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1. 2. 6 Preliminary Numerical Essay
2.6 Preliminary numerical results max κ Using a permeability field κ whose initial value is shown in
Figure 2, and the contrast minκ is increasing as maxκ = 1000e250t, the solutions at two different
time instants T = 0.01 and min κ
T = 0.02 can be computed. The observations are on the coarse grid with σ1 = σL. The fine grid is
100 × 100 and the coarse grid is 10 × 10. Here 2 permanent basis functions per coarse neighborhood
are used to compute "fixed" solution and Bayesian framework is used to seek additional basis
functions by solving small global problems. In this example, 25% of the total local regions at which
residual is the largest and multiscale basis functions are added. In these coarse blocks, both
sequential sampling and full ... Show more content on Helpwriting.net ...
2.7 Some important questions to be investigated
The PI proposes to investigate the following issues.
1 Different type of pde system. Current development and the numerical results are shown in the
proposal concentrates on flow equation/heat equation type model or in general
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Figure 6: Plots of sample standard deviation of numerical solution at T = 0.02: sequential sampling
(left), full sampling (right).
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Figure 7: History of occurrence probability against basis functions using sequential sampling (red
dotted line) and full sampling (blue solid line): at time T = 0.01 (left), at time T = 0.02 (right).
parabolic equation with an aim towards porous media flow characterization. Different kind of
pde/ode arising from other mathematical system needs to be investigated.
2 Posterior Approximation. Posterior approximation method such as Laplace approxima– tion needs
to studied carefully where observations are non linear function of the solution. Other posterior
approximation methods such as ABC type approximation can also be con– sidered in this context.
3 Tuning Parameter. Choosing σL2 the tuning parameter for the pde model is important and can
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4. Factors Influencing The Factor Analysis
Factor analysis According to Maria& Eva, the factor analysis is a technique in the statistics to
observe variability in the correlated variables in terms of lowers number of unobserved variables,
which is necessary for factorization (Maria& Eva, 2012). Dehak, Kenn, Dehak, Dumouchel, &
Ouellet, further stated that, the factor analysis is useful technique to investigate the relationship
between the variables in complex concepts and the main purpose of the factor analysis is to reduce
the number of variables associated with the measure and to detect the structures of the relationship
between the variables (Dehak, Kenn, Dehak, Dumouchel, & Ouellet, 2011) .
The application of factor analysis widely used in social research (Steinfeld, Navon, Creech, Yakhini,
& Tsalenko, 2014). The current study employs factor analysis to reduce the items' in the
questionnaire for data reduction as per the recommendations of In addition, the factor analysis is
used to construct the factor based on the items' in the scale (Wang & Ahmed, 2004). Hence, the
factor analysis is used for data reduction and structuring the variables. Factor analysis has two types
as discussed below, exploratory and confirmatory factor analysis (Costello & Osborne, 2005).
Exploratory Factor Analysis (EFA) Costello & Osborne, (2005) said that, factor analysis is used to
uncover the structure of relatively large set of variables in the data(Costello & Osborne, 2005). EFA
identifies the underlying relationship between the
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6. Advantages And Disadvantages Of Eye And Face Recognition
Sudeep Sarkar et.al.[10] Researchers have suggested that the ear may have advantages over the face
for biometric recognition. Our previous experiments with ear and face recognition, using the
standard principal component analysis approach, showed lower recognition performance using ear
images. We report results of similar experiments on larger data sets that are more rigorously
controlled for relative quality of face and ear images. We find that recognition performance is not
significantly different between the face and the ear.
Haitao Zhao, Pong Chi Yuen says face recognition has been an active research area in the computer–
vision and pattern–recognition societies [11] in the last two decades. Since the original input–image
space has a very high dimension, a dimensionality–reduction technique is usually employed before
classification takes place. Principal component analysis (PCA) is one of the most popular
representation methods for face recognition. It does not only reduce the image dimension, but also
provides a compact feature for representing a face image. In 1997, PCA was also employed for
dimension reduction for linear discriminant . PCA is ... Show more content on Helpwriting.net ...
The images forming the training set (database) are projected onto the major eigenvectors and the
projection values are computed. In the recognition stage the projection value of the input image is
also found and the distance from the known projection values is calculated to identify who the
individual is.Neural Network Based Face Recognition procedure is followed for forming the
eigenvectors as in the Eigenface approach, which are then fed into the Neural Network Unit to train
it on those vectors and the knowledge gained from the training phase is subsequently used for
recognizing new input images. The training and recognition phases can be implemented using
several neural network models and
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8. Generator Protection : Generator Monitoring Subsynchronous...
Generator Protection Subsynchronous Resonance
Sokhom Sim
Department of Electrical Engineering
California State University, Long Beach
Long Beach, CA 90840–8303 ericsokhomsim0@gmail.com Abstract–the transmission lines are
complex systems and the power generation sources are generally far away from the distributing
center. The transmission lines congesting or overloading are always a challenge for utilities
protection engineers. As a result, a series compensating capacitors are being used to alleviate the
transmission lines, and increased the loads capability. However, adding series compensating
capacitors (SCC) to the transmission lines will increase the potential risk of subsynchronous
resonance (SSR). In fact, SSR leads to a possible torsional interaction and damage the turbine
generator shaft when the mechanical frequency falls below the electrical frequency. In this paper, a
brief analysis of SSR phenomenon will be addressed by computing the eigenvalues, eigenvectors,
and MATLAB/Simulink for simulation incorporated with the IEEE first and second benchmark
Model # 1 system.
Keywords–Subsynchronous Resonance, Torsional oscillation, Series Compensating Capacitor,
Transient Torque, generator induction effect.
Introduction
The growing demand of electricity in a large cities increase the need of high power transmission
from remote generating stations. Therefore, for reducing the cost–effective in power transmission,
series compensating capacitor (SSC) is used to
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10. Face Recognition Essay
SECURITY SURVEILLANCE SYSTEM
Security Through Image Processing
Prof.Vishal Meshram,Jayendra More
Department of Electronics & Telecommunication Engineering (ExTC), University Of Mumbai
Vishwatmak Om Gurudev College Of Engineering Maharashtra State Highway 79, Mohili,
Maharashtra 421601, India.
Vishalmmeshram19@gmail.com, more.jayendra@yahoo.in
Bhagyesh Birari,Swapnil Mahajan
Department of Electronics & Telecommunication Engineering (ExTC), University Of Mumbai
Vishwatmak Om Gurudev College Of Engineering Maharashtra State Highway 79, Mohili,
Maharashtra 421601, India. bhagyeshbb86@gmail.com, swapnilmahajan939@gmail.com
Abstract–Automatic recognition of people is a challenging problem which has received much
attention during recent years due to its many applications in different fields. Face recognition is one
of those challenging problems and up to date, there is no technique that provides a robust solution to
all situations. This paper presents a technique for human face recognition. A self–organizing
program is used to identify if the subject in the input image is "present" or "not present" in the image
database. Face recognition with Eigen values is carried out by classifying Eigen values in both
images. The main advantage of this technique is its high–speed processing capability and low
computational requirements, in terms of both speed, accuracy and memory utilization. The goal is to
implement the system for a particular face and distinguish it
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12. Face Recognition Using Orthogonal Locality Preserving...
FACE RECOGNITION USING ORTHOGONAL LOCALITY PRESERVING PROJECTIONS.
Dr. Ravish R Singh Ronak K Khandelwal Manoj Chavan
Academic Advisor EXTC Engineering EXTC Engineering Thakur Educational Trust L.R.Tiwari
COE Thakur COE
Mumbai, India. Mumbai,India. Mumbai, India. ravishrsingh@yahoo.com
ronakkhandelwal2804@gmail.com prof.manoj@gmail.com
Abstract: In this paper a hybrid technique is used for determining the face from an image. Face
detection is one of the tedious job to achieve with very high accuracy. In this paper we proposed a
method that combines two techniques that is Orthogonal Laplacianface (OLPP) and Particle Swarm
Optimization (PSO). The formula for the OLPP relies on the Locality Preserving Projection (LPP)
formula, which aims at finding a linear approximation to the Eigen functions of the astronomer
Beltrami operator on the face manifold. However, LPP is non–orthogonal and this makes it difficult
to reconstruct the information. When the set of features is found by the OLPP, with the help of the
PSO, the grouping of the image features is done and the one with the best match from the database
is given as the result. This hybrid technique gives a higher accuracy in less processing time.
Keywords: OLPP, PSO,
INTRODUCTION:
Recently, appearance–based face recognition has received tons of attention. In general, a face image
of size n1 × n2 is delineating as a vector within the image house Rn1 × n2. We have a tendency to
denote
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