Displaying publications 21 - 40 of 266 in total

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  1. Characterization of spatial patterns in river water quality using chemometric techniques
    Norshidah Baharuddin, Nor'ashikin Saim, Sharifuddin M. Zain, Hafizan Juahir, Rozita Osman, Aziah Aziz
    Sains Malaysiana, 2014;43:1355-1362.
    Water pollution has become a growing threat to human society and natural ecosystem in recent decades, increasing the need to better understand the variabilities of pollutants within aquatic systems. This study presents the application of two chemometric techniques, namely, cluster analysis (CA) and principal component analysis (PCA). This is to classify and identify the water quality variables into groups of similarities or dissimilarities and to determine their significance. Six stations along Kinta River, Perak, were monitored for 30 physical and chemical parameters during the period of 1997-2006. Using CA, the 30 physical and chemical parameters were classified into 4 clusters; PCA was applied to the datasets and resulted in 10 varifactors with a total variance of 78.06%. The varifactors obtained indicated the significance of each of the variables to the pollution of Kinta River.
    Matched MeSH terms: Principal Component Analysis
  2. Fulltext Determination of elements (Cd, Cu, Fe, Pb and Zn) in Universiti Teknologi MARA (UiTM) Hostel Shah Alam, Malaysia
    Nurzulaifa Shaheera Erne Mohd Yasim, Siti Khadijah Mat Yaacob, Noradila Mohamed
    Science Letters, 2018;12(2):28-36.
    MyJurnal
    The purpose of this study is to determine the concentration of the selected elemental composition in a multi-storey hostel. Dust samples were taken from three random rooms at each level of the student hostel by sweeping the floor. The concentrations of elements (Cd, Cu, Fe, Pb and Zn) were determined by using Inductively Coupled Plasma-Optical Emission Spectrometer (ICPOES) after digestion with nitric acid and sulfuric acid solutions. Dust samples analysis has shown the different levels of sampling point does not affect the concentration of the elements. The concentration of elements in investigated microenvironment was in the order of Fe > Zn > Cu > Pb > Cd. The correlation analysis was applied to elements variable in order to identify the sources of an airborne contaminant. It was discovered the strong positive correlation between Cu-Zn which indicates the sources come from traffic emission and street dust. This result was supported by the Principal Component Analysis (PCA) that revealed the presence of elements in the student hostel originated from the outdoor sources.
    Matched MeSH terms: Principal Component Analysis
  3. Fulltext The effect of resting morphological lip shape during lip movement: A three-dimensional motion analysis study
    Nasir SH, Popat H, Richmond S
    Heliyon, 2020 Jun;6(6):e04093.
    PMID: 32514484 DOI: 10.1016/j.heliyon.2020.e04093
    Purpose: The aim of this study was to determine the influence of different morphological lip shape during lip movement.

    Method: A sample of 80 individuals with three-dimensional facial images at rest and during speech were recorded. Subjects were asked to pronounce four bilabial words in a relaxed manner and scanned using the 3dMDFace™ Dynamic System at 48 frames per second. Six lip landmarks were identified at rest and the landmark displacement vectors for the frame of maximal lip movement for all six visemes were recorded. Principal component analysis was applied to isolate relationship between lip traits and their registered coordinates. Eight specific resting morphological lip traits were identified for each individual. The principal component (PC) scores for each viseme were labelled by lip morphological trait and were graphically visualized as ellipses to discriminate any differences in lip movement.

    Results: The first five PCs accounted for up to 95% of the total variance in lip shape during movement, with PC1 accounting for at least 38%. There was no clear discrimination between PC1, PC2 and PC3 for any of the resting morphological lip traits.

    Conclusion: Lip shapes during movement are more uniform between individuals and resting morphological lip shape does not influence movement of the lips.

    Matched MeSH terms: Principal Component Analysis
  4. Fulltext Ambient factors influencing Photovoltaic module temperature in Klang valley
    Hedzlin Zainuddin, Maisarah Ismail, Nurul Hidayah Bostamam, Muhamad Mukhzani Muhamad Hanifah, Mohamad Fariz Mohamad Taib, Mohamad Zhafran Hussin
    Science Letter, 2016;10(2):23-25.
    MyJurnal
    The study is conducted to evaluate the significance of solar irradiance, ambient temperature and relative humidity as predictors and to quantify the relative contribution of these ambient parameters as predictors for photovoltaic module temperature model. The module temperature model was developed from experimental data of mono-crystalline and poly-crystalline PV modules retrofitted on metal roof in Klang Valley. The model was developed and analyzed using Multiple Linear Regressions (MLR) and Principle Component Analysis (PCA) Techniques. Solar irradiance, ambient temperature and relative humidity have been proven to be the significant predictors for module temperature. For poly-crystalline PV module, the relative contribution of solar irradiance, ambient temperature and relative humidity are 64.28 %, 17.45 % and 12.64 % respectively. For mono-crystalline PV module, the relative contribution of solar irradiance, ambient temperature and relative humidity are 66.12 %, 17.46 % and 12.48 % respectively. Thus, there is no significant difference in terms of relative contribution of these ambient parameters towards photovoltaic module temperature between poly-crystalline and mono-crystalline PV module technologies.
    Matched MeSH terms: Principal Component Analysis
  5. Fulltext Effects of baseline correction algorithms on forensic classification of paper based on ATR-FTIR spectrum and Principal Component Analysis (PCA)
    Lee, L.C., Liong, C-Y., Khairul, O., Jemain, A.A.
    Pertanika Journal of Science & Technology, 2017;25(3):767-774.
    MyJurnal
    Spectral data is often required to be pre-processed prior to applying a multivariate modelling technique. Baseline correction of spectral data is one of the most important and frequently applied pre-processing procedures. This preliminary study aims to investigate the impacts of six types of baseline correction algorithms on classifying 150 infrared spectral data of three varieties of paper. The algorithms investigated were Iterative Restricted Least Squares, Asymmetric Least Squares (ALS), Low-pass FFT Filter, Median Window (MW), Fill Peaks and Modified Polynomial Fitting. Processed spectral data were then analysed using Principal Component Analysis (PCA) to visually examine the clustering among the three varieties of paper. Results show that separation among the three varieties of paper is greatly improved after baseline correction via ALS, FP and MW algorithms.
    Matched MeSH terms: Principal Component Analysis
  6. Fulltext Point Set Denoising Using Bootstrap-Based Radial Basis Function
    Liew KJ, Ramli A, Abd Majid A
    PLoS One, 2016;11(6):e0156724.
    PMID: 27315105 DOI: 10.1371/journal.pone.0156724
    This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.
    Matched MeSH terms: Principal Component Analysis
  7. Statistical validation for the profiling of heroin by associating simulated postcut samples with the corresponding precut sample
    Chan KW, Tan GH, Wong RC
    J Forensic Sci, 2013 Jan;58 Suppl 1:S199-207.
    PMID: 23013257 DOI: 10.1111/j.1556-4029.2012.02285.x
    Statistical validation is crucial for the clustering of unknown samples. This study aims to demonstrate how statistical techniques can be optimized using simulated heroin samples containing a range of analyte concentrations that are similar to those of the case samples. Eight simulated heroin distribution links consisting of 64 postcut samples were prepared by mixing one of two mixtures of paracetamol-caffeine-dextromethorphan at different proportions with eight precut samples. Analyte contents and compositional variation of the prepared samples were investigated. A number of data pretreatments were evaluated by associating the postcut samples with the corresponding precut samples using principal component analysis and discriminant analysis. Subsequently, combinations of seven linkage methods and five distance measures were explored using hierarchical cluster analysis. In this study, Ward-Manhattan showed better distinctions between unrelated links and was able to cluster all related samples in very close distance under the known links on a dendogram. A similar discriminative outcome was also achieved by 90 unknown case samples when clustered via Ward-Manhattan.
    Matched MeSH terms: Principal Component Analysis
  8. Development of an objective method for the comparison of fired projectiles using an air pistol as a template
    Hamzah NH
    Forensic Sci Int, 2016 07;264:106-12.
    PMID: 27081766 DOI: 10.1016/j.forsciint.2016.03.050
    The ability to objectify ballistic evidence is a challenge faced by firearms examiners around the world. A number of researchers are trying to improve bullet-identification systems to address deficiencies detailed within the National Academy of Science report (2009). More recently focus has turned to making use of more sophisticated imaging modalities to view entire regions of the projectile and the development of automated systems for the comparison of the topographical surfaces recorded. Projectiles from a newly bought air pistol with 0.177 calibre pellets (unjacketed), fired series of 609 pellets were examined using an optical microscope. A mathematical methodology was developed to pre-process the resultant topographical maps generating point data for comparison, analysed using the principal component analysis (PCA). In most cases limited to reasonable success was achieved. The objective method still requires an operator to identify the Land Engraved Areas to be scanned, however the mathematical alignments were objectively achieved. The PCA results illustrated that the striation marks were neither exclusive nor specific to the LEA regions but rather crossed over regions. This study also proves that a single weapon does not necessarily leave identical marks of projectiles on its surface.
    Matched MeSH terms: Principal Component Analysis
  9. Fulltext Dimension manifestation of users’ psychographic traits towards online travel agency
    Abdul Aziz Azdel, Khairil Wahidin Awang, Raja Nerina Raja Yusof, Salleh Mohd Radzi, Mohd Noor Ismawi Ismail
    Journal of Academia Universiti Teknologi MARA Negeri Sembilan, 2019;7(101):91-101.
    MyJurnal
    The purpose of this study is to assess the determinants of psychological traits towards users’ technology experience specifically on Online Travel Agencies (OTA) via exploratory factor analysis (EFA). Working on these issues and after sequences of analyses to verify reliability and factor structure, the final 16 items of Technology Readiness 2.0 (TR2.0) with 4 items for each dimension (Optimism, Innovativeness, Discomfort, and Insecurity) have been congregated. Through an online survey, the technology readiness determinants were administered to 100 travelers at KLIA2 who have experienced on OTA. EFA using Principal Component Analysis with Varimax Rotation indicated 14 items, with 4 factors final solution with the following subscales: Innovativeness (4 items); Optimism (4 items); Discomfort (3 items); and Insecurities (3 items). All in all, only two items were removed from the original total of 16 items by the factor analysis based on the factor loading matrix for this final solution. This study basically plays an important role in contributing to the existing literature on the OTA users’ standpoint by using an approach which is very powerful to redefine the factors within Technology Readiness. This enhancement has reorganized the items according to their importance specifically towards new perspective which are OTA users in Malaysia setting.
    Matched MeSH terms: Principal Component Analysis
  10. Fulltext Development of long-term talent in sports model based on anthropometric and motor fitness components using multivariate analysis
    Liyana Daud, Mohamad Razali Abdullah, Siti Musliha Mat-Rasid, Ahmad Bisyri Husin Musawi Maliki, Amr Alnaimat, Muhammad Rabani Hashim, et al.
    Malaysian Journal of Applied Sciences, 2019;4(1):38-47.
    MyJurnal
    The study attempts to use multivariate analysis to evaluate the profile of male player for developments of Long-Term Talent in Sports (LT-TiS) model based on anthropometric and motor fitness components. Data of anthropometric and motor fitness included power, flexibility, coordination and speed were obtained from 2019 respondents aged 7.32±0.52 year. Data interpretations were carried out using multivariate analysis of Principle Components Analysis (PCA) and Discriminant analysis (DA). The adequacy of sampling has been measured using Bartletts tests on sphericity and Kaiser-Meyer-Olkin (KMO) has been used, with this conformance of running the Principal Component Analysis (PCA). Then, Discriminant Analysis (DA) were used to validate the correctness of group classification by LT-TiS model. Then, Discriminant Analysis (DA) were used to validate the correctness of group classification by LT-TiS. As a result, two factors with eigenvalues greater than 1 were extracted which accounted for 55.00% of the variations present in the original variables was found. The two factors were used to obtain the factor score coefficients explained by 27.86% and 27.21% of the variations in player performance respectively. Factor 1 revealed high factor loading on motor fitness compared to factor 2 as it was significantly related to anthropometrics. A model was obtained using standardized coefficient of factor 1. Three clusters of performance were shaped in view by categorizing; LT−TiS≥65%, 40%≤LT−TiS
    Matched MeSH terms: Principal Component Analysis
  11. RERS-CC: Robotic facial recognition system for improving the accuracy of human face identification using HRI
    Jing W, Tao H, Rahman MA, Kabir MN, Yafeng L, Zhang R, et al.
    Work, 2021;68(3):923-934.
    PMID: 33612534 DOI: 10.3233/WOR-203426
    BACKGROUND: Human-Computer Interaction (HCI) is incorporated with a variety of applications for input processing and response actions. Facial recognition systems in workplaces and security systems help to improve the detection and classification of humans based on the vision experienced by the input system.

    OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.

    RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.

    CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.

    Matched MeSH terms: Principal Component Analysis
  12. Fulltext Principal Component Analysis of the Running Ground Reaction Forces With Different Speeds
    Yu L, Mei Q, Xiang L, Liu W, Mohamad NI, István B, et al.
    Front Bioeng Biotechnol, 2021;9:629809.
    PMID: 33842444 DOI: 10.3389/fbioe.2021.629809
    Ground reaction force (GRF) is a key metric in biomechanical research, including parameters of loading rate (LR), first impact peak, second impact peak, and transient between first and second impact peaks in heel strike runners. The GRFs vary over time during stance. This study was aimed to investigate the variances of GRFs in rearfoot striking runners across incremental speeds. Thirty female and male runners joined the running tests on the instrumented treadmill with speeds of 2.7, 3.0, 3.3, and 3.7 m/s. The discrete parameters of vertical average loading rate in the current study are consistent with the literature findings. The principal component analysis was modeled to investigate the main variances (95%) in the GRFs over stance. The females varied in the magnitude of braking and propulsive forces (PC1, 84.93%), whereas the male runners varied in the timing of propulsion (PC1, 53.38%). The female runners dominantly varied in the transient between the first and second peaks of vertical GRF (PC1, 36.52%) and LR (PC2, 33.76%), whereas the males variated in the LR and second peak of vertical GRF (PC1, 78.69%). Knowledge reported in the current study suggested the difference of the magnitude and patterns of GRF between male and female runners across different speeds. These findings may have implications for the prevention of sex-specific running-related injuries and could be integrated with wearable signals for the in-field prediction and estimation of impact loadings and GRFs.
    Matched MeSH terms: Principal Component Analysis
  13. Morphological Study of the Relationships between Weedy Rice Accessions (Oryza sativa Complex) and Commercial Rice Varieties in Pulau Pinang Rice Granary Area
    Hussain ZP, Man A, Othman AS
    Trop Life Sci Res, 2010 Dec;21(2):27-40.
    PMID: 24575197
    Weedy rice (WR) is found in many direct-seeded rice fields. WR possesses morphological characteristics that are similar to cultivated rice varieties in the early stage of growth, making them more difficult to control than other weeds. A comparative morphological study was conducted by collecting WR accessions from four sites within the Pulau Pinang rice growing areas. The objective of the study was to characterise WR accessions of the Pulau Pinang rice granary by comparing their morphological characteristics to those of commercially grown rice in the area. Their morphometric relations were established by comparing 17 morphological characteristics of the WR accessions and the commercial varieties. A total of 36 WR morphotypes were identified from these 4 sites based on 17 characteristics, which included grain shattering habit and germination rate. The Principal Component Analysis (PCA) showed that 45.88% of the variation observed among the WR accessions and commercial varieties were within the first 3 axes. PB6, PP2 and SGA5 WR accessions had a higher number of tillers and longer panicle lengths, culm heights and leaf lengths compared to the commercial rice. The grain sizes of the commercial varieties were slightly longer, and the chlorophyll contents at 60-70 days after sowing (DAS) were higher than those of the WR accessions. Results from this study are useful for predicting potential WR accession growth, which might improve WR management and agriculture practices that control WR in the future.
    Matched MeSH terms: Principal Component Analysis
  14. Fulltext Morphological study of the relationships between weedy rice accessions (Oryza sativa Complex) and commercial rice varieties in Pulau Pinang rice granary area
    Zainudin PMD Hussain, Azmi Man, Ahmad Sofiman Othman
    Trop Life Sci Res, 2010;21(2):-.
    MyJurnal
    Weedy rice (WR) is found in many direct-seeded rice fields. WR possesses morphological characteristics that are similar to cultivated rice varieties in the early stage of growth, making them more difficult to control than other weeds. A comparative morphological study was conducted by collecting WR accessions from four sites within the Pulau Pinang rice growing areas. The objective of the study was to characterise WR accessions of the Pulau Pinang rice granary by comparing their morphological characteristics to those of commercially grown rice in the area. Their morphometric relations were established by comparing 17 morphological characteristics of the WR accessions and the commercial varieties. A total of 36 WR morphotypes were identified from these 4 sites based on 17 characteristics, which included grain shattering habit and germination rate. The Principal Component Analysis (PCA) showed that 45.88% of the variation observed among the WR accessions and commercial varieties were within the first 3 axes. PB6, PP2 and SGA5 WR accessions had a higher number of tillers and longer panicle lengths, culm heights and leaf lengths compared to the commercial rice. The grain
    sizes of the commercial varieties were slightly longer, and the chlorophyll contents at 60–70 days after sowing (DAS) were higher than those of the WR accessions. Results from this study are useful for predicting potential WR accession growth, which might improve WR management and agriculture practices that control WR in the future.
    Matched MeSH terms: Principal Component Analysis
  15. Fulltext Morphological Variability Identification of Harumanis Mango (Mangifera indica L.) Harvested from Different Location and Tree Age
    Yusuf SNA, Rahman AMA, Zakaria Z, Subbiah VK, Masnan MJ, Wahab Z
    Trop Life Sci Res, 2020 Jul;31(2):107-143.
    PMID: 32922671 DOI: 10.21315/tlsr2020.31.2.6
    Harumanis is one of the main signatures of Perlis with regards to its delightful taste, pleasant aroma and expensive price. Harumanis authenticity and productivity had become the remarks among the farmers, entrepreneurs, consumers and plant breeders due to the existence of morphological characteristics variation among the fruits and high production cost. Assessment of Harumanis morphological characteristics of natural population and different tree ages may represent a possible source of important characteristics for development and breeding purposes of Harumanis. The aim of this study is to evaluate the morphological variation of Harumanis collected from different location in Perlis and tree age. A total of 150 Harumanis fruits from 50 trees with three different stages of development (young, middle-aged and old) were characterised using 11 traits; 10 quantitative and one qualitative morphological trait. The ANOVA analyses in combination with Dunn's pairwise and Kruskal-Wallis multiple comparison test able to point out the existence of environmental factor and age influence towards the significant different of identified morphological traits except for Total Soluble Solid (TSS) and pulp percentage. Five clusters of 50 Harumanis accessions reflect a grouping pattern which not according to neither geographical region nor age. The result of Principal Component Analysis (PCA) using the first two principal components (PCs) provided a good approximation of the data explaining 84.09% of the total variance which majorly contributed by parameters of weight, fruit dimensional characteristics, peel percentage and hue angle, h. Preliminary screening of important morphological characteristics which contribute to the phenotypic diversity of Harumanis is successfully achieved. The findings can be employed by the plant breeders and farmers for the establishment of standard grading of Harumanis and advancement of breeding crop of Harumanis in future.
    Matched MeSH terms: Principal Component Analysis
  16. Fulltext Evaluation of solvent extraction of Amaranth betacyanins using multivariate analysis
    Chong, P.H., Yusof, Y.A., Aziz, M.G., Mohd. Nazli, N., Chin, N.L., Syed Muhammad, S.K.
    International Food Research Journal, 2014;21(4):1569-1573.
    MyJurnal
    The present study was aimed at assessing the effect of solvents on the yield and the color properties of amaranth extract. Two species of amaranth namely Amaranthus gangeticus and Amaranthus blitum were extracted with water, methanol and ethanol. Seven parameters like betacyanin content, total soluble solids, lightness (L*), redness (a*), yellowness (b*), hue angle (h*) and chroma (c*) were analyzed to assess extraction efficiency. Correlation analysis was carried out to assess the linear association among the analytical variables. Principal component analysis was used to establish the relationships between the different analytical variables and to detect the most important factors of variability. Among the two varieties, Amaranthus gangeticus extract contained about two and half time more betacyanin with half of total soluble solids compared to Amaranthus blitum. Water is the best as solvent for extracting betacyanin from Amaranthus gangeticus and ethanol in case of Amaranthus blitum. Among the analytical parameters, a* and c* were perfectly correlated. Three principal components were found among the seven analytical variables accounting 88% of total variability. The first principal components mostly reflected the redness (a*), whereas the second principal components reflected the betacyanin content, total soluble solids and lightness (L* value).
    Matched MeSH terms: Principal Component Analysis
  17. Fulltext Diagnosing angina using a simple neural network architecture
    Bulgiba, A.M.
    JUMMEC, 2006;9(1):39-43.
    MyJurnal
    The aim of the study was to research the use of a simple neural network in diagnosing angina in patients complaining of chest pain. A total of 887 records were extracted from the electronic medical record system (EMR) in Selayang Hospital, Malaysia. Simple neural networks (simple perceptrons) were built and trained using a subset of 470 records with and without pre-processing using principal components analysis (PCA). These were subsequently tested on another subset of 417 records. Average sensitivity of 80.75% (95% CI 79.54%, 81.96%), specificity of 41.64% (95% CI 40.13%, 43.15%), PPV of 46.73% (95% CI 45.20%, 48.26%) and NPV of 77.39% (95% CI 76.11%, 78.67%) were achieved with the simple perceptron. When PCA pre-processing was used, the perceptrons had a sensitivity of 1.43% (95% CI 1.06%, 1.80%), specificity of 98.32% (95% CI 97.92%, 98.72%), PPV of 32.95% (95% CI 31.51%, 34.39%) and NPV of 61.33% (95% CI 59.84%, 62.82%). These results show that it is possible for a simple neural network to have respectable sensitivity and specificity levels for angina.
    Matched MeSH terms: Principal Component Analysis
  18. Fulltext Chromatographic fingerprint and Chemometric approach for quality control of Tongkat Ali (Eurycoma Longifolia)
    Nor Nasriah Zaini, Mardiana Saaid, Hafizan Juahir, Rozita Osman
    Pertanika Journal of Science & Technology, 2017;25(103):345-354.
    MyJurnal
    Tongkat Ali (Eurycoma longifolia) is one of the most popular tropical herbal plants as it is believed to enhance virility and sexual prowess. This study looked examined chromatographic fingerprint of Tongkat Ali roots and its products generated using online solid phase-extraction liquid chromatography (SPE-LC) combined with chemometric approaches. The aim was to determine its quality. Pressurised liquid extraction (PLE) technique was used prior to online SPE-LC using polystyrene divinyl benzene (PSDVB) and C18 columns. Seventeen Tongkat Ali roots and 10 products (capsules) were analysed. Chromatographic dataset was subjected to chemometric techniques, namely cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) using 37 selected peaks. The samples were grouped into three clusters based on their quality. The PCA resulted in 11 latent factors describing 90.8% of the whole variance. Pattern matching analysis showed no significant difference (p>0.05) between the roots and products within the same CA grouping. The findings showed the combination of chromatographic fingerprint and chemometric techniques provided comprehensive evaluation for efficient quality control of Tongkat Ali formulation.
    Matched MeSH terms: Principal Component Analysis
  19. Fulltext Classification of Partial Discharge Measured under Different Levels of Noise Contamination
    Jee Keen Raymond W, Illias HA, Abu Bakar AH
    PLoS One, 2017;12(1):e0170111.
    PMID: 28085953 DOI: 10.1371/journal.pone.0170111
    Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.
    Matched MeSH terms: Principal Component Analysis
  20. Distribution of polycyclic aromatic hydrocarbons (PAHs) in Surface Sediments of Langkawi Island, Malaysia
    Terence Ricky Chiu, Md Firoz Khan, Mohd Shahrul Mohd Nadzir, Haris Hafizal Abdul Hamid, Mohd Talib Latif, Mohd Shahrul Mohd Nadzir, et al.
    Sains Malaysiana, 2018;47:871-882.
    The individual compounds and sources of polycyclic aromatic hydrocarbon (PAHs) were studied in the surface sediments
    at 32 locations in the tourism area of Langkawi Island. A total of 15 PAHs were determined and quantified by gas
    chromatography coupled with mass spectrometry (GC-MS). The total PAH concentrations of surface sediments from
    Langkawi Island ranged from 228.13 to 990.25 ng/g and they were classified as being in the low to moderate pollution
    range. All sampling stations were dominated by high molecular weight PAHs with 4 rings (31.59%) and 5-6 rings (42.73%).
    The diagnostic ratio results showed that in most cases, the sampling stations have pyrogenic input. Further analysis
    using principal component analysis (PCA) combined with absolute principal component score (APCS) and multiple linear
    regression (MLR) showed that the natural gas emissions contributed to 57% of the total PAH concentration, 22% from the
    incomplete combustion and pyrolysis of fuel, 15% from pyrogenic and petrogenic sources and 6% from an undefined source.
    Matched MeSH terms: Principal Component Analysis
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