Statistical-based monitoring of multivariate non-Gaussian systems

The monitoring of multivariate systems that exhibit non-Gaussian behavior is addressed. Existing work advocates the use of independent component analysis (ICA) to extract the underlying non-Gaussian data structure. Since some of the source signals may be Gaussian, the use of principal component analysis (PCA) is proposed to capture the Gaussian and non-Gaussian source signals. A subsequent application of ICA then allows the extraction of non-Gaussian components from the retained principal components (PCs). A further contribution is the utilization of a support vector data description to determine a confidence limit for the non-Gaussian components. Finally, a statistical test is developed for determining how many non-Gaussian components are encapsulated within the retained PCs, and associated monitoring statistics are defined. The utility of the proposed scheme is demonstrated by a simulation example, and the analysis of recorded data from an industrial melter.

Live-Cell Tracking Using SIFT Features in DIC Microscopic Videos

In this paper, a novel motion-tracking scheme using scale-invariant features is proposed for automatic cell motility analysis in gray-scale microscopic videos, particularly for the live-cell tracking in low-contrast differential interference contrast (DIC) microscopy. In the proposed approach, scale-invariant feature transform (SIFT) points around live cells in the microscopic image are detected, and a structure locality preservation (SLP) scheme using Laplacian Eigenmap is proposed to track the SIFT feature points along successive frames of low-contrast DIC videos. Experiments on low-contrast DIC microscopic videos of various live-cell lines shows that in comparison with principal component analysis (PCA) based SIFT tracking, the proposed Laplacian-SIFT can significantly reduce the error rate of SIFT feature tracking. With this enhancement, further experimental results demonstrate that the proposed scheme is a robust and accurate approach to tackling the challenge of live-cell tracking in DIC microscopy.

Dietary patterns and bone mineral status in young adults: the Northern Ireland Young Hearts Project

Studies of individual nutrients or foods have revealed much about dietary influences on bone. Multiple food or nutrient approaches, such as dietary pattern analysis, could offer further insight but research is limited and largely confined to older adults. We examined the relationship between dietary patterns, obtained by a posteriori and a priori methods, and bone mineral status (BMS; collective term for bone mineral content (BMC) and bone mineral density (BMD)) in young adults (20-25 years; n 489). Diet was assessed by 7 d diet history and BMD and BMC were determined at the lumbar spine and femoral neck (FN). A posteriori dietary patterns were derived using principal component analysis (PCA) and three a priori dietary quality scores were applied (dietary diversity score (DDS), nutritional risk score and Mediterranean diet score). For the PCA-derived dietary patterns, women in the top compared to the bottom fifth of the 'Nuts and Meat' pattern had greater FN BMD by 0.074 g/cm(2) (P=0.049) and FN BMC by 0.40 g (P=0.034) after adjustment for confounders. Similarly, men in the top compared to the bottom fifth of the 'Refined' pattern had lower FN BMC by 0.41 g (P-0.049). For the a priori DDS, women in the top compared to the bottom third had lower FN BMD by 0.05 g/cm(2) after adjustments (P=0.052), but no other relationships with BMS were identified. In conclusion, adherence to a 'Nuts and Meat' dietary pattern may be associated with greater BMS in young women and a 'Refined' dietary pattern may be detrimental for bone health in young men.

2D silhouette and 3D skeletal models for human detection and tracking

In this paper we propose a statistical model for detection and tracking of human silhouette and the corresponding 3D skeletal structure in gait sequences. We follow a point distribution model (PDM) approach using a Principal Component Analysis (PCA). The problem of non-lineal PCA is partially resolved by applying a different PDM depending of pose estimation; frontal, lateral and diagonal, estimated by Fisher's linear discriminant. Additionally, the fitting is carried out by selecting the closest allowable shape from the training set by means of a nearest neighbor classifier. To improve the performance of the model we develop a human gait analysis to take into account temporal dynamic to track the human body. The incorporation of temporal constraints on the model increase reliability and robustness.

Fault Diagnostics for Internal Combustion Engines – Current and Future Techniques

The adoption of each new level of automotive emissions legislation often requires the introduction of additional emissions reduction techniques or the development of existing emissions control systems. This, in turn, usually requires the implementation of new sensors and hardware which must subsequently be monitored by the on-board fault detection systems. The reliable detection and diagnosis of faults in these systems or sensors, which result in the tailpipe emissions rising above the progressively lower failure thresholds, provides enormous challenges for OBD engineers. This paper gives a review of the field of fault detection and diagnostics as used in the automotive industry. Previous work is discussed and particular emphasis is placed on the various strategies and techniques employed. Methodologies such as state estimation, parity equations and parameter estimation are explained with their application within a physical model diagnostic structure. The utilization of symptoms and residuals in the diagnostic process is also discussed. These traditional physical model based diagnostics are investigated in terms of their limitations. The requirements from the OBD legislation are also addressed. Additionally, novel diagnostic techniques, such as principal component analysis (PCA) are also presented as a potential method of achieving the monitoring requirements of current and future OBD legislation.

Behavioural and physiological responses of heifer calves to acute stressors: long-term consistency and relationship with adult reactivity to milking

The present study investigated the long-term consistency of individual differences in dairy cattles’ responses in tests of behavioural and hypothalamo–pituitary–adrenocortical (HPA) axis reactivity, as well as the relationship between responsiveness in behavioural tests and the reaction to first milking. Two cohorts of heifer calves, Cohorts 1 (N = 25) and 2 (N = 16), respectively, were examined longitudinally from the rearing period until adulthood. Cohort 1 heifers were subjected to open field (OF), novel object (NO), restraint, and response to a human tests at 7 months of age, and were again observed in an OF test during first pregnancy between 22 and 24 months of age. Subsequently, inhibition of milk ejection and stepping and kicking behaviours were recorded in Cohort 1 heifers during their first machine milking. Cohort 2 heifers were individually subjected to OF and NO tests as well as two HPA axis reactivity tests (determining ACTH and/or cortisol response profiles after administration of exogenous CRH and ACTH, respectively) at 6 months of age and during first lactation at approximately 29 months of age. Principal component analysis (PCA) was used to condense correlated response measures (to behavioural tests and to milking) within ages into independent dimensions underlying heifers’ reactivity. Heifers demonstrated consistent individual differences in locomotion and vocalisation during an OF test from rearing to first pregnancy (Cohort 1) or first lactation (Cohort 2). Individual differences in struggling in a restraint test at 7 months of age reliably predicted those in OF locomotion during first pregnancy in Cohort 1 heifers. Cohort 2 animals with high cortisol responses to OF and NO tests and high avoidance of the novel object at 6 months of age also exhibited enhanced cortisol responses to OF and NO tests at 29 months of age. Measures of HPA axis reactivity, locomotion, vocalisation and adrenocortical and behavioural responses to novelty were largely uncorrelated, supporting the idea that stress responsiveness in dairy cows is mediated by multiple independent underlying traits. Inhibition of milk ejection and stepping and kicking behaviours during first machine milking were not related to earlier struggling during restraint, locomotor responses to OF and NO tests, or the behavioural interaction with a novel object. Heifers with high rates of OF and NO vocalisation and short latencies to first contact with the human at 7 months of age exhibited better milk ejection during first machine milking. This suggests that low underlying sociality might be implicated in the inhibition of milk ejection at the beginning of lactation in heifers.

Profiling the ionome of rice and its use in discriminating geographical origins at the regional scale, China

Element profile was investigated for their use to trace the geographical origin of rice (Oryza sativa L.) samples. The concentrations of 13 elements (calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), boron (B), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), arsenic (As), selenium (Se), molybdenum (Mo), and cadmium (Cd)) were determined in the rice samples by inductively coupled plasma optical emission and mass spectrometry. Most of the essential elements for human health in rice were within normal ranges except for Mo and Se. Mo concentrations were twice as high as those in rice from Vietnam and Spain. Meanwhile, Se concentrations were three times lower in the whole province compared to the Chinese average level of 0.088 mg/kg. About 12% of the rice samples failed the Chinese national food safety standard of 0.2 mg/kg for Cd. Combined with the multi-elemental profile in rice, the principal component analysis (PCA), discriminant function analysis (DFA) and Fibonacci index analysis (FIA) were applied to discriminate geographical origins of the samples. Results indicated that the FIA method could achieve a more effective geographical origin classification compared with PCA and DFA, due to its efficiency in making the grouping even when the elemental variability was so high that PCA and DFA showed little discriminatory power. Furthermore, some elements were identified as the most powerful indicators of geographical origin: Ca, Ni, Fe and Cd. This suggests that the newly established methodology of FIA based on the ionome profile can be applied to determine the geographical origin of rice.

Chemometric techniques in multivariate statistical modelling of process plant

The techniques of principal component analysis (PCA) and partial least squares (PLS) are introduced from the point of view of providing a multivariate statistical method for modelling process plants. The advantages and limitations of PCA and PLS are discussed from the perspective of the type of data and problems that might be encountered in this application area. These concepts are exemplified by two case studies dealing first with data from a continuous stirred tank reactor (CSTR) simulation and second a literature source describing a low-density polyethylene (LDPE) reactor simulation.

A statistical process control approach for automatic anti-islanding detection using synchrophasors

Anti-islanding protection is becoming increasingly important due to the rapid installation of distributed generation from renewable resources like wind, tidal and wave, solar PV, bio-fuels, as well as from other resources like diesel. Unintentional islanding presents a potential risk for damaging utility plants and equipment connected from the demand side, as well as to public and personnel in utility plants. This paper investigates automatic islanding detection. This is achieved by deploying a statistical process control approach for fault detection with the real-time data acquired through a wide area measurement system, which is based on Phasor Measurement Unit (PMU) technology. In particular, the principal component analysis (PCA) is used to project the data into principal component subspace and residual space, and two statistics are used to detect the occurrence of fault. Then a fault reconstruction method is used to identify the fault and its development over time. The proposed scheme has been used in a real system and the results have confirmed that the proposed method can correctly identify the fault and islanding site.

S21 Culture Independent Identification Of Bacterial Communities In The Respiratory Tract Of Patients With Copd, Healthy Non-smokers And Healthy Smokers

Introduction and aims: The role bacteria play in the development and progression of Chronic Obstructive Pulmonary Disease (COPD) is unclear. We used culture-independent methods to describe differences and/or similarities in microbial communities in the lower airways of patients with COPD, healthy non-smokers and smokers.

Methods: Bronchial wash samples were collected from patients with COPD (GOLD 1–3; n = 18), healthy non-smokers (HV; n = 11) and healthy smokers (HS; n = 8). Samples were processed using the Illumina MiSeq platform. The Shannon-Wiener Index (SW) of diversity, lung obstruction (FEV1/FVC ratio) and ordination by Non-Metric Multidimensional Scaling (NMDS) on Bray-Curtis dissimilarity indices were analysed to evaluate how samples were related. Principal component analysis (PCA) was performed to assess the effect specific taxa had within each cohort. Characteristics of each cohort are shown in Table 1.

Results: There was no difference in taxa richness between cohorts (range: 69–71; p = 0.954). Diversity (SW Index) was significantly lower in COPD samples compared to samples from HV and HS (p = 0.009 and p = 0.033, respectively). There was no significant difference between HV and HS (p = 0.186). The FEV1/FVC ratio was significantly lower for COPD compared to HV (p = 9*10–8) and HS (p = 2*10–6), respectively. NMDS analysis showed that communities belonging to either of the healthy groups were more similar to each other than they were to samples belonging to the COPD group. PCA analysis showed that members of Streptococcus sp. and Haemophilus sp. had the largest effect on the variance explained in COPD. In HS, Haemophilus sp., Fusobaterium sp., Actinomyces sp., Prevotella sp. and Veillonella sp. had the largest effect on the variance explained, while in HV Neisseria sp., Porphyromonas sp., Actinomyces sp., Atopobium sp., Prevotella and Veillonella sp. had the largest effect on the variance explained.

Conclusions: The study demonstrates that microbial communities in the lower airways of patients with COPD are significantly different from that seen in healthy comparison groups. Patients with COPD had lower microbial diversity than either of the healthy comparison groups, higher relative abundance of members of Streptococcus sp. and lower relative abundance of a number of key anaerobes.Characteristics

Hydrophilic Interaction Ultra-High Performance Liquid Chromatography Coupled with Triple-Quadrupole Mass Spectrometry for Determination of Nucleosides and Nucleobases in Animal Horns

Traditional Chinese Medicines (TCMs) derived from animal horns are one of the most important types of Chinese medicine. In the present study, a fast and sensitive analytical method was established for qualitative and quantitative determination of 14 nucleosides and nucleobases in animal horns using hydrophilic interaction ultra-high performance liquid chromatography coupled with triple-quadruple tandem mass spectrometry (HILIC-UPLC-QQQ-MS/MS) in selective reaction monitoring (SRM) mode. The method was optimized and validated, and showed good linearity, precision, repeatability, and accuracy. The method was successfully used to determine contents of the 14 nucleosides and nucleobases in 25 animal horn samples. Hierarchical clustering analysis (HCA) and principal component analysis (PCA) were performed and the 25 samples were thereby divided into two groups, which agreed with taxonomy. The method may enable quick and effective search of substitutes for precious horns.

Neural Network Based Attack on a Masked Implementation of AES

Masked implementations of cryptographic algorithms are often used in commercial embedded cryptographic devices to increase their resistance to side channel attacks. In this work we show how neural networks can be used to both identify the mask value, and to subsequently identify the secret key value with a single attack trace with high probability. We propose the use of a pre-processing step using principal component analysis (PCA) to significantly increase the success of the attack. We have developed a classifier that can correctly identify the mask for each trace, hence removing the security provided by that mask and reducing the attack to being equivalent to an attack against an unprotected implementation. The attack is performed on the freely available differential power analysis (DPA) contest data set to allow our work to be easily reproducible. We show that neural networks allow for a robust and efficient classification in the context of side-channel attacks.

Implication of modified molecular structure of lipid through heat-related process to fatty acids supply in Brassica carinata seed.

This study was conducted to explore the effect of different autoclave heating times (30, 60 and 90 min) on fatty acids supply and molecular stability in Brassica carinata seed. Multivariate spectral analyses and correlation analyses were also carried out in our study. The results showed that autoclaving treatments significantly decreased the total fatty acids content in a linear fashion in B. carinata seed as heating time increased. Reduced concentrations were also observed in C18:3n3, C20:1, C22:1n9, monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), omega 3 (ω-3) and 9 (ω-9) fatty acids. Correspondingly, the heated seeds showed dramatic reductions in all the peak intensities within lipid-related spectral regions. Results from agglomerative hierarchical cluster analysis (AHCA) and principal component analysis (PCA) indicated that the raw oilseed had completely different structural make-up from the autoclaved seeds in both CH3 and CH2 asymmetric and symmetric stretching region (ca. 2999–2800 cm−1) and lipid ester Cdouble bond; length as m-dashO carbonyl region (ca. 1787–1706 cm−1). However, the oilseeds heated for 30, 60 and 90 min were not grouped into separate classes or ellipses in all the lipid-related regions, indicating that there still exhibited similarities in lipid biopolymer conformations among autoclaved B. carinata seeds. Moreover, strong correlations between spectral information and fatty acid compositions observed in our study could imply that lipid-related spectral parameters might have a potential to predict some fatty acids content in oilseed samples, i.e. B. carinata. However, more data from large sample size and diverse range would be necessary and helpful to draw up a final conclusion.

Exploring the joint compositional variability of major components and trace elements in the Tellus soil geochemistry survey (Northern Ireland)

The complexity of modern geochemical data sets is increasing in several aspects (number of available samples, number of elements measured, number of matrices analysed, geological-environmental variability covered, etc), hence it is becoming increasingly necessary to apply statistical methods to elucidate their structure. This paper presents an exploratory analysis of one such complex data set, the Tellus geochemical soil survey of Northern Ireland (NI). This exploratory analysis is based on one of the most fundamental exploratory tools, principal component analysis (PCA) and its graphical representation as a biplot, albeit in several variations: the set of elements included (only major oxides vs. all observed elements), the prior transformation applied to the data (none, a standardization or a logratio transformation) and the way the covariance matrix between components is estimated (classical estimation vs. robust estimation). Results show that a log-ratio PCA (robust or classical) of all available elements is the most powerful exploratory setting, providing the following insights: the first two processes controlling the whole geochemical variation in NI soils are peat coverage and a contrast between “mafic” and “felsic” background lithologies; peat covered areas are detected as outliers by a robust analysis, and can be then filtered out if required for further modelling; and peat coverage intensity can be quantified with the %Br in the subcomposition (Br, Rb, Ni).

Process monitoring approach using fast moving window PCA

This paper introduces a fast algorithm for moving window principal component analysis (MWPCA) which will adapt a principal component model. This incorporates the concept of recursive adaptation within a moving window to (i) adapt the mean and variance of the process variables, (ii) adapt the correlation matrix, and (iii) adjust the PCA model by recomputing the decomposition. This paper shows that the new algorithm is computationally faster than conventional moving window techniques, if the window size exceeds 3 times the number of variables, and is not affected by the window size. A further contribution is the introduction of an N-step-ahead horizon into the process monitoring. This implies that the PCA model, identified N-steps earlier, is used to analyze the current observation. For monitoring complex chemical systems, this work shows that the use of the horizon improves the ability to detect slowly developing drifts.