Displaying publications 1 - 20 of 167 in total

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  1. Chun TS, Malek MA, Ismail AR
    Water Sci Technol, 2015;71(4):524-8.
    PMID: 25746643 DOI: 10.2166/wst.2014.451
    The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method - namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, non-linear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.
    Matched MeSH terms: Least-Squares Analysis
  2. Lee SK, Yeoh HK, Chua AS, Ngoh GC
    Water Sci Technol, 2012;66(3):620-6.
    PMID: 22744694 DOI: 10.2166/wst.2012.216
    The titrimetric method is used for on-site measurement of the concentration of volatile fatty acids (VFAs) in anaerobic treatment. In current practice, specific and interpolated pH-volume data points are used to obtain the concentration of VFA by solving simultaneous equations iteratively to convergence (denoted as SEq). Here, the least squares method (LSM) is introduced as an elegant alternative. Known concentrations of VFA (acetic acid and/or propionic acid) ranging from to 200 to 1,000 mg/L were determined using SEq and LSM. Using standard numbers of data points, SEq gave more accurate results compared with LSM. However, results favoured LSM when all data points in the range were included without any interpolation. For model refinement, unit monovalent activity coefficient (f(m) = 1) was found reasonable and arithmetic averages of dissociation constants and molecular weight of 80 mol% acetic acid were recommended in the model for VFA determination of mixtures. An accurate result was obtained with a mixture containing more VFA (butyric acid and valeric acid). In a typical VFA measurement of real anaerobic effluent, a satisfactory result with an error of 14% was achieved. LSM appears to be a promising mathematical model solver for determination of concentration of VFA in the titrimetric method. Validation of LSM in the presence of other electrolytes deserves further exploration.
    Matched MeSH terms: Least-Squares Analysis*
  3. Lim JM, Hong AG, Raman S, Shyamala N
    Ultrasound Obstet Gynecol, 2000 Feb;15(2):131-7.
    PMID: 10775996
    To determine whether racial differences affect the relationship between the fetal femur diaphysis length and the neonatal crown-heel length.
    Matched MeSH terms: Least-Squares Analysis
  4. Veerasamy R, Rajak H
    Turk J Pharm Sci, 2021 04 20;18(2):151-156.
    PMID: 33900700 DOI: 10.4274/tjps.galenos.2020.45556
    Objectives: The present study aimed to establish significant and validated quantitative structure-activity relationship (QSAR) models for neuraminidase inhibitors and correlate their physicochemical, steric, and electrostatic properties with their anti-influenza activity.

    Materials and Methods: We have developed and validated 2D and 3D QSAR models by using multiple linear regression, partial least square regression, and k-nearest neighbor-molecular field analysis methods.

    Results: 2D QSAR models had q2: 0.950 and pred_r2: 0.877 and 3D QSAR models had q2: 0.899 and pred_r2: 0.957. These results showed that the models werere predictive.

    Conclusion: Parameters such as hydrogen count and hydrophilicity were involved in 2D QSAR models. The 3D QSAR study revealed that steric and hydrophobic descriptors were negatively contributed to neuraminidase inhibitory activity. The results of this study could be used as platform for design of better anti-influenza drugs.

    Matched MeSH terms: Least-Squares Analysis
  5. Saadatian-Elahi M, Alexander N, Möhlmann T, Langlois-Jacques C, Suer R, Ahmad NW, et al.
    Trials, 2021 May 30;22(1):374.
    PMID: 34053466 DOI: 10.1186/s13063-021-05298-2
    BACKGROUND: In common with many South East Asian countries, Malaysia is endemic for dengue. Dengue control in Malaysia is currently based on reactive vector management within 24 h of a dengue case being reported. Preventive rather than reactive vector control approaches, with combined interventions, are expected to improve the cost-effectiveness of dengue control programs. The principal objective of this cluster randomized controlled trial is to quantify the effectiveness of a preventive integrated vector management (IVM) strategy on the incidence of dengue as compared to routine vector control efforts.

    METHODS: The trial is conducted in randomly allocated clusters of low- and medium-cost housing located in the Federal Territory of Kuala Lumpur and Putrajaya. The IVM approach combines: targeted outdoor residual spraying with K-Othrine Polyzone, deployment of mosquito traps as auto-dissemination devices, and community engagement activities. The trial includes 300 clusters randomly allocated in a 1:1 ratio. The clusters receive either the preventive IVM in addition to the routine vector control activities or the routine vector control activities only. Epidemiological data from monthly confirmed dengue cases during the study period will be obtained from the Vector Borne Disease Sector, Malaysian Ministry of Health e-Dengue surveillance system. Entomological surveillance data will be collected in 12 clusters randomly selected from each arm. To measure the effectiveness of the IVM approach on dengue incidence, a negative binomial regression model will be used to compare the incidence between control and intervention clusters. To quantify the effect of the interventions on the main entomological outcome, ovitrap index, a modified ordinary least squares regression model using a robust standard error estimator will be used.

    DISCUSSION: Considering the ongoing expansion of dengue burden in Malaysia, setting up proactive control strategies is critical. Despite some limitations of the trial such as the use of passive surveillance to identify cases, the results will be informative for a better understanding of effectiveness of proactive IVM approach in the control of dengue. Evidence from this trial may help justify investment in preventive IVM approaches as preferred to reactive case management strategies.

    TRIAL REGISTRATION: ISRCTN ISRCTN81915073 . Retrospectively registered on 17 April 2020.

    Matched MeSH terms: Least-Squares Analysis
  6. Javed S, Ahmad NA
    ScientificWorldJournal, 2014;2014:625280.
    PMID: 24688412 DOI: 10.1155/2014/625280
    An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs.
    Matched MeSH terms: Least-Squares Analysis*
  7. Rohman A, Ariani R
    ScientificWorldJournal, 2013;2013:740142.
    PMID: 24319381 DOI: 10.1155/2013/740142
    Fourier transform infrared spectroscopy (FTIR) combined with multivariate calibration of partial least square (PLS) was developed and optimized for the analysis of Nigella seed oil (NSO) in binary and ternary mixtures with corn oil (CO) and soybean oil (SO). Based on PLS modeling performed, quantitative analysis of NSO in binary mixtures with CO carried out using the second derivative FTIR spectra at combined frequencies of 2977-3028, 1666-1739, and 740-1446 cm(-1) revealed the highest value of coefficient of determination (R (2), 0.9984) and the lowest value of root mean square error of calibration (RMSEC, 1.34% v/v). NSO in binary mixtures with SO is successfully determined at the combined frequencies of 2985-3024 and 752-1755 cm(-1) using the first derivative FTIR spectra with R (2) and RMSEC values of 0.9970 and 0.47% v/v, respectively. Meanwhile, the second derivative FTIR spectra at the combined frequencies of 2977-3028 cm(-1), 1666-1739 cm(-1), and 740-1446 cm(-1) were selected for quantitative analysis of NSO in ternary mixture with CO and SO with R (2) and RMSEC values of 0.9993 and 0.86% v/v, respectively. The results showed that FTIR spectrophotometry is an accurate technique for the quantitative analysis of NSO in binary and ternary mixtures with CO and SO.
    Matched MeSH terms: Least-Squares Analysis
  8. Cappellini MD, Viprakasit V, Taher AT, Georgiev P, Kuo KHM, Coates T, et al.
    N Engl J Med, 2020 03 26;382(13):1219-1231.
    PMID: 32212518 DOI: 10.1056/NEJMoa1910182
    BACKGROUND: Patients with transfusion-dependent β-thalassemia need regular red-cell transfusions. Luspatercept, a recombinant fusion protein that binds to select transforming growth factor β superfamily ligands, may enhance erythroid maturation and reduce the transfusion burden (the total number of red-cell units transfused) in such patients.

    METHODS: In this randomized, double-blind, phase 3 trial, we assigned, in a 2:1 ratio, adults with transfusion-dependent β-thalassemia to receive best supportive care plus luspatercept (at a dose of 1.00 to 1.25 mg per kilogram of body weight) or placebo for at least 48 weeks. The primary end point was the percentage of patients who had a reduction in the transfusion burden of at least 33% from baseline during weeks 13 through 24 plus a reduction of at least 2 red-cell units over this 12-week interval. Other efficacy end points included reductions in the transfusion burden during any 12-week interval and results of iron studies.

    RESULTS: A total of 224 patients were assigned to the luspatercept group and 112 to the placebo group. Luspatercept or placebo was administered for a median of approximately 64 weeks in both groups. The percentage of patients who had a reduction in the transfusion burden of at least 33% from baseline during weeks 13 through 24 plus a reduction of at least 2 red-cell units over this 12-week interval was significantly greater in the luspatercept group than in the placebo group (21.4% vs. 4.5%, P<0.001). During any 12-week interval, the percentage of patients who had a reduction in transfusion burden of at least 33% was greater in the luspatercept group than in the placebo group (70.5% vs. 29.5%), as was the percentage of those who had a reduction of at least 50% (40.2% vs. 6.3%). The least-squares mean difference between the groups in serum ferritin levels at week 48 was -348 μg per liter (95% confidence interval, -517 to -179) in favor of luspatercept. Adverse events of transient bone pain, arthralgia, dizziness, hypertension, and hyperuricemia were more common with luspatercept than placebo.

    CONCLUSIONS: The percentage of patients with transfusion-dependent β-thalassemia who had a reduction in transfusion burden was significantly greater in the luspatercept group than in the placebo group, and few adverse events led to the discontinuation of treatment. (Funded by Celgene and Acceleron Pharma; BELIEVE ClinicalTrials.gov number, NCT02604433; EudraCT number, 2015-003224-31.).

    Matched MeSH terms: Least-Squares Analysis
  9. Noor Dalila IZA, Rosnah I, Ismail NH
    Med J Malaysia, 2019 04;74(2):160-167.
    PMID: 31079128
    INTRODUCTION: Psychosocial stressors appear to alter the state of mind and adoption of overeating behaviour, resulting in high body mass index. This study was conducted to determine the magnitude of psychosocial stressors on male employees' well-being.

    METHOD: This study used secondary data retrieved from a cross-sectional study involving 492 male employees' completed data. Eligible participants completed validated questionnaires of the Psychosocial Safety Climate (PSC-12) scale, short version Demand Induced Strain Compensation (DISQ 2.1), Oldenburg Burnout Inventory - Emotional Exhaustion domain and the Three Eating Factor Questionnaire (TEFQ) -Uncontrolled Eating domain; assessing psychosocial safety climate, job demands and job resources, emotional exhaustion, and uncontrolled eating behaviour, respectively. Body mass index (BMI) was calculated based on weight and height. The research statistical model was tested by two-steps of assessment replicating partial least squares structural equation modelling (PLS-SEM).

    RESULT: The results show that psychosocial stressors (psychosocial safety climate, job demands and job resources) had significant effects on emotional exhaustion (β= -0.149, p=0.004; β= 0.223, p<0.001; β= -0.127, p=0.013). Emotional exhaustion predicted by work stressors may act as a chain reaction which could result in uncontrolled eating (β=0.138, p=0.005) and high BMI (β=0.185, p<0.001). Emotional exhaustion does mediate the relationship between PSC and uncontrolled eating behaviour (β= -0.021 [95% boot CI bias corrected: -0.048, -0.002]).

    CONCLUSION: The psychosocial stressors at work are significant factors for emotional exhaustion, which further signifies the positive effect on uncontrolled eating behaviour and BMI among Malaysian male employees.

    Matched MeSH terms: Least-Squares Analysis
  10. Isa ZM, Tawfiq OF, Noor NM, Shamsudheen MI, Rijal OM
    J Prosthet Dent, 2010 Mar;103(3):182-8.
    PMID: 20188241 DOI: 10.1016/S0022-3913(10)60028-5
    In rehabilitating edentulous patients, selecting appropriately sized teeth in the absence of preextraction records is problematic.
    Matched MeSH terms: Least-Squares Analysis
  11. Lee LC, Liong CY, Jemain AA
    Analyst, 2018 Jul 23;143(15):3526-3539.
    PMID: 29947623 DOI: 10.1039/c8an00599k
    Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. Over the past two decades, PLS-DA has demonstrated great success in modelling high-dimensional datasets for diverse purposes, e.g. product authentication in food analysis, diseases classification in medical diagnosis, and evidence analysis in forensic science. Despite that, in practice, many users have yet to grasp the essence of constructing a valid and reliable PLS-DA model. As the technology progresses, across every discipline, datasets are evolving into a more complex form, i.e. multi-class, imbalanced and colossal. Indeed, the community is welcoming a new era called big data. In this context, the aim of the article is two-fold: (a) to review, outline and describe the contemporary PLS-DA modelling practice strategies, and (b) to critically discuss the respective knowledge gaps that have emerged in response to the present big data era. This work could complement other available reviews or tutorials on PLS-DA, to provide a timely and user-friendly guide to researchers, especially those working in applied research.
    Matched MeSH terms: Least-Squares Analysis
  12. Lee LC, Jemain AA
    Analyst, 2019 Apr 08;144(8):2670-2678.
    PMID: 30849143 DOI: 10.1039/c8an02074d
    In response to our review paper [L. C. Lee et al., Analyst, 2018, 143, 3526-3539], we present a study that compares empirical differences between PLS1-DA and PLS2-DA algorithms in modelling a colossal ATR-FTIR spectral dataset. Over the past two decades, partial least squares-discriminant analysis (PLS-DA) has gained wide acceptance and huge popularity in the field of applied research, partly due to its dimensionality reduction capability and ability to handle multicollinear and correlated variables. To solve a K-class problem (K > 2) using PLS-DA and high-dimensional data like infrared spectra, one can construct either K one-versus-all PLS1-DA models or only one PLS2-DA model. The aim of this work is to explore empirical differences between the two PLS-DA algorithms in modeling a colossal ATR-FTIR spectral dataset. The practical task is to build a prediction model using the imbalanced, high dimensional, colossal and multi-class ATR-FTIR spectra of blue gel pen inks. Four different sub-datasets were prepared from the principal dataset by considering the raw and asymmetric least squares (AsLS) preprocessed forms: (a) Raw-global region; (b) Raw-local region; (c) AsLS-global region; and (d) AsLS-local region. A series of 50 models which includes the first 50 PLS components incrementally was constructed repeatedly using the four sub-datasets. Each model was evaluated using six different variants of v-fold cross validation, autoprediction and external testing methods. As a result, each PLS-DA algorithm was represented by a number of figures of merit. The differences between PLS1-DA and PLS2-DA algorithms were assessed using hypothesis tests with respect to model accuracy, stability and fitting. On the other hand, confusion matrices of the two PLS-DA algorithms were inspected carefully for assessment of model parsimony. Overall, both the algorithms presented satisfactory model accuracy and stability. Nonetheless, PLS1-DA models showed significantly higher accuracy rates than PLS2-DA models, whereas PLS2-DA models seem to be much more stable compared to PLS1-DA models. Eventually, PLS2-DA also proved to be less prone to overfitting and is more parsimonious than PLS1-DA. In conclusion, the relatively high accuracy of the PLS1-DA algorithm is achieved at the cost of rather low parsimony and stability, and with an increased risk of overfitting.
    Matched MeSH terms: Least-Squares Analysis
  13. Goh KM, Maulidiani M, Rudiyanto R, Wong YH, Ang MY, Yew WM, et al.
    Talanta, 2019 Jun 01;198:215-223.
    PMID: 30876552 DOI: 10.1016/j.talanta.2019.01.111
    The technique of Fourier transform infrared spectroscopy is widely used to generate spectral data for use in the detection of food contaminants. Monochloropropanediol (MCPD) is a refining process-induced contaminant that is found in palm-based fats and oils. In this study, a chemometric approach was used to evaluate the relationship between the FTIR spectra and the total MCPD content of a palm-based cooking oil. A total of 156 samples were used to develop partial least squares regression (PLSR), artificial neural network (nnet), average artificial neural network (avNNET), random forest (RF) and cubist models. In addition, a consensus approach was used to generate fusion result consisted from all the model mentioned above. All the models were evaluated based on validation performed using training and testing datasets. In addition, the box plot of coefficient of determination (R2), root mean square error (RMSE), slopes and intercepts by 100 times randomization was also compared. Evaluation of performance based on the testing R2 and RMSE suggested that the cubist model predicted total MCPD content with the highest accuracy, followed by the RF, avNNET, nnet and PLSR models. The overfitting tendency was assessed based on differences in R2 and RMSE in the training and testing calibrations. The observations showed that the cubist and avNNET models possessed a certain degree of overfitting. However, the accuracy of these models in predicting the total MCPD content was high. Results of the consensus model showed that it slightly improved the accuracy of prediction as well as significantly reduced its uncertainty. The important variables derived from the cubist and RF models suggested that the wavenumbers corresponding to the MCPDs originated from the -CH=CH2 or CH=CH (990-900 cm-1) and C-Cl stretch (800-700 cm-1) regions of the FTIR spectrum data. In short, chemometrics in combination with FTIR analysis especially for the consensus model represent a potential and flexible technique for estimating the total MCPD content of refined vegetable oils.
    Matched MeSH terms: Least-Squares Analysis
  14. Ong P, Chen S, Tsai CY, Chuang YK
    PMID: 33744842 DOI: 10.1016/j.saa.2021.119657
    In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R2) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R2 values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.
    Matched MeSH terms: Least-Squares Analysis
  15. Basri KN, Hussain MN, Bakar J, Sharif Z, Khir MFA, Zoolfakar AS
    Spectrochim Acta A Mol Biomol Spectrosc, 2017 Feb 15;173:335-342.
    PMID: 27685001 DOI: 10.1016/j.saa.2016.09.028
    Short wave near infrared spectroscopy (NIR) method was used to detect the presence of lard adulteration in palm oil. MicroNIR was set up in two different scan modes to study the effect of path length to the performance of spectral measurement. Pure and adulterated palm oil sample were classified using soft independent modeling class analogy (SIMCA) algorithm with model accuracy more than 0.95 reported for both transflectance and transmission modes. Additionally, by employing partial least square (PLS) regression, the coefficient of determination (R2) of transflectance and transmission were 0.9987 and 0.9994 with root mean square error of calibration (RMSEC) of 0.5931 and 0.6703 respectively. In order to remove the uninformative variables, variable selection using cumulative adaptive reweighted sampling (CARS) has been performed. The result of R2 and RMSEC after variable selection for transflectance and transmission were improved significantly. Based on the result of classification and quantification analysis, the transmission mode has yield better prediction model compared to the transflectance mode to distinguish the pure and adulterated palm oil.
    Matched MeSH terms: Least-Squares Analysis
  16. Ong P, Jian J, Li X, Zou C, Yin J, Ma G
    PMID: 37356390 DOI: 10.1016/j.saa.2023.123037
    The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380-1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm.
    Matched MeSH terms: Least-Squares Analysis
  17. Ong P, Jian J, Li X, Yin J, Ma G
    PMID: 37804706 DOI: 10.1016/j.saa.2023.123477
    Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220-1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.
    Matched MeSH terms: Least-Squares Analysis
  18. Rahmat MF, Isa MD, Rahim RA, Hussin TA
    Sensors (Basel), 2009;9(12):10291-308.
    PMID: 22303174 DOI: 10.3390/s91210291
    Electrical charge tomography (EChT) is a non-invasive imaging technique that is aimed to reconstruct the image of materials being conveyed based on data measured by an electrodynamics sensor installed around the pipe. Image reconstruction in electrical charge tomography is vital and has not been widely studied before. Three methods have been introduced before, namely the linear back projection method, the filtered back projection method and the least square method. These methods normally face ill-posed problems and their solutions are unstable and inaccurate. In order to ensure the stability and accuracy, a special solution should be applied to obtain a meaningful image reconstruction result. In this paper, a new image reconstruction method - Least squares with regularization (LSR) will be introduced to reconstruct the image of material in a gravity mode conveyor pipeline for electrical charge tomography. Numerical analysis results based on simulation data indicated that this algorithm efficiently overcomes the numerical instability. The results show that the accuracy of the reconstruction images obtained using the proposed algorithm was enhanced and similar to the image captured by a CCD Camera. As a result, an efficient method for electrical charge tomography image reconstruction has been introduced.
    Matched MeSH terms: Least-Squares Analysis
  19. Ravanfar SA, Razak HA, Ismail Z, Monajemi H
    Sensors (Basel), 2015;15(9):22750-75.
    PMID: 26371005 DOI: 10.3390/s150922750
    This paper reports on a two-step approach for optimally determining the location and severity of damage in beam structures under flexural vibration. The first step focuses on damage location detection. This is done by defining the damage index called relative wavelet packet entropy (RWPE). The damage severities of the model in terms of loss of stiffness are assessed in the second step using the inverse solution of equations of motion of a structural system in the wavelet domain. For this purpose, the connection coefficient of the scaling function to convert the equations of motion in the time domain into the wavelet domain is applied. Subsequently, the dominant components based on the relative energies of the wavelet packet transform (WPT) components of the acceleration responses are defined. To obtain the best estimation of the stiffness parameters of the model, the least squares error minimization is used iteratively over the dominant components. Then, the severity of the damage is evaluated by comparing the stiffness parameters of the identified model before and after the occurrence of damage. The numerical and experimental results demonstrate that the proposed method is robust and effective for the determination of damage location and accurate estimation of the loss in stiffness due to damage.
    Matched MeSH terms: Least-Squares Analysis
  20. Silalahi DD, Midi H, Arasan J, Mustafa MS, Caliman JP
    Sensors (Basel), 2020 Sep 03;20(17).
    PMID: 32899292 DOI: 10.3390/s20175001
    The extraction of relevant wavelengths from a large dataset of Near Infrared Spectroscopy (NIRS) is a significant challenge in vibrational spectroscopy research. Nonetheless, this process allows the improvement in the chemical interpretability by emphasizing the chemical entities related to the chemical parameters of samples. With the complexity in the dataset, it may be possible that irrelevant wavelengths are still included in the multivariate calibration. This yields the computational process to become unnecessary complex and decreases the accuracy and robustness of the model. In multivariate analysis, Partial Least Square Regression (PLSR) is a method commonly used to build a predictive model from NIR spectral data. However, in the PLSR method and common commercial chemometrics software, there is no standard wavelength selection procedure applied to screen the irrelevant wavelengths. In this study, a new robust wavelength selection procedure called the modified VIP-MCUVE (mod-VIP-MCUVE) using Filter-Wrapper method and input scaling strategy is introduced. The proposed method combines the modified Variable Importance in Projection (VIP) and modified Monte Carlo Uninformative Variable Elimination (MCUVE) to calculate the scale matrix of the input variable. The modified VIP uses the orthogonal components of Partial Least Square (PLS) in investigating the informative variable in the model by applying the amount of variation both in X and y{SSX,SSY}, simultaneously. The modified MCUVE uses a robust reliability coefficient and a robust tolerance interval in the selection procedure. To evaluate the superiority of the proposed method, the classical VIP, MCUVE, and autoscaling procedure in classical PLSR were also included in the evaluation. Using artificial data with Monte Carlo simulation and NIR spectral data of oil palm (Elaeis guineensis Jacq.) fruit mesocarp, the study shows that the proposed method offers advantages to improve model interpretability, to be computationally extensive, and to produce better model accuracy.
    Matched MeSH terms: Least-Squares Analysis
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