Abstract: Multi-agent system is composed by several agents
capable of reaching the goal cooperatively. The system needs an agent
platform for efficient and stable interaction between intelligent agents.
In this paper we propose a flexible and scalable agent platform by
composing the containers with multiple hierarchical agent groups. It
also allows efficient implementation of multiple domain presentations
of the agents unlike JADE. The proposed platform provides both
group management and individual management of agents for
efficiency. The platform has been implemented and tested, and it can
be used as a flexible foundation of the dynamic multi-agent system
targeting seamless delivery of ubiquitous services.
Abstract: Timetabling problems are often hard and timeconsuming
to solve. Most of the methods of solving them concern
only one problem instance or class. This paper describes a universal
method for solving large, highly constrained timetabling problems
from different domains. The solution is based on evolutionary
algorithm-s framework and operates on two levels – first-level
evolutionary algorithm tries to find a solution basing on given set of
operating parameters, second-level algorithm is used to establish
those parameters. Tabu search is employed to speed up the solution
finding process on first level. The method has been used to solve
three different timetabling problems with promising results.
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: This paper deals with the tuning of parameters for Automatic Generation Control (AGC). A two area interconnected hydrothermal system with PI controller is considered. Genetic Algorithm (GA) and Particle Swarm optimization (PSO) algorithms have been applied to optimize the controller parameters. Two objective functions namely Integral Square Error (ISE) and Integral of Time-multiplied Absolute value of the Error (ITAE) are considered for optimization. The effectiveness of an objective function is considered based on the variation in tie line power and change in frequency in both the areas. MATLAB/SIMULINK was used as a simulation tool. Simulation results reveal that ITAE is a better objective function than ISE. Performances of optimization algorithms are also compared and it was found that genetic algorithm gives better results than particle swarm optimization algorithm for the problems of AGC.
Abstract: Human activity is a major concern in a wide variety of
applications, such as video surveillance, human computer interface
and face image database management. Detecting and recognizing
faces is a crucial step in these applications. Furthermore, major
advancements and initiatives in security applications in the past years
have propelled face recognition technology into the spotlight. The
performance of existing face recognition systems declines significantly
if the resolution of the face image falls below a certain level.
This is especially critical in surveillance imagery where often, due to
many reasons, only low-resolution video of faces is available. If these
low-resolution images are passed to a face recognition system, the
performance is usually unacceptable. Hence, resolution plays a key
role in face recognition systems. In this paper we introduce a new
low resolution face recognition system based on mixture of expert
neural networks. In order to produce the low resolution input images
we down-sampled the 48 × 48 ORL images to 12 × 12 ones using
the nearest neighbor interpolation method and after that applying
the bicubic interpolation method yields enhanced images which is
given to the Principal Component Analysis feature extractor system.
Comparison with some of the most related methods indicates that
the proposed novel model yields excellent recognition rate in low
resolution face recognition that is the recognition rate of 100% for
the training set and 96.5% for the test set.
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: The development of Internet technology in recent years has led to a more active role of users in creating Web content. This has significant effects both on individual learning and collaborative knowledge building. This paper will present an integrative framework model to describe and explain learning and knowledge building with shared digital artifacts on the basis of Luhmann-s systems theory and Piaget-s model of equilibration. In this model, knowledge progress is based on cognitive conflicts resulting from incongruities between an individual-s prior knowledge and the information which is contained in a digital artifact. Empirical support for the model will be provided by 1) applying it descriptively to texts from Wikipedia, 2) examining knowledge-building processes using a social network analysis, and 3) presenting a survey of a series of experimental laboratory studies.
Abstract: In the urban traffic network, the intersections are the
“bottleneck point" of road network capacity. And the arterials are the
main body in road network and the key factor which guarantees the
normal operation of the city-s social and economic activities. The
rapid increase in vehicles leads to seriously traffic jam and cause the
increment of vehicles- delay. Most cities of our country are
traditional single control system, which cannot meet the need for the
city traffic any longer. In this paper, Synchro6.0 as a platform to
minimize the intersection delay, optimizesingle signal cycle and split
for Zhonghua Street in Handan City. Meanwhile, linear control
system uses to optimize the phase for the t arterial road in this
system. Comparing before and after use the control, capacities and
service levels of this road and the adjacent road have improved
significantly.
Abstract: In this paper, the discrete-time fuzzy BAM neural network with delays and impulses is studied. Sufficient conditions are obtained for the existence and global stability of a unique equilibrium of this class of fuzzy BAM neural networks with Lipschitzian activation functions without assuming their boundedness, monotonicity or differentiability and subjected to impulsive state displacements at fixed instants of time. Some numerical examples are given to demonstrate the effectiveness of the obtained results.
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: A microchannel with two inlets and two outlets was tested as a potential reactor to carry out two-phase catalytic phase transfer reaction with phase separation at the exit of the microchannel. The catalytic phase transfer reaction between benzyl chloride and sodium sulfide was chosen as a model reaction. The effect of operational time on the conversion was studied. By utilizing a multiphase parallel flow inside the microchannel reactor with the aid of a guideline structure, the catalytic phase reaction followed by phase separation could be ensured. The organic phase could be separated completely from one exit and part of the aqueous phase was separated purely and could be reused with slightly affecting the catalytic phase transfer reaction.
Abstract: In this paper we considered the Neumann problem for
the fourth order differential equation. First we define the weighted Sobolev space
2 Wα and generalized solution for this equation. Then we consider the existence and uniqueness of the generalized solution,
as well as give the description of the spectrum and of the domain of definition of the corresponding operator.
Abstract: Long Term Evolution (LTE) is a 4G wireless
broadband technology developed by the Third Generation
Partnership Project (3GPP) release 8, and it's represent the
competitiveness of Universal Mobile Telecommunications System
(UMTS) for the next 10 years and beyond. The concepts for LTE
systems have been introduced in 3GPP release 8, with objective of
high-data-rate, low-latency and packet-optimized radio access
technology. In this paper, performance of different TCP variants
during LTE network investigated. The performance of TCP over
LTE is affected mostly by the links of the wired network and total
bandwidth available at the serving base station. This paper describes
an NS-2 based simulation analysis of TCP-Vegas, TCP-Tahoe, TCPReno,
TCP-Newreno, TCP-SACK, and TCP-FACK, with full
modeling of all traffics of LTE system. The Evaluation of the
network performance with all TCP variants is mainly based on
throughput, average delay and lost packet. The analysis of TCP
performance over LTE ensures that all TCP's have a similar
throughput and the best performance return to TCP-Vegas than other
variants.
Abstract: IT infrastructures are becoming more and more
difficult. Therefore, in the first industrial IT systems, the P2P
paradigm has replaced the traditional client server and methods of
self-organization are gaining more and more importance. From the
past it is known that especially regular structures like grids may
significantly improve the system behavior and performance. This
contribution introduces a new algorithm based on a biologic
analogue, which may provide the growth of several regular structures
on top of anarchic grown P2P- or social network structures.
Abstract: The design problem of Infinite Impulse Response (IIR)
digital filters is usually expressed as the minimization problem of
the complex magnitude error that includes both the magnitude and
phase information. However, the group delay of the filter obtained
by solving such design problem may be far from the desired group
delay. In this paper, we propose a design method of stable IIR digital
filters with prespecified maximum group delay errors. In the proposed
method, the approximation problems of the magnitude-phase and
group delay are separately defined, and these two approximation
problems are alternately solved using successive projections. As a
result, the proposed method can design the IIR filters that satisfy the
prespecified allowable errors for not only the complex magnitude but
also the group delay by alternately executing the coefficient update
for the magnitude-phase and the group delay approximation. The
usefulness of the proposed method is verified through some examples.
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: Obtaining labeled data in supervised learning is often
difficult and expensive, and thus the trained learning algorithm tends
to be overfitting due to small number of training data. As a result,
some researchers have focused on using unlabeled data which may
not necessary to follow the same generative distribution as the labeled
data to construct a high-level feature for improving performance on
supervised learning tasks. In this paper, we investigate the impact of
the relationship between unlabeled and labeled data for classification
performance. Specifically, we will apply difference unlabeled data
which have different degrees of relation to the labeled data for
handwritten digit classification task based on MNIST dataset. Our
experimental results show that the higher the degree of relation
between unlabeled and labeled data, the better the classification
performance. Although the unlabeled data that is completely from
different generative distribution to the labeled data provides the lowest
classification performance, we still achieve high classification performance.
This leads to expanding the applicability of the supervised
learning algorithms using unsupervised learning.
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: Higher-order Statistics (HOS), also known as
cumulants, cross moments and their frequency domain counterparts,
known as poly spectra have emerged as a powerful signal processing
tool for the synthesis and analysis of signals and systems. Algorithms
used for the computation of cross moments are computationally
intensive and require high computational speed for real-time
applications. For efficiency and high speed, it is often advantageous
to realize computation intensive algorithms in hardware. A promising
solution that combines high flexibility together with the speed of a
traditional hardware is Field Programmable Gate Array (FPGA). In
this paper, we present FPGA-based parallel architecture for the
computation of third-order cross moments. The proposed design is
coded in Very High Speed Integrated Circuit (VHSIC) Hardware
Description Language (VHDL) and functionally verified by
implementing it on Xilinx Spartan-3 XC3S2000FG900-4 FPGA.
Implementation results are presented and it shows that the proposed
design can operate at a maximum frequency of 86.618 MHz.
Abstract: Banishing hunger from the face of earth has been
frequently expressed in various international, national and regional
level conferences since 1974. Providing food security has become
important issue across the world particularly in developing countries.
In a developing country like India, where growth rate of population is
more than that of the food grains production, food security is a
question of great concern. According to the International Food Policy
Research Institute's Global Hunger Index, 2011, India ranks 67 of the
81 countries of the world with the worst food security status. After
Green Revolution, India became a food surplus country. Its
production has increased from 74.23 million tonnes in 1966-67 to
257.44 million tonnes in 2011-12. But after achieving selfsufficiency
in food during last three decades, the country is now
facing new challenges due to increasing population, climate change,
stagnation in farm productivity. Therefore, the main objective of the
present paper is to examine the food security situation at national
level in the country and further to explain the paradox of food
insecurity in a food surplus state of India i.e in Punjab at micro level.
In order to achieve the said objectives, secondary data collected from
the Ministry of Agriculture and the Agriculture department of Punjab
State was analyzed. The result of the study showed that despite
having surplus food production the country is still facing food
insecurity problem at micro level. Within the Kandi belt of Punjab
state, the area adjacent to plains is food secure while the area along
the hills falls in food insecure zone.
The present paper is divided into following three sections (i)
Introduction, (ii) Analysis of food security situation at national level
as well as micro level (Kandi belt of Punjab State) (iii) Concluding
Observations