Worth of radar data in the real-time prediction of mean areal rainfall by nonadvective physically based models

1991 ◽  
Vol 27 (2) ◽  
pp. 185-197 ◽  
Author(s):  
Konstantine P. Georgakakos ◽  
Witold F. Krajewski
Keyword(s):  
Real Time ◽  
Radar Data ◽  
The Real ◽  
Time Prediction ◽  
Physically Based ◽  
Physically Based Models ◽  
Areal Rainfall

scholarly journals MRSaiFE: An AI-based Approach Towards the Real-Time Prediction of Specific Absorption Rate

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sayim Gokyar ◽  
Fraser J. L. Robb ◽  
Wolfgang Kainz ◽  
Akshay Chaudhari ◽  
Simone Angela Winkler
Keyword(s):  
Real Time ◽  
Absorption Rate ◽  
Specific Absorption Rate ◽  
Specific Absorption ◽  
The Real ◽  
Time Prediction

The Error Correction Model on Kalman Filter

2014 ◽  
Vol 644-650 ◽  
pp. 3968-3971
Author(s):  
Ya Qiu Hao
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Arrival Time ◽  
Running Speed ◽  
Correction Model ◽  
The Real ◽  
Time Prediction ◽  
Speed Prediction ◽  
Arrival Time Prediction ◽  
Bus Arrival Time Prediction

In this paper, authors extracted the data from the GPS equipment on the bus and established the real-time bus arrival time prediction model and bus running speed prediction model based on Kalman filtering technique. Analyse the error and build the error correction model. Firstly the bus running speed was predicted in the next section with the bus running speed prediction model, and then the bus arrival time was predicted with the real-time bus arrival time prediction model. Applying the newest information of bus running speed and bus arrival time, we were able to predict the real-time bus arrival time dynamically. The bus running speed prediction model and the real-time bus arrival time prediction model were assessed with the data of transit route NO.300 in Beijing. Lastly we assessed the real-time bus arrival time with the error between bus arrival time and real-time bus arrival time so that the prediction error was improved to 10 seconds which has higher prediction accuracy.

scholarly journals The real-time prediction and inhibition of linguistic outcomes: Effects of language and literacy skill

2016 ◽  
Vol 171 ◽  
pp. 72-84 ◽  
Author(s):  
Anuenue Kukona ◽  
David Braze ◽  
Clinton L. Johns ◽  
W. Einar Mencl ◽  
Julie A. Van Dyke ◽  
...  
Keyword(s):  
Real Time ◽  
Literacy Skill ◽  
Language And Literacy ◽  
The Real ◽  
Time Prediction

Data Center Cooling Prediction Using Artificial Neural Network

2007 ◽  
Author(s):  
Saurabh K. Shrivastava ◽  
James W. VanGilder ◽  
Bahgat G. Sammakia
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Data Center ◽  
Cooling Performance ◽  
The Real ◽  
Time Prediction ◽  
Linear Relationships ◽  
The Neural Network ◽  
Data Center Cooling ◽  
Computational Fluid Dynamics Cfd

An analytical approach using artificial intelligence has been developed for assessing the cooling performance of data centers. This paper discusses the use of a Neural Network (NN) model in the real-time prediction of the cooling performance of a cluster of equipment in a data center environment. The NN model is used to predict the Capture Index (CI) [1] as a function of rack power, cooler airflow and physical/geometric arrangement for a cluster located in a simple room environment. The Neural Network is “trained” on thousands of hypothetical but realistic cluster variations for which CI values have been computed using either PDA [2] or full Computational Fluid Dynamics (CFD). The great value of the NN approach lies in its ability to capture the non-linear relationships between input parameters and corresponding capture indices. The accuracy of the NN approach is 3.8% (Root Mean Square Error) for a set of example scenarios discussed here. Because of the real-time nature of the calculations, the NN approach readily facilitates optimization studies. Example cases are discussed which show the integration of the NN approach and a genetic algorithm used for optimization.

scholarly journals Two-Stream Dense Feature Fusion Network Based on RGB-D Data for the Real-Time Prediction of Weed Aboveground Fresh Weight in a Field Environment

2021 ◽  
Vol 13 (12) ◽  
pp. 2288
Author(s):  
Longzhe Quan ◽  
Hengda Li ◽  
Hailong Li ◽  
Wei Jiang ◽  
Zhaoxia Lou ◽  
...  
Keyword(s):  
Real Time ◽  
Precision Agriculture ◽  
Feature Fusion ◽  
Reference Method ◽  
Fresh Weight ◽  
Herbicide Application ◽  
Convolutional Network ◽  
Important Indicator ◽  
The Real ◽  
Time Prediction

The aboveground fresh weight of weeds is an important indicator that reflects their biomass and physiological activity and directly affects the criteria for determining the amount of herbicides to apply. In precision agriculture, the development of models that can accurately locate weeds and predict their fresh weight can provide visual support for accurate, variable herbicide application in real time. In this work, we develop a two-stream dense feature fusion convolutional network model based on RGB-D data for the real-time prediction of the fresh weight of weeds. A data collection method is developed for the compilation and production of RGB-D data sets. The acquired images undergo data enhancement, and a depth transformation data enhancement method suitable for depth data is proposed. The main idea behind the approach in this study is to use the YOLO-V4 model to locate weeds and use the two-stream dense feature fusion network to predict their aboveground fresh weight. In the two-stream dense feature fusion network, DenseNet and NiN methods are used to construct a Dense-NiN-Block structure for deep feature extraction and fusion. The Dense-NiN-Block module was embedded in five convolutional neural networks for comparison, and the best results were achieved with DenseNet201. The test results show that the predictive ability of the convolutional network using RGB-D as the input is better than that of the network using RGB as the input without the Dense-NiN-Block module. The mAP of the proposed network is 75.34% (IoU value of 0.5), the IoU is 86.36%, the detection speed of the fastest model with a RTX2080Ti NVIDIA graphics card is 17.8 fps, and the average relative error is approximately 4%. The model proposed in this paper can provide visual technical support for precise, variable herbicide application. The model can also provide a reference method for the non-destructive prediction of crop fresh weight in the field and can contribute to crop breeding and genetic improvement.

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