Journal articles: 'Recursive partial least squares'

1

Helland, Kristian, Hans E. Berntsen, Odd S. Borgen, and Harald Martens. "Recursive algorithm for partial least squares regression." Chemometrics and Intelligent Laboratory Systems 14, no. 1-3 (1992): 129–37. http://dx.doi.org/10.1016/0169-7439(92)80098-o.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Merino, A., D. Garcia-Alvarez, G. I. Sainz-Palmero, L. F. Acebes, and M. J. Fuente. "Knowledge based recursive non-linear partial least squares (RNPLS)." ISA Transactions 100 (May 2020): 481–94. http://dx.doi.org/10.1016/j.isatra.2020.01.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Eliseyev, Andrey, and Tetiana Aksenova. "Recursive N-Way Partial Least Squares for Brain-Computer Interface." PLoS ONE 8, no. 7 (2013): e69962. http://dx.doi.org/10.1371/journal.pone.0069962.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Wang, Xun, Uwe Kruger, and Barry Lennox. "Recursive partial least squares algorithms for monitoring complex industrial processes." Control Engineering Practice 11, no. 6 (2003): 613–32. http://dx.doi.org/10.1016/s0967-0661(02)00096-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Ni, Wangdong, Soon Keat Tan, Wun Jern Ng, and Steven D. Brown. "Localized, Adaptive Recursive Partial Least Squares Regression for Dynamic System Modeling." Industrial & Engineering Chemistry Research 51, no. 23 (2012): 8025–39. http://dx.doi.org/10.1021/ie203043q.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Vahidpour, Vahid, Amir Rastegarnia, Azam Khalili, and Saeid Sanei. "Analysis of partial diffusion recursive least squares adaptation over noisy links." IET Signal Processing 11, no. 6 (2017): 749–57. http://dx.doi.org/10.1049/iet-spr.2016.0544.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Arablouei, Reza, Kutluyil Dogancay, Stefan Werner, and Yih-Fang Huang. "Adaptive Distributed Estimation Based on Recursive Least-Squares and Partial Diffusion." IEEE Transactions on Signal Processing 62, no. 14 (2014): 3510–22. http://dx.doi.org/10.1109/tsp.2014.2327005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Vijaysai, P., R. D. Gudi, and S. Lakshminarayanan. "Identification on Demand Using a Blockwise Recursive Partial Least-Squares Technique†." Industrial & Engineering Chemistry Research 42, no. 3 (2003): 540–54. http://dx.doi.org/10.1021/ie020042r.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Poerio, Dominic V., and Steven D. Brown. "A frequency-localized recursive partial least squares ensemble for soft sensing." Journal of Chemometrics 32, no. 5 (2018): e2999. http://dx.doi.org/10.1002/cem.2999.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Bayrak, Elif Seyma, Kamuran Turksoy, Ali Cinar, Lauretta Quinn, Elizabeth Littlejohn, and Derrick Rollins. "Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models." Journal of Diabetes Science and Technology 7, no. 1 (2013): 206–14. http://dx.doi.org/10.1177/193229681300700126.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Matias, Tiago, Francisco Souza, Rui Araújo, Nuno Gonçalves, and João P. Barreto. "On-line sequential extreme learning machine based on recursive partial least squares." Journal of Process Control 27 (March 2015): 15–21. http://dx.doi.org/10.1016/j.jprocont.2015.01.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Ciaccio, Mark F., Vincent C. Chen, Richard B. Jones, and Neda Bagheri. "The DIONESUS algorithm provides scalable and accurate reconstruction of dynamic phosphoproteomic networks to reveal new drug targets." Integrative Biology 7, no. 7 (2015): 776–91. http://dx.doi.org/10.1039/c5ib00065c.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Chi, Yuejie, Yonina C. Eldar, and Robert Calderbank. "PETRELS: Parallel Subspace Estimation and Tracking by Recursive Least Squares From Partial Observations." IEEE Transactions on Signal Processing 61, no. 23 (2013): 5947–59. http://dx.doi.org/10.1109/tsp.2013.2282910.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Rinnan, Åsmund, Martin Andersson, Carsten Ridder, and Søren Balling Engelsen. "Recursive weighted partial least squares (rPLS): an efficient variable selection method using PLS." Journal of Chemometrics 28, no. 5 (2013): 439–47. http://dx.doi.org/10.1002/cem.2582.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Zumoffen, David, Lautaro Braccia, Patricio Luppi, and Juan C. Gómez. "Subspace-based identification integrated to recursive PLS modeling: A preliminary result." Latin American Applied Research - An international journal 55, no. 2 (2025): 153–58. https://doi.org/10.52292/j.laar.2025.3447.

Full text

Abstract:

In this work a new multivariable dynamic recursive estimation approach is presented. The overall methodology is based on the classical subspace state-space identification (4SID) theory and integrated with the recursive partial least squares (RPLS) tools. The main theory and concepts are shown and some preliminary results are displayed for the classical Shell fractionator process.

APA, Harvard, Vancouver, ISO, and other styles

16

Fu, Y., W. Yang, O. Xu, L. Zhou, and J. Wang. "Soft sensor modelling by time difference, recursive partial least squares and adaptive model updating." Measurement Science and Technology 28, no. 4 (2017): 045101. http://dx.doi.org/10.1088/1361-6501/aa57e2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Khan, Aftab A., J. R. Moyne, and D. M. Tilbury. "Virtual metrology and feedback control for semiconductor manufacturing processes using recursive partial least squares." Journal of Process Control 18, no. 10 (2008): 961–74. http://dx.doi.org/10.1016/j.jprocont.2008.04.014.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Haavisto, Olli, and Heikki Hyötyniemi. "Recursive multimodel partial least squares estimation of mineral flotation slurry contents using optical reflectance spectra." Analytica Chimica Acta 642, no. 1-2 (2009): 102–9. http://dx.doi.org/10.1016/j.aca.2008.11.017.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Zhang, Mingjin, Shizhi Zhang, and Jibran Iqbal. "Key wavelengths selection from near infrared spectra using Monte Carlo sampling–recursive partial least squares." Chemometrics and Intelligent Laboratory Systems 128 (October 2013): 17–24. http://dx.doi.org/10.1016/j.chemolab.2013.07.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Poerio, Dominic V., and Steven D. Brown. "Highly-overlapped, recursive partial least squares soft sensor with state partitioning via local variable selection." Chemometrics and Intelligent Laboratory Systems 175 (April 2018): 104–15. http://dx.doi.org/10.1016/j.chemolab.2018.02.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Islam, Md Nahidul, Glenn Nielsen, Søren Stærke, Anders Kjær, Bjarke Jørgensen, and Merete Edelenbos. "Noninvasive Determination of Firmness and Dry Matter Content of Stored Onion Bulbs Using Shortwave Infrared Imaging with Whole Spectra and Selected Wavelengths." Applied Spectroscopy 72, no. 10 (2018): 1467–78. http://dx.doi.org/10.1177/0003702818792282.

Full text

Abstract:

A firm texture of dry onions is important for consumer acceptance. Both the texture and dry matter content decline during storage, influencing the market value of onions. The main goal of this study was to develop predictive models that in future might form the basis for automated sorting of onions for firmness and dry matter content in the industry. Hyperspectral scanning was conducted in reflectance mode for six commercial batches of onions that were monitored three times during storage. Mean spectra from the region of interest were extracted and partial least squares regression (PLSR) models were constructed. Feature wavelengths were identified using variable selection techniques resulting from interval partial least squares and recursive partial least squares analyses. The PLSR model for firmness gave a root mean square error of cross-validation (RMSECV) of 0.84 N, and a root mean square error of prediction (RMSEP) of 0.73 N, with coefficients of determination ( R2) of 0.72 and 0.83, respectively. The RMSECV and RMSEP of the PLSR model for dry matter content were 0.10% and 0.08%, respectively, with a R2 of 0.58 and 0.79, respectively. The whole wavelength range and selected wavelengths showed nearly similar results for both dry matter content and firmness. The results obtained from this study clearly reveal that hyperspectral imaging of onion bulbs with selected wavelengths, coupled with chemometric modeling, can be used for the noninvasive determination of the firmness and dry matter content of stored onion bulbs.

APA, Harvard, Vancouver, ISO, and other styles

22

Tirta, I. Made, Nawal Ika Susanti, and Yuliani Setia Dewi. "Structural Equation Modeling of the Factors Affecting the Nutritional Status of Children Under Five in Banyuwangi Region using Recursive (one-way) GSCA." Jurnal ILMU DASAR 16, no. 1 (2015): 1. http://dx.doi.org/10.19184/jid.v16i1.534.

Full text

Abstract:

Structural Equation Modeling is one among popular multivariate analysis, especially applied in pschology and marketing. There are two main types of Structural Equation Modeling namely covariance-based or CB-SEM and variance-based or Partial Least Square (PLS)- SEM. Both types have advantages and disadvantage. To overcome its limitation, Generalized Structured Component Analysis (GSCA) was then proposed as an extension of PLS-SEM. In estimating the parameters, GSCA uses Alternating Least Squares (ALS) and in estimating the standard error of the parameter estimates it uses the bootstrap method. In this paper, GSCA is applied to study the causality model of Infant nutritional status, in relation with socio-economic status and infantcare status in Banyuwangi Region. The results show that both socio-economic and infantcare status have significant positive influence on infant nutritional status.Keywords: Alternating least square, generalized structural component analysis, nutritional status of infants, structural equation modelling

APA, Harvard, Vancouver, ISO, and other styles

23

Zhao, Junyu, Lin Jiang, Yuanyuan Shi, et al. "A Hyperspectral Inversion Model of Forest Soil Organic Carbon in Subtropical Red Soil Area Based on Orthogonal Partial Least Square." Journal of Biobased Materials and Bioenergy 16, no. 3 (2022): 474–80. http://dx.doi.org/10.1166/jbmb.2022.2183.

Full text

Abstract:

Soil organic carbon (SOC) is a measureable component of soil organic matter, the widely used partial least squares (PLS) have limited ability in screening variables, a large amount of redundancy in soil hyperspectral data leads to the complexity and instability of the inversion model. In this study, the Eucalyptus plantation soil in subtropical red soil area of southern China was analyzed, orthogonal partial least square (OPLS) was applied to construct models, combined with recursive feature elimination (RFE) for bands screening, and the organic carbon content inversion models with full-band, significant-band, and an RFE feature set was established. The results showed that the number of important principal components of the OPLS inversion model was lower than that of PLS, indicating that the addition of orthogonal verification improved accuracy in the selection of independent variables. Using first derivative and logarithmic first derivative transformation can significantly reduce the redundant data and enhance the sensitivity of hyperspectra to SOC. In conclusion, the OPLS method improves the prediction of traditional SOC linear modelling, reduces the number of dependent variables, and the amount of computation during modelling, which significantly improves the accuracy and stability of the established models.

APA, Harvard, Vancouver, ISO, and other styles

24

Jiang, Ze, Xiaoyan Huang, and Wenping Cao. "RLS-Based Algorithm for Detecting Partial Demagnetization under Both Stationary and Nonstationary Conditions." Energies 15, no. 10 (2022): 3509. http://dx.doi.org/10.3390/en15103509.

Full text

Abstract:

An algorithm was developed detect the partial demagnetization of permanent-magnet synchronous motors (PMSMs) under both stationary and nonstationary conditions. On the basis of the recursive least-squares (RLS) method, the vital component of fault-related harmonics in the current could be extracted on the line, and its proportion to fundamental component could be regarded as the indicator of partial demagnetization faults. The proposed algorithm is fairly easy to realize and could substitute conventional and complicated signal processing methods such as Fourier transform and wavelet transform when detecting partial demagnetization. Experiments with inverter-fed healthy and partially demagnetized PMSMs are carried out to substantiate the effectiveness of proposed algorithm under both stationary and nonstationary conditions. At the end, a way to eliminate the impact of eccentricity fault on the partial demagnetization diagnosis is given.

APA, Harvard, Vancouver, ISO, and other styles

25

Wang, Yongjian, Hongguang Li, and Bo Yang. "Modeling of furnace operation with a new adaptive data echo state network method integrating block recursive partial least squares." Applied Thermal Engineering 171 (May 2020): 115088. http://dx.doi.org/10.1016/j.applthermaleng.2020.115088.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Nørgaard, L., A. Saudland, J. Wagner, J. P. Nielsen, L. Munck, and S. B. Engelsen. "Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy." Applied Spectroscopy 54, no. 3 (2000): 413–19. http://dx.doi.org/10.1366/0003702001949500.

Full text

Abstract:

A new graphically oriented local modeling procedure called interval partial least-squares ( iPLS) is presented for use on spectral data. The iPLS method is compared to full-spectrum partial least-squares and the variable selection methods principal variables (PV), forward stepwise selection (FSS), and recursively weighted regression (RWR). The methods are tested on a near-infrared (NIR) spectral data set recorded on 60 beer samples correlated to original extract concentration. The error of the full-spectrum correlation model between NIR and original extract concentration was reduced by a factor of 4 with the use of iPLS ( r = 0.998, and root mean square error of prediction equal to 0.17% plato), and the graphic output contributed to the interpretation of the chemical system under observation. The other methods tested gave a comparable reduction in the prediction error but suffered from the interpretation advantage of the graphic interface. The intervals chosen by iPLS cover both the variables found by FSS and all possible combinations as well as the variables found by PV and RWR, and iPLS is still able to utilize the first-order advantage.

APA, Harvard, Vancouver, ISO, and other styles

27

Cobbinah, Elizabeth, Oliver Generalao, Sathish Kumar Lageshetty, Indra Adrianto, Seema Singh, and Gerard G. Dumancas. "Using Near-Infrared Spectroscopy and Stacked Regression for the Simultaneous Determination of Fresh Cattle and Poultry Manure Chemical Properties." Chemosensors 10, no. 10 (2022): 410. http://dx.doi.org/10.3390/chemosensors10100410.

Full text

Abstract:

Excessive use of animal manure as fertilizers can lead to pollution through the introduction of nitrogen, phosphorus, and other mineral compounds to the environment. Wet chemical analytical methods are traditionally used to determine the precise chemical composition of manure to manage the application of animal waste to the soil. However, such methods require significant resources to carry out the processes. Affordable, rapid, and accurate methods of analyses of various chemical components present in animal manure, therefore, are valuable in managing soil and mitigating water pollution. In this study, a stacked regression ensemble approach using near-infrared spectroscopy was developed to simultaneously determine the amount of dry matter, total ammonium nitrogen, total nitrogen, phosphorus pentoxide, calcium oxide, magnesium oxide, and potassium oxide contents in both cattle and poultry manure collected from livestock production areas in France and Reunion Island. The performance of the stacked regression, an ensemble learning algorithm that is formed by collating the well-performing models for prediction was then compared with that of various other machine learning techniques, including support vector regression (linear, polynomial, and radial), least absolute shrinkage and selection operator, ridge regression, elastic net, partial least squares, random forests, recursive partitioning and regression trees, and boosted trees. Results show that stack regression performed optimally well in predicting the seven abovementioned chemical constituents in the testing set and may provide an alternative to the traditional partial least squares method for a more accurate and simultaneous method in determining the chemical properties of animal manure.

APA, Harvard, Vancouver, ISO, and other styles

28

Zhao, Huasheng, Bjørn Ursin, and Lasse Amundsen. "Frequency‐wavenumber elastic inversion of marine seismic data." GEOPHYSICS 59, no. 12 (1994): 1868–81. http://dx.doi.org/10.1190/1.1443574.

Full text

Abstract:

We present an inversion method for determining the velocities, densities, and layer thicknesses of a horizontally stratified medium with an acoustic layer at the top and a stack of elastic layers below. The multioffset reflection response of the medium generated by a compressional point source is transformed from the time‐space domain into the frequency‐wavenumber domain where the inversion is performed by minimizing the difference between the reference data and the modeled data using a least‐squares technique. The forward modeling is based on the reflectivity method where the solution for each frequency‐wavenumber component is found by computing the generalized reflection and transmission matrices recursively. The gradient of the objective function is computed from analytical expressions of the Jacobian matrix derived directly from the recursive modeling equations. The partial derivatives of the reflection response of the stratified medium are then computed simultaneously with the reflection response by layer‐recursive formulas. The limited‐aperture and discretization effects in time and space of the reference data are included by applying a pair of frequency and wavenumber dependent filters to the predicted data and to the Jacobian matrix at each iteration. Numerical experiments performed with noise‐free synthetic data prove that the proposed inversion method satisfactorily reconstructs the elastic parameters of a stratified medium. The low‐frequency trends of the S‐wave velocity and density are found when the initial P‐wave velocity model gives approximately correct traveltimes. The convergence of the iterative minimization algorithm is fast.

APA, Harvard, Vancouver, ISO, and other styles

29

Wang, Jing Fang. "Recursive PLS Modeling of Aromatics and Olefins in Gasoline." Advanced Materials Research 740 (August 2013): 267–72. http://dx.doi.org/10.4028/www.scientific.net/amr.740.267.

Full text

Abstract:

The separate calibration models of aromatics and olefins were established for gasoline through recursive partial least square (R-PLS) method in this paper.The some oil refining enterprise application has achieved better effect on the software being realized by R-PLS method. The calibration models were validated through comparison of the results determined by fluorescent indicator adsorption (FIA) and near infrared spectroscopy (NIR) methods.The NIR analysis results were well coincident with those of FIA method.The NIR can not only raise the analysis efficiency and lower the analysis cost,but also has better precision compared with FIA method.

APA, Harvard, Vancouver, ISO, and other styles

30

Gao, Tianyi, Hao Luo, Shen Yin, and Okyay Kaynak. "A recursive modified partial least square aided data-driven predictive control with application to continuous stirred tank heater." Journal of Process Control 89 (May 2020): 108–18. http://dx.doi.org/10.1016/j.jprocont.2020.03.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Li, Chunfu, Jie Zhang, and Guizeng Wang. "BATCH-TO-BATCH OPTIMAL CONTROL OF BATCH PROCESSES BASED ON RECURSIVELY UPDATED NONLINEAR PARTIAL LEAST SQUARES MODELS." Chemical Engineering Communications 194, no. 3 (2006): 261–79. http://dx.doi.org/10.1080/00986440600829796.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Wei, Hsiang-En, Miles Grafton, Michael Bretherton, Matthew Irwin, and Eduardo Sandoval. "Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation." Remote Sensing 13, no. 16 (2021): 3198. http://dx.doi.org/10.3390/rs13163198.

Full text

Abstract:

Monitoring and management of plant water status over the critical period between flowering and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R2 = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards.

APA, Harvard, Vancouver, ISO, and other styles

33

Sun, Xiaoyu, Mudassir Rashid, Nicole Hobbs, Rachel Brandt, Mohammad Reza Askari, and Ali Cinar. "Incorporating Prior Information in Adaptive Model Predictive Control for Multivariable Artificial Pancreas Systems." Journal of Diabetes Science and Technology 16, no. 1 (2021): 19–28. http://dx.doi.org/10.1177/19322968211059149.

Full text

Abstract:

Background: Adaptive model predictive control (MPC) algorithms that recursively update the glucose prediction model are shown to be promising in the development of fully automated multivariable artificial pancreas systems. However, the recursively updated glycemic prediction models do not explicitly consider prior knowledge in the identification of the model parameters. Prior information of the glycemic effects of meals and physical activity can improve model accuracy and yield better glycemic control algorithms. Methods: A glucose prediction model based on regularized partial least squares (rPLS) method where the prior information is encoded as the regularization term is developed to provide accurate predictions of the future glucose concentrations. An adaptive MPC is developed that incorporates dynamic trajectories for the glucose setpoint and insulin dosing constraints based on the estimated plasma insulin concentration (PIC). The proposed adaptive MPC algorithm is robust to disturbances caused by unannounced meals and physical activities even in cases with missing glucose measurements. The effectiveness of the proposed adaptive MPC based on rPLS is investigated with in silico subjects of the multivariable glucose-insulin-physiological variables simulator (mGIPsim). Results: The efficacy of the proposed adaptive MPC strategy in regulating the blood glucose concentration (BGC) of people with T1DM is assessed using the average percent time in range (TIR) for glucose, defined as 70 to 180 mg/dL inclusive, and the average percent time in hypoglycemia (<70 and >54 mg/dL) and level 2 hypoglycemia (≤54 mg/dL). The TIR for a cohort of 20 virtual subjects of mGIPsim is 81.9% ± 7.4% (with no hypoglycemia or severe hypoglycemia) for the proposed MPC compared with 73.9% ± 7.6% (0.2% ± 0.1% in hypoglycemia and 0.1% ± 0.1% in level 2 hypoglycemia) for an MPC based on a recursive autoregressive exogenous (ARX) model. Conclusions: The adaptive MPC algorithm that incorporates prior knowledge in the recursive updating of the glucose prediction model can contribute to the development of fully automated artificial pancreas systems that can mitigate meal and physical activity disturbances.

APA, Harvard, Vancouver, ISO, and other styles

34

Ding, Jianli, Aixia Yang, Jingzhe Wang, Vasit Sagan, and Danlin Yu. "Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy." PeerJ 6 (October 17, 2018): e5714. http://dx.doi.org/10.7717/peerj.5714.

Full text

Abstract:

Soil organic carbon (SOC) is an important soil property that has profound impact on soil quality and plant growth. With 140 soil samples collected from Ebinur Lake Wetland National Nature Reserve, Xinjiang Uyghur Autonomous Region of China, this research evaluated the feasibility of visible/near infrared (VIS/NIR) spectroscopy data (350–2,500 nm) and simulated EO-1 Hyperion data to estimate SOC in arid wetland regions. Three machine learning algorithms including Ant Colony Optimization-interval Partial Least Squares (ACO-iPLS), Recursive Feature Elimination-Support Vector Machine (RF-SVM), and Random Forest (RF) were employed to select spectral features and further estimate SOC. Results indicated that the feature wavelengths pertaining to SOC were mainly within the ranges of 745–910 nm and 1,911–2,254 nm. The combination of RF-SVM and first derivative pre-processing produced the highest estimation accuracy with the optimal values of Rt (correlation coefficient of testing set), RMSEt and RPD of 0.91, 0.27% and 2.41, respectively. The simulated EO-1 Hyperion data combined with Support Vector Machine (SVM) based recursive feature elimination algorithm produced the most accurate estimate of SOC content. For the testing set, Rt was 0.79, RMSEt was 0.19%, and RPD was 1.61. This practice provides an efficient, low-cost approach with potentially high accuracy to estimate SOC contents and hence supports better management and protection strategies for desert wetland ecosystems.

APA, Harvard, Vancouver, ISO, and other styles

35

Forsberg, Jakob, Per Munk Nielsen, Søren Balling Engelsen, and Klavs Martin Sørensen. "On-Line Real-Time Monitoring of a Rapid Enzymatic Oil Degumming Process: A Feasibility Study Using Free-Run Near-Infrared Spectroscopy." Foods 10, no. 10 (2021): 2368. http://dx.doi.org/10.3390/foods10102368.

Full text

Abstract:

Enzymatic degumming is a well established process in vegetable oil refinement, resulting in higher oil yield and a more stable downstream processing compared to traditional degumming methods using acid and water. During the reaction, phospholipids in the oil are hydrolyzed to free fatty acids and lyso-phospholipids. The process is typically monitored by off-line laboratory measurements of the free fatty acid content in the oil, and there is a demand for an automated on-line monitoring strategy to increase both yield and understanding of the process dynamics. This paper investigates the option of using Near-Infrared spectroscopy (NIRS) to monitor the enzymatic degumming reaction. A new method for balancing spectral noise and keeping the chemical information in the spectra obtained from a rapid changing chemical process is suggested. The effect of a varying measurement averaging window width (0 to 300 s), preprocessing method and variable selection algorithm is evaluated, aiming to obtain the most accurate and robust calibration model for prediction of the free fatty acid content (% (w/w)). The optimal Partial Least Squares (PLS) model includes eight wavelength variables, as found by rPLS (recursive PLS) calibration, and yields an RMSECV (Root Mean Square Error of Cross Validation) of 0.05% (w/w) free fatty acid using five latent variables.

APA, Harvard, Vancouver, ISO, and other styles

36

Mageed Hag Elamin, Khalid Abd El. "Particle Filtering for Enhanced Parameter Estimation in Bilinear Systems Under Colored Noise." Current Research in Statistics & Mathematics 3, no. 3 (2024): 01–20. http://dx.doi.org/10.33140/crsm.03.03.01.

Full text

Abstract:

This paper addresses the challenging problem of parameter estimation in bilinear systems under colored noise. A novel approach, termed B-PF-RLS, is proposed, combining a particle filter (PF) with a recursive least squares (RLS) estimator. The B-PF-RLS algorithm tackles the complexities arising from system nonlinearities and colored noise by effectively estimating unknown system states using the particle filter, which are then integrated into the RLS parameter estimation process. Furthermore, the paper introduces an enhanced particle filter that eliminates the need for explicit knowledge of the measurement noise variance, enhancing the method's practicality for real-world applications. Numerical examples demonstrate the B-PF-RLS algorithm's superior performance in accurately estimating both system parameters and states, even under uncertain noise conditions. This work offers a robust and effective solution for system identification in various engineering applications involving bilinear models subject to complex noise environments.

APA, Harvard, Vancouver, ISO, and other styles

37

Pei, Zuo, Zhang, and Wang. "Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis." Molecules 24, no. 14 (2019): 2559. http://dx.doi.org/10.3390/molecules24142559.

Full text

Abstract:

Origin traceability is important for controlling the effect of Chinese medicinal materials and Chinese patent medicines. Paris polyphylla var. yunnanensis is widely distributed and well-known all over the world. In our study, two spectroscopic techniques (Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR)) were applied for the geographical origin traceability of 196 wild P. yunnanensis samples combined with low-, mid-, and high-level data fusion strategies. Partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to establish classification models. Feature variables extraction (principal component analysis—PCA) and important variables selection models (recursive feature elimination and Boruta) were applied for geographical origin traceability, while the classification ability of models with the former model is better than with the latter. FT-MIR spectra are considered to contribute more than NIR spectra. Besides, the result of high-level data fusion based on principal components (PCs) feature variables extraction is satisfactory with an accuracy of 100%. Hence, data fusion of FT-MIR and NIR signals can effectively identify the geographical origin of wild P. yunnanensis.

APA, Harvard, Vancouver, ISO, and other styles

38

Rao, D. Govind, N. S. Murthy, and A. Vengadarajan. "Design and Implementation of Digital Beam Former Architecture for Phased Array Radar." International Journal of Systems Applications, Engineering & Development 16 (January 5, 2022): 9–13. http://dx.doi.org/10.46300/91015.2022.16.2.

Full text

Abstract:

This paper deals with the design and implementation of a digital beam former architecture which is developed for 4/8/12/16 element phased array radar. This technique employs a very high performance FPGA to handle large no of parallel complex arithmetic operations including digital down conversion and filtering. A 3MHz echo signal riding on an IF carrier of 60 MHz is under sampled at 50 MHz and down converted digitally to bring the spectrum to echo signal baseband. After suitable decimation filtering, the I and Q channels are multiplied with Recursive Least Squares based optimized complex weights to form partial beams. The prototype architecture employs techniques of pipelining and parallelism to generate multiple beams simultaneously from a 16 element array within 1 μsec. This can be extended to several number of arrays. The critical components employed in this design are eight 16 bit 125 MS/s ADCs and a very high performance state of the art Xilinx FPGA device Virtex-5 FX 130T having several on-chip resources and 150 MHz clock generators.

APA, Harvard, Vancouver, ISO, and other styles

39

Rizky, Fadila, and Gilang Nugroho. "Determinasi Kepuasan Kerja dan Implikasi terhadap Turnover Intention: Studi pada PT Pupuk Indonesia (Persero)." Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan 18, no. 1 (2024): 96. http://dx.doi.org/10.35931/aq.v18i1.2997.

Full text

Abstract:

Tujuan dari penelitian ini adalah untuk memahami bagaimana kompensasi finansial dan kondisi tenaga kerja mempengaruhi keinginan berpindah yang dikomunikasikan oleh karyawan PT Pupuk Indonesia. Metodologi yang digunakan dalam penelitian ini adalah kuantitatif-deskriptif. Penelitian ini menggunakan data yang dikumpulkan melalui kuesioner terhadap 100 karyawan PT Pupuk Indonesia. Analisis data yang digunakan dalam tulisan ini menggunakan teknik recursive line up untuk mengetahui signifikansi variabel mediator. Analisis data pada penelitian ini menggunakan algoritma Partial Least Squares (PLS) yang diimplementasikan menggunakan aplikasi smartPLS. Hasil penelitian ini menunjukkan bahwa tempat kerja mempunyai pengaruh yang signifikan dan positif terhadap prestasi kerja sedangkan kompensasi finansial mempunyai pengaruh positif terhadap prestasi kerja. Selanjutnya, kepuasan kerja tidak berpengaruh signifikan terhadap turnover intention. penelitian ini juga membuktikan bahwa lingkungan kerja dan kompensasi finansial berpengaruh signifikan terhadap kepuasan kerja secara simultan. Tetapi lingkungan kerja, kompensasi finansial dan kepuasan kerja tidak berpengaruh signifikan terhadap turnover intention secara simultan.

APA, Harvard, Vancouver, ISO, and other styles

40

Schlosser, Aletta Dóra, Gergely Szabó, László Bertalan, Zsolt Varga, Péter Enyedi, and Szilárd Szabó. "Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation." Remote Sensing 12, no. 15 (2020): 2397. http://dx.doi.org/10.3390/rs12152397.

Full text

Abstract:

Urban sprawl related increase of built-in areas requires reliable monitoring methods and remote sensing can be an efficient technique. Aerial surveys, with high spatial resolution, provide detailed data for building monitoring, but archive images usually have only visible bands. We aimed to reveal the efficiency of visible orthophotographs and photogrammetric dense point clouds in building detection with segmentation-based machine learning (with five algorithms) using visible bands, texture information, and spectral and morphometric indices in different variable sets. Usually random forest (RF) had the best (99.8%) and partial least squares the worst overall accuracy (~60%). We found that >95% accuracy can be gained even in class level. Recursive feature elimination (RFE) was an efficient variable selection tool, its result with six variables was like when we applied all the available 31 variables. Morphometric indices had 82% producer’s and 85% user’s Accuracy (PA and UA, respectively) and combining them with spectral and texture indices, it had the largest contribution in the improvement. However, morphometric indices are not always available but by adding texture and spectral indices to red-green-blue (RGB) bands the PA improved with 12% and the UA with 6%. Building extraction from visual aerial surveys can be accurate, and archive images can be involved in the time series of a monitoring.

APA, Harvard, Vancouver, ISO, and other styles

41

Safder, Usman, Jongrack Kim, Gijung Pak, Gahee Rhee, and Kwangtae You. "Investigating Machine Learning Applications for Effective Real-Time Water Quality Parameter Monitoring in Full-Scale Wastewater Treatment Plants." Water 14, no. 19 (2022): 3147. http://dx.doi.org/10.3390/w14193147.

Full text

Abstract:

Environmental sensors are utilized to collect real-time data that can be viewed and interpreted using a visual format supported by a server. Machine learning (ML) methods, on the other hand, are excellent in statistically evaluating complicated nonlinear systems to assist in modeling and prediction. Moreover, it is important to implement precise online monitoring of complex nonlinear wastewater treatment plants to increase stability. Thus, in this study, a novel modeling approach based on ML methods is suggested that can predict the effluent concentration of total nitrogen (TNeff) a few hours ahead. The method consists of different ML algorithms in the training stage, and the best selected models are concatenated in the prediction stage. Recursive feature elimination is utilized to reduce overfitting and the curse of dimensionality by finding and eliminating irrelevant features and identifying the optimal subset of features. Performance indicators suggested that the multi-attention-based recurrent neural network and partial least squares had the highest accurate prediction performance, representing a 41% improvement over other ML methods. Then, the proposed method was assessed to predict the effluent concentration with multistep prediction horizons. It predicted 1-h ahead TNeff with a 98.1% accuracy rate, whereas 3-h ahead effluent TN was predicted with a 96.3% accuracy rate.

APA, Harvard, Vancouver, ISO, and other styles

42

Srisrisawang, Nitikorn, and Gernot R. Müller-Putz. "Transfer Learning in Trajectory Decoding: Sensor or Source Space?" Sensors 23, no. 7 (2023): 3593. http://dx.doi.org/10.3390/s23073593.

Full text

Abstract:

In this study, across-participant and across-session transfer learning was investigated to minimize the calibration time of the brain–computer interface (BCI) system in the context of continuous hand trajectory decoding. We reanalyzed data from a study with 10 able-bodied participants across three sessions. A leave-one-participant-out (LOPO) model was utilized as a starting model. Recursive exponentially weighted partial least squares regression (REW-PLS) was employed to overcome the memory limitation due to the large pool of training data. We considered four scenarios: generalized with no update (Gen), generalized with cumulative update (GenC), and individual models with cumulative (IndC) and non-cumulative (Ind) updates, with each one trained with sensor-space features or source-space features. The decoding performance in generalized models (Gen and GenC) was lower than the chance level. In individual models, the cumulative update (IndC) showed no significant improvement over the non-cumulative model (Ind). The performance showed the decoder’s incapability to generalize across participants and sessions in this task. The results suggested that the best correlation could be achieved with the sensor-space individual model, despite additional anatomical information in the source-space features. The decoding pattern showed a more localized pattern around the precuneus over three sessions in Ind models.

APA, Harvard, Vancouver, ISO, and other styles

43

Zhang, Xianglin, Jie Xue, Yi Xiao, Zhou Shi, and Songchao Chen. "Towards Optimal Variable Selection Methods for Soil Property Prediction Using a Regional Soil Vis-NIR Spectral Library." Remote Sensing 15, no. 2 (2023): 465. http://dx.doi.org/10.3390/rs15020465.

Full text

Abstract:

Soil visible and near-infrared (Vis-NIR, 350–2500 nm) spectroscopy has been proven as an alternative to conventional laboratory analysis due to its advantages being rapid, cost-effective, non-destructive and environmentally friendly. Different variable selection methods have been used to deal with the high redundancy, heavy computation, and model complexity of using full spectra in spectral modelling. However, most previous studies used a linear algorithm in the variable selection, and the application of a non-linear algorithm remains poorly explored. To address the current knowledge gap, based on a regional soil Vis-NIR spectral library (1430 soil samples), we evaluated seven variable selection algorithms together with three predictive algorithms in predicting seven soil properties. Our results showed that Cubist overperformed partial least squares regression (PLSR) and random forests (RF) in most soil properties (R2 > 0.75 for soil organic matter, total nitrogen and pH) when using the full spectra. Most of variable selection can greatly reduce the number of spectral bands and therefore simplified predictive models without losing accuracy. The results also showed that there was no silver bullet for the optimal variable selection algorithm among different predictive algorithms: (1) competitive adaptive reweighted sampling (CARS) always performed best for the PLSR algorithm, followed by forward recursive feature selection (FRFS); (2) recursive feature elimination (RFE) and genetic algorithm (GA) generally had better accuracy than others for the Cubist algorithm; and (3) FRFS had the best model performance for the RF algorithm. In addition, the performance was generally better when the algorithm used in the variable selection matched the predictive algorithm. The outcome of this study provides a valuable reference for predicting soil information using spectroscopic techniques together with variable selection algorithms.

APA, Harvard, Vancouver, ISO, and other styles

44

Chan, S. C., H. C. Wu, and K. M. Tsui. "A New Method for Preliminary Identification of Gene Regulatory Networks from Gene Microarray Cancer Data Using Ridge Partial Least Squares With Recursive Feature Elimination and Novel Brier and Occurrence Probability Measures." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 42, no. 6 (2012): 1514–28. http://dx.doi.org/10.1109/tsmca.2012.2199302.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Mcmurray, Samuel, and Ali Hassan Sodhro. "A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications." Sensors 23, no. 7 (2023): 3470. http://dx.doi.org/10.3390/s23073470.

Full text

Abstract:

Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-Cycle (SDLC). As the prevalence of software systems increases and becomes more integrated into our daily lives, so the complexity of these systems increases the risks of widespread defects. With reliance on these systems increasing, the ability to accurately identify a defective model using Machine Learning (ML) has been overlooked and less addressed. Thus, this article contributes an investigation of various ML techniques for SDP. An investigation, comparative analysis and recommendation of appropriate Feature Extraction (FE) techniques, Principal Component Analysis (PCA), Partial Least Squares Regression (PLS), Feature Selection (FS) techniques, Fisher score, Recursive Feature Elimination (RFE), and Elastic Net are presented. Validation of the following techniques, both separately and in combination with ML algorithms, is performed: Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Decision Tree (DT), and ensemble learning methods Bootstrap Aggregation (Bagging), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Random Forest(RF), and Generalized Stacking (Stacking). Extensive experimental setup was built and the results of the experiments revealed that FE and FS can both positively and negatively affect performance over the base model or Baseline. PLS, both separately and in combination with FS techniques, provides impressive, and the most consistent, improvements, while PCA, in combination with Elastic-Net, shows acceptable improvement.

APA, Harvard, Vancouver, ISO, and other styles

46

Chatraei Azizabadi, Ehsan, Mohamed El-Shetehy, Xiaodong Cheng, Ali Youssef, and Nasem Badreldin. "In-Season Potato Nitrogen Prediction Using Multispectral Drone Data and Machine Learning." Remote Sensing 17, no. 11 (2025): 1860. https://doi.org/10.3390/rs17111860.

Full text

Abstract:

Assessing nitrogen (N) status in potato (Solanum tuberosum L.) during the growing season is crucial for optimizing fertilizer application, aligning it with crop demand, and improving N use efficiency, particularly in Western Canada, where extensive potato cultivation supports the agricultural industry. This study evaluated the performance of three machine learning (ML) models—Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Regression (GBR)—for predicting potato N status and examined the impact of feature selection techniques, including Partial Least Squares Regression (PLSR), Boruta, and Recursive Feature Elimination (RFE). A field experiment was conducted in 2023 and 2024 near Carberry, Manitoba, Canada, with plots receiving different N rates from various fertilizer sources. Multispectral drone imagery was collected throughout the growing seasons, and key vegetation indices (VIs) related to plant N concentration were extracted for model training. Among the VIs, Cl green exhibited the highest correlation with petiole NO3-N concentration (PNC). The results indicate that RF outperformed SVM and GBR, achieving the highest coefficient of determination (R2 = 0.571) and the lowest mean absolute error (MAE = 0.365%) using the RFE feature selection method. Feature selection enhanced model performance in specific cases, notably RF with RFE, and both SVM and GBR with Boruta. These findings highlight the potential of ML-based approaches for in-season potato N monitoring and emphasize the importance of feature selection in enhancing predictive accuracy.

APA, Harvard, Vancouver, ISO, and other styles

47

Li, Hongbo, Dapeng Jiang, Wanjing Dong, Jin Cheng, and Xihai Zhang. "Towards Sustainable and Dynamic Modeling Analysis in Korean Pine Nuts: An Online Learning Approach with NIRS." Foods 13, no. 17 (2024): 2857. http://dx.doi.org/10.3390/foods13172857.

Full text

Abstract:

Due to its advantages such as speed and noninvasive nature, near-infrared spectroscopy (NIRS) technology has been widely used in detecting the nutritional content of nut food. This study aims to address the problem of offline quantitative analysis models producing unsatisfactory results for different batches of samples due to complex and unquantifiable factors such as storage conditions and origin differences of Korean pine nuts. Based on the offline model, an online learning model was proposed using recursive partial least squares (RPLS) regression with online multiplicative scatter correction (OMSC) preprocessing. This approach enables online updates of the original detection model using a small amount of sample data, thereby improving its generalization ability. The OMSC algorithm reduces the prediction error caused by the inability to perform effective scatter correction on the updated dataset. The uninformative variable elimination (UVE) algorithm appropriately increases the number of selected feature bands during the model updating process to expand the range of potentially relevant features. The final model is iteratively obtained by combining new sample feature data with RPLS. The results show that, after OMSC preprocessing, with the number of features increased to 100, the new online model’s R2 value for the prediction set is 0.8945. The root mean square error of prediction (RMSEP) is 3.5964, significantly outperforming the offline model, which yields values of 0.4525 and 24.6543, respectively. This indicates that the online model has dynamic and sustainable characteristics that closely approximate practical detection, and it provides technical references and methodologies for the design and development of detection systems. It also offers an environmentally friendly tool for rapid on-site analysis for nut food regulatory agencies and production enterprises.

APA, Harvard, Vancouver, ISO, and other styles

48

Xue, Weifeng, Fang Li, Xuemei Li, and Ying Liu. "A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize." Molecules 29, no. 13 (2024): 3026. http://dx.doi.org/10.3390/molecules29133026.

Full text

Abstract:

The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography–tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method’s practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.

APA, Harvard, Vancouver, ISO, and other styles

49

Bolla, Marianna, and Fatma Abdelkhalek. "Kalman's filtering technique in structural equation modeling." Studia Universitatis Babes-Bolyai Matematica 66, no. 1 (2021): 179–96. http://dx.doi.org/10.24193/subbmath.2021.1.15.

Full text

Abstract:

"Structural equation modeling finds linear relations between exogenous and endogenous latent and observable random vectors. In this paper, the model equations are considered as a linear dynamical system to which the celebrated R.~E.~K\'alm\'an's filtering technique is applicable. An artificial intelligence is developed, where the partial least squares algorithm of H.~Wold and the block Cholesky decomposition of H.~Kiiveri et al. are combined to estimate the parameter matrices from a training sample. Then the filtering technique introduced is capable to predict the latent variable case values along with the prediction error covariance matrices in the test sample. The recursion goes from case to case along the test sample, without having to re-estimate the parameter matrices. The algorithm is illustrated on real life sociological data."

APA, Harvard, Vancouver, ISO, and other styles

50

R., Vincy, and J. William. "Optimization of Acoustic Echo and Noise Reduction in Non Stationary Environment." International Journal of Advance Research and Innovation 3, no. 1 (2015): 104–8. http://dx.doi.org/10.51976/ijari.311519.

Full text

Abstract:

Optimized speech enhancement method combines acoustic echo reduction and noise reduction in a unified framework for non stationary environment. Simultaneous optimization of noise and echo reduction is already done in stationary environment. In most of the times in transmission, signal properties change over time. We need to remove the artifacts of sound in those conditions. Recursive least square method proposed for noise and echo reduction. It gives little amount of mean square error and better results. Normally, partial optimization of acoustic echo reduction and noise reduction does not lead to total optimization. A cascade method of multiple functions causes mutual interference between these functions and degrades eventual speech enhancement performance. Unlike cascade methods, the proposed method combines all functions to optimize eventual speech enhancement performance based on a unified framework, which is also robust against the mutual interference problem. With the proposed method, in addition to time-invariant linear filters, time-varying filters are used to reduce residual acoustic echo signal, and background noise signal which cannot be reduced using time-invariant filters. These time-invariant filters and time-varying filters are also optimized based on a unified likelihood function to avoid the mutual interference problem. Under this, all the parameters are optimized simultaneously based on the expectation-maximization algorithm and calculates a minimum mean squared error estimate of a desired signal. The experimental results show that the proposed method is superior to the cascade methods.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Recursive partial least squares'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles