Modelling and Prediction of Global Magnetic Disturbance in Near-Earth Space: a Case Study for Kp Index using NARX Models

Severe geomagnetic disturbances can be hazardous for mod-ern technological systems. The reliable forecast of parameters related to thestate of the magnetosphere can facilitate the mitigation of adverse effects ofspace weather. This study is devoted to the modeling and forecasting of theevolution of the Kp index related to global geomagnetic disturbances. Through-out this work the Nonlinear AutoRegressive with eXogenous inputs (NARX)methodology is applied. Two approaches are presented: i) a recursive slid-ing window approach, and ii) a direct approach. These two approaches arestudied separately and are then compared to evaluate their performances.It is shown that the direct approach outperforms the recursive approach, butboth tend to produce predictions slightly biased from the true values for lowand high disturbances

Age-related association of venom gene expression and diet of predatory gastropods

Abstract Background Venomous organisms serve as wonderful systems to study the evolution and expression of genes that are directly associated with prey capture. To evaluate the relationship between venom gene expression and prey utilization, we examined these features among individuals of different ages of the venomous, worm-eating marine snail Conus ebraeus. We determined expression levels of six genes that encode venom components, used a DNA-based approach to evaluate the identity of prey items, and compared patterns of venom gene expression and dietary specialization. Results C. ebraeus exhibits two major shifts in diet with age—an initial transition from a relatively broad dietary breadth to a narrower one and then a return to a broader diet. Venom gene expression patterns also change with growth. All six venom genes are up-regulated in small individuals, down-regulated in medium-sized individuals, and then either up-regulated or continued to be down-regulated in members of the largest size class. Venom gene expression is not significantly different among individuals consuming different types of prey, but instead is coupled and slightly delayed with shifts in prey diversity. Conclusion These results imply that changes in gene expression contribute to intraspecific variation of venom composition and that gene expression patterns respond to changes in the diversity of food resources during different growth stages.http://deepblue.lib.umich.edu/bitstream/2027.42/116871/1/12862_2016_Article_592.pd

Predictive Maintenance on the Machining Process and Machine Tool

This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring

The influence of HIV-1 genomic target region selection and sequence length on the accuracy of inferred phylogenies and clustering outcomes.

Masters Degree. University of KwaZulu-Natal, Durban.To improve the methodology of HIV-1 cluster analysis, we addressed how analysis of HIV-1 clustering is associated with parameters that can affect the outcome of viral clustering. The extent of HIV clustering, tree certainty, subtype diversity ratio (SDR), subtype diversity variance (SDV) and Shimodaira-Hasegawa (SH)-like support values were compared between 2881 HIV-1 full genome sequences and sub-genomic regions of which 2567 were retrieved from the LANL HIV Database and 314 were sequenced from blood samples from a cohort in KwaZulu-Natal. Sliding window analysis was based on 99 windows of 1000 bp, 45 windows of 2000 bp and 27 windows of 3000 bp. Clusters were enumerated for each window sequence length, and the optimal sequence length for cluster identification was probed. Potential associations between the extent of HIV clustering and sequence length were also evaluated. The phylogeny based on the full-genome sequences showed the best tree accuracy; it ranked highest with regards to both tree certainty and SH-like support. Product 4, a region associated with env, had the best tree accuracy among the sub-genomic regions. Among the HIV-1 structural genes, env had the best tree certainty, SH-like support, SDR score and the best SDV score overall. The hierarchy of cluster phylotype enumeration mirrored the tree accuracy analysis, with the full genome phylogeny showing the highest extent of clustering, and the product 4 region being second best. Among the structural genes, the highest number of phylotypes was enumerated from the pol phylogeny, followed by env. The extent of HIV-1 clustering was slightly higher for sliding windows of 3 000 bp than 2000 bp and 1000 bp, thus 3000 bp was found to be the optimal length for phylogenetic cluster analysis. We found a moderate association between the length of sequences used and proportion of HIV sequences in clusters; the influence of viral sequence length may have been diminished by the substantial number of taxa. Full-genome sequences could provide the most informative HIV cluster analysis. Selected sub-genomic regions with the best combination of high extent of HIV clustering and high tree accuracy, such as env, could also be considered as a second choice

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

Forecasting wind power for the day-ahead market using numerical weather prediction models and computational intelligence techniques

Wind power forecasting is essential for the integration of large amounts of wind power into the electric grid, especially during large rapid changes of wind generation. These changes, known as ramp events, may cause instability in the power grid. Therefore, detailed information of future ramp events could potentially improve the backup allocation process during the Day Ahead (DA) market (12 to 36 hours before the actual operation), allowing the reduction of resources needed, costs and environmental impact. It is well established in the literature that meteorological models are necessary when forecasting more than six hours into the future. Most state-of-the-art forecasting tools use a combination of Numerical Weather Prediction (NWP) forecasts and observations to estimate the power output of a single wind turbine or a whole wind farm. Although NWP systems can model meteorological processes that are related to large changes in wind power, these might be misplaced i.e. in the wrong physical position. A standard way to quantify such errors is by the use of NWP ensembles. However, these are computationally expensive. Here, an alternative is to use spatial fields, which are used to explore different numerical grid points to quantify variability. This strategy can achieve comparable results to typical numerical ensembles, which makes it a potential candidate for ramp characterisation

Machine Learning in Tribology

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology

Feature-based time-series analysis

This work presents an introduction to feature-based time-series analysis. The time series as a data type is first described, along with an overview of the interdisciplinary time-series analysis literature. I then summarize the range of feature-based representations for time series that have been developed to aid interpretable insights into time-series structure. Particular emphasis is given to emerging research that facilitates wide comparison of feature-based representations that allow us to understand the properties of a time-series dataset that make it suited to a particular feature-based representation or analysis algorithm. The future of time-series analysis is likely to embrace approaches that exploit machine learning methods to partially automate human learning to aid understanding of the complex dynamical patterns in the time series we measure from the world.Comment: 28 pages, 9 figure