Abstract: This paper investigates the encryption efficiency of RC6 block cipher application to digital images, providing a new mathematical measure for encryption efficiency, which we will call the encryption quality instead of visual inspection, The encryption quality of RC6 block cipher is investigated among its several design parameters such as word size, number of rounds, and secret key length and the optimal choices for the best values of such design parameters are given. Also, the security analysis of RC6 block cipher for digital images is investigated from strict cryptographic viewpoint. The security estimations of RC6 block cipher for digital images against brute-force, statistical, and differential attacks are explored. Experiments are made to test the security of RC6 block cipher for digital images against all aforementioned types of attacks. Experiments and results verify and prove that RC6 block cipher is highly secure for real-time image encryption from cryptographic viewpoint. Thorough experimental tests are carried out with detailed analysis, demonstrating the high security of RC6 block cipher algorithm. So, RC6 block cipher can be considered to be a real-time secure symmetric encryption for digital images.
Abstract: Vortices can develop in intakes of turbojet and turbo
fan aero engines during high power operation in the vicinity of solid
surfaces. These vortices can cause catastrophic damage to the engine.
The factors determining the formation of the vortex include both
geometric dimensions as well as flow parameters. It was shown that
the threshold at which the vortex forms or disappears is also
dependent on the initial flow condition (i.e. whether a vortex forms
after stabilised non vortex flow or vice-versa). A computational fluid
dynamics study was conducted to determine the difference in
thresholds between the two conditions. This is the first reported
numerical investigation of the “memory effect". The numerical
results reproduce the phenomenon reported in previous experimental
studies and additional factors, which had not been previously studied,
were investigated. They are the rate at which ambient velocity
changes and the initial value of ambient velocity. The former was
found to cause a shift in the threshold but not the later. It was also
found that the varying condition thresholds are not symmetrical about
the neutral threshold. The vortex to no vortex threshold lie slightly
further away from the neutral threshold compared to the no vortex to
vortex threshold. The results suggests that experimental investigation
of vortex formation threshold performed either in vortex to no vortex
conditions, or vice versa, solely may introduce mis-predictions
greater than 10%.
Abstract: The use of neural networks for recognition application is generally constrained by their inherent parameters inflexibility after the training phase. This means no adaptation is accommodated for input variations that have any influence on the network parameters. Attempts were made in this work to design a neural network that includes an additional mechanism that adjusts the threshold values according to the input pattern variations. The new approach is based on splitting the whole network into two subnets; main traditional net and a supportive net. The first deals with the required output of trained patterns with predefined settings, while the second tolerates output generation dynamically with tuning capability for any newly applied input. This tuning comes in the form of an adjustment to the threshold values. Two levels of supportive net were studied; one implements an extended additional layer with adjustable neuronal threshold setting mechanism, while the second implements an auxiliary net with traditional architecture performs dynamic adjustment to the threshold value of the main net that is constructed in dual-layer architecture. Experiment results and analysis of the proposed designs have given quite satisfactory conducts. The supportive layer approach achieved over 90% recognition rate, while the multiple network technique shows more effective and acceptable level of recognition. However, this is achieved at the price of network complexity and computation time. Recognition generalization may be also improved by accommodating capabilities involving all the innate structures in conjugation with Intelligence abilities with the needs of further advanced learning phases.
Abstract: The aim of this paper is to identify an optimum
control strategy of three-phase shunt active filters to minimize the total harmonic distortion factor of the supply current. A classical PIPI cascade control solution of the output current of the active filterand the voltage across the DC capacitor based on Modulus–Optimum
criterion is taken into consideration. The control system operation
has been simulated using Matlab-Simulink environment and the results agree with the theoretical expectation. It is shown that there is
an optimum value of the DC-bus voltage which minimizes the supply current harmonic distortion factor. It corresponds to the equality of the apparent power at the output of the active filter and the apparent power across the capacitor. Finally, predicted results are verified experimentally on a MaxSine active power filter.
Abstract: The goal of a network-based intrusion detection
system is to classify activities of network traffics into two major
categories: normal and attack (intrusive) activities. Nowadays, data
mining and machine learning plays an important role in many
sciences; including intrusion detection system (IDS) using both
supervised and unsupervised techniques. However, one of the
essential steps of data mining is feature selection that helps in
improving the efficiency, performance and prediction rate of
proposed approach. This paper applies unsupervised K-means
clustering algorithm with information gain (IG) for feature selection
and reduction to build a network intrusion detection system. For our
experimental analysis, we have used the new NSL-KDD dataset,
which is a modified dataset for KDDCup 1999 intrusion detection
benchmark dataset. With a split of 60.0% for the training set and the
remainder for the testing set, a 2 class classifications have been
implemented (Normal, Attack). Weka framework which is a java
based open source software consists of a collection of machine
learning algorithms for data mining tasks has been used in the testing
process. The experimental results show that the proposed approach is
very accurate with low false positive rate and high true positive rate
and it takes less learning time in comparison with using the full
features of the dataset with the same algorithm.
Abstract: In this paper, several improvements are proposed to
previous work of automated classification of alcoholics and nonalcoholics.
In the previous paper, multiplayer-perceptron neural
network classifying energy of gamma band Visual Evoked Potential
(VEP) signals gave the best classification performance using 800
VEP signals from 10 alcoholics and 10 non-alcoholics. Here, the
dataset is extended to include 3560 VEP signals from 102 subjects:
62 alcoholics and 40 non-alcoholics. Three modifications are
introduced to improve the classification performance: i) increasing
the gamma band spectral range by increasing the pass-band width of
the used filter ii) the use of Multiple Signal Classification algorithm
to obtain the power of the dominant frequency in gamma band VEP
signals as features and iii) the use of the simple but effective knearest
neighbour classifier. To validate that these two modifications
do give improved performance, a 10-fold cross validation
classification (CVC) scheme is used. Repeat experiments of the
previously used methodology for the extended dataset are performed
here and improvement from 94.49% to 98.71% in maximum
averaged CVC accuracy is obtained using the modifications. This
latest results show that VEP based classification of alcoholics is
worth exploring further for system development.
Abstract: Keys to high-quality face-to-face education are ensuring flexibility in the way lectures are given, and providing care and responsiveness to learners. This paper describes a face-to-face education support system that is designed to raise the satisfaction of learners and reduce the workload on instructors. This system consists of a lecture adaptation assistance part, which assists instructors in adapting teaching content and strategy, and a Q&A assistance part, which provides learners with answers to their questions. The core component of the former part is a “learning achievement map", which is composed of a Bayesian network (BN). From learners- performance in exercises on relevant past lectures, the lecture adaptation assistance part obtains information required to adapt appropriately the presentation of the next lecture. The core component of the Q&A assistance part is a case base, which accumulates cases consisting of questions expected from learners and answers to them. The Q&A assistance part is a case-based search system equipped with a search index which performs probabilistic inference. A prototype face-to-face education support system has been built, which is intended for the teaching of Java programming, and this approach was evaluated using this system. The expected degree of understanding of each learner for a future lecture was derived from his or her performance in exercises on past lectures, and this expected degree of understanding was used to select one of three adaptation levels. A model for determining the adaptation level most suitable for the individual learner has been identified. An experimental case base was built to examine the search performance of the Q&A assistance part, and it was found that the rate of successfully finding an appropriate case was 56%.
Abstract: Safe drinking water is one of the biggest issues facing
the planet this century. The primary aim of this paper is to present our
research focused on theoretical and experimental analysis of potable
water and in-building water distribution systems from the point of
view of microbiological risk on the basis of confrontation between
the theoretical analysis and synthesis of gathered information in
conditions of the Slovak Republic. The presence of the bacteria
Legionella in water systems, especially in hot water distribution
system, represents in terms of health protection of inhabitants the
crucial problem which cannot be overlooked. Legionella
pneumophila discovery, its classification and its influence on
installations inside buildings are relatively new. There are a lot of
guidelines and regulations developed in many individual countries for
the design, operation and maintenance for tap water systems to avoid
the growth of bacteria Legionella pneumophila, but in Slovakia we
don-t have any. The goal of this paper is to show the necessity of
prevention and regulations for installations inside buildings verified
by simulation methods.
Abstract: The optimal extraction condition of dried
Echinocactus grusonii powder was studied. The three independent
variables are raw material drying temperature, extraction
temperature, and extraction time. The dependent variables are both
yield percentage of crude extract and total phenolic quantification as
gallic acid equivalent in crude extract. The experimental design was
based on central composite design. Highest yield percentage of crude
extract could get from extraction condition at raw material drying
temperature at 60°C, extraction temperature at 15°C, and extraction
time for 25 min °C. Moreover, the crude extract with highest phenolic
occurred by extraction condition of raw material drying temperature
at 60°C, extraction temperature at 35 °C, and extraction lasting 25
min.
Abstract: A new algorithm called Character-Comparison to
Character-Access (CCCA) is developed to test the effect of both: 1)
converting character-comparison and number-comparison into
character-access and 2) the starting point of checking on the
performance of the checking operation in string searching. An
experiment is performed; the results are compared with five
algorithms, namely, Naive, BM, Inf_Suf_Pref, Raita, and Circle.
With the CCCA algorithm, the results suggest that the evaluation
criteria of the average number of comparisons are improved up to
74.0%. Furthermore, the results suggest that the clock time required
by the other algorithms is improved in range from 28% to 68% by the
new CCCA algorithm
Abstract: With a rapid growth in 3D graphics technology over the last few years, people are desired to see more flexible reacting motions of a biped in animations. In particular, it is impossible to anticipate all reacting motions of a biped while facing a perturbation. In this paper, we propose a three-level tracking method for animating a 3D humanoid character. First, we take the laws of physics into account to attach physical attributes, such as mass, gravity, friction, collision, contact, and torque, to bones and joints of a character. The next step is to employ PD controller to follow a reference motion as closely as possible. Once the character cannot tolerate a strong perturbation to prevent itself from falling down, we are capable of tracking a desirable falling-down action to avoid any falling condition inaccuracy. From the experimental results, we demonstrate the effectiveness and flexibility of the proposed method in comparison with conventional data-driven approaches.
Abstract: In-situ chemical oxidation (ISCO) has been widely
used for source zone remediation of Dense Nonaqueous Phase
Liquids (DNAPLs) in subsurface environments. DNAPL source
zones for karst aquifers are generally located in epikarst where the
DNAPL mass is trapped either in karst soil or at the regolith contact
with carbonate bedrock. This study aims to investigate the
performance of oxidation of residual trichloroethylene found in such
environments by potassium permanganate. Batch and flow cell
experiments were conducted to determine the kinetics and the mass
removal rate of TCE. pH change, Cl production, TCE and MnO4
destruction were monitored routinely during experiments. Nonreactive
tracer tests were also conducted prior and after the oxidation
process to determine the influence of oxidation on flow conditions.
The results show that oxidant consumption rate of the calcareous
epikarst soil was significant and the oxidant demand was determined
to be 20 g KMnO4/kg soil. Oxidation rate of residual TCE (1.26x10-3
s-1) was faster than the oxidant consumption rate of the soil (2.54 -
2.92x10-4 s-1) at only high oxidant concentrations (> 40 mM
KMnO4). Half life of TCE oxidation ranged from 7.9 to 10.7 min.
Although highly significant fraction of residual TCE mass in the
system was destroyed by permanganate oxidation, TCE
concentration in the effluent remained above its MCL. Flow
interruption tests indicate that efficiency of ISCO was limited by the
rate of TCE dissolution and the rate-limited desorption of TCE. The
residence time and the initial concentration of the oxidant in the
source zone also controlled the efficiency of ISCO in epikarst.
Abstract: Non-premixed turbulent combustion Computational Fluid Dynamics (CFD) has been carried out in a simplified methanefuelled coaxial jet combustor employing Large Eddy Simulation (LES). The objective of this study is to evaluate the performance of LES in modelling non-premixed combustion using a commercial software, FLUENT, and investigate the effects of the grid density and chemistry models employed on the accuracy of the simulation results. A comparison has also been made between LES and Reynolds Averaged Navier-Stokes (RANS) predictions. For LES grid sensitivity test, 2.3 and 6.2 million cell grids are employed with the equilibrium model. The chemistry model sensitivity analysis is achieved by comparing the simulation results from the equilibrium chemistry and steady flamelet models. The predictions of the mixture fraction, axial velocity, species mass fraction and temperature by LES are in good agreement with the experimental data. The LES results are similar for the two chemistry models but influenced considerably by the grid resolution in the inner flame and near-wall regions.
Abstract: The DNA microarray technology concurrently monitors the expression levels of thousands of genes during significant biological processes and across the related samples. The better understanding of functional genomics is obtained by extracting the patterns hidden in gene expression data. It is handled by clustering which reveals natural structures and identify interesting patterns in the underlying data. In the proposed work clustering gene expression data is done through an Advanced Nelder Mead (ANM) algorithm. Nelder Mead (NM) method is a method designed for optimization process. In Nelder Mead method, the vertices of a triangle are considered as the solutions. Many operations are performed on this triangle to obtain a better result. In the proposed work, the operations like reflection and expansion is eliminated and a new operation called spread-out is introduced. The spread-out operation will increase the global search area and thus provides a better result on optimization. The spread-out operation will give three points and the best among these three points will be used to replace the worst point. The experiment results are analyzed with optimization benchmark test functions and gene expression benchmark datasets. The results show that ANM outperforms NM in both benchmarks.
Abstract: Optimization of a microwave-assisted extraction of cherry laurel (Prunus laurocerasus) fruit using methanol was studied. The influence of process parameters (microwave power, plant material-to-solvent ratio and the extraction time) on the extraction efficiency were optimized by using response surface methodology. The predicted maximum yield of extractive substances (41.85 g/100 g fresh plant material) was obtained at microwave power of 600 W and plant material to solvent ratio of 0.2 g/cm3 after 26 minutes of extraction, while a mean value of 40.80±0.41 g/100 g fresh plant material was obtained from laboratory experiments. This proves applicability of the model in predicting optimal extraction conditions with minimal laborious and time consuming. The results indicated that all process parameters were effective on the extraction efficiency, while the most important factor was extraction time. In order to rationalize production the optimal economical condition which gave a large total extract yield with minimal energy and solvent consumption was found.
Abstract: In this work, sorption of nickel from aqueous solution on hypnea valentiae, red macro algae, was investigated. Batch experiments have been carried out to find the effect of various parameters such as pH, temperature, sorbent dosage, metal concentration and contact time on the sorption of nickel using hypnea valentiae. Response surface methodology (RSM) is employed to optimize the process parameters. Based on the central composite design, quadratic model was developed to correlate the process variables to the response. The most influential factor on each experimental design response was identified from the analysis of variance (ANOVA). The optimum conditions for the sorption of nickel were found to be: pH – 5.1, temperature – 36.8oC, sorbent dosage – 5.1 g/L, metal concentration – 100 mg/L and contact time – 30 min. At these optimized conditions the maximum removal of nickel was found to be 91.97%. A coefficient of determination R2 value 0.9548 shows the fitness of response surface methodology in this work.
Abstract: This article is presented an experimental and modeling
study of a four-bed pressure swing adsorption process using
zeolite13X to provide oxygen-enriched air. The binary mixture N2/O2
(79/21 vol %) was used as a feed stream. The effects of purge/feed
ratio (P/F), adsorption pressure, cyclic time and product flow rate on
product purity and recovery under nonisothermal condition were
studied. The adsorption dynamics of process were determined using
a mathematical model incorporated mass and energy balances. A
Mathlab code using finite difference method was developed to solve
the set of coupled differential-algebraic equations, and the simulation
results are agreed well with experimental results.
Abstract: Identification of cancer genes that might anticipate
the clinical behaviors from different types of cancer disease is
challenging due to the huge number of genes and small number of
patients samples. The new method is being proposed based on
supervised learning of classification like support vector machines
(SVMs).A new solution is described by the introduction of the
Maximized Margin (MM) in the subset criterion, which permits to
get near the least generalization error rate. In class prediction
problem, gene selection is essential to improve the accuracy and to
identify genes for cancer disease. The performance of the new
method was evaluated with real-world data experiment. It can give
the better accuracy for classification.
Abstract: This paper examines the available experiment data for a copper bromide vapor laser (CuBr laser), emitting at two wavelengths - 510.6 and 578.2nm. Laser output power is estimated based on 10 independent input physical parameters. A classification and regression tree (CART) model is obtained which describes 97% of data. The resulting binary CART tree specifies which input parameters influence considerably each of the classification groups. This allows for a technical assessment that indicates which of these are the most significant for the manufacture and operation of the type of laser under consideration. The predicted values of the laser output power are also obtained depending on classification. This aids the design and development processes considerably.
Abstract: Mammalian genomes contain large number of
retroelements (SINEs, LINEs and LTRs) which could affect
expression of protein coding genes through associated transcription
factor binding sites (TFBS). Activity of the retroelement-associated
TFBS in many genes is confirmed experimentally but their global
functional impact remains unclear. Human SINEs (Alu repeats) and
mouse SINEs (B1 and B2 repeats) are known to be clustered in GCrich
gene rich genome segments consistent with the view that they
can contribute to regulation of gene expression. We have shown
earlier that Alu are involved in formation of cis-regulatory modules
(clusters of TFBS) in human promoters, and other authors reported
that Alu located near promoter CpG islands have an increased
frequency of CpG dinucleotides suggesting that these Alu are
undermethylated. Human Alu and mouse B1/B2 elements have an
internal bipartite promoter for RNA polymerase III containing
conserved sequence motif called B-box which can bind basal
transcription complex TFIIIC. It has been recently shown that TFIIIC
binding to B-box leads to formation of a boundary which limits
spread of repressive chromatin modifications in S. pombe. SINEassociated
B-boxes may have similar function but conservation of
TFIIIC binding sites in SINEs located near mammalian promoters
has not been studied earlier. Here we analysed abundance and
distribution of retroelements (SINEs, LINEs and LTRs) in annotated
sequences of the Database of mammalian transcription start sites
(DBTSS). Fractions of SINEs in human and mouse promoters are
slightly lower than in all genome but >40% of human and mouse
promoters contain Alu or B1/B2 elements within -1000 to +200 bp
interval relative to transcription start site (TSS). Most of these SINEs
is associated with distal segments of promoters (-1000 to -200 bp
relative to TSS) indicating that their insertion at distances >200 bp
upstream of TSS is tolerated during evolution. Distribution of SINEs
in promoters correlates negatively with the distribution of CpG
sequences. Using analysis of abundance of 12-mer motifs from the
B1 and Alu consensus sequences in genome and DBTSS it has been
confirmed that some subsegments of Alu and B1 elements are poorly
conserved which depends in part on the presence of CpG
dinucleotides. One of these CpG-containing subsegments in B1
elements overlaps with SINE-associated B-box and it shows better
conservation in DBTSS compared to genomic sequences. It has been
also studied conservation in DBTSS and genome of the B-box
containing segments of old (AluJ, AluS) and young (AluY) Alu
repeats and found that CpG sequence of the B-box of old Alu is
better conserved in DBTSS than in genome. This indicates that Bbox-
associated CpGs in promoters are better protected from
methylation and mutation than B-box-associated CpGs in genomic
SINEs. These results are consistent with the view that potential
TFIIIC binding motifs in SINEs associated with human and mouse
promoters may be functionally important. These motifs may protect
promoters from repressive histone modifications which spread from
adjacent sequences. This can potentially explain well known
clustering of SINEs in GC-rich gene rich genome compartments and
existence of unmethylated CpG islands.