energy transfer (IDET) is able to provide both the wireless data transfer (WDT) and wireless energy transfer (WET) services towards low-power devices. In this paper, a FAMA assisted IDET system is studied, where N access points (APs) provide dedicated IDET services towards N user equipments (UEs). Each UE is equipped with a single fluid antenna. The performance of WDT and WET, i.e., the WDT outage probability, the WET outage probability, the reliable throughput and the average energy harvesting amount, are analysed theoretically by using time switching (TS) between WDT and WET. Numerical results validate our theoretical analysis, which reveals that the number of UEs and TS ratio should be optimized to achieve a tradeoff between the WDT and WET performance. Moreover, FAMA assisted IDET achieves a better performance in terms of both WDT and WET than traditional MIMO with the same antenna size.

关键词： Fluid Antenna Multiple Access (FAMA) Integrated data and Energy Transfer (IDET) Time Switching (TS) Performance Analysis

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A data-driven approach for context definition in situation-aware wearable computing systems 20

A data-driven approach for context definition in situation-a...

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ieee 20th international conference on Automation science and Engineering (CASE)

作者： D'Aniello, Giuseppe Fortino, Giancarlo Gaeta, Matteo Rehman, Zia Ur Univ Salerno Dept Informat & Elect Engn & Appl Math Fisciano SA Italy Univ Calabria Dept Comp Engn Modeling Elect & Syst Arcavacata Di Rende CS Italy

ISBN: (纸本)9798350358513;9798350358520

A situation-aware wearable computing system is defined as a wearable device with the capability to perceive, comprehend, and project the situations occurring in the environment in order to adapt its behavior accordingly. For this kind of devices, the identification of complex situations related to the activities performed by users in various contexts is of great interest. This requires the capability to identify context states. A well-known technique for this task is the Context Space Theory (CST), which provides a multidimensional space representing contexts. Although powerful and lightweight, this technique has the drawback of requiring manual definition of such a space, a time-consuming process that involves domain experts. To address this issue, this work proposes a data-driven approach for defining context spaces in CST using kernel density estimation. This approach is compared with a state-of-theart expert-based CST technique and a fuzzy inference system for context representation, demonstrating superior performance on the Extrasensory dataset.

关键词： Wearable computers

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data-mechanism-driven Product Performance Optimization with Multiple Parameters Under Uncertainties in Manufacturing Automation systems 20

Data-mechanism-driven Product Performance Optimization with ...

引用

ieee 20th international conference on Automation science and Engineering (CASE)

作者： Cui, Kaiyue Hong, Zhaoxi Song, Xiuju Zhou, Mengchu Li, Zhiwu Feng, Yixiong Tan, Jianrong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Ningbo Innovat Ctr Ningbo 315100 Zhejiang Peoples R China Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Guizhou Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

ISBN: (纸本)9798350358513;9798350358520

High-end equipment with robust and excellent performance is of great significance to manufacturing automation systems. Product parameters are the foundation of performance. Usually, there is a lack of clear physical models to reveal the relationship between parameters and performance. Simulation is an important way to understand the design results of product performance parameters. However, simulations are often particularly time and resource-consuming. To address these issues, this work proposes a data-mechanism-driven product performance optimization method, which introduces Taguchi' method, nonparametric correlation testing, and multiple attribute decision making (MADM), to efficiently obtain the optimal parameter scheme. A novel MADM method is designed, which is combined with the Multi-Objective Optimization on the basis of a Ratio Analysis plus the full MULTIplicative form (MULTIMOORA) and the Variable Neighborhood Search. It has been proven that it performs better than the MULTIMOORA based on the well-known Simulated Annealing and Tabu search. Finally, taking the cutting unit of EBZ200i as a case study, we have successfully obtained the optimal parameter scheme that comprehensively performs better in terms of wear performance, robustness, and environmental economic performance.

关键词： Simulated annealing

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Do data Scientists Dream About Their Skills' Assessment? - Transforming a Competition Platform Into an Assessment Platform

Do Data Scientists Dream About Their Skills' Assessment? - T...

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2024 ieee international conference on Big data, Bigdata 2024

作者： Slȩzak, Dominik Janusz, Andrzej Swiechowski, MacIej Chądzynska-Krasowska, Agnieszka Kaminski, Jacek Qed Software Poland University of Warsaw Institute of Informatics Poland Polish-Japanese Academy of Information Technology Poland Qut School of Information Systems Australia

ISBN: (纸本)9798350362480

We present a platform for automatic assessment of technical data science skills (hard skills) and competencies that help to apply those technical skills in practice (soft skills). The platform serves so-called assessment platform tasks that resemble data-focused tasks typical for online data science competitions. Actually, these tasks are designed based on international conference competitions that we have been organizing on our competition platform ***. The main idea relies on our observation that the given person's behavior during a competition (dynamics of submitting solutions, activity on competition forum, etc.) can be correlated with his/her soft competencies. Accordingly, our goal is to translate the specifics of international conference competitions into a framework that investigates the behaviors of data science job candidates, employees who want to improve their work in data science projects, students who wish to build their future professional careers on AI solutions, etc. We claim that such behaviors - if properly measured based on solving our assessment platform tasks - can effectively indicate both hard and soft types of competencies. © 2024 ieee.

关键词： data science

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A data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets

A Data-Informed Analysis of Scalable Supervision for Safety ...

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2024 international conference on Intelligent Robots and systems

作者： Hickert, Cameron Yan, Zhongxia Wu, Cathy MIT Inst Data Syst & Soc Cambridge MA 02139 USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA MIT Lab Informat & Decis Syst Cambridge MA 02139 USA MIT Dept Civil & Environm Engn Cambridge MA 02139 USA

ISBN: (纸本)9798350377712;9798350377705

Autonomous driving is a highly anticipated approach toward eliminating roadway fatalities. At the same time, the bar for safety is both high and costly to verify. This work considers the role of remotely-located human operators supervising a fleet of autonomous vehicles (AVs) for safety. Such a 'scalable supervision' concept was previously proposed to bridge the gap between still-maturing autonomy technology and the pressure to begin commercial offerings of autonomous driving. The present article proposes DISCES, a framework for data-Informed Safety-Critical Event Simulation, to investigate the practicality of this concept from a dynamic network loading standpoint. With a focus on the safety-critical context of AVs merging into mixed-autonomy traffic, vehicular arrival processes at 1,097 highway merge points are modeled using microscopic traffic reconstruction with historical data from interstates across three California counties. Combined with a queuing theoretic model, these results characterize the dynamic supervision requirements and thereby scalability of the teleoperation approach. Across all scenarios we find reductions in operator requirements greater than 99% as compared to in-vehicle supervisors for the time period analyzed. The work also demonstrates two methods for reducing these empirical supervision requirements: (i) the use of cooperative connected AVs - which are shown to produce an average 3.67 orders-of-magnitude system reliability improvement across the scenarios studied - and (ii) aggregation across larger regions.

关键词： Autonomous vehicles

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MoLDy: Open-source Library for data-based Modeling and Nonlinear Model Predictive Control of Soft Robots 7

MoLDy: Open-source Library for Data-based Modeling and Nonli...

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7th international conference on Soft Robotics (ROBOSOFT)

作者： Cheney, Daniel G. Killpack, Marc D. Brigham Young Univ Robot & Dynam Lab Provo UT 84602 USA

ISBN: (纸本)9798350381818

Aggressive and accurate control of complex dynamical systems, such as soft robots, is especially challenging due to the difficulty of obtaining an accurate and tractable model for real-time control. Learned dynamic models are incredibly useful because they do not require derivation of an analytical model, they can represent complex, nonlinear behavior directly from data, and they can be evaluated quickly on graphics-processing units (GPUs). In this paper, we present an open-source Python library to further current research in model-based control of soft robot systems. Our library for Modeling of Learned Dynamics (MoLDy), is designed to generate learned forward models of complex systems through a data-driven approach to hyperparameter optimization and learned model training. Included in the MoLDy library, we present an open-source version of NEMPC (Nonlinear Evolutionary Model Predictive Control), a previously published control algorithm validated on soft robots. We demonstrate the ability of MoLDy and NEMPC to accurately perform model-based control on a physical pneumatic continuum joint. We also present a benchmarking study on the effect of the loss metric used in model training on control performance. The results of this paper serve to guide other researchers in creating learned dynamic models of novel systems and using them in closed-loop control tasks.

关键词： Model predictive control

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Sensor-based injury prediction in football using a new interval-valued fuzzy inference library

Sensor-based injury prediction in football using a new inter...

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ieee international conference on Fuzzy systems (FUZZ-ieee)

作者： Dyczkowski, Krzysztof Grzelak, Bartlomiej Gorecki, Tomasz Pilka, Tomasz Sadurska, Aleksandra Pekala, Barbara Owen, Adam Lee Adam Mickiewicz Univ Poznan Poland KKS Lech Poznan Poznan Poland Univ Rzeszow Rzeszow Poland Lyon 1 Univ Lyon France

ISBN: (纸本)9798350319552;9798350319545

In many computer science and engineering applications, uncertainty is a common feature that must be addressed. In this paper, our primary emphasis is on forecasting lower-body injuries in football (soccer) players, caused by overtraining or undertraining, that do not involve direct physical contact. The dataset employed in the research comes from sensors worn during training and matches by players from the KKS Lech Poznan team, which often supply distorted or incomplete data. To address this issue, an uncertainty approach is necessary. Interval fuzzy set theory, particularly the interval inference model (generalized fuzzy inference), has proven to be effective in dealing with uncertainty in this context. This analysis aims to develop decision-making models for monitoring the workload of football players and predicting an increased risk of injury during the upcoming microcycle. In pursuit of this goal, we introduce a novel Python library that is both versatile and user-friendly, created to facilitate the definition, analysis, and interpretation of interval-valued fuzzy inference systems. The enhanced library allows an intuitive specification of interval-valued fuzzy sets and rules, enabling comprehensive fuzzy inference. It operates on a dataset encompassing athlete external load parameters, offering the capacity to forecast injury risk while considering uncertainties.

关键词： interval-valued fuzzy inference injury prediction sport data analysis sensor data

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