Abstract: In this paper, we present a new learning algorithm for
anomaly based network intrusion detection using improved self
adaptive naïve Bayesian tree (NBTree), which induces a hybrid of
decision tree and naïve Bayesian classifier. The proposed approach
scales up the balance detections for different attack types and keeps
the false positives at acceptable level in intrusion detection. In
complex and dynamic large intrusion detection dataset, the detection
accuracy of naïve Bayesian classifier does not scale up as well as
decision tree. It has been successfully tested in other problem
domains that naïve Bayesian tree improves the classification rates in
large dataset. In naïve Bayesian tree nodes contain and split as
regular decision-trees, but the leaves contain naïve Bayesian
classifiers. The experimental results on KDD99 benchmark network
intrusion detection dataset demonstrate that this new approach scales
up the detection rates for different attack types and reduces false
positives in network intrusion detection.
Abstract: H.264/AVC offers a considerably higher improvement
in coding efficiency compared to other compression standards such
as MPEG-2, but computational complexity is increased significantly.
In this paper, we propose selective mode decision schemes for fast
intra prediction mode selection. The objective is to reduce the
computational complexity of the H.264/AVC encoder without
significant rate-distortion performance degradation. In our proposed
schemes, the intra prediction complexity is reduced by limiting the
luma and chroma prediction modes using the directional information
of the 16×16 prediction mode. Experimental results are presented to
show that the proposed schemes reduce the complexity by up to 78%
maintaining the similar PSNR quality with about 1.46% bit rate
increase in average.
Abstract: In this paper the authors propose a flexible electronic solution, to improve the energetic efficiency of a thermo plant. This is achieved by replacing the mechanical gear box, placed traditionally between a gas turbine and a synchronous generator; by a power electronic converter. After reminding problematic of gear boxes and interest of a proposed electronic solution in high power plants, the authors describe a new control strategy for an indirect frequency converter, which is characterized by its high efficiency due to the use of SWM: Square Wave Modulation. The main advantage of this mode is the quasi absence of switching losses. A control method is also proposed to resolve some problems incurred by using square wave modulation, in particular to reduce the harmonics distortion of the output inverter voltage and current. Simulation examples as well as experimental results are included.
Abstract: In this study, in order to clarify wind-induced
phenomena, especially vertical mixing of density stratification in a
closed water area with floating-leaved plants, we conducted hydraulic
experiments on wind flow characteristics, wind wave characteristics,
entrainment phenomena and turbulent structure by using a wind tunnel
test tank and simulated floating-leaved plants. From the experimental
results of wind flow and wind wave characteristics, we quantified the
impact of the occupancy rate of the plants on their resistance
characteristics. From the experimental results of entrainment
phenomena, we defined the parameter that could explain the
magnitude of mixing between the density stratifications, and
quantified the impact of the occupancy rate on vertical mixing
between stratifications. From the experimental results of the turbulent
structure of the upper layer, we clarified the differences in small-scale
turbulence components at each occupancy rate and quantified the
impact of the occupancy rate on the turbulence characteristics. For a
summary of this study, we theoretically quantified wind-induced
entrainment phenomena in a closed water area with luxuriant growth
of floating-leaved plants. The results indicated that the impact of
luxuriant growth of floating-leaved plants in a closed water body
could be seen in the difference in small-scale fluid characteristics, and
these characteristics could be expressed using the small-scale
turbulent components.
Abstract: A zero dimensional model has been used to investigate
the combustion performance of a single cylinder direct injection
diesel engine fueled by biofuels with options like supercharging and
exhaust gas recirculation. The numerical simulation was performed at
constant speed. The indicated pressure, temperature diagrams are
plotted and compared for different fuels. The emissions of soot and
nitrous oxide are computed with phenomenological models. The
experimental work was also carried out with biodiesel (palm stearin
methyl ester) diesel blends, ethanol diesel blends to validate
simulation results with experimental results, and observed that the
present model is successful in predicting the engine performance with
biofuels.
Abstract: Data compression is used operationally to reduce bandwidth and storage requirements. An efficient method for achieving lossless weather radar data compression is presented. The characteristics of the data are taken into account and the optical linear prediction is used for the PPI images in the weather radar data in the proposed method. The next PPI image is identical to the current one and a dramatic reduction in source entropy is achieved by using the prediction algorithm. Some lossless compression methods are used to compress the predicted data. Experimental results show that for the weather radar data, the method proposed in this paper outperforms the other methods.
Abstract: This paper presents a method for determining the
uniaxial tensile properties such as Young-s modulus, yield strength
and the flow behaviour of a material in a virtually non-destructive
manner. To achieve this, a new dumb-bell shaped miniature
specimen has been designed. This helps in avoiding the removal of
large size material samples from the in-service component for the
evaluation of current material properties. The proposed miniature
specimen has an advantage in finite element modelling with respect
to computational time and memory space. Test fixtures have been
developed to enable the tension tests on the miniature specimen in a
testing machine. The studies have been conducted in a chromium
(H11) steel and an aluminum alloy (AR66). The output from the
miniature test viz. load-elongation diagram is obtained and the finite
element simulation of the test is carried out using a 2D plane stress
analysis. The results are compared with the experimental results. It is
observed that the results from the finite element simulation
corroborate well with the miniature test results. The approach seems
to have potential to predict the mechanical properties of the
materials, which could be used in remaining life estimation of the
various in-service structures.
Abstract: This paper describes new computer vision algorithms
that have been developed to track moving objects as part of a
long-term study into the design of (semi-)autonomous vehicles. We
present the results of a study to exploit variable kernels for tracking in
video sequences. The basis of our work is the mean shift
object-tracking algorithm; for a moving target, it is usual to define a
rectangular target window in an initial frame, and then process the data
within that window to separate the tracked object from the background
by the mean shift segmentation algorithm. Rather than use the
standard, Epanechnikov kernel, we have used a kernel weighted by the
Chamfer distance transform to improve the accuracy of target
representation and localization, minimising the distance between the
two distributions in RGB color space using the Bhattacharyya
coefficient. Experimental results show the improved tracking
capability and versatility of the algorithm in comparison with results
using the standard kernel. These algorithms are incorporated as part of
a robot test-bed architecture which has been used to demonstrate their
effectiveness.
Abstract: The excessive consumption of fossil energies (electrical energy) during summer caused by the technological development involves more and more climate warming.
In order to reduce the worst impact of gas emissions produced from classical air conditioning, heat driven solar absorption chiller is pretty promising; it consists on using solar as motive energy which is clean and environmentally friendly to provide cold.
Solar absorption machine is composed by four components using Lithium Bromide /water as a refrigerating couple. LiBr- water is the most promising in chiller applications due to high safety, high volatility ratio, high affinity, high stability and its high latent heat. The lithium bromide solution is constitute by the salt lithium bromide which absorbs water under certain conditions of pressure and temperature however if the concentration of the solution is high in the absorption chillers; which exceed 70%, the solution will crystallize.
The main aim of this article is to study the phenomena of the crystallization and to evaluate how the dependence between the electric conductivity and the concentration which should be controlled.
Abstract: The goal of this work is to describe a new algorithm for finding the optimal variable order, number of nodes for any order and other ROBDD parameters, based on a tabular method. The tabular method makes use of a pre-built backend database table that stores the ROBDD size for selected combinations of min-terms. The user uses the backend table and the proposed algorithm to find the necessary ROBDD parameters, such as best variable order, number of nodes etc. Experimental results on benchmarks are given for this technique.
Abstract: This paper discusses a method for improving accuracy
of fuzzy-rule-based classifiers using particle swarm optimization
(PSO). Two different fuzzy classifiers are considered and optimized.
The first classifier is based on Mamdani fuzzy inference system
(M_PSO fuzzy classifier). The second classifier is based on Takagi-
Sugeno fuzzy inference system (TS_PSO fuzzy classifier). The
parameters of the proposed fuzzy classifiers including premise
(antecedent) parameters, consequent parameters and structure of
fuzzy rules are optimized using PSO. Experimental results show that
higher classification accuracy can be obtained with a lower number
of fuzzy rules by using the proposed PSO fuzzy classifiers. The
performances of M_PSO and TS_PSO fuzzy classifiers are compared
to other fuzzy based classifiers
Abstract: This paper presented the technique of robot control by event-related potentials (ERPs) of brain waves. Based on the proposed technique, severe physical disabilities can free browse outside world. A specific component of ERPs, N2P3, was found and used to control the movement of robot and the view of camera on the designed brain-computer interface (BCI). Users only required watching the stimuli of attended button on the BCI, the evoked potentials of brain waves of the target button, N2P3, had the greatest amplitude among all control buttons. An experimental scene had been constructed that the robot required walking to a specific position and move the view of camera to see the instruction of the mission, and then completed the task. Twelve volunteers participated in this experiment, and experimental results showed that the correct rate of BCI control achieved 80% and the average of execution time was 353 seconds for completing the mission. Four main contributions included in this research: (1) find an efficient component of ERPs, N2P3, for BCI control, (2) embed robot's viewpoint image into user interface for robot control, (3) design an experimental scene and conduct the experiment, and (4) evaluate the performance of the proposed system for assessing the practicability.
Abstract: Gesture recognition is a challenging task for extracting
meaningful gesture from continuous hand motion. In this paper, we propose an automatic system that recognizes isolated gesture,
in addition meaningful gesture from continuous hand motion for Arabic numbers from 0 to 9 in real-time based on Hidden Markov Models (HMM). In order to handle isolated gesture, HMM using
Ergodic, Left-Right (LR) and Left-Right Banded (LRB) topologies is applied over the discrete vector feature that is extracted from stereo
color image sequences. These topologies are considered to different
number of states ranging from 3 to 10. A new system is developed to recognize the meaningful gesture based on zero-codeword detection
with static velocity motion for continuous gesture. Therefore, the
LRB topology in conjunction with Baum-Welch (BW) algorithm for
training and forward algorithm with Viterbi path for testing presents the best performance. Experimental results show that the proposed system can successfully recognize isolated and meaningful gesture and achieve average rate recognition 98.6% and 94.29% respectively.
Abstract: Cooperative learning has been defined as learners working together as a team to solve a problem to complete a task or to accomplish a common goal, which emphasizes the importance of interactions among members to promote the whole learning performance. With the popularity of society networks, cooperative learning is no longer limited to traditional classroom teaching activities. Since society networks facilitate to organize online learners, to establish common shared visions, and to advance learning interaction, the online community and online learning community have triggered the establishment of web-based societies. Numerous research literatures have indicated that the collaborative learning community is a critical issue to enhance learning performance. Hence, this paper proposes a learning community recommendation approach to facilitate that a learner joins the appropriate learning communities, which is based on k-nearest neighbor (kNN) classification. To demonstrate the viability of the proposed approach, the proposed approach is implemented for 117 students to recommend learning communities. The experimental results indicate that the proposed approach can effectively recommend appropriate learning communities for learners.
Abstract: In this study, a robust intelligent backstepping tracking control (RIBTC) system combined with adaptive output recurrent cerebellar model articulation control (AORCMAC) and H∞ control technique is proposed for wheeled inverted pendulums (WIPs) real-time control with exact system dynamics unknown. Moreover, a robust H∞ controller is designed to attenuate the effect of the residual approximation errors and external disturbances with desired attenuation level. The experimental results indicate that the WIPs can stand upright stably when using the proposed RIBTC.
Abstract: In this paper, we propose a novel spatiotemporal fuzzy
based algorithm for noise filtering of image sequences. Our proposed algorithm uses adaptive weights based on a triangular membership
functions. In this algorithm median filter is used to suppress noise.
Experimental results show when the images are corrupted by highdensity
Salt and Pepper noise, our fuzzy based algorithm for noise filtering of image sequences, are much more effective in suppressing
noise and preserving edges than the previously reported algorithms such as [1-7]. Indeed, assigned weights to noisy pixels are very
adaptive so that they well make use of correlation of pixels. On the other hand, the motion estimation methods are erroneous and in highdensity noise they may degrade the filter performance. Therefore, our
proposed fuzzy algorithm doesn-t need any estimation of motion trajectory. The proposed algorithm admissibly removes noise without having any knowledge of Salt and Pepper noise density.
Abstract: The majority of today's IR systems base the IR task on two main processes: indexing and searching. There exists a special group of dynamic IR systems where both processes (indexing and searching) happen simultaneously; such a system discards obsolete information, simultaneously dealing with the insertion of new in¬formation, while still answering user queries. In these dynamic, time critical text document databases, it is often important to modify index structures quickly, as documents arrive. This paper presents a method for dynamization which may be used for this task. Experimental results show that the dynamization process is possible and that it guarantees the response time for the query operation and index actualization.
Abstract: Understanding road features such as lanes, the color
of lanes, and sidewalks in a live video captured from a moving
vehicle is essential to build video-based navigation systems. In this
paper, we present a novel idea to understand the road features using
support vector machines. Various feature vectors including color
components of road markings and the difference between two
regions, i.e., chosen AOIs, and so on are fed into SVM, deciding
colors of lanes and sidewalks robustly. Experimental results are
provided to show the robustness of the proposed idea.
Abstract: Hierarchical Mobile IPv6 (HMIPv6) was designed to
support IP micro-mobility management in the Next Generation
Networks (NGN) framework. The main design behind this protocol is
the usage of Mobility Anchor Point (MAP) located at any level router
of network to support hierarchical mobility management. However,
the distance MAP selection in HMIPv6 causes MAP overloaded and
increase frequent binding update as the network grows. Therefore, to
address the issue in designing MAP selection scheme, we propose a
dynamic load control mechanism integrates with a speed detection
mechanism (DMS-DLC). From the experimental results we obtain
that the proposed scheme gives better distribution in MAP load and
increase handover speed.
Abstract: In this paper, we propose a texture feature-based
language identification using wavelet-domain BDIP (block difference
of inverse probabilities) and BVLC (block variance of local
correlation coefficients) features and FFT (fast Fourier transform)
feature. In the proposed method, wavelet subbands are first obtained
by wavelet transform from a test image and denoised by Donoho-s
soft-thresholding. BDIP and BVLC operators are next applied to the
wavelet subbands. FFT blocks are also obtained by 2D (twodimensional)
FFT from the blocks into which the test image is
partitioned. Some significant FFT coefficients in each block are
selected and magnitude operator is applied to them. Moments for each
subband of BDIP and BVLC and for each magnitude of significant
FFT coefficients are then computed and fused into a feature vector. In
classification, a stabilized Bayesian classifier, which adopts variance
thresholding, searches the training feature vector most similar to the
test feature vector. Experimental results show that the proposed
method with the three operations yields excellent language
identification even with rather low feature dimension.