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Cyber-Resilient Control for a 100% Inverter-Based Microgrid: Analysis and Real-Time Simulation

Cyber-Resilient Control for a 100% Inverter-Based Microgrid:...

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international conference on intelligent Systems Applications to Power Systems (ISAP)

作者： Milad Beikbabaei Nguyen Khoa Nguyen Ali Mehrizi-Sani The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

As the U.S. moves toward cleaner electricity generation, the number of installed inverter-based resources such as wind and photovoltaic (PV) are on the rise. These inverter-based resources (IBR) rely on communication to update their real and reactive power set point; however, it opens room for cyberattacks. Previous work proposed ways to detect and mitigate cyberattacks for fully inverter-based microgrids using software simulation, but the feasibility of these methods needs to be tested using control hardware-in-the-loop (CHIL). This work develops a CHIL testbed using Typhoon HIL 402, Raspberry Pi 4, and a network switch to implement a cyber-resilient inverter control against false data injection (FDI) attacks. The Raspberry Pi 4 receives the electrical measurements from the Typhoon HIL using user datagram protocol (UDP) communication, runs the LSTM-based detection and mitigation code using the received measurements and sends back the corrected set point if an FDI attack is detected. The proposed method is tested on a fully inverter-based microgrid with four inverters under various scenarios.

关键词： Reactive power Protocols Tropical cyclones Prevention and mitigation Electric variables measurement Microgrids Switches Control systems Inverters Real-time systems

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AI-Driven Assessment of Safety Risk at Road Intersections Using Drone Videos

AI-Driven Assessment of Safety Risk at Road Intersections Us...

引用

IEEE international conference on Industrial Informatics (IndIN)

作者： Shile Zhang Yan Wang Yongjun Yan Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong China School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China University

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Urban intersections pose significant safety challenges due to the convergence of diverse traffic flows from various directions, resulting in a disturbingly high rate of annual road fatalities. Specifically, the presence of permissive left-turn signals introduces potential conflicts between vehicles making left turns and those proceeding straight from opposite directions. To address this issue, this article introduces a dynamic safety assessment model tailored for urban intersections. Leveraging drone videos, high-resolution vehicle trajectory data is extracted, enabling the conflicts identification of rear-end and angular. Through an ensemble learning approach utilizing artificial intelligence (AI) techniques, conflict frequency is estimated at a granular five-minute interval. Notably, the Random Forest model exhibits superior performance, yielding a mean squared error (MSE) of 5.35. Further analysis employing SHAP (SHapley Additive exPlanations) highlights the critical role of variables such as vehicle deceleration rate and speed. The insights garnered from this study can inform the development of proactive traffic management systems, facilitating real-time assessment of intersection safety conditions and the implementation of measures to mitigate potential risks.

关键词： Roads Real-time systems Frequency estimation Safety Trajectory Ensemble learning Vehicle dynamics Random forests Videos Drones

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Semantic Segmentation with Perceiver IO

Semantic Segmentation with Perceiver IO

引用

international conference on Control, Automation and Systems ( ICCAS)

作者： Keong-Hun Choi Jong-Eun Ha Graduate School of Automotive Engineering Seoul National University of Science and Technology Seoul Korea Dept. of Mechanical and Automotive Engineering Seoul National University of Science and Technology Seoul Korea

Recently, in deep learning, the transformer is replacing the convolutional neural network (CNN) due to its performance and simple design. In particular, in recent studies, constructing an encoder of the transformer that effectively extracts features on an image has been widely used. However, even in these cases, models utilizing existing deep neural network structures needed to use a form suitable for each data format according to input modality. Recently, the Perceiver IO [6] has been proposed to overcome this limitation. It can process various data formats through one structure to extract a characteristic value. Also, it uses an output query to output data as we want. In this paper, a semantic segmentation model using the characteristics of the Perceiver IO is presented. Two types of input configuration are suggested, and experimental results show the feasibility of the proposed method.

关键词： Deep learning Semantic segmentation Semantics Neural networks Feature extraction Transformers data models

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Developments on Support Vector Machines for Multiple-Expert learning 22nd

Developments on Support Vector Machines for Multiple-Expert ...

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22nd international conference on intelligent data engineering and automated learning, IDEAL 2021

作者： Umaquinga-Criollo, Ana C. Tamayo-Quintero, Juan D. Moreno-García, María N. Aalaila, Yahya Peluffo-Ordóñez, Diego H. Universidad de Salamanca Salamanca Spain Universidad Técnica del Norte Ibarra Ecuador Universidad Nacional de Colombia Manizales Colombia Tecnológico de Antoquia Medellín Colombia Cadi Ayyad University Marrakesh Morocco Mohammed VI Polytechnic University Ben Guerir Morocco SDAS Research Group Ben Guerir Morocco

ISBN: (纸本)9783030916077

In supervised learning scenarios, some applications require solve a classification problem wherein labels are not given as a single ground truth. Instead, the criteria of a set of experts is used to provide labels aimed at compensating for the erroneous influence with respect to a single labeler as well as the error bias (excellent or lousy) due to the level of perception and experience of each expert. This paper aims to briefly outline mathematical developments on support vector machines (SVM), and overview SVM-based approaches for multiple expert learning (MEL). Such MEL approaches are posed by modifying the formulation of a least-squares SVM, which enables to obtain a set of reliable, objective labels while penalizing the evaluation quality of each expert. Particularly, this work studies both two-class (binary) MEL classifier (BMLC) and its extension to multiclass through one-against all (OaA-MLC) including penalization of each expert’s influence. Formal mathematical developments are stated, as well as remarkable discussion on key aspects about the least-squares SVM formulation and penalty factors are provided. © 2021, Springer Nature Switzerland AG.

关键词： Support vector machines

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Personalized speech enhancement through self-supervised data augmentation and purification 22

Personalized speech enhancement through self-supervised data...

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22nd Annual conference of the international Speech Communication Association, INTERSPEECH 2021

作者： Sivaraman, Aswin Kim, Sunwoo Kim, Minje Indiana University Department of Intelligent Systems Engineering United States

ISBN: (纸本)9781713836902

Training personalized speech enhancement models is innately a no-shot learning problem due to privacy constraints and limited access to noise-free speech from the target user. If there is an abundance of unlabeled noisy speech from the test-time user, one may train a personalized speech enhancement model using self-supervised learning. One straightforward approach to model personalization is to use the target speaker’s noisy recordings as pseudo-sources. Then, a pseudo denoising model learns to remove injected training noises and recover the pseudo-sources. However, this approach is volatile as it depends on the quality of the pseudo-sources, which may be too noisy. To remedy this, we propose a data purification step that refines the self-supervised approach. We first train an SNR predictor model to estimate the frame-by-frame SNR of the pseudo-sources. Then, we convert the predictor’s estimates into weights that adjust the pseudo-sources’ frame-by-frame contribution towards training the personalized model. We empirically show that the proposed data purification step improves the usability of the speaker-specific noisy data in the context of personalized speech enhancement. Our approach may be seen as privacy-preserving as it does not rely on any clean speech recordings or speaker embeddings. Copyright © 2021 ISCA.

关键词： Speech enhancement

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Estimating SIR Model Parameters for Predicting Covid-19 Cases in Bandung City

Estimating SIR Model Parameters for Predicting Covid-19 Case...

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intelligent Cybernetics Technology & Applications (ICICyTA), international conference on

作者： Kahfi M. Bintang Norma binti Alias Wandi Yusuf Kurniawan Ni Kadek Winda Patrianingsih Gde Palguna Reganata Putu Harry Gunawan School of Computing Telkom University Bandung Indonesia Faculty of Science University Teknologi Malaysia Skudai Johor Malaysia Informatics Department Universitas Bali Internasional Denpasar Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

The COVID-19 pandemic has profoundly impacted global public health, with Indonesia among the most severely af-fected. In response, the Bandung City Government implemented various control measures, including the Mayor's Circular on COVID-19 prevention and enforcing restrictions on community activities, known as PPKM. This study applies the Susceptible-Infected-Recovered (SIR) model to analyze the progression of COVID-19 in Bandung City, specifically during the first and second waves. The SIR model was calibrated using historical case data and incorporated the finite probability of recovered individuals becoming susceptible again, which is critical for generating oscillatory solutions in epidemic modeling. The study focused on determining the best transmission rate for both waves. The first wave, occurring between July 4th and October 22nd, 2021, exhibited an optimal transmission rate of 0.38 (MAE: 0.0424). In contrast, the second wave, spanning February 1st to May 15th, 2022, required a higher transmission rate of 0.54 (MAE: 0.0142), reflecting the increased transmissibility of the Omicron variant. Meanwhile, the recovery rate was calculated based on an average incubation period of 14 days, equivalent to 1/14, approximately 0.0714. The results demonstrate that the SIR model successfully captures the epidemic dynamics in the region, providing insights into the effectiveness of governmental policies and restrictions in controlling the virus. Future studies may explore the inclusion of additional parameters, such as vaccination coverage and mobility patterns, which could enhance the model's predictive accuracy. Moreover, hybrid modeling approaches integrating SIR with machine learning may offer more adaptive solutions to rapidly changing epidemic conditions.

关键词： COVID-19 Adaptation models Pandemics Urban areas Government Machine learning Predictive models Mathematical models data models Vaccines

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SOC Estimation of Lithium-Ion Batteries based on Transfer learning under Low Temperature Conditions

SOC Estimation of Lithium-Ion Batteries based on Transfer Le...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Xinglong Yang Tianzhu Jiang Ming Xu Shun Li Changjian Ji Xueying Liu Bin Ma FAW Bestune Automotive Co.Ltd Changchun China School of Artificial Intelligence Changchun China College of Communication Engineering Jilin University Changchun China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Accurate estimation of the SOC of lithium-ion batteries is important for range prediction, battery health monitoring and optimization of driving strategies in pure electric vehicles. However, due to the dynamic complexity and uncertainty of the actual operating conditions of vehicles, it is difficult to develop an accurate SOC estimation algorithm that is applicable to different operating conditions. In this paper, we propose a battery SOC estimation method that combines CNNLSTM network and Kalman filter (KF) and apply a migration learning method to accelerate the model training process. Firstly, the CNN-LSTM network is used to learn the nonlinear relationship between SOC and the measured values, then KF is applied to refine the output of the LSTM network, and finally the migration learning method is used to migrate the model parameters from one operating condition to other operating conditions. By using the data from three different temperatures under CLTC conditions for training and validation, the results show a high prediction accuracy at different temperatures for both the base model and the migration learning model, where the root mean square error is less than 3%. In addition, the model training part and model prediction part of the method can be deployed in the cloud and the vehicle to realize vehiclecloud cooperative estimation, respectively, and can be extended to multi-state cooperative estimation.

关键词： Training Lithium-ion batteries Temperature measurement learning systems Temperature distribution Accuracy Transfer learning Estimation Predictive models State of charge

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Graph data Mining in Recommender Systems 22nd

Graph Data Mining in Recommender Systems

引用

22nd international conference on Web Information Systems engineering (WISE)

作者： Chen, Hongxu Li, Yicong Yang, Haoran Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9783030915605;9783030915599

With the rapid development of e-commerce, massive data is generated from various e-commerce platforms. Most of the generated data can be represented in the forms of graph, which is capable to demonstrate the complicated relations among various entities, for example, graphs describe the interactions history between users and items. It is critical for the platforms to mine graph data to formulate recommendation strategy to gain more profits. For instance, in a user-item interaction graph, we can utilize graph data mining techniques to capture users' behavioral patterns to make personalized recommendation strategies. Graph data mining in recommendation is currently a research topic attracts more and more attentions from industry and academic fields. In this half-day tutorial, we will present some key graph data mining methods and its applications in recommendation. We hope to find out the directions for the future work and that more theoretical models can be applied under real-world scenarios.

关键词： Graph data mining Recommender systems Graph neural networks Explainable machine learning Self-supervised learning

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Health Risk Assessment and Monitoring System Based on Multi-sensor Information Fusion and Contrast learning

Health Risk Assessment and Monitoring System Based on Multi-...

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international conference on Communication Technology (ICCT)

作者： Nannan Chong Kunpeng Chen Dehao Kong School of Information and Intelligence Engineering Tianjin Renai College Tianjin China School of Electronic and Information Engineering Hebei University of Technology Tianjin China

The number of solitaries is on the rise worldwide. In order to improve the risk resistance ability of loners to unexpected events, and to evaluate their life and health status in real-time and automatically warn their risks, a health risk assessment and monitoring system based on multi-sensor information fusion based on comparative self-supervised learning are proposed. The scheme predicts the vital signs and safety level of the solitary person by observing and calculating the preset multi-modal sensor information, integrates the information, identifies the routine, risk, emergency, and other states. The model was trained, verified, and tested by the physical signs data of 143 anonymous volunteers. The results show that the health risk assessment and monitoring system can effectively filter the errors of sensor information data, and the steady-state error is less than 5%, which has higher accuracy and efficiency than other recent information fusion methods. The model trained with sufficient historical data can significantly improve the ability of loners to cope with unexpected events.

关键词： Temperature sensors Multimodal sensors Instruments Self-supervised learning data models Real-time systems Steady-state

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Multi-Layer Perceptron-based Beamformer Design for Next-Generation Full-Duplex Cellular Systems 22

Multi-Layer Perceptron-based Beamformer Design for Next-Gene...

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22nd IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Fall)

作者： Biswas, Sudip Singh, Umesh Nag, Kaustuv Indian Inst Informat Technol Guwahati Dept Elect & Commun Engn Gauhati India Jawaharlal Nehru Univ Sch Computat & Integrat Sci New Delhi India Indian Inst Informat Technol Guwahati Dept Comp Sci & Engn Gauhati India

ISBN: (纸本)9781665404037

An in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) radio's self-interference (SI) and co-channel interference (CCI) cancellation strengths usually determine its performance gains over conventional half-duplex ones. Accordingly, this paper explores an alternative to traditional optimization driven design (ODD) techniques available in the literature for beamformer design in IBFD radios. In particular, to mitigate the residual SI and CCI, we propose a run-time data-driven prediction approach to predict the beamforming matrices at the uplink users and the base station. First, we formulate an ODD-based beamforming design problem, which we structurally optimize through sum-rate maximization, and cast it as a second-order cone programming problem. Then, we repeatedly solve this problem to generate a dataset forming a multiple multivariate regression problem. We use the dataset to train a multi-layer perceptron (MLP) employing a supervised learning scheme to solve the associated regression problem. Experimental results demonstrate that the MLP based beamformer design achieves a near-optimal performance at a remarkably high speed for reasonable residual SI and CCI cancellation without the need for explicit channel estimation.

关键词： 5G & Beyond in-band full-duplex communication data-driven prediction multi-layer perceptron optimization-driven design multivariate regression

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