Abstract: The present study presents a new approach to automatic
data clustering and classification problems in large and complex
databases and, at the same time, derives specific types of explicit rules
describing each cluster. The method works well in both sparse and
dense multidimensional data spaces. The members of the data space
can be of the same nature or represent different classes. A number
of N-dimensional ellipsoids are used for enclosing the data clouds.
Due to the geometry of an ellipsoid and its free rotation in space
the detection of clusters becomes very efficient. The method is based
on genetic algorithms that are used for the optimization of location,
orientation and geometric characteristics of the hyper-ellipsoids. The
proposed approach can serve as a basis for the development of
general knowledge systems for discovering hidden knowledge and
unexpected patterns and rules in various large databases.
Abstract: This paper presents a new heuristic algorithm useful
for long-term planning of survivable WDM networks. A multi-period
model is formulated that combines network topology design and
capacity expansion. The ability to determine network expansion
schedules of this type becomes increasingly important to the
telecommunications industry and to its customers. The solution
technique consists of a Genetic Algorithm that allows generating
several network alternatives for each time period simultaneously and
shortest-path techniques to deduce from these alternatives a least-cost
network expansion plan over all time periods. The multi-period
planning approach is illustrated on a realistic network example.
Extensive simulations on a wide range of problem instances are
carried out to assess the cost savings that can be expected by
choosing a multi-period planning approach instead of an iterative
network expansion design method.
Abstract: An alarming emergence of multidrug-resistant strains
of the tuberculosis pathogen Mycobacterium tuberculosis and
continuing high worldwide incidence of tuberculosis has invigorated
the search for novel drug targets. The enzyme glutamate racemase
(MurI) in bacteria catalyzes the stereoconversion of L-glutamate to
D-glutamate which is a component of the peptidoglycan cell wall of
the bacterium. The inhibitors targeted against MurI from several
bacterial species have been patented and are advocated as promising
antibacterial agents. However there are none available against MurI
from Mycobacterium tuberculosis, due to the lack of its threedimensional
structure. This work accomplished two major objectives.
First, the tertiary structure of MtMurI was deduced computationally
through homology modeling using the templates from bacterial
homologues. It is speculated that like in other Gram-positive bacteria,
MtMurI exists as a dimer and many of the protein interactions at the
dimer interface are also conserved. Second, potent candidate
inhibitors against MtMurI were identified through docking against
already known inhibitors in other organisms.
Abstract: The school / university orientation interests a broad and
often badly informed public. Technically, it is an important
multicriterion decision problem, which supposes the combination of
much academic professional and/or lawful knowledge, which in turn
justifies software resorting to the techniques of Artificial Intelligence.
CORUS is an expert system of the "Conseil et ORientation
Universitaire et Scolaire", based on a knowledge representation
language (KRL) with rules and objects, called/ known as Ibn Rochd.
CORUS was developed thanks to DéGSE, a workshop of cognitive
engineering which supports this LRC. CORUS works out many
acceptable solutions for the case considered, and retains the most
satisfactory among them. Several versions of CORUS have extended
its services gradually.
Abstract: The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.
Abstract: This article illustrates a model selection management approach for virtual prototypes in interactive simulations. In those numerical simulations, the virtual prototype and its environment are modelled as a multiagent system, where every entity (prototype,human, etc.) is modelled as an agent. In particular, virtual prototyp ingagents that provide mathematical models of mechanical behaviour inform of computational methods are considered. This work argues that selection of an appropriate model in a changing environment,supported by models? characteristics, can be managed by the deter-mination a priori of specific exploitation and performance measures of virtual prototype models. As different models exist to represent a single phenomenon, it is not always possible to select the best one under all possible circumstances of the environment. Instead the most appropriate shall be selecting according to the use case. The proposed approach consists in identifying relevant metrics or indicators for each group of models (e.g. entity models, global model), formulate their qualification, analyse the performance, and apply the qualification criteria. Then, a model can be selected based on the performance prediction obtained from its qualification. The authors hope that this approach will not only help to inform engineers and researchers about another approach for selecting virtual prototype models, but also assist virtual prototype engineers in the systematic or automatic model selection.
Abstract: This paper presents a time control liquids mixing
system in the tanks as an application of fuzzy time control discrete
model. The system is designed for a wide range of industrial
applications. The simulation design of control system has three
inputs: volume, viscosity, and selection of product, along with the
three external control adjustments for the system calibration or to
take over the control of the system autonomously in local or
distributed environment. There are four controlling elements: rotatory
motor, grinding motor, heating and cooling units, and valves
selection, each with time frame limit. The system consists of three
controlled variables measurement through its sensing mechanism for
feed back control. This design also facilitates the liquids mixing
system to grind certain materials in tanks and mix with fluids under
required temperature controlled environment to achieve certain
viscous level. Design of: fuzzifier, inference engine, rule base,
deffuzifiers, and discrete event control system, is discussed. Time
control fuzzy rules are formulated, applied and tested using
MATLAB simulation for the system.
Abstract: Environment-assisted cracking (EAC) is one of the most serious causes of structural failure over a broad range of industrial applications including offshore structures. In EAC condition there is not a definite relation such as Paris equation in Linear Elastic Fracture Mechanics (LEFM). According to studying and searching a lot what the researchers said either a material has contact with hydrogen or any other corrosive environment, phenomenon of electrical and chemical reactions of material with its environment will be happened. In the literature, there are many different works to consider fatigue crack growing and solve it but they are experimental works. Thus, in this paper, authors have an aim to evaluate mathematically the pervious works in LEFM. Obviously, if an environment is more sour and corrosive, the changes of stress intensity factor is more and the calculation of stress intensity factor is difficult. A mathematical relation to deal with the stress intensity factor during the diffusion of sour environment especially hydrogen in a marine pipeline is presented. By using this relation having and some experimental relation an analytical formulation will be presented which enables the fatigue crack growth and critical crack length under cyclic loading to be predicted. In addition, we can calculate KSCC and stress intensity factor in the pipeline caused by EAC.
Abstract: Interest in Human Consciousness has been revived in the late 20th century from different scientific disciplines. Consciousness studies involve both its understanding and its application. In this paper, a computational model of the minimum consciousness functions necessary in my point of view for Artificial Intelligence applications is presented with the aim of improving the way computations will be made in the future. In section I, human consciousness is briefly described according to the scope of this paper. In section II, a minimum set of consciousness functions is defined - based on the literature reviewed - to be modelled, and then a computational model of these functions is presented in section III. In section IV, an analysis of the model is carried out to describe its functioning in detail.
Abstract: In order to provide and maintain effective pedagogy for the burgeoning virtual reality community, it is vital to have trained faculty in the institutions of higher education who will teach these courses and be able to make full use of their academic knowledge and expertise. As the number of online courses continues to grow, there is a need for these institutions to establish mentoring programs that will support the novice online instructor. The environment in which this takes place and the factors that ensure its success are critical to the adoption of the new instructional delivery format taught by both seasoned educators and adjunct instructors. Effective one-on-one mentoring promotes a professional, compassionate and collegial faculty who will provide a consistent and rigorous academic program for students online.
Abstract: Enhancement of the performance of a reverse osmosis
(RO) unit through periodic control is studied. The periodic control
manipulates the feed pressure and flow rate of the RO unit. To ensure
the periodic behavior of the inputs, the manipulated variables (MV)
are transformed into the form of sinusoidal functions. In this case, the
amplitude and period of the sinusoidal functions become the
surrogate MV and are thus regulated via nonlinear model predictive
control algorithm. The simulation results indicated that the control
system can generate cyclic inputs necessary to enhance the closedloop
performance in the sense of increasing the permeate production
and lowering the salt concentration. The proposed control system can
attain its objective with arbitrary set point for the controlled outputs.
Successful results were also obtained in the presence of modeling
errors.
Abstract: Face Recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal Component Analysis (PCA), Self Organizing Maps (SOM) and Independent Component Analysis (ICA) are the three techniques among many others as proposed by different researchers for Face Recognition, known as the unsupervised techniques. This paper proposes integration of the two techniques, SOM and PCA, for dimensionality reduction and feature selection. Simulation results show that, though, the individual techniques SOM and PCA itself give excellent performance but the combination of these two can also be utilized for face recognition. Experimental results also indicate that for the given face database and the classifier used, SOM performs better as compared to other unsupervised learning techniques. A comparison of two proposed methodologies of SOM, Local and Global processing, shows the superiority of the later but at the cost of more computational time.
Abstract: We demonstrate a 1×4 coarse wavelength
division-multiplexing (CWDM) planar concave grating
multiplexer/demultiplexer and its application in re-configurable
optical add/drop multiplexer (ROADM) system in silicon-on-insulator
substrate. The wavelengths of the demonstrated concave grating
multiplexer align well with the ITU-T standard. We demonstrate a
prototype of ROADM comprising two such concave gratings and four
wide-band thermo-optical MZI switches. Undercut technology which
removes the underneath silicon substrate is adopted in optical switches
in order to minimize the operation power. For all the thermal heaters,
the operation voltage is smaller than 1.5 V, and the switch power is
~2.4 mW. High throughput pseudorandom binary sequence (PRBS)
data transmission with up to 100 Gb/s is demonstrated, showing the
high-performance ROADM functionality.
Abstract: In the present communication, we have studied
different variations in the entropy measures in the different states of
queueing processes. In case of steady state queuing process, it has
been shown that as the arrival rate increases, the uncertainty
increases whereas in the case of non-steady birth-death process, it is
shown that the uncertainty varies differently. In this pattern, it first
increases and attains its maximum value and then with the passage of
time, it decreases and attains its minimum value.
Abstract: Contour filter strips planted with perennial vegetation
can be used to improve surface and ground water quality by reducing
pollutant, such as NO3-N, and sediment outflow from cropland to a
river or lake. Meanwhile, the filter strips of perennial grass with biofuel
potentials also have economic benefits of producing ethanol. In
this study, The Soil and Water Assessment Tool (SWAT) model was
applied to the Walnut Creek Watershed to examine the effectiveness
of contour strips in reducing NO3-N outflows from crop fields to the
river or lake. Required input data include watershed topography,
slope, soil type, land-use, management practices in the watershed and
climate parameters (precipitation, maximum/minimum air
temperature, solar radiation, wind speed and relative humidity).
Numerical experiments were conducted to identify potential
subbasins in the watershed that have high water quality impact, and
to examine the effects of strip size and location on NO3-N reduction
in the subbasins under various meteorological conditions (dry,
average and wet). Variable sizes of contour strips (10%, 20%, 30%
and 50%, respectively, of a subbasin area) planted with perennial
switchgrass were selected for simulating the effects of strip size and
location on stream water quality. Simulation results showed that a
filter strip having 10%-50% of the subbasin area could lead to 55%-
90% NO3-N reduction in the subbasin during an average rainfall
year. Strips occupying 10-20% of the subbasin area were found to be
more efficient in reducing NO3-N when placed along the contour
than that when placed along the river. The results of this study can
assist in cost-benefit analysis and decision-making in best water
resources management practices for environmental protection.
Abstract: Gated community has gained its dominant in residential areas development that it has become the standard development pattern of the newly built residential areas in contemporary China. The form of gated community has its own advantages and rationality that meet the needs of quite a lot of residents, but it-s also believed by researchers that the form has great damage to the urban morphology and development, and has a negative impact on residents- living style. However, there is still a considerable controversy of the origins and outcomes. Though recognized as a global phenomenon, gated community developed in China is greatly to do with the specific local forces, respect to the unique historical, political and socio-cultural momentums. A historical review of the traditional settlements in China and the trends that how Gated community has gained its contemporary form, is indispensable for comprehending the local forces, and provide a new perspective to solve the controversy.
Abstract: In this paper, an efficient structural approach for
recognizing on-line handwritten digits is proposed. After reading
the digit from the user, the slope is estimated and normalized for
adjacent nodes. Based on the changing of signs of the slope values,
the primitives are identified and extracted. The names of these
primitives are represented by strings, and then a finite state
machine, which contains the grammars of the digits, is traced to
identify the digit. Finally, if there is any ambiguity, it will be
resolved. Experiments showed that this technique is flexible and
can achieve high recognition accuracy for the shapes of the digits
represented in this work.
Abstract: Nature conducts its action in a very private manner. To
reveal these actions classical science has done a great effort. But
classical science can experiment only with the things that can be seen
with eyes. Beyond the scope of classical science quantum science
works very well. It is based on some postulates like qubit,
superposition of two states, entanglement, measurement and
evolution of states that are briefly described in the present paper.
One of the applications of quantum computing i.e.
implementation of a novel quantum evolutionary algorithm(QEA) to
automate the time tabling problem of Dayalbagh Educational Institute
(Deemed University) is also presented in this paper. Making a good
timetable is a scheduling problem. It is NP-hard, multi-constrained,
complex and a combinatorial optimization problem. The solution of
this problem cannot be obtained in polynomial time. The QEA uses
genetic operators on the Q-bit as well as updating operator of
quantum gate which is introduced as a variation operator to converge
toward better solutions.
Abstract: This work evaluated the effect of temperature on headspace solid-phase microextraction of volatile and furanic compounds in coated fish sticks. The major goal was the analysis of the samples as consumed, to reproduce volatile compounds people feel when consuming those products. Extraction at 37 ºC (the human body temperature) throughout the HS-SPME analysis of volatile and furanic compounds in coated fish was compared with higher extraction temperatures, which are frequently used for this kind of determinations. The profile of volatile compounds found in deepfried (F) and non-fried (NF) coated fish at 37 and 50 ºC was different from that obtained at 80 ºC. Concerning furan and its derivatives, an extra formation of these compounds was observed at higher extraction temperatures. The analysis of volatile and furanic compounds in fish coated sticks simulating the cooking and eating conditions can be reliably carried out setting the headspace absorption temperature at 37 ºC.
Abstract: In this study, we explore the use of information for inventory decision in the healthcare organization (HO). We consider the scenario when the HO can make use of the information collected from some correlated products to enhance its inventory planning. Motivated by our real world observations that HOs adopt RFID and bar-coding system for information collection purpose, we examine the effectiveness of these systems for inventory planning with Bayesian information updating. We derive the optimal ordering decision and study the issue of Pareto improvement in the supply chain. Our analysis demonstrates that RFID system will outperform the bar-coding system when the RFID system installation cost and the tag cost reduce to a level that is comparable with that of the barcoding system. We also show how an appropriately set wholesale pricing contract can achieve Pareto improvement in the HO supply chain.