Abstract: In this paper we present an adaptive method for image
compression that is based on complexity level of the image. The
basic compressor/de-compressor structure of this method is a multilayer
perceptron artificial neural network. In adaptive approach
different Back-Propagation artificial neural networks are used as
compressor and de-compressor and this is done by dividing the
image into blocks, computing the complexity of each block and then
selecting one network for each block according to its complexity
value. Three complexity measure methods, called Entropy, Activity
and Pattern-based are used to determine the level of complexity in
image blocks and their ability in complexity estimation are evaluated
and compared. In training and evaluation, each image block is
assigned to a network based on its complexity value. Best-SNR is
another alternative in selecting compressor network for image blocks
in evolution phase which chooses one of the trained networks such
that results best SNR in compressing the input image block. In our
evaluations, best results are obtained when overlapping the blocks is
allowed and choosing the networks in compressor is based on the
Best-SNR. In this case, the results demonstrate superiority of this
method comparing with previous similar works and JPEG standard
coding.
Abstract: Generally speaking, the mobile robot is capable of
sensing its surrounding environment, interpreting the sensed
information to obtain the knowledge of its location and the
environment, planning a real-time trajectory to reach the object. In
this process, the issue of obstacle avoidance is a fundamental topic to
be challenged. Thus, an adaptive path-planning control scheme is
designed without detailed environmental information, large memory
size and heavy computation burden in this study for the obstacle
avoidance of a mobile robot. In this scheme, the robot can gradually
approach its object according to the motion tracking mode, obstacle
avoidance mode, self-rotation mode, and robot state selection. The
effectiveness of the proposed adaptive path-planning control scheme
is verified by numerical simulations of a differential-driving mobile
robot under the possible occurrence of obstacle shapes.
Abstract: This paper explores gender related barriers to interagency collaboration in statutory children safeguard partnerships against a theoretical framework that considers individuals, professions and organisations interacting as part of a complex adaptive system. We argue that gender-framed obstacles to effective communication between culturally discrepant agencies can ultimately impact on the effectiveness of policy delivery,. We focused our research on three partnership structures in Sefton Metropolitan Borough in order to observe how interactions occur, whether the agencies involved perceive their occupational environment as being gender affected and whether they believe this can hinder effective collaboration with other biased organisations. Our principal empirical findings indicate that there is a general awareness amongst professionals of the role that gender plays in each of the agencies reviewed, that gender may well constitute a barrier to effective communication, but there is a sense in which there is little scope for change in the short term. We aim to signal here, however, the need to change against the risk of service failure.
Abstract: Our study proposes an alternative method in building
Fuzzy Rule-Based System (FRB) from Support Vector Machine
(SVM). The first set of fuzzy IF-THEN rules is obtained through
an equivalence of the SVM decision network and the zero-ordered
Sugeno FRB type of the Adaptive Network Fuzzy Inference System
(ANFIS). The second set of rules is generated by combining the
first set based on strength of firing signals of support vectors using
Gaussian kernel. The final set of rules is then obtained from the
second set through input scatter partitioning. A distinctive advantage
of our method is the guarantee that the number of final fuzzy IFTHEN
rules is not more than the number of support vectors in the
trained SVM. The final FRB system obtained is capable of performing
classification with results comparable to its SVM counterpart, but it
has an advantage over the black-boxed SVM in that it may reveal
human comprehensible patterns.
Abstract: Decrease in hardware costs and advances in computer
networking technologies have led to increased interest in the use of
large-scale parallel and distributed computing systems. One of the
biggest issues in such systems is the development of effective
techniques/algorithms for the distribution of the processes/load of a
parallel program on multiple hosts to achieve goal(s) such as
minimizing execution time, minimizing communication delays,
maximizing resource utilization and maximizing throughput.
Substantive research using queuing analysis and assuming job
arrivals following a Poisson pattern, have shown that in a multi-host
system the probability of one of the hosts being idle while other host
has multiple jobs queued up can be very high. Such imbalances in
system load suggest that performance can be improved by either
transferring jobs from the currently heavily loaded hosts to the lightly
loaded ones or distributing load evenly/fairly among the hosts .The
algorithms known as load balancing algorithms, helps to achieve the
above said goal(s). These algorithms come into two basic categories -
static and dynamic. Whereas static load balancing algorithms (SLB)
take decisions regarding assignment of tasks to processors based on
the average estimated values of process execution times and
communication delays at compile time, Dynamic load balancing
algorithms (DLB) are adaptive to changing situations and take
decisions at run time.
The objective of this paper work is to identify qualitative
parameters for the comparison of above said algorithms. In future this
work can be extended to develop an experimental environment to
study these Load balancing algorithms based on comparative
parameters quantitatively.
Abstract: In our modern world, more physical transactions are being substituted by electronic transactions (i.e. banking, shopping, and payments), many businesses and companies are performing most of their operations through the internet. Instead of having a physical commerce, internet visitors are now adapting to electronic commerce (e-Commerce). The ability of web users to reach products worldwide can be greatly benefited by creating friendly and personalized online business portals. Internet visitors will return to a particular website when they can find the information they need or want easily. Dealing with this human conceptualization brings the incorporation of Artificial/Computational Intelligence techniques in the creation of customized portals. From these techniques, Fuzzy-Set technologies can make many useful contributions to the development of such a human-centered endeavor as e-Commerce. The main objective of this paper is the implementation of a Paradigm for the Intelligent Design and Operation of Human-Computer interfaces. In particular, the paradigm is quite appropriate for the intelligent design and operation of software modules that display information (such Web Pages, graphic user interfaces GUIs, Multimedia modules) on a computer screen. The human conceptualization of the user personal information is analyzed throughout a Cascaded Fuzzy Inference (decision-making) System to generate the User Ascribe Qualities, which identify the user and that can be used to customize portals with proper Web links.
Abstract: Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.
Abstract: Speed sensorless systems are intensively studied during recent years; this is mainly due to their economical benefit and fragility of mechanical sensors and also the difficulty of installing this type of sensor in many applications. These systems suffer from instability problems and sensitivity to parameter mismatch at low speed operation. In this paper an analysis of adaptive observer stability with stator resistance estimation is given.
Abstract: The present work demonstrates the design and simulation of a fuzzy control of an air conditioning system at different pressures. The first order Sugeno fuzzy inference system is utilized to model the system and create the controller. In addition, an estimation of the heat transfer rate and water mass flow rate injection into or withdraw from the air conditioning system is determined by the fuzzy IF-THEN rules. The approach starts by generating the input/output data. Then, the subtractive clustering algorithm along with least square estimation (LSE) generates the fuzzy rules that describe the relationship between input/output data. The fuzzy rules are tuned by Adaptive Neuro-Fuzzy Inference System (ANFIS). The results show that when the pressure increases the amount of water flow rate and heat transfer rate decrease within the lower ranges of inlet dry bulb temperatures. On the other hand, and as pressure increases the amount of water flow rate and heat transfer rate increases within the higher ranges of inlet dry bulb temperatures. The inflection in the pressure effect trend occurs at lower temperatures as the inlet air humidity increases.
Abstract: The experiment was conducted to evaluate
digestibility quantities of protein in Canola Meals (CMs) between
caecectomised and intact adult Rhode Island Red (RIR) cockerels
with using conventional addition method (CAM) for 7 d: a 4-d
adaptation and a 3-d experiment period on the basis of a completely
randomized design with 4 replicates. Results indicated that
caecectomy decreased (P
Abstract: The paper presents a part of the results obtained in a
complex research project on Romanian Grey Steppe breed, owner of
some remarkable qualities such as hardiness, longevity, adaptability,
special resistance to ban weather and diseases and included in the
genetic fund (G.D. no. 822/2008.) from Romania.
Following the researches effectuated, we identified alleles of six
loci, codifying the six types of major milk proteins: alpha-casein S1
(α S1-cz); beta-casein (β-cz); kappa-casein (K-cz); beta-lactoglobulin
(β-lg); alpha-lactalbumin (α-la) and alpha-casein S2 (α S2-cz). In
system αS1-cz allele αs1-Cn B has the highest frequency (0.700), in
system β-cz allele β-Cn A2 ( 0.550 ), in system K-cz allele k-CnA2 (
0.583 ) and heterozygote genotype AB ( 0.416 ) and BB (0.375), in
system β-lg allele β-lgA1 has the highest frequency (0.542 ) and
heterozygote genotype AB ( 0.500 ), in system α-la there is
monomorphism for allele α-la B and similarly in system αS2-cz for
allele αs2-Cn A.
The milk analysis by the isoelectric focalization technique (I.E.F.)
allowed the identification of a new allele for locus αS1-casein, for two
of the individuals under analysis, namely allele called αS1-casein
IRV. When experiments were repeated, we noticed that this is not a
proteolysis band and it really was a new allele that has not been
registered in the specialized literature so far. We identified two
heterozygote individuals, carriers of this allele, namely: BIRV and
CIRV. This discovery is extremely important if focus is laid on the
national genetic patrimony.
Abstract: The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy architecture based on Extended Kalman filter. To test the performance and applicability of the proposed neuro-fuzzy model, simulation study of nonlinear complex dynamic system is carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction of financial time series. A benchmark case studie is used to demonstrate that the proposed model is a superior neuro-fuzzy modeling technique.
Abstract: In this paper, we propose a novel adaptive voltage control strategy for boost converter via Inverse LQ Servo-Control. Our presented strategy is based on an analytical formula of Inverse Linear Quadratic (ILQ) design method, which is not necessary to solve Riccati’s equation directly. The optimal and adaptive controller of the voltage control system is designed. The stability and the robust control are analyzed. Whereas, we can get the analytical solution for the optimal and robust voltage control is achieved through the natural angular velocity within a single parameter and we can change the responses easily via the ILQ control theory. Our method provides effective results as the stable responses and the response times are not drifted even if the condition is changed widely.
Abstract: Nowadays over-consumption of fossil energy in
buildings especially in residential buildings and also considering the
increase in populations, the crisis of energy shortage in a near future
is predictable. The recent performance of developed countries in
construction with the aim of decreasing fossil energies shows that
these countries have understood the incoming crisis and has taken
reasonable and basic actions in this regard. However, Iranian
architecture, with several thousands years of history, has acquired
and executed invaluable experiences in designing, adapting and
coordinating with the nature.
Architectural studies during the recent decades show that imitating
modern western architecture results in high energy wastage beside
the fact that it not reasonably adaptable and corresponded with the
habits and customs of people unlike the architecture in the past which
was compatible and adaptable with the climatic conditions and this
necessitates optimal using of renewable energies more than ever. This
paper studies problems of design, execution and living in today's
houses and reviews the characteristics of climatic elements paying
special attention to the performance of trombe wall and solar
greenhouse in traditional houses and offers some suggestions for
combining these two elements and a climatic strategy.
Abstract: In this study, we developed a model to predict the
temperature and the pressure variation in an internal combustion
engine operated in HCCI (Homogeneous charge compression ignition)
mode. HCCI operation begins from aspirating of homogeneous charge
mixture through intake valve like SI (Spark ignition) engine and the
premixed charge is compressed until temperature and pressure of
mixture reach autoignition point like diesel engine. Combustion phase
was described by double-Wiebe function. The single zone model
coupled with an double-Wiebe function were performed to simulated
pressure and temperature between the period of IVC (Inlet valve close)
and EVO (Exhaust valve open). Mixture gas properties were
implemented using STANJAN and transfer the results to main model.
The model has considered the engine geometry and enables varying in
fuelling, equivalence ratio, manifold temperature and pressure. The
results were compared with the experiment and showed good
correlation with respect to combustion phasing, pressure rise, peak
pressure and temperature. This model could be adapted and use to
control start of combustion for HCCI engine.
Abstract: An adaptive Helmholtz resonator was designed and
adapted to hydraulics. The resonator was controlled by open- and
closed-loop controls so that 20 dB attenuation of the peak-to-peak
value of the pulsating pressure was maintained. The closed-loop
control was noted to be better, albeit it was slower because of its low
pressure and temperature variation, which caused variation in the
effective bulk modulus of the hydraulic system. Low-pressure
hydraulics contains air, which affects the stiffness of the hydraulics,
and temperature variation changes the viscosity of the oil. Thus, an
open-loop control loses its efficiency if a condition such as
temperature or the amount of air changes after calibration. The
instability of the low-pressure hydraulic system reduced the
operational frequency range of the Helmholtz resonator when
compared with the results of an analytical model.
Different dampers for hydraulics are presented. Then analytical
models of a hydraulic pipe and a hydraulic pipe with a Helmholtz
resonator are presented. The analytical models are based on the wave
equation of sound pressure. Finally, control methods and the results
of experiments are presented.
Abstract: We propose a multi-agent based utilitarian approach
to model and understand information flows in social networks that
lead to Pareto optimal informational exchanges. We model the
individual expected utility function of the agents to reflect the net
value of information received. We show how this model, adapted
from a theorem by Karl Borch dealing with an actuarial Risk
Exchange concept in the Insurance industry, can be used for social
network analysis. We develop a utilitarian framework that allows us
to interpret Pareto optimal exchanges of value as potential
information flows, while achieving a maximization of a sum of
expected utilities of information of the group of agents. We examine
some interesting conditions on the utility function under which the
flows are optimal. We illustrate the promise of this new approach to
attach economic value to information in networks with a synthetic
example.
Abstract: Context awareness is a capability whereby mobile
computing devices can sense their physical environment and adapt
their behavior accordingly. The term context-awareness, in
ubiquitous computing, was introduced by Schilit in 1994 and has
become one of the most exciting concepts in early 21st-century
computing, fueled by recent developments in pervasive computing
(i.e. mobile and ubiquitous computing). These include computing
devices worn by users, embedded devices, smart appliances, sensors
surrounding users and a variety of wireless networking technologies.
Context-aware applications use context information to adapt
interfaces, tailor the set of application-relevant data, increase the
precision of information retrieval, discover services, make the user
interaction implicit, or build smart environments. For example: A
context aware mobile phone will know that the user is currently in a
meeting room, and reject any unimportant calls. One of the major
challenges in providing users with context-aware services lies in
continuously monitoring their contexts based on numerous sensors
connected to the context aware system through wireless
communication. A number of context aware frameworks based on
sensors have been proposed, but many of them have neglected the
fact that monitoring with sensors imposes heavy workloads on
ubiquitous devices with limited computing power and battery. In this
paper, we present CALEEF, a lightweight and energy efficient
context aware framework for resource limited ubiquitous devices.
Abstract: Sparse representation has long been studied and several
dictionary learning methods have been proposed. The dictionary
learning methods are widely used because they are adaptive. In this
paper, a new dictionary learning method for audio is proposed. Signals
are at first decomposed into different degrees of Intrinsic Mode
Functions (IMF) using Empirical Mode Decomposition (EMD)
technique. Then these IMFs form a learned dictionary. To reduce the
size of the dictionary, the K-means method is applied to the dictionary
to generate a K-EMD dictionary. Compared to K-SVD algorithm, the
K-EMD dictionary decomposes audio signals into structured
components, thus the sparsity of the representation is increased by
34.4% and the SNR of the recovered audio signals is increased by
20.9%.
Abstract: It is known that the heart interacts with and adapts to its venous and arterial loading conditions. Various experimental studies and modeling approaches have been developed to investigate the underlying mechanisms. This paper presents a model of the left ventricle derived based on nonlinear stress-length myocardial characteristics integrated over truncated ellipsoidal geometry, and second-order dynamic mechanism for the excitation-contraction coupling system. The results of the model presented here describe the effects of the viscoelastic damping element of the electromechanical coupling system on the hemodynamic response. Different heart rates are considered to study the pacing effects on the performance of the left-ventricle against constant preload and afterload conditions under various damping conditions. The results indicate that the pacing process of the left ventricle has to take into account, among other things, the viscoelastic damping conditions of the myofilament excitation-contraction process. The effects of left ventricular dimensions on the hemdynamic response have been examined. These effects are found to be different at different viscoelastic and pacing conditions.