Abstract: In this paper, a comparative study of application of
supervised and unsupervised learning algorithms on illumination
invariant face recognition has been carried out. The supervised
learning has been carried out with the help of using a bi-layered
artificial neural network having one input, two hidden and one output
layer. The gradient descent with momentum and adaptive learning
rate back propagation learning algorithm has been used to implement
the supervised learning in a way that both the inputs and
corresponding outputs are provided at the time of training the
network, thus here is an inherent clustering and optimized learning of
weights which provide us with efficient results.. The unsupervised
learning has been implemented with the help of a modified
Counterpropagation network. The Counterpropagation network
involves the process of clustering followed by application of Outstar
rule to obtain the recognized face. The face recognition system has
been developed for recognizing faces which have varying
illumination intensities, where the database images vary in lighting
with respect to angle of illumination with horizontal and vertical
planes. The supervised and unsupervised learning algorithms have
been implemented and have been tested exhaustively, with and
without application of histogram equalization to get efficient results.
Abstract: Complex assemblies of interacting proteins carry out
most of the interesting jobs in a cell, such as metabolism, DNA
synthesis, mitosis and cell division. These physiological properties
play out as a subtle molecular dance, choreographed by underlying
regulatory networks that control the activities of cyclin-dependent
kinases (CDK). The network can be modeled by a set of nonlinear
differential equations and its behavior predicted by numerical
simulation. In this paper, an innovative approach has been proposed
that uses genetic algorithms to mine a set of behavior data output by
a biological system in order to determine the kinetic parameters of
the system. In our approach, the machine learning method is
integrated with the framework of existent biological information in a
wiring diagram so that its findings are expressed in a form of system
dynamic behavior. By numerical simulations it has been illustrated
that the model is consistent with experiments and successfully shown
that such application of genetic algorithms will highly improve the
performance of mathematical model of the cell division cycle to
simulate such a complicated bio-system.
Abstract: The issue of real-time and reliable report delivery is extremely important for taking effective decision in a real world mission critical Wireless Sensor Network (WSN) based application. The sensor data behaves differently in many ways from the data in traditional databases. WSNs need a mechanism to register, process queries, and disseminate data. In this paper we propose an architectural framework for data placement and management. We propose a reliable and real time approach for data placement and achieving data integrity using self organized sensor clusters. Instead of storing information in individual cluster heads as suggested in some protocols, in our architecture we suggest storing of information of all clusters within a cell in the corresponding base station. For data dissemination and action in the wireless sensor network we propose to use Action and Relay Stations (ARS). To reduce average energy dissipation of sensor nodes, the data is sent to the nearest ARS rather than base station. We have designed our architecture in such a way so as to achieve greater energy savings, enhanced availability and reliability.
Abstract: A fundamental model consisting of charged particles
moving in free space exposed to alternating and direct current (ACDC)
electromagnetic fields is analyzed. Effects of charged particles
initial position and initial velocity to cyclotron resonance frequency
are observed. Strong effects are observed revealing that effects of
electric and magnetic fields on a charged particle in free space
varies with the initial conditions. This indicates the frequency where
maximum displacement occur can be changed. At this frequency
the amplitude of oscillation of the particle displacement becomes
unbounded.
Abstract: Image clustering is a process of grouping images
based on their similarity. The image clustering usually uses the color
component, texture, edge, shape, or mixture of two components, etc.
This research aims to explore image clustering using color
composition. In order to complete this image clustering, three main
components should be considered, which are color space, image
representation (feature extraction), and clustering method itself. We
aim to explore which composition of these factors will produce the
best clustering results by combining various techniques from the
three components. The color spaces use RGB, HSV, and L*a*b*
method. The image representations use Histogram and Gaussian
Mixture Model (GMM), whereas the clustering methods use KMeans
and Agglomerative Hierarchical Clustering algorithm. The
results of the experiment show that GMM representation is better
combined with RGB and L*a*b* color space, whereas Histogram is
better combined with HSV. The experiments also show that K-Means
is better than Agglomerative Hierarchical for images clustering.
Abstract: Factor analysis was applied to two stages biogas
production from banana stem waste allowing a screening of the
experimental variables second stage temperature (T), organic loading
rates (OLR) and hydraulic retention times (HRT). Biogas production
was found to be strongly influenced by all the above experimental
variables. Results from factorial analysis have shown that all
variables which were HRT, OLR and T have significant effect to
biogas production. Increased in HRT and OLR could increased the
biogas yield. The performance was tested under the conditions of
various T (35oC-60oC), OLR (0.3 g TS/l.d–1.9 gTS/l.d), and HRT (3
d–15 d). Conditions for temperature, OLR and HRT in this study
were based on the best range obtained from literature review.
Abstract: Selecting the routes and the assignment of link flow in a computer communication networks are extremely complex combinatorial optimization problems. Metaheuristics, such as genetic or simulated annealing algorithms, are widely applicable heuristic optimization strategies that have shown encouraging results for a large number of difficult combinatorial optimization problems. This paper considers the route selection and hence the flow assignment problem. A genetic algorithm and simulated annealing algorithm are used to solve this problem. A new hybrid algorithm combining the genetic with the simulated annealing algorithm is introduced. A modification of the genetic algorithm is also introduced. Computational experiments with sample networks are reported. The results show that the proposed modified genetic algorithm is efficient in finding good solutions of the flow assignment problem compared with other techniques.
Abstract: In this study rack systems that are structural storage
units of warehouses have been analyzed as structural with Finite
Element Method (FEA). Each cell of discussed rack system storages
pallets which have from 800 kg to 1000 kg weights and
0.80x1.15x1.50 m dimensions. Under this load, total deformations
and equivalent stresses of structural elements and principal stresses,
tensile stresses and shear stresses of connection elements have been
analyzed. The results of analyses have been evaluated according to
resistance limits of structural and connection elements. Obtained
results have been presented as visual and magnitude.
Abstract: Gliding during night without electric power is an efficient method to enhance endurance performance of solar aircrafts. The properties of maximum gliding endurance path are studied in this paper. The problem is formulated as an optimization problem about maximum endurance can be sustained by certain potential energy storage with dynamic equations and aerodynamic parameter constrains. The optimal gliding path is generated based on gauss pseudo-spectral method. In order to analyse relationship between altitude, velocity of solar UAVs and its endurance performance, the lift coefficient in interval of [0.4, 1.2] and flight envelopes between 0~30km are investigated. Results show that broad range of lift coefficient can improve solar aircrafts- long endurance performance, and it is possible for a solar aircraft to achieve the aim of long endurance during whole night just by potential energy storage.
Abstract: In vitro gastro-duodenal digestion model was used to investigate the changes of emulsions under digestion conditions. Oil in water emulsions stabilized by whey proteins (2%) and stabilized by whey proteins (2%) with addition of carboxymethyl cellulose (0.75%) as gelling agent of continuous phase were prepared at pH7. Both emulsions were destabilized under gastric conditions; however the protective role of carboxymethyl cellulose was indicated by recording delay of fat digestibility of this emulsion. In the presence of carboxymethyl cellulose whey proteins on the interfacial surface of droplets were more resistant to gastric degradation causing limited hydrolysis of fat due to the poor acceptability of lipids for the enzymes. Studies of emulsions using in vivo model supported results from in vitro studies. Lower content of triglycerides in blood serum and higher amount of fecal fat of rats were determined when rats were fed by diet containing emulsion made with whey proteins and carboxymethyl cellulose.
Abstract: In this paper we propose a method which improves the efficiency of video coding. Our method combines an adaptive GOP (group of pictures) structure and the shot cut detection. We have analyzed different approaches for shot cut detection with aim to choose the most appropriate one. The next step is to situate N frames to the positions of detected cuts during the process of video encoding. Finally the efficiency of the proposed method is confirmed by simulations and the obtained results are compared with fixed GOP structures of sizes 4, 8, 12, 16, 32, 64, 128 and GOP structure with length of entire video. Proposed method achieved the gain in bit rate from 0.37% to 50.59%, while providing PSNR (Peak Signal-to-Noise Ratio) gain from 1.33% to 0.26% in comparison to simulated fixed GOP structures.
Abstract: Real world Speaker Identification (SI) application
differs from ideal or laboratory conditions causing perturbations that
leads to a mismatch between the training and testing environment
and degrade the performance drastically. Many strategies have been
adopted to cope with acoustical degradation; wavelet based Bayesian
marginal model is one of them. But Bayesian marginal models
cannot model the inter-scale statistical dependencies of different
wavelet scales. Simple nonlinear estimators for wavelet based
denoising assume that the wavelet coefficients in different scales are
independent in nature. However wavelet coefficients have significant
inter-scale dependency. This paper enhances this inter-scale
dependency property by a Circularly Symmetric Probability Density
Function (CS-PDF) related to the family of Spherically Invariant
Random Processes (SIRPs) in Log Gabor Wavelet (LGW) domain
and corresponding joint shrinkage estimator is derived by Maximum
a Posteriori (MAP) estimator. A framework is proposed based on
these to denoise speech signal for automatic speaker identification
problems. The robustness of the proposed framework is tested for
Text Independent Speaker Identification application on 100 speakers
of POLYCOST and 100 speakers of YOHO speech database in three
different noise environments. Experimental results show that the
proposed estimator yields a higher improvement in identification
accuracy compared to other estimators on popular Gaussian Mixture
Model (GMM) based speaker model and Mel-Frequency Cepstral
Coefficient (MFCC) features.
Abstract: The purpose of this research was to study five vital
factors related to employees’ job performance. A total of 250
respondents were sampled from employees who worked at a public
warehouse organization, Bangkok, Thailand. Samples were divided
into two groups according to their work experience. The average
working experience was about 9 years for group one and 28 years for
group two. A questionnaire was utilized as a tool to collect data.
Statistics utilized in this research included frequency, percentage,
mean, standard deviation, t-test analysis, one way ANOVA, and
Pearson Product-moment correlation coefficient. Data were analyzed
by using Statistical Package for the Social Sciences. The findings
disclosed that the majority of respondents were female between 23-
31 years old, single, and hold an undergraduate degree. The average
income of respondents was less than 30,900 baht. The findings also
revealed that the factors of organization chart awareness, job process
and technology, internal environment, employee loyalty, and policy
and management were ranked as medium level. The hypotheses
testing revealed that difference in gender, age, and position had
differences in terms of the awareness of organization chart, job
process and technology, internal environment, employee loyalty, and
policy and management in the same direction with low level.
Abstract: We study different types of aggregation operators and
the decision making process with minimization of regret. We analyze
the original work developed by Savage and the recent work
developed by Yager that generalizes the MMR method creating a
parameterized family of minimal regret methods by using the ordered
weighted averaging (OWA) operator. We suggest a new method that
uses different types of geometric operators such as the weighted
geometric mean or the ordered weighted geometric operator (OWG)
to generalize the MMR method obtaining a new parameterized family
of minimal regret methods. The main result obtained in this method
is that it allows to aggregate negative numbers in the OWG operator.
Finally, we give an illustrative example.
Abstract: This paper presents a new and efficient approach for capacitor placement in radial distribution systems that determine the optimal locations and size of capacitor with an objective of improving the voltage profile and reduction of power loss. The solution methodology has two parts: in part one the loss sensitivity factors are used to select the candidate locations for the capacitor placement and in part two a new algorithm that employs Plant growth Simulation Algorithm (PGSA) is used to estimate the optimal size of capacitors at the optimal buses determined in part one. The main advantage of the proposed method is that it does not require any external control parameters. The other advantage is that it handles the objective function and the constraints separately, avoiding the trouble to determine the barrier factors. The proposed method is applied to 9, 34, and 85-bus radial distribution systems. The solutions obtained by the proposed method are compared with other methods. The proposed method has outperformed the other methods in terms of the quality of solution.
Abstract: When an assignable cause(s) manifests itself to a multivariate process and the process shifts to an out-of-control condition, a root-cause analysis should be initiated by quality engineers to identify and eliminate the assignable cause(s) affected the process. A root-cause analysis in a multivariate process is more complex compared to a univariate process. In the case of a process involved several correlated variables an effective root-cause analysis can be only experienced when it is possible to identify the required knowledge including the out-of-control condition, the change point, and the variable(s) responsible to the out-of-control condition, all simultaneously. Although literature addresses different schemes to monitor multivariate processes, one can find few scientific reports focused on all the required knowledge. To the best of the author’s knowledge this is the first time that a multi task model based on artificial neural network (ANN) is reported to monitor all the required knowledge at the same time for a multivariate process with more than two correlated quality characteristics. The performance of the proposed scheme is evaluated numerically when different step shifts affect the mean vector. Average run length is used to investigate the performance of the proposed multi task model. The simulated results indicate the multi task scheme performs all the required knowledge effectively.
Abstract: The main emphasis of metallurgists has been to process the materials to obtain the balanced mechanical properties for the given application. One of the processing routes to alter the properties is heat treatment. Nearly 90% of the structural applications are related to the medium carbon an alloyed steels and hence are regarded as structural steels. The major requirement in the conventional steel is to improve workability, toughness, hardness and grain refinement. In this view, it is proposed to study the mechanical and tribological properties of unalloyed structural (AISI 1140) steel with different thermal (heat) treatments like annealing, normalizing, tempering and hardening and compared with as brought (cold worked) specimen. All heat treatments are carried out in atmospheric condition. Hardening treatment improves hardness of the material, a marginal decrease in hardness value with improved ductility is observed in tempering. Annealing and normalizing improve ductility of the specimen. Normalized specimen shows ultimate ductility. Hardened specimen shows highest wear resistance in the initial period of slide wear where as above 25KM of sliding distance, as brought steel dominates the hardened specimen. Both mild and severe wear regions are observed. Microstructural analysis shows the existence of pearlitic structure in normalized specimen, lath martensitic structure in hardened, pearlitic, ferritic structure in annealed specimen.
Abstract: In this paper, the energy performance of a selected
UHDE Ammonia plant is optimized by conducting heat integration through waste heat recovery and the synthesis of a heat exchange
network (HEN). Minimum hot and cold utility requirements were estimated through IChemE spreadsheet. Supporting simulation was
carried out using HYSYS software. The results showed that there is
no need for heating utility while the required cold utility was found to
be around 268,714 kW. Hence a threshold pinch case was faced. Then, the hot and cold streams were matched appropriately. Also,
waste heat recovered resulted with savings in HP and LP steams of
approximately 51.0% and 99.6%, respectively. An economic analysis
on proposed HEN showed very attractive overall payback period not
exceeding 3 years. In general, a net saving approaching 35% was
achieved in implementing heat optimization of current studied UHDE Ammonia process.
Abstract: In this paper the features of multiple material gate
silicon-on-insulator MOSFETs are presented and compared with
single material gate silicon-on-insulator MOSFET structures. The
results indicate that the multiple material gate structures reduce short
channel effects such as drain induce barrier lowering, hot electron
effect and better current characteristics in comparison with single
material structures
Abstract: This study was carried out to develop a flotation
procedure for an oxide copper ore from a Region in Central Africa
for producing an 18% copper concentrate for downstream processing
at maximum recovery from a 4% copper feed grade. The copper
recoveries achieved from the test work were less than 50% despite
changes in reagent conditions (multistage sulphidisation, use of RCA
emulsion and mixture, use of AM 2, etc). The poor recoveries were
attributed to the mineralogy of the ore from which copper silicates
accounted for approximately 70% (mass) of the copper minerals in
the ore. These can be complex and difficult to float using
conventional flotation methods. Best results were obtained using
basic sulphidisation procedures, a high flotation temperature and
extended flotation residence time.