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  1. A controller based on Optimal Type-2 Fuzzy Logic: systematic design, optimization and real-time implementation
    Fayek HM, Elamvazuthi I, Perumal N, Venkatesh B
    ISA Trans, 2014 Sep;53(5):1583-91.
    PMID: 24962934 DOI: 10.1016/j.isatra.2014.06.001
    A computationally-efficient systematic procedure to design an Optimal Type-2 Fuzzy Logic Controller (OT2FLC) is proposed. The main scheme is to optimize the gains of the controller using Particle Swarm Optimization (PSO), then optimize only two parameters per type-2 membership function using Genetic Algorithm (GA). The proposed OT2FLC was implemented in real-time to control the position of a DC servomotor, which is part of a robotic arm. The performance judgments were carried out based on the Integral Absolute Error (IAE), as well as the computational cost. Various type-2 defuzzification methods were investigated in real-time. A comparative analysis with an Optimal Type-1 Fuzzy Logic Controller (OT1FLC) and a PI controller, demonstrated OT2FLC׳s superiority; which is evident in handling uncertainty and imprecision induced in the system by means of noise and disturbances.
  2. Fulltext An algorithmic framework for multiobjective optimization
    Ganesan T, Elamvazuthi I, Shaari KZ, Vasant P
    ScientificWorldJournal, 2013;2013:859701.
    PMID: 24470795 DOI: 10.1155/2013/859701
    Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.
  3. Detection of Voice Pathology using Fractal Dimension in a Multiresolution Analysis of Normal and Disordered Speech Signals
    Ali Z, Elamvazuthi I, Alsulaiman M, Muhammad G
    J Med Syst, 2016 Jan;40(1):20.
    PMID: 26531753 DOI: 10.1007/s10916-015-0392-2
    Voice disorders are associated with irregular vibrations of vocal folds. Based on the source filter theory of speech production, these irregular vibrations can be detected in a non-invasive way by analyzing the speech signal. In this paper we present a multiband approach for the detection of voice disorders given that the voice source generally interacts with the vocal tract in a non-linear way. In normal phonation, and assuming sustained phonation of a vowel, the lower frequencies of speech are heavily source dependent due to the low frequency glottal formant, while the higher frequencies are less dependent on the source signal. During abnormal phonation, this is still a valid, but turbulent noise of source, because of the irregular vibration, affects also higher frequencies. Motivated by such a model, we suggest a multiband approach based on a three-level discrete wavelet transformation (DWT) and in each band the fractal dimension (FD) of the estimated power spectrum is estimated. The experiments suggest that frequency band 1-1562 Hz, lower frequencies after level 3, exhibits a significant difference in the spectrum of a normal and pathological subject. With this band, a detection rate of 91.28 % is obtained with one feature, and the obtained result is higher than all other frequency bands. Moreover, an accuracy of 92.45 % and an area under receiver operating characteristic curve (AUC) of 95.06 % is acquired when the FD of all levels is fused. Likewise, when the FD of all levels is combined with 22 Multi-Dimensional Voice Program (MDVP) parameters, an improvement of 2.26 % in accuracy and 1.45 % in AUC is observed.
  4. Automatic Voice Pathology Detection With Running Speech by Using Estimation of Auditory Spectrum and Cepstral Coefficients Based on the All-Pole Model
    Ali Z, Elamvazuthi I, Alsulaiman M, Muhammad G
    J Voice, 2016 Nov;30(6):757.e7-757.e19.
    PMID: 26522263 DOI: 10.1016/j.jvoice.2015.08.010
    BACKGROUND AND OBJECTIVE: Automatic voice pathology detection using sustained vowels has been widely explored. Because of the stationary nature of the speech waveform, pathology detection with a sustained vowel is a comparatively easier task than that using a running speech. Some disorder detection systems with running speech have also been developed, although most of them are based on a voice activity detection (VAD), that is, itself a challenging task. Pathology detection with running speech needs more investigation, and systems with good accuracy (ACC) are required. Furthermore, pathology classification systems with running speech have not received any attention from the research community. In this article, automatic pathology detection and classification systems are developed using text-dependent running speech without adding a VAD module.

    METHOD: A set of three psychophysics conditions of hearing (critical band spectral estimation, equal loudness hearing curve, and the intensity loudness power law of hearing) is used to estimate the auditory spectrum. The auditory spectrum and all-pole models of the auditory spectrums are computed and analyzed and used in a Gaussian mixture model for an automatic decision.

    RESULTS: In the experiments using the Massachusetts Eye & Ear Infirmary database, an ACC of 99.56% is obtained for pathology detection, and an ACC of 93.33% is obtained for the pathology classification system. The results of the proposed systems outperform the existing running-speech-based systems.

    DISCUSSION: The developed system can effectively be used in voice pathology detection and classification systems, and the proposed features can visually differentiate between normal and pathological samples.

  5. Fulltext Assessments of Process Parameters on Cutting Force and Surface Roughness during Drilling of AA7075/TiB2 In Situ Composite
    Parasuraman S, Elamvazuthi I, Kanagaraj G, Natarajan E, Pugazhenthi A
    Materials (Basel), 2021 Mar 31;14(7).
    PMID: 33807476 DOI: 10.3390/ma14071726
    Reinforced aluminum composites are the basic class of materials for aviation and transport industries. The machinability of these composites is still an issue due to the presence of hard fillers. The current research is aimed to investigate the drilling topographies of AA7075/TiB2 composites. The samples were prepared with 0, 3, 6, 9 and 12 wt.% of fillers and experiments were conducted by varying the cutting speed, feed, depth of cut and tool nose radius. The machining forces and surface topographies, the structure of the cutting tool and chip patterns were examined. The maximum cutting force was recorded upon increase in cutting speed because of thermal softening, loss of strength discontinuity and reduction of the built-up-edge. The increased plastic deformation with higher cutting speed resulted in the excess metal chip. In addition, the increase in cutting speed improved the surface roughness due to decrease in material movement. The cutting force was decreased upon high loading of TiB2 due to the deterioration of chips caused by fillers. Further introduction of TiB2 particles above 12 wt.% weakened the composite; however, due to the impact of the microcutting action of the fillers, the surface roughness was improved.
  6. Measurement of range of motions of L3-L4 healthy spine through offsetting reflective markers and in silico analysis of meshed model
    Kosalishkwaran G, Parasuraman S, Singh DKJ, Natarajan E, Elamvazuthi I, George J
    Med Biol Eng Comput, 2019 Oct;57(10):2305-2318.
    PMID: 31444622 DOI: 10.1007/s11517-019-02026-6
    Degenerative disc disease (DDD) is a common condition in elderly population that can be painful and can significantly affect individual's quality of life. Diagnosis of DDD allows prompt corrective actions but it is challenging due to the absence of any symptoms at early stages. In studying disc degeneration, measurement of the range of motion (RoM) and loads acting on the spine are crucial factors. However, direct measurement of RoM involves increased instrumentation and risk. In this paper, an innovative method is proposed for calculating RoM, emphasizing repeatability and reliability by considering the posterior thickness of the spine. This is achieved by offsetting the position of markers in relation to the actual vertebral loci. Three geometrically identical finite element models of L3-L4 are developed from a CT scan with different types of elements, and thereafter, mesh element-related metrics are provided for the assessment of the quality of models. The model with the best mesh quality is used for further analysis, where RoM are within ranges as reported in literature and in vivo experiment results. Various kinds of stresses acting on individual components including facet joints are analysed for normal and abnormal loading conditions. The results showed that the stresses in abnormal load conditions for all components including cortical (76.67 MPa), cancellous (69.18 MPa), annulus (6.30 MPa) and nucleus (0.343 MPa) are significantly greater as compared to normal loads (49.96 MPa, 44.2 MPa, 4.28 MPa and 0.23 MPa respectively). However, stress levels for both conditions are within safe limits (167-215 MPa for cortical, 46 MPa for the annulus and 3 MPa for facets). The results obtained could be used as a baseline motion and stresses of healthy subjects based on their respective lifestyles, which could benefit clinicians to suggest corrective actions for those affected by DDD.
  7. Fulltext Compression after Impact Behaviour and Failure Analysis of Nanosilica-Toughened Thin Epoxy/GFRP Composite Laminates
    Lal LPJ, Ramesh S, Parasuraman S, Natarajan E, Elamvazuthi I
    Materials (Basel), 2019 Sep 20;12(19).
    PMID: 31547117 DOI: 10.3390/ma12193057
    Nanosilica particles were utilized as secondary reinforcement to enhance the strength of the epoxy resin matrix. Thin glass fibre reinforced polymer (GFRP) composite laminates of 3 ± 0.25 mm were developed with E-Glass mats of 610 GSM and LY556 epoxy resin. Nanosilica fillers were mixed with epoxy resin in the order of 0.25, 0.5, 0.75 and 1 wt% through mechanical stirring followed by an ultrasonication method. Thereafter, the damage was induced on toughened laminates through low-velocity drop weight impact tests and the induced damage was assessed through an image analysis tool. The residual compression strength of the impacted laminates was assessed through compression after impact (CAI) experiments. Laminates with nanosilica as secondary reinforcement exhibited enhanced compression strength, stiffness, and damage suppression. Results of Fourier-transform infrared spectroscopy revealed that physical toughening mechanisms enhanced the strength of the nanoparticle-reinforced composite. Failure analysis of the damaged area through scanning electron microscopy (SEM) evidenced the presence of key toughening mechanisms like damage containment through micro-cracks, enhanced fiber-matrix bonding, and load transfer.
  8. Fulltext Improved Active Disturbance Rejection Control for Trajectory Tracking Control of Lower Limb Robotic Rehabilitation Exoskeleton
    Aole S, Elamvazuthi I, Waghmare L, Patre B, Meriaudeau F
    Sensors (Basel), 2020 Jun 30;20(13).
    PMID: 32630115 DOI: 10.3390/s20133681
    Neurological disorders such as cerebral paralysis, spinal cord injuries, and strokes, result in the impairment of motor control and induce functional difficulties to human beings like walking, standing, etc. Physical injuries due to accidents and muscular weaknesses caused by aging affect people and can cause them to lose their ability to perform daily routine functions. In order to help people recover or improve their dysfunctional activities and quality of life after accidents or strokes, assistive devices like exoskeletons and orthoses are developed. Control strategies for control of exoskeletons are developed with the desired intention of improving the quality of treatment. Amongst recent control strategies used for rehabilitation robots, active disturbance rejection control (ADRC) strategy is a systematic way out from a robust control paradox with possibilities and promises. In this modern era, we always try to find the solution in order to have minimum resources and maximum output, and in robotics-control, to approach the same condition observer-based control strategies is an added advantage where it uses a state estimation method which reduces the requirement of sensors that is used for measuring every state. This paper introduces improved active disturbance rejection control (I-ADRC) controllers as a combination of linear extended state observer (LESO), tracking differentiator (TD), and nonlinear state error feedback (NLSEF). The proposed controllers were evaluated through simulation by investigating the sagittal plane gait trajectory tracking performance of two degrees of freedom, Lower Limb Robotic Rehabilitation Exoskeleton (LLRRE). This multiple input multiple output (MIMO) LLRRE has two joints, one at the hip and other at the knee. In the simulation study, the proposed controllers show reduced trajectory tracking error, elimination of random, constant, and harmonic disturbances, robustness against parameter variations, and under the influence of noise, with improvement in performance indices, indicates its enhanced tracking performance. These promising simulation results would be validated experimentally in the next phase of research.
  9. Fulltext EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review
    Al-Quraishi MS, Elamvazuthi I, Daud SA, Parasuraman S, Borboni A
    Sensors (Basel), 2018 Oct 07;18(10).
    PMID: 30301238 DOI: 10.3390/s18103342
    Electroencephalography (EEG) signals have great impact on the development of assistive rehabilitation devices. These signals are used as a popular tool to investigate the functions and the behavior of the human motion in recent research. The study of EEG-based control of assistive devices is still in early stages. Although the EEG-based control of assistive devices has attracted a considerable level of attention over the last few years, few studies have been carried out to systematically review these studies, as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used for assistive technology. Therefore, this research has three main goals. The first aim is to systematically gather, summarize, evaluate and synthesize information regarding the accuracy and the value of previous research published in the literature between 2011 and 2018. The second goal is to extensively report on the holistic, experimental outcomes of this domain in relation to current research. It is systematically performed to provide a wealthy image and grounded evidence of the current state of research covering EEG-based control for assistive rehabilitation devices to all the experts and scientists. The third goal is to recognize the gap of knowledge that demands further investigation and to recommend directions for future research in this area.
  10. Fulltext Routing Based Multi-Agent System for Network Reliability in the Smart Microgrid
    Singh N, Elamvazuthi I, Nallagownden P, Ramasamy G, Jangra A
    Sensors (Basel), 2020 May 25;20(10).
    PMID: 32466240 DOI: 10.3390/s20102992
    Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman-Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%-43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids.
  11. Fulltext Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework
    Yahya N, Musa H, Ong ZY, Elamvazuthi I
    Sensors (Basel), 2019 Nov 08;19(22).
    PMID: 31717412 DOI: 10.3390/s19224878
    In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.
  12. Fulltext Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors
    Ku Abd Rahim KN, Elamvazuthi I, Izhar LI, Capi G
    Sensors (Basel), 2018 Nov 26;18(12).
    PMID: 30486242 DOI: 10.3390/s18124132
    Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.
  13. Fulltext Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: a focused assistive diagnostic method
    Gupta R, Elamvazuthi I, Dass SC, Faye I, Vasant P, George J, et al.
    Biomed Eng Online, 2014;13:157.
    PMID: 25471386 DOI: 10.1186/1475-925X-13-157
    Disorders of rotator cuff tendons results in acute pain limiting the normal range of motion for shoulder. Of all the tendons in rotator cuff, supraspinatus (SSP) tendon is affected first of any pathological changes. Diagnosis of SSP tendon using ultrasound is considered to be operator dependent with its accuracy being related to operator's level of experience.
  14. Fulltext Impacting clinical evaluation of anterior talofibular ligament injuries through analysis of ultrasound images
    Singh V, Elamvazuthi I, Jeoti V, George J, Swain A, Kumar D
    Biomed Eng Online, 2016;15:13.
    PMID: 26838596 DOI: 10.1186/s12938-016-0129-6
    Anterior talofibular ligament (ATFL) is considered as the weakest ankle ligament that is most prone to injuries. Ultrasound imaging with its portable, non-invasive and non-ionizing radiation nature is increasingly being used for ATFL diagnosis. However, diagnosis of ATFL injuries requires its segmentation from ultrasound images that is a challenging task due to the existence of homogeneous intensity regions, homogeneous textures and low contrast regions in ultrasound images. To address these issues, this research has developed an efficient ATFL segmentation framework that would contribute to accurate and efficient diagnosis of ATFL injuries for clinical evaluation.
  15. Fulltext Bimodal Data Fusion of Simultaneous Measurements of EEG and fNIRS during Lower Limb Movements
    Al-Quraishi MS, Elamvazuthi I, Tang TB, Al-Qurishi M, Adil SH, Ebrahim M
    Brain Sci, 2021 May 27;11(6).
    PMID: 34071982 DOI: 10.3390/brainsci11060713
    Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have temporal and spatial characteristics that may complement each other and, therefore, pose an intriguing approach for brain-computer interaction (BCI). In this work, the relationship between the hemodynamic response and brain oscillation activity was investigated using the concurrent recording of fNIRS and EEG during ankle joint movements. Twenty subjects participated in this experiment. The EEG was recorded using 20 electrodes and hemodynamic responses were recorded using 32 optodes positioned over the motor cortex areas. The event-related desynchronization (ERD) feature was extracted from the EEG signal in the alpha band (8-11) Hz, and the concentration change of the oxy-hemoglobin (oxyHb) was evaluated from the hemodynamics response. During the motor execution of the ankle joint movements, a decrease in the alpha (8-11) Hz amplitude (desynchronization) was found to be correlated with an increase of the oxyHb (r = -0.64061, p < 0.00001) observed on the Cz electrode and the average of the fNIRS channels (ch28, ch25, ch32, ch35) close to the foot area representation. Then, the correlated channels in both modalities were used for ankle joint movement classification. The result demonstrates that the integrated modality based on the correlated channels provides a substantial enhancement in ankle joint classification accuracy of 93.01 ± 5.60% (p < 0.01) compared with single modality. These results highlight the potential of the bimodal fNIR-EEG approach for the development of future BCI for lower limb rehabilitation.
  16. Intra- and Inter-database Study for Arabic, English, and German Databases: Do Conventional Speech Features Detect Voice Pathology?
    Ali Z, Alsulaiman M, Muhammad G, Elamvazuthi I, Al-Nasheri A, Mesallam TA, et al.
    J Voice, 2017 May;31(3):386.e1-386.e8.
    PMID: 27745756 DOI: 10.1016/j.jvoice.2016.09.009
    A large population around the world has voice complications. Various approaches for subjective and objective evaluations have been suggested in the literature. The subjective approach strongly depends on the experience and area of expertise of a clinician, and human error cannot be neglected. On the other hand, the objective or automatic approach is noninvasive. Automatic developed systems can provide complementary information that may be helpful for a clinician in the early screening of a voice disorder. At the same time, automatic systems can be deployed in remote areas where a general practitioner can use them and may refer the patient to a specialist to avoid complications that may be life threatening. Many automatic systems for disorder detection have been developed by applying different types of conventional speech features such as the linear prediction coefficients, linear prediction cepstral coefficients, and Mel-frequency cepstral coefficients (MFCCs). This study aims to ascertain whether conventional speech features detect voice pathology reliably, and whether they can be correlated with voice quality. To investigate this, an automatic detection system based on MFCC was developed, and three different voice disorder databases were used in this study. The experimental results suggest that the accuracy of the MFCC-based system varies from database to database. The detection rate for the intra-database ranges from 72% to 95%, and that for the inter-database is from 47% to 82%. The results conclude that conventional speech features are not correlated with voice, and hence are not reliable in pathology detection.
  17. Stepease™ diabetic socks: An answer to efficacious indoor foot pressure relief- A prospective study
    Soh EZF, Htwe O, Naicker AS, Nasirabadi AR, Ghazali MJ, Mohd Mustafah N, et al.
    J Tissue Viability, 2020 May;29(2):104-109.
    PMID: 32014382 DOI: 10.1016/j.jtv.2020.01.005
    BACKGROUND: Diabetic foot ulcer is commonly seen in people with diabetes mellitus. Inadequate plantar pressure offloading has been identified as a contributing factor to development of diabetic foot ulcers. Various pressure off-loading footwear are widely available in the market but poor compliance has been reported especially for indoor usage. StepEase™ diabetic socks have been designed using Ethylene Vinyl Acetate (EVA) microspheres for better redistribution of plantar pressure. The objective of this study was to determine the efficacy of StepEase™ in redistributing the foot plantar pressure and to assess patients' satisfaction on the usage of the socks.

    METHODS: This was a prospective non randomized clinical trial conducted on 31 patients with diabetes mellitus with high risk foot (King's classification stage II) over a 12 weeks period. Dynamic foot plantar pressure reading was recorded at day 0, 6 weeks and 12 weeks intervals, both barefoot and with StepEase™, using Novel Pedar-X system (Novel GmbH, Munich, Germany). Patients' satisfaction and usage practice were assessed by a questionnaire.

    RESULTS: The mean age of subjects was 57.9 years with mean body mass index (BMI) of 26 kg/m2. The mean duration of diagnosis with diabetes mellitus was 10.2 years. The mean peak plantar pressure was found to be highest at the right forefoot and left heel region, 267.6 kPa (SD113.5 kPa) and 266.3 kPa (SD 94.6 kPa) respectively. There was a statistically significant reduction of mean peak pressure (P 

  18. Fulltext Dataset on the effect of receiver size and acceptance half-angle on the aperture width and height of compound parabolic concentrator for low-concentrating photovoltaic applications using RSM
    Masood F, Nor NBM, Nallagownden P, Elamvazuthi I, Alam MA, Yusuf M, et al.
    Data Brief, 2021 Dec;39:107630.
    PMID: 34988268 DOI: 10.1016/j.dib.2021.107630
    The combined effect of design control factors on the response variables gives valuable information for geometric design optimization of the compound parabolic concentrator. This study presents the data related to the statistical modeling and analysis of variance for aperture width and height of a low concentration symmetric compound parabolic concentrator designed for photovoltaic applications. The design matrix was generated using the response surface modeling approach. The geometric design equations of the proposed concentrator were developed and solved analytically using MATLAB. The empirical models were developed to establish relationships between the control factors and response variables of the proposed system. The analysis of variance was conducted for two significant response variables. The developed statistical models can be used to predict the selected response variables within the permissible range. The presented data can be used for statistical modeling and design optimization of the two-dimensional symmetric compound parabolic concentrator.
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