Frequent Subgraph Mining in Outerplanar Graphs

In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases. Existing approaches have therefore resorted to various heuristic strategies and restrictions of the search space, but have not identified a practically relevant tractable graph class beyond trees. In this paper, we define the class of so called tenuous outerplanar graphs, a strict generalization of trees, develop a frequent subgraph mining algorithm for tenuous outerplanar graphs that works in incremental polynomial time, and evaluate the algorithm empirically on the NCI molecular graph dataset

Frequent Subgraph Mining in Outerplanar Graphs

In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases. Existing approaches have therefore resorted to various heuristic strategies and restrictions of the search space, but have not identified a practically relevant tractable graph class beyond trees. In this paper, we define the class of so called tenuous outerplanar graphs, a strict generalization of trees, develop a frequent subgraph mining algorithm for tenuous outerplanar graphs that works in incremental polynomial time, and evaluate the algorithm empirically on the NCI molecular graph dataset

Maximal Closed Set and Half-Space Separations in Finite Closure Systems

Several problems of artificial intelligence, such as predictive learning, formal concept analysis or inductive logic programming, can be viewed as a special case of half-space separation in abstract closure systems over finite ground sets. For the typical scenario that the closure system is given via a closure operator, we show that the half-space separation problem is NP-complete. As a first approach to overcome this negative result, we relax the problem to maximal closed set separation, give a greedy algorithm solving this problem with a linear number of closure operator calls, and show that this bound is sharp. For a second direction, we consider Kakutani closure systems and prove that they are algorithmically characterized by the greedy algorithm. As a first special case of the general problem setting, we consider Kakutani closure systems over graphs, generalize a fundamental characterization result based on the Pasch axiom to graph structured partitioning of finite sets, and give a sufficient condition for this kind of closures systems in terms of graph minors. For a second case, we then focus on closure systems over finite lattices, give an improved adaptation of the greedy algorithm for this special case, and present two applications concerning formal concept and subsumption lattices. We also report some experimental results to demonstrate the practical usefulness of our algorithm.Comment: An early version of this paper was presented at ECML/PKDD 2019 and has appeared in the Lecture Notes in Computer Science, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 201

Life Predicted in a Probabilistic Design Space for Brittle Materials With Transient Loads

Analytical techniques have progressively become more sophisticated, and now we can consider the probabilistic nature of the entire space of random input variables on the lifetime reliability of brittle structures. This was demonstrated with NASA s CARES/Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code combined with the commercially available ANSYS/Probabilistic Design System (ANSYS/PDS), a probabilistic analysis tool that is an integral part of the ANSYS finite-element analysis program. ANSYS/PDS allows probabilistic loads, component geometry, and material properties to be considered in the finite-element analysis. CARES/Life predicts the time dependent probability of failure of brittle material structures under generalized thermomechanical loading--such as that found in a turbine engine hot-section. Glenn researchers coupled ANSYS/PDS with CARES/Life to assess the effects of the stochastic variables of component geometry, loading, and material properties on the predicted life of the component for fully transient thermomechanical loading and cyclic loading

Condensation Modelling of Expanding Cold Gas Jets during Hypersonic Retro-Propulsion Manoeuvres within the RETPRO Project

The RETPRO project (Validation of Wind Tunnel Test and CFD Techniques for Retropropulsion), as part of ESA’s Future Launchers Preparatory Programme, aims at preparing the tools, necessary for a reliable design and simulation of future rocket launchers or spacecraft. A particular focus is assigned to vertical take-off and landing configurations using retro propulsion as part of their control concept for entry, descent, and landing manoeuvres. Wind tunnel tests and computational fluid dynamics are used to generate a comprehensive aerodynamic database, which is required for flight dynamics simulations, enabling mission and performance analyses of possible future launcher designs. Windtunnel tests are conducted in the DLR Cologne H2K facility, with room temperature dry air ejected through selected nozzles to simulate the exhaust plume. Condensation effects might occur in the plume due to the low static freestream pressure at Mach 7, combined with the expanding flow in the nozzle. This paper presents results from numerical investigations including a vapour-equilibirum model which evaluate the potential influence of plume condensation on measured data in the wind tunnel. A qualitative comparison between experimental and numerical results is presented through Schlieren photographs. Condensation was observed in the numerical results, causing the flow path in and around the plume to be altered. Surface pressure coefficients in the condensation case were observed to be approximately 5% lower than when using the standard ideal gas model. Finally, the shock stand off distance was reduced, but not significantly. The comparison with tunnel data was therefore more-or-less the same as with the ideal gas model and the use of the condensation model was not deemed necessary for subsequent computations

Probabilistic Prediction of Lifetimes of Ceramic Parts

ANSYS/CARES/PDS is a software system that combines the ANSYS Probabilistic Design System (PDS) software with a modified version of the Ceramics Analysis and Reliability Evaluation of Structures Life (CARES/Life) Version 6.0 software. [A prior version of CARES/Life was reported in Program for Evaluation of Reliability of Ceramic Parts (LEW-16018), NASA Tech Briefs, Vol. 20, No. 3 (March 1996), page 28.] CARES/Life models effects of stochastic strength, slow crack growth, and stress distribution on the overall reliability of a ceramic component. The essence of the enhancement in CARES/Life 6.0 is the capability to predict the probability of failure using results from transient finite-element analysis. ANSYS PDS models the effects of uncertainty in material properties, dimensions, and loading on the stress distribution and deformation. ANSYS/CARES/PDS accounts for the effects of probabilistic strength, probabilistic loads, probabilistic material properties, and probabilistic tolerances on the lifetime and reliability of the component. Even failure probability becomes a stochastic quantity that can be tracked as a response variable. ANSYS/CARES/PDS enables tracking of all stochastic quantities in the design space, thereby enabling more precise probabilistic prediction of lifetimes of ceramic components

Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields.</p

MR-IMPACT II: Magnetic Resonance Imaging for Myocardial Perfusion Assessment in Coronary artery disease Trial: perfusion-cardiac magnetic resonance vs. single-photon emission computed tomography for the detection of coronary artery disease: a comparative multicentre, multivendor trial

Aims Perfusion-cardiac magnetic resonance (CMR) has emerged as a potential alternative to single-photon emission computed tomography (SPECT) to assess myocardial ischaemia non-invasively. The goal was to compare the diagnostic performance of perfusion-CMR and SPECT for the detection of coronary artery disease (CAD) using conventional X-ray coronary angiography (CXA) as the reference standard. Methods and results In this multivendor trial, 533 patients, eligible for CXA or SPECT, were enrolled in 33 centres (USA and Europe) with 515 patients receiving MR contrast medium. Single-photon emission computed tomography and CXA were performed within 4 weeks before or after CMR in all patients. The prevalence of CAD in the sample was 49%. Drop-out rates for CMR and SPECT were 5.6 and 3.7%, respectively (P = 0.21). The primary endpoint was non-inferiority of CMR vs. SPECT for both sensitivity and specificity for the detection of CAD. Readers were blinded vs. clinical data, CXA, and imaging results. As a secondary endpoint, the safety profile of the CMR examination was evaluated. For CMR and SPECT, the sensitivity scores were 0.67 and 0.59, respectively, with the lower confidence level for the difference of +0.02, indicating superiority of CMR over SPECT. The specificity scores for CMR and SPECT were 0.61 and 0.72, respectively (lower confidence level for the difference: −0.17), indicating inferiority of CMR vs. SPECT. No severe adverse events occurred in the 515 patients. Conclusion In this large multicentre, multivendor study, the sensitivity of perfusion-CMR to detect CAD was superior to SPECT, while its specificity was inferior to SPECT. Cardiac magnetic resonance is a safe alternative to SPECT to detect perfusion deficits in CA