Abstract: Economically transformers constitute one of the largest investments in a Power system. For this reason, transformer condition assessment and management is a high priority task. If a transformer fails, it would have a significant negative impact on revenue and service reliability. Monitoring the state of health of power transformers has traditionally been carried out using laboratory Dissolved Gas Analysis (DGA) tests performed at periodic intervals on the oil sample, collected from the transformers. DGA of transformer oil is the single best indicator of a transformer-s overall condition and is a universal practice today, which started somewhere in the 1960s. Failure can occur in a transformer due to different reasons. Some failures can be limited or prevented by maintenance. Oil filtration is one of the methods to remove the dissolve gases and prevent the deterioration of the oil. In this paper we analysis the DGA data by regression method and predict the gas concentration in the oil in the future. We bring about a comparative study of different traditional methods of regression and the errors generated out of their predictions. With the help of these data we can deduce the health of the transformer by finding the type of fault if it has occurred or will occur in future. Additional in this paper effect of filtration on the transformer health is highlight by calculating the probability of failure of a transformer with and without oil filtrating.
Abstract: The approach of subset selection in polynomial
regression model building assumes that the chosen fixed full set of
predefined basis functions contains a subset that is sufficient to
describe the target relation sufficiently well. However, in most cases
the necessary set of basis functions is not known and needs to be
guessed – a potentially non-trivial (and long) trial and error process.
In our research we consider a potentially more efficient approach –
Adaptive Basis Function Construction (ABFC). It lets the model
building method itself construct the basis functions necessary for
creating a model of arbitrary complexity with adequate predictive
performance. However, there are two issues that to some extent
plague the methods of both the subset selection and the ABFC,
especially when working with relatively small data samples: the
selection bias and the selection instability. We try to correct these
issues by model post-evaluation using Cross-Validation and model
ensembling. To evaluate the proposed method, we empirically
compare it to ABFC methods without ensembling, to a widely used
method of subset selection, as well as to some other well-known
regression modeling methods, using publicly available data sets.
Abstract: In this paper a stochastic scenario-based model predictive control applied to molten salt storage systems in concentrated solar tower power plant is presented. The main goal of this study is to build up a tool to analyze current and expected future resources for evaluating the weekly power to be advertised on electricity secondary market. This tool will allow plant operator to maximize profits while hedging the impact on the system of stochastic variables such as resources or sunlight shortage.
Solving the problem first requires a mixed logic dynamic modeling of the plant. The two stochastic variables, respectively the sunlight incoming energy and electricity demands from secondary market, are modeled by least square regression. Robustness is achieved by drawing a certain number of random variables realizations and applying the most restrictive one to the system. This scenario approach control technique provides the plant operator a confidence interval containing a given percentage of possible stochastic variable realizations in such a way that robust control is always achieved within its bounds. The results obtained from many trajectory simulations show the existence of a ‘’reliable’’ interval, which experimentally confirms the algorithm robustness.
Abstract: The main goal of this paper is to study Statistical Process Control (SPC) with Exponentially Weighted Moving Average (EWMA) control chart when observations are serially-correlated. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be enough large, so-called (ARL0). Otherwise it should be small when the process is out-of-control, so-called Average of Delay Time (ARL1) or a mean of true alarm. We find explicit formulas of ARL for EWMA control chart for Seasonal Autoregressive and Moving Average processes (SARMA) with Exponential white noise. The results of ARL obtained from explicit formula and Integral equation are in good agreement. In particular, this formulas for evaluating (ARL0) and (ARL1) be able to get a set of optimal parameters which depend on smoothing parameter (λ) and width of control limit (H) for designing EWMA chart with minimum of (ARL1).
Abstract: Recently, it has been suggested that thought control strategies aimed at controlling unwanted thoughts may be used to cope with paranoid thoughts in both clinical and nonclinical samples. The current study aims to examine the type of thought control strategies that were associated with the frequency of paranoid thoughts in nonclinical samples. A total of 159 Japanese undergraduate students completed the two scales–the Paranoia Checklist and the Thought Control Questionnaire. A hierarchical multiple regression analysis demonstrated that worry-based control strategies were associated with paranoid thoughts, whereas distraction- and social-based control strategies were inversely associated with paranoid thoughts. Our findings suggest that in a nonclinical population, worry-based strategies may be especially maladaptive, whereas distraction- and social-based strategies may be adaptive to paranoid thoughts.
Abstract: Load forecasting has always been the essential part of
an efficient power system operation and planning. A novel approach
based on support vector machines is proposed in this paper for annual
power load forecasting. Different kernel functions are selected to
construct a combinatorial algorithm. The performance of the new
model is evaluated with a real-world dataset, and compared with two
neural networks and some traditional forecasting techniques. The
results show that the proposed method exhibits superior performance.
Abstract: This paper presents a technical speaker adaptation
method called WMLLR, which is based on maximum likelihood linear
regression (MLLR). In MLLR, a linear regression-based transform
which adapted the HMM mean vectors was calculated to maximize the
likelihood of adaptation data. In this paper, the prior knowledge of the
initial model is adequately incorporated into the adaptation. A series of
speaker adaptation experiments are carried out at a 30 famous city
names database to investigate the efficiency of the proposed method.
Experimental results show that the WMLLR method outperforms the
conventional MLLR method, especially when only few utterances
from a new speaker are available for adaptation.
Abstract: To evaluate the ability to predict xerostomia after
radiotherapy, we constructed and compared neural network and
logistic regression models. In this study, 61 patients who completed a
questionnaire about their quality of life (QoL) before and after a full
course of radiation therapy were included. Based on this questionnaire,
some statistical data about the condition of the patients’ salivary
glands were obtained, and these subjects were included as the inputs of
the neural network and logistic regression models in order to predict
the probability of xerostomia. Seven variables were then selected from
the statistical data according to Cramer’s V and point-biserial
correlation values and were trained by each model to obtain the
respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65
for SSE, and 13.7% and 19.0% for MAPE, respectively. These
parameters demonstrate that both neural network and logistic
regression methods are effective for predicting conditions of parotid
glands.
Abstract: It-s known that incorporating prior knowledge into support
vector regression (SVR) can help to improve the approximation
performance. Most of researches are concerned with the incorporation
of knowledge in the form of numerical relationships. Little work,
however, has been done to incorporate the prior knowledge on the
structural relationships among the variables (referred as to Structural
Prior Knowledge, SPK). This paper explores the incorporation of SPK
in SVR by constructing appropriate admissible support vector kernel
(SV kernel) based on the properties of reproducing kernel (R.K).
Three-levels specifications of SPK are studied with the corresponding
sub-levels of prior knowledge that can be considered for the method.
These include Hierarchical SPK (HSPK), Interactional SPK (ISPK)
consisting of independence, global and local interaction, Functional
SPK (FSPK) composed of exterior-FSPK and interior-FSPK. A
convenient tool for describing the SPK, namely Description Matrix
of SPK is introduced. Subsequently, a new SVR, namely Motivated
Support Vector Regression (MSVR) whose structure is motivated
in part by SPK, is proposed. Synthetic examples show that it is
possible to incorporate a wide variety of SPK and helpful to improve
the approximation performance in complex cases. The benefits of
MSVR are finally shown on a real-life military application, Air-toground
battle simulation, which shows great potential for MSVR to
the complex military applications.
Abstract: The effects of down slope steepness on soil splash distribution under a water drop impact have been investigated in this study. The equipment used are the burette to simulate a water drop, a splash cup filled with sandy soil which forms the source area and a splash board to collect the ejected particles. The results found in this study have shown that the apparent mass increased with increasing downslope angle following a linear regression equation with high coefficient of determination. In the same way, the radial soil splash distribution over the distance has been analyzed statistically, and an exponential function was the best fit of the relationship for the different slope angles. The curves and the regressions equations validate the well known FSDF and extend the theory of Van Dijk.
Abstract: Gold passbook is an investing tool that is especially
suitable for investors to do small investment in the solid gold. The gold
passbook has the lower risk than other ways investing in gold, but its
price is still affected by gold price. However, there are many factors
can cause influences on gold price. Therefore, building a model to
predict the price of gold passbook can both reduce the risk of
investment and increase the benefits. This study investigates the
important factors that influence the gold passbook price, and utilize
the Group Method of Data Handling (GMDH) to build the predictive
model. This method can not only obtain the significant variables but
also perform well in prediction. Finally, the significant variables of
gold passbook price, which can be predicted by GMDH, are US dollar
exchange rate, international petroleum price, unemployment rate,
whole sale price index, rediscount rate, foreign exchange reserves,
misery index, prosperity coincident index and industrial index.
Abstract: Evolutionary Algorithms are population-based,
stochastic search techniques, widely used as efficient global
optimizers. However, many real life optimization problems often
require finding optimal solution to complex high dimensional,
multimodal problems involving computationally very expensive
fitness function evaluations. Use of evolutionary algorithms in such
problem domains is thus practically prohibitive. An attractive
alternative is to build meta models or use an approximation of the
actual fitness functions to be evaluated. These meta models are order
of magnitude cheaper to evaluate compared to the actual function
evaluation. Many regression and interpolation tools are available to
build such meta models. This paper briefly discusses the
architectures and use of such meta-modeling tools in an evolutionary
optimization context. We further present two evolutionary algorithm
frameworks which involve use of meta models for fitness function
evaluation. The first framework, namely the Dynamic Approximate
Fitness based Hybrid EA (DAFHEA) model [14] reduces
computation time by controlled use of meta-models (in this case
approximate model generated by Support Vector Machine
regression) to partially replace the actual function evaluation by
approximate function evaluation. However, the underlying
assumption in DAFHEA is that the training samples for the metamodel
are generated from a single uniform model. This does not take
into account uncertain scenarios involving noisy fitness functions.
The second model, DAFHEA-II, an enhanced version of the original
DAFHEA framework, incorporates a multiple-model based learning
approach for the support vector machine approximator to handle
noisy functions [15]. Empirical results obtained by evaluating the
frameworks using several benchmark functions demonstrate their
efficiency
Abstract: The purpose of this study was to evaluate and
compare new indices based on the discrete wavelet transform
with another spectral parameters proposed in the literature as
mean average voltage, median frequency and ratios between
spectral moments applied to estimate acute exercise-induced
changes in power output, i.e., to assess peripheral muscle
fatigue during a dynamic fatiguing protocol. 15 trained
subjects performed 5 sets consisting of 10 leg press, with 2
minutes rest between sets. Surface electromyography was
recorded from vastus medialis (VM) muscle. Several surface
electromyographic parameters were compared to detect
peripheral muscle fatigue. These were: mean average voltage
(MAV), median spectral frequency (Fmed), Dimitrov spectral
index of muscle fatigue (FInsm5), as well as other five
parameters obtained from the discrete wavelet transform
(DWT) as ratios between different scales. The new wavelet
indices achieved the best results in Pearson correlation
coefficients with power output changes during acute dynamic
contractions. Their regressions were significantly different
from MAV and Fmed. On the other hand, they showed the
highest robustness in presence of additive white gaussian
noise for different signal to noise ratios (SNRs). Therefore,
peripheral impairments assessed by sEMG wavelet indices
may be a relevant factor involved in the loss of power output
after dynamic high-loading fatiguing task.
Abstract: This study investigated the number of Aedes larvae,
the key breeding sites of Aedes sp., and the relationship between
climatic factors and the incidence of DHF in Samui Islands. We
conducted our questionnaire and larval surveys from randomly
selected 105 households in Samui Islands in July-September 2006.
Pearson-s correlation coefficient was used to explore the primary
association between the DHF incidence and all climatic factors.
Multiple stepwise regression technique was then used to fit the
statistical model. The results showed that the positive indoor
containers were small jars, cement tanks, and plastic tanks. The
positive outdoor containers were small jars, cement tanks, plastic
tanks, used cans, tires, plastic bottles, discarded objects, pot saucers,
plant pots, and areca husks. All Ae. albopictus larval indices (i.e., CI,
HI, and BI) were higher than Ae. aegypti larval indices in this area.
These larval indices were higher than WHO standard. This indicated
a high risk of DHF transmission at Samui Islands. The multiple
stepwise regression model was y = –288.80 + 11.024xmean temp. The
mean temperature was positively associated with the DHF incidence
in this area.
Abstract: This paper examines the effect of corporate diversification on the profitability of the Financial services sector in Nigeria. The study relied on historic accounting data generated from financial (annual) reports and accounts of sampled banks between the period 1998 and 2007 (a ten-year period). A regression equation was formulated, in line with previous studies to shed light on the effect of corporate diversification on the profitability of the Financial services sector in Nigeria. The results of the regression analysis revealed that diversification impacts strongly on banks profitability. Conclusively the paper produces strong evidence to assert that diversification impacts positively and significantly on banks profitability because among other things such diversified banks can pool their internally generated funds and allocate them properly.
Abstract: Whilst there is growing evidence that activity
across the lifespan is beneficial for improved health, there are
also many changes involved with the aging process and
subsequently the potential for reduced indices of health. The
nexus between health, physical activity and aging is complex
and has raised much interest in recent times due to the
realization that a multifaceted approached is necessary in
order to counteract a growing obesity epidemic. By
investigating age based trends within a population adhering to
competitive sport at older ages, further insight might be
gleaned to assist in understanding one of many factors
influencing this relationship.
BMI was derived using data gathered on a total of 6,071
masters athletes (51.9% male, 48.1% female) aged 25 to 91
years ( =51.5, s =±9.7), competing at the Sydney World
Masters Games (2009). Using linear and loess regression it
was demonstrated that the usual tendency for prevalence of
higher BMI increasing with age was reversed in the sample.
This trend in reversal was repeated for both male and female
only sub-sets of the sample participants, indicating the
possibility of improved prevalence of BMI with increasing
age for both the sample as a whole and these individual subgroups.
This evidence of improved classification in one index of
health (reduced BMI) for masters athletes (when compared to
the general population) implies there are either improved
levels of this index of health with aging due to adherence to
sport or possibly the reduced BMI is advantageous and
contributes to this cohort adhering (or being attracted) to
masters sport at older ages. Demonstration of this
proportionately under-investigated World Masters Games
population having an improved relationship between BMI and
increasing age over the general population is of particular
interest in the context of the measures being taken globally to
curb an obesity epidemic.
Abstract: Maintenance is one of the most important activities in
the shipyard industry. However, sometimes it is not supported by
adequate services from the shipyard, where inaccuracy in estimating
the duration of the ship maintenance is still common. This makes
estimation of ship maintenance duration is crucial. This study uses
Data Mining approach, i.e., CART (Classification and Regression
Tree) to estimate the duration of ship maintenance that is limited to
dock works or which is known as dry docking. By using the volume
of dock works as an input to estimate the maintenance duration, 4
classes of dry docking duration were obtained with different linear
model and job criteria for each class. These linear models can then be
used to estimate the duration of dry docking based on job criteria.
Abstract: Flour from Mucuna beans (Mucuna pruriens) were
used in producing texturized meat analogue using a single screw
extruder to monitor modifications on the proximate composition and
the functional properties at high moisture level. Response surface
methodology based on Box Behnken design at three levels of barrel
temperature (110, 120, 130°C), screw speed (100,120,140rpm) and
feed moisture (44, 47, 50%) were used in 17 runs. Regression models
describing the effect of variables on the product responses were
obtained. Descriptive profile analyses and consumer acceptability
test were carried out on optimized flavoured extruded meat analogue.
Responses were mostly affected by barrel temperature and moisture
level and to a lesser extent by screw speed. Optimization results
based on desirability concept indicated that a barrel temperature of
120.15°C, feed moisture of 47% and screw speed of 119.19 rpm
would produce meat analogue of preferable proximate composition,
functional and sensory properties which reveals consumers` likeness
for the product.
Abstract: Droughts are complex, natural hazards that, to a
varying degree, affect some parts of the world every year. The range
of drought impacts is related to drought occurring in different stages
of the hydrological cycle and usually different types of droughts,
such as meteorological, agricultural, hydrological, and socioeconomical
are distinguished. Streamflow drought was analyzed by
the method of truncation level (at 70% level) on daily discharges
measured in 54 hydrometric stations in southwestern Iran. Frequency
analysis was carried out for annual maximum series (AMS) of
drought deficit volume and duration series. Some factors including
physiographic, climatic, geologic, and vegetation cover were studied
as influential factors in the regional analysis. According to the results
of factor analysis, six most effective factors were identified as area,
rainfall from December to February, the percent of area with
Normalized Difference Vegetation Index (NDVI)
Abstract: It is well known that Logistic Regression is the gold
standard method for predicting clinical outcome, especially
predicting risk of mortality. In this paper, the Decision Tree method
has been proposed to solve specific problems that commonly use
Logistic Regression as a solution. The Biochemistry and
Haematology Outcome Model (BHOM) dataset obtained from
Portsmouth NHS Hospital from 1 January to 31 December 2001 was
divided into four subsets. One subset of training data was used to
generate a model, and the model obtained was then applied to three
testing datasets. The performance of each model from both methods
was then compared using calibration (the χ2 test or chi-test) and
discrimination (area under ROC curve or c-index). The experiment
presented that both methods have reasonable results in the case of the
c-index. However, in some cases the calibration value (χ2) obtained
quite a high result. After conducting experiments and investigating
the advantages and disadvantages of each method, we can conclude
that Decision Trees can be seen as a worthy alternative to Logistic
Regression in the area of Data Mining.