scholarly journals A neural network for prediction of high intensity resonance modes in magnetic multilayers

2021 ◽  
Author(s):  
Andres Felipe Franco
Keyword(s):  
Neural Network ◽  
High Intensity ◽  
Magnetic Multilayers ◽  
Resonance Modes

scholarly journals Susceptibility to Seismic Amplification and Earthquake Probability Estimation Using Recurrent Neural Network (RNN) Model in Odisha, India

2020 ◽  
Vol 10 (15) ◽  
pp. 5355 ◽  
Author(s):  
Ratiranjan Jena ◽  
Biswajeet Pradhan ◽  
Abdullah M. Alamri
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
High Intensity ◽  
Intensity Variation ◽  
The State ◽  
Earthquake Catalog ◽  
Alluvial Deposits ◽  
Earthquake Probability ◽  
Probability Map ◽  
Seismic Amplification

The eastern region of India, including the coastal state of Odisha, is a moderately seismic-prone area under seismic zones II and III. However, no major studies have been conducted on earthquake probability (EPA) and hazard assessment (EHA) in Odisha. This paper had two main objectives: (1) to assess the susceptibility of seismic wave amplification (SSA) and (2) to estimate EPA in Odisha. In total, 12 indicators were employed to assess the SSA and EPA. Firstly, using the historical earthquake catalog, the peak ground acceleration (PGA) and intensity variation was observed for the Indian subcontinent. We identified high amplitude and frequency locations for estimated PGA and the periodograms were plotted. Secondly, several indicators such as slope, elevation, curvature, and amplification values of rocks were used to generate SSA using predefined weights of layers. Thirdly, 10 indicators were implemented in a developed recurrent neural network (RNN) model to create an earthquake probability map (EPM). According to the results, recent to quaternary unconsolidated sedimentary rocks and alluvial deposits have great potential to amplify earthquake intensity and consequently lead to acute ground motion. High intensity was observed in coastal and central parts of the state. Complicated morphometric structures along with high intensity variation could be other parameters that influence deposits in the Mahanadi River and its delta with high potential. The RNN model was employed to create a probability map (EPM) for the state. Results show that the Mahanadi basin has dominant structural control on earthquakes that could be found in the western parts of the state. Major faults were pointed towards a direction of WNW–ESE, NE–SW, and NNW–SSE, which may lead to isoseismic patterns. Results also show that the western part is highly probable for events while the eastern coastal part is highly susceptible to seismic amplification. The RNN model achieved an accuracy of 0.94, precision (0.94), recall (0.97), F1 score (0.96), critical success index (CSI) (0.92), and a Fowlkes–Mallows index (FM) (0.95).

scholarly journals Passenger Flow Prediction Based on Land Use around Metro Stations: A Case Study

2020 ◽  
Vol 12 (17) ◽  
pp. 6844 ◽  
Author(s):  
Ciyun Lin ◽  
Kang Wang ◽  
Dayong Wu ◽  
Bowen Gong
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
High Intensity ◽  
Service Level ◽  
Land Uses ◽  
Metro Station ◽  
Traffic Demand ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Management Standard

High-density land uses cause high-intensity traffic demand. Metro as an urban mass transit mode is considered as a sustainable strategy to balance the urban high-density land uses development and the high-intensity traffic demand. However, the capacity of the metro cannot always meet the traffic demand during rush hours. It calls for traffic agents to reinforce the operation and management standard to improve the service level. Passenger flow prediction is the foremost and pivotal technology in improving the management standard and service level of metro. It is an important technological means in ensuring sustainable and steady development of urban transportation. This paper uses mathematical and neural network modeling methods to predict metro passenger flow based on the land uses around the metro stations, along with considering the spatial correlation of metro stations within the metro line and the temporal correlation of time series in passenger flow prediction. It aims to provide a feasible solution to predict the passenger flow based on land uses around the metro stations and then potentially improving the understanding of the land uses around the metro station impact on the metro passenger flow, and exploring the potential association between the land uses and the metro passenger flow. Based on the data source from metro line 2 in Qingdao, China, the perdition results show the proposed methods have a good accuracy, with Mean Absolute Percentage Errors (MAPEs) of 11.6%, 3.24%, and 3.86 corresponding to the metro line prediction model with Categorical Regression (CATREG), single metro station prediction model with Artificial Neural Network (ANN), and single metro station prediction model with Long Short-Term Memory (LSTM), respectively.

A New High Intensity Grid Cap for RCA-EMU-3 Electron Microscopes

1968 ◽  
Vol 26 ◽  
pp. 316-317
Author(s):  
George Christov ◽  
Bolivar J. Lloyd
Keyword(s):  
Electron Beam ◽  
Electron Microscope ◽  
High Voltage ◽  
High Intensity ◽  
Electron Gun ◽  
Tungsten Filament ◽  
Fluorescent Screen ◽  
Electron Microscopes ◽  
Pass Through ◽  
Ground Potential

A new high intensity grid cap has been designed for the RCA-EMU-3 electron microscope. Various parameters of the new grid cap were investigated to determine its characteristics. The increase in illumination produced provides ease of focusing on the fluorescent screen at magnifications from 1500 to 50,000 times using an accelerating voltage of 50 KV.The EMU-3 type electron gun assembly consists of a V-shaped tungsten filament for a cathode with a thin metal threaded cathode shield and an anode with a central aperture to permit the beam to course the length of the column. The cathode shield is negatively biased at a potential of several hundred volts with respect to the filament. The electron beam is formed by electrons emitted from the tip of the filament which pass through an aperture of 0.1 inch diameter in the cap and then it is accelerated by the negative high voltage through a 0.625 inch diameter aperture in the anode which is at ground potential.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

260: High Intensity Focused Ultrasound Combined with Endocrine Therapy in Treating High or Intermediate Risk Prostate Cancer

2006 ◽  
Vol 175 (4S) ◽  
pp. 86-86
Author(s):  
Makoto Sumitomo ◽  
Junichi Asakuma ◽  
Yasumasa Hanawa ◽  
Kazuhiko Nagakura ◽  
Masamichi Hayakawa
Keyword(s):  
Prostate Cancer ◽  
Endocrine Therapy ◽  
High Intensity ◽  
Focused Ultrasound ◽  
High Intensity Focused Ultrasound ◽  
Intermediate Risk ◽  
Risk Prostate Cancer
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