Abstract: Some physico-chemical characteristics and mineral
composition of 'Karayemis' (Prunus laurocerasus L.) fruits which
grown naturally in Norteast Turkey was studied. 28 minerals ( Al,
Mg, B, Mn, Co, Na, Ca, Ni, Cd, P, Cr, Pb, Cu, S, Fe, Zn, K, Sr, Li,
As, V, Ag, Ba, Br, Ga, In, Se, Ti) were analyzed and 19 minerals
were present at ascertainable levels. Karayemis fruit was richest in
potassium (7938.711 ppm), magnesium (1242.186 ppm) and calcium
(1158.853 ppm). And some physico-chemical characteristics of
Karayemis fruit was investigated. Fruit length, fruit width, fruit
thickness, fruit weight, total soluble solids, colour, protein, crude ash,
crude fiber, crude oil values were determined as 2.334 cm, 1.884 cm,
2.112 cm, 5.35 g, 20.1 %, S99M99Y99, 0.29 %, 0.22 %, 6.63 % and
0.001 %, respectively. The seed of fruit mean weight, length, width
and thickness were found to be 0.41 g, 1.303 cm, 0.921 cm and
0.803, respectively.
Abstract: A new approach for facial expressions recognition based on face context and adaptively weighted sub-pattern PCA (Aw-SpPCA) has been presented in this paper. The facial region and others part of the body have been segmented from the complex environment based on skin color model. An algorithm has been proposed to accurate detection of face region from the segmented image based on constant ratio of height and width of face (δ= 1.618). The paper also discusses on new concept to detect the eye and mouth position. The desired part of the face has been cropped to analysis the expression of a person. Unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub patterns partitioned from an original whole pattern and separately extracts features from them. Aw-SpPCA can adaptively compute the contributions of each part and a classification task in order to enhance the robustness to both expression and illumination variations. Experiments on single standard face with five types of facial expression database shows that the proposed method is competitive.
Abstract: Intensive changes of environment and strong market
competition have raised management of information and knowledge
to the strategic level of companies. In a knowledge based economy
only those organizations are capable of living which have up-to-date,
special knowledge and they are able to exploit and develop it.
Companies have to know what knowledge they have by taking a
survey of organizational knowledge and they have to fix actual and
additional knowledge in organizational memory. The question is how
to identify, acquire, fix and use knowledge effectively. The paper will
show that over and above the tools of information technology
supporting acquisition, storage and use of information and
organizational learning as well as knowledge coming into being as a
result of it, fixing and storage of knowledge in the memory of a
company play an important role in the intelligence of organizations
and competitiveness of a company.
Abstract: Smoothing or filtering of data is first preprocessing step
for noise suppression in many applications involving data analysis.
Moving average is the most popular method of smoothing the data,
generalization of this led to the development of Savitzky-Golay filter.
Many window smoothing methods were developed by convolving
the data with different window functions for different applications;
most widely used window functions are Gaussian or Kaiser. Function
approximation of the data by polynomial regression or Fourier
expansion or wavelet expansion also gives a smoothed data. Wavelets
also smooth the data to great extent by thresholding the wavelet
coefficients. Almost all smoothing methods destroys the peaks and
flatten them when the support of the window is increased. In certain
applications it is desirable to retain peaks while smoothing the data
as much as possible. In this paper we present a methodology called
as peak-wise smoothing that will smooth the data to any desired level
without losing the major peak features.
Abstract: In a world of climate change and limited fossil fuel resources, renewable energy sources are playing an increasingly important role. Due to industrializations and population growth our economy and technologies today largely depend upon natural resources, which are not replaceable. Approximately 90% of our energy consumption comes from fossil fuels (viz. coal, oil and natural gas). The irony is that these resources are depleting. Also, the huge consumption of fossil fuels has caused visible damage to the environment in various forms viz. global warming, acid rains etc.
Abstract: There have been widespread applications of fluidized beds in industries which are related to the combination of gas-solid particles during the last decade. For instance, in order to crack the catalyses in petrochemical industries or as a drier in food industries. High capacity of fluidized bed in heat and mass transfer has made this device very popular. In order to achieve a higher efficiency of fluidized beds, a particular attention has been paid to beds with pulsating air flow. In this paper, a fluidized bed device with pulsating flow has been designed and constructed. Size of particles have been used during the test are in the range of 40 to 100μm. The purpose of this experimental test is to investigate the air flow regime, observe the particles- movement and measure the pressure loss along the bed. The effects of pulsation can be evaluated by comparing the results for both continuous and pulsating flow. Results of both situations are compared for various gas speeds. Moreover the above experiment is numerically simulated by using Fluent software and its numerical results are compared with the experimental results.
Abstract: This paper presents a novel approach for tuning unified power flow controller (UPFC) based damping controller in order to enhance the damping of power system low frequency oscillations. The design problem of damping controller is formulated as an optimization problem according to the eigenvalue-based objective function which is solved using iteration particle swarm optimization (IPSO). The effectiveness of the proposed controller is demonstrated through eigenvalue analysis and nonlinear time-domain simulation studies under a wide range of loading conditions. The simulation study shows that the designed controller by IPSO performs better than CPSO in finding the solution. Moreover, the system performance analysis under different operating conditions show that the δE based controller is superior to the mB based controller.
Abstract: The population structure of the Tor tambroides was
investigated with morphometric data (i.e. morphormetric
measurement and truss measurement). A morphometric analysis was
conducted to compare specimens from three waterfalls: Sunanta, Nan
Chong Fa and Wang Muang waterfalls at Khao Nan National Park,
Nakhon Si Thammarat, Southern Thailand. The results of stepwise
discriminant analysis on seven morphometric variables and 21 truss
variables per individual were the same as from a neural network. Fish
from three waterfalls were separated into three groups based on their
morphometric measurements. The morphometric data shows that the
nerual network model performed better than the stepwise
discriminant analysis.
Abstract: The computer has become an essential tool in modern
life, and the combined use of a computer with a projector is very
common in teaching and presentations. However, as typical computer
operating devices involve a mouse or keyboard, when making
presentations, users often need to stay near the computer to execute
functions such as changing pages, writing, and drawing, thus, making
the operation time-consuming, and reducing interactions with the
audience. This paper proposes a laser pointer interaction system able
to simulate mouse functions in order that users need not remain near
the computer, but can directly use laser pointer operations from at a
distance. It can effectively reduce the users- time spent by the
computer, allowing for greater interactions with the audience.
Abstract: This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.
Abstract: Recently electric vehicles are becoming popular as an
alternative of conventional fossil fuel vehicles. Conventional Internal
Combustion Engine (ICE) vehicle uses fossil fuel which contributing
a major part of overall carbon emission in the environment. Carbon
and other green house gas emission are responsible for global
warming and resulting climate change. It becomes vital to evaluate
performance of vehicle based on emission. In this paper an effort has
been made to depict the picture of emission caused by vehicle and
scenario of Australia has taken into account. Effort has been made to
compare the fossil based vehicle with electric vehicle in phases. The
study also evaluates advancement in electric vehicle technology,
required infrastructure for sustainability and future scope of
developments. This paper also includes the evaluation of electric
vehicle concept for pollution control and sustainable transport
systems in future. This study can be a benchmark for development of
electric vehicle as low carbon emission alternative for the cities of
tomorrow.
Abstract: Rarefied gas flows are often occurred in micro electro
mechanical systems and classical CFD could not precisely anticipate
the flow and thermal behavior due to the high Knudsen number.
Therefore, the heat transfer and the fluid dynamics characteristics of
rarefied gas flows in both a two-dimensional simple microchannel
and geometry similar to single Knudsen compressor have been
investigated with a goal of increasing performance of a actual
Knudsen compressor by using a particle simulation method. Thermal
transpiration and thermal creep, which are rarefied gas dynamic
phenomena, that cause movement of the flow from less to higher
temperature is generated by using two different longitude temperature
gradients (Linear, Step) along the walls of the flow microchannel. In
this study the influence of amount of temperature gradient and
governing pressure in various Knudsen numbers and length-to-height
ratios have been examined.
Abstract: This paper proposes a new parameter identification
method based on Linear Fractional Transformation (LFT). It is
assumed that the target linear system includes unknown parameters.
The parameter deviations are separated from a nominal system via
LFT, and identified by organizing I/O signals around the separated
deviations of the real system. The purpose of this paper is to apply LFT
to simultaneously identify the parameter deviations in systems with
fewer outputs than unknown parameters. As a fundamental example,
this method is implemented to one degree of freedom vibratory system.
Via LFT, all physical parameters were simultaneously identified in this
system. Then, numerical simulations were conducted for this system to
verify the results. This study shows that all the physical parameters of a
system with fewer outputs than unknown parameters can be effectively
identified simultaneously using LFT.
Abstract: Mechanical interaction between endothelial cells (ECs) and the extracellular matrix (or collagen gel) is known to influence the sprouting response of endothelial cells during angiogenesis. This influence is believed to impact on the capability of endothelial cells to sense soluble chemical cues. Quantitative analysis of endothelial-cell-mediated displacement of the collagen gel provides a means to explore this mechanical interaction. Existing analysis in this context is generally limited to 2D settings. In this paper, we investigate the mechanical interaction between endothelial cells and the extracellular matrix in terms of the endothelial-cellmediated displacement of the collagen gel in both 2D and 3D. Digital image correlation and Digital volume correlation are applied on confocal reflectance image stacks to analyze cell-mediated displacement of the gel. The skeleton of the sprout is extracted from phase contrast images and superimposed on the displacement field to further investigate the link between the development of the sprout and the displacement of the gel.
Abstract: Tea is consumed by a big part of the world-s
population. It has an enormous importance for the Turkish culture.
Nearly it is brewed every morning and evening at the all houses. Also it is consumed with lemon wedge. Habitual drinking of tea
infusions may significantly contribute to daily dietary requirements of elements.
Different instrumental techniques are used for determination of
these elements. But atomic and mass spectroscopic methods are preferred most. In these study chromium, iron and selenium contents
after the hot water brewing of black and green tea were determined
by Optical Emission Spectroscopy (ICP-OES). Furthermore, effect
of lemon addition on chromium, iron and selenium concentration tea
infusions is investigated.
Results of the investigation showed that concentration of
chromium, iron and selenium increased in black tea with lemon addition. On the other hand only selenium is increased with lemon
addition in green tea. And iron concentration is not detected in green
tea but its concentration is determined as 1.420 ppm after lemon addition.
Abstract: In this paper, a new K-means clustering based
approach for identification of voltage control areas is developed.
Voltage control areas are important for efficient reactive power
management in power systems operating under deregulated
environment. Although, voltage control areas are formed using
conventional hierarchical clustering based method, but the present
paper investigate the capability of K-means clustering for the
purpose of forming voltage control areas. The proposed method is
tested and compared for IEEE 14 bus and IEEE 30 bus systems. The
results show that this K-means based method is competing with
conventional hierarchical approach
Abstract: Topical photodynamic therapy (PDT) with
5-aminolevulinic acid (ALA) is an alternative therapy for treating
superficial cancer, especially for skin or oral cancer. ALA, a precursor
of the photosensitizer protoporphyrin IX (PpIX), is present as
zwitterions and hydrophilic property which make the low permeability
through the cell membrane. Collagen is a traditional carrier; its
molecular composed various amino acids which bear positive charge
and negative charge. In order to utilize the ion-pairs with ALA and
collagen, the study employed various pH values adjusting the net
charge. The aim of this study was to compare a series collagen form,
including solution, gel and sponge to investigate the topical delivery
behavior of ALA. The in vivo confocal laser scanning microscopy
(CLSM) study demonstrated that PpIX generation ability was different
pattern after apply for 6 h. Gel type could generate high PpIX, and
archived more deep of skin depth.
Abstract: Power flow (PF) study, which is performed to
determine the power system static states (voltage magnitudes and
voltage angles) at each bus to find the steady state operating
condition of a system, is very important and is the most frequently
carried out study by power utilities for power system planning,
operation and control. In this paper, a counterpropagation neural
network (CPNN) is proposed to solve power flow problem under
different loading/contingency conditions for computing bus voltage
magnitudes and angles of the power system. The counterpropagation
network uses a different mapping strategy namely
counterpropagation and provides a practical approach for
implementing a pattern mapping task, since learning is fast in this
network. The composition of the input variables for the proposed
neural network has been selected to emulate the solution process of a
conventional power flow program. The effectiveness of the proposed
CPNN based approach for solving power flow is demonstrated by
computation of bus voltage magnitudes and voltage angles for
different loading conditions and single line-outage contingencies in
IEEE 14-bus system.
Abstract: This work presents a multiple objective linear programming (MOLP) model based on the desirability function approach for solving the aggregate production planning (APP) decision problem upon Masud and Hwang-s model. The proposed model minimises total production costs, carrying or backordering costs and rates of change in labor levels. An industrial case demonstrates the feasibility of applying the proposed model to the APP problems with three scenarios of inventory levels. The proposed model yields an efficient compromise solution and the overall levels of DM satisfaction with the multiple combined response levels. There has been a trend to solve complex planning problems using various metaheuristics. Therefore, in this paper, the multi-objective APP problem is solved by hybrid metaheuristics of the hunting search (HuSIHSA) and firefly (FAIHSA) mechanisms on the improved harmony search algorithm. Results obtained from the solution of are then compared. It is observed that the FAIHSA can be used as a successful alternative solution mechanism for solving APP problems over three scenarios. Furthermore, the FAIHSA provides a systematic framework for facilitating the decision-making process, enabling a decision maker interactively to modify the desirability function approach and related model parameters until a good optimal solution is obtained with proper selection of control parameters when compared.
Abstract: In the present simulation work, an attempt is made to study the switching dynamics of an optically controlled 4HSiC thyristor power semiconductor device with the use of GaAs optically triggered power transistor. The half-cell thyristor has the forward breakdown of 200 V and reverse breakdown of more than 1000 V. The optically controlled thyristor has a rise time of 0.14 μs and fall time of 0.065 μs. The turn-on and turn-off delays are 0.1 μs and 0.06 μs, respectively. In addition, this optically controlled thyristor is used as a control switch for the DC-DC Boost converter. The pn-diode used for the converter has the forward drop of 2.8 V and reverse breakdown of around 400 V.