Prediction of steam/water stratified flow characteristics in NPPs transients using SVM learning algorithm with combination of thermal-hydraulic model and new data mapping technique

2022 ◽  
Vol 166 ◽  
pp. 108699
Author(s):  
Khalil Moshkbar-Bakhshayesh ◽  
Mohsen Ghafari
Keyword(s):  
Learning Algorithm ◽  
Flow Characteristics ◽  
Stratified Flow ◽  
Hydraulic Model ◽  
Mapping Technique ◽  
Data Mapping

Performance of Corrosion Inhibitors at High CO2 Pressures

2008 ◽  
Author(s):  
Patchareeporn Sintoorahat ◽  
Aree Wairatpanich ◽  
Suchada Chimam ◽  
Dayin Mongkholkhajornsilp ◽  
Cheolho Kang
Keyword(s):  
Corrosion Inhibitors ◽  
Liquid Velocity ◽  
Flow Characteristics ◽  
Stratified Flow ◽  
Inhibitor Concentration ◽  
Flow Loop ◽  
Corrosion Rates ◽  
Offshore Pipeline ◽  
Carbon Dioxide Pressure ◽  
Corrosion Rate Measurement

The objective of this study was to evaluate the performance of two corrosion inhibitors (CI-A and CI-B) under conditions similar to the second PTT’s offshore pipeline. The experiments were carried out in flow-loop system, 36 m long, 10.16 cm diameter at 10.5 and 14 bar of carbon dioxide pressure, a temperature at 50°C. The performances of corrosion inhibitors were examined under conditions of superficial liquid velocity of 0.03 m/s and gas velocities of 6, 8 and 10 m/s in 0 and 3 degree inclinations using the ER probe and X65 weight-loss coupons for corrosion rate measurement at the top and bottom of pipe. According to flow characteristics, it was found that the smooth and wavy stratified flow occurred in 0 degree. For 3 degree inclination, wavy stratified flow with big waves was dominantly presented for all conditions. Corrosion inhibitor B showed a better performance than inhibitor A in all cases. For inhibitor B, the target corrosion rates of less than 0.1 mm/yr were achieved in all conditions with 50 ppm of inhibitor concentration whereas the amount of 75 ppm inhibitor concentration was required for CI-A. The color, turbidity, and emulsion tendency with corrosion inhibitors will be also discussed in this paper.

scholarly journals Coronary artery decision algorithm trained by two-step machine learning algorithm

RSC Advances ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 4014-4022
Author(s):  
Young Woo Kim ◽  
Hee-Jin Yu ◽  
Jung-Sun Kim ◽  
Jinyong Ha ◽  
Jongeun Choi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Decision Making ◽  
Coronary Artery ◽  
Learning Algorithm ◽  
Flow Characteristics ◽  
Machine Learning Algorithm ◽  
Decision Algorithm ◽  
Computational Fluid Dynamics Cfd ◽  
Biometric Features

A two-step machine learning (ML) algorithm for coronary artery decision making is introduced, to increase the data quality by providing flow characteristics and biometric features by aid of computational fluid dynamics (CFD).

scholarly journals Using Slit-Lamp Images for Deep Learning–Based Identification of Bacterial and Fungal Keratitis

2021 ◽  
Author(s):  
Ning Hung ◽  
Eugene Yu-Chuan Kang ◽  
Andy Guan-Yu Shih ◽  
Chi-Hung Lin ◽  
Ming‐Tse Kuo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Learning Algorithm ◽  
Model Performance ◽  
Area Under The Curve ◽  
Fungal Keratitis ◽  
Classification Model ◽  
Mapping Technique ◽  
Slit Lamp ◽  
Deep Learning Algorithm

In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between January 1, 2010, and December 31, 2019, from two medical centers in Taiwan. We constructed a deep learning algorithm, consisting of a segmentation model for cropping cornea images and a classification model that applies convolutional neural networks to differentiate between FK and BK. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heatmap of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved an average diagnostic accuracy of 80.00%. The diagnostic accuracy for BK ranged from 79.59% to 95.91% and that for FK ranged from 26.31% to 63.15%. DenseNet169 showed the best model performance, with an AUC of 0.78 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.

scholarly journals Earthquake Analyzer using Prediction Commands

2020 ◽  
Vol 10 (1) ◽  
pp. 36-38
Keyword(s):  
Linear Regression ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Climatic Conditions ◽  
Data Exploration ◽  
Model Description ◽  
Correct Prediction ◽  
Data Mapping ◽  
Cognitive Content ◽  
Input Variables

Destructive earthquakes usually causes gargantuan casualties. So, to cut back these inimical casualties’ analysis are made to reduce despicable and forlorn impacts which they left upon others to just ponder and become lugubrious. These factors measure the decisive casualties it brings and also earthquake and therefore the development of rational prediction model to casualties become a crucial analysis topic, as a result of quality and cognitive content of gift prediction methodology of price, an additional correct prediction model is mentioned by gray correlation theory and BP neural networks. The earthquake can be analyzed succinct by using various technique mainly predictive commands to marshal all the calculated time and magnitude of a potential earthquake have been the topic of the many studies varied ways are tried mistreatment several input variables like temperature exorable, seismic movements and particularly the variable climatic conditions. The relation between recorded seismal-acoustic information associate degreed occurring an abnormal seismic process (ASP). However, it's obstreperous to predict all parameters the placement, time and magnitude of the earthquake by mistreatment this information. This model description is different from others as with the help of the prediction commands most of the paragons and domains are identified and tend to explore the activity of serious Earthquakes. We use the preemptive data information which is collected around the planet. We retrieved the data to perceive that associate degree earthquake reaches the class of exceeds a grade range of eight on Richter Scale. The two main affected areas are in the field of Data Exploration and Data Mapping. Number of occurrences of an earthquake with different magnitude ranges, severity of an earthquake. Mapping is thereby crucial to identify highly affected areas based on Magnitude and Correlation between depth and magnitude. So, based on the above explorations we have made the following predictions. Predictions Magnitude based on depth. Magnitude based on Latitude and Longitude. Depth based on Latitude and Longitude The primitive algorithm used here are the Machine Learning Algorithm I.e. Linear Regression and K- Means Clustering. Firstly, we have made all the predictions via Linear Regression and made different clusters of the Earthquakes which belong to the same subdivision as that of Magnitude or Depth. Keyword: Data Exploration and Data Mapping.

scholarly journals Fast Recognition using Bar-based Data Mapping for Gamelan Music Genre Classification

2021 ◽  
pp. 374-383
Author(s):  
Khafiizh Hastuti ◽  
Pulung Nurtantio Andono ◽  
Arry Maulana Syarif ◽  
Azhari Azhari
Keyword(s):  
Audio Signal ◽  
Mapping Technique ◽  
Data Mapping ◽  
Fast Recognition ◽  
Genre Classification ◽  
Feed Forward Neural Networks ◽  
Music Genre ◽  
The One ◽  
Musical Mode ◽  
Music Genre Classification

This research aims to develop a gamelan music genre classifier based on the musical mode system determined based on the dominant notes in a certain order. Only experts can discriminate the musical mode system of compositions. The Feed Forward Neural Networks method was used to classify gamelan compositions into three musical mode systems. The challenge is to recognize the musical mode system of compositions between the initial melody without having to analyze the entire melody using a small amount of data for the dataset. Instead of conducting a melodic extraction from audio signal data, the text-based skeletal melody data, which is a form of extracted melodic features, are used for the dataset. Unique corpuses are controlled based on the cardinality of the one-to-many relationship, and a data mapping technique based on the bars is used to increase the number of corpuses. The results show that the proposed method is suitable to solve the specified problems, where the accuracy in recognizing the class of unseen compositions between the initial melody achieves at 86.7%.

scholarly journals Design and Simulation of parallel CDMA System Based on 3D-Hadamard Transform

2013 ◽  
Vol 6 (4) ◽  
pp. 45-69
Author(s):  
Ali T. Shaheen
Keyword(s):  
Fading Channels ◽  
Spectral Efficiency ◽  
Hadamard Matrix ◽  
Additive White Gaussian Noise ◽  
Mapping Technique ◽  
Cdma System ◽  
Data Mapping ◽  
Two Dimensional ◽  
And Performance ◽  
Code Division Multiple

Future wireless systems aim to provide higher transmission data rates, improved spectral efficiency and greater capacity. In this paper a spectral efficient two dimensional (2-D) parallel code division multiple access (CDMA) system is proposed for generating and transmitting (2-D CDMA) symbols through 2-D Inter-Symbol Interference (ISI) channel to increase the transmission speed. The 3D-Hadamard matrix is used to generate the 2-D spreading codes required to spread the two-dimensional data for each user row wise and column wise. The quadrature amplitude modulation (QAM) is used as a data mapping technique due to the increased spectral efficiency offered. The new structure simulated using MATLAB and a comparison of performance for serial one-dimensional (1-D) CDMA and parallel (2-D) CDMA is made under Additive White Gaussian Noise (AWGN), flat fading and multi-path selective fading channels conditions. It is found that 2-D CDMA has better speed and performance than serial 1-D CDMA.

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