Abstract: In this paper, determining the optimal proportionalintegral-
derivative (PID) controller gains of an single-area load
frequency control (LFC) system using genetic algorithm (GA) is
presented. The LFC is notoriously difficult to control optimally using
conventionally tuning a PID controller because the system parameters
are constantly changing. It is for this reason the GA as tuning strategy
was applied. The simulation has been conducted in MATLAB
Simulink package for single area power system. the simulation results
shows the effectiveness performance of under various disturbance.
Abstract: Although lots of research work has been done for
human pose recognition, the view-point of cameras is still critical
problem of overall recognition system. In this paper, view-point
insensitive human pose recognition is proposed. The aims of the
proposed system are view-point insensitivity and real-time processing.
Recognition system consists of feature extraction module, neural
network and real-time feed forward calculation. First, histogram-based
method is used to extract feature from silhouette image and it is
suitable for represent the shape of human pose. To reduce the
dimension of feature vector, Principle Component Analysis(PCA) is
used. Second, real-time processing is implemented by using Compute
Unified Device Architecture(CUDA) and this architecture improves
the speed of feed-forward calculation of neural network. We
demonstrate the effectiveness of our approach with experiments on
real environment.
Abstract: In this paper, we proposed the distribution of mesh
normal vector direction as a feature descriptor of a 3D model. A
normal vector shows the entire shape of a model well. The
distribution of normal vectors was sampled in proportion to each
polygon's area so that the information on the surface with less surface
area may be less reflected on composing a feature descriptor in order
to enhance retrieval performance. At the analysis result of ANMRR,
the enhancement of approx. 12.4%~34.7% compared to the existing
method has also been indicated.
Abstract: In this paper, a new approach for quality assessment
tasks in lossy compressed digital video is proposed. The research
activity is based on the visual fixation data recorded by an eye
tracker. The method involved both a new paradigm for subjective
quality evaluation and the subsequent statistical analysis to match
subjective scores provided by the observer to the data obtained from
the eye tracker experiments. The study brings improvements to the
state of the art, as it solves some problems highlighted in literature.
The experiments prove that data obtained from an eye tracker can be
used to classify videos according to the level of impairment due to
compression. The paper presents the methodology, the experimental
results and their interpretation. Conclusions suggest that the eye
tracker can be useful in quality assessment, if data are collected and
analyzed in a proper way.
Abstract: In this paper, a neural network tuned fuzzy controller
is proposed for controlling Multi-Input Multi-Output (MIMO)
systems. For the convenience of analysis, the structure of MIMO
fuzzy controller is divided into single input single-output (SISO)
controllers for controlling each degree of freedom. Secondly,
according to the characteristics of the system-s dynamics coupling, an
appropriate coupling fuzzy controller is incorporated to improve the
performance. The simulation analysis on a two-level mass–spring
MIMO vibration system is carried out and results show the
effectiveness of the proposed fuzzy controller. The performance
though improved, the computational time and memory used is
comparatively higher, because it has four fuzzy reasoning blocks and
number may increase in case of other MIMO system. Then a fuzzy
neural network is designed from a set of input-output training data to
reduce the computing burden during implementation. This control
strategy can not only simplify the implementation problem of fuzzy
control, but also reduce computational time and consume less
memory.
Abstract: In the closed quantum system, if the control system is
strongly regular and all other eigenstates are directly coupled to the
target state, the control system can be asymptotically stabilized at the
target eigenstate by the Lyapunov control based on the state error.
However, if the control system is not strongly regular or as long as
there is one eigenstate not directly coupled to the target state, the
situations will become complicated. In this paper, we propose an
implicit Lyapunov control method based on the state error to solve the
convergence problems for these two degenerate cases. And at the same
time, we expand the target state from the eigenstate to the arbitrary
pure state. Especially, the proposed method is also applicable in the
control system with multi-control Hamiltonians. On this basis, the
convergence of the control systems is analyzed using the LaSalle
invariance principle. Furthermore, the relation between the implicit
Lyapunov functions of the state distance and the state error is
investigated. Finally, numerical simulations are carried out to verify
the effectiveness of the proposed implicit Lyapunov control method.
The comparisons of the control effect using the implicit Lyapunov
control method based on the state distance with that of the state error
are given.
Abstract: This paper presents a novel template-based method to
detect objects of interest from real images by shape matching. To
locate a target object that has a similar shape to a given template
boundary, the proposed method integrates three components: contour
grouping, partial shape matching, and boundary verification. In the
first component, low-level image features, including edges and
corners, are grouped into a set of perceptually salient closed contours
using an extended ratio-contour algorithm. In the second component,
we develop a partial shape matching algorithm to identify the
fractions of detected contours that partly match given template
boundaries. Specifically, we represent template boundaries and
detected contours using landmarks, and apply a greedy algorithm to
search the matched landmark subsequences. For each matched
fraction between a template and a detected contour, we estimate an
affine transform that transforms the whole template into a hypothetic
boundary. In the third component, we provide an efficient algorithm
based on oriented edge lists to determine the target boundary from
the hypothetic boundaries by checking each of them against image
edges. We evaluate the proposed method on recognizing and
localizing 12 template leaves in a data set of real images with clutter
back-grounds, illumination variations, occlusions, and image noises.
The experiments demonstrate the high performance of our proposed
method1.
Abstract: This paper presents a method to estimate load profile
in a multiple power flow solutions for every minutes in 24 hours per
day. A method to calculate multiple solutions of non linear profile is
introduced. The Power System Simulation/Engineering (PSS®E) and
python has been used to solve the load power flow. The result of this
power flow solutions has been used to estimate the load profiles for
each load at buses using Independent Component Analysis (ICA)
without any knowledge of parameter and network topology of the
systems. The proposed algorithm is tested with IEEE 69 test bus
system represents for distribution part and the method of ICA has
been programmed in MATLAB R2012b version. Simulation results
and errors of estimations are discussed in this paper.
Abstract: This paper introduces an intelligent system, which can be applied in the monitoring of vehicle speed using a single camera. The ability of motion tracking is extremely useful in many automation problems and the solution to this problem will open up many future applications. One of the most common problems in our daily life is the speed detection of vehicles on a highway. In this paper, a novel technique is developed to track multiple moving objects with their speeds being estimated using a sequence of video frames. Field test has been conducted to capture real-life data and the processed results were presented. Multiple object problems and noisy in data are also considered. Implementing this system in real-time is straightforward. The proposal can accurately evaluate the position and the orientation of moving objects in real-time. The transformations and calibration between the 2D image and the actual road are also considered.
Abstract: In this paper, numerical solution for the generalized Rosenau-Burgers equation is considered and Crank-Nicolson finite difference scheme is proposed. Existence of the solutions for the difference scheme has been shown. Stability, convergence and priori error estimate of the scheme are proved. Numerical results demonstrate that the scheme is efficient and reliable.
Abstract: This research paper presents a framework on how to
build up malware dataset.Many researchers took longer time to
clean the dataset from any noise or to transform the dataset into a
format that can be used straight away for testing. Therefore, this
research is proposing a framework to help researchers to speed up
the malware dataset cleaningprocesses which later can be used for
testing. It is believed, an efficient malware dataset cleaning
processes, can improved the quality of the data, thus help to improve
the accuracy and the efficiency of the subsequent analysis. Apart
from that, an in-depth understanding of the malware taxonomy is
also important prior and during the dataset cleaning processes. A
new Trojan classification has been proposed to complement this
framework.This experiment has been conducted in a controlled lab
environment and using the dataset from VxHeavens dataset. This
framework is built based on the integration of static and dynamic
analyses, incident response method and knowledge database
discovery (KDD) processes.This framework can be used as the basis
guideline for malware researchers in building malware dataset.
Abstract: To increase reliability of face recognition system, the
system must be able to distinguish real face from a copy of face such
as a photograph. In this paper, we propose a fast and memory efficient
method of live face detection for embedded face recognition system,
based on the analysis of the movement of the eyes. We detect eyes in
sequential input images and calculate variation of each eye region to
determine whether the input face is a real face or not. Experimental
results show that the proposed approach is competitive and promising
for live face detection.
Abstract: This work proposes an optical fiber system (OF) for
sensing various volatile organic compounds (VOCs) in human breath
for the diagnosis of some metabolic disorders as a non-invasive
methodology. The analyzed VOCs are alkanes (i.e., ethane, pentane,
heptane, octane, and decane), and aromatic compounds (i.e., benzene,
toluene, and styrene). The OF displays high analytical performance
since it provides near real-time responses, rapid analysis, and low
instrumentation costs, as well as it exhibits useful linear range and
detection limits; the developed OF sensor is also comparable to a
reference methodology (gas chromatography-mass spectrometry) for
the eight tested VOCs.
Abstract: The purpose of determining impact significance is to
place value on impacts. Environmental impact assessment review is a
process that judges whether impact significance is acceptable or not in
accordance with the scientific facts regarding environmental,
ecological and socio-economical impacts described in environmental
impact statements (EIS) or environmental impact assessment reports
(EIAR). The first aim of this paper is to summarize the criteria of
significance evaluation from the past review results and accordingly
utilize fuzzy logic to incorporate these criteria into scientific facts. The
second aim is to employ data mining technique to construct an EIS or
EIAR prediction model for reviewing results which can assist
developers to prepare and revise better environmental management
plans in advance. The validity of the previous prediction model
proposed by authors in 2009 is 92.7%. The enhanced validity in this
study can attain 100.0%.
Abstract: In this paper, a vision based system has been used for
controlling an industrial 3P Cartesian robot. The vision system will
recognize the target and control the robot by obtaining images from
environment and processing them. At the first stage, images from
environment are changed to a grayscale mode then it can diverse and
identify objects and noises by using a threshold objects which are
stored in different frames and then the main object will be
recognized. This will control the robot to achieve the target. A vision
system can be an appropriate tool for measuring errors of a robot in a
situation where the experimental test is conducted for a 3P robot.
Finally, the international standard ANSI/RIA R15.05-2 is used for
evaluating the path-related characteristics of the robot. To evaluate
the performance of the proposed method experimental test is carried
out.
Abstract: Risk Assessment Tool (RAT) is an expert system that
assesses, monitors, and gives preliminary treatments automatically
based on the project plan. In this paper, a review was taken out for
the current project time management risk assessment tools for SME
software development projects, analyze risk assessment parameters,
conditions, scenarios, and finally propose risk assessment tool (RAT)
model to assess, treat, and monitor risks. An implementation prototype
system is developed to validate the model.
Abstract: In many sensor network applications, sensor nodes are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the node's cryptographic keys. False sensing report can be injected through compromised nodes, which can lead to not only false alarms but also the depletion of limited energy resource in battery powered networks. Ye et al. proposed a statistical en-route filtering scheme (SEF) to detect such false reports during the forwarding process. In this scheme, the choice of a security threshold value is important since it trades off detection power and overhead. In this paper, we propose a fuzzy logic for determining a security threshold value in the SEF based sensor networks. The fuzzy logic determines a security threshold by considering the number of partitions in a global key pool, the number of compromised partitions, and the energy level of nodes. The fuzzy based threshold value can conserve energy, while it provides sufficient detection power.
Abstract: The recent development of Information and Communication Technology (ICT) enables new ways of "democratic" decision-making such as a page-ranking system, which estimates the importance of a web page based on indirect trust on that page shared by diverse group of unorganized individuals. These kinds of "democracy" have not been acclaimed yet in the world of real politics. On the other hand, a large amount of data about personal relations including trust, norms of reciprocity, and networks of civic engagement has been accumulated in a computer-readable form by computer systems (e.g., social networking systems). We can use these relations as a new type of social capital to construct a new democratic decision-making system based on a delegation network. In this paper, we propose an effective decision-making support system, which is based on empowering someone's vote whom you trust. For this purpose, we propose two new techniques: the first is for estimating entire vote distribution from a small number of votes, and the second is for estimating active voter choice to promote voting using a delegation network. We show that these techniques could increase the voting ratio and credibility of the whole decision by agent-based simulations.
Abstract: A wideband 2-1-1 cascaded ΣΔ modulator with a
single-bit quantizer in the two first stages and a 4-bit quantizer in the
final stage is developed. To reduce sensitivity of digital-to-analog
converter (DAC) nonlinearities in the feedback of the last stage,
dynamic element matching (DEM) is introduced. This paper presents
two modelling approaches: The first is MATLAB description and the
second is VHDL-AMS modelling of the proposed architecture and
exposes some high-level-simulation results allowing a behavioural
study. The detail of both ideal and non-ideal behaviour modelling are
presented. Then, the study of the effect of building blocks
nonidealities is presented; especially the influences of nonlinearity,
finite operational amplifier gain, amplifier slew rate limitation and
capacitor mismatch. A VHDL-AMS description presents a good
solution to predict system-s performances and can provide sensitivity
curves giving the impact of nonidealities on the system performance.
Abstract: Analyses carried out on examples of detected defects
echoes showed clearly that one can describe these detected forms according to a whole of characteristic parameters in order to be able to make discrimination between a planar defect and a volumic defect.
This work answers to a problem of ultrasonics NDT like Identification of the defects. The problems as well as the objective of
this realized work, are divided in three parts: Extractions of the parameters of wavelets from the ultrasonic echo of the detected defect - the second part is devoted to principal components analysis
(PCA) for optimization of the attributes vector. And finally to establish the algorithm of classification (SVM, Support Vector Machine) which allows discrimination between a plane defect and a
volumic defect. We have completed this work by a conclusion where we draw up a summary of the completed works, as well as the robustness of the
various algorithms proposed in this study.