Abstract: Multimedia security is an incredibly significant area
of concern. A number of papers on robust digital watermarking have
been presented, but there are no standards that have been defined so
far. Thus multimedia security is still a posing problem. The aim of
this paper is to design a robust image-watermarking scheme, which
can withstand a different set of attacks. The proposed scheme
provides a robust solution integrating image moment normalization,
content dependent watermark and discrete wavelet transformation.
Moment normalization is useful to recover the watermark even in
case of geometrical attacks. Content dependent watermarks are a
powerful means of authentication as the data is watermarked with its
own features. Discrete wavelet transforms have been used as they
describe image features in a better manner. The proposed scheme
finds its place in validating identification cards and financial
instruments.
Abstract: This paper presents a multi-objective order allocation
planning problem with the consideration of various real-world
production features. A novel hybrid intelligent optimization model,
integrating a multi-objective memetic optimization process, a Monte
Carlo simulation technique and a heuristic pruning technique, is
proposed to handle this problem. Experiments based on industrial data
are conducted to validate the proposed model. Results show that (1)
the proposed model can effectively solve the investigated problem by
providing effective production decision-making solutions, which
outperformsan NSGA-II-based optimization process and an industrial
method.
Abstract: Resource-constrained project scheduling is an NPhard
optimisation problem. There are many different heuristic
strategies how to shift activities in time when resource requirements
exceed their available amounts. These strategies are frequently based
on priorities of activities. In this paper, we assume that a suitable
heuristic has been chosen to decide which activities should be
performed immediately and which should be postponed and
investigate the resource-constrained project scheduling problem
(RCPSP) from the implementation point of view. We propose an
efficient routine that, instead of shifting the activities, extends their
duration. It makes it possible to break down their duration into active
and sleeping subintervals. Then we can apply the classical Critical
Path Method that needs only polynomial running time. This
algorithm can simply be adapted for multiproject scheduling with
limited resources.
Abstract: Decisions are regularly made during a project or
daily life. Some decisions are critical and have a direct impact on
project or human success. Formal evaluation is thus required,
especially for crucial decisions, to arrive at the optimal solution
among alternatives to address issues. According to microeconomic
theory, all people-s decisions can be modeled as indifference curves.
The proposed approach supports formal analysis and decision by
constructing indifference curve model from the previous experts-
decision criteria. These knowledge embedded in the system can be
reused or help naïve users select alternative solution of the similar
problem. Moreover, the method is flexible to cope with unlimited
number of factors influencing the decision-making. The preliminary
experimental results of the alternative selection are accurately
matched with the expert-s decisions.
Abstract: Recently, distributed generation technologies have received much attention for the potential energy savings and reliability assurances that might be achieved as a result of their widespread adoption. Fueling the attention have been the possibilities of international agreements to reduce greenhouse gas emissions, electricity sector restructuring, high power reliability requirements for certain activities, and concern about easing transmission and distribution capacity bottlenecks and congestion. So it is necessary that impact of these kinds of generators on distribution feeder reconfiguration would be investigated. This paper presents an approach for distribution reconfiguration considering Distributed Generators (DGs). The objective function is summation of electrical power losses A Tabu search optimization is used to solve the optimal operation problem. The approach is tested on a real distribution feeder.
Abstract: In this paper we present a new approach to deal with
image segmentation. The fact that a single segmentation result do not
generally allow a higher level process to take into account all the
elements included in the image has motivated the consideration of
image segmentation as a multiobjective optimization problem. The
proposed algorithm adopts a split/merge strategy that uses the result
of the k-means algorithm as input for a quantum evolutionary
algorithm to establish a set of non-dominated solutions. The
evaluation is made simultaneously according to two distinct features:
intra-region homogeneity and inter-region heterogeneity. The
experimentation of the new approach on natural images has proved
its efficiency and usefulness.
Abstract: An edge based local search algorithm, called ELS, is proposed for the maximum clique problem (MCP), a well-known combinatorial optimization problem. ELS is a two phased local search method effectively £nds the near optimal solutions for the MCP. A parameter ’support’ of vertices de£ned in the ELS greatly reduces the more number of random selections among vertices and also the number of iterations and running times. Computational results on BHOSLIB and DIMACS benchmark graphs indicate that ELS is capable of achieving state-of-the-art-performance for the maximum clique with reasonable average running times.
Abstract: In this paper, two matrix iterative methods are presented to solve the matrix equation A1X1B1 + A2X2B2 + ... + AlXlBl = C the minimum residual problem l i=1 AiXiBi−CF = minXi∈BRni×ni l i=1 AiXiBi−CF and the matrix nearness problem [X1, X2, ..., Xl] = min[X1,X2,...,Xl]∈SE [X1,X2, ...,Xl] − [X1, X2, ..., Xl]F , where BRni×ni is the set of bisymmetric matrices, and SE is the solution set of above matrix equation or minimum residual problem. These matrix iterative methods have faster convergence rate and higher accuracy than former methods. Paige’s algorithms are used as the frame method for deriving these matrix iterative methods. The numerical example is used to illustrate the efficiency of these new methods.
Abstract: A parallel block method based on Backward
Differentiation Formulas (BDF) is developed for the parallel solution
of stiff Ordinary Differential Equations (ODEs). Most common
methods for solving stiff systems of ODEs are based on implicit
formulae and solved using Newton iteration which requires repeated
solution of systems of linear equations with coefficient matrix, I -
hβJ . Here, J is the Jacobian matrix of the problem. In this paper,
the matrix operations is paralleled in order to reduce the cost of the
iterations. Numerical results are given to compare the speedup and
efficiency of parallel algorithm and that of sequential algorithm.
Abstract: This paper describes an automatic algorithm to restore
the shape of three-dimensional (3D) left ventricle (LV) models created
from magnetic resonance imaging (MRI) data using a geometry-driven
optimization approach. Our basic premise is to restore the LV shape
such that the LV epicardial surface is smooth after the restoration. A
geometrical measure known as the Minimum Principle Curvature (κ2)
is used to assess the smoothness of the LV. This measure is used to
construct the objective function of a two-step optimization process.
The objective of the optimization is to achieve a smooth epicardial
shape by iterative in-plane translation of the MRI slices.
Quantitatively, this yields a minimum sum in terms of the magnitude
of κ
2, when κ2 is negative. A limited memory quasi-Newton algorithm,
L-BFGS-B, is used to solve the optimization problem. We tested our
algorithm on an in vitro theoretical LV model and 10 in vivo
patient-specific models which contain significant motion artifacts. The
results show that our method is able to automatically restore the shape
of LV models back to smoothness without altering the general shape of
the model. The magnitudes of in-plane translations are also consistent
with existing registration techniques and experimental findings.
Abstract: We provide a maximum norm analysis of a finite
element Schwarz alternating method for a nonlinear elliptic boundary
value problem of the form -Δu = f(u), on two overlapping sub
domains with non matching grids. We consider a domain which is
the union of two overlapping sub domains where each sub domain
has its own independently generated grid. The two meshes being
mutually independent on the overlap region, a triangle belonging to
one triangulation does not necessarily belong to the other one. Under
a Lipschitz assumption on the nonlinearity, we establish, on each sub
domain, an optimal L∞ error estimate between the discrete Schwarz
sequence and the exact solution of the boundary value problem.
Abstract: Acute kidney injury (AKI) is a new worldwide public
health problem. A diagnosis of this disease using creatinine is still a
problem in clinical practice. Therefore, a measurement of biomarkers
responsible for AKI has received much attention in the past couple
years. Cytokine interleukin-18 (IL-18) was reported as one of the
early biomarkers for AKI. The most commonly used method to
detect this biomarker is an immunoassay. This study used a planar
platform to perform an immunoassay using fluorescence for
detection. In this study, anti-IL-18 antibody was immobilized onto a
microscope slide using a covalent binding method. Make-up samples
were diluted at the concentration between 10 to 1000 pg/ml to create
a calibration curve. The precision of the system was determined
using a coefficient of variability (CV), which was found to be less
than 10%. The performance of this immunoassay system was
compared with the measurement from ELISA.
Abstract: A new conserving approach in the context of Immersed Boundary Method (IBM) is presented to simulate one dimensional, incompressible flow in a moving boundary problem. The method employs control volume scheme to simulate the flow field. The concept of ghost node is used at the boundaries to conserve the mass and momentum equations. The Present method implements the conservation laws in all cells including boundary control volumes. Application of the method is studied in a test case with moving boundary. Comparison between the results of this new method and a sharp interface (Image Point Method) IBM algorithm shows a well distinguished improvement in both pressure and velocity fields of the present method. Fluctuations in pressure field are fully resolved in this proposed method. This approach expands the IBM capability to simulate flow field for variety of problems by implementing conservation laws in a fully Cartesian grid compared to other conserving methods.
Abstract: How to coordinate the behaviors of the agents through
learning is a challenging problem within multi-agent domains.
Because of its complexity, recent work has focused on how
coordinated strategies can be learned. Here we are interested in using
reinforcement learning techniques to learn the coordinated actions of a
group of agents, without requiring explicit communication among
them. However, traditional reinforcement learning methods are based
on the assumption that the environment can be modeled as Markov
Decision Process, which usually cannot be satisfied when multiple
agents coexist in the same environment. Moreover, to effectively
coordinate each agent-s behavior so as to achieve the goal, it-s
necessary to augment the state of each agent with the information
about other existing agents. Whereas, as the number of agents in a
multiagent environment increases, the state space of each agent grows
exponentially, which will cause the combinational explosion problem.
Profit sharing is one of the reinforcement learning methods that allow
agents to learn effective behaviors from their experiences even within
non-Markovian environments. In this paper, to remedy the drawback
of the original profit sharing approach that needs much memory to
store each state-action pair during the learning process, we firstly
address a kind of on-line rational profit sharing algorithm. Then, we
integrate the advantages of modular learning architecture with on-line
rational profit sharing algorithm, and propose a new modular
reinforcement learning model. The effectiveness of the technique is
demonstrated using the pursuit problem.
Abstract: This paper proposes a new solution to string matching problem. This solution constructs an inverted list representing a string pattern to be searched for. It then uses a new algorithm to process an input string in a single pass. The preprocessing phase takes 1) time complexity O(m) 2) space complexity O(1) where m is the length of pattern. The searching phase time complexity takes 1) O(m+α ) in average case 2) O(n/m) in the best case and 3) O(n) in the worst case, where α is the number of comparing leading to mismatch and n is the length of input text.
Abstract: Sparse representation which can represent high dimensional
data effectively has been successfully used in computer vision
and pattern recognition problems. However, it doesn-t consider the
label information of data samples. To overcome this limitation,
we develop a novel dimensionality reduction algorithm namely
dscriminatively regularized sparse subspace learning(DR-SSL) in this
paper. The proposed DR-SSL algorithm can not only make use of
the sparse representation to model the data, but also can effective
employ the label information to guide the procedure of dimensionality
reduction. In addition,the presented algorithm can effectively deal
with the out-of-sample problem.The experiments on gene-expression
data sets show that the proposed algorithm is an effective tool for
dimensionality reduction and gene-expression data classification.
Abstract: This paper introduces a framework based on the collaboration of multi agent and hyper-heuristics to find a solution of the real single machine production problem. There are many techniques used to solve this problem. Each of it has its own advantages and disadvantages. By the collaboration of multi agent system and hyper-heuristics, we can get more optimal solution. The hyper-heuristics approach operates on a search space of heuristics rather than directly on a search space of solutions. The proposed framework consists of some agents, i.e. problem agent, trainer agent, algorithm agent (GPHH, GAHH, and SAHH), optimizer agent, and solver agent. Some low level heuristics used in this paper are MRT, SPT, LPT, EDD, LDD, and MON
Abstract: Truss optimization problem has been vastly studied
during the past 30 years and many different methods have been
proposed for this problem. Even though most of these methods
assume that the design variables are continuously valued, in reality,
the design variables of optimization problems such as cross-sectional
areas are discretely valued. In this paper, an improved hill climbing
and an improved simulated annealing algorithm have been proposed
to solve the truss optimization problem with discrete values for crosssectional
areas. Obtained results have been compared to other
methods in the literature and the comparison represents that the
proposed methods can be used more efficiently than other proposed
methods
Abstract: The healthcare environment is generally perceived as
being information rich yet knowledge poor. However, there is a lack
of effective analysis tools to discover hidden relationships and trends
in data. In fact, valuable knowledge can be discovered from
application of data mining techniques in healthcare system. In this
study, a proficient methodology for the extraction of significant
patterns from the Coronary Heart Disease warehouses for heart
attack prediction, which unfortunately continues to be a leading cause
of mortality in the whole world, has been presented. For this purpose,
we propose to enumerate dynamically the optimal subsets of the
reduced features of high interest by using rough sets technique
associated to dynamic programming. Therefore, we propose to
validate the classification using Random Forest (RF) decision tree to
identify the risky heart disease cases. This work is based on a large
amount of data collected from several clinical institutions based on
the medical profile of patient. Moreover, the experts- knowledge in
this field has been taken into consideration in order to define the
disease, its risk factors, and to establish significant knowledge
relationships among the medical factors. A computer-aided system is
developed for this purpose based on a population of 525 adults. The
performance of the proposed model is analyzed and evaluated based
on set of benchmark techniques applied in this classification problem.
Abstract: During the last years, the genomes of more and more
species have been sequenced, providing data for phylogenetic recon-
struction based on genome rearrangement measures. A main task in
all phylogenetic reconstruction algorithms is to solve the median of
three problem. Although this problem is NP-hard even for the sim-
plest distance measures, there are exact algorithms for the breakpoint
median and the reversal median that are fast enough for practical use.
In this paper, this approach is extended to the transposition median as
well as to the weighted reversal and transposition median. Although
there is no exact polynomial algorithm known even for the pairwise
distances, we will show that it is in most cases possible to solve
these problems exactly within reasonable time by using a branch and
bound algorithm.