Abstract: Abrasive waterjet is a novel machining process capable of processing wide range of hard-to-machine materials. This research addresses modeling and optimization of the process parameters for this machining technique. To model the process a set of experimental data has been used to evaluate the effects of various parameter settings in cutting 6063-T6 aluminum alloy. The process variables considered here include nozzle diameter, jet traverse rate, jet pressure and abrasive flow rate. Depth of cut, as one of the most important output characteristics, has been evaluated based on different parameter settings. 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. The pairwise effects of process parameters settings on process response outputs are also shown graphically. The proposed model is then embedded into a Simulated Annealing algorithm to optimize the process parameters. The optimization is carried out for any desired values of depth of cut. The objective is to determine proper levels of process parameters in order to obtain a certain level of depth of cut. Computational results demonstrate that the proposed solution procedure is quite effective in solving such multi-variable problems.
Abstract: Investigation of soil properties like Cation Exchange
Capacity (CEC) plays important roles in study of environmental
reaserches as the spatial and temporal variability of this property
have been led to development of indirect methods in estimation of
this soil characteristic. Pedotransfer functions (PTFs) provide an
alternative by estimating soil parameters from more readily available
soil data. 70 soil samples were collected from different horizons of
15 soil profiles located in the Ziaran region, Qazvin province, Iran.
Then, multivariate regression and neural network model (feedforward
back propagation network) were employed to develop a
pedotransfer function for predicting soil parameter using easily
measurable characteristics of clay and organic carbon. The
performance of the multivariate regression and neural network model
was evaluated using a test data set. In order to evaluate the models,
root mean square error (RMSE) was used. The value of RMSE and
R2 derived by ANN model for CEC were 0.47 and 0.94 respectively,
while these parameters for multivariate regression model were 0.65
and 0.88 respectively. Results showed that artificial neural network
with seven neurons in hidden layer had better performance in
predicting soil cation exchange capacity than multivariate regression.
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: Researchers have long had trouble in measurement of
Exchangeable Sodium Ratio (ESR) at salt-affected soils. this
parameter are often determined using laborious and time consuming
laboratory tests, but it may be more appropriate and economical to
develop a method which uses a more simple soil salinity index. The
aim of this study was to determine the relationship between
exchangeable sodium ratio (ESR) and sodium adsorption ratio (SAR)
in some salt-affected soils of Khuzestan plain. To this purpose, two
experimental areas (S1, S2) of Khuzestan province-IRAN were
selected and four treatments with three replications by series of
double rings were applied. The treatments were included 25cm,
50cm, 75cm and 100cm water application. The statistical results of
the study indicated that in order to predict soil ESR based on soil
SAR the linear regression model ESR=0.2048+0.0066 SAR
(R2=0.53) & ESR=0.0564+0.0171 SAR (R2=0.76) can be
recommended in Pilot S1 and S2 respectively.
Abstract: In this study, a network quality of service (QoS)
evaluation system was proposed. The system used a combination of
fuzzy C-means (FCM) and regression model to analyse and assess the
QoS in a simulated network. Network QoS parameters of multimedia
applications were intelligently analysed by FCM clustering
algorithm. The QoS parameters for each FCM cluster centre were
then inputted to a regression model in order to quantify the overall
QoS. The proposed QoS evaluation system provided valuable
information about the network-s QoS patterns and based on this
information, the overall network-s QoS was effectively quantified.
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: 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 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: 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: In this paper, the sum of squares in linear regression is
reduced to sum of squares in semi-parametric regression. We
indicated that different sums of squares in the linear regression are
similar to various deviance statements in semi-parametric regression.
In addition to, coefficient of the determination derived in linear
regression model is easily generalized to coefficient of the
determination of the semi-parametric regression model. Then, it is
made an application in order to support the theory of the linear
regression and semi-parametric regression. In this way, study is
supported with a simulated data example.
Abstract: In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.
Abstract: Cluster analysis divides data into groups that are
meaningful, useful, or both. Analysis of biological data is creating a
new generation of epidemiologic, prognostic, diagnostic and
treatment modalities. Clustering of protein sequences is one of the
current research topics in the field of computer science. Linear
relation is valuable in rule discovery for a given data, such as if value
X goes up 1, value Y will go down 3", etc. The classical linear
regression models the linear relation of two sequences perfectly.
However, if we need to cluster a large repository of protein sequences
into groups where sequences have strong linear relationship with
each other, it is prohibitively expensive to compare sequences one by
one. In this paper, we propose a new technique named General
Regression Model Technique Clustering Algorithm (GRMTCA) to
benignly handle the problem of linear sequences clustering. GRMT
gives a measure, GR*, to tell the degree of linearity of multiple
sequences without having to compare each pair of them.
Abstract: In this paper, estimation of the linear regression
model is made by ordinary least squares method and the
partially linear regression model is estimated by penalized
least squares method using smoothing spline. Then, it is
investigated that differences and similarity in the sum of
squares related for linear regression and partial linear
regression models (semi-parametric regression models). It is
denoted that the sum of squares in linear regression is reduced
to sum of squares in partial linear regression models.
Furthermore, we indicated that various sums of squares in the
linear regression are similar to different deviance statements in
partial linear regression. In addition to, coefficient of the
determination derived in linear regression model is easily
generalized to coefficient of the determination of the partial
linear regression model. For this aim, it is made two different
applications. A simulated and a real data set are considered to
prove the claim mentioned here. In this way, this study is
supported with a simulation and a real data example.
Abstract: The authors have been developing several models
based on artificial neural networks, linear regression models, Box-
Jenkins methodology and ARIMA models to predict the time series
of tourism. The time series consist in the “Monthly Number of Guest
Nights in the Hotels" of one region. Several comparisons between the
different type models have been experimented as well as the features
used at the entrance of the models. The Artificial Neural Network
(ANN) models have always had their performance at the top of the
best models. Usually the feed-forward architecture was used due to
their huge application and results. In this paper the author made a
comparison between different architectures of the ANNs using
simply the same input. Therefore, the traditional feed-forward
architecture, the cascade forwards, a recurrent Elman architecture and
a radial based architecture were discussed and compared based on the
task of predicting the mentioned time series.
Abstract: The main aim of this study is to identify the most
influential variables that cause defects on the items produced by a
casting company located in Turkey. To this end, one of the items
produced by the company with high defective percentage rates is
selected. Two approaches-the regression analysis and decision treesare
used to model the relationship between process parameters and
defect types. Although logistic regression models failed, decision tree
model gives meaningful results. Based on these results, it can be
claimed that the decision tree approach is a promising technique for
determining the most important process variables.
Abstract: Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.
Abstract: Uncertainties of a serial production line affect on the
production throughput. The uncertainties cannot be prevented in a
real production line. However the uncertain conditions can be
controlled by a robust prediction model. Thus, a hybrid model
including autoregressive integrated moving average (ARIMA) and
multiple polynomial regression, is proposed to model the nonlinear
relationship of production uncertainties with throughput. The
uncertainties under consideration of this study are demand, breaktime,
scrap, and lead-time. The nonlinear relationship of production
uncertainties with throughput are examined in the form of quadratic
and cubic regression models, where the adjusted R-squared for
quadratic and cubic regressions was 98.3% and 98.2%. We optimized
the multiple quadratic regression (MQR) by considering the time
series trend of the uncertainties using ARIMA model. Finally the
hybrid model of ARIMA and MQR is formulated by better adjusted
R-squared, which is 98.9%.
Abstract: This is a cross-cultural study that determines South
African multinational enterprises (MNEs) entry strategies as they
invest in Africa. An integrated theoretical framework comprising the
transaction cost theory, Uppsala model, eclectic paradigm and the
distance framework was adopted. A sample of 40 South African
MNEs with 415 existing FDI entries in Africa was drawn. Using an
ordered logistic regression model, the impact of culture on the choice
of degree of control by South African MNEs in Africa was
determined. Cultural distance was one of significant factors that
influenced South African MNEs- choice of degree of control.
Furthermore, South African MNEs are risk averse in all countries in
Africa but minimize the risks differently across sectors. Service
sectors chooses to own their subsidiaries 100% and avoid dealing
with the locals while manufacturing, resources and construction
choose to have a local partner to share the risk.
Abstract: Based on assumptions of neo-classical economics and
rational choice / public choice theory, this paper investigates the
regulation of industrial land use in Taiwan by homeowners
associations (HOAs) as opposed to traditional government
administration. The comparison, which applies the transaction cost
theory and a polynomial regression analysis, manifested that HOAs
are superior to conventional government administration in terms of
transaction costs and overall efficiency. A case study that compares
Taiwan-s commonhold industrial park, NangKang Software Park, to
traditional government counterparts using limited data on the costs
and returns was analyzed. This empirical study on the relative
efficiency of governmental and private institutions justified the
important theoretical proposition. Numerical results prove the
efficiency of the established model.
Abstract: This article provides empirical evidence on the effect
of domestic and international factors on the U.S. current account
deficit. Linear dynamic regression and vector autoregression models
are employed to estimate the relationships during the period from 1986
to 2011. The findings of this study suggest that the current and lagged
private saving rate and foreign current account for East Asian
economies have played a vital role in affecting the U.S. current
account. Additionally, using Granger causality tests and variance
decompositions, the change of the productivity growth and foreign
domestic demand are determined to influence significantly the change
of the U.S. current account. To summarize, the empirical relationship
between the U.S. current account deficit and its determinants is
sensitive to alternative regression models and specifications.