scholarly journals Geomagnetic storm forecasting service StormFocus: 5 years online

2018 ◽  
Vol 8 ◽  
pp. A22 ◽  
Author(s):  
Tatiana Podladchikova ◽  
Anatoly Petrukovich ◽  
Yuri Yermolaev
Keyword(s):  
Solar Wind ◽  
Real Time ◽  
Geomagnetic Storm ◽  
Geomagnetic Storms ◽  
Prediction Errors ◽  
Time Operation ◽  
Real Time Operation ◽  
Cycle 24 ◽  
Processing Errors

Forecasting geomagnetic storms is highly important for many space weather applications. In this study, we review performance of the geomagnetic storm forecasting service StormFocus during 2011–2016. The service was implemented in 2011 at SpaceWeather.Ru and predicts the expected strength of geomagnetic storms as measured by Dst index several hours ahead. The forecast is based on L1 solar wind and IMF measurements and is updated every hour. The solar maximum of cycle 24 is weak, so most of the statistics are on rather moderate storms. We verify quality of selection criteria, as well as reliability of real-time input data in comparison with the final values, available in archives. In real-time operation 87% of storms were correctly predicted while the reanalysis running on final OMNI data predicts successfully 97% of storms. Thus the main reasons for prediction errors are discrepancies between real-time and final data (Dst, solar wind and IMF) due to processing errors, specifics of datasets.

scholarly journals Efficient Deep Neural Network for Digital Image Compression Employing Rectified Linear Neurons

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Farhan Hussain ◽  
Jechang Jeong
Keyword(s):  
Image Compression ◽  
Real Time ◽  
Deep Neural Networks ◽  
Compression Technique ◽  
Training Time ◽  
Time Compression ◽  
Time Operation ◽  
Real Time Operation ◽  
Digital Image Compression

A compression technique for still digital images is proposed with deep neural networks (DNNs) employing rectified linear units (ReLUs). We tend to exploit the DNNs capabilities to find a reasonable estimate of the underlying compression/decompression relationships. We aim for a DNN for image compression purpose that has better generalization property and reduced training time and support real time operation. The use of ReLUs which map more plausibly to biological neurons, makes the training of our DNN significantly faster, shortens the encoding/decoding time, and improves its generalization ability. The introduction of the ReLUs establishes an efficient gradient propagation, induces sparsity in the proposed network, and is efficient in terms of computations making these networks suitable for real time compression systems. Experiments performed on standard real world images show that using ReLUs instead of logistic sigmoid units speeds up the training of the DNN by converging markedly faster. The evaluation of objective and subjective quality of reconstructed images also proves that our DNN achieves better generalization as most of the images are never seen by the network before.

Real-Time Operation and Characterization of a High-Performance Time-Based Accelerometer

2015 ◽  
Vol 24 (6) ◽  
pp. 1703-1711 ◽  
Author(s):  
Rosana Alves Dias ◽  
Filipe Serra Alves ◽  
Margaret Costa ◽  
Helder Fonseca ◽  
Jorge Cabral ◽  
...  
Keyword(s):  
Real Time ◽  
High Performance ◽  
Performance Time ◽  
Time Operation ◽  
Real Time Operation

Cultural Change in ALS Real Time Operation and Optimization in Vaca Muerta Through CWE

2018 ◽  
Author(s):  
J. I. Alvarez Claramunt ◽  
P. E. Bizzotto ◽  
F. Sapag ◽  
E. Ferrigno ◽  
J. L. Barros ◽  
...  
Keyword(s):  
Real Time ◽  
Cultural Change ◽  
Time Operation ◽  
Vaca Muerta ◽  
Real Time Operation

scholarly journals TDOA-based microwave imaging algorithm for real-time microwave ablation monitoring

2017 ◽  
Vol 10 (2) ◽  
pp. 169-178 ◽  
Author(s):  
Shouhei Kidera ◽  
Luz Maria Neira ◽  
Barry D. Van Veen ◽  
Susan C. Hagness
Keyword(s):  
Real Time ◽  
Microwave Ablation ◽  
Microwave Imaging ◽  
Ablation Zone ◽  
Real Time Monitoring ◽  
Imaging Algorithm ◽  
Array Measurements ◽  
Time Operation ◽  
Real Time Operation ◽  
Propagation Media

Microwave ablation is widely recognized as a promising minimally invasive tool for treating cancer. Real-time monitoring of the dimensions of the ablation zone is indispensable for ensuring an effective and safe treatment. In this paper, we propose a microwave imaging algorithm for monitoring the evolution of the ablation zone. Our proposed algorithm determines the boundary of the ablation zone by exploiting the time difference of arrival (TDOA) between signals received before and during the ablation at external antennas surrounding the tissue, using the interstitial ablation antenna as the transmitter. A significant advantage of this method is that it requires few assumptions about the dielectric properties of the propagation media. Also the simplicity of the signal processing, wherein the TDOA is determined from a cross-correlation calculation, allows real-time monitoring and provides robust performance in the presence of noise. We investigate the performance of this approach for the application of breast tumor ablation. We use simulated array measurements obtained from finite-difference time-domain simulations of magnetic resonance imaging-derived numerical breast phantoms. The results demonstrate that our proposed method offers the potential to achieve millimeter-order accuracy and real-time operation in estimating the boundary of the ablation zone in heterogeneous and dispersive breast tissue.

scholarly journals CONTROLLING AND MONITORING OF AUTOMATION WATER SUPPLY SYSTEM BASED ON IOT WITH THEFT IDENTIFICATION

2017 ◽  
Vol 5 (5) ◽  
pp. 320-325
Author(s):  
Ahmad T. Jaiad ◽  
Hamzah Sabr Ghayyib
Keyword(s):  
Water Supply ◽  
Real Time ◽  
Water Conservation ◽  
Water Distribution ◽  
Network Connectivity ◽  
Raspberry Pi ◽  
Human Beings ◽  
Residential Areas ◽  
Time Operation ◽  
Real Time Operation

Water is the most precious and valuable because it’s a basic need of all the human beings but, now a day water supply department are facing problem in real time operation this is because less amount of water in resources due to less rain fall. With increase in Population, urban residential areas have increased because of this reasons water has become a crucial problem which affects the problem of water distribution, interrupted water supply, water conservation, water consumption and also the water quality so, to overcome water supply related problems and make system efficient there is need of proper monitoring and controlling system. In this project, we are focusing on continuous and real time monitoring of water supply in IOT platform. Water supply with continuous monitoring makes a proper distribution so that, we can have a record of available amount of water in tanks, flow rate, abnormality in distribution line. Internet of things is nothing but the network of physical objects embedded with electronics, sensors, software, and network connectivity. Monitoring can be done from anywhere as central office. Using Adafruit as free sever data continuously pushed on cloud so we can see data in real time operation. Using different sensors with controller and raspberry pi as Mini computer can monitor data and also control operation from cloud with efficient client server communication.

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