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 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 studies 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: Discrimination between different classes of environmental
sounds is the goal of our work. The use of a sound recognition
system can offer concrete potentialities for surveillance and
security applications. The first paper contribution to this research
field is represented by a thorough investigation of the applicability
of state-of-the-art audio features in the domain of environmental
sound recognition. Additionally, a set of novel features obtained by
combining the basic parameters is introduced. The quality of the
features investigated is evaluated by a HMM-based classifier to which
a great interest was done. In fact, we propose to use a Multi-Style
training system based on HMMs: one recognizer is trained on a
database including different levels of background noises and is used
as a universal recognizer for every environment. In order to enhance
the system robustness by reducing the environmental variability, we
explore different adaptation algorithms including Maximum Likelihood
Linear Regression (MLLR), Maximum A Posteriori (MAP)
and the MAP/MLLR algorithm that combines MAP and MLLR.
Experimental evaluation shows that a rather good recognition rate
can be reached, even under important noise degradation conditions
when the system is fed by the convenient set of features.
Abstract: Regression testing is a maintenance activity applied to
modified software to provide confidence that the changed parts are
correct and that the unchanged parts have not been adversely affected
by the modifications. Regression test selection techniques reduce the
cost of regression testing, by selecting a subset of an existing test
suite to use in retesting modified programs. This paper presents the
first general regression-test-selection technique, which based on code
and allows selecting test cases for any programs written in any
programming language. Then it handles incomplete program. We
also describe RTSDiff, a regression-test-selection system that
implements the proposed technique. The results of the empirical
studied that performed in four programming languages java, C#, Cµ
and Visual basic show that the efficiency and effective in reducing
the size of test suit.
Abstract: The critical period for weed control (CPWC) is the period in the crop growth cycle during which weeds must be controlled to prevent unacceptable yield losses. Field studies were conducted in 2005 and 2006 in the University of Birjand at the south east of Iran to determine CPWC of corn using a randomized complete block design with 14 treatments and four replications. The treatments consisted of two different periods of weed interference, a critical weed-free period and a critical time of weed removal, were imposed at V3, V6, V9, V12, V15, and R1 (based on phonological stages of corn development) with a weedy check and a weed-free check. The CPWC was determined with the use of 2.5, 5, 10, 15 and 20% acceptable yield loss levels by non-linear Regression method and fitting Logistic and Gompertz nonlinear equations to relative yield data. The CPWC of corn was from 5- to 15-leaf stage (19-55 DAE) to prevent yield losses of 5%. This period to prevent yield losses of 2.5, 10 and 20% was 4- to 17-leaf stage (14-59 DAE), 6- to 12-leaf stage (25-47 DAE) and 8- to 9-leaf stage (31-36 DAE) respectively. The height and leaf area index of corn were significantly decreased by weed competition in both weed free and weed infested treatments (P
Abstract: Enzymatic hydrolysis of starch from natural sources
finds potential application in commercial production of alcoholic
beverage and bioethanol. In this study the effect of starch
concentration, temperature, time and enzyme concentration were
studied and optimized for hydrolysis of Potato starch powder (of
mesh 80/120) into glucose syrup by immobilized (using Sodium
arginate) α-amylase using central composite design. The
experimental result on enzymatic hydrolysis of Potato starch was
subjected to multiple linear regression analysis using MINITAB 14
software. Positive linear effect of starch concentration, enzyme
concentration and time was observed on hydrolysis of Potato starch
by α-amylase. The statistical significance of the model was validated
by F-test for analysis of variance (p ≤ 0.01). The optimum value of
starch concentration, enzyme concentration, temperature, time and
were found to be 6% (w/v), 2% (w/v), 40°C and 80min respectively.
The maximum glucose yield at optimum condition was 2.34 mg/mL.
Abstract: In this paper, a set of experimental data has been used to assess the influence of abrasive water jet (AWJ) process parameters in cutting 6063-T6 aluminum alloy. The process variables considered here include nozzle diameter, jet traverse rate, jet pressure and abrasive flow rate. The effects of these input parameters are studied on depth of cut (h); one of most important characteristics of AWJ. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. In the next stage, the proposed model is embedded into a Simulated Annealing (SA) algorithm to optimize the AWJ process parameters. The objective is to determine a suitable set of process parameters that can produce a desired depth of cut, considering the ranges of the process parameters. Computational results prove the effectiveness of the proposed model and optimization procedure.
Abstract: Formulation of biological profile is one of the modern roles of forensic anthropologist. The present study was conducted to estimate height using foot and shoeprint length of Malaysian population. The present work can be very useful information in the process of identification of individual in forensic cases based on shoeprint evidence. It can help to narrow down suspects and ease the police investigation. Besides, stature is important parameters in determining the partial identify of unidentified and mutilated bodies. Thus, this study can help the problem encountered in cases of mass disaster, massacre, explosions and assault cases. This is because it is very hard to identify parts of bodies in these cases where people are dismembered and become unrecognizable. Samples in this research were collected from 200 Malaysian adults (100 males and 100 females) with age ranging from 20 to 45 years old. In this research, shoeprint length were measured based on the print of the shoes made from the flat shoes. Other information like gender, foot length and height of subject were also recorded. The data was analyzed using IBM® SPSS Statistics 19 software. Results indicated that, foot length has a strong correlation with stature than shoeprint length for both sides of the feet. However, in the unknown, where the gender was undetermined have shown a better correlation in foot length and shoeprint length parameter compared to males and females analyzed separately. In addition, prediction equations are developed to estimate the stature using linear regression analysis of foot length and shoeprint length. However, foot lengths give better prediction than shoeprint length.
Abstract: The paper investigates the relationship between the foreign direct investment (FDI) and the corporate governance or transparency by investigating the country-level FDI flows, FDI inward performance, corporate governance and transparency variables. From the regression analysis with Newey-West estimator of 28 country panel data from 1990- 2002, we find strong positive relationships between corporate governance or transparency level of hosting countries and FDI inward performance within hosting countries. A strong positive relationship is found between anti-director rights level or number of analysts of hosting countries and FDI inward performance within hosting countries. Also, we find a positive relationship between the number of analysts of hosting countries and FDI inflows. The empirical results are consistent with stock market liberalizations and corporate governance explanations of reasons for FDI.
Abstract: Dichotomization of the outcome by a single cut-off point is an important part of various medical studies. Usually the relationship between the resulted dichotomized dependent variable and explanatory variables is analyzed with linear regression, probit regression or logistic regression. However, in many real-life situations, a certain cut-off point dividing the outcome into two groups is unknown and can be specified only approximately, i.e. surrounded by some (small) uncertainty. It means that in order to have any practical meaning the regression model must be robust to this uncertainty. In this paper, we show that neither the beta in the linear regression model, nor its significance level is robust to the small variations in the dichotomization cut-off point. As an alternative robust approach to the problem of uncertain medical categories, we propose to use the linear regression model with the fuzzy membership function as a dependent variable. This fuzzy membership function denotes to what degree the value of the underlying (continuous) outcome falls below or above the dichotomization cut-off point. In the paper, we demonstrate that the linear regression model of the fuzzy dependent variable can be insensitive against the uncertainty in the cut-off point location. In the paper we present the modeling results from the real study of low hemoglobin levels in infants. We systematically test the robustness of the binomial regression model and the linear regression model with the fuzzy dependent variable by changing the boundary for the category Anemia and show that the behavior of the latter model persists over a quite wide interval.
Abstract: In this paper is reported an analysis about the outdoor air pollution of the urban centre of the city of Messina. The variations of the most critical pollutants concentrations (PM10, O3, CO, C6H6) and their trends respect of climatic parameters and vehicular traffic have been studied. Linear regressions have been effectuated for representing the relations among the pollutants; the differences between pollutants concentrations on weekend/weekday were also analyzed. In order to evaluate air pollution and its effects on human health, a method for calculating a pollution index was implemented and applied in the urban centre of the city. This index is based on the weighted mean of the most detrimental air pollutants concentrations respect of their limit values for protection of human health. The analyzed data of the polluting substances were collected by the Assessorship of the Environment of the Regional Province of Messina in the year 2004. A statistical analysis of the air quality index trends is also reported.
Abstract: In July 1, 2007, Taiwan Stock Exchange (TWSE) on
market observation post system (MOPS) adds a new "Financial
reference database" for investors to do investment reference. This
database as a warning to public offering companies listed on the
public financial information and it original within eight targets. In
this paper, this database provided by the indicators for the application
of company financial crisis early warning model verify that the
database provided by the indicator forecast for the financial crisis,
whether or not companies have a high accuracy rate as opposed to
domestic and foreign scholars have positive results. There is use of
Logistic Regression Model application of the financial early warning
model, in which no joined back-conditions is the first model, joined it
in is the second model, has been taken occurred in the financial crisis
of companies to research samples and then business took place
before the financial crisis point with T-1 and T-2 sample data to do
positive analysis. The results show that this database provided the
debt ratio and net per share for the best forecast variables.
Abstract: This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines.
Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the
dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the
effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.
Abstract: A Matlab based software for logistic regression is developed to enhance the process of teaching quantitative topics and assist researchers with analyzing wide area of applications where categorical data is involved. The software offers an option of performing stepwise logistic regression to select the most significant predictors. The software includes a feature to detect influential observations in data, and investigates the effect of dropping or misclassifying an observation on a predictor variable. The input data may consist either as a set of individual responses (yes/no) with the predictor variables or as grouped records summarizing various categories for each unique set of predictor variables' values. Graphical displays are used to output various statistical results and to assess the goodness of fit of the logistic regression model. The software recognizes possible convergence constraints when present in data, and the user is notified accordingly.
Abstract: An inflation–extension test with human vena cava
inferior was performed with the aim to fit a material model. The vein
was modeled as a thick–walled tube loaded by internal pressure and
axial force. The material was assumed to be an incompressible
hyperelastic fiber reinforced continuum. Fibers are supposed to be
arranged in two families of anti–symmetric helices. Considered
anisotropy corresponds to local orthotropy. Used strain energy
density function was based on a concept of limiting strain
extensibility. The pressurization was comprised by four pre–cycles
under physiological venous loading (0 – 4kPa) and four cycles under
nonphysiological loading (0 – 21kPa). Each overloading cycle was
performed with different value of axial weight. Overloading data
were used in regression analysis to fit material model. Considered
model did not fit experimental data so good. Especially predictions
of axial force failed. It was hypothesized that due to
nonphysiological values of loading pressure and different values of
axial weight the material was not preconditioned enough and some
damage occurred inside the wall. A limiting fiber extensibility
parameter Jm was assumed to be in relation to supposed damage.
Each of overloading cycles was fitted separately with different values
of Jm. Other parameters were held the same. This approach turned out
to be successful. Variable value of Jm can describe changes in the
axial force – axial stretch response and satisfy pressure – radius
dependence simultaneously.
Abstract: In the micro and nano-technology industry, the
«clean-rooms» dedicated to manufacturing chip, are equipped with
the most sophisticated equipment-tools. There use a large number of
resources in according to strict specifications for an optimum
working and result. The distribution of «utilities» to the production is
assured by teams who use a supervision tool.
The studies show the interest to control the various parameters of
production or/and distribution, in real time, through a reliable and
effective supervision tool. This document looks at a large part of the
functions that the supervisor must assure, with complementary
functionalities to help the diagnosis and simulation that prove very
useful in our case where the supervised installations are complexed
and in constant evolution.
Abstract: Manufacturing components of fiber-reinforced
thermoplastics requires three steps: heating the matrix, forming and
consolidation of the composite and terminal cooling the matrix. For
the heating process a pre-determined temperature distribution through
the layers and the thickness of the pre-consolidated sheets is
recommended to enable forming mechanism. Thus, a design for the
heating process for forming composites with thermoplastic matrices
is necessary. To obtain a constant temperature through thickness and
width of the sheet, the heating process was analyzed by the help of
the finite element method. The simulation models were validated by
experiments with resistance thermometers as well as with an infrared
camera. Based on the finite element simulation, heating methods for
infrared radiators have been developed. Using the numeric
simulation many iteration loops are required to determine the process
parameters. Hence, the initiation of a model for calculating relevant
process parameters started applying regression functions.
Abstract: The construction industry is the pillar industry in
China, accounting for about 6% of the gross domestic product. Along
with changes in the external environment of the construction industry
in China, the construction firm faces fierce competition. The paper
aims to investigate the relationship between diversified types of
construction firm and its performance in China. Based on generalist
and specialist strategy in organizational ecology, we think a generalist
organization can be applied to an enterprise with diversified
developments, while specialist groups are extended to professional
enterprises .This study takes advantage of annual financial data of
listed construction firm to empirically verify the relationship between
diversification and corporation performance establishing a regression
equation to econometric analysis. We find that: 1) Specialization can
significantly improve the level of profitability of listed construction
firms, and there is a significant positive relationship with corporate
performance; 2) The level of operating performance of listed
construction enterprises which engage in unrelated diversification is
higher than those with related diversification; 3) The relationship
between state-owned construction firms and corporate performance is
negative. The more the year of foundation is, the higher performance
will be; however, the more the year of being listed, the lower
performance will be.
Abstract: In wavelet regression, choosing threshold value is a crucial issue. A too large value cuts too many coefficients resulting in over smoothing. Conversely, a too small threshold value allows many coefficients to be included in reconstruction, giving a wiggly estimate which result in under smoothing. However, the proper choice of threshold can be considered as a careful balance of these principles. This paper gives a very brief introduction to some thresholding selection methods. These methods include: Universal, Sure, Ebays, Two fold cross validation and level dependent cross validation. A simulation study on a variety of sample sizes, test functions, signal-to-noise ratios is conducted to compare their numerical performances using three different noise structures. For Gaussian noise, EBayes outperforms in all cases for all used functions while Two fold cross validation provides the best results in the case of long tail noise. For large values of signal-to-noise ratios, level dependent cross validation works well under correlated noises case. As expected, increasing both sample size and level of signal to noise ratio, increases estimation efficiency.
Abstract: This paper investigates how the use of machine learning techniques can significantly predict the three major dimensions of learner-s emotions (pleasure, arousal and dominance) from brainwaves. This study has adopted an experimentation in which participants were exposed to a set of pictures from the International Affective Picture System (IAPS) while their electrical brain activity was recorded with an electroencephalogram (EEG). The pictures were already rated in a previous study via the affective rating system Self-Assessment Manikin (SAM) to assess the three dimensions of pleasure, arousal, and dominance. For each picture, we took the mean of these values for all subjects used in this previous study and associated them to the recorded brainwaves of the participants in our study. Correlation and regression analyses confirmed the hypothesis that brainwave measures could significantly predict emotional dimensions. This can be very useful in the case of impassive, taciturn or disabled learners. Standard classification techniques were used to assess the reliability of the automatic detection of learners- three major dimensions from the brainwaves. We discuss the results and the pertinence of such a method to assess learner-s emotions and integrate it into a brainwavesensing Intelligent Tutoring System.
Abstract: The purpose of this paper is to explore the relationship
between the customers- issues in company corporate governance and
the financial performance. At the beginning theoretical background
consisting stakeholder theory and corporate governance is presented.
On this theoretical background, the empirical research is built,
collecting data of 60 Czech joint stock companies- boards
considering their relationships with customers. Correlation analysis
and multivariate regression analysis were employed to test the sample
on two hypotheses. The weak positive correlation between
stakeholder approach and the company size was identified. But both
hypotheses were not supported, because there was no significant
relation of independent variables to financial performance.