Abstract: An adaptive spatial Gaussian mixture model is proposed for clustering based color image segmentation. A new clustering objective function which incorporates the spatial information is introduced in the Bayesian framework. The weighting parameter for controlling the importance of spatial information is made adaptive to the image content to augment the smoothness towards piecewisehomogeneous region and diminish the edge-blurring effect and hence the name adaptive spatial finite mixture model. The proposed approach is compared with the spatially variant finite mixture model for pixel labeling. The experimental results with synthetic and Berkeley dataset demonstrate that the proposed method is effective in improving the segmentation and it can be employed in different practical image content understanding applications.
Abstract: This paper presents preliminary results on modeling
and control of a quadrotor UAV. With aerodynamic concepts, a
mathematical model is firstly proposed to describe the dynamics
of the quadrotor UAV. Parameters of this model are identified by
experiments with Matlab Identify Toolbox. A group of PID controllers
are then designed based on the developed model. To verify
the developed model and controllers, simulations and experiments for
altitude control, position control and trajectory tracking are carried
out. The results show that the quadrotor UAV well follows the
referenced commands, which clearly demonstrates the effectiveness
of the proposed approach.
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: There are many approaches proposed for solving
Sudoku puzzles. One of them is by modelling the puzzles as block
world problems. There have been three model for Sudoku solvers
based on this approach. Each model expresses Sudoku solver as
a parameterized multi agent systems. In this work, we propose a
new model which is an improvement over the existing models. This
paper presents the development of a Sudoku solver that implements
all the proposed models. Some experiments have been conducted to
determine the performance of each model.
Abstract: A computer model of Quantum Theory (QT) has been
developed by the author. Major goal of the computer model was
support and demonstration of an as large as possible scope of QT.
This includes simulations for the major QT (Gedanken-) experiments
such as, for example, the famous double-slit experiment.
Besides the anticipated difficulties with (1) transforming exacting
mathematics into a computer program, two further types of problems
showed up, namely (2) areas where QT provides a complete mathematical
formalism, but when it comes to concrete applications the
equations are not solvable at all, or only with extremely high effort;
(3) QT rules which are formulated in natural language and which do
not seem to be translatable to precise mathematical expressions, nor
to a computer program.
The paper lists problems in all three categories and describes also
the possible solutions or circumventions developed for the computer
model.
Abstract: This research proposes a Preemptive Possibilistic
Linear Programming (PPLP) approach for solving multiobjective
Aggregate Production Planning (APP) problem with interval demand
and imprecise unit price and related operating costs. The proposed
approach attempts to maximize profit and minimize changes of
workforce. It transforms the total profit objective that has imprecise
information to three crisp objective functions, which are maximizing
the most possible value of profit, minimizing the risk of obtaining the
lower profit and maximizing the opportunity of obtaining the higher
profit. The change of workforce level objective is also converted.
Then, the problem is solved according to objective priorities. It is
easier than simultaneously solve the multiobjective problem as
performed in existing approach. Possible range of interval demand is
also used to increase flexibility of obtaining the better production
plan. A practical application of an electronic company is illustrated to
show the effectiveness of the proposed model.
Abstract: Every commercial bank optimises its asset portfolio
depending on the profitability of assets and chosen or imposed
constraints. This paper proposes and applies a stylized model for
optimising banks' asset and liability structure, reflecting profitability
of different asset categories and their risks as well as costs associated
with different liability categories and reserve requirements. The level
of detail for asset and liability categories is chosen to create a
suitably parsimonious model and to include the most important
categories in the model. It is shown that the most appropriate
optimisation criterion for the model is the maximisation of the ratio
of net interest income to assets. The maximisation of this ratio is
subject to several constraints. Some are accounting identities or
dictated by legislative requirements; others vary depending on the
market objectives for a particular bank. The model predicts variable
amount of assets allocated to loan provision.
Abstract: Image synthesis is an important area in image processing.
To synthesize images various systems are proposed in
the literature. In this paper, we propose a bio-inspired system to
synthesize image and to study the generating power of the system, we
define the class of languages generated by our system. We call image
as array in this paper. We use a primitive called iso-array to synthesize
image/array. The operation is double splicing on iso-arrays. The
double splicing operation is used in DNA computing and we use
this to synthesize image. A comparison of the family of languages
generated by the proposed self restricted double splicing systems on
iso-arrays with the existing family of local iso-picture languages is
made. Certain closure properties such as union, concatenation and
rotation are studied for the family of languages generated by the
proposed model.
Abstract: A new approach for facial expressions recognition based on face context and adaptively weighted sub-pattern PCA (Aw-SpPCA) has been presented in this paper. The facial region and others part of the body have been segmented from the complex environment based on skin color model. An algorithm has been proposed to accurate detection of face region from the segmented image based on constant ratio of height and width of face (δ= 1.618). The paper also discusses on new concept to detect the eye and mouth position. The desired part of the face has been cropped to analysis the expression of a person. Unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub patterns partitioned from an original whole pattern and separately extracts features from them. Aw-SpPCA can adaptively compute the contributions of each part and a classification task in order to enhance the robustness to both expression and illumination variations. Experiments on single standard face with five types of facial expression database shows that the proposed method is competitive.
Abstract: This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.
Abstract: This work presents a multiple objective linear programming (MOLP) model based on the desirability function approach for solving the aggregate production planning (APP) decision problem upon Masud and Hwang-s model. The proposed model minimises total production costs, carrying or backordering costs and rates of change in labor levels. An industrial case demonstrates the feasibility of applying the proposed model to the APP problems with three scenarios of inventory levels. The proposed model yields an efficient compromise solution and the overall levels of DM satisfaction with the multiple combined response levels. There has been a trend to solve complex planning problems using various metaheuristics. Therefore, in this paper, the multi-objective APP problem is solved by hybrid metaheuristics of the hunting search (HuSIHSA) and firefly (FAIHSA) mechanisms on the improved harmony search algorithm. Results obtained from the solution of are then compared. It is observed that the FAIHSA can be used as a successful alternative solution mechanism for solving APP problems over three scenarios. Furthermore, the FAIHSA provides a systematic framework for facilitating the decision-making process, enabling a decision maker interactively to modify the desirability function approach and related model parameters until a good optimal solution is obtained with proper selection of control parameters when compared.
Abstract: The model-based approach to user interface design
relies on developing separate models capturing various aspects about
users, tasks, application domain, presentation and dialog structures.
This paper presents a task modeling approach for user interface
design and aims at exploring mappings between task, domain and
presentation models. The basic idea of our approach is to identify
typical configurations in task and domain models and to investigate
how they relate each other. A special emphasis is put on applicationspecific
functions and mappings between domain objects and
operational task structures. In this respect, we will address two
layers in task decomposition: a functional (planning) layer and an
operational layer.
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: The prediction of financial time series is a very
complicated process. If the efficient market hypothesis holds, then the predictability of most financial time series would be a rather
controversial issue, due to the fact that the current price contains already all available information in the market. This paper extends
the Adaptive Neuro Fuzzy Inference System for High Frequency
Trading which is an expert system that is capable of using fuzzy reasoning combined with the pattern recognition capability of neural networks to be used in financial forecasting and trading in high
frequency. However, in order to eliminate unnecessary input in the
training phase a new event based volatility model was proposed.
Taking volatility and the scaling laws of financial time series into consideration has brought about the development of the Intraday Seasonality Observation Model. This new model allows the observation of specific events and seasonalities in data and subsequently removes any unnecessary data. This new event based
volatility model provides the ANFIS system with more accurate input
and has increased the overall performance of the system.
Abstract: Analysis of the elastic scattering of protons on 6,7Li
nuclei has been done in the framework of the optical model at the
beam energies up to 50 MeV. Differential cross sections for the 6,7Li +
p scattering were measured over the proton laboratory–energy range
from 400 to 1050 keV. The elastic scattering of 6,7Li+p data at
different proton incident energies have been analyzed using singlefolding
model. In each case the real potential obtained from the
folding model was supplemented by a phenomenological imaginary
potential, and during the fitting process the real potential was
normalized and the imaginary potential optimized. Normalization
factor NR is calculated in the range between 0.70 and 0.84.
Abstract: With the development of virtual communities, there is
an increase in the number of members in Virtual Communities (VCs).
Many join VCs with the objective of sharing their knowledge and
seeking knowledge from others. Despite the eagerness of sharing
knowledge and receiving knowledge through VCs, there is no
standard of assessing ones knowledge sharing capabilities and
prospects of knowledge sharing. This paper developed a vector space
model to assess the knowledge sharing prospect of VC users.
Abstract: The resistive-inductive-capacitive behavior of long
interconnects which are driven by CMOS gates are presented in this
paper. The analysis is based on the ¤Ç-model of a RLC load and is
developed for submicron devices. Accurate and analytical
expressions for the output load voltage, the propagation delay and the
short circuit power dissipation have been proposed after solving a
system of differential equations which accurately describe the
behavior of the circuit. The effect of coupling capacitance between
input and output and the short circuit current on these performance
parameters are also incorporated in the proposed model. The
estimated proposed delay and short circuit power dissipation are in
very good agreement with the SPICE simulation with average
relative error less than 6%.
Abstract: Cross sections of As radionuclides in the interaction of natGe with 14-30 MeV protons have been deduced by off-line y-ray spectroscopy to find optimal reaction channels leading to radiotracers for positron emission tomography. The experimental results were compared with the previous results and those estimated by the compound nucleus reaction model.
Abstract: In 2002 an amendment to SOLAS opened for
lightweight material constructions in vessels if the same fire safety as
in steel constructions could be obtained. FISPAT (FIreSPread
Analysis Tool) is a computer application that simulates fire spread
and fault injection in cruise vessels and identifies fire sensitive areas.
It was developed to analyze cruise vessel designs and provides a
method to evaluate network layout and safety of cruise vessels. It
allows fast, reliable and deterministic exhaustive simulations and
presents the result in a graphical vessel model. By performing the
analysis iteratively while altering the cruise vessel design it can be
used along with fire chamber experiments to show that the
lightweight design can be as safe as a steel construction and that
SOLAS regulations are fulfilled.
Abstract: Discrete choice model is the most used methodology for studying traveler-s mode choice and demand. However, to calibrate the discrete choice model needs to have plenty of questionnaire survey. In this study, an aggregative model is proposed. The historical data of passenger volumes for high speed rail and domestic civil aviation are employed to calibrate and validate the model. In this study, different models are compared so as to propose the best one. From the results, systematic equations forecast better than single equation do. Models with the external variable, which is oil price, are better than models based on closed system assumption.