Letteratura scientifica selezionata sul tema "Matrix linear regression"
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Articoli di riviste sul tema "Matrix linear regression"
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Lutay, V. N., e N. S. Khusainov. "The selective regularization of a linear regression model". Journal of Physics: Conference Series 2099, n. 1 (1 novembre 2021): 012024. http://dx.doi.org/10.1088/1742-6596/2099/1/012024.
Testo completoAbstract (sommario):
Abstract This paper discusses constructing a linear regression model with regularization of the system matrix of normal equations. In contrast to the conventional ridge regression, where positive parameters are added to all diagonal terms of a matrix, in the method proposed only those matrix diagonal entries that correspond to the data with a high correlation are increased. This leads to a decrease in the matrix conditioning and, therefore, to a decrease in the corresponding coefficients of the regression equation. The selection of the entries to be increased is based on the triangular decomposition of the correlation matrix of the original dataset. The effectiveness of the method is tested on a known dataset, and it is performed not only with a ridge regression, but also with the results of applying the widespread algorithms LARS and Lasso.
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Nakonechnyi, Alexander G., Grigoriy I. Kudin, Petr N. Zinko e Taras P. Zinko. "Perturbation Method in Problems of Linear Matrix Regression". Journal of Automation and Information Sciences 52, n. 1 (2020): 1–12. http://dx.doi.org/10.1615/jautomatinfscien.v52.i1.10.
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Zhang, Jiawei, Peng Wang e Ning Zhang. "Distribution Network Admittance Matrix Estimation With Linear Regression". IEEE Transactions on Power Systems 36, n. 5 (settembre 2021): 4896–99. http://dx.doi.org/10.1109/tpwrs.2021.3090250.
Testo completo
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Ivashnev, L. I. "Methods of linear multiple regression in a matrix form". Izvestiya MGTU MAMI 9, n. 4-4 (20 agosto 2015): 35–41. http://dx.doi.org/10.17816/2074-0530-67011.
Testo completoAbstract (sommario):
The article contains a summary of three basic and two weighted linear multiple regression tech- niques in matrix form, together with the method of least squares of Gauss constitute a new tool re- gression analysis. The article contains a matrix formula that can be used to obtain equations of line- ar multiple regression and the basic weighted least-squares method to obtain regression equations without constant term and the method of obtaining the regression equations of general form. The article provides an example of use of matrix methods to obtain the coefficients of regression equa- tion of the general form, ie equation of equal performance.
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Mahaboob, B., J. P. Praveen, B. V. A. Rao, Y. Harnath, C. Narayana e G. B. Prakash. "A STUDY ON MULTIPLE LINEAR REGRESSION USING MATRIX CALCULUS". Advances in Mathematics: Scientific Journal 9, n. 7 (2 agosto 2020): 4863–72. http://dx.doi.org/10.37418/amsj.9.7.52.
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Aubin, Elisete da Conceição Q., e Gauss M. Cordeiro. "BIAS in linear regression models with unknown covariance matrix". Communications in Statistics - Simulation and Computation 26, n. 3 (gennaio 1997): 813–28. http://dx.doi.org/10.1080/03610919708813413.
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Bargiela, Andrzej, e Joanna K. Hartley. "Orthogonal linear regression algorithm based on augmented matrix formulation". Computers & Operations Research 20, n. 8 (ottobre 1993): 829–36. http://dx.doi.org/10.1016/0305-0548(93)90104-q.
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Livadiotis, George. "Linear Regression with Optimal Rotation". Stats 2, n. 4 (28 settembre 2019): 416–25. http://dx.doi.org/10.3390/stats2040028.
Testo completoAbstract (sommario):
The paper shows how the linear regression depends on the selection of the reference frame. The slope of the fitted line and the corresponding Pearson’s correlation coefficient are expressed in terms of the rotation angle. The correlation coefficient is found to be maximized for a certain optimal angle, for which the slope attains a special optimal value. The optimal angle, the value of the optimal slope, and the corresponding maximum correlation coefficient were expressed in terms of the covariance matrix, but also in terms of the values of the slope, derived from the fitting at the nonrotated and right-angle-rotated axes. The potential of the new method is to improve the derived values of the fitting parameters by detecting the optimal rotation angle, that is, the one that maximizes the correlation coefficient. The presented analysis was applied to the linear regression of density and temperature measurements characterizing the proton plasma in the inner heliosheath, the outer region of our heliosphere.
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Klen, Kateryna, Vadym Martynyuk e Mykhailo Yaremenko. "Prediction of the wind speed change function by linear regression method". Computational Problems of Electrical Engineering 9, n. 2 (10 novembre 2019): 28–33. http://dx.doi.org/10.23939/jcpee2019.02.028.
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In the article the approximation of the function of wind speed changes by linear functions based on Walsh functions and the prediction of function values by linear regression method is made. It is shown that under the condition of a linear change of the internal resistance of the wind generator over time, it is advisable to introduce the wind speed change function with linear approximation. The system of orthonormal linear functions based on Walsh functions is given. As an example, the approximation of the linear-increasing function with a system of 4, 8 and 16 linear functions based on the Walsh functions is given. The result of the approximation of the wind speed change function with a system of 8 linear functions based on Walsh functions is shown. Decomposition coefficients, mean-square and average relative approximation errors for such approximation are calculated. In order to find the parameters of multiple linear regression the method of least squares is applied. The regression equation in matrix form is given. The example of application of the prediction method of linear regression to simple functions is shown. The restoration result for wind speed change function is shown. Decomposition coefficients, mean-square and average relative approximation errors for restoration of wind speed change function with linear regression method are calculated.
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Srivastava, A. K. "Estimation of linear regression model with rank deficient observations matrix under linear restrictions". Microelectronics Reliability 36, n. 1 (gennaio 1996): 109–10. http://dx.doi.org/10.1016/0026-2714(95)00018-w.
Testo completoTesi sul tema "Matrix linear regression"
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Kuljus, Kristi. "Rank Estimation in Elliptical Models : Estimation of Structured Rank Covariance Matrices and Asymptotics for Heteroscedastic Linear Regression". Doctoral thesis, Uppsala universitet, Matematisk statistik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9305.
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This thesis deals with univariate and multivariate rank methods in making statistical inference. It is assumed that the underlying distributions belong to the class of elliptical distributions. The class of elliptical distributions is an extension of the normal distribution and includes distributions with both lighter and heavier tails than the normal distribution. In the first part of the thesis the rank covariance matrices defined via the Oja median are considered. The Oja rank covariance matrix has two important properties: it is affine equivariant and it is proportional to the inverse of the regular covariance matrix. We employ these two properties to study the problem of estimating the rank covariance matrices when they have a certain structure. The second part, which is the main part of the thesis, is devoted to rank estimation in linear regression models with symmetric heteroscedastic errors. We are interested in asymptotic properties of rank estimates. Asymptotic uniform linearity of a linear rank statistic in the case of heteroscedastic variables is proved. The asymptotic uniform linearity property enables to study asymptotic behaviour of rank regression estimates and rank tests. Existing results are generalized and it is shown that the Jaeckel estimate is consistent and asymptotically normally distributed also for heteroscedastic symmetric errors.
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Shrewsbury, John Stephen. "Calibration of trip distribution by generalised linear models". Thesis, University of Canterbury. Department of Civil and Natuaral Resources Engineering, 2012. http://hdl.handle.net/10092/7685.
Testo completoAbstract (sommario):
Generalised linear models (GLMs) provide a flexible and sound basis for calibrating gravity models for trip distribution, for a wide range of deterrence functions (from steps to splines), with K factors and geographic segmentation. The Tanner function fitted Wellington Transport Strategy Model data as well as more complex functions and was insensitive to the formulation of intrazonal and external costs. Weighting from variable expansion factors and interpretation of the deviance under sparsity are addressed.
An observed trip matrix is disaggregated and fitted at the household, person and trip levels with consistent results. Hierarchical GLMs (HGLMs) are formulated to fit mixed logit models, but were unable to reproduce the coefficients of simple nested logit models.
Geospatial analysis by HGLM showed no evidence of spatial error patterns, either as random K factors or as correlations between them. Equivalence with hierarchical mode choice, duality with trip distribution, regularisation, lorelograms, and the modifiable areal unit problem are considered.
Trip distribution is calibrated from aggregate data by the MVESTM matrix estimation package, incorporating period and direction factors in the intercepts. Counts across four screenlines showed a significance similar to a thousand-household travel survey. Calibration was possible only in conjuction with trip end data. Criteria for validation against screenline counts were met, but only if allowance was made for error in the trip end data.
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Wang, Shuo. "An Improved Meta-analysis for Analyzing Cylindrical-type Time Series Data with Applications to Forecasting Problem in Environmental Study". Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/386.
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This thesis provides a case study on how the wind direction plays an important role in the amount of rainfall, in the village of Somi$acute{o}$. The primary goal is to illustrate how a meta-analysis, together with circular data analytic methods, helps in analyzing certain environmental issues. The existing GLS meta-analysis combines the merits of usual meta-analysis that yields a better precision and also accounts for covariance among coefficients. But, it is quite limited since information about the covariance among coefficients is not utilized. Hence, in my proposed meta-analysis, I take the correlations between adjacent studies into account when employing the GLS meta-analysis. Besides, I also fit a time series linear-circular regression as a comparable model. By comparing the confidence intervals of parameter estimates, covariance matrix, AIC, BIC and p-values, I discuss an improvement on the GLS meta analysis model in its application to forecasting problem in Environmental study.
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Kim, Jingu. "Nonnegative matrix and tensor factorizations, least squares problems, and applications". Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42909.
Testo completoAbstract (sommario):
Nonnegative matrix factorization (NMF) is a useful dimension reduction method that has been investigated and applied in various areas. NMF is considered for high-dimensional data in which each element has a nonnegative value, and it provides a low-rank approximation formed by factors whose elements are also nonnegative. The nonnegativity constraints imposed on the low-rank factors not only enable natural interpretation but also reveal the hidden structure of data. Extending the benefits of NMF to multidimensional arrays, nonnegative tensor factorization (NTF) has been shown to be successful in analyzing complicated data sets. Despite the success, NMF and NTF have been actively developed only in the recent decade, and algorithmic strategies for computing NMF and NTF have not been fully studied. In this thesis, computational challenges regarding NMF, NTF, and related least squares problems are addressed.
First, efficient algorithms of NMF and NTF are investigated based on a connection from the NMF and the NTF problems to the nonnegativity-constrained least squares (NLS) problems. A key strategy is to observe typical structure of the NLS problems arising in the NMF and the NTF computation and design a fast algorithm utilizing the structure. We propose an accelerated block principal pivoting method to solve the NLS problems, thereby significantly speeding up the NMF and NTF computation. Implementation results with synthetic and real-world data sets validate the efficiency of the proposed method.
In addition, a theoretical result on the classical active-set method for rank-deficient NLS problems is presented. Although the block principal pivoting method appears generally more efficient than the active-set method for the NLS problems, it is not applicable for rank-deficient cases. We show that the active-set method with a proper starting vector can actually solve the rank-deficient NLS problems without ever running into rank-deficient least squares problems during iterations.
Going beyond the NLS problems, it is presented that a block principal pivoting strategy can also be applied to the l1-regularized linear regression. The l1-regularized linear regression, also known as the Lasso, has been very popular due to its ability to promote sparse solutions. Solving this problem is difficult because the l1-regularization term is not differentiable. A block principal pivoting method and its variant, which overcome a limitation of previous active-set methods, are proposed for this problem with successful experimental results.
Finally, a group-sparsity regularization method for NMF is presented. A recent challenge in data analysis for science and engineering is that data are often represented in a structured way. In particular, many data mining tasks have to deal with group-structured prior information, where features or data items are organized into groups. Motivated by an observation that features or data items that belong to a group are expected to share the same sparsity pattern in their latent factor representations, We propose mixed-norm regularization to promote group-level sparsity. Efficient convex optimization methods for dealing with the regularization terms are presented along with computational comparisons between them. Application examples of the proposed method in factor recovery, semi-supervised clustering, and multilingual text analysis are presented.
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Nasseri, Sahand. "Application of an Improved Transition Probability Matrix Based Crack Rating Prediction Methodology in Florida’s Highway Network". Scholar Commons, 2008. https://scholarcommons.usf.edu/etd/424.
Testo completoAbstract (sommario):
With the growing need to maintain roadway systems for provision of safety and comfort for travelers, network level decision-making becomes more vital than ever. In order to keep pace with this fast evolving trend, highway authorities must maintain extremely effective databases to keep track of their highway maintenance needs. Florida Department of Transportation (FDOT), as a leader in transportation innovations in the U.S., maintains Pavement Condition Survey (PCS) database of cracking, rutting, and ride information that are updated annually.
Crack rating is an important parameter used by FDOT for making maintenance decisions and budget appropriation. By establishing a crack rating threshold below which traveler comfort is not assured, authorities can screen the pavement sections which are in need of Maintenance and Rehabilitation (M&R). Hence, accurate and reliable prediction of crack thresholds is essential to optimize the rehabilitation budget and manpower. Transition Probability Matrices (TPM) can be utilized to accurately predict the deterioration of crack ratings leading to the threshold. Such TPMs are usually developed by historical data or expert or experienced maintenance engineers' opinion. When historical data are used to develop TPMs, deterioration trends have been used vii indiscriminately, i.e. with no discrimination made between pavements that degrade at different rates. However, a more discriminatory method is used in this thesis to develop TPMs based on classifying pavements first into two groups. They are pavements with relatively high traffic and, pavements with a history of excessive degradation due to delayed rehabilitation.
The new approach uses a multiple non-linear regression process to separately optimize TPMs for the two groups selected by prior screening of the database. The developed TPMs are shown to have minimal prediction errors with respect to crack ratings in the database that were not used in the TPM formation. It is concluded that the above two groups are statistically different from each other with respect to the rate of cracking. The observed significant differences in the deterioration trends would provide a valuable tool for the authorities in making critical network-level decisions. The same methodology can be applied in other transportation agencies based on the corresponding databases.
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Кір’ян, М. П. "Веб-система загальноосвітноьої школи з використанням алгоритму оцінювання та збору статистики". Master's thesis, Сумський державний університет, 2019. http://essuir.sumdu.edu.ua/handle/123456789/76750.
Testo completoAbstract (sommario):
Виконано дослідження предметної області, проведений аналіз існуючих рішень, здійснений вибір
методів рішення поставленої задачі. Розроблена зовнішня та внутрішня структура веб-
додатку. Розроблено веб-систему з використанням алгоритму оцінювання та збору
статистики на базі загальноосвітньої школи. Реалізовано алгоритм з використанням Баєсової
лінійної регресії, оскільки на невеликій кількості даних він надає достатньо високу точність.
Розроблено систему з використанням новітніх технологій, які дозволять легко підтримувати
та оновлювати систему у майбутньому.
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Bettache, Nayel. "Matrix-valued Time Series in High Dimension". Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAG002.
Testo completoAbstract (sommario):
L'objectif de cette thèse est de modéliser des séries temporelles à valeurs matricielles dans un cadre de grande dimension. Pour ce faire, la totalité de l'étude est présentée dans un cadre non asymptotique. Nous fournissons d'abord une procédure de test capable de distinguer dans le cas de vecteurs ayant une loi centrée stationnaire si leur matrice de covariance est égale à l'identité ou si elle possède une structure de Toeplitz sparse. Dans un second temps, nous proposons une extension de la régression linéaire matricielle de faible rang à une régression à deux paramètres matriciels qui créent des corrélations entre les lignes et les colonnes des observations. Enfin nous introduisons et estimons un topiques-modèle dynamique où l'espérance des observations est factorisée en une matrice statique et une matrice qui évolue dans le temps suivant un processus autorégressif d'ordre un à valeurs dans un simplexeThe objective of this thesis is to model matrix-valued time series in a high-dimensional framework. To this end, the entire study is presented in a non-asymptotic framework. We first provide a test procedure capable of distinguishing whether the covariance matrix of centered random vectors with centered stationary distribution is equal to the identity or has a sparse Toeplitz structure. Secondly, we propose an extension of low-rank matrix linear regression to a regression model with two matrix-parameters which create correlations between the rows and he columns of the output random matrix. Finally, we introduce and estimate a dynamic topic model where the expected value of the observations is factorizes into a static matrix and a time-dependent matrix following a simplex-valued auto-regressive process of order one
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Žiupsnys, Giedrius. "Klientų duomenų valdymas bankininkystėje". Master's thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20110709_152442-86545.
Testo completoAbstract (sommario):
Darbas apima banko klientų kredito istorinių duomenų dėsningumų tyrimą. Pirmiausia nagrinėjamos banko duomenų saugyklos, siekiant kuo geriau perprasti bankinius duomenis. Vėliau naudojant banko duomenų imtis, kurios apima kreditų grąžinimo istoriją, siekiama įvertinti klientų nemokumo riziką. Tai atliekama adaptuojant algoritmus bei programinę įrangą duomenų tyrimui, kuris pradedamas nuo informacijos apdorojimo ir paruošimo. Paskui pritaikant įvairius klasifikavimo algoritmus, sudarinėjami modeliai, kuriais siekiama kuo tiksliau suskirstyti turimus duomenis, nustatant nemokius klientus. Taip pat siekiant įvertinti kliento vėluojamų mokėti paskolą dienų skaičių pasitelkiami regresijos algoritmai bei sudarinėjami prognozės modeliai. Taigi darbo metu atlikus numatytus tyrimus, pateikiami duomenų vitrinų modeliai, informacijos srautų schema. Taip pat nurodomi klasifikavimo ir prognozavimo modeliai bei algoritmai, geriausiai įvertinantys duotas duomenų imtis.This work is about analysing regularities in bank clients historical credit data. So first of all bank information repositories are analyzed to comprehend banks data. Then using data mining algorithms and software for bank data sets, which describes credit repayment history, clients insolvency risk is being tried to estimate. So first step in analyzis is information preprocessing for data mining. Later various classification algorithms is used to make models wich classify our data sets and help to identify insolvent clients as accurate as possible. Besides clasiffication, regression algorithms are analyzed and prediction models are created. These models help to estimate how long client are late to pay deposit. So when researches have been done data marts and data flow schema are presented. Also classification and regressions algorithms and models, which shows best estimation results for our data sets, are introduced.
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NÓBREGA, Caio Santos Bezerra. "Uma estratégia para predição da taxa de aprendizagem do gradiente descendente para aceleração da fatoração de matrizes". Universidade Federal de Campina Grande, 2014. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/362.
Testo completoAbstract (sommario):
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Capes
Sugerir os produtos mais apropriados aos diversos tipos de consumidores não é uma tarefa trivial, apesar de ser um fator chave para aumentar satisfação e lealdade destes. Devido a esse fato, sistemas de recomendação têm se tornado uma ferramenta importante para diversas aplicações, tais como, comércio eletrônico, sites personalizados e redes sociais. Recentemente, a fatoração de matrizes se tornou a técnica mais bem sucedida de implementação de sistemas de recomendação. Os parâmetros do modelo de fatoração de matrizes são tipicamente aprendidos por meio de métodos numéricos, tal como o gradiente descendente. O desempenho do gradiente descendente está diretamente relacionada à configuração da taxa de aprendizagem, a qual é tipicamente configurada para valores pequenos, com o objetivo de não perder um mínimo local. Consequentemente, o algoritmo pode levar várias iterações para convergir. Idealmente,é desejada uma taxa de aprendizagem que conduza a um mínimo local nas primeiras iterações, mas isto é muito difícil de ser realizado dada a alta complexidade do espaço de valores a serem pesquisados. Começando com um estudo exploratório em várias bases de dados de sistemas de recomendação, observamos que, para a maioria das bases, há um padrão linear entre a taxa de aprendizagem e o número de iterações necessárias para atingir a convergência. A partir disso, propomos utilizar modelos de regressão lineares simples para predizer, para uma base de dados desconhecida, um bom valor para a taxa de aprendizagem inicial. A ideia é estimar uma taxa de aprendizagem que conduza o gradiente descendenteaummínimolocalnasprimeirasiterações. Avaliamosnossatécnicaem8bases desistemasderecomendaçãoreaisecomparamoscomoalgoritmopadrão,oqualutilizaum valorfixoparaataxadeaprendizagem,ecomtécnicasqueadaptamataxadeaprendizagem extraídas da literatura. Nós mostramos que conseguimos reduzir o número de iterações até em 40% quando comparados à abordagem padrão.
Suggesting the most suitable products to different types of consumers is not a trivial task, despite being a key factor for increasing their satisfaction and loyalty. Due to this fact, recommender systems have be come an important tool for many applications, such as e-commerce, personalized websites and social networks. Recently, Matrix Factorization has become the most successful technique to implement recommendation systems. The parameters of this model are typically learned by means of numerical methods, like the gradient descent. The performance of the gradient descent is directly related to the configuration of the learning rate, which is typically set to small values, in order to do not miss a local minimum. As a consequence, the algorithm may take several iterations to converge. Ideally, one wants to find a learning rate that will lead to a local minimum in the early iterations, but this is very difficult to achieve given the high complexity of search space. Starting with an exploratory study on several recommendation systems datasets, we observed that there is an over all linear relationship between the learnin grate and the number of iterations needed until convergence. From this, we propose to use simple linear regression models to predict, for a unknown dataset, a good value for an initial learning rate. The idea is to estimate a learning rate that drives the gradient descent as close as possible to a local minimum in the first iteration. We evaluate our technique on 8 real-world recommender datasets and compared it with the standard Matrix Factorization learning algorithm, which uses a fixed value for the learning rate over all iterations, and techniques fromt he literature that adapt the learning rate. We show that we can reduce the number of iterations until at 40% compared to the standard approach.
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Cavalcanti, Alexsandro Bezerra. "Aperfeiçoamento de métodos estatísticos em modelos de regressão da família exponencial". Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-05082009-170043/.
Testo completoAbstract (sommario):
Neste trabalho, desenvolvemos três tópicos relacionados a modelos de regressão da família exponencial. No primeiro tópico, obtivemos a matriz de covariância assintótica de ordem $n^$, onde $n$ é o tamanho da amostra, dos estimadores de máxima verossimilhança corrigidos pelo viés de ordem $n^$ em modelos lineares generalizados, considerando o parâmetro de precisão conhecido. No segundo tópico calculamos o coeficiente de assimetria assintótico de ordem n^{-1/2} para a distribuição dos estimadores de máxima verossimilhança dos parâmetros que modelam a média e dos parâmetros de precisão e dispersão em modelos não-lineares da família exponencial, considerando o parâmetro de dispersão desconhecido, porém o mesmo para todas as observações. Finalmente, obtivemos fatores de correção tipo-Bartlett para o teste escore em modelos não-lineares da família exponencial, considerando covariáveis para modelar o parâmetro de dispersão. Avaliamos os resultados obtidos nos três tópicos desenvolvidos por meio de estudos de simulação de Monte CarloIn this work, we develop three topics related to the exponential family nonlinear regression. First, we obtain the asymptotic covariance matrix of order $n^$, where $n$ is the sample size, for the maximum likelihood estimators corrected by the bias of order $n^$ in generalized linear models, considering the precision parameter known. Second, we calculate an asymptotic formula of order $n^{-1/2}$ for the skewness of the distribution of the maximum likelihood estimators of the mean parameters and of the precision and dispersion parameters in exponential family nonlinear models considering that the dispersion parameter is the same although unknown for all observations. Finally, we obtain Bartlett-type correction factors for the score test in exponential family nonlinear models assuming that the precision parameter is modelled by covariates. Monte Carlo simulation studies are developed to evaluate the results obtained in the three topics.
Libri sul tema "Matrix linear regression"
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Puntanen, Simo, George P. H. Styan e Jarkko Isotalo. Formulas Useful for Linear Regression Analysis and Related Matrix Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32931-9.
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Grafarend, Erik. Linear and Nonlinear Models: Fixed effects, random effects, and total least squares. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
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Formulas Useful For Linear Regression Analysis And Related Matrix Theory Its Only Formulas But We Like Them. Springer, 2012.
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Formulas Useful for Linear Regression Analysis and Related Matrix Theory: It's Only Formulas but We Like Them. Springer London, Limited, 2013.
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Optimization of Objective Functions: Analytics. Numerical Methods. Design of Experiments. Moscow, Russia: Fizmatlit Publisher, 2009.
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Sobczyk, Eugeniusz Jacek. Uciążliwość eksploatacji złóż węgla kamiennego wynikająca z warunków geologicznych i górniczych. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN, 2022. http://dx.doi.org/10.33223/onermin/0222.
Testo completoAbstract (sommario):
Hard coal mining is characterised by features that pose numerous challenges to its current operations and cause strategic and operational problems in planning its development. The most important of these include the high capital intensity of mining investment projects and the dynamically changing environment in which the sector operates, while the long-term role of the sector is dependent on factors originating at both national and international level. At the same time, the conditions for coal mining are deteriorating, the resources more readily available in active mines are being exhausted, mining depths are increasing, temperature levels in pits are rising, transport routes for staff and materials are getting longer, effective working time is decreasing, natural hazards are increasing, and seams with an increasing content of waste rock are being mined. The mining industry is currently in a very difficult situation, both in technical (mining) and economic terms. It cannot be ignored, however, that the difficult financial situation of Polish mining companies is largely exacerbated by their high operating costs. The cost of obtaining coal and its price are two key elements that determine the level of efficiency of Polish mines. This situation could be improved by streamlining the planning processes. This would involve striving for production planning that is as predictable as possible and, on the other hand, economically efficient. In this respect, it is helpful to plan the production from operating longwalls with full awareness of the complexity of geological and mining conditions and the resulting economic consequences. The constraints on increasing the efficiency of the mining process are due to the technical potential of the mining process, organisational factors and, above all, geological and mining conditions. The main objective of the monograph is to identify relations between geological and mining parameters and the level of longwall mining costs, and their daily output. In view of the above, it was assumed that it was possible to present the relationship between the costs of longwall mining and the daily coal output from a longwall as a function of onerous geological and mining factors. The monograph presents two models of onerous geological and mining conditions, including natural hazards, deposit (seam) parameters, mining (technical) parameters and environmental factors. The models were used to calculate two onerousness indicators, Wue and WUt, which synthetically define the level of impact of onerous geological and mining conditions on the mining process in relation to: —— operating costs at longwall faces – indicator WUe, —— daily longwall mining output – indicator WUt. In the next research step, the analysis of direct relationships of selected geological and mining factors with longwall costs and the mining output level was conducted. For this purpose, two statistical models were built for the following dependent variables: unit operating cost (Model 1) and daily longwall mining output (Model 2). The models served two additional sub-objectives: interpretation of the influence of independent variables on dependent variables and point forecasting. The models were also used for forecasting purposes. Statistical models were built on the basis of historical production results of selected seven Polish mines. On the basis of variability of geological and mining conditions at 120 longwalls, the influence of individual parameters on longwall mining between 2010 and 2019 was determined. The identified relationships made it possible to formulate numerical forecast of unit production cost and daily longwall mining output in relation to the level of expected onerousness. The projection period was assumed to be 2020–2030. On this basis, an opinion was formulated on the forecast of the expected unit production costs and the output of the 259 longwalls planned to be mined at these mines. A procedure scheme was developed using the following methods: 1) Analytic Hierarchy Process (AHP) – mathematical multi-criteria decision-making method, 2) comparative multivariate analysis, 3) regression analysis, 4) Monte Carlo simulation. The utilitarian purpose of the monograph is to provide the research community with the concept of building models that can be used to solve real decision-making problems during longwall planning in hard coal mines. The layout of the monograph, consisting of an introduction, eight main sections and a conclusion, follows the objectives set out above. Section One presents the methodology used to assess the impact of onerous geological and mining conditions on the mining process. Multi-Criteria Decision Analysis (MCDA) is reviewed and basic definitions used in the following part of the paper are introduced. The section includes a description of AHP which was used in the presented analysis. Individual factors resulting from natural hazards, from the geological structure of the deposit (seam), from limitations caused by technical requirements, from the impact of mining on the environment, which affect the mining process, are described exhaustively in Section Two. Sections Three and Four present the construction of two hierarchical models of geological and mining conditions onerousness: the first in the context of extraction costs and the second in relation to daily longwall mining. The procedure for valuing the importance of their components by a group of experts (pairwise comparison of criteria and sub-criteria on the basis of Saaty’s 9-point comparison scale) is presented. The AHP method is very sensitive to even small changes in the value of the comparison matrix. In order to determine the stability of the valuation of both onerousness models, a sensitivity analysis was carried out, which is described in detail in Section Five. Section Six is devoted to the issue of constructing aggregate indices, WUe and WUt, which synthetically measure the impact of onerous geological and mining conditions on the mining process in individual longwalls and allow for a linear ordering of longwalls according to increasing levels of onerousness. Section Seven opens the research part of the work, which analyses the results of the developed models and indicators in individual mines. A detailed analysis is presented of the assessment of the impact of onerous mining conditions on mining costs in selected seams of the analysed mines, and in the case of the impact of onerous mining on daily longwall mining output, the variability of this process in individual fields (lots) of the mines is characterised. Section Eight presents the regression equations for the dependence of the costs and level of extraction on the aggregated onerousness indicators, WUe and WUt. The regression models f(KJC_N) and f(W) developed in this way are used to forecast the unit mining costs and daily output of the designed longwalls in the context of diversified geological and mining conditions. The use of regression models is of great practical importance. It makes it possible to approximate unit costs and daily output for newly designed longwall workings. The use of this knowledge may significantly improve the quality of planning processes and the effectiveness of the mining process.
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Marques, Marcia Alessandra Arantes, a cura di. Estudos Avançados em Ciências Agrárias. Bookerfield Editora, 2022. http://dx.doi.org/10.53268/bkf22040700.
Testo completoAbstract (sommario):
Esta obra tem por objetivo apresentar produções acadêmicas que possuem em comum a grande área Ciências Agrárias. Permeando por este vasto tema, nas próximas páginas serão apresentados trabalhos que abordam sobre Ciência e Tecnologia de Alimentos, Engenharia Agrícola, bem como na Ciência Animal. Desta forma, para melhor direcionar o fluxo da leitura, o livro está dividido em capítulos, nos quais os primeiros apresentados abordam o tema “Ciência e Tecnologia em Alimentos” e apresenta trabalhos desenvolvidos com ênfase em controle de qualidade, aproveitamento de subprodutos e planejamento experimental. Acredito que o controle de qualidade de alimentos e o aproveitamento de subprodutos são temas de grande relevância para nosso país e desta forma, nós como professores e pesquisadores, devemos, por meio da ciência sempre trazer novas pesquisas a fim de preencher lacunas no conhecimento e apresentar novas possibilidades e soluções para o melhor aproveitamento e utilização dos alimentos. Na sequência, são apresentados trabalhos desenvolvidos na temática de produção e caracterização de forrageiras de cereais de inverno, predição da produtividade da cultura da soja por meio da aplicação de modelos de regressão linear, bem como relatar um estudo casos de onfalite em bezerros. Neste sentido, os trabalhos aqui apresentados, alinham-se a estas demandas e trazem novas analises que condizem com as necessidades emergentes da nossa sociedade. Profª. Drª. Heloisa Gabriel Falcão. Instituto Federal de Educação (IFG) – Campus Inhumas O crescimento da economia e da taxa de urbanização de alguns países, especialmente da Ásia, resultou em significativas mudanças no estilo de vida das populações neles residentes, com incrementos no consumo de bens duráveis, energia e alimentos. Além disso, estima-se que a população mundial ultrapassará 8,5 bilhões de pessoas até 2030 e que a maior porção desse crescimento demográfico ocorrerá na China, Índia e Indonésia. Esse contexto representa um desafio para a segurança alimentar e energética mundial, uma vez que, se as tendências atuais forem mantidas, a área agrícola deverá aumentar em cerca de 42 milhões hectares até 2027. Contudo, a limitação de terras agricultáveis permitirá um crescimento de apenas 10% em escala mundial, sendo que, quase metade disso se dará no Brasil e na Argentina. Assim, a América do Sul será a mais importante fonte de expansão agrícola do mundo. Com abundantes recursos naturais e grande potencial de desenvolvimento agropecuário, a América do Sul configura importante elemento estratégico para melhorar a segurança alimentar global. Em particular, o setor agropecuário brasileiro é reconhecido internacionalmente pela elevada inserção no mercado globalizado, com destaque para produção de carne de frango, açúcar, suco de laranja, fumo, café e soja... produtos do agronegócio brasileiro que são campeões no ranking de exportações do mercado global. Outros produtos agropecuários brasileiros que merecem grande destaque por configurarem entre as primeiras posições no ranking mundial de produção e exportação são: carne bovina, óleo de soja, farelo de soja, milho e leite bovino. A pandemia de Covid-19 impactou negativamente a economia mundial em razão das necessidades sanitárias e de distanciamento social. Ainda assim, mesmo em momentos de maiores restrições de circulação e transportes, vários segmentos agropecuários do Brasil experimentaram expressiva elevação na produção e vendas nacionais e internacionais. Isso ocorreu em razão das políticas preventivas de vários países no sentido de garantir a segurança alimentar de suas populações, restringindo as exportações e aumentando as importações de alimentos para ampliar suas reservas estratégicas. Essas políticas preventivas não foram adotadas pelo Brasil e, devido ao desmonte dos estoques reguladores e da redução substancial dos recursos destinados a agricultura familiar desde 2017, o mercado interno foi drasticamente afetado pelas exportações record de 2020 e 2021. A redução da quantidade de milho, soja e carnes, principalmente bovina, no mercado interno promoveu expressivo aumento dos preços num momento onde houve aumento de desemprego e queda de renda das classes menos abastadas da população brasileira. O Brasil, que já tinha voltado ao mapa da fome em 2018, sofreu um aumento de 14% no número de domicílios com algum tipo de insegurança alimentar entre 2018 e 2020. Estima-se que mais de 55% da população brasileira sofreu de insegurança alimentar entre 2020 e 2021, conforme dados da rede Penssan e da Organização das Nações Unidas. Nesse contexto, apesar das reduções dramáticas no volume de recursos públicos destinados a produção cientifica no Brasil, tornou-se ainda mais imprescindível a produção de pesquisas e a disseminação do conhecimento resultante delas. Composto por sete capítulos que apresentam pesquisas relevantes, esse livro pretende contribuir com subsídios significativos para o enfrentamento desse imenso desafio que se apresenta, ainda mais intenso nesses tempos de pós-Covid-19, que é elevar a eficiência da produção agropecuária a fim de garantir melhores condições de segurança alimentar para a população brasileira. O primeiro capítulo apresenta uma proposta de utilização da farinha de okara para o enriquecimento do hamburguer de carne bovina. Um dos produtos mais conhecidos do processamento da soja é o leite de soja ou extrato aquoso de soja. Ele é obtido a partir da lavagem, maceração, aquecimento e filtração dos grãos de soja. O okara é o subproduto solido do processo de filtração que separa o leite de soja. Aproximadamente, 250 g de farinha de okara são obtidos a partir do processamento de cada quilo de soja. Trata-se de um alimento altamente nutritivo, fonte de isoflavonas, antioxidantes, fibras solúveis e insolúveis que, além de auxiliar na redução de colesterol e triglicerídeos, previne a ação carcinogênica do bolo fecal. Os capítulos 2 e 3 apresentam um estudo que desenvolveu e avaliou as características químicas, físicas e funcionais de biscoitos, tipo cookie, com substituição parcial de farinha de trigo por farinha de gérmen de milho. Essa proposta se mostra extremamente relevante do ponto de vista econômico e nutricional. Uma vez que o advento do conflito bélico entre Rússia e Ucrânia tende a reduzir a oferta de trigo no mercado global e elevar seus preços. O Brasil é o segundo maior produtor de milho do planeta e apenas o 21º produtor de trigo. O resultado disso é que o Brasil importa cerca de 50% do trigo consumido no mercado interno. Além disso, o aumento da prevalência de pessoas com sensibilidade ao glúten, apontado pela pesquisa nacional de saúde do IBGE em 2017, torna esse tipo de experimento, muito relevante para o aumento de alternativas alimentares para esse público. O capítulo 4 compreende um estudo que identificou os agentes causadores de mastite em vacas leiteiras. Além disso, avaliou a relação entre a sua ocorrência de mastite e a qualidade do leite. A mastite é uma reação inflamatória da glândula mamária, geralmente associada à presença de microrganismos, que reduz a qualidade do leite e seus derivados, bem como a segurança do consumidor em razão de alterações na composição físico-química e sensorial dos produtos. Trata-se de uma pesquisa de grande relevância, uma vez que a retomada das exportações de leite para a China em 2021 tende a reduzir a oferta no mercado interno. Ainda sem as exportações para a China, o Brasil vendeu cerca de 29 milhões de toneladas de leite para Argélia, Venezuela, Estados Unidos, Argentina e Uruguai em 2021. Isso explica parte da pressão inflacionaria sobre o produto desde o início das medidas de contenção da Covid-19. Nesse contexto, contribuições que auxiliem na melhoria da qualidade e aumento da produtividade são salutares. O capitulo 5 nos relata um experimento que analisou as características químicas e bromatológicas de forragens de cereais de inverno em duas alturas de corte do solo e os benefícios da manutenção da cobertura vegetal na forma de matéria seca. Cereais de inverno, como centeio, trigo, triticale, cevada e aveia, além de produzirem grãos utilizados na alimentação humana, podem servir de alimento para aves, suínos, bovinos de corte, ovinos e, principalmente, vacas leiteiras. Na região Sul do Brasil, durante o inverno, não é incomum que grande parte de áreas agrícolas e máquinas fiquem ociosas. Dessa forma, a produção de cereais de inverno para forragear os rebanhos e para formar reservas para épocas de escassez parece ser uma estratégia viável para melhorar a constância da produtividade animal, gerando renda e diluindo os custos fixos da propriedade rural. Ademais, a manutenção de matéria seca no solo contribui para a redução de custos por meio da conservação da fertilidade do solo e redução da perda de carbono e necessidade de insumos. O sexto capítulo trata da utilização de técnicas de sensoriamento remoto para estimar a produtividade da cultura da soja, com a utilização de imagens de satélite. São apresentados modelos de regressão múltipla para prever a produtividade a partir de índices de vegetação (NDVI, SAVI, NDWI e EVI2). Ainda que pesquisas oficiais com as do IBGE e CONAB estimem a produtividade da soja com relativa precisão em escala estadual, elas são baseadas em abordagens qualitativas com grupos focais. Assim, o desenvolvimento de novas técnicas para o acompanhamento das culturas em escala microrregional pode contribuir para a redução de custos e maior precisão nas pesquisas oficiais. Além disso, os produtores e operadores do agronegócio podem fazer uso de insumos específicos para o planejamento da cultura e tomada de decisões. O capitulo 7, último desse livro, relata um estudo de 30 casos de onfalite em bezerros, dos quais 15 animais foram tratados conservadoramente e 15 submetidos ao tratamento cirúrgico. A onfalite constitui uma infecção dos remanescentes umbilicais cuja evolução pode resultar em óbito do animal ou comprometer o crescimento e rentabilidade do sistema produtivo desse. Os escassos estudos epidemiológicos brasileiros, a respeito dessa afecção umbilical, relatam que entre 21% e 45% dos bezerros neonatos desenvolverão algum nível dessa infecção e desses, entre 5,5% e 10% irão a óbito. Os resultados do estudo descrito nesse capítulo são extremamente relevantes para que criadores, zootecnistas e médicos veterinários tenham maios evidencias na tomada de decisão a respeito dos procedimentos a serem adotadas diante de tal situação. João Francisco Severo Santos. Doutor em Ciências do Ambiente – UFT. Analista de Pesquisas Agropecuárias - IBGE
Capitoli di libri sul tema "Matrix linear regression"
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Groß, Jürgen. "Matrix Algebra". In Linear Regression, 331–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55864-1_7.
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Groß, Jürgen. "The Covariance Matrix of the Error Vector". In Linear Regression, 259–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55864-1_5.
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von Frese, Ralph R. B. "Matrix Linear Regression". In Basic Environmental Data Analysis for Scientists and Engineers, 127–40. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429291210-7.
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Brown, Jonathon D. "Simple Linear Regression". In Linear Models in Matrix Form, 39–67. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11734-8_2.
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Brown, Jonathon D. "Polynomial Regression". In Linear Models in Matrix Form, 341–75. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11734-8_10.
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Brown, Jonathon D. "Multiple Regression". In Linear Models in Matrix Form, 105–45. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11734-8_4.
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Lange, Kenneth. "Linear Regression and Matrix Inversion". In Numerical Analysis for Statisticians, 93–111. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5945-4_7.
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Dinov, Ivo D. "Linear Algebra, Matrix Computing, and Regression Modeling". In The Springer Series in Applied Machine Learning, 149–213. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17483-4_3.
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Hines, Benjamin, Yuriy Kuleshov e Guoqi Qian. "Spatial Modelling of Linear Regression Coefficients for Gauge Measurements Against Satellite Estimates". In 2019-20 MATRIX Annals, 217–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62497-2_11.
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Puntanen, Simo, Jarkko Isotalo e George P. H. Styan. "Formulas Useful for Linear Regression Analysis and Related Matrix Theory". In Formulas Useful for Linear Regression Analysis and Related Matrix Theory, 1–116. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32931-9_1.
Testo completoAtti di convegni sul tema "Matrix linear regression"
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Chen, Xiaojun, Guowen Yuan, Feiping Nie e Joshua Zhexue Huang. "Semi-supervised Feature Selection via Rescaled Linear Regression". In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/211.
Testo completoAbstract (sommario):
With the rapid increase of complex and high-dimensional sparse data, demands for new methods to select features by exploiting both labeled and unlabeled data have increased. Least regression based feature selection methods usually learn a projection matrix and evaluate the importances of features using the projection matrix, which is lack of theoretical explanation. Moreover, these methods cannot find both global and sparse solution of the projection matrix. In this paper, we propose a novel semi-supervised feature selection method which can learn both global and sparse solution of the projection matrix. The new method extends the least square regression model by rescaling the regression coefficients in the least square regression with a set of scale factors, which are used for ranking the features. It has shown that the new model can learn global and sparse solution. Moreover, the introduction of scale factors provides a theoretical explanation for why we can use the projection matrix to rank the features. A simple yet effective algorithm with proved convergence is proposed to optimize the new model. Experimental results on eight real-life data sets show the superiority of the method.
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Chou, Wu. "Maximum a posterior linear regression with elliptically symmetric matrix variate priors". In 6th European Conference on Speech Communication and Technology (Eurospeech 1999). ISCA: ISCA, 1999. http://dx.doi.org/10.21437/eurospeech.1999-4.
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Dufrenois, F., e J. C. Noyer. "Discriminative Hat Matrix: A new tool for outlier identification and linear regression". In 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose). IEEE, 2011. http://dx.doi.org/10.1109/ijcnn.2011.6033300.
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Youwen Zhu, Zhikuan Wang, Cheng Qian e Jian Wang. "On efficiently harnessing cloud to securely solve linear regression and other matrix operations". In 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS). IEEE, 2016. http://dx.doi.org/10.1109/iwqos.2016.7590402.
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Krishna, Y. Hari, G. V. Arunamayi, K. Ramesh Babu, S. Nanda Kishore, M. Rajaiah e B. Mahaboob. "Several matrix algebra applications in linear regression analysis, information theory, ODE and geometry". In FOURTH INTERNATIONAL CONFERENCE ON ADVANCES IN PHYSICAL SCIENCES AND MATERIALS: ICAPSM 2023. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0216119.
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Jing, Wang, Zhou Huizhi, Liu Dichen, Guo Ke e Han Xiangyu. "Research on real-time admittance matrix identification based on WAMS and multiple linear regression". In 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2014. http://dx.doi.org/10.1109/appeec.2014.7066186.
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Lopez, Oscar, Daniel Dunlavy e Richard Lehoucq. "Zero-Truncated Poisson Regression for Multiway Count Data." In Proposed for presentation at the Conference on Random Matrix Theory and Numerical Linear Algebra held June 20-24, 2022 in Seattle, WA. US DOE, 2022. http://dx.doi.org/10.2172/2003556.
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Thiyagarajan, A., e K. Anbazhagan. "Confusion matrix analysis of personal loan fraud detection using novel random forest algorithm and linear regression algorithm". In INTERNATIONAL CONFERENCE ON SCIENCE, ENGINEERING, AND TECHNOLOGY 2022: Conference Proceedings. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0173705.
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Wu, Hsiao-Chun, Shih Yu Chang e Tho Le-Ngoc. "Efficient Rank-Adaptive Least-Square Estimation and Multiple-Parameter Linear Regression Using Novel Dyadically Recursive Hermitian Matrix Inversion". In 2008 International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, 2008. http://dx.doi.org/10.1109/iwcmc.2008.185.
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Thiradathanapattaradecha, Thanapon, Roungsan Chaisricharoen e Thongchai Yooyativong. "The strategic planning of e-commerce business to deployment with TOWS matrix by using K-mean and linear regression". In 2017 International Conference on Digital Arts, Media and Technology (ICDAMT). IEEE, 2017. http://dx.doi.org/10.1109/icdamt.2017.7905001.
Testo completoRapporti di organizzazioni sul tema "Matrix linear regression"
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Castellano, Mike J., Abraham G. Shaviv, Raphael Linker e Matt Liebman. Improving nitrogen availability indicators by emphasizing correlations between gross nitrogen mineralization and the quality and quantity of labile soil organic matter fractions. United States Department of Agriculture, gennaio 2012. http://dx.doi.org/10.32747/2012.7597926.bard.
Testo completoAbstract (sommario):
A major goal in Israeli and U.S. agroecosystems is to maximize nitrogen availability to crops while minimizing nitrogen losses to air and water resources. This goal has presented a significant challenge to global agronomists and scientists because crops require large inputs of nitrogen (N) fertilizer to maximize yield, but N fertilizers are easily lost to surrounding ecosystems where they contribute to water pollution and greenhouse gas concentrations. Determination of the optimum N fertilizer input is complex because the amount of N produced from soil organic matter varies with time, space and management. Indicators of soil N availability may help to guide requirements for N fertilizer inputs and are increasingly viewed as indicators of soil health To address these challenges and improve N availability indicators, project 4550 “Improving nitrogen availability indicators by emphasizing correlations between gross nitrogen mineralization and the quality and quantity of labile organic matter fractions” addressed the following objectives: Link the quantity and quality of labile soil organic matter fractions to indicators of soil fertility and environmental quality including: i) laboratory potential net N mineralization ii) in situ gross N mineralization iii) in situ N accumulation on ion exchange resins iv) crop uptake of N from mineralized soil organic matter sources (non-fertilizer N), and v) soil nitrate pool size. Evaluate and compare the potential for hot water extractable organic matter (HWEOM) and particulate organic matter quantity and quality to characterize soil N dynamics in biophysically variable Israeli and U.S. agroecosystems that are managed with different N fertility sources. Ultimately, we sought to determine if nitrogen availability indicators are the same for i) gross vs. potential net N mineralization processes, ii) diverse agroecosystems (Israel vs. US) and, iii) management strategies (organic vs. inorganic N fertility sources). Nitrogen availability indicators significantly differed for gross vs. potential N mineralization processes. These results highlight that different mechanisms control each process. Although most research on N availability indicators focuses on potential net N mineralization, new research highlights that gross N mineralization may better reflect plant N availability. Results from this project identify the use of ion exchange resin (IERs) beads as a potential technical advance to improve N mineralization assays and predictors of N availability. The IERs mimic the rhizosphere by protecting mineralized N from loss and immobilization. As a result, the IERs may save time and money by providing a measurement of N mineralization that is more similar to the costly and time consuming measurement of gross N mineralization. In further search of more accurate and cost-effective predictors of N dynamics, Excitation- Emission Matrix (EEM) spectroscopy analysis of HWEOM solution has the potential to provide reliable indicators for changes in HWEOM over time. These results demonstrated that conventional methods of labile soil organic matter quantity (HWEOM) coupled with new analyses (EEM) may be used to obtain more detailed information about N dynamics. Across Israeli and US soils with organic and inorganic based N fertility sources, multiple linear regression models were developed to predict gross and potential N mineralization. The use of N availability indicators is increasing as they are incorporated into soil health assessments and agroecosystem models that guide N inputs. Results from this project suggest that some soil variables can universally predict these important ecosystem process across diverse soils, climate and agronomic management. BARD Report - Project4550 Page 2 of 249
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Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs e Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, ottobre 1994. http://dx.doi.org/10.32747/1994.7570549.bard.
Testo completoAbstract (sommario):
The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,