A study of a two phases medium in pipe vessel using gaussian filter method and k-NN classifier

2013 ◽  
Vol 25 (3) ◽  
pp. 635-641 ◽  
Author(s):  
Mohd Rizal Manan ◽  
Muhammad Naufal Mansor ◽  
Mohd Hafiz Fazalul Rahiman ◽  
Ruzairi Abdul Rahim ◽  
Sazali Yaacob
Keyword(s):  
Filter Method ◽  
Gaussian Filter ◽  
Two Phases

scholarly journals Aspect Category Extraction for Sentiment Analysis using Multivariate Filter Method of Feature Selection

2019 ◽  
Vol 8 (3) ◽  
pp. 2138-2143
Keyword(s):  
Feature Selection ◽  
Sentiment Analysis ◽  
Correlation Coefficient ◽  
Computation Time ◽  
Filter Method ◽  
Term Frequency ◽  
Aspect Extraction ◽  
Selection Strategies ◽  
Two Phases ◽  
Feature Selection Techniques

Aspect-oriented sentiment analysis is done in two phases like aspect term identification from review and determining related opinion. To carry out this analysis, features play an important role to determine the accuracy of the model. Feature extraction and feature selection techniques contribute to increase the classification accuracy. Feature selection strategies reduce computation time, improve prediction performance, and provides a higher understanding of the information in machine learning and pattern recognition applications etc. This work specifically focuses on aspect extraction from restaurant review dataset but can also be used for other datasets. In this system, we proposed a multivariate filter strategy of feature selection which works on lemma features. This method helps to select relevant features and avoid redundant ones. Initially, the extracted features undergo preprocessing and then the “term-frequency matrix” is generated which contains the occurrence count of features with respect to aspect category. In the next phase, different feature selection strategies are applied which includes selecting features based on correlation, weighted term frequency and weighted term frequency with the correlation coefficient. The performance of weighted term frequency with correlation coefficient approach is compared with the existing system and shows significant improvement in F1 score

Mine Location Algorithm Based on Multiple Linear Regression

2011 ◽  
Vol 58-60 ◽  
pp. 1830-1835
Author(s):  
Xiao Ying Wang ◽  
Ying Ge Chen
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Coefficient ◽  
Training Sample ◽  
Filter Method ◽  
Gaussian Filter ◽  
Location Error ◽  
Measuring Range ◽  
Mine Environment ◽  
Location Algorithm

This paper put forward a mine location algorithm based on multiple linear regression, which using only simple RSSI value to get a higher location accuracy under long narrow and sensitive mine environment. General RSSI measurement method and its drawbacks are discussed in the paper. In order to acquire smaller location error, we filtered some abnormal RSSI data through Gaussian filter method. And we deduced regression equation according to multiple linear regression principle. Combined with training sample, we got their regression parameter. We did relevant location experiment again in the same environment---40m long and narrow bomb shelter which may imitate mine tunnel to a great extent, which shows that the total errors are limited in 3m and 75% errors are less than 2m. What’s more, it can be extended to infinite measuring range with the same set regression coefficient in similar environment.

Noise Qualification in Bali Palm Leaf Image with Gaussian Filter Method

2020 ◽  
Vol 8 (3) ◽  
pp. 301
Author(s):  
Wayan Adhitya Prathama ◽  
I Gede Arta Wibawa
Keyword(s):  
Image Quality ◽  
Signal To Noise Ratio ◽  
Filter Method ◽  
Gaussian Filter ◽  
Signal To Noise ◽  
Filter Noise ◽  
Leaf Image ◽  
Palm Leaf ◽  
Better Than

In improving the quality of the image basically makes the appearance of an image better than before. One thing that is done in improving the image quality is noise qualification. This noise qualification aims to reduce the level of noise contained in a digital image. In this study, the image used is the image of Bali palm leaf. There are many methods that can be used to qualify for noise. One of them is the Gaussian filter. In this study, Gaussian Filter is used as a method to qualify the noise contained in the palm leaf image. The image quality after the noise qualification process is calculated using PSNR (Peak Signal to Noise Ratio). The higher the PSNR value obtained, the better the image quality. In this study, the PSNR value obtained in the palm leaf image after processing the noise qualification is 54.7625 db. Keywords: Gaussian Filter, Noise, PSNR, Bali Palm Leaf, Image

scholarly journals Derivative-free nonlinear optimization filter simplex

2010 ◽  
Vol 20 (4) ◽  
pp. 679-688 ◽  
Author(s):  
Aldina Correia ◽  
João Matias ◽  
Pedro Mestre ◽  
Carlos Serodio
Keyword(s):  
Objective Function ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Objective Function Value ◽  
Optimization Methods ◽  
Direct Search ◽  
Filter Method ◽  
Filter Methods ◽  
Derivative Free ◽  
Two Phases

Derivative-free nonlinear optimization filter simplexThe filter method is a technique for solving nonlinear programming problems. The filter algorithm has two phases in each iteration. The first one reduces a measure of infeasibility, while in the second the objective function value is reduced. In real optimization problems, usually the objective function is not differentiable or its derivatives are unknown. In these cases it becomes essential to use optimization methods where the calculation of the derivatives or the verification of their existence is not necessary: direct search methods or derivative-free methods are examples of such techniques. In this work we present a new direct search method, based on simplex methods, for general constrained optimization that combines the features of simplex and filter methods. This method neither computes nor approximates derivatives, penalty constants or Lagrange multipliers.

Precipitation of NiHfsi phase in NiAl single crystals containing Hf

1995 ◽  
Vol 53 ◽  
pp. 532-533
Author(s):  
A. Garg ◽  
R. D. Noebe ◽  
R. Darolia
Keyword(s):  
High Temperature ◽  
Single Crystals ◽  
High Temperature Strength ◽  
Bridgman Technique ◽  
Shell Mold ◽  
Third Phase ◽  
Unknown Phase ◽  
Transmission Electron ◽  
Two Phases ◽  
Nial Matrix

Small additions of Hf to NiAl produce a significant increase in the high-temperature strength of single crystals. Hf has a very limited solubility in NiAl and in the presence of Si, results in a high density of G-phase (Ni16Hf6Si7) cuboidal precipitates and some G-platelets in a NiAl matrix. These precipitates have a F.C.C structure and nucleate on {100}NiAl planes with almost perfect coherency and a cube-on-cube orientation-relationship (O.R.). However, G-phase is metastable and after prolonged aging at high temperature dissolves at the expense of a more stable Heusler (β'-Ni2AlHf) phase. In addition to these two phases, a third phase was shown to be present in a NiAl-0.3at. % Hf alloy, but was not previously identified (Fig. 4 of ref. 2 ). In this work, we report the morphology, crystal-structure, O.R., and stability of this unknown phase, which were determined using conventional and analytical transmission electron microscopy (TEM).Single crystals of NiAl containing 0.5at. % Hf were grown by a Bridgman technique. Chemical analysis indicated that these crystals also contained Si, which was not an intentional alloying addition but was picked up from the shell mold during directional solidification.

Imaging of Al-Li-Cu alloys with a Scanning Ion Microprobe

1990 ◽  
Vol 48 (2) ◽  
pp. 314-315
Author(s):  
K.K. Soni ◽  
D.B. Williams ◽  
J.M. Chabala ◽  
R. Levi-Setti ◽  
D.E. Newbury
Keyword(s):  
Mass Spectrometry ◽  
Secondary Ion Mass Spectrometry ◽  
Equilibrium Phase ◽  
Icosahedral Symmetry ◽  
Ion Microprobe ◽  
X Ray ◽  
Cu Alloys ◽  
Two Phases ◽  
The University ◽  
Secondary Ion

In contrast to the inability of x-ray microanalysis to detect Li, secondary ion mass spectrometry (SIMS) generates a very strong Li+ signal. The latter’s potential was recently exploited by Williams et al. in the study of binary Al-Li alloys. The present study of Al-Li-Cu was done using the high resolution scanning ion microprobe (SIM) at the University of Chicago (UC). The UC SIM employs a 40 keV, ∼70 nm diameter Ga+ probe extracted from a liquid Ga source, which is scanned over areas smaller than 160×160 μm2 using a 512×512 raster. During this experiment, the sample was held at 2 × 10-8 torr.In the Al-Li-Cu system, two phases of major importance are T1 and T2, with nominal compositions of Al2LiCu and Al6Li3Cu respectively. In commercial alloys, T1 develops a plate-like structure with a thickness <∼2 nm and is therefore inaccessible to conventional microanalytical techniques. T2 is the equilibrium phase with apparent icosahedral symmetry and its presence is undesirable in industrial alloys.

Defect structures of Nb1+αS2 sulfidation scales

1992 ◽  
Vol 50 (1) ◽  
pp. 38-39
Author(s):  
Chuxin Zhou ◽  
L. W. Hobbs
Keyword(s):  
Stacking Faults ◽  
Rhombohedral Structure ◽  
Stacking Sequence ◽  
Defect Structures ◽  
X Ray Diffraction ◽  
X Ray ◽  
Plane Normal ◽  
Lower Case ◽  
Sulfur Layer ◽  
Two Phases

One of the major purposes in the present work is to study the high temperature sulfidation properties of Nb in severe sulfidizing environments. Kinetically, the sulfidation rate of Nb is satisfactorily slow, but the microstructures and non-stoichiometry of Nb1+αS2 challenge conventional oxidation/sulfidation theory and defect models of non-stoichiometric compounds. This challenge reflects our limited knowledge of the dependence of kinetics and atomic migration processes in solid state materials on their defect structures.Figure 1 shows a high resolution image of a platelet from the middle portion of the Nb1+αS2 scale. A thin lamellar heterogeneity (about 5nm) is observed. From X-ray diffraction results, we have shown that Nb1+αS2 scale is principally rhombohedral structure, but 2H-NbS2 can result locally due to stacking faults, because the only difference between these 2H and 3R phases is variation in the stacking sequence along the c axis. Following an ABC notation, we use capital letters A, B and C to represent the sulfur layer, and lower case letters a, b and c to refer to Nb layers. For example, the stacking sequence of 2H phase is AbACbCA, which is a ∼12Å period along the c axis; the stacking sequence of 3R phase is AbABcBCaCA to form an ∼18Å period along the c axis. Intergrowth of these two phases can take place at stacking faults or by a shear in the basal plane normal to the c axis.

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