Smart grid health monitoring via dynamic compressive sensing

2013 ◽  
Author(s):  
Jinping Hao ◽  
Robert J. Piechocki ◽  
Dritan Kaleshi ◽  
Woon Hau Ching ◽  
Zhong Fan
Keyword(s):  
Smart Grid ◽  
Compressive Sensing ◽  
Health Monitoring

scholarly journals Didática para Ensino de IoT Otimiza Recursos Públicos / Didactics for IoT Optimizes Public Transportation

2019 ◽  
Vol 5 (1) ◽  
pp. 1-7
Author(s):  
Helbert Da Rocha ◽  
Tânia Lucia Monteiro
Keyword(s):  
Smart Grid ◽  
Health Monitoring ◽  
Public Transportation ◽  
Preventive Maintenance ◽  
New Technologies ◽  
Smart Cities ◽  
Technological Paradigm ◽  
City Hall ◽  
Public Resources ◽  
Two Cities

ABSTRACTInternet of Things (IoT) is a technological paradigm and one of the most important tendency of TI nowadays, it is having a broad scope of research in the most different areas. IoT is playing an important role in the concept of smart cities, smart grid, smart health monitoring, smart clothes, preventive maintenance, etc. The IoT contribution to cities in order to make them intelligent, creates a hall of possibilities where there is almost no limit. The knowledge of this topic was presented to the students of the discipline Advanced Topics in Computer Networks. Unorthodoxly and using new technologies, resulted in the project “Vou de ônibus”. The project, aims to optimize public resources, used in the transportation of university students between two cities in the interior of Brazil, provided daily to academics by one of the local city hall. The project has the goal to determine the partial number of students that will use the transportation on a certain day of the week.RESUMOInternet of Things (IoT) é um paradigma tecnológico e uma das tendências de TI mais importantes da atualidade, tendo amplo escopo de pesquisa nas mais diversas áreas. IoT está desempenhando um papel importante no conceito de cidades inteligentes, smart grid, monitoramento de saúde inteligente, roupas inteligentes, manutenção preditiva, etc. A contribuição da IoT para cidades, com finalidade de torná-las inteligentes cria um hall de possibilidades onde quase não há limite. O conhecimento deste tema foi apresentado aos alunos da Disciplina de Tópicos Avançados em Redes de Computadores, de forma não ortodoxa e utilizando novas tecnologias, tendo como resultado o projeto “Vou de ônibus”. O projeto, visa otimizar recursos públicos, utilizados no transporte de universitários entre duas cidades no interior do Brasil, fornecido diariamente aos acadêmicos locais por uma das prefeituras. Tendo como objetivo determinar o número parcial de alunos que irá utilizar o transporte em determinado dia da semana.

Compressive sensing for smart-grid security and reliability

2017 ◽  
pp. 347-404
Author(s):  
Mohammad Babakmehr Babakmehr ◽  
Marcelo Godoy Simoes Simoes ◽  
Ahmed Al-Durra Al-Durra
Keyword(s):  
Smart Grid ◽  
Compressive Sensing ◽  
Grid Security ◽  
Smart Grid Security ◽  
Security And Reliability

scholarly journals A Computerized Bioinspired Methodology for Lightweight and Reliable Neural Telemetry

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6461
Author(s):  
Olufemi Adeluyi ◽  
Miguel A. Risco-Castillo ◽  
María Liz Crespo ◽  
Andres Cicuttin ◽  
Jeong-A Lee
Keyword(s):  
Compressive Sensing ◽  
Health Monitoring ◽  
Bayesian Learning ◽  
Structural Similarity ◽  
Weakly Electric Fish ◽  
Neural Signals ◽  
Differential Signal ◽  
Monitoring Process ◽  
Average Coherence ◽  
Weakly Electric

Personalized health monitoring of neural signals usually results in a very large dataset, the processing and transmission of which require considerable energy, storage, and processing time. We present bioinspired electroceptive compressive sensing (BeCoS) as an approach for minimizing these penalties. It is a lightweight and reliable approach for the compression and transmission of neural signals inspired by active electroceptive sensing used by weakly electric fish. It uses a signature signal and a sensed pseudo-sparse differential signal to transmit and reconstruct the signals remotely. We have used EEG datasets to compare BeCoS with the block sparse Bayesian learning-bound optimization (BSBL-BO) technique—A popular compressive sensing technique used for low-energy wireless telemonitoring of EEG signals. We achieved average coherence, latency, compression ratio, and estimated per-epoch power values that were 35.38%, 62.85%, 53.26%, and 13 mW better than BSBL-BO, respectively, while structural similarity was only 6.295% worse. However, the original and reconstructed signals remain visually similar. BeCoS senses the signals as a derivative of a predefined signature signal resulting in a pseudo-sparse signal that significantly improves the efficiency of the monitoring process. The results show that BeCoS is a promising approach for the health monitoring of neural signals.

scholarly journals Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach

2019 ◽  
Vol 19 (1) ◽  
pp. 293-304 ◽  
Author(s):  
Yuequan Bao ◽  
Zhiyi Tang ◽  
Hui Li
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Compressive Sensing ◽  
Health Monitoring ◽  
Applied Mathematics ◽  
Modal Identification ◽  
Data Reconstruction ◽  
Wireless Data ◽  
Sensing Data ◽  
Structural Health

Compressive sensing has been studied and applied in structural health monitoring for data acquisition and reconstruction, wireless data transmission, structural modal identification, and spare damage identification. The key issue in compressive sensing is finding the optimal solution for sparse optimization. In the past several years, many algorithms have been proposed in the field of applied mathematics. In this article, we propose a machine learning–based approach to solve the compressive-sensing data-reconstruction problem. By treating a computation process as a data flow, the solving process of compressive sensing–based data reconstruction is formalized into a standard supervised-learning task. The prior knowledge, i.e. the basis matrix and the compressive sensing–sampled signals, is used as the input and the target of the network; the basis coefficient matrix is embedded as the parameters of a certain layer; and the objective function of conventional compressive sensing is set as the loss function of the network. Regularized by l1-norm, these basis coefficients are optimized to reduce the error between the original compressive sensing–sampled signals and the masked reconstructed signals with a common optimization algorithm. In addition, the proposed network is able to handle complex bases, such as a Fourier basis. Benefiting from the nature of a multi-neuron layer, multiple signal channels can be reconstructed simultaneously. Meanwhile, the disassembled use of a large-scale basis makes the method memory-efficient. A numerical example of multiple sinusoidal waves and an example of field-test wireless data from a suspension bridge are carried out to illustrate the data-reconstruction ability of the proposed approach. The results show that high reconstruction accuracy can be obtained by the machine learning–based approach. In addition, the parameters of the network have clear meanings; the inference of the mapping between input and output is fully transparent, making the compressive-sensing data-reconstruction neural network interpretable.

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