Automated operational states detection for drilling systems control in critical conditions

Critical events in industrial drilling should be overcome by engineers while they maintain safety and achieve their targeted operational drilling plans. Geophysical drilling requires maximum awareness of critical situations such as “Kicks”, “Fluid loss” and “Stuck pipe”. These may compromise safety and potentially halt operations with the need of staff rapid evacuations from rigs. In this paper, a robust method for the detection of operational states is proposed. Specifically, Echo State Networks (ESNs) were benchmarked and tested rigorously despite the challenging unbalanced datasets used for training. Nevertheless, these challenges were overcome and led to acceptable ESNs performances

Model-based approaches for predicting gait changes over time

Interest in automated biometrics continues to increase, but has little consideration of time which are especially important in surveillance and scan control. This paper deals with a problem of recognition by gait when time-dependent covariates are added, i.e. when $6$ or $12$ months have passed between recording of the gallery and the probe sets. Moreover, in some cases some extra covariates present as well. We have shown previously how recognition rates fall significantly when data is captured between lengthy time intervals. Under the assumption that it is possible to have some subjects from the probe for training and that similar subjects have similar changes in gait over time, we suggest predictive models of changes in gait due both to time and now to time-invariant covariates. Our extended time-dependent predictive model derives high recognition rates when time-dependent or subject-dependent covariates are added. However it is not able to cope with time-invariant covariates, therefore a new time-invariant predictive model is suggested to accommodate extra covariates. These are combined to achieve a predictive model which takes into consideration all types of covariates. A considerable improvement in recognition capability is demonstrated, showing that changes can be modelled successfully by the new approach

Automatic Workflow Monitoring in Industrial Environments

Robust automatic workflow monitoring using visual sensors in industrial environments is still an unsolved problem. This is mainly due to the difficulties of recording data in work settings and the environmental conditions (large occlusions, similar background/foreground) which do not allow object detection/tracking algorithms to perform robustly. Hence approaches analysing trajectories are limited in such environments. However, workflow monitoring is especially needed due to quality and safety requirements. In this paper we propose a robust approach for workflow classification in industrial environments. The proposed approach consists of a robust scene descriptor and an efficient time series analysis method. Experimental results on a challenging car manufacturing dataset showed that the proposed scene descriptor is able to detect both human and machinery related motion robustly and the used time series analysis method can classify tasks in a given workflow automatically

Business Process Risk Management and Simulation Modelling for Digital Audio-Visual Media Preservation.

Digitised and born-digital Audio-Visual (AV) content presents new challenges for preservation and Quality Assurance (QA) to ensure that cultural heritage is accessible for the long term. Digital archives have developed strategies for avoiding, mitigating and recovering from digital AV loss using IT-based systems, involving QA tools before ingesting files into the archive and utilising file-based replication to repair files that may be damaged while in the archive. However, while existing strategies are effective for addressing issues related to media degradation, issues such as format obsolescence and failures in processes and people pose significant risk to the long-term value of digital AV content. We present a Business Process Risk management framework (BPRisk) designed to support preservation experts in managing risks to long-term digital media preservation. This framework combines workflow and risk specification within a single risk management process designed to support continual improvement of workflows. A semantic model has been developed that allows the framework to incorporate expert knowledge from both preservation and security experts in order to intelligently aid workflow designers in creating and optimising workflows. The framework also provides workflow simulation functionality, allowing users to a) understand the key vulnerabilities in the workflows, b) target investments to address those vulnerabilities, and c) minimise the economic consequences of risks. The application of the BPRisk framework is demonstrated on a use case with the Austrian Broadcasting Corporation (ORF), discussing simulation results and an evaluation against the outcomes of executing the planned workflow

Apophis planetary defense campaign

We describe results of a planetary defense exercise conducted during the close approach to Earth by the near-Earth asteroid (99942) Apophis during 2020 December–2021 March. The planetary defense community has been conducting observational campaigns since 2017 to test the operational readiness of the global planetary defense capabilities. These community-led global exercises were carried out with the support of NASA's Planetary Defense Coordination Office and the International Asteroid Warning Network. The Apophis campaign is the third in our series of planetary defense exercises. The goal of this campaign was to recover, track, and characterize Apophis as a potential impactor to exercise the planetary defense system including observations, hypothetical risk assessment and risk prediction, and hazard communication. Based on the campaign results, we present lessons learned about our ability to observe and model a potential impactor. Data products derived from astrometric observations were available for inclusion in our risk assessment model almost immediately, allowing real-time updates to the impact probability calculation and possible impact locations. An early NEOWISE diameter measurement provided a significant improvement in the uncertainty on the range of hypothetical impact outcomes. The availability of different characterization methods such as photometry, spectroscopy, and radar provided robustness to our ability to assess the potential impact risk

Sex difference and intra-operative tidal volume: Insights from the LAS VEGAS study

BACKGROUND: One key element of lung-protective ventilation is the use of a low tidal volume (VT). A sex difference in use of low tidal volume ventilation (LTVV) has been described in critically ill ICU patients.OBJECTIVES: The aim of this study was to determine whether a sex difference in use of LTVV also exists in operating room patients, and if present what factors drive this difference.DESIGN, PATIENTS AND SETTING: This is a posthoc analysis of LAS VEGAS, a 1-week worldwide observational study in adults requiring intra-operative ventilation during general anaesthesia for surgery in 146 hospitals in 29 countries.MAIN OUTCOME MEASURES: Women and men were compared with respect to use of LTVV, defined as VT of 8 ml kg-1 or less predicted bodyweight (PBW). A VT was deemed 'default' if the set VT was a round number. A mediation analysis assessed which factors may explain the sex difference in use of LTVV during intra-operative ventilation.RESULTS: This analysis includes 9864 patients, of whom 5425 (55%) were women. A default VT was often set, both in women and men; mode VT was 500 ml. Median [IQR] VT was higher in women than in men (8.6 [7.7 to 9.6] vs. 7.6 [6.8 to 8.4] ml kg-1 PBW, P < 0.001). Compared with men, women were twice as likely not to receive LTVV [68.8 vs. 36.0%; relative risk ratio 2.1 (95% CI 1.9 to 2.1), P < 0.001]. In the mediation analysis, patients' height and actual body weight (ABW) explained 81 and 18% of the sex difference in use of LTVV, respectively; it was not explained by the use of a default VT.CONCLUSION: In this worldwide cohort of patients receiving intra-operative ventilation during general anaesthesia for surgery, women received a higher VT than men during intra-operative ventilation. The risk for a female not to receive LTVV during surgery was double that of males. Height and ABW were the two mediators of the sex difference in use of LTVV.TRIAL REGISTRATION: The study was registered at Clinicaltrials.gov, NCT01601223

Metadata representation and risk management framework for preservation processes in AV archives

This paper proposes an approach to assessing risks related to audiovisual (AV) preservation processes through gathering and representing metadata. We define a model for process metadata,which is interoperable with both business process models and other preservation metadata formats. A risk management framework is also suggested to help key decision makers to plan and execute preservation processes in a manner that reduces the risk of ‘damage’ to AV content. The framework uses a plan, do,check, act cycle to continuously improve the process based on risk measures and impact model. The process metadata serves as the interface between the steps in the framework and enables a unified approach to data gathering from the heterogeneous toolsand devices used in an AV preservation workflow

Benchmarking Knowledge-assisted Kriging for Automated Spatial Interpolation of Wind Measurements

Abstract - We have benchmarked a novel knowledge-assisted kriging algorithm that allows regions of spatial cohesion to be specified and variograms calculated for each region. The variogram calculation itself is automated and spatial regions are created via offline automated segmentation of either expert-drawn Google Earth polygons or NASA altitude data. Our use-case is to create wind interpolation grids for input into a bathing water quality model of microbial contamination. We benchmark our knowledge-assisted kriging algorithm against 7 other algorithms on UK met-office wind data (189 sensors). Our wind estimation results are comparable, but not better than ordinary kriging, but the kriging error maps are much sharper and reflect the known spatial features better. These results are very promising when considering it is an automated approach and allows on-demand datasets to be selected and thus real-time interpolation of previously unknown measurements. Automation is important in progressing towards a pan-European interpolation service capability

Data analytics for drilling operational states classifications

This paper provides benchmarks for the identification of best performance classifiers for the detection of operational states in industrial drilling operations. Multiple scenarios for the detection of the operational states are tested on a rig with various drilling wells. Drilling data are extremely challenging due to their non-linear and stochastic natures, notwithstanding the embedded noise in them and unbalancing. Nevertheless, there is a possibility to deploy robust classifiers to overcome such challenges and achieve good automated detection of states. Three classifiers with best classification rates of drilling operational states were identified in this study

Generic sensor data fusion services for Web-enabled environmental risk management and decision-support systems

The advancement of smart sensor technology in the last few years has led to an increase in the deployment of affordable sensors for monitoring the environment around Europe. This is generating large amounts of sensor observation information and inevitably leading to problems about how to manage large volumes of data as well as making sense out the data for decision-making.In addition, the various European Directives (Water Framework Directives, Bathing Water Directives, Habitat Directives, etc.. ) which regulate human activities in the environment and the INSPIRE Directive on spatial information management regulations have implicitly led the designated European Member States environment agencies and authorities to put in place new sensor monitoring infrastructure and share information about environmental regions under their statutory responsibilities. They will need to work cross border and collectively reach environmental quality standards. They will also need to regularly report to the EC on the quality of the environments of which they are responsible and make such information accessible to the members of the public.In recent years, early pioneering work on the design of service oriented architecture using sensor networks has been achieved. Information web-services infrastructure using existing data catalogues and web-GIS map services can now be enriched with the deployment of new sensor observation and data fusion and modelling services using OGC standards. The deployment of the new services which describe sensor observations and intelligent data-processing using data fusion techniques can now be implemented and provide added value information with spatial-temporal uncertainties to the next generation of decision support service systems. The new decision support service systems have become key to implement across Europe in order to comply with EU environmental regulations and INSPIRE.In this paper, data fusion services using OGC standards with sensor observation data streams are described in context of a geo-distributed service infrastructure specialising in multiple environmental risk management and decision-support. The sensor data fusion services are deployed and validated in two use cases. These are respectively concerned with: 1) Microbial risks forecast in bathing waters; and 2) Geohazards in urban zones during underground tunneling activities. This research was initiated in the SANY Integrated Project (www.sany-ip.org) and funded by the European Commission under the 6th Framework Programme