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grid Voltage Sensor-Less State-Observer based OSS-MPCC for grid-Tied Inverter with LCL Filter 11

Grid Voltage Sensor-Less State-Observer based OSS-MPCC for G...

引用

11th ieee international conference on Power electronics, Drives and energy Systems, PeDeS 2024

作者： Anees, Mohammad Anas Mekhilef, Saad Mubin, Marizan Binti Kermadi, Mostefa Siddique, Marif Daula Seyedmahmoudian, Mehdi Universiti Malaya Pearl Lab Kuala Lumpur Malaysia School of Science Computing and Engg. Tech. Swinburne University of Technology Melbourne Australia School of Engineering University of Leicester United Kingdom

This paper presents an enhanced reduced-sensor Optimal-Switching-Sequence Model-Predictive-Current-Control (OSS-MPCC) algorithm for grid-tied inverters with LCL filters, using only injected grid current measurements. The OSS-MPCC algorithm relies on four signals: estimated symmetrical components of grid voltage from a virtual flux observer, state estimates from Kalman Filter based estimation, reference positive sequence grid current from SOGI-QSG symmetrical voltage estimations, and inverter switching states for predictive current tracking. The proposed algorithm offers multiple benefits: it limits the switching frequency spectrum of the inverter voltage, reduces sensor count from nine to three, maintains low grid current THD even with voltage unbalance, and its computational time is comparable to previous OSS-MPC algorithms. Numerical validation has been conducted through MATLAB simulations. © 2024 ieee.

关键词： grid-tied inverters Model-Predictive-Control Optimal-Switching-Sequence state-observer

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Research on electric Vehicle Charging Load Forecasting Based on the MCBDL-TCN Model

Research on Electric Vehicle Charging Load Forecasting Based...

引用

2024 Asia conference on Advances in electrical and Power engineering, ACePe 2024

作者： Wang, Yang Yan, Dazhi Xu, Xiaolong Gao, Peng Yang, Hao Zhao, Yingjie State Grid Shandong Electric Power Company Department of Zaozhuang Power Supply Company Zaozhuang China

ISBN: (纸本)9798350390513

This study introduces a new method for predicting the charging load of electric vehicles (eVs) by combining a Temporal Convolutional Network (TCN) and Multiple Cascaded Bayesian Deep Learning (MCBDL) model. The study focuses on short-term eV charging demand prediction in an urban region of a northern Chinese metropolis, utilizing data from 38 public DC charging stations. Long-term trends and short-term oscillations in load data are both well captured by the MCBDL-TCN model. experiments show that MCBDL-TCN is superior to LSTM and ANN traditional models in terms of RMSe, MAe and MAPe indexes. The results of predicting the four seasons, as well as workdays and non-workdays, demonstrate the model's outstanding performance in forecasting and addressing uncertainty, thus enhancing the accuracy and flexibility of complex time series data. This shows that the ability of the model to deal with uncertainty and extract rich feature information from long time series data is improved by combining LSTM-BDL unit with TCN. © 2024 ieee.

关键词： Charging stations

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exodus Bi-Directional LSTM Based Multivariate Texmoji Classification 11

Exodus Bi-Directional LSTM Based Multivariate Texmoji Classi...

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11th ieee international conference on Cloud computing in emerging Markets, CCeM 2023

作者： Raman, Sreekumar Nedumpally Muruganandam, Narendran Sobanapuram Jeyaseelan, Charles Arayalpuram, Gautham Parapurathe, Sreeshanth Anand, Anushob Kavikkal School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India

ISBN: (纸本)9798350360059

Stories that contain text and emojis manifests humans' emotions in a very broad way. emojis mixed with regular words on many social media sites has changed how we talk. It lets people send feelings without having to describe them in a standard way. The proposed work here mainly focuses to collect such kind of texts combined with the emojis and predict the 8 basic human emotions revealed through it i.e., Anger, fear, sadness, disgust, surprise, anticipation, trust and joy. The collected instances are summarized with transformers summarization algorithm, which then we calculate the emotional intensity of each emotion and perform a multi-label classification using Bi-Directional LSTM network, and rank the emotions as per the predicted emotional inteneity. © 2023 ieee.

关键词： Classification (of information)

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CADeT: Control-Aware Dynamic edge computing for Real-Time Target Tracking in UAV Systems 48

CADET: Control-Aware Dynamic Edge Computing for Real-Time Ta...

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48th ieee international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Florenzan Reyes, Luis Felipe Smarra, Francesco D'Innocenzo, Alessandro Levorato, Marco University of l'Aquila Department of Information Engineering Computer Science and Mathematics AQ 67100 Italy University of California Department of Computer Science IrvineCA92697 United States

ISBN: (纸本)9781728163277

The autonomous operations of unmanned aerial vehicles (UAV) necessitate the real-time analysis of information-rich signals, such as camera and LiDAR feeds, where the analysis algorithms often take the form of extremely complex deep neural networks (DNN). The continuous execution of such models onboard the UAV imposes a considerable resource consumption (e.g., energy), while offloading the execution of the models to edge servers requires the transmission of the input signals over capacity constrained, time-varying, wireless channels. In this paper, we propose an innovative approach - CADeT - to control where sensor signals are processed in the system. In addition to traditional features and measures, such as channel state, energy consumption and channel usage, CADeT makes dynamic task routing decisions - local computing vs edge computing - based on the state of the flight controller. The proposed methodology is based on Markov jump switched linear systems, where an embedded filter predicts and controls the state of the joint motion/computing dynamics. © 2023 ieee.

关键词： edge computing

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Automated Biological Analysis System: Integration of Chatbots and edge computing 26

Automated Biological Analysis System: Integration of Chatbot...

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2024 ieee international conference on Automation/26th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2024

作者： Reveco-Toledo, Florencia Soto, Ismael Garcia, Veronica University of Santiago of Chile C.I.M.T.T. Department of Electrical Engineering Santiago Chile University of Santiago of Chile C.E.C.T.A. Department of Food Science and Technology Santiago Chile

ISBN: (纸本)9798350378115

This article presents an automated system for counting and analyzing biological samples, integrating edge computing, computer vision, and Natural Language Processing (NLP) through a chatbot interface. The system processes biological samples locally using advanced image processing techniques, reducing latency and improving the accuracy of results. Additionally, the system allows for remote user interaction via a natural language interface and stores spatiotemporal data in the cloud for enhanced management. The system achieves a 6 5 % success rate in Colony-forming unit (CFU) detection and 85% in RFID/QR label recognition, with identified areas for improvement in image quality and standardization. © 2024 ieee.

关键词： Natural language processing systems

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A Dynamic Routing Strategy for Hybrid Power Communication Networks 23

A Dynamic Routing Strategy for Hybrid Power Communication Ne...

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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： Qi, Zengqing Xiao, Jingpeng Xu, Zhiqiang State Grid Research Institute of Hunan Province Economics and Technology China Hunan Economy Institute Electric Power Design Co. Ltd. China Hunan Engineering Research Center of Large-scale Battery Energy Storage Application Technology China Hunan Key Laboratory of Energy Internet Supply-demand and Operation China

ISBN: (纸本)9781665494571

With the rapid development of smart grid, the business data generated during production and management will exceed the load balancing capability of the grid, and the reliability of the network will also be affected. To address the issues in hybrid power communication networks, this paper proposes and implements a dynamic routing scheme to meet the requirements of different service data, which can be recorded in the IP header of packets. At the access side, the data packets then can be delivered over different paths by comparing their requirements with the performance that the current condition of networks can support. In this paper, the network performance is some type of statistical proof of historical packets. The test results show that the strategy can not only effectively achieve load balance between the 5G public network and the power private network itself, but also improve the end-to-end transmission performance. © 2021 ieee.

关键词： Protocols 5G mobile communication Smart cities Heuristic algorithms Routing Hybrid power systems Smart grids

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A Semantic Feature-Driven Multiscale grid Dehazing Method 9

A Semantic Feature-Driven Multiscale Grid Dehazing Method

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9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Zhang, Hao Jin, Hongmei Li, Zhanli He, Ning Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9798350376548

Aiming at the problems of incomplete dehazing and detail distortion in the dehazing process of existing algorithms, an end-to-end dehazing method SemanticgridDehazeNet is proposed, which is semantic feature-driven and combines multi-scale feature fusion. The method consists of three modules: preprocessing, backbone, and post-processing. First, preprocessing is performed using a newly designed residual dense fusion block (RDFB) and convolutional layer. The Backbone module is based on the extended gridN et, which contains three processing scales, and semantic features extracted based on VGG16 Net are added in front of each scale to enhance learning. each scale contains five RDFB blocks to fuse the feature map to the last column. Then an RDFB post-processing module improves the output quality. The main innovations include: (1) the introduction of the RDFB fusion module to focus on haze features, dynamically adjust the level of attention paid to channel or pixel locations to more effectively learn and utilize key information about the haze distribution in the input data;(2) Semantic features are integrated into the image dehazing process by incorporating semantic features extracted from VGG16 Net before each processing scale. This enhances multi-scale learning and enables more accurate focus on haze features, improving dehazing effectiveness. experimental results demonstrate superior performance in target enhancement, robustness, and generalization. The method effectively removes residual hzae and addresses detail distortion issues. © 2024 ieee.

关键词： Demulsification

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Investigating the Impact of Grafted Molecule Types on Frequency Domain Spectroscopy of Polypropylene Insulation 5

Investigating the Impact of Grafted Molecule Types on Freque...

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5th ieee international conference on Dielectrics, ICD 2024

作者： Zhao, Shuai Li, Zhonglei Yin, Yifan Du, Boxue School of Electrical and Information Engineering Tianjin University Key Laboratory of Smart Grid of Education Ministry Tianjin300072 China

ISBN: (纸本)9798350308976

Thermoplastic insulation materials represented by polypropylene (PP) have become an important direction for future research on cable insulation materials. To develop high performance PP cable insulation materials, the Havriliak-Negami (H-N) model is used to study the impact of grafted molecule types on frequency domain spectroscopy (FDS) of PP insulation in this paper. Research results demonstrate that PP grafted with the irganox 3052 effectively scavenges free radicals within the material, and increases its oxidation induction time (OIT), thus enhancing the thermal stability of PP. Both the voltage stabilizer DA and the irganox 3052 can increase the barrier height (eδ) for the thermionic migration in PP. PP-g-3052 exhibits a larger eδ compared to PP-g-DA, enabling better suppression of thermionic migration and consequently reducing the thermionic polarization. Concurrently, the relationship between conductivity activation energy (esigma;) and eδ is further studied, with consistent changes observed in both. PP-g-3052 possesses the largest eσ, exerts the strongest inhibitory effect on carrier migration, and more effectively enhances the insulation properties of PP. © 2024 ieee.

关键词： Polypropylenes

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A web computing tool for a SIR model: Immunity loss and vaccination

A web computing tool for a SIR model: Immunity loss and vacc...

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2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Casquilho, Miguel Da Silva, Pascoal M. Caridade, Cristina M. R. De Miranda, Joao L. Ist University of Lisbon and Cerena Depart. of Chemical Engineering Lisboa Portugal Polytechnic University of Coimbra IPC/ISEC and Cmuc Depart. of Physics and Mathematics Coimbra Portugal Polytechnic University of Coimbra IPC/ISEC and Cernas Depart. of Physics and Mathematics Coimbra Portugal Polytechnic Institute of Portalegre IPPortalegre and Cerena Depart. of Technologies Portalegre Portugal

ISBN: (纸本)9798350386226

The goal of this short work is to present an online tool for modeling an epidemic in a given population. It is based on the typical SIR model, where the population is made up of three groups of individuals: susceptible, infected and recovered, where the recovered can lose immunity and the susceptible can be vaccinated. Computer languages and solvers such as PHP, Python and 'gnuplot' were used. This example with the solution web page demonstrates the use of the Web for both academia and industry, without requiring the user to install special software or have machine power. The user can simulate different situations, depending on the options that the application has available to manage the model, and can choose several numerical methods to solve the problem. © 2024 ieee.

关键词： epidemic mathematical models numerical methods ordinary differential equations web-based computing

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Processing model for fog computing applied to internet of medical things (IoMT)

Processing model for fog computing applied to internet of me...

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international conference on Computational science and Computational Intelligence (CSCI)

作者： Ramirez Lopez, Leonardo Juan Ramirez Maldonado, engler Rojas Reales, Wilson Mauro Univ Militar Nueva Granada Telecommun Engn Bogota DC Colombia Univ Militar Nueva Granada Bogota DC Colombia Univ El Bosque Syst Engn Bogota Colombia

ISBN: (纸本)9798350361513;9798350372304

Internet of Things (IoT) platforms are software systems that connect and manage IoT devices, applications, and data, act as intermediaries between devices and applications, providing a common framework for them to communicate with each other. Furthermore, in fog computing architecture, the fog layer refers to the layer of computing infrastructure that is located between the edge devices and the cloud data center. Therefore, fog layer is comprised of inter-mediate nodes, which are computing resources that are located closer to the edge devices than the cloud data center, allows to improve the efficiency and reduce the latency of data processing by bringing computing and storage closer to the devices that generate and use data. This study presents a new model of real-time data processing in the fog layer to take advantage of own resources instead of sending data to a centralized cloud data center for processing. The method used was supervised machine learning, which consists of creating a model that makes predictions using evidence supported by uncertainty applied in e-health, using intermediate nodes within a platform used for the detection of heartbeat anomalies in an Internet of Things (IoT) network. The evaluation used was a set of statistical validation metrics. The results may identify important criteria for choosing appropriate machine learning techniques, which may include the statistical and inherent effectiveness of ML methods or their adaptability to intermediate nodes within an IoT platform. Translated with *** (free version) the less resource-intensive methods like Simple Linear Regression, Logistic Regression, and K Nearest Neighbors are suitable for intermediate nodes, as they demand minimal processing and storage. Consequently, adopting a cognitive network approach for intermediate nodes in IoT platforms reduces the cloud computing processing costs and shifts the burden to the fog layer.

关键词： Medical IoT Fog layer Detection Heartrate

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