Abstract: A novel typical day prediction model have been built and validated by the measured data of a grid-connected solar photovoltaic (PV) system in Macau. Unlike conventional statistical method used by previous study on PV systems which get results by averaging nearby continuous points, the present typical day statistical method obtain the value at every minute in a typical day by averaging discontinuous points at the same minute in different days. This typical day statistical method based on discontinuous point averaging makes it possible for us to obtain the Gaussian shape dynamical distributions for solar irradiance and output power in a yearly or monthly typical day. Based on the yearly typical day statistical analysis results, the maximum possible accumulated output energy in a year with on site climate conditions and the corresponding optimal PV system running time are obtained. Periodic Gaussian shape prediction models for solar irradiance, output energy and system energy efficiency have been built and their coefficients have been determined based on the yearly, maximum and minimum monthly typical day Gaussian distribution parameters, which are obtained from iterations for minimum Root Mean Squared Deviation (RMSD). With the present model, the dynamical effects due to time difference in a day are kept and the day to day uncertainty due to weather changing are smoothed but still included. The periodic Gaussian shape correlations for solar irradiance, output power and system energy efficiency have been compared favorably with data of the PV system in Macau and proved to be an improvement than previous models.
Abstract: In this paper, we consider components of discrete event imitating model, implementing a simulation model by using JAVA and performing an input analysis of the data and an output analysis of the simulation results. Was lead development of imitating model of mass service system with n (n≥1) devices of service. On the basis of the developed process of a multithreading simulated the distributed processes with presence of synchronization. Was developed the algorithm of event-oriented simulation, was received results of system functioning with n devices of service.
Abstract: The increasing interest on processing data created by
sensor networks has evolved into approaches to implement sensor
networks as databases. The aggregation operator, which calculates a
value from a large group of data such as computing averages or sums,
etc. is an essential function that needs to be provided when
implementing such sensor network databases. This work proposes to
add the DURING clause into TinySQL to calculate values during a
specific long period and suggests a way to implement the aggregation
service in sensor networks by applying materialized view and
incremental view maintenance techniques that is used in data
warehouses. In sensor networks, data values are passed from child
nodes to parent nodes and an aggregation value is computed at the root
node. As such root nodes need to be memory efficient and low
powered, it becomes a problem to recompute aggregate values from all
past and current data. Therefore, applying incremental view
maintenance techniques can reduce the memory consumption and
support fast computation of aggregate values.
Abstract: Considering non-ideal behavior of fluids and its effects on hydrodynamic and mass transfer in multiphase flow is very essential. Simulations were performed that takes into account the effects of mass transfer and mixture non-ideality on hydrodynamics reported by Irani et al. In this paper, by assuming the density of phases to be constant and Raullt-s law instead of using EOS and fugacity coefficient definition, respectively for both the liquid and gas phases, the importance of non-ideality effects on mass transfer and hydrodynamic behavior was studied. The results for a system of octane/propane (T=323 K, P =445 kpa) also indicated that the assumption of constant density in simulation had major role to diverse from experimental data. Furthermore, comparison between obtained results and the previous report indicated significant differences between experimental data and simulation results with more ideal assumptions.
Abstract: The purpose of this study was to develop and examine a
Teaching Commitment Scale of Health and Physical Education
(TCS-HPE) for Taiwanese elementary school teachers. First of all,
based on teaching commitment related theory and literatures to
develop a original scale with 40 items, later both stratified random
sampling and cluster sampling were used to sample participants.
During the first stage, 300 teachers were sampled and 251 valid scales
(83.7%) returned. Later, the data was analyzed by exploratory factor
analysis to obtain 74.30% of total variance for the construct validity.
The Cronbach-s alpha coefficient of sum scale reliability was 0.94, and
subscale coefficients were between 0.80 and 0.96. In the second stage,
400 teachers were sampled and 318 valid scales (79.5%) returned.
Finally, this study used confirmatory factor analysis to test validity and
reliability of TCS-HPE. The result showed that the fit indexes reached
acceptable criteria(¤ç2
(246 ) =557.64 , p
Abstract: This paper presents a new approach for busbar protection with stable operation of current transformer during saturation, using fuzzy neuro and symmetrical components theory. This technique uses symmetrical components of current signals to learn the hidden relationship existing in the input patterns. Simulation studies are preformed and the influence of changing system parameters such as inception fault and source impedance is studied. Details of the design procedure and the results of performance studies with the proposed relay are given in the paper. An analysis of the performance of the proposed technique during ct saturation conditions is presented. The performance of the technique was investigated for a variety of operating conditions and for several busbar configurations. Data generated by EMTDC simulations of model power systems were used in the investigations. The results indicate that the proposed technique is stable during ct saturation conditions.
Abstract: Electrocardiogram (ECG) is considered to be the
backbone of cardiology. ECG is composed of P, QRS & T waves and
information related to cardiac diseases can be extracted from the
intervals and amplitudes of these waves. The first step in extracting
ECG features starts from the accurate detection of R peaks in the
QRS complex. We have developed a robust R wave detector using
wavelets. The wavelets used for detection are Daubechies and
Symmetric. The method does not require any preprocessing therefore,
only needs the ECG correct recordings while implementing the
detection. The database has been collected from MIT-BIH arrhythmia
database and the signals from Lead-II have been analyzed. MatLab
7.0 has been used to develop the algorithm. The ECG signal under
test has been decomposed to the required level using the selected
wavelet and the selection of detail coefficient d4 has been done based
on energy, frequency and cross-correlation analysis of decomposition
structure of ECG signal. The robustness of the method is apparent
from the obtained results.
Abstract: This paper explores the opportunity of using tri-axial
wireless accelerometers for supervised monitoring of sports
movements. A motion analysis system for the upper extremities of
lawn bowlers in particular is developed. Accelerometers are placed
on parts of human body such as the chest to represent the shoulder
movements, the back to capture the trunk motion, back of the hand,
the wrist and one above the elbow, to capture arm movements. These
sensors placement are carefully designed in order to avoid restricting
bowler-s movements. Data is acquired from these sensors in soft-real
time using virtual instrumentation; the acquired data is then
conditioned and converted into required parameters for motion
regeneration. A user interface was also created to facilitate in the
acquisition of data, and broadcasting of commands to the wireless
accelerometers. All motion regeneration in this paper deals with the
motion of the human body segment in the X and Y direction, looking
into the motion of the anterior/ posterior and lateral directions
respectively.
Abstract: It is well known that during the developments in the
economic sector and through the financial crises occur everywhere in
the whole world, volatility measurement is the most important
concept in financial time series. Therefore in this paper we discuss
the volatility for Amman stocks market (Jordan) for certain period of
time. Since wavelet transform is one of the most famous filtering
methods and grows up very quickly in the last decade, we compare
this method with the traditional technique, Fast Fourier transform to
decide the best method for analyzing the volatility. The comparison
will be done on some of the statistical properties by using Matlab
program.
Abstract: The paper presents the modeling of nonlinear
longitudinal aerodynamics using flight data of Hansa-3 aircraft at
high angles of attack near stall. The Kirchhoff-s quasi-steady stall
model has been used to incorporate nonlinear aerodynamic effects in
the aerodynamic model used to estimate the parameters, thereby,
making the aerodynamic model nonlinear. The Maximum Likelihood
method has been applied to the flight data (at high angles of attack)
for the estimation of parameters (aerodynamic and stall
characteristics) using the nonlinear aerodynamic model. To improve
the accuracy level of the estimates, an approach of fixing the strong
parameters has also been presented.
Abstract: The purpose of this research was to investigate Thai Muslims’ way of life through the way their clothes. The data of this qualitative research were collected from related documents and research reports, ancient cloths and clothing, and in-depth interviews with clothes owners and weavers.
The research found that in the 18th century Thai Muslims in the three southern border provinces used many types of clothing in their life. At home women wore plain clothes. They used checked cloths to cover the upper part of their body from the breasts down to the waist. When going out, they used Lima cloth and So Kae with a piece of Pla-nging cloth as a head scarf. For men, they wore a checked sarong as a lower garment, and wore no upper garment. However, when going out, they wore Puyo Potong. In addition, Thai Muslims used cloths in various religious rites, namely, the rite of placing a baby in a cradle, the Masoyawi rite, the Nikah rite, and the burial rite. These types of cloths were related to the way of life of Thai Muslims from birth to death. They reflected the race, gender, age, social status, values, and beliefs in traditions that have been inherited.
Practical Implication: Woven in these cloths are the lost local wisdom, and therefore, aesthetics on the cloths are like mirrors reflecting the background of people in this region that is fading away. These cloths are pages of a local history book that is of importance and value worth for preservation and publicity so that they are treasured. Government organizations can expand and materialize the knowledge received from the study in accordance with government policy in supporting the One Tambon, One Product project.
Abstract: An empirical study of web applications that use
software frameworks is presented here. The analysis is based on two
approaches. In the first, developers using such frameworks are
required, based on their experience, to assign weights to parameters
such as database connection. In the second approach, a performance
testing tool, OpenSTA, is used to compute start time and other such
measures. From such an analysis, it is concluded that open source
software is superior to proprietary software. The motivation behind
this research is to examine ways in which a quantitative assessment
can be made of software in general and frameworks in particular.
Concepts such as metrics and architectural styles are discussed along
with previously published research.
Abstract: This paper proposed a novel model for short term load
forecast (STLF) in the electricity market. The prior electricity
demand data are treated as time series. The model is composed of
several neural networks whose data are processed using a wavelet
technique. The model is created in the form of a simulation program
written with MATLAB. The load data are treated as time series data.
They are decomposed into several wavelet coefficient series using
the wavelet transform technique known as Non-decimated Wavelet
Transform (NWT). The reason for using this technique is the belief
in the possibility of extracting hidden patterns from the time series
data. The wavelet coefficient series are used to train the neural
networks (NNs) and used as the inputs to the NNs for electricity load
prediction. The Scale Conjugate Gradient (SCG) algorithm is used as
the learning algorithm for the NNs. To get the final forecast data, the
outputs from the NNs are recombined using the same wavelet
technique. The model was evaluated with the electricity load data of
Electronic Engineering Department in Mandalay Technological
University in Myanmar. The simulation results showed that the
model was capable of producing a reasonable forecasting accuracy in
STLF.
Abstract: The purpose of this study was to explore the learning
effects on dance domain in Arts Curriculum at junior and senior high
levels. A total of 1,366 students from 9th to 11th grade of different
areas from Taiwan were administered a self-designed dance
achievement test. Data were analyzed through descriptive analysis,
independent sample t test, one-way ANOVA and Post hoc comparison
analysis using Scheffé Test. The results showed (1) female students
Abstract: In recent years, everything is trending toward digitalization
and with the rapid development of the Internet technologies,
digital media needs to be transmitted conveniently over the network.
Attacks, misuse or unauthorized access of information is of great
concern today which makes the protection of documents through
digital media a priority problem. This urges us to devise new data
hiding techniques to protect and secure the data of vital significance.
In this respect, steganography often comes to the fore as a tool for
hiding information. Steganography is a process that involves hiding
a message in an appropriate carrier like image or audio. It is of
Greek origin and means "covered or hidden writing". The goal of
steganography is covert communication. Here the carrier can be sent
to a receiver without any one except the authenticated receiver only
knows existence of the information. Considerable amount of work
has been carried out by different researchers on steganography. In this
work the authors propose a novel Steganographic method for hiding
information within the spatial domain of the gray scale image. The
proposed approach works by selecting the embedding pixels using
some mathematical function and then finds the 8 neighborhood of
the each selected pixel and map each bit of the secret message in
each of the neighbor pixel coordinate position in a specified manner.
Before embedding a checking has been done to find out whether the
selected pixel or its neighbor lies at the boundary of the image or not.
This solution is independent of the nature of the data to be hidden
and produces a stego image with minimum degradation.
Abstract: The given article deals with the usage of the concept
in many spheres of science, including its place in the Kazakh
linguistics One of such concepts is the role of the “бақыт”
(“happiness”) concept in the Kazakh outlook. The work tells us about
its studying. The data about studying of the “happiness” concept in
the sphere of philosophy, psychology, cognitive linguistics, lingo
cultural study, logics, psycho-linguistic are given in this work.
Particularly dwelling at length on the studying level of the concept in
the sphere of cognitive linguistics, analysis have been made
pertaining linguist point of views. It was pointed out that the concept
of “happiness” hasn’t been studied yet in the Kazakh linguistics and
it is necessary to find out the meaning of the language units related to
this concept, i.e. blessings, proverbs, sayings and phrasiological units.
Abstract: The paper deals with an application of quantitative analysis – the Data Envelopment Analysis (DEA) method to performance evaluation of the European Union Member States, in the reference years 2000 and 2011. The main aim of the paper is to measure efficiency changes over the reference years and to analyze a level of productivity in individual countries based on DEA method and to classify the EU Member States to homogeneous units (clusters) according to efficiency results. The theoretical part is devoted to the fundamental basis of performance theory and the methodology of DEA. The empirical part is aimed at measuring degree of productivity and level of efficiency changes of evaluated countries by basic DEA model – CCR CRS model, and specialized DEA approach – the Malmquist Index measuring the change of technical efficiency and the movement of production possibility frontier. Here, DEA method becomes a suitable tool for setting a competitive/uncompetitive position of each country because there is not only one factor evaluated, but a set of different factors that determine the degree of economic development.
Abstract: This study sought to determine whether there were relationships existed among leisure satisfaction, self-esteem, and spiritual wellness. Four hundred survey instruments were distributed, and 334 effective instruments were returned, for an effective rate of 83.5%. The participants were recruited from a purposive sampling that subjects were at least 60 years of age and retired in Tainan City, Taiwan. Three instruments were used in this research: Leisure Satisfaction Scale (LSS), Self-Esteem Scale (SES), and Spirituality Assessment Scale (SAS). The collected data were analyzed statistically. The findings of this research were as follows: 1. There is significantly correlated between leisure satisfaction and spiritual wellness. 2. There is significantly correlated between leisure satisfaction and self-esteem. 3. There is significantly correlated between spiritual wellness and self-esteem.
Abstract: Intravitreal injection (IVI) is the most common treatment for eye posterior segment diseases such as endopthalmitis, retinitis, age-related macular degeneration, diabetic retinopathy, uveitis, and retinal detachment. Most of the drugs used to treat vitreoretinal diseases, have a narrow concentration range in which they are effective, and may be toxic at higher concentrations. Therefore, it is critical to know the drug distribution within the eye following intravitreal injection. Having knowledge of drug distribution, ophthalmologists can decide on drug injection frequency while minimizing damage to tissues. The goal of this study was to develop a computer model to predict intraocular concentrations and pharmacokinetics of intravitreally injected drugs. A finite volume model was created to predict distribution of two drugs with different physiochemical properties in the rabbit eye. The model parameters were obtained from literature review. To validate this numeric model, the in vivo data of spatial concentration profile from the lens to the retina were compared with the numeric data. The difference was less than 5% between the numerical and experimental data. This validation provides strong support for the numerical methodology and associated assumptions of the current study.
Abstract: We propose a fast and robust hierarchical face detection system which finds and localizes face images with a cascade of classifiers. Three modules contribute to the efficiency of our detector. First, heterogeneous feature descriptors are exploited to enrich feature types and feature numbers for face representation. Second, a PSO-Adaboost algorithm is proposed to efficiently select discriminative features from a large pool of available features and reinforce them into the final ensemble classifier. Compared with the standard exhaustive Adaboost for feature selection, the new PSOAdaboost algorithm reduces the training time up to 20 times. Finally, a three-stage hierarchical classifier framework is developed for rapid background removal. In particular, candidate face regions are detected more quickly by using a large size window in the first stage. Nonlinear SVM classifiers are used instead of decision stump functions in the last stage to remove those remaining complex nonface patterns that can not be rejected in the previous two stages. Experimental results show our detector achieves superior performance on the CMU+MIT frontal face dataset.