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Adaptive Divided Difference Filter for Power systems Dynamic State Estimation With Outliers and Unknown Noise Covariance

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ieee TRANSACTIONS ON INDUSTRY applications 2023年第6期59卷 7529-7544页

作者： Chatterjee, Sayantan Chowdhury, Arindam Dey, Aritro Thakur, S. S. Indian Inst Technol Dept Elect Engn Kanpur 208016 India Azure Power India Pvt Ltd New Delhi 110017 India Natl Inst Technol Dept Elect Engn Durgapur 713209 India

Power systems Dynamic State Estimation (PSDSE) using hybrid measurements from Phasor Measurement Units (PMUs) and Remote Terminal Units (RTUs) in the presence of partially characterized and non-stationary Gaussian as well as non-Gaussian measurement noise has been addressed in this work. For Gaussian noise, Adaptive Divided Difference Filter (ADDF) with anomaly detection has been tested in comparison to the adaptive versions of Unscented Kalman Filter (AUKF) and Cubature Kalman Filter (ACKF) for ieee 30 bus test system and Northern Regional Power Grid (NRPG) 246 bus Indian practical system. In addition, a novel technique termed Adaptive Student's-t Divided Difference Filter (AST-DDF), has been proposed which deals with the presence of outliers in real-time data in the context of synchronous and asynchronous reporting rate of the RTUs. The non-Gaussian measurement noise is assumed to follow student's-t distribution, which intrinsically accommodates outliers. In view of partially characterized time-varying noise statistics, AST-DDF has been validated in ieee 30 bus and ieee 118 bus benchmark systems in the presence of bad data and transients caused during the faults in power systems. Results reveal that the adaptive versions of DDF for both the Gaussian and non-Gaussian noise substantiate satisfactory estimation accuracy at a reasonable computational burden and hence recommended for the future Energy Management systems (EMS).

关键词： Adaptive estimators cubature kalman filter divided difference filter non-stationary noise phasor measurement units student's-t distribution

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Predicting Cloud applications Failures from Infrastructure Level Data 53

Predicting Cloud Applications Failures from Infrastructure L...

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53rd Annual ieee/IFIP international conference on Dependable systems and Networks (DSN)

作者： Domingos, Jomar Cerveira, Frederico Barbosa, Raul Madeira, Henrique Univ Coimbra CISUC DEI Coimbra Portugal

ISBN: (纸本)9798350325430

Cloud computing assumes a crucial role in the internet applications and services landscape, serving as the default infrastructure for deploying and delivering web applications. To harness the cloud's technical and financial benefits, one must address the challenges of the increased consequences of software faults. Online failure prediction has emerged as a solution allowing for actions to be taken before the failures. However, conventionally, each target application requires a specific online failure predictor to be trained from application-specific data, which is impractical. In this paper we explore and assess the possibility to achieve online failure prediction for cloud applications from infrastructure-level data, i.e., data from the hypervisor. For this purpose, we present a method to accomplish this type of failure prediction, through fault injection on the hypervisor and machine learning techniques to create failure prediction models. From our experiments we find that it is possible to predict application failures from low-level infrastructural data, i.e., the infrastructure data presented enough application failure predictive power (with an accuracy of 96%, precision of 95% and a recall of 64%), showing the great potential that infrastructural data has to contribute to this task. Additionally, our study also opens interesting future research directions, such as multilevel failure prediction or generic failure prediction models.

关键词： failure prediction machine learning feature engineering time series forecast dependability cloud computing

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Object Detection: A comprehensive study of famous Deep Learning approaches with case study of Helmet Detection dataset 2

Object Detection: A comprehensive study of famous Deep Learn...

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2nd ieee international conference on Advancements and Key Challenges in Green Energy and computing, AKGEC 2024

作者： Tajane, Kapil Khutale, Prathmesh Kore, Atharva Khairmode, Atharva Girawale, Sachin Pimpri Chinchwad College of Engineering Computer Engineering Department Pune India

ISBN: (纸本)9798350390179

An important challenge of computer vision, object detection is critical to numerous uses, such as augmented reality, autonomous driving, and monitoring. To provide a concise example on helmet detection, this paper discusses the latest deep learning architectures for the object detection task. Thus, using a number of data sets the performance of such models as YOLO, SSD, R-CNN is estimated as thoroughly as possible. Our work provides both the strengths and weakness of each technique for object detection in different contexts and scales based on comprehensive investigations and comparisons. This study further enriches the existing knowledge base and possibilities for the field of not only object detection but also demonstrates potential implications of the methodology within the real-world scenarios. We emphasize the importance of accurate object detection and also present useful information for other authors who focuses on creating better models. The last case of applying object detection is helmet detection, where object detection can be used to enforce compliance with safety rules at construction sites or in traffic monitoring. In the real-time detection, we discovered that YOLO offered higher accuracy because of its speed, making it better than the other models, whereas, SSD served as a better compromise between accuracy and speed as opposed to other models. R-CNN was faster but less accurate, while the much more accurate F-FR-CNN was slower and more computationally expensive. Our results indicate that it is highly dependent on the intended application, whether in need of real-time evaluation or accuracy. For future work it is recommended to further adapt these models to run on edge-devices and afforded to provide insights for enhancing their resilience under varying environmental conditions so as to support their practical uses in real-world applications. © 2024 ieee.

关键词： Knowledge based systems

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IoT based Thermal Management and State of Charge Estimation of LFP battery 3

IoT based Thermal Management and State of Charge Estimation ...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Anand, Deepthi Paraag, V. Naik, Sahana Kavana, T.M. Vasudha, Karanam Shubha Rao, K. Bnm Institute of Technology Dept. of Eee Bangalore India

ISBN: (纸本)9798331515911

Efficient storage of electricity is pivotal in modern applications, necessitating the use of batteries. However, enclosing batteries in compact spaces, such as power plants or electric vehicles, often leads to excessive heat generation, diminishing efficiency and longevity. Heat generation primarily stems from the charging and discharging processes. A liquid cooling system, powered by a motor connected to the battery system, circulates fluid externally around the battery layers to manage temperature, optimizing the battery's performance within the ideal range of 25°C to 35°C. Thermal Modelling of the battery is implemented in systems to understand how temperature changes affect their performance, efficiency, and reliability. IoT-based Battery Thermal Management System (IoT-BTMS) is introduced resulting in a sophisticated solution utilizing sensors, communication technologies, and data analytics. This system monitors and regulates battery temperature in real-time, based on sensed data a relay will enable cooling system if temperature values exceed the limit values or when the system becomes unstable, the cooling system continues until the system stabilizes and temperatures fall within limits. © 2025 ieee.

关键词： Cooling systems

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Edge computing and Robotic applications in Modern Agriculture 6

Edge Computing and Robotic Applications in Modern Agricultur...

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6th international Congress on Human-Computer Interaction, Optimization and Robotic applications, HORA 2024

作者： Ayranci, Ahmet Aytuğ Erkmen, Burcu Department of Electrical and Electronics Engineering İstanbul Kültür University İstanbul Turkey Department of Electronics and Communication Engineering Yıldız Technical University İstanbul Turkey

ISBN: (纸本)9798350394634

The modernization of agricultural practices prominently features robotics as a key technology. Efforts are concentrated on achieving automation and enhancing efficiency in agriculture through advancements in robotic applications. The widespread integration of remote sensing systems into agricultural areas facilitates real-time information acquisition, enabling drones and robots to operate with enhanced efficiency and effectiveness. Robotics plays a crucial role in the evolution of agriculture 4.0 and agriculture 5.0 strategies, marking significant strides in agricultural technology. Specifically designed robots for agricultural use are currently employed in tasks like planting, fertilizing, irrigating, pest controlling, and harvesting, proving a certain level of effectiveness. Edge computing is crucial in enhancing efficiency and sustainability within modern agricultural practices. Edge computing can instantly process data from numerous devices, mitigating network congestion effectively. In modern agricultural applications, it is possible to perform multiple tasks in a coordinated and effective manner by using Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) together. These devices can serve as both data collection and edge devices in the network. Multiple agricultural robot applications and benefits of these applications are explained in the study. © 2024 ieee.

关键词： Agricultural robots

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2024 IFIP/ieee 32nd international conference on Very Large Scale Integration, VLSI-SoC 2024

2024 IFIP/IEEE 32nd International Conference on Very Large S...

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32nd IFIP/ieee international conference on Very Large Scale Integration, VLSI-SoC 2024

ISBN: (纸本)9798331539672

The proceedings contain 55 papers. The topics discussed include: FVDCLS: functional verification of RISCV based dual-core lockstep feature using fault injection mechanism;time-to-digital converter based self-timed ring oscillator: an FPGA implementation;design co-processor based on partially homomorphic encryption execution using open-source tool;mean: mixture-of-experts based neural receiver;fortified-edge 5.0: federated learning for secure and reliable PUF in authentication systems;AFSRAM-CIM: adder free SRAM-based digital computation-in-memory for BNN;embedded and real-time anomalous command classification in unmanned ground vehicle operations;benchmarking microfluidic design automation flows;commercial evaluation of zero-skipping MAC design for bit sparsity exploitation in DL inference;and diagnostic coverage estimation for automotive SoCs based on colored stochastic petri nets.

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Fast Finite-time Adaptive Neural Control for Pure-Feedback Stochastic Nonlinear systems

Fast Finite-Time Adaptive Neural Control for Pure-Feedback S...

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2023 ieee international conference on Memristive computing and applications, ICMCA 2023

作者： Zhao, Ximei Zhang, Hanyu Shao, Lijun Huang, Lili Li, Jiao School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Qilu Institute of Technology Jinan China College of Computer and Communication Engineering Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350316230

In this article, we solve the fast finite-time stabilization as well as adaptive neural control design issues for a class uncertain stochastic nonlinear systems. By employing the mean value theorem, the pure-feedback form can be transformed into strict-feedback case. Firstly, a new fast finite-time control scheme is proposed in virtue of generalizing the fast finite-time adaptive control strategy for deterministic systems to the stochastic case. Next, a novel adaptive neural control strategy is developed to simultaneously deal with the stochastic nonlinear systems with completely unknown nonlinearities as well as the disturbances term. Then, stability analysis have been given based on a Jensen's inequality. © 2023 ieee.

关键词： Feedback

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Transient Recovery of Energy Storage Balance in DC Microgrids with MARL- Based Power Control 10

Transient Recovery of Energy Storage Balance in DC Microgrid...

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10th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Al-Saadi, Mudhafar Short, Michael Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough England

ISBN: (纸本)9798350373981;9798350373974

Multi-Agent Reinforcement Learning (MARL) - based primary -secondary -control has been shown to exhibit high-performance in Microgrid -based power and energy applications. However, in highly dynamic environments, such as within vehicle-to-grid applications, loss of performance can occur. Specifically, transient loss of accuracy in the synchronization of energy-storage-balance after dynamic topology changes is a known defect, which can overload batteries and reduce stability margins. In this work a newly developed methodology for transient recovery and fault ride-through in battery-based DC-Microgrids is developed and validated. Specifically, an enhancement to MARL-based control utilizing a planned policy with compensation for the DC infrastructure influence is developed, and regional assessment of energy-flow efficiency is examined. The real-time results with quasi-random battery insertion and removal under realistic environmental conditions confirms a reduction in transient recovery time (0.66-13.366%), coupled with enhanced voltage stability (2.637-3.24%) and smoothness (2.9739-3.8462%), better load steadiness (6.666-37.091%), energy saving (2.94%), energy-flow balance enrichment (6.468 %), and raised efficiency (2.626%).

关键词： Microgrids Battery Energy Storage systems Dynamic Reconfiguration Multiagent Control Vehicle-2-Grid

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A Few-Shot Learning-Based Domain-Adaptive Lightweight CNN for Robust Hand Gesture Recognition in real-time applications 2

A Few-Shot Learning-Based Domain-Adaptive Lightweight CNN fo...

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2nd ieee international conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Shobana, K.B. Kaladeep Yalagi, Pratibha C. Ammu, V. Aldo Stalin, J.L. Kumar, Prashant Richard, Titus Department of Artificial Intelligence and Data Science Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Walchand Institute of Technology Solapur India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Information Technology Sona College of Technology Salem India Department of Information Technology Project Circle BSNL Lalkuthi Kolkata India Department of English Vels Institute of Science Technology & Advance Studies Pallavaram Chennai India

ISBN: (纸本)9798331504960

Surface electromyogram-based hand gesture recognition (sEMG) has promising prospects in human-machine interaction applications, including the neural rehabilitation and assistive technologies. However, this technique also has several disadvantages, such as poor generalization across subjects and days, demanding a large amount of data for calibration, and computational complexity that does not enable the real-time deployment of the system. The proposed work incorporates a novel few-shot learning-based domain-adaptive lightweight CNN that is specifically designed to be used in robust and efficient hand gesture recognition. This minimizes the dependency on the labeled data of other new subjects or days, hence allowing better adaptability in real-world scenarios. The proposed lightweight CNN architecture adapts well to wearable devices with real-time performance but high accuracy. Another would be adding domain-adaptive feature learning to account for variations between inter-subject and inter-day domains while preserving the accuracy of classification and improving model generalization. The proposed framework is able to achieve competitive accuracy using a small calibration set in gesture-based human-machine interfaces continuously and broadly. Results on high-density sEMG datasets have shown that our model achieves a good balance between the accuracy (95.5%) and efficiency on experimental results and holds promise for providing a reliable solution for real-time cross-domain hand gesture recognition applications. © 2024 ieee.

关键词： Calibration

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Improved real time Simulation of Fluid-Solid Interactions for Virtual Maritime Test Sites

Improved Real Time Simulation of Fluid-Solid Interactions fo...

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2024 international conference on Industrial Engineering, applications and Manufacturing, ICIEAM 2024

作者： Minin, Maksim S. Saint-Petersburg State Marine Technical University Cyberphysical Systems Department Saint Petersburg Russia

ISBN: (纸本)9798350395013

In this paper, we present an improved real-time simulation approach for fluid-solid interactions using the Smoothed-Particle Hydrodynamics (SPH) method. It is widely applied in a variety of scientific disciplines, such as astrophysics, ballistics, volcanology, and oceanography. The essence of the SPH approach involves the segmentation of a fluid into a collection of discrete units, known as particles, which allows for the detailed simulation of motion within uniform media. This technique is particularly advantageous for the real-time simulation of interactions between fluids and solids, especially in the creation and testing of virtual maritime environments. In the field of astrophysics, the SPH method is crucial for the exploration of phenomena such as the formation of galaxies and stars, the dynamics of stellar collisions, the events of supernovae, and the impacts of meteors, among others. Although highly effective, the application of the SPH method is computation-intensive, necessitating the use of cutting-edge parallel processing techniques and computational platforms to enhance efficiency. © 2024 ieee.

关键词： Computational fluid dynamics

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