150 research outputs found

    Latent Variable Models with Applications to Spectral Data Analysis

    Get PDF
    Recent technological advances in automatic data acquisition have created an ever increasing need to extract meaningful information from huge amount of data. Multivariate predictive models have become important statistical tools in solving modern engineering problems. The purpose of this thesis is to develop novel predictive methods based on latent variable models and validate these methods by applying them into spectral data analysis. In this thesis, hybrid models of principal components regression (PCR) and partial least squares regression (PLS) is proposed. The basic idea of hybrid models is to develop more accurate prediction techniques by combining the merits of PCR and PLS. In the hybrid models, both principal components in PCR and latent variables in PLS are involved in the common regression process. Another major contribution of this work is to propose the robust probabilistic multivariate calibration model (RPMC) to overcome the drawback of Gaussian assumption in most latent variable models. The RPMC was designed to be robust to outliers by adopting a Student-t distribution instead of the Gaussian distribution. An efficient Expectation- Maximization algorithm was derived for parameter estimation in the RPMC. It can also be shown that some popular latent variables such as probabilistic PCA (PPCA) and supervised probabilistic PCA (SPPCA) are special cases of the RPMC. Both the predictive models developed in this thesis were assessed on the real-life spectral data datasets. The hybrid models were applied into the shaft misalignment prediction problem and the RPMC are tested on the near-infrared (NIR) dataset. For the classification problem on the NIR data, the fusion of the regularized discriminant analysis (RDA) and principal components analysis (PCA) was also proposed. The experimental results have shown the effectiveness and efficiency of the proposed methods

    Multivariate Analysis in Management, Engineering and the Sciences

    Get PDF
    Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field

    Improving Energy Efficiency and Motion Control in Load-Carrying Applications using Self-Contained Cylinders

    Get PDF
    Because of an increasing focus on environmental impact, including CO2 emissions and fluid spill pollution, inefficient hydraulic systems are being replaced by more environmentally friendly alternatives in several industries. For instance, in some offshore applications that have multiple diesel generators continuously running to produce electricity, all hydraulic rotating actuators supplied from a central hydraulic power unit have been replaced with AC induction motors containing a variable frequency drive and gearbox. However, hydraulic linear actuators are still needed in most load-carrying applications mainly because of their high reliability associated with external impact shocks. Moreover, their force capacity is higher than that of their linear electromechanical counterparts. Valve-controlled linear actuators (cylinders) supplied from a centralized hydraulic power unit are standard in offshore load-carrying applications. In addition to the advantages mentioned above of hydraulic linear actuators, they have, nevertheless, a number of important drawbacks, which include: 1) a high level of energy consumption due to significant power losses caused by flow throttling in both the pipelines and valves, 2) reduced motion performance due to the influence of load-holding valves, 3) high CO2 emissions and fuel costs related to the diesel generator that supplies electricity to the hydraulic power unit, 4) significant potential for hydraulic fluid leakage because of many leakage points, 5) demanding efforts with respect to installation and maintenance, as well as 6) costly piping due to the centralized hydraulic power supply. The work presented in this dissertation and the appended papers are devoted to replacing inefficient hydraulic linear actuation systems traditionally used in offshore load-carrying applications with more environmentally friendly solutions. Two alternative technologies are identified, namely electro-mechanical and electro-hydraulic self-contained cylinders. The feasibility of replacing conventional valve-controlled cylinders with self-contained cylinder concepts is investigated in two relevant case studies.publishedVersio

    Advanced Sensors for Real-Time Monitoring Applications

    Get PDF
    It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications

    Precision Agriculture Technology for Crop Farming

    Get PDF
    This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

    Soybean

    Get PDF
    Soybean is an agricultural crop of tremendous economic importance. Soybean and food items derived from it form dietary components of numerous people, especially those living in the Orient. The health benefits of soybean have attracted the attention of nutritionists as well as common people

    Microbial and non-microbial volatile fingerprints : potential clinical applications of electronic nose for early diagnoses and detection of diseases

    Get PDF
    This is the first study to explore the potential applications of using qualitative volatile fingerprints (electronic nose) for early detection and diagnosis of diseases such as dermatophytosis, ventilator associated pneumonia and upper gastrointestinal cancer. The investigations included in vitro analysis of various dermatophyte species and strains, antifungal screening, bacterial cultures and associated clinical specimens and oesophageal cell lines. Mass spectrometric analyses were attempted to identify possible markers. The studies that involved e-nose comparisons indicated that the conducting polymer system was unable to differentiate between any of the treatments over the experimental period (120 hours). Metal oxide-based sensor arrays were better suited and differentiated between four dermatophyte species within 96 hours of growth using principal component analysis and cluster analysis (Euclidean distance and Ward’s linkage) based on their volatile profile patterns. Studies on the sensitivity of detection showed that for Trichophyton mentagrophytes and T. rubrum it was possible to differentiate between log3, log5 and log7 inoculum levels within 96 hours. The probabilistic neural network model had a high prediction accuracy of 88 to 96% depending on the number of sensors used. Temporal volatile production patterns studied at a species level for a Microsporum species, two Trichophyton species and at a strain level for the two Trichophyton species; showed possible discrimination between the species from controls after 120 hours. The predictive neural network model misclassified only one sample. Data analysis also indicated probable differentiation between the strains of T. rubrum while strains of T. mentagrophytes clustered together showing good similarity between them. Antifungal treatments with itraconazole on T. mentagrophytes and T. rubrum showed that the e-nose could differentiate between untreated fungal species from the treated fungal species at both temperatures (25 and 30°C). However, the different antifungal concentrations of 50% fungal inhibition and 2 ppm could not be separated from each other or the controls based on their volatiles. Headspace analysis of bacterial cultures in vitro indicated that the e-nose could differentiate between the microbial species and controls in 83% of samples (n=98) based on a four group model (gram-positive, gram-negative, fungi and no growth). Volatile fingerprint analysis of the bronchoalveolar lavage fluid accurately separated growth and no growth in 81% of samples (n=52); however only 63% classification accuracy was achieved with a four group model. 12/31 samples were classified as infected by the e-nose but had no microbiological growth, further analysis suggested that the traditional clinical pulmonary infection score (CPIS) system correlated with the e-nose prediction of infection in 68% of samples (n=31). No clear distinction was observed between various human cell lines (oesophageal and colorectal) based on volatile fingerprints within one to four hours of incubation, although they were clearly separate from the blank media. However, after 24 hours one of the cell lines could be clearly differentiated from the others and the controls. The different gastrointestinal pathologies (forming the clinical samples) did not show any specific pattern and thus could not be distinguished. Mass spectrometric analysis did not detect distinct markers within the fungal and cell line samples, but potential identifiers in the fungal species such as 3-Octanone, 1-Octen-3-ol and methoxybenzene including high concentration of ammonia, the latter mostly in T. mentagrophytes, followed by T. rubrum and Microsporum canis, were found. These detailed studies suggest that the approach of qualitative volatile fingerprinting shows promise for use in clinical settings, enabling rapid detection/diagnoses of diseases thus eventually reducing the time to treatment significantly.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
    • …
    corecore