scholarly journals Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images

Forecasting ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 194-210 ◽  
Author(s):  
Marino Marrocu ◽  
Luca Massidda
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
State Of The Art ◽  
Performance Comparison ◽  
Data Set ◽  
Radar Images ◽  
The Neural Network ◽  
Meteorological Radar ◽  
One Year ◽  
Better Than

In this article, a nowcasting technique for meteorological radar images based on a generative neural network is presented. This technique’s performance is compared with state-of-the-art optical flow procedures. Both methods have been validated using a public domain data set of radar images, covering an area of about 104 km2 over Japan, and a period of five years with a sampling frequency of five minutes. The performance of the neural network, trained with three of the five years of data, forecasts with a time horizon of up to one hour, evaluated over one year of the data, proved to be significantly better than those obtained with the techniques currently in use.

scholarly journals Applying Data Mining Techniques on Continuous Sensed Data for Daily Living Activity Recognition

2015 ◽  
Vol 738-739 ◽  
pp. 191-196
Author(s):  
Yun Jie Li ◽  
Hui Song
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Daily Living ◽  
Tree Model ◽  
Data Set ◽  
Data Mining Techniques ◽  
Daily Living Activity ◽  
The Neural Network ◽  
Better Than

In this paper, several data mining techniques were discussed and analyzed in order to achieve the objective of human daily activities recognition based on a continuous sensing data set. The data mining techniques of decision tree, Naïve Bayes and Neural Network were successfully applied to the data set. The paper also proposed an idea of combining the Neural Network with the Decision Tree, the result shows that it works much better than the typical Neural Network and the typical Decision Tree model.

scholarly journals STUDY OF A MA16-BASED VERTEX TRIGGER FOR THE CDF EXPERIMENT

1995 ◽  
Vol 06 (05) ◽  
pp. 681-692
Author(s):  
R. ODORICO
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Impact Parameter ◽  
Electronic Components ◽  
The Neural Network ◽  
Viable Solution ◽  
Impact Parameters ◽  
Input Variables ◽  
Better Than

A Neural Network trigger for [Formula: see text] events based on the SVT microvertex processor of experiment CDF at Fermilab is presented. It exploits correlations among track impact parameters and azimuths calculated by the SVT from the SVX microvertex detector data. The neural trigger is meant for implementation on the systolic Siemens microprocessor MA16, which has already been used in a neural-network trigger for experiment WA92 at CERN. A suitable set of input variables is found, which allows a viable solution for the preprocessing task using standard electronic components. The response time of the neural-network stage of the trigger, including preprocessing, can be estimated ~10 μs. Its precise value depends on the quantitative specifications of the output signals of the SVT, which is still in development. The performance of the neural-network trigger is found to be significantly better than that of a conventional trigger exclusively based on impact parameter data.

Watermelon Sorting Process by Frequency Identification and Artificial Neural Network

2021 ◽  
Author(s):  
Komsan Wongkalasin ◽  
Teerapon Upachaban ◽  
Wacharawish Daosawang ◽  
Nattadon Pannucharoenwong ◽  
Phadungsak Ratanadecho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Selection Process ◽  
Network Processor ◽  
Data Set ◽  
The Neural Network ◽  
Frequency Identification ◽  
Sorting Process ◽  
Artificial Neural ◽  
Reasonable Prediction

This research aims to enhance the watermelon’s quality selection process, which was traditionally conducted by knocking the watermelon fruit and sort out by the sound’s character. The proposed method in this research is generating the sound spectrum through the watermelon and then analyzes the response signal’s frequency and the amplitude by Fast Fourier Transform (FFT). Then the obtained data were used to train and verify the neural network processor. The result shows that, the frequencies of 129 and 172 Hz were suit to be used in the comparison. Thirty watermelons, which were randomly selected from the orchard, were used to create a data set, and then were cut to manually check and match to the fruits’ quality. The 129 Hz frequency gave the response ranging from 13.57 and above in 3 groups of watermelons quality, including, not fully ripened, fully ripened, and close to rotten watermelons. When the 172 Hz gave the response between 11.11–12.72 in not fully ripened watermelons and those of 13.00 or more in the group of close to rotten and hollow watermelons. The response was then used as a training condition for the artificial neural network processor of the sorting machine prototype. The verification results provided a reasonable prediction of the ripeness level of watermelon and can be used as a pilot prototype to improve the efficiency of the tools to obtain a modern-watermelon quality selection tool, which could enhance the competitiveness of the local farmers on the product quality control.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

scholarly journals Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)

2018 ◽  
Author(s):  
Georgy Ayzel ◽  
Maik Heistermann ◽  
Tanja Winterrath
Keyword(s):  
Optical Flow ◽  
State Of The Art ◽  
Model Development ◽  
Software Library ◽  
Flow Models ◽  
Urban Sewage ◽  
Radar Images ◽  
Software Implementations ◽  
Python Programming ◽  
Prediction Approach

Abstract. Quantitative precipitation nowcasting (QPN) has become an essential technique in various application contexts, such as early warning or urban sewage control. A common heuristic prediction approach is to track the motion of precipitation features from a sequence of weather radar images, and then to extrapolate that motion to the imminent future (minutes to hours), assuming that the intensity of the features remains constant (Lagrangian persistence). In that context, optical flow has become one of the most popular tracking techniques. Yet, the present landscape of computational QPN models still struggles with producing open software implementations. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step, and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. Our software library (rainymotion) for precipitation nowcasting is written in Python programming language, and openly available at GitHub (https://github.com/hydrogo/rainymotion). That way, the library may serve as a tool for providing fast, free and transparent solutions that could serve as a benchmark for further model development and hypothesis testing – a benchmark that is far more advanced than the conventional benchmark of Eulerian persistence commonly used in QPN verification experiments.

scholarly journals Constructive Learning of Deep Neural Networks for Bigdata Analysis

2020 ◽  
Vol 9 (12) ◽  
pp. 311-322
Author(s):  
Soha Abd Mohamed El-Moamen ◽  
Marghany Hassan Mohamed ◽  
Mohammed F. Farghally
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Binary Classification ◽  
Network Models ◽  
Classification Model ◽  
Neural Network Models ◽  
Constructive Learning ◽  
The Neural Network ◽  
Rapid Pace ◽  
Better Than

The need for tracking and evaluation of patients in real-time has contributed to an increase in knowing people’s actions to enhance care facilities. Deep learning is good at both a rapid pace in collecting frameworks of big data healthcare and good predictions for detection the lung cancer early. In this paper, we proposed a constructive deep neural network with Apache Spark to classify images and levels of lung cancer. We developed a binary classification model using threshold technique classifying nodules to benign or malignant. At the proposed framework, the neural network models training, defined using the Keras API, is performed using BigDL in a distributed Spark clusters. The proposed algorithm has metrics AUC-0.9810, a misclassifying rate from which it has been shown that our suggested classifiers perform better than other classifiers.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

scholarly journals Bootstrapping a Neural Morphological Generator from Morphological Analyzer Output for Inuktitut

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Jeffrey Micher
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Data Set ◽  
Set Size ◽  
The Neural Network ◽  
Surface Character ◽  
Finite State ◽  
Character Sequences ◽  
Finite State Transducer

We present a method for building a morphological generator from the output of an existing analyzer for Inuktitut, in the absence of a two-way finite state transducer which would normally provide this functionality. We make use of a sequence to sequence neural network which “translates” underlying Inuktitut morpheme sequences into surface character sequences. The neural network uses only the previous and the following morphemes as context. We report a morpheme accuracy of approximately 86%. We are able to increase this accuracy slightly by passing deep morphemes directly to output for unknown morphemes. We do not see significant improvement when increasing training data set size, and postulate possible causes for this.

scholarly journals NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION

2019 ◽  
Vol 2019 (02) ◽  
pp. 89-98
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Original Data ◽  
Data Set ◽  
Cancer Prediction ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
The Neural Networks ◽  
Human Nervous System

Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.

Tunicate swarm algorithm-trained multi-layered perceptron for data centre energy demand forecasting and relative percentage contribution analysis of input parameters

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oluwafemi Ajayi ◽  
Reolyn Heymann
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Data Set ◽  
Content Type ◽  
Demand Pattern ◽  
The Neural Network ◽  
Input Parameters ◽  
Demand Profile

Purpose Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly increasing global demand for cloud services and other technological services. This projected sectoral growth is expected to translate into increased energy demand from the sector, which is already considered a major energy consumer unless innovative steps are used to drive effective energy management systems. The purpose of this study is to provide insights into the expected energy demand of the DC and the impact each measured parameter has on the building's energy demand profile. This serves as a basis for the design of an effective energy management system. Design/methodology/approach This study proposes novel tunicate swarm algorithm (TSA) for training an artificial neural network model used for predicting the energy demand of a DC. The objective is to find the optimal weights and biases of the model while avoiding commonly faced challenges when using the backpropagation algorithm. The model implementation is based on historical energy consumption data of an anonymous DC operator in Cape Town, South Africa. The data set provided consists of variables such as ambient temperature, ambient relative humidity, chiller output temperature and computer room air conditioning air supply temperature, which serve as inputs to the neural network that is designed to predict the DC’s hourly energy consumption for July 2020. Upon preprocessing of the data set, total sample number for each represented variable was 464. The 80:20 splitting ratio was used to divide the data set into training and testing set respectively, making 452 samples for the training set and 112 samples for the testing set. A weights-based approach has also been used to analyze the relative impact of the model’s input parameters on the DC’s energy demand pattern. Findings The performance of the proposed model has been compared with those of neural network models trained using state of the art algorithms such as moth flame optimization, whale optimization algorithm and ant lion optimizer. From analysis, it was found that the proposed TSA outperformed the other methods in training the model based on their mean squared error, root mean squared error, mean absolute error, mean absolute percentage error and prediction accuracy. Analyzing the relative percentage contribution of the model's input parameters based on the weights of the neural network also shows that the ambient temperature of the DC has the highest impact on the building’s energy demand pattern. Research limitations/implications The proposed novel model can be applied to solving other complex engineering problems such as regression and classification. The methodology for optimizing the multi-layered perceptron neural network can also be further applied to other forms of neural networks for improved performance. Practical implications Based on the forecasted energy demand of the DC and an understanding of how the input parameters impact the building's energy demand pattern, neural networks can be deployed to optimize the cooling systems of the DC for reduced energy cost. Originality/value The use of TSA for optimizing the weights and biases of a neural network is a novel study. The application context of this study which is DCs is quite untapped in the literature, leaving many gaps for further research. The proposed prediction model can be further applied to other regression tasks and classification tasks. Another contribution of this study is the analysis of the neural network's input parameters, which provides insight into the level to which each parameter influences the DC’s energy demand profile.

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