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A New Affine Arithmetic-based power flow analysis for Weakly Meshed Distribution systems under Uncertainty

A New Affine Arithmetic-based power flow analysis for Weakly...

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20th ieee international conference on Environment and Electrical Engineering (EEEIC) / 4th ieee Industrial and Commercial Power systems Europe conference (I and CPS Europe)

作者： Raj, Vinod Kumar, Bodetti Kalyan Indian Inst Technol Madras Elect Engn Dept Chennai Tamil Nadu India

ISBN: (纸本)9781728174556

An affine arithmetic based load flow for weakly meshed distribution systems with uncertainty using optimized solution intervals is proposed in the present work. Uncertainty is considered in loads and distributed Generation (DG) connected at buses. Affine Arithmetic (AA) is used to represent uncertainty in distribution load flow. Bus Injection to Branch Current (BIBC) and Branch Current to Bus Voltage (BCBV) matrices are used in the load flow. Noise intervals for the initial affine solution are optimised with respect to the specified uncertainty in power injections. the proposed method, existing AA and Monte Carlo simulations based power flow analyses are tested on weakly meshed 33 and 118 bus distribution systems.

关键词： Affine Arithmetic distributed Generation Distribution load flow Uncertainty

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Comparison of Control Methods for Single-Phase Inverters to Meet the Requirements in ieee Standard 1547-2018

Comparison of Control Methods for Single-Phase Inverters to ...

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20th ieee international conference on Environment and Electrical Engineering (EEEIC) / 4th ieee Industrial and Commercial Power systems Europe conference (I and CPS Europe)

作者： Tencate, Timothy Oriti, Giovanna Julian, Alexander US Navy Monterey CA 93940 USA Naval Postgrad Sch Monterey CA USA

ISBN: (纸本)9781728174556

this paper compares the performance of different control strategies for a single-phase inverter interfacing a distributed energy resource during abnormal transients or failures of the utility electric power system voltage. the performance of the various control methods is presented and compared to ieee Standard 1547-2018 using a physicsbased model. the simulation scenarios are created to compare the controller responses to the intentional island and ride-through recommended timing specified in ieee Standard 1547-2018. the physics-based model is validated by experimental measurements on a laboratory prototype with a Field Programmable Gate Array - based controller.

关键词： distributed energy resource grid disconnection ride-through ieee Standard

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(POSTER) A Software-Defined Underwater Visible Light Communication Testbed

(POSTER) A Software-Defined Underwater Visible Light Communi...

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international conference on distributed computing in sensor systems (DCOSS)

作者： Clifford O. Boakye-Mensah Dontez V. Vann Javionn J. Ramsey Hongzhi Guo Engineering Department Norfolk State University Norfolk USA

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

Underwater Visible Light Communication (VLC) uses lasers or LEDs can be used for wireless sensor networks and robotic networks. Compared with underwater acoustic communication and radio frequency communication, VLC has its unique advantages in high data rates and low latency. In this paper, a fully reconfigurable low-cost VLC testbed is designed for underwater communication. the testbed uses a green laser as the transmitter. USRP is utilized to collect the received signals, which are processed in MATLAB. A demodulation algorithm is designed and Bit-Error-Rate (BER) is measured under various conditions. the results indicate that reliable communication can be achieved in the absence of some high-cost elements from typical VLC testbeds. the communication system provides an average BER of 5.5×10 −3 with a data rate of 4 Mbps.

关键词： Wireless sensor networks Underwater communication Lasers Radio transmitters Measurement by laser beam Robot sensing systems sensor systems

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Means of Electromagnetic Compatibility Providing In Local Electricity Networks Under distributed Generation Development 4

Means of Electromagnetic Compatibility Providing In Local El...

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4th ieee international conference on Intelligent Energy and Power systems (IEPS)

作者： Zharkin, Andrii Novskyi, Volodymyr Palachov, Sergij Pazieiev, Andrii Malakhatka, Denys Natl Acad Sci Ukraine Inst Electrodynam Dept Stabilizat Electromagnet Energy Parameters Kiev Ukraine

ISBN: (纸本)9780738105680

the main issues of ensuring the electromagnetic compatibility in local electric networks under distributed generation development are considered, in particular, in the micro-grids with embedded renewable energy sources, that use solar and wind energy, as well as energy storage systems with bidirectional energy converters. the main ways of development of local systems with artificial neutral, which allows to increase the reliability of power supply and improve power quality at the points of connection of loads, that are sensitive to the voltage distortion, are proposed and investigated.

关键词： electromagnetic compatibility power quality local electric network energy storage system bidirectional converters zero-sequence current filter

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A Deep Learning Approach for Short Term Prediction of Industrial Plant Working Status 7

A Deep Learning Approach for Short Term Prediction of Indust...

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7th ieee international conference on Big Data computing Service and Applications, BigDataService 2021

作者： Bellini, Pierfrancesco Cenni, Daniele Palesi, Luciano Alessandro Ipsaro Nesi, Paolo Pantaleo, Gianni Distributed Systems and Internet Tech lab Department of Information Engineering University of Florence DISIT Lab Italy

ISBN: (纸本)9781665434836

Predictive Maintenance has gained more and more research and commercial interests, being a pivotal topic for improving the efficiency of many production industrial plants to minimize downtimes, as well as to reduce operational costs for interventions. Solutions reviewed in literature are increasingly based on machine learning and deep learning methods for prediction of fault proneness with respect to normal working conditions. Many state-of-the art solutions are not actually applied in real scenarios, and have restrictions to be executed in real-time in the production environment. In this paper, a framework for predictive maintenance is presented. It has been built upon a deep learning model based on Long-Short Term Memory Neural Networks, LSTM and Convolutional LSTM. the proposed model provides a one-hour prediction of the plant status and indications on the areas in which the intervention should be performed by using explainable LSTM technique. the solution has been validated against real data of ALTAIR chemical plant, demonstrating an high accuracy with the capability of being executed in real-time in a production operative scenario. the paper also introduced business intelligence tools on maintenance data and the architectural infrastructure for the integration of predictive maintenance approach. © 2021 ieee.

关键词： Long short-term memory

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ARIS: A Real Time Edge Computed Accident Risk Inference System 7

ARIS: A Real Time Edge Computed Accident Risk Inference Syst...

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7th ieee international conference on Smart computing (SMARTCOMP)

作者： Ovi, Pretom Roy Dey, Emon Roy, Nirmalya Gangopadhyay, Aryya Univ Maryland Baltimore Cty Ctr Real Time Distributed Sensing & Auton Dept Informat Syst Baltimore MD 21228 USA

ISBN: (纸本)9781665412520

To deploy an intelligent transport system in urban environment, an effective and real-time accident risk prediction method is required that can help maintain road safety, provide adequate level of medical assistance and transport in case of an emergency. Reducing traffic accidents is an important problem for increasing public safety, so accident analysis and prediction have been a subject of extensive research in recent time. Even if a traffic hazard occurs, a readily deployable structure with an accurate prediction of accident can contribute to better management of rescue resources. But the significant shortcomings of current studies are the use of small-scale datasets with minimal scope, being based on extensive data sets, and not being applicable for real-time purposes. To overcome these challenges, we propose ARIS: a system for real-time traffic accident prediction built on a traffic accident dataset named `US-Accidents' which covers 49 states of United States, collected from February 2016 to June 2020. Our approach is based on a deep neural network model that utilizes a variety of data characteristics, such as time-sensitive weather data, textual information, and discerning factors. We have tested ARIS against multiple baselines through a comprehensive series of experiments across several major cities of USA, and we have noticed significant improvement during inference especially in detecting accident classes. Additionally, to make our model edge-implementable we have compressed our model using a joint technique of magnitude-based weight pruning and model quantization. We have also demonstrated the inference results along with power consumption profiling after deploying the model on a resource constrained environment that consists of Intel Neural Compute Stick 2 (NCS2) with Raspberry Pi 4B (RPi4). Our investigation and observations indicate major improvements to predict unusual traffic accident event even after model compression and deployment. We have managed

关键词： accident risk prediction real-time quantization pruning device implementation

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Experimental Demonstration of a Visible Light Communications Crosswalk Assistance System 15

Experimental Demonstration of a Visible Light Communications...

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15th international Symposium on Electronics and Telecommunications, ISETC 2022

作者： Beguni, Catalin Zadobrischi, Eduard Avatamanitei, Sebastian-Andrei Cailean, Alin-Mihai Department of Computers Electronics and Automation Stefan Cel Mare University of Suceava Suceava720229 Romania Integrated Center for Research Development and Innovation in Advanced Materials Nanotechnologies and Distributed Systems for Fabrication and Control Stefan Cel Mare University of Suceava Suceava Romania Technical University of Cluj-Napoca Cluj-Napoca Romania Laboratoire D Ingénierie des Systèmes de Versailles Paris-Saclay University Vélizy-Villacoublay78140 France

ISBN: (纸本)9781665451505

In the context in which pedestrians are the most exposed road user category in urban areas, this paper presents the results of an experimental demonstration of a new system designed to assist pedestrians in safely using a crosswalk. the proposed architecture consists of a pedestrian detection and localization unit that detects pedestrians in the crosswalk area, a visible light communications component that transmits this information toward the approaching vehicles using the visible light communications technology and a graphical user interface that enables the driver to receive relevant traffic safety information. the experimental investigation has shown that based on a multi-sensor data fusion approach, pedestrians are detected with an overall 100% detection rate, whereas the low power crosswalk sign VLC emitter can provide a communication range of up to 27 ― 40 meters, with a data rate of 100 kb/s and a BER in the 10-7― 10-4 range. therefore, the experimental results indicate that the proposed VLC system has high performance in traffic situations that could pose serious issues to other concepts. © 2022 ieee.

关键词： Visible light communication

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Message from the IoTI4 2021 Workshop Chairs

Message from the IoTI4 2021 Workshop Chairs

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international conference on distributed computing in sensor systems (DCOSS)

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

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Object Recognition: distributed Architecture Based on Heterogeneous Devices to Integrate sensor Information 16th

Object Recognition: Distributed Architecture Based on Hetero...

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16th international Symposium on distributed computing and Artificial Intelligence (DCAI)

作者： Poza-Lujan, Jose-Luis Posadas-Yague, Juan-Luis Munera, Eduardo Simo, Jose E. Blanes, Francisco Univ Politecn Valencia Univ Inst Control Syst & Ind Comp Ai2 Camino Vera S-N E-46022 Valencia Spain

ISBN: (纸本)9783030238872;9783030238865

Object recognition is a necessary task for many areas of technology, such as robot navigation or the intelligent reconstruction of environments in order a robot can interact with these objects. this article presents an architecture that integrates distributed heterogeneous information to recognise objects. the architecture uses devices that can process sensory data locally to send information to other devices. In order to perform data processing actions, the devices must have a layer of intelligent connectivity. these devices are called Smart Resources that offer to the distributed nodes the processed sensor data by means of information services. To validate the architecture, a system with two smart resources, equipped with different sensors, has been implemented. Experiments carried out show that it is better to select objects as soon as possible to improve the object recognition rate. Consequently, in the distributed system, devices should, when possible, deliver the process in advance.

关键词： Smart environment distributed systems distributed architectures Object detection Information integration

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Smart watering system based on framework of low-bandwidth distributed applications (LBDA) in cloud computing 12th

Smart watering system based on framework of low-bandwidth di...

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12th international conference on Intelligent Networking and Collaborative systems, INCoS 2020

作者： Sirimorok, Nurdiansyah As, Mansur Yoshida, Kaori Köppen, Mario Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Creative Informatics Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9783030577957

the estimation of soil moisture content is required for agriculture, mainly to build the irrigation scheduling model. In this study, we present a smart watering system to deal with various factors derived from the stochastic information in the agricultural operational, i.e., air temperature, air humidity, soil moisture, soil temperature, and light intensity. the methodology consists of exploiting the Internet of things (IoT) using Low-Bandwidth distributed Applications (LBDA) in cloud computing to integrate the real data sets collected by the several sensor technologies. We conducted experiments for the watering system used two types of soil and different plant. Here, the Long Short Term Memory Networks (LSTMs) approach in deep learning techniques used to build smart decisions concerning watering requirements and deal with heterogeneous information coming from agricultural environments. Results show that our models can effectively improve the prediction accuracy for the watering system over various soil and plants. © Springer Nature Switzerland AG 2021.

关键词： Cloud computing

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