A data-driven approach to anomaly detection and vulnerability dynamic analysis for large-scale integrated energy systems

2021 ◽  
Vol 234 ◽  
pp. 113926
Author(s):  
Li Zhang ◽  
Huai Su ◽  
Enrico Zio ◽  
Zhien Zhang ◽  
Lixun Chi ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Dynamic Analysis ◽  
Large Scale ◽  
Energy Systems ◽  
Data Driven ◽  
Integrated Energy Systems ◽  
Data Driven Approach

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

scholarly journals Improving the management of type 2 diabetes through large-scale general practice: the role of a data-driven and technology-enabled education programme

2021 ◽  
Vol 10 (1) ◽  
pp. e001087
Author(s):  
Tarek F Radwan ◽  
Yvette Agyako ◽  
Alireza Ettefaghian ◽  
Tahira Kamran ◽  
Omar Din ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Large Scale ◽  
Education Programme ◽  
Educational Programme ◽  
Data Driven ◽  
Treatment Targets ◽  
Care Processes ◽  
Data Driven Approach

A quality improvement (QI) scheme was launched in 2017, covering a large group of 25 general practices working with a deprived registered population. The aim was to improve the measurable quality of care in a population where type 2 diabetes (T2D) care had previously proved challenging. A complex set of QI interventions were co-designed by a team of primary care clinicians and educationalists and managers. These interventions included organisation-wide goal setting, using a data-driven approach, ensuring staff engagement, implementing an educational programme for pharmacists, facilitating web-based QI learning at-scale and using methods which ensured sustainability. This programme was used to optimise the management of T2D through improving the eight care processes and three treatment targets which form part of the annual national diabetes audit for patients with T2D. With the implemented improvement interventions, there was significant improvement in all care processes and all treatment targets for patients with diabetes. Achievement of all the eight care processes improved by 46.0% (p<0.001) while achievement of all three treatment targets improved by 13.5% (p<0.001). The QI programme provides an example of a data-driven large-scale multicomponent intervention delivered in primary care in ethnically diverse and socially deprived areas.

A comparative study of data-driven and physics-based gas turbine fault recognition approaches

2021 ◽  
pp. 1748006X2198964
Author(s):  
Juan Luis Pérez-Ruiz ◽  
Igor Loboda ◽  
Iván González-Castillo ◽  
Víctor Manuel Pineda-Molina ◽  
Karen Anaid Rendón-Cortés ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Recognition Accuracy ◽  
Data Driven ◽  
Fault Identification ◽  
Fault Recognition ◽  
Data Driven Approach ◽  
Diagnostic Approaches ◽  
Artificial Neural Network Ann ◽  
Two Stages

The present paper compares the fault recognition capabilities of two gas turbine diagnostic approaches: data-driven and physics-based (a.k.a. gas path analysis, GPA). The comparison takes into consideration two differences between the approaches, the type of diagnostic space and diagnostic decision rule. To that end, two stages are proposed. In the first one, a data-driven approach with an artificial neural network (ANN) that recognizes faults in the space of measurement deviations is compared with a hybrid GPA approach that employs the same type of ANN to recognize faults in the space of estimated fault parameter. Different case studies for both anomaly detection and fault identification are proposed to evaluate the diagnostic spaces. They are formed by varying the classification, type of diagnostic analysis, and deviation noise scheme. In the second stage, the original GPA is reconstructed replacing the ANN with a tolerance-based rule to make diagnostic decisions. Here, two aspects are under analysis: the comparison of GPA classification rules and whole approaches. The results reveal that for simple classifications both spaces are equally accurate for anomaly detection and fault identification. However, for complex scenarios, the data-driven approach provides on average slightly better results for fault identification. The use of a hybrid GPA with ANN for a full classification instead of an original GPA with tolerance-based rule causes an increase of 12.49% in recognition accuracy for fault identification and up to 54.39% for anomaly detection. As for the whole approach comparison, the application of a data-driven approach instead of the original GPA can lead to an improvement of 12.14% and 53.26% in recognition accuracy for fault identification and anomaly detection, respectively.

scholarly journals RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID

PLoS Genetics ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. e1009315
Author(s):  
Ardalan Naseri ◽  
Junjie Shi ◽  
Xihong Lin ◽  
Shaojie Zhang ◽  
Degui Zhi
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Scale Up ◽  
Data Driven ◽  
Genome Wide Association Studies ◽  
Inference Method ◽  
Genome Wide ◽  
Familial Relationship ◽  
Kinship Coefficients ◽  
Data Driven Approach

Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.

Modeling of Large-Scale Integrated Energy Systems

2019 ◽  
pp. 15-38
Author(s):  
Qing-Hua Wu ◽  
Jiehui Zheng ◽  
Zhaoxia Jing ◽  
Xiaoxin Zhou
Keyword(s):  
Large Scale ◽  
Energy Systems ◽  
Integrated Energy Systems

Data-driven anomaly detection in cyber-physical production systems

2015 ◽  
Vol 63 (10) ◽  
Author(s):  
Oliver Niggemann ◽  
Christian Frey
Keyword(s):  
Anomaly Detection ◽  
Production Systems ◽  
Data Driven ◽  
Plant Analysis ◽  
Product Complexity ◽  
Automation Systems ◽  
Data Driven Approach ◽  
Assistance Systems ◽  
Cyber Physical Production Systems ◽  
Intelligent Assistance

AbstractDue to global competition and increasing product complexity, the complexity of production systems has grown significantly in recent years. This places an increasing burden on automation developers, systems engineers and plant constructors. Intelligent assistance systems and smart automation systems are a possible solution to face this complexity: The machines, i.e. the software and assistance systems, take over tasks that were previously carried out manually by experts. At the heart of this concept are intelligent anomaly detection approaches based on models of the system behaviors. Intelligent assistance systems learn these models automatically: Based on data, these systems extract most necessary knowledge about the diagnosis task. This paper outlines this data-driven approach to plant analysis using several use cases from industry.

scholarly journals Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks

2018 ◽  
Vol 115 (37) ◽  
pp. 9300-9305 ◽  
Author(s):  
Shuo Wang ◽  
Erik D. Herzog ◽  
István Z. Kiss ◽  
William J. Schwartz ◽  
Guy Bloch ◽  
...  
Keyword(s):  
Network Topology ◽  
Large Scale ◽  
Data Driven ◽  
Electrical Networks ◽  
Chemical Oscillators ◽  
Complex Interactions ◽  
Dynamic Topology ◽  
Estimation Problems ◽  
Data Driven Approach ◽  
Fast Chemical

Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity among mice over weeks.

scholarly journals Screening performance of abbreviated versions of the UPSIT smell test

2018 ◽  
Author(s):  
Theresita Joseph ◽  
Stephen D. Auger ◽  
Luisa Peress ◽  
Daniel Rack ◽  
Jack Cuzick ◽  
...  
Keyword(s):  
Large Scale ◽  
Effective Means ◽  
Cost Effective ◽  
Data Driven ◽  
Screening Performance ◽  
Smell Test ◽  
Identification Test ◽  
Data Driven Approach ◽  
Smell Identification ◽  
Smell Tests

ABSTRACTBackgroundHyposmia features in several neurodegenerative conditions, including Parkinson’s disease (PD). The University of Pennsylvania Smell Identification Test (UPSIT) is a widely used screening tool for detecting hyposmia, but is time-consuming and expensive when used on a large scale.MethodsWe assessed shorter subsets of UPSIT items for their ability to detect hyposmia in 891 healthy participants from the PREDICT-PD study. Established shorter tests included Versions A and B of both the 4-item Pocket Smell Test (PST) and 12-item Brief Smell Identification Test (BSIT). Using a data-driven approach, we evaluated screening performances of 23,231,378 combinations of 1-7 smell items from the full UPSIT.ResultsPST Versions A and B achieved sensitivity/specificity of 76.8%/64.9% and 86.6%/45.9% respectively, whilst BSIT Versions A and B achieved 83.1%/79.5% and 96.5%/51.8% for detecting hyposmia defined by the longer UPSIT. From the data-driven analysis, two optimised sets of 7 smells surpassed the screening performance of the 12 item BSITs (with validation sensitivity/specificities of 88.2%/85.4% and 100%/53.5%). A set of 4 smells (Menthol, Clove, Gingerbread and Orange) had higher sensitivity for hyposmia than PST-A, -B and even BSIT-A (with validation sensitivity 91.2%). The same 4 smells also featured amongst those most commonly misidentified by 44 individuals with PD compared to 891 PREDICT-PD controls and a screening test using these 4 smells would have identified all hyposmic patients with PD.ConclusionUsing abbreviated smell tests could provide a cost-effective means of screening for hyposmia in large cohorts, allowing more targeted administration of the UPSIT or similar smell tests.

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