Abstract: In all industries which are related to heat, suitable
thermal ranges are defined for each device to operate well.
Consideration of these limits requires a thermal control unit beside
the main system. The Satellite Thermal Control Unit exploits from
different methods and facilities individually or mixed. For enhancing
heat transfer between primary surface and the environment,
utilization of radiating extended surfaces are common. Especially for
large temperature differences; variable thermal conductivity has a
strong effect on performance of such a surface .In most literatures,
thermo-physical properties, such as thermal conductivity, are
assumed as constant. However, in some recent researches the
variation of these parameters is considered. This may be helpful for
the evaluation of fin-s temperature distribution in relatively large
temperature differences. A new method is introduced to evaluate
temperature-dependent thermal conductivity values. The finite
volume method is employed to simulate numerically the temperature
distribution in a space radiating fin. The present modeling is carried
out for Aluminum as fin material and compared with previous
method. The present results are also compared with those of two
other analytical methods and good agreement is shown.
Abstract: Non-isothermal stagnation-point flow with consideration of thermal radiation is studied numerically. A set of partial differential equations that governing the fluid flow and energy is converted into a set of ordinary differential equations which is solved by Runge-Kutta method with shooting algorithm. Dimensionless wall temperature gradient and temperature boundary layer thickness for different combinaton of values of Prandtl number Pr and radiation parameter NR are presented graphically. Analyses of results show that the presence of thermal radiation in the stagnation-point flow is to increase the temperature boundary layer thickness and decrease the dimensionless wall temperature gradient.
Abstract: In this work, sorption of nickel from aqueous solution on hypnea valentiae, red macro algae, was investigated. Batch experiments have been carried out to find the effect of various parameters such as pH, temperature, sorbent dosage, metal concentration and contact time on the sorption of nickel using hypnea valentiae. Response surface methodology (RSM) is employed to optimize the process parameters. Based on the central composite design, quadratic model was developed to correlate the process variables to the response. The most influential factor on each experimental design response was identified from the analysis of variance (ANOVA). The optimum conditions for the sorption of nickel were found to be: pH – 5.1, temperature – 36.8oC, sorbent dosage – 5.1 g/L, metal concentration – 100 mg/L and contact time – 30 min. At these optimized conditions the maximum removal of nickel was found to be 91.97%. A coefficient of determination R2 value 0.9548 shows the fitness of response surface methodology in this work.
Abstract: Electrical discharge machining (EDM) is well
established machining technique mainly used to machine complex
geometries on difficult-to-machine materials and high strength
temperature resistant alloys. In the present research, the objective is
to study the shape of the electrode and establish the application of
liquid nitrogen in reducing distortion of the electrode during
electrical discharge machining of M2 grade high speed steel using
copper electrodes. Study of roundness was performed on the
electrode to observe the shape of the electrode for both conventional
EDM and EDM with cryogenically cooled electrode. Scanning
Electron Microscope (SEM) has been used to study the shape of
electrode tip. The effect of various parameters such as discharge
current and pulse on time has been studied to understand the behavior
of distortion of electrode. It has been concluded that the shape
retention is better in case of liquid nitrogen cooled electrode.
Abstract: RFID system, in which we give identification number to each item and detect it with radio frequency, supports more variable service than barcode system can do. For example, a refrigerator with RFID reader and internet connection will automatically notify expiration of food validity to us. But, in spite of its convenience, RFID system has some security threats, because anybody can get ID information of item easily. One of most critical threats is privacy invasion. Existing privacy protection schemes or systems have been proposed, and these schemes or systems defend normal users from attempts that any attacker tries to get information using RFID tag value. But, these systems still have weakness that attacker can get information using analogous value instead of original tag value. In this paper, we mention this type of attack more precisely and suggest 'Tag Broker Model', which can defend it. Tag broker in this model translates original tag value to random value, and user can only get random value. Attacker can not use analogous tag value, because he/she is not able to know original one from it.
Abstract: Collaborative planning, forecasting and
replenishment (CPFR) coordinates the various supply chain
management activities including production and purchase planning,
demand forecasting and inventory replenishment between supply
chain trading partners. This study proposes a systematic way of
analyzing CPFR supporting factors using fuzzy cognitive map
(FCM) approach. FCMs have proven particularly useful for solving
problems in which a number of decision variables and
uncontrollable variables are causally interrelated. Hence the FCMs
of CPFR are created to show the relationships between the factors
that influence on effective implementation of CPFR in the supply
chain.
Abstract: In this paper, we validate crater detection in moon surface image using FLDA. This proposal assumes that it is applied to SLIM (Smart Lander for Investigating Moon) project aiming at the pin-point landing to the moon surface. The point where the lander should land is judged by the position relations of the craters obtained via camera, so the real-time image processing becomes important element. Besides, in the SLIM project, 400kg-class lander is assumed, therefore, high-performance computers for image processing cannot be equipped. We are studying various crater detection methods such as Haar-Like features, LBP, and PCA. And we think these methods are appropriate to the project, however, to identify the unlearned images obtained by actual is insufficient. In this paper, we examine the crater detection using FLDA, and compare with the conventional methods.
Abstract: We present a preliminary x-ray study on human-hair
microstructures for a health-state indicator, in particular a cancer
case. As an uncomplicated and low-cost method of x-ray technique,
the human-hair microstructure was analyzed by wide-angle x-ray
diffractions (XRD) and small-angle x-ray scattering (SAXS). The
XRD measurements exhibited the simply reflections at the d-spacing
of 28 Å, 9.4 Å and 4.4 Å representing to the periodic distance of the
protein matrix of the human-hair macrofibrous and the diameter and
the repeated spacing of the polypeptide alpha helixes of the
photofibrils of the human-hair microfibrous, respectively. When
compared to the normal cases, the unhealthy cases including to the
breast- and ovarian-cancer cases obtained higher normalized ratios of
the x-ray diffracting peaks of 9.4 Å and 4.4 Å. This likely resulted
from the varied distributions of microstructures by a molecular
alteration. As an elemental analysis by x-ray fluorescence (XRF), the
normalized quantitative ratios of zinc(Zn)/calcium(Ca) and
iron(Fe)/calcium(Ca) were determined. Analogously, both Zn/Ca and
Fe/Ca ratios of the unhealthy cases were obtained higher than both of
the normal cases were. Combining the structural analysis by XRD
measurements and the elemental analysis by XRF measurements
exhibited that the modified fibrous microstructures of hair samples
were in relation to their altered elemental compositions. Therefore,
these microstructural and elemental analyses of hair samples will be
benefit to associate with a diagnosis of cancer and genetic diseases.
This functional method would lower a risk of such diseases by the
early diagnosis. However, the high-intensity x-ray source, the highresolution
x-ray detector, and more hair samples are necessarily
desired to develop this x-ray technique and the efficiency would be
enhanced by including the skin and fingernail samples with the
human-hair analysis.
Abstract: In this study we present the effect of elevated
temperatures from 300K to 400K on the electrical properties of
copper Phthalocyanine (CuPc) based organic field effect transistors
(OFET). Thin films of organic semiconductor CuPc (40nm) and
semitransparent Al (20nm) were deposited in sequence, by vacuum
evaporation on a glass substrate with previously deposited Ag source
and drain electrodes with a gap of 40 μm. Under resistive mode of
operation, where gate was suspended it was observed that drain
current of this organic field effect transistor (OFET) show an
increase with temperature. While in grounded gate condition metal
(aluminum) – semiconductor (Copper Phthalocyanine) Schottky
junction dominated the output characteristics and device showed
switching effect from low to high conduction states like Zener diode
at higher bias voltages. This threshold voltage for switching effect
has been found to be inversely proportional to temperature and shows
an abrupt decrease after knee temperature of 360K. Change in
dynamic resistance (Rd = dV/dI) with respect to temperature was
observed to be -1%/K.
Abstract: A novel application of neural network approach to
fault classification and fault location of Medium voltage cables is
demonstrated in this paper. Different faults on a protected cable
should be classified and located correctly. This paper presents the use
of neural networks as a pattern classifier algorithm to perform these
tasks. The proposed scheme is insensitive to variation of different
parameters such as fault type, fault resistance, and fault inception
angle. Studies show that the proposed technique is able to offer high
accuracy in both of the fault classification and fault location tasks.
Abstract: The Beshar River is one of the most important aquatic ecosystems in the upstream of the Karun watershed in south of Iran which is affected by point and non point pollutant sources . This study was done in order to evaluate the effects of pollutants activities on the water quality of the Beshar river and its aquatic ecosystems. This river is approximately 190 km in length and situated at the geographical positions of 51° 20´ to 51° 48´ E and 30° 18´ to 30° 52´ N it is one of the most important aquatic ecosystems of Kohkiloye and Boyerahmad province in south-west Iran. In this research project, five study stations were selected to examine water pollution in the Beshar River systems. Human activity is now one of the most important factors affecting on hydrology and water quality of the Beshar river. Humans use large amounts of resources to sustain various standards of living, although measures of sustainability are highly variable depending on how sustainability is defined. The Beshar river ecosystems are particularly sensitive and vulnerable to human activities. Therefore, to determine the impact of human activities on the Beshar River, the most important water quality parameters such as pH, dissolve oxygen (DO), Biological Oxygen Demand (BOD5), Total Dissolve Solids (TDS), Nitrates (NO3-N) and Phosphates (PO4) were estimated at the five stations. As the results show, the most important pollution index parameters such as BOD5, NO3 and PO4 increase and DO and pH decrease according to human activities (P
Abstract: Prediction of sinusoidal signals with time-varying
frequencies has been an important research topic in power electronics
systems. To solve this problem, we propose a new fuzzy
predictive filtering scheme, which is based on a Finite Impulse
Response (FIR) filter bank. Fuzzy logic is introduced here to provide
appropriate interpolation of individual filter outputs. Therefore,
instead of regular 'hard' switching, our method has the advantageous
'soft' switching among different filters. Simulation
comparisons between the fuzzy predictive filtering and conventional
filter bank-based approach are made to demonstrate that the
new scheme can achieve an enhanced prediction performance for
slowly changing sinusoidal input signals.
Abstract: Nano fibers produced by electrospinning are of industrial and scientific attention due to their special characteristics such as long length, small diameter and high surface area. Applications of electrospun structures in nanotechnology are included tissue scaffolds, fibers for drug delivery, composite reinforcement, chemical sensing, enzyme immobilization, membrane-based filtration, protective clothing, catalysis, solar cells, electronic devices and others. Many polymer and ceramic precursor nano fibers have been successfully electrospun with diameters in the range from 1 nm to several microns. The process is complex so that fiber diameter is influenced by various material, design and operating parameters. The objective of this work is to apply genetic algorithm on the parameters of electrospinning which have the most significant effect on the nano fiber diameter to determine the optimum parameter values before doing experimental set up. Effective factors including initial polymer concentration, initial jet radius, electrical potential, relaxation time, initial elongation, viscosity and distance between nozzle and collector are considered to determine finest diameter which is selected by user.
Abstract: Realistic systems generally are systems with various
inputs and outputs also known as Multiple Input Multiple Output
(MIMO). Such systems usually prove to be complex and difficult to
model and control purposes. Therefore, decomposition was used to
separate individual inputs and outputs. A PID is assigned to each
individual pair to regulate desired settling time. Suitable parameters
of PIDs obtained from Genetic Algorithm (GA), using Mean of
Squared Error (MSE) objective function.
Abstract: Studies in economics domain tried to reveal the correlation between stock markets. Since the globalization era, interdependence between stock markets becomes more obvious. The Dynamic Interaction Network (DIN) algorithm, which was inspired by a Gene Regulatory Network (GRN) extraction method in the bioinformatics field, is applied to reveal important and complex dynamic relationship between stock markets. We use the data of the stock market indices from eight countries around the world in this study. Our results conclude that DIN is able to reveal and model patterns of dynamic interaction from the observed variables (i.e. stock market indices). Furthermore, it is also found that the extracted network models can be utilized to predict movement of the stock market indices with a considerably good accuracy.
Abstract: Rotor Flux based Model Reference Adaptive System
(RF-MRAS) is the most popularly used conventional speed
estimation scheme for sensor-less IM drives. In this scheme, the
voltage model equations are used for the reference model. This
encounters major drawbacks at low frequencies/speed which leads to
the poor performance of RF-MRAS. Replacing the reference model
using Neural Network (NN) based flux estimator provides an
alternate solution and addresses such drawbacks. This paper
identifies an NN based flux estimator using Single Neuron Cascaded
(SNC) Architecture. The proposed SNC-NN model replaces the
conventional voltage model in RF-MRAS to form a novel MRAS
scheme named as SNC-NN-MRAS. Through simulation the proposed
SNC-NN-MRAS is shown to be promising in terms of all major
issues and robustness to parameter variation. The suitability of the
proposed SNC-NN-MRAS based speed estimator and its advantages
over RF-MRAS for sensor-less induction motor drives is
comprehensively presented through extensive simulations.
Abstract: This paper presents an optimal and unsupervised satellite image segmentation approach based on Pearson system and k-Means Clustering Algorithm Initialization. Such method could be considered as original by the fact that it utilised K-Means clustering algorithm for an optimal initialisation of image class number on one hand and it exploited Pearson system for an optimal statistical distributions- affectation of each considered class on the other hand. Satellite image exploitation requires the use of different approaches, especially those founded on the unsupervised statistical segmentation principle. Such approaches necessitate definition of several parameters like image class number, class variables- estimation and generalised mixture distributions. Use of statistical images- attributes assured convincing and promoting results under the condition of having an optimal initialisation step with appropriated statistical distributions- affectation. Pearson system associated with a k-means clustering algorithm and Stochastic Expectation-Maximization 'SEM' algorithm could be adapted to such problem. For each image-s class, Pearson system attributes one distribution type according to different parameters and especially the Skewness 'β1' and the kurtosis 'β2'. The different adapted algorithms, K-Means clustering algorithm, SEM algorithm and Pearson system algorithm, are then applied to satellite image segmentation problem. Efficiency of those combined algorithms was firstly validated with the Mean Quadratic Error 'MQE' evaluation, and secondly with visual inspection along several comparisons of these unsupervised images- segmentation.
Abstract: Generalization is one of the most challenging issues
of Learning Classifier Systems. This feature depends on the
representation method which the system used. Considering the
proposed representation schemes for Learning Classifier System, it
can be concluded that many of them are designed to describe the
shape of the region which the environmental states belong and the
other relations of the environmental state with that region was
ignored. In this paper, we propose a new representation scheme
which is designed to show various relationships between the
environmental state and the region that is specified with a particular
classifier.
Abstract: Motivated by the recent work of Herbert, Hayen, Macaskill and Walter [Interval estimation for the difference of two independent variances. Communications in Statistics, Simulation and Computation, 40: 744-758, 2011.], we investigate, in this paper, new confidence intervals for the difference between two normal population variances based on the generalized confidence interval of Weerahandi [Generalized Confidence Intervals. Journal of the American Statistical Association, 88(423): 899-905, 1993.] and the closed form method of variance estimation of Zou, Huo and Taleban [Simple confidence intervals for lognormal means and their differences with environmental applications. Environmetrics 20: 172-180, 2009]. Monte Carlo simulation results indicate that our proposed confidence intervals give a better coverage probability than that of the existing confidence interval. Also two new confidence intervals perform similarly based on their coverage probabilities and their average length widths.
Abstract: Mycophenolic acid (MPA) is a secondary metabolite
produced by Penicillium brevicompactum, which has antibiotic and
immunosuppressive properties. In this study, the first, mycophenolic
acid was produced in a fermentation process by Penicillium
brevicompactum MUCL 19011 in shake flask using a base medium.
The maximum MPA production, product yield and productivity of
process were 1.379 g/L, 18.6 mg/g glucose and 4.9 mg/L. h,
respectively. Also the glucose consumption, biomass and MPA
production profiles were investigated during batch cultivation.
Obtained results showed that MPA production starts approximately
after 180 hours and reaches to a maximum at 280 h. In the next step,
the effects of some various concentrations of enzymatically
hydrolyzed casein on MPA production were evaluated. Maximum
MPA production, product yield and productivity as 3.63 g/L, 49
mg/g glucose and 12.96 mg/L.h, respectively were obtained with
using 30 g/L enzymatically hydrolyzed casein in culture medium.
These values show an enhanced MPA production, product yield and
process productivity pr as 116.8%, 132.8% and 163.2%, respectively.