Abstract: In today-s competitive market, most companies
develop manufacturing systems that can help in cost reduction and
maximum quality. Human issues are an important part of
manufacturing systems, yet most companies ignore their effects on
production performance. This paper aims to developing an integrated
workforce planning system that incorporates the human being.
Therefore, a multi-objective mixed integer nonlinear programming
model is developed to determine the amount of hiring, firing,
training, overtime for each worker type. This paper considers a
workforce planning model including human aspects such as skills,
training, workers- personalities, capacity, motivation, and learning
rates. This model helps to minimize the hiring, firing, training and
overtime costs, and maximize the workers- performance. The results
indicate that the workers- differences should be considered in
workforce scheduling to generate realistic plans with minimum costs.
This paper also investigates the effects of human learning rates on the
performance of the production systems.
Abstract: Learning using labeled and unlabelled data has
received considerable amount of attention in the machine learning
community due its potential in reducing the need for expensive
labeled data. In this work we present a new method for combining
labeled and unlabeled data based on classifier ensembles. The model
we propose assumes each classifier in the ensemble observes the
input using different set of features. Classifiers are initially trained
using some labeled samples. The trained classifiers learn further
through labeling the unknown patterns using a teaching signals that is
generated using the decision of the classifier ensemble, i.e. the
classifiers self-supervise each other. Experiments on a set of object
images are presented. Our experiments investigate different classifier
models, different fusing techniques, different training sizes and
different input features. Experimental results reveal that the proposed
self-supervised ensemble learning approach reduces classification
error over the single classifier and the traditional ensemble classifier
approachs.
Abstract: This paper considers the design of a motion planner
that will simultaneously accomplish control and motion planning of a
n-link nonholonomic mobile manipulator, wherein, a n-link
holonomic manipulator is coupled with a nonholonomic mobile
platform, within an obstacle-ridden environment. This planner,
derived from the Lyapunov-based control scheme, generates
collision-free trajectories from an initial configuration to a final
configuration in a constrained environment cluttered with stationary
solid objects of different shapes and sizes. We demonstrate the
efficiency of the control scheme and the resulting acceleration
controllers of the mobile manipulator with results through computer
simulations of an interesting scenario.
Abstract: Exchange algorithm with constraints on magnitude and phase error separately in new way is presented in this paper. An important feature of the algorithms presented in this paper is that they allow for design constraints which often arise in practical filter design problems. Meeting required minimum stopband attenuation or a maximum deviation from the desired magnitude and phase responses in the passbands are common design constraints that can be handled by the methods proposed here. This new algorithm may have important advantages over existing technique, with respect to the speed and stability of convergence, memory requirement and low ripples.
Abstract: In this paper, some experiments of liquid dispersion flow driven by explosion in vertical plane were carried out using a liquid explosive dispersion device with film cylindrical constraints. The separated time series describing the breakup shape and dispersion process of liquid were recorded with high speed CMOS camera. The experimental results were analyzed and some essential characteristics of liquid dispersing flow are presented.
Abstract: HIV-1 genome is highly heterogeneous. Due to this
variation, features of HIV-I genome is in a wide range. For this
reason, the ability to infection of the virus changes depending on
different chemokine receptors. From this point of view, R5 HIV
viruses use CCR5 coreceptor while X4 viruses use CXCR5 and
R5X4 viruses can utilize both coreceptors. Recently, in
Bioinformatics, R5X4 viruses have been studied to classify by using
the experiments on HIV-1 genome.
In this study, R5X4 type of HIV viruses were classified using
Auto Regressive (AR) model through Artificial Neural Networks
(ANNs). The statistical data of R5X4, R5 and X4 viruses was
analyzed by using signal processing methods and ANNs. Accessible
residues of these virus sequences were obtained and modeled by AR
model since the dimension of residues is large and different from
each other. Finally the pre-processed data was used to evolve various
ANN structures for determining R5X4 viruses. Furthermore ROC
analysis was applied to ANNs to show their real performances. The
results indicate that R5X4 viruses successfully classified with high
sensitivity and specificity values training and testing ROC analysis
for RBF, which gives the best performance among ANN structures.
Abstract: The impact of rain attenuation on wireless communication signals is predominant because of the used high frequency (above 10 GHz). The knowledge of statistics of attenuation is very important for planning point-to-point microwave links operating in high frequency band. Describing the statistics of attenuation is possible for instance with fade duration or level crossing rate. In our examination we determine these statistics from one year measured data for a given microwave link, and we are going to make an attempt to transform the level crossing rate statistic to fade duration statistic.
Abstract: The purpose of this work was to inspect the potential
of vincristine-dextran complex loaded solid lipid nanoparticles for
drug delivery to the brain.
The nanoparticles were stained with a fluorescence dye and their
plasma pharmacokinetic and brain concentrations were investigated
following injection to rats.
The result revealed a significant improvement in the plasma
concentration profile of the SLN injected animals as well as a sharp
increased concentration in the brains.
Abstract: Liners are made to protect the groundwater table from
the infiltration of leachate which normally carries different kinds of
toxic materials from landfills. Although these liners are engineered to
last for long period of time; unfortunately these liners fail; therefore,
toxic materials pass to groundwater. This paper focuses on the
changes of the hydraulic conductivity of a sand-bentonite liner due to
the infiltration of biofuel and ethanol fuel. Series of laboratory tests
were conducted in 20-cm-high PVC columns. Several compositions
of sand-bentonite liners were tested: 95% sand: 5% bentonite; 90%
sand: 10% bentonite; and 100% sand (passed mesh #40). The
columns were subjected to extreme pressures of 40 kPa, and 100 kPa
to evaluate the transport of alternative fuels (biofuel and ethanol
fuel). For comparative studies, similar tests were carried out using
water. Results showed that hydraulic conductivity increased due to
the infiltration of alternative fuels through the liners. Accordingly,
the increase in the hydraulic conductivity showed significant
dependency on the type of liner mixture and the characteristics of the
liquid. The hydraulic conductivity of a liner (subjected to biofuel
infiltration) consisting of 5% bentonite: 95% sand under pressure of
40 kPa and 100 kPa had increased by one fold. In addition, the
hydraulic conductivity of a liner consisting of 10% bentonite: 90%
sand under pressure of 40 kPa and 100 kPa and infiltrated by biofuel
had increased by three folds. On the other hand, the results obtained
by water infiltration under 40 kPa showed lower hydraulic
conductivities of 1.50×10-5 and 1.37×10-9 cm/s for 5% bentonite:
95% sand, and 10% bentonite: 90% sand, respectively. Similarly,
under 100 kPa, the hydraulic conductivities were 2.30×10-5 and
1.90×10-9 cm/s for 5% bentonite: 95% sand, and 10% bentonite: 90%
sand, respectively.
Abstract: Language Reforms and potential use of ICTs has been a focal area of Higher Education Commission of Pakistan. Efforts are being accelerated to incorporate fast expanding ICTs to bring qualitative improvement in language instruction in higher education. This paper explores how university teachers are benefitting from ICTs to make their English class effective and what type of problems they face in practicing ICTs during their lectures. An in-depth qualitative study was employed to understand why language teachers tend to use ICTs in their instruction and how they are practicing it. A sample of twenty teachers from five universities located in Islamabad, three from public sector and two from private sector, was selected on non-random (Snowball) sampling basis. An interview with 15 semi-structured items was used as research instruments to collect data. The findings reveal that business English teaching is facilitated and improved through the use of ICTs. The language teachers need special training regarding the practices and implementation of ICTs. It is recommended that initiatives might be taken to equip university language teachers with modern methodology incorporating ICTs as focal area and efforts might be made to remove barriers regarding the training of language teachers and proper usage of ICTs.
Abstract: The error monitoring and processing system, EMPS is
the system located in the substantia nigra of the midbrain, basal
ganglia and cortex of the forebrain, and plays a leading role in error
detection and correction. The main components of EMPS are the
dopaminergic system and anterior cingulate cortex. Although, recent
studies show that alcohol disrupts the EMPS, the ways in which
alcohol affects this system are poorly understood. Based on current
literature data, here we suggest a hypothesis of alcohol-related
glucose-dependent system of error monitoring and processing, which
holds that the disruption of the EMPS is related to the competency of
glucose homeostasis regulation, which in turn may determine the
dopamine level as a major component of EMPS. Alcohol may
indirectly disrupt the EMPS by affecting dopamine level through
disorders in blood glucose homeostasis regulation.
Abstract: Mobile learning (M-learning) integrates mobile
devices and wireless computing technology to enhance the current
conventional learning system. However, there are constraints which
are affecting the implementation of platform and device independent
M-learning. The main aim of this research is to fulfill the following
main objectives: to develop platform independent mobile learning
tool (M-LT) for structured programming course, and evaluate its
effectiveness and usability using ADDIE instructional design model
(ISD) as M-LT life cycle. J2ME (Java 2 micro edition) and XML
(Extensible Markup Language) were used to develop platform
independent M-LT. It has two modules lecture materials and quizzes.
This study used Quasi experimental design to measure effectiveness
of the tool. Meanwhile, questionnaire is used to evaluate the usability
of the tool. Finally, the results show that the system was effective and
also usability evaluation was positive.
Abstract: In this paper, we introduce an mobile agent framework
with proactive load balancing for ambient intelligence (AmI) environments.
One of the main obstacles of AmI is the scalability in
which the openness of AmI environment introduces dynamic resource
requirements on agencies. To mediate this scalability problem, our
framework proposes a load balancing module to proactively analyze
the resource consumption of network bandwidth and preferred agencies
to suggest the optimal communication method to its user. The
framework generally formulates an AmI environment that consists
of three main components: (1) mobile devices, (2) hosts or agencies,
and (3) directory service center (DSC). A preliminary implementation
was conducted with NetLogo and the experimental results show that
the proposed approach provides enhanced system performance by
minimizing the network utilization to provide users with responsive
services.
Abstract: This paper presents a sensor-based motion planning algorithm for 3-DOF car-like robots with a nonholonomic constraint. Similar to the classic Bug family algorithms, the proposed algorithm enables the car-like robot to navigate in a completely unknown environment using only the range sensor information. The car-like robot uses the local range sensor view to determine the local path so that it moves towards the goal. To guarantee that the robot can approach the goal, the two modes of motion are repeated, termed motion-to-goal and wall-following. The motion-to-goal behavior lets the robot directly move toward the goal, and the wall-following behavior makes the robot circumnavigate the obstacle boundary until it meets the leaving condition. For each behavior, the nonholonomic motion for the car-like robot is planned in terms of the instantaneous turning radius. The proposed algorithm is implemented to the real robot and the experimental results show the performance of proposed algorithm.
Abstract: This paper presents a cold flow simulation study of a small gas turbine combustor performed using laboratory scale test rig. The main objective of this investigation is to obtain physical insight of the main vortex, responsible for the efficient mixing of fuel and air. Such models are necessary for predictions and optimization of real gas turbine combustors. Air swirler can control the combustor performance by assisting in the fuel-air mixing process and by producing recirculation region which can act as flame holders and influences residence time. Thus, proper selection of a swirler is needed to enhance combustor performance and to reduce NOx emissions. Three different axial air swirlers were used based on their vane angles i.e., 30°, 45°, and 60°. Three-dimensional, viscous, turbulent, isothermal flow characteristics of the combustor model operating at room temperature were simulated via Reynolds- Averaged Navier-Stokes (RANS) code. The model geometry has been created using solid model, and the meshing has been done using GAMBIT preprocessing package. Finally, the solution and analysis were carried out in a FLUENT solver. This serves to demonstrate the capability of the code for design and analysis of real combustor. The effects of swirlers and mass flow rate were examined. Details of the complex flow structure such as vortices and recirculation zones were obtained by the simulation model. The computational model predicts a major recirculation zone in the central region immediately downstream of the fuel nozzle and a second recirculation zone in the upstream corner of the combustion chamber. It is also shown that swirler angles changes have significant effects on the combustor flowfield as well as pressure losses.
Abstract: The development of the power electronics has allowed
increasing the precision and reliability of the electrical trainings,
thanks to the adjustable inverters, as the Pulse Wide Modulation
(PWM) five level inverters, which is the object of study in this
article.The authors treat the relation between the law order adopted for
a given system and the oscillations of the electrical and mechanical
parameters of which the tolerance depends on the process with which
they are integrated (paper factory, lifting of the heavy loads,
etc.).Thus the best choice of the regulation indexes allows us to
achieve stability and safety training without investment (management
of existing equipment).
Abstract: Abovepresented work deals with the new scope of application of information and communication technologies for the improvement of the election process in the biased environment. We are introducing a new concept of construction of the information-communication system for the election participant. It consists of four main components: Software, Physical Infrastructure, Structured Information and the Trained Stuff. The Structured Information is the bases of the whole system and is the collection of all possible events (irregularities among them) at the polling stations, which are structured in special templates, forms and integrated in mobile devices.The software represents a package of analytic modules, which operates with the dynamic database. The application of modern communication technologies facilities the immediate exchange of information and of relevant documents between the polling stations and the Server of the participant. No less important is the training of the staff for the proper functioning of the system. The e-training system with various modules should be applied in this respect. The presented methodology is primarily focused on the election processes in the countries of emerging democracies.It can be regarded as the tool for the monitoring of elections process by the political organization(s) and as one of the instruments to foster the spread of democracy in these countries.
Abstract: Recognition of Indian languages scripts is challenging problems. In Optical Character Recognition [OCR], a character or symbol to be recognized can be machine printed or handwritten characters/numerals. There are several approaches that deal with problem of recognition of numerals/character depending on the type of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent. Our work focused on a technique in feature extraction i.e. global based approach using end-points information, which is extracted from images of isolated numerals. These feature vectors are fed to neuro-memetic model [18] that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. . In proposed scheme data sets are fed to neuro-memetic algorithm, which identifies the rule with highest fitness value of nearly 100 % & template associates with this rule is nothing but identified numerals. Experimentation result shows that recognition rate is 92-97 % compared to other models.
Abstract: In this study, the effect of mechanical activation on the synthesis of Fe3Al/Al2O3 nanocomposite has been investigated by using mechanochemical method. For this purpose, Aluminum powder and hematite as precursors, with stoichiometric ratio, have been utilized and other effective parameters in milling process were kept constant. Phase formation analysis, crystallite size measurement and lattice strain were studied by X-ray diffraction (XRD) by using Williamson-Hall method as well as microstructure and morphology were explored by Scanning electron microscopy (SEM). Also, Energy-dispersive X-ray spectroscopy (EDX) analysis was used in order to probe the particle distribution. The results showed that after 30-hour milling, the reaction was started, combustibly done and completed.
Abstract: Water level forecasting using records of past time series is of importance in water resources engineering and management. For example, water level affects groundwater tables in low-lying coastal areas, as well as hydrological regimes of some coastal rivers. Then, a reliable prediction of sea-level variations is required in coastal engineering and hydrologic studies. During the past two decades, the approaches based on the Genetic Programming (GP) and Artificial Neural Networks (ANN) were developed. In the present study, the GP is used to forecast daily water level variations for a set of time intervals using observed water levels. The measurements from a single tide gauge at Urmia Lake, Northwest Iran, were used to train and validate the GP approach for the period from January 1997 to July 2008. Statistics, the root mean square error and correlation coefficient, are used to verify model by comparing with a corresponding outputs from Artificial Neural Network model. The results show that both these artificial intelligence methodologies are satisfactory and can be considered as alternatives to the conventional harmonic analysis.