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2022 ◽  
pp. 423-442
Author(s):  
Archana Yashodip Chaudhari ◽  
Preeti Mulay

Intelligent electricity meters (IEMs) form a key infrastructure necessary for the growth of smart grids. IEMs generate a considerable amount of electricity data incrementally. However, on an influx of new data, traditional clustering task re-cluster all of the data from scratch. The incremental clustering method is an essential way to solve the problem of clustering with dynamic data. Given the volume of IEM data and the number of data types involved, an incremental clustering method is highly complex. Microsoft Azure provide the processing power necessary to handle incremental clustering analytics. The proposed Cloud4NFICA is a scalable platform of a nearness factor-based incremental clustering algorithm. This research uses the real dataset of Irish households collected by IEMs and related socioeconomic data. Cloud4NFICA is incremental in nature, hence accommodates the influx of new data. Cloud4NFICA was designed as an infrastructure as a service. It is visible from the study that the developed system performs well on the scalability aspect.


Author(s):  
Mohammad Behdad Jamshidi ◽  
Saeed Roshani ◽  
Jakub Talla ◽  
Maryam S. Sharifi-Atashgah ◽  
Sobhan Roshani ◽  
...  

2021 ◽  
Author(s):  
Prakarsh Kaushik ◽  
Ashwin Murali Rao ◽  
Devang Pratap Singh ◽  
Swati Vashisht ◽  
Shubhi Gupta

2021 ◽  
pp. 125-139
Author(s):  
Ігор Борисович Туркін ◽  
В'ячеслав Андрійович Лезновскій

The subject of study in the article is a digital platform for vibration diagnostics of industrial equipment. The aim is to increase the informativeness of vibration diagnostics processes of industrial equipment by developing and implementing IoT-oriented solutions based on the concept of intelligent sensors and actuators according to the IEEE standard 1451.0-2007. Tasks: to substantiate the feasibility of using platform-oriented technologies for vibration diagnostics of industrial equipment and choose a cloud service for the implementation of the platform, to develop software and hardware solutions for IoT-platform for vibration diagnostics of industrial equipment; calibrate the vibration diagnostic system and check the accuracy of the measurement. The methods used are microservice approach, multilevel architecture, methods for assessing the condition of equipment by vibration indicators. The following results were obtained. The Microsoft Azure IoT platform, which provides the infrastructure for creating and managing cloud applications, was chosen as the cloud computing platform for the industrial equipment vibration diagnostic system. Azure Internet of Things Suite is a Microsoft Azure IoT service designed to integrate and organize data flows, analyze, and present data in a format that helps people make informed decisions. The architecture of the IoT-system of vibration diagnostics of industrial equipment developed and presented in the article is three-level. The level of autonomous sensors provides reading of vibration acceleration indicators and through the digital wireless data transmission channel BLE transmits data to the Hub level, which is implemented based on a single-board microcomputer BeagleBone. The computing power of BeagleBone provides work with artificial intelligence algorithms. At the third level of the server platform, the tasks of diagnosing and predicting the state of the equipment are solved, for which the Dictionary Learning algorithm implemented in the Python programming language is used. Conclusions. Tests of the IoT system for vibration diagnostics of industrial equipment were performed using a special stand, which allows the calibration of sensors and verification of the accuracy of the measuring system. The correctness of the entire system is confirmed by the coincidence of expected and measured results. The direction of development of the IoT-system for vibration diagnostics of industrial equipment is the development of additional microservices, which will add the possibility of using modern artificial intelligence technologies for complex diagnostics and forecasting of equipment status.


2021 ◽  
Author(s):  
Matheus Xavier Sampaio ◽  
Regis Pires Magalhães ◽  
Ticiana Linhares Coelho da Silva ◽  
Lívia Almada Cruz ◽  
Davi Romero de Vasconcelos ◽  
...  

Automatic Speech Recognition (ASR) is an essential task for many applications like automatic caption generation for videos, voice search, voice commands for smart homes, and chatbots. Due to the increasing popularity of these applications and the advances in deep learning models for transcribing speech into text, this work aims to evaluate the performance of commercial solutions for ASR that use deep learning models, such as Facebook Wit.ai, Microsoft Azure Speech, and Google Cloud Speech-to-Text. The results demonstrate that the evaluated solutions slightly differ. However, Microsoft Azure Speech outperformed the other analyzed APIs.


Author(s):  
D. Subbarao ◽  
Bhagya Raju ◽  
Farha Anjum ◽  
Ch venkateswara Rao ◽  
B. Mahender Reddy

Author(s):  
Ricardo Rosero ◽  
Sebastián Bahamonde ◽  
Ana Zambrano

El presente trabajo propone un sistema de distribución para leer y visualizar el consumo de agua de manera remota, por medio de una página web y un aplicativo móvil. Para la implementación del sistema se lo ha dividido en tres etapas que son: módulo electrónico, módulo web y módulo móvil, y, para su desarrollo se ha establecido una arquitectura de tres niveles: presentación, aplicación y datos. Cada uno de estos niveles se encuentran alojados en el Cloud Computing de Microsoft Azure. El módulo electrónico se encarga de medir y registrar el consumo de agua y los datos obtenidos se guardan en una base de datos, el módulo web se encarga de gestionar los usuarios y administradores, permite las consultas sobre el consumo de agua de los diferentes controladores y en el módulo móvil se encuentra un APK que puede ser instalado en un dispositivo móvil para consultar el consumo de agua de los diferentes controladores.


Author(s):  
Ravi Helon M. S. Ferreira ◽  
Lucas O. de Figueiredo ◽  
Rafael B. C. Lima ◽  
Luiz Antonio Pereira Silva ◽  
Pericles R. Barros

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