Abstract: In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.
Abstract: The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
Abstract: In this paper we propose and examine an Adaptive
Neuro-Fuzzy Inference System (ANFIS) in Smoothing Transition
Autoregressive (STAR) modeling. Because STAR models follow
fuzzy logic approach, in the non-linear part fuzzy rules can be
incorporated or other training or computational methods can be
applied as the error backpropagation algorithm instead to nonlinear
squares. Furthermore, additional fuzzy membership functions can be
examined, beside the logistic and exponential, like the triangle,
Gaussian and Generalized Bell functions among others. We examine
two macroeconomic variables of US economy, the inflation rate and
the 6-monthly treasury bills interest rates.
Abstract: Customer-supplier collaboration enables firms to
achieve greater success than acting independently. Nevertheless, not
many firms have fully utilized the potential of collaboration. This
paper presents organizational and human related success factors for
collaboration in manufacturing supply chains in casting industry. Our
research approach was a case study including multiple cases. Data
was gathered by interviews and group discussions in two different
research projects. In the first research project we studied seven firms
and in the second five. It was found that the success factors are
interrelated, in other words, organizational and human factors
together enable success but not any of them alone. Some of the found
success factors are a culture of following agreements, and a speed of
informing the partner about changes affecting to the product or the
delivery chain.
Abstract: Does a communication modality matter in delivering e-learning information? With the recent growth of broadcasting systems, media technologies and e-learning contents, various systems with different communication modalities have been introduced. In accordance with these trends, this study examines the effects of the information delivery modality on psychology of students. Findings from an experiment indicated that the delivering information which includes a video modality elicited higher degrees of credibility, quality, representativeness of content, and perceived suitability for delivering information than those of auditory information. However, there is no difference between content liking and attitude. The Implications of the findings and the limitations are discussed.
Abstract: Starch/chitosan blend have been prepared via the
solution casting technique. Ionic conductivity for the system was
conducted over a wide range of frequency between 50 Hz-1 MHz and
at temperatures between 303 K and 373 K. Sample with 35 wt% of
NH4NO3 shows the highest conductivity of 3.89 ± 0.79 x 10-5 Scm-1
at room temperature. Conductivity-temperature relationship suggests
that samples are Arrhenian. Power law exponent was obtained
through dielectric loss variation and the trend suggests that the
conduction mechanism of the ions can be represented by the
correlated barrier hopping (CBH) model.
Abstract: Digital Video Terrestrial Broadcasting (DVB-T)
allows combining broadcasting, telephone and data services in one
network. It has facilitated mobile TV broadcasting. Mobile TV
broadcasting is dominated by fragmentation of standards in use in
different continents. In Asia T-DMB and ISDB-T are used while
Europe uses mainly DVB-H and in USA it is MediaFLO. Issues of
royalty for developers of these different incompatible technologies,
investments made and differing local conditions shall make it
difficult to agree on a unified standard in a very near future. Despite
this shortcoming, mobile TV has shown very good market potential.
There are a number of challenges that still exist for regulators,
investors and technology developers but the future looks bright.
There is need for mobile telephone operators to cooperate with
content providers and those operating terrestrial digital broadcasting
infrastructure for mutual benefit.
Abstract: By systematically applying different engineering
methods, difficult financial problems become approachable. Using a
combination of theory and techniques such as wavelet transform,
time series data mining, Markov chain based discrete stochastic
optimization, and evolutionary algorithms, this work formulated a
strategy to characterize and forecast non-linear time series. It
attempted to extract typical features from the volatility data sets of
S&P100 and S&P500 indices that include abrupt drops, jumps and
other non-linearity. As a result, accuracy of forecasting has reached
an average of over 75% surpassing any other publicly available
results on the forecast of any financial index.
Abstract: Estimates of temperature values at a specific time of day, from daytime and daily profiles, are needed for a number of environmental, ecological, agricultural and technical applications, ranging from natural hazards assessments, crop growth forecasting to design of solar energy systems. The scope of this research is to investigate the efficiency of data mining techniques in estimating minimum, maximum and mean temperature values. For this reason, a number of experiments have been conducted with well-known regression algorithms using temperature data from the city of Patras in Greece. The performance of these algorithms has been evaluated using standard statistical indicators, such as Correlation Coefficient, Root Mean Squared Error, etc.
Abstract: Social-economic variables influence transportation
demand largely. Analyses of discrete choice model consider
social-economic variables to study traveler-s mode choice and
demand. However, to calibrate the discrete choice model needs to have
plenty of questionnaire survey. Also, an aggregative model is
proposed. The historical data of passenger volumes for high speed rail
and domestic civil aviation are employed to calibrate and validate the
model. In this study, models with different social-economic variables,
which are oil price, GDP per capita, CPI and economic growth rate,
are compared. From the results, the model with the oil price is better
than models with the other social-economic variables.
Abstract: In this study, ZnO nano rods and ZnO ultrafine particles were synthesized by Gel-casting method. The synthesized ZnO powder has a hexagonal zincite structure. The ZnO aggregates with rod-like morphology are typically 1.4 μm in length and 120 nm in diameter, which consist of many small nanocrystals with diameters of 10 nm. Longer wires connected by many hexahedral ZnO nanocrystals were obtained after calcinations at the temperature over 600° C.The crystalline structures and morphologies of the powder have been characterized by X-ray diffraction(XRD) and Scaning electron microscopy (SEM).The result shows that the different preparation conditions such as concentration H2O, calcinations time and calcinations temperature have a lot of influences upon the properties of nano ZnO powders, an increase in the temperature of the calcinations results in an increase of the grain size and also the increase of the calcinations time in high temperature makes the size of the grains bigger. The existences of extra watter prevent nano grains from improving like rod morphology. We have obtained the smallest grain size of ZnO powder by controlling the process conditions. Finally In a suitable condition, a novel nanostructure, namely bi-rod-like ZnO nano rods was found which is different from known ZnO nanostructures.
Abstract: Ranking of fuzzy numbers play an important role in
decision making, optimization, forecasting etc. Fuzzy numbers must
be ranked before an action is taken by a decision maker. In this
paper, with the help of several counter examples it is proved that
ranking method proposed by Chen and Chen (Expert Systems with
Applications 36 (2009) 6833-6842) is incorrect. The main aim of this
paper is to propose a new approach for the ranking of generalized
trapezoidal fuzzy numbers. The main advantage of the proposed
approach is that the proposed approach provide the correct ordering
of generalized and normal trapezoidal fuzzy numbers and also the
proposed approach is very simple and easy to apply in the real life
problems. It is shown that proposed ranking function satisfies all
the reasonable properties of fuzzy quantities proposed by Wang and
Kerre (Fuzzy Sets and Systems 118 (2001) 375-385).
Abstract: Financial forecasting using machine learning techniques has received great efforts in the last decide . In this ongoing work, we show how machine learning of graphical models will be able to infer a visualized causal interactions between different banks in the Saudi equities market. One important discovery from such learned causal graphs is how companies influence each other and to what extend. In this work, a set of graphical models named Gaussian graphical models with developed ensemble penalized feature selection methods that combine ; filtering method, wrapper method and a regularizer will be shown. A comparison between these different developed ensemble combinations will also be shown. The best ensemble method will be used to infer the causal relationships between banks in Saudi equities market.
Abstract: This paper describes a 3D modeling system in
Augmented Reality environment, named 3DARModeler. It can be
considered a simple version of 3D Studio Max with necessary
functions for a modeling system such as creating objects, applying
texture, adding animation, estimating real light sources and casting
shadows. The 3DARModeler introduces convenient, and effective
human-computer interaction to build 3D models by combining both
the traditional input method (mouse/keyboard) and the tangible input
method (markers). It has the ability to align a new virtual object with
the existing parts of a model. The 3DARModeler targets nontechnical
users. As such, they do not need much knowledge of
computer graphics and modeling techniques. All they have to do is
select basic objects, customize their attributes, and put them together
to build a 3D model in a simple and intuitive way as if they were
doing in the real world. Using the hierarchical modeling technique,
the users are able to group several basic objects to manage them as a
unified, complex object. The system can also connect with other 3D
systems by importing and exporting VRML/3Ds Max files. A
module of speech recognition is included in the system to provide
flexible user interfaces.
Abstract: The paper evaluates several hundred one-day-ahead
VaR forecasting models in the time period between the years 2004
and 2009 on data from six world stock indices - DJI, GSPC, IXIC,
FTSE, GDAXI and N225. The models model mean using the ARMA
processes with up to two lags and variance with one of GARCH,
EGARCH or TARCH processes with up to two lags. The models are
estimated on the data from the in-sample period and their forecasting
accuracy is evaluated on the out-of-sample data, which are more
volatile. The main aim of the paper is to test whether a model
estimated on data with lower volatility can be used in periods with
higher volatility. The evaluation is based on the conditional coverage
test and is performed on each stock index separately. The primary
result of the paper is that the volatility is best modelled using a
GARCH process and that an ARMA process pattern cannot be found
in analyzed time series.
Abstract: In H.264/AVC video encoding, rate-distortion
optimization for mode selection plays a significant role to achieve
outstanding performance in compression efficiency and video quality.
However, this mode selection process also makes the encoding
process extremely complex, especially in the computation of the ratedistortion
cost function, which includes the computations of the sum
of squared difference (SSD) between the original and reconstructed
image blocks and context-based entropy coding of the block. In this
paper, a transform-domain rate-distortion optimization accelerator
based on fast SSD (FSSD) and VLC-based rate estimation algorithm
is proposed. This algorithm could significantly simplify the hardware
architecture for the rate-distortion cost computation with only
ignorable performance degradation. An efficient hardware structure
for implementing the proposed transform-domain rate-distortion
optimization accelerator is also proposed. Simulation results
demonstrated that the proposed algorithm reduces about 47% of total
encoding time with negligible degradation of coding performance.
The proposed method can be easily applied to many mobile video
application areas such as a digital camera and a DMB (Digital
Multimedia Broadcasting) phone.
Abstract: Due to the liberalization of countless electricity markets, load forecasting has become crucial to all public utilities for which electricity is a strategic variable. With the goal of contributing to the forecasting process inside public utilities, this paper addresses the issue of applying the Holt-Winters exponential smoothing technique and the time series analysis for forecasting the hourly electricity load curve of the Italian railways. The results of the analysis confirm the accuracy of the two models and therefore the relevance of forecasting inside public utilities.
Abstract: Time series models have been used to make predictions of academic enrollments, weather, road accident, casualties and stock prices, etc. Based on the concepts of quartile regression models, we have developed a simple time variant quantile based fuzzy time series forecasting method. The proposed method bases the forecast using prediction of future trend of the data. In place of actual quantiles of the data at each point, we have converted the statistical concept into fuzzy concept by using fuzzy quantiles using fuzzy membership function ensemble. We have given a fuzzy metric to use the trend forecast and calculate the future value. The proposed model is applied for TAIFEX forecasting. It is shown that proposed method work best as compared to other models when compared with respect to model complexity and forecasting accuracy.
Abstract: In this paper, based on the past project cost and time
performance, a model for forecasting project cost performance is
developed. This study presents a probabilistic project control concept
to assure an acceptable forecast of project cost performance. In this
concept project activities are classified into sub-groups entitled
control accounts. Then obtain the Stochastic S-Curve (SS-Curve), for
each sub-group and the project SS-Curve is obtained by summing
sub-groups- SS-Curves. In this model, project cost uncertainties are
considered through Beta distribution functions of the project
activities costs required to complete the project at every selected time
sections through project accomplishment, which are extracted from a
variety of sources. Based on this model, after a percentage of the
project progress, the project performance is measured via Earned
Value Management to adjust the primary cost probability distribution
functions. Then, accordingly the future project cost performance is
predicted by using the Monte-Carlo simulation method.
Abstract: This research focuses on the effect of weight
percentage variation and size variation of MgFeSi added,
gating system design and reaction chamber design on inmold
process. By using inmold process, well-known problem of
fading is avoided because the liquid iron reacts with
magnesium in the mold and not, as usual, in the ladle. During
the pouring operation, liquid metal passes through the
chamber containing the magnesium, where the reaction of the
metal with magnesium proceeds in the absence of atmospheric
oxygen [1].In this paper, the results of microstructural
characteristic of ductile iron on this parameters are mentioned.
The mechanisms of the inmold process are also described [2].
The data obtained from this research will assist in producing
the vehicle parts and other machinery parts for different
industrial zones and government industries and in transferring
the technology to all industrial zones in Myanmar. Therefore,
the inmold technology offers many advantages over traditional
treatment methods both from a technical and environmental,
as well as an economical point of view. The main objective of
this research is to produce ductile iron castings in all industrial
sectors in Myanmar more easily with lower costs. It will also
assist the sharing of knowledge and experience related to the
ductile iron production.