16 resultados para genetic algorithm-kernel partial least squares
em Universidade Federal do Rio Grande do Norte(UFRN)
Resumo:
In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma
Resumo:
The aim of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) as a rapid and non-destructive method to determine the soluble solid content (SSC), pH and titratable acidity of intact plums. Samples of plum with a total solids content ranging from 5.7 to 15%, pH from 2.72 to 3.84 and titratable acidity from 0.88 a 3.6% were collected from supermarkets in Natal-Brazil, and NIR spectra were acquired in the 714 2500 nm range. A comparison of several multivariate calibration techniques with respect to several pre-processing data and variable selection algorithms, such as interval Partial Least Squares (iPLS), genetic algorithm (GA), successive projections algorithm (SPA) and ordered predictors selection (OPS), was performed. Validation models for SSC, pH and titratable acidity had a coefficient of correlation (R) of 0.95 0.90 and 0.80, as well as a root mean square error of prediction (RMSEP) of 0.45ºBrix, 0.07 and 0.40%, respectively. From these results, it can be concluded that NIR spectroscopy can be used as a non-destructive alternative for measuring the SSC, pH and titratable acidity in plums
Resumo:
In recent decades the public sector comes under pressure in order to improve its performance. The use of Information Technology (IT) has been a tool increasingly used in reaching that goal. Thus, it has become an important issue in public organizations, particularly in institutions of higher education, determine which factors influence the acceptance and use of technology, impacting on the success of its implementation and the desired organizational results. The Technology Acceptance Model - TAM was used as the basis for this study and is based on the constructs perceived usefulness and perceived ease of use. However, when it comes to integrated management systems due to the complexity of its implementation,organizational factors were added to thus seek further explanation of the acceptance of such systems. Thus, added to the model five TAM constructs related to critical success factors in implementing ERP systems, they are: support of top management, communication, training, cooperation, and technological complexity (BUENO and SALMERON, 2008). Based on the foregoing, launches the following research problem: What factors influence the acceptance and use of SIE / module academic at the Federal University of Para, from the users' perception of teachers and technicians? The purpose of this study was to identify the influence of organizational factors, and behavioral antecedents of behavioral intention to use the SIE / module academic UFPA in the perspective of teachers and technical users. This is applied research, exploratory and descriptive, quantitative with the implementation of a survey, and data collection occurred through a structured questionnaire applied to a sample of 229 teachers and 30 technical and administrative staff. Data analysis was carried out through descriptive statistics and structural equation modeling with the technique of partial least squares (PLS). Effected primarily to assess the measurement model, which were verified reliability, convergent and discriminant validity for all indicators and constructs. Then the structural model was analyzed using the bootstrap resampling technique like. In assessing statistical significance, all hypotheses were supported. The coefficient of determination (R ²) was high or average in five of the six endogenous variables, so the model explains 47.3% of the variation in behavioral intention. It is noteworthy that among the antecedents of behavioral intention (BI) analyzed in this study, perceived usefulness is the variable that has a greater effect on behavioral intention, followed by ease of use (PEU) and attitude (AT). Among the organizational aspects (critical success factors) studied technological complexity (TC) and training (ERT) were those with greatest effect on behavioral intention to use, although these effects were lower than those produced by behavioral factors (originating from TAM). It is pointed out further that the support of senior management (TMS) showed, among all variables, the least effect on the intention to use (BI) and was followed by communications (COM) and cooperation (CO), which exert a low effect on behavioral intention (BI). Therefore, as other studies on the TAM constructs were adequate for the present research. Thus, the study contributed towards proving evidence that the Technology Acceptance Model can be applied to predict the acceptance of integrated management systems, even in public. Keywords: Technology
Resumo:
The study aims to identify the factors that influence the behavior intention to adopt an academic Information System (SIE), in an environment of mandatory use, applied in the procurement process at the Federal University of Pará (UFPA). For this, it was used a model of innovation adoption and technology acceptance (TAM), focused in attitudes and intentions regarding the behavior intention. The research was conducted a quantitative survey, through survey in a sample of 96 administrative staff of the researched institution. For data analysis, it was used structural equation modeling (SEM), using the partial least squares method (Partial Least Square PLS-PM). As to results, the constructs attitude and subjective norms were confirmed as strong predictors of behavioral intention in a pre-adoption stage. Despite the use of SIE is required, the perceived voluntariness also predicts the behavior intention. Regarding attitude, classical variables of TAM, like as ease of use and perceived usefulness, appear as the main influence of attitude towards the system. It is hoped that the results of this study may provide subsidies for more efficient management of the process of implementing systems and information technologies, particularly in public universities
Resumo:
In this work we used chemometric tools to classify and quantify the protein content in samples of milk powder. We applied the NIR diffuse reflectance spectroscopy combined with multivariate techniques. First, we carried out an exploratory method of samples by principal component analysis (PCA), then the classification of independent modeling of class analogy (SIMCA). Thus it became possible to classify the samples that were grouped by similarities in their composition. Finally, the techniques of partial least squares regression (PLS) and principal components regression (PCR) allowed the quantification of protein content in samples of milk powder, compared with the Kjeldahl reference method. A total of 53 samples of milk powder sold in the metropolitan areas of Natal, Salvador and Rio de Janeiro were acquired for analysis, in which after pre-treatment data, there were four models, which were employed for classification and quantification of samples. The methods employed after being assessed and validated showed good performance, good accuracy and reliability of the results, showing that the NIR technique can be a non invasive technique, since it produces no waste and saves time in analyzing the samples
Resumo:
In this work calibration models were constructed to determine the content of total lipids and moisture in powdered milk samples. For this, used the near-infrared spectroscopy by diffuse reflectance, combined with multivariate calibration. Initially, the spectral data were submitted to correction of multiplicative light scattering (MSC) and Savitzsky-Golay smoothing. Then, the samples were divided into subgroups by application of hierarchical clustering analysis of the classes (HCA) and Ward Linkage criterion. Thus, it became possible to build regression models by partial least squares (PLS) that allowed the calibration and prediction of the content total lipid and moisture, based on the values obtained by the reference methods of Soxhlet and 105 ° C, respectively . Therefore, conclude that the NIR had a good performance for the quantification of samples of powdered milk, mainly by minimizing the analysis time, not destruction of the samples and not waste. Prediction models for determination of total lipids correlated (R) of 0.9955, RMSEP of 0.8952, therefore the average error between the Soxhlet and NIR was ± 0.70%, while the model prediction to content moisture correlated (R) of 0.9184, RMSEP, 0.3778 and error of ± 0.76%
Resumo:
This work is combined with the potential of the technique of near infrared spectroscopy - NIR and chemometrics order to determine the content of diclofenac tablets, without destruction of the sample, to which was used as the reference method, ultraviolet spectroscopy, which is one of the official methods. In the construction of multivariate calibration models has been studied several types of pre-processing of NIR spectral data, such as scatter correction, first derivative. The regression method used in the construction of calibration models is the PLS (partial least squares) using NIR spectroscopic data of a set of 90 tablets were divided into two sets (calibration and prediction). 54 were used in the calibration samples and the prediction was used 36, since the calibration method used was crossvalidation method (full cross-validation) that eliminates the need for a validation set. The evaluation of the models was done by observing the values of correlation coefficient R 2 and RMSEC mean square error (calibration error) and RMSEP (forecast error). As the forecast values estimated for the remaining 36 samples, which the results were consistent with the values obtained by UV spectroscopy
Resumo:
Aiming to consumer s safety the presence of pathogenic contaminants in foods must be monitored because they are responsible for foodborne outbreaks that depending on the level of contamination can ultimately cause the death of those who consume them. In industry is necessary that this identification be fast and profitable. This study shows the utility and application of near-infrared (NIR) transflectance spectroscopy as an alternative method for the identification and classification of Escherichia coli and Salmonella Enteritidis in commercial fruit pulp (pineapple). Principal Component Analysis (PCA), Independent Modeling of Class Analogy (SIMCA) and Discriminant Analysis Partial Least Squares (PLS-DA) were used in the analysis. It was not possible to obtain total separation between samples using PCA and SIMCA. The PLS-DA showed good performance in prediction capacity reaching 87.5% for E. coli and 88.3% for S. Enteritides, respectively. The best models were obtained for the PLS-DA with second derivative spectra treated with a sensitivity and specificity of 0.87 and 0.83, respectively. These results suggest that the NIR spectroscopy and PLS-DA can be used to discriminate and detect bacteria in the fruit pulp
Antecedentes da intenção de uso de comentários de viagem on-line na escolha de um meio de hospedagem
Resumo:
The Internet is present in each step of a trip planning. The constant technological advances has made major changes in the tourism industry. This is noticeable by the growing number of people who share their travel experiences on the Internet. This study has aimed to analyze the factors that influence the use of the Online Travel Reviews (OTR) in choosing an accommodation. It was done an investigation into the comments available on the internet about information on touristic products and services, specifically about accommodations. The research proposed to understand the influencing factors of OTR, in the Brazilian context, through the Technology Acceptance Model, Motivational Theory, Similarity, and Trustworthiness. The methodology used was a descriptive-exploratory study, with a quantitative approach, and bibliographic research. The study used a Structural Equation Modeling technique called Partial Least Squares (PLS), to test and evaluate the proposed research model. Data collection was performed with 308 guests hosted in five hotels in Ponta Negra (Natal/RN), who have used the OTRs in choosing an accommodation. The research tested fifteen hypotheses, where nine were confirmed, and six were rejected. The results showed that guests have attitude and intention to use the OTRs to choose an accommodation.
Resumo:
In recent decades the public sector comes under pressure in order to improve its performance. The use of Information Technology (IT) has been a tool increasingly used in reaching that goal. Thus, it has become an important issue in public organizations, particularly in institutions of higher education, determine which factors influence the acceptance and use of technology, impacting on the success of its implementation and the desired organizational results. The Technology Acceptance Model - TAM was used as the basis for this study and is based on the constructs perceived usefulness and perceived ease of use. However, when it comes to integrated management systems due to the complexity of its implementation,organizational factors were added to thus seek further explanation of the acceptance of such systems. Thus, added to the model five TAM constructs related to critical success factors in implementing ERP systems, they are: support of top management, communication, training, cooperation, and technological complexity (BUENO and SALMERON, 2008). Based on the foregoing, launches the following research problem: What factors influence the acceptance and use of SIE / module academic at the Federal University of Para, from the users' perception of teachers and technicians? The purpose of this study was to identify the influence of organizational factors, and behavioral antecedents of behavioral intention to use the SIE / module academic UFPA in the perspective of teachers and technical users. This is applied research, exploratory and descriptive, quantitative with the implementation of a survey, and data collection occurred through a structured questionnaire applied to a sample of 229 teachers and 30 technical and administrative staff. Data analysis was carried out through descriptive statistics and structural equation modeling with the technique of partial least squares (PLS). Effected primarily to assess the measurement model, which were verified reliability, convergent and discriminant validity for all indicators and constructs. Then the structural model was analyzed using the bootstrap resampling technique like. In assessing statistical significance, all hypotheses were supported. The coefficient of determination (R ²) was high or average in five of the six endogenous variables, so the model explains 47.3% of the variation in behavioral intention. It is noteworthy that among the antecedents of behavioral intention (BI) analyzed in this study, perceived usefulness is the variable that has a greater effect on behavioral intention, followed by ease of use (PEU) and attitude (AT). Among the organizational aspects (critical success factors) studied technological complexity (TC) and training (ERT) were those with greatest effect on behavioral intention to use, although these effects were lower than those produced by behavioral factors (originating from TAM). It is pointed out further that the support of senior management (TMS) showed, among all variables, the least effect on the intention to use (BI) and was followed by communications (COM) and cooperation (CO), which exert a low effect on behavioral intention (BI). Therefore, as other studies on the TAM constructs were adequate for the present research. Thus, the study contributed towards proving evidence that the Technology Acceptance Model can be applied to predict the acceptance of integrated management systems, even in public. Keywords: Technology
Resumo:
The study aims to identify the factors that influence the behavior intention to adopt an academic Information System (SIE), in an environment of mandatory use, applied in the procurement process at the Federal University of Pará (UFPA). For this, it was used a model of innovation adoption and technology acceptance (TAM), focused in attitudes and intentions regarding the behavior intention. The research was conducted a quantitative survey, through survey in a sample of 96 administrative staff of the researched institution. For data analysis, it was used structural equation modeling (SEM), using the partial least squares method (Partial Least Square PLS-PM). As to results, the constructs attitude and subjective norms were confirmed as strong predictors of behavioral intention in a pre-adoption stage. Despite the use of SIE is required, the perceived voluntariness also predicts the behavior intention. Regarding attitude, classical variables of TAM, like as ease of use and perceived usefulness, appear as the main influence of attitude towards the system. It is hoped that the results of this study may provide subsidies for more efficient management of the process of implementing systems and information technologies, particularly in public universities
Resumo:
The pattern classification is one of the machine learning subareas that has the most outstanding. Among the various approaches to solve pattern classification problems, the Support Vector Machines (SVM) receive great emphasis, due to its ease of use and good generalization performance. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen to achieve satisfactory results in a given task. Despite the LS-SVMs having high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles, in other words, a combination of several classifiers. In this work, our proposal is to use an ensemble and a Genetic Algorithm (GA), search algorithm based on the evolution of species, to enhance the LSSVM classification. In the construction of this ensemble, we use a random selection of attributes of the original problem, which it splits the original problem into smaller ones where each classifier will act. So, we apply a genetic algorithm to find effective values of the LS-SVM parameters and also to find a weight vector, measuring the importance of each machine in the final classification. Finally, the final classification is obtained by a linear combination of the decision values of the LS-SVMs with the weight vector. We used several classification problems, taken as benchmarks to evaluate the performance of the algorithm and compared the results with other classifiers
Resumo:
Several mobile robots show non-linear behavior, mainly due friction phenomena between the mechanical parts of the robot or between the robot and the ground. Linear models are efficient in some cases, but it is necessary take the robot non-linearity in consideration when precise displacement and positioning are desired. In this work a parametric model identification procedure for a mobile robot with differential drive that considers the dead-zone in the robot actuators is proposed. The method consists in dividing the system into Hammerstein systems and then uses the key-term separation principle to present the input-output relations which shows the parameters from both linear and non-linear blocks. The parameters are then simultaneously estimated through a recursive least squares algorithm. The results shows that is possible to identify the dead-zone thresholds together with the linear parameters
Resumo:
This work proposes a new technique for phasor estimation applied in microprocessor numerical relays for distance protection of transmission lines, based on the recursive least squares method and called least squares modified random walking. The phasor estimation methods have compromised their performance, mainly due to the DC exponential decaying component present in fault currents. In order to reduce the influence of the DC component, a Morphological Filter (FM) was added to the method of least squares and previously applied to the process of phasor estimation. The presented method is implemented in MATLABr and its performance is compared to one-cycle Fourier technique and conventional phasor estimation, which was also based on least squares algorithm. The methods based on least squares technique used for comparison with the proposed method were: forgetting factor recursive, covariance resetting and random walking. The techniques performance analysis were carried out by means of signals synthetic and signals provided of simulations on the Alternative Transient Program (ATP). When compared to other phasor estimation methods, the proposed method showed satisfactory results, when it comes to the estimation speed, the steady state oscillation and the overshoot. Then, the presented method performance was analyzed by means of variations in the fault parameters (resistance, distance, angle of incidence and type of fault). Through this study, the results did not showed significant variations in method performance. Besides, the apparent impedance trajectory and estimated distance of the fault were analysed, and the presented method showed better results in comparison to one-cycle Fourier algorithm
Resumo:
Waste stabilization ponds (WSP) have been widely used for sewage treatment in hot climate regions because they are economic and environmentally sustainable. In the present study a WSP complex comprising a primary facultative pond (PFP) followed by two maturation ponds (MP-1 and MP-2) was studied, in the city of Natal-RN. The main objective was to study the bio-degradability of organic matter through the determination of the kinetic constant k throughout the system. The work was carried out in two phases. In the first, the variability in BOD, COD and TOC concentrations and an analysis of the relations between these parameters, in the influent raw sewage, pond effluents and in specific areas inside the ponds was studied. In the second stage, the decay rate for organic matter (k) was determined throughout the system based on BOD tests on the influent sewage, pond effluents and water column samples taken from fixed locations within the ponds, using the mathematical methods of Least Squares and the Thomas equation. Subsequently k was estimated as a function of a hydrodynamic model determined from the dispersion number (d), using empirical methods and a Partial Hydrodynamic Evaluation (PHE), obtained from tracer studies in a section of the primary facultative pond corresponding to 10% of its total length. The concentrations of biodegradable organic matter, measured as BOD and COD, gradually reduced through the series of ponds, giving overall removal efficiencies of 71.95% for BOD and of 52.45% for COD. Determining the values for k, in the influent and effluent samples of the ponds using the mathematical method of Least Squares, gave the following values respectively: primary facultative pond (0,23 day-1 and 0,09 day-1), maturation 1 (0,04 day-1 and 0,03 day-1) and maturation 2 (0,03 day-1 and 0,08 day-1). When using the Thomas method, the values of k in the influents and effluents of the ponds were: primary facultative pond (0,17 day-1 and 0,07 day-1), maturation 1 (0,02 day-1 and 0,01 day-1) and maturation 2 (0,01 day-1 and 0,02 day-1). From the Partial Hydrodynamic Evaluation, in the first section of the facultative pond corresponding to 10% of its total length, it can be concluded from the dispersion number obtained of d = 0.04, that the hydraulic regime is one of dispersed flow with a kinetic constant value of 0.20 day-1