Abstract: With the prevalence of computer and development of information technology, Geographic Information Systems (GIS) have long used for a variety of applications in electrical engineering. GIS are designed to support the analysis, management, manipulation and mapping of spatial data. This paper presents several usages of GIS in power utilities such as automated route selection for the construction of new power lines which uses a dynamic programming model for route optimization, load forecasting and optimizing planning of substation-s location and capacity with comprehensive algorithm which involves an accurate small-area electric load forecasting procedure and simulates the different cost functions of substations.
Abstract: Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a suitable neural network model for monthly precipitation mapping of Myanmar. Using 3-layerd neural network models, 100 cases are tested by changing the number of input and hidden nodes from 1 to 10 nodes, respectively, and only one outputnode used. The optimum model with the suitable number of nodes is selected in accordance with the minimum forecast error. In measuring network performance using Root Mean Square Error (RMSE), experimental results significantly show that 3 inputs-10 hiddens-1 output architecture model gives the best prediction result for monthly precipitation in Myanmar.
Abstract: According to the mobility of the satellite network nodes and the characteristic of management domain dynamic partition in the satellite network, the login and logout mechanism of the satellite network dynamic management domain partition was proposed in the paper. In the mechanism, a ground branch-station sends the packets of login broadcasting to satellites in view. After received the packets, the SNMP agents on the satellites adopt link-delay test to respond. According to the mechanism, the SNMP primitives were extended, and the new added primitives were as follows: broadcasting, login, login confirmation,delay_testing, test responses, and logout. The definition of primitives, which followed RFC1157 criterion, could be encoded by the BER coding. The policy of the dynamic management domain partition on the basis of the login and logout mechanism, which was supported by the SNMP protocol, was realized by the design of the extended primitives.
Abstract: The electric power industry is currently undergoing an unprecedented reform. One of the most exciting and potentially profitable recent developments is increasing usage of artificial intelligence techniques. The intention of this paper is to give an overview of using neural network (NN) techniques in power systems. According to the growth rate of NNs application in some power system subjects, this paper introduce a brief overview in fault diagnosis, security assessment, load forecasting, economic dispatch and harmonic analyzing. Advantages and disadvantages of using NNs in above mentioned subjects and the main challenges in these fields have been explained, too.
Abstract: This study investigates the performance of radial basis function networks (RBFN) in forecasting the monthly CO2 emissions of an electric power utility. We also propose a method for input variable selection. This method is based on identifying the general relationships between groups of input candidates and the output. The effect that each input has on the forecasting error is examined by removing all inputs except the variable to be investigated from its group, calculating the networks parameter and performing the forecast. Finally, the new forecasting error is compared with the reference model. Eight input variables were identified as the most relevant, which is significantly less than our reference model with 30 input variables. The simulation results demonstrate that the model with the 8 inputs selected using the method introduced in this study performs as accurate as the reference model, while also being the most parsimonious.
Abstract: Photo-crosslinked rice starch-based biodegradable
films were prepared by casting film-solution on leveled trays and
ultra violet (UV) irradiation was applied for 10 minute. The effect of
the content (3%, 6% and 9 wt. %)of photosensitiser (sodium
benzoate) on mechanical properties, water vapor permeability (WVP)
and structural properties of rice starch films were investigated. The
tensile strength increased while elongation at break and water
resistance properties of rice starch films decreased with addition and
increasing content of photosensitiser. The % crystallinity of rice
starch films were decreased when the content of photosensitiser
increased and UV were applied. The results showed that the
carboxylate group band of sodium benzoate was found in the FTIR
spectrum of rice starch films and found that incorporation of 6% of
photosensitiser into the films showed a higher absorption band of
resulted films. This result pointed out the highest interaction between
starch molecules was occurred.
Abstract: Forecasting the values of the indicators, which
characterize the effectiveness of performance of organizations is of
great importance for their successful development. Such forecasting
is necessary in order to assess the current state and to foresee future
developments, so that measures to improve the organization-s
activity could be undertaken in time. The article presents an
overview of the applied mathematical and statistical methods for
developing forecasts. Special attention is paid to artificial neural
networks as a forecasting tool. Their strengths and weaknesses are
analyzed and a synopsis is made of the application of artificial neural
networks in the field of forecasting of the values of different
education efficiency indicators. A method of evaluation of the
activity of universities using the Balanced Scorecard is proposed and
Key Performance Indicators for assessment of e-learning are
selected. Resulting indicators for the evaluation of efficiency of the
activity are proposed. An artificial neural network is constructed and
applied in the forecasting of the values of indicators for e-learning
efficiency on the basis of the KPI values.
Abstract: This paper proposes a novel spectrum sensing technique
for the digital video broadcasting-terrestrial (DVB-T) systems, which
utilizes the periodicity of pilot signals in the orthogonal frequency
division multiplexing (OFDM) symbols. The proposed scheme can
overcome the effect of the timing synchronization error by recorrelating
the correlation values in the same sample distances. The
numerical results demonstrate that the detection probability performance
of the proposed scheme outperforms that of the conventional
scheme when there exists a timing synchronization error.
Abstract: The selection for plantation of a particular type of
mustard plant depending on its productivity (pod yield) at the stage
of maturity. The growth of mustard plant dependent on some
parameters of that plant, these are shoot length, number of leaves,
number of roots and roots length etc. As the plant is growing, some
leaves may be fall down and some new leaves may come, so it can
not gives the idea to develop the relationship with the seeds weight at
mature stage of that plant. It is not possible to find the number of
roots and root length of mustard plant at growing stage that will be
harmful of this plant as roots goes deeper to deeper inside the land.
Only the value of shoot length which increases in course of time can
be measured at different time instances. Weather parameters are
maximum and minimum humidity, rain fall, maximum and minimum
temperature may effect the growth of the plant. The parameters of
pollution, water, soil, distance and crop management may be
dominant factors of growth of plant and its productivity. Considering
all parameters, the growth of the plant is very uncertain, fuzzy
environment can be considered for the prediction of shoot length at
maturity of the plant. Fuzzification plays a greater role for
fuzzification of data, which is based on certain membership
functions. Here an effort has been made to fuzzify the original data
based on gaussian function, triangular function, s-function,
Trapezoidal and L –function. After that all fuzzified data are
defuzzified to get normal form. Finally the error analysis
(calculation of forecasting error and average error) indicates the
membership function appropriate for fuzzification of data and use to
predict the shoot length at maturity. The result is also verified using
residual (Absolute Residual, Maximum of Absolute Residual, Mean
Absolute Residual, Mean of Mean Absolute Residual, Median of
Absolute Residual and Standard Deviation) analysis.
Abstract: This paper presents an application of Artificial Neural Network (ANN) to forecast actual cost of a project based on the earned value management system (EVMS). For this purpose, some projects randomly selected based on the standard data set , and it is produced necessary progress data such as actual cost ,actual percent complete , baseline cost and percent complete for five periods of project. Then an ANN with five inputs and five outputs and one hidden layer is trained to produce forecasted actual costs. The comparison between real and forecasted data show better performance based on the Mean Absolute Percentage Error (MAPE) criterion. This approach could be applicable to better forecasting the project cost and result in decreasing the risk of project cost overrun, and therefore it is beneficial for planning preventive actions.
Abstract: In this paper we apply an Adaptive Network-Based
Fuzzy Inference System (ANFIS) with one input, the dependent
variable with one lag, for the forecasting of four macroeconomic
variables of US economy, the Gross Domestic Product, the inflation
rate, six monthly treasury bills interest rates and unemployment rate.
We compare the forecasting performance of ANFIS with those of the
widely used linear autoregressive and nonlinear smoothing transition
autoregressive (STAR) models. The results are greatly in favour of
ANFIS indicating that is an effective tool for macroeconomic
forecasting used in academic research and in research and application
by the governmental and other institutions
Abstract: S-Curves are commonly used in technology forecasting. They show the paths of product performance in relation to time or investment in R&D. It is a useful tool to describe the inflection points and the limit of improvement of a technology. Companies use this information to base their innovation strategies.
However inadequate use and some limitations of this technique lead
to problems in decision making. In this paper first technology
forecasting and its importance for company level strategies will be
discussed. Secondly the S-Curve and its place among other
forecasting techniques will be introduced. Thirdly its use in
technology forecasting will be discussed based on its advantages,
disadvantages and limitations. Finally an application of S-curve on
3D TV technology using patent data will also be presented and the
results will be discussed.
Abstract: In this paper we present a Feed-Foward Neural
Networks Autoregressive (FFNN-AR) model with genetic algorithms
training optimization in order to predict the gross domestic product
growth of six countries. Specifically we propose a kind of weighted
regression, which can be used for econometric purposes, where the
initial inputs are multiplied by the neural networks final optimum
weights from input-hidden layer of the training process. The
forecasts are compared with those of the ordinary autoregressive
model and we conclude that the proposed regression-s forecasting
results outperform significant those of autoregressive model.
Moreover this technique can be used in Autoregressive-Moving
Average models, with and without exogenous inputs, as also the
training process with genetics algorithms optimization can be
replaced by the error back-propagation algorithm.
Abstract: Antimicrobial (AM) starch-based films were
developed by incorporating chitosan and lauric acid as antimicrobial
agent into starch-based film. Chitosan has wide range of applications
as a biomaterial, but barriers still exist to its broader use due to its
physical and chemical limitations. In this work, a series of
starch/chitosan (SC) blend films containing 8% of lauric acid was
prepared by casting method. The structure of the film was
characterized by Fourier transform infrared spectroscopy (FTIR), Xray
diffraction (XRD), and scanning electron microscopy (SEM).
The results indicated that there were strong interactions were present
between the hydroxyl groups of starch and the amino groups of
chitosan resulting in a good miscibility between starch and chitosan
in the blend films. Physical properties and optical properties of the
AM starch-based film were evaluated. The AM starch-based films
incorporated with chitosan and lauric acid showed an improvement in
water vapour transmission rate (WVTR) and addition of starch
content provided more transparent films while the yellowness of the
film attributed to the higher chitosan content. The improvement in
water barrier properties was mainly attributed to the hydrophobicity
of lauric acid and optimum chitosan or starch content. AM starch
based film also showed excellent oxygen barrier. Obtaining films
with good oxygen permeability would be an indication of the
potential use of these antimicrobial packaging as a natural packaging
and an alternative packaging to the synthetic polymer to protect food
from oxidation reactions
Abstract: The Address Resolution Protocol (ARP) is used by
computers to map logical addresses (IP) to physical addresses
(MAC). However ARP is an all trusting protocol and is stateless
which makes it vulnerable to many ARP cache poisoning attacks
such as Man-in-the-Middle (MITM) and Denial of service (DoS)
attacks. These flaws result in security breaches thus weakening the
appeal of the computer for exchange of sensitive data. In this paper
we describe ARP, outline several possible ARP cache poisoning
attacks and give the detailed of some attack scenarios in network
having both wired and wireless hosts. We have analyzed each of
proposed solutions, identify their strengths and limitations. Finally
get that no solution offers a feasible solution. Hence, this paper
presents an efficient and secure version of ARP that is able to cope
up with all these types of attacks and is also a feasible solution. It is a
stateful protocol, by storing the information of the Request frame in
the ARP cache, to reduce the chances of various types of attacks in
ARP. It is more efficient and secure by broadcasting ARP Reply
frame in the network and storing related entries in the ARP cache
each time when communication take place.
Abstract: The alumosilicate ceramics with mullite crystalline phase are used in various branches of science and technique. The mullite refractory ceramics with high porosity serve as a heat insulator and as a constructional materials [1], [2]. The purpose of the work was to sinter high porosity ceramic and to increase the quantity of mullite phase in this mullite, mullite-corundum ceramics. Two types of compositions were prepared at during the experiment. The first type is compositions with commercial alumina and silica oxides. The second type is from mixing these oxides with 10, 20 and 30 wt.%. of kaolin. In all samples the Al2O3 and SiO2 were in 2.57:1 ratio, because that was conformed to mullite stechiometric compositions (3Al2O3.2SiO2). The types of alumina oxides were α-Al2O3 (d50=4µm) and γ-Al2O3 (d50=80µm). Ratios of α-: γ-Al2O3 were (1:1) or (1:3). The porous materials were prepared by slip casting of suspension of raw materials. The aluminium paste (0.18 wt.%) was used as a pore former. Water content in the suspensions was 26-47 wt.%. Pore formation occurred as a result of hydrogen formation in chemical reaction between aluminium paste and water [2]. The samples were sintered at the temperature of 1650°C and 1750°C for one hour. The increasing amount of kaolin, α-: γ-Al2O3 at the ratio (1:3) and sintering at the highest temperature raised the quantity of mullite phase. The mullite phase began to dominate over the corundum phase.
Abstract: This paper deals with heterogeneous autoregressive
models of realized volatility (HAR-RV models) on high-frequency
data of stock indices in the USA. Its aim is to capture the behavior of
three groups of market participants trading on a daily, weekly and
monthly basis and assess their role in predicting the daily realized
volatility. The benefits of this work lies mainly in the application of
heterogeneous autoregressive models of realized volatility on stock
indices in the USA with a special aim to analyze an impact of the
global financial crisis on applied models forecasting performance.
We use three data sets, the first one from the period before the global
financial crisis occurred in the years 2006-2007, the second one from
the period when the global financial crisis fully hit the U.S. financial
market in 2008-2009 years, and the last period was defined over
2010-2011 years. The model output indicates that estimated realized
volatility in the market is very much determined by daily traders and
in some cases excludes the impact of those market participants who
trade on monthly basis.
Abstract: Hazard rate estimation is one of the important topics
in forecasting earthquake occurrence. Forecasting earthquake
occurrence is a part of the statistical seismology where the main
subject is the point process. Generally, earthquake hazard rate is
estimated based on the point process likelihood equation called the
Hazard Rate Likelihood of Point Process (HRLPP). In this research,
we have developed estimation method, that is hazard rate single
decrement HRSD. This method was adapted from estimation method
in actuarial studies. Here, one individual associated with an
earthquake with inter event time is exponentially distributed. The
information of epicenter and time of earthquake occurrence are used
to estimate hazard rate. At the end, a case study of earthquake hazard
rate will be given. Furthermore, we compare the hazard rate between
HRLPP and HRSD method.
Abstract: recurrent neural network (RNN) is an efficient tool for
modeling production control process as well as modeling services. In
this paper one RNN was combined with regression model and were
employed in order to be checked whether the obtained data by the
model in comparison with actual data, are valid for variable process
control chart. Therefore, one maintenance process in workshop of
Esfahan Oil Refining Co. (EORC) was taken for illustration of
models. First, the regression was made for predicting the response
time of process based upon determined factors, and then the error
between actual and predicted response time as output and also the
same factors as input were used in RNN. Finally, according to
predicted data from combined model, it is scrutinized for test values
in statistical process control whether forecasting efficiency is
acceptable. Meanwhile, in training process of RNN, design of
experiments was set so as to optimize the RNN.
Abstract: This paper describes interconnection between
technical and economical making decision. The reason of this dealing
could be different: poor technical condition, change of substation
(electrical network) regime, power transformer owner budget deficit
and increasing of tariff on electricity. Establishing of recommended
practice as well as to give general advice and guidance in economical
sector, testing, diagnostic power transformers to establish its
conditions, identify problems and provide potential remedies.