Search

In the era of big data lean optimisation, a technologically advanced tool for Gas Turbine (GT) performance analysis is necessary. This need is due to big data taken every 5 min of about 4-years, totalling about 6 million data points (1.5×4 data points) for a single unit of GE 7FA engine of 211MW capacity. The use of actual engine data to quantify degradation using a time-base is limited and challenging. This restraint is due to the influence of varying ambient conditions, compressor inlet conditions with different power settings and loads. This study presents a software development kit (SDK) for real-time processing and evaluation of engine data using different control modes with a statistical method of filtering the data using the inlet guide vane (IGV) opening. The study also examines the analysis of GT health monitoring using time-based machine-generated data. The output of this investigation includes obtaining an average degradation trend line for the power output and heat rate increase as a function of time that serves as the basis for the investigation of the economic analysis. Implementing the SDK has reduced the labour cost, enhanced the accuracy of the analysis, and improved the efficiency of service delivery. The validation of the result with the available literature indicated a fair deviation of 1.4%. The utilisation of data for fuel flow recorded for the standard and extended correction was 24.3% and 16.3% at a range of 9.06–9.43 kg/s and 8.95–9.31 kg/s, respectively. The average percentage of fouling degradation level amounted to a 7.2% reduction of power output and a 1.6% increase in heat rate for one year of operation.

Determination of whether or not the electroencephalography (EEG) ambient signal is Gaussian-distributed is important for a variety of engineering applications, such as cognitive studies and cortical source localization, as well as for a theoretical understanding of the brain’s physiology. The statistical normality of EEG ambient signals has variably been affirmed/negated in the research literature. Therefore, in light of the evolving applications of EEG, one prime objective of this work is to statistically characterize the baseline EEG signal obtained from healthy subjects to be used as a reference in future studies that involve EEG signals. In addition, this present work will critically examine the alleged normality trend by introducing more appropriately rigorous statistical techniques to reveal/characterize the statistical normality of EEG datasets. Then, this work will apply these statistical tools quantitatively to a dataset collected from healthy subjects using high-density EEG recordings with over an order-of-magnitude more electrodes compared to previously reported works. The analysis results show that the maximal data-observation duration is found to be at most 0.2 seconds for a normality-test positivity rate of 98%; for 95%, 90%, 85%, and 80%, the respective ceilings can relax, respectively, to 0.3, 1.0, 1.6, and 2.9 seconds. [ABSTRACT FROM AUTHOR]

With the deployment of radar in versatile scenarios and a wide variety of potential targets, demand for automatic classification of various targets is increasing. The wide variety of radar signatures from physically smaller targets due to lower velocity / radar cross-section thresholds and the increased deployment of radar-based sensors do crowd the radar screen with misinterpreted targets. Micro-Doppler signatures have been widely employed by researchers for the recognition of those targets that exhibit micro-motions. This review article presents the evolution and recent advances in radar micro-Doppler based signature analysis and feature extraction. A review of the micro-Doppler-based target classification techniques along with their applications in defense and commercial sectors, has also been presented. This article provides the first review paper in the open literature that systematically covers the major steps along with the adopted practices in micro-Doppler based target recognition. Moreover, the limitations and future trends in the field are also discussed. [ABSTRACT FROM AUTHOR]

A “p-u probe” (also known as a “p-v probe”) comprises one pressure-sensor (which is isotropic) and one uni-axial particle-velocity sensor (which has a “figure-8” bi-directional spatial directivity). This p-u probe may be generalized, by allowing the figure-8 bi-directional sensor to have a higher order of directivity. This higher-order p-u probe has not previously been investigated anywhere in the open literature (to the best knowledge of the present authors). For such a sensing system, this paper is first (1) to develop closed-form eigen-based signal-processing algorithms for azimuth-elevation direction finding; (2) to analytically derive the associated Cramér-Rao lower bounds (CRB), which are expressed explicitly in terms of the two constituent sensors' spatial geometry and in terms of the figure-8 sensor's directivity order; (3) to verify (via Monte Carlo simulations) the proposed direction-of-arrival estimators' efficacy and closeness to the respective CRB. Here, the higher-order p-u probe's two constituent sensors may be spatially displaced. [ABSTRACT FROM AUTHOR]

The concept of the cloud-to-thing continuum addresses advancements made possible by the widespread adoption of cloud, edge, and IoT resources. It opens the possibility of combining classical symbolic AI with advanced machine learning approaches in a meaningful way. In this paper, we present a thing registry and an agent-based orchestration framework, which we combine to support semantic orchestration of IoT use cases across several federated cloud environments. We use the concept of virtual sensors based on machine learning (ML) services as abstraction, mediating between the instance level and the semantic level. We present examples of virtual sensors based on ML models for activity recognition and describe an approach to remedy the problem of missing or scarce training data. We illustrate the approach with a use case from an assisted living scenario. [ABSTRACT FROM AUTHOR]