scholarly journals An artificial neural network based b jet identification algorithm at the CDF experiment

2012 ◽  
Vol 663 (1) ◽  
pp. 37-47 ◽  
Author(s):  
J. Freeman ◽  
W. Ketchum ◽  
J.D. Lewis ◽  
S. Poprocki ◽  
A. Pronko ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Identification Algorithm ◽  
Artificial Neural

A Non - Intrusive Load Identification Algorithm Combined with Event Detection

2021 ◽  
Vol 14 ◽  
Author(s):  
Runhai Jiao ◽  
Qihang Zhou ◽  
Liangqiu Lyu ◽  
Guangwei Yan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
The State ◽  
Event Recognition ◽  
Identification Algorithm ◽  
Load Identification ◽  
Fusion Model ◽  
Public Data ◽  
Artificial Neural

Background: The traditional state-based non-intrusive load monitoring method mainly deploys the aggregate load as the characteristic to identify the states of every electrical appliance. Each identification is relatively independent, and there is no correlation between the identification results. Objective: This paper combines the event detection results with the state-based non-intrusive load identification algorithm to improve accuracy. Methods: Firstly, the load recognition model based on an artificial neural network is constructed, and the state-based recognition results are obtained. An event recognition and detection model is then built to identify electrical state transitions, that is, the current moment based on the event recognition results obtained from the previous moment. Finally, a reasonable decision method is constructed to determine the identification result of the electrical states. Result: Experimental results on the public data set REDD show that in the Long Short-Term Memory (LSTM) fusion model, the macro-F1 is increased by an average of 6%, and the macro-F1 of the Artificial Neural Network (ANN) fusion model is increased by an average of 5.3% compared with LSTM and ANN. Conclusion: The proposed model can effectively improve the accuracy of identification compared with the state-based load identification method.

scholarly journals Designing a Fruit Identification Algorithm in Orchard Conditions to Develop Robots Using Video Processing and Majority Voting Based on Hybrid Artificial Neural Network

2020 ◽  
Vol 10 (1) ◽  
pp. 383 ◽  
Author(s):  
Sajad Sabzi ◽  
Razieh Pourdarbani ◽  
Davood Kalantari ◽  
Thomas Panagopoulos
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Video Processing ◽  
Color Space ◽  
Majority Voting ◽  
Identification Algorithm ◽  
Natural Conditions ◽  
Artificial Neural ◽  
Majority Voting Method ◽  
Voting Method

The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.

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

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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