Improved Process Monitoring Scheme Using Multi-Scale Independent Component Analysis SpringerLink

Use deterministic or stochastic material models for results including A and B-basis allowables. Figure 1 shows the distribution of submillimetre compact sources from the GCC catalogue. The range of angular sizes (40–130″) corresponds to approximately 0.15–0.50 pc, implying that each source is likely an individual dense core, although in some cases the better resolution of short-wavelength maps reveals that the source is substructured.

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This is combined with the molecular gas emission observed in the millimetre range with the IRAM 30 m telescope. The dynamics at larger scales and its relationship with the core scale are addressed in the next papers of the series. Caseins are approximately 130–160 nm and can form microstructures in a wide density range depending on manufacturing conditions like heat treatment (Diaz-Bustamante et al., 2020; Lucey et al., 2021; Spyropoulos et al., 2019). For instance, acid-induced dairy gels from heated milk are less porous than unheated milk and exhibit a homogeneous and rigid microstructure (Guinee 2016; Spyropoulos et al., 2019). Moreover, high processing temperatures produce a less dense crystal network and a protein/water distribution change in butter .

Data-Driven Fault Detection of Three-Tank System Applying MWAT-ICA

Figure9 illustrates optimum decomposition depth computation for MSICA strategy. The idea is to design an appropriate stochastic macro-scale model and to estimate its corresponding stochastic parameters such that the aleatoric meso-scale information is preserved. We have examined the core-scale dust and gas observables in the cloud G202.3+2.5. The evolutionary classification of compact sources was improved compared to Montillaud et al. to analyse the star formation activity. It is striking that this region lies at the junction of three branches of the ramified structure of G202.3+2.5, suggesting that these branches follow a convergent dynamics that has enhanced the star formation activity in the junction region. Determining the origin of this enhancement now requires extending the analysis to larger scales, to examine the relationship between cores, filaments, and their environment.

multi-scale product analysis

A.2.Radial profiles in CO , 13CO , and C18O integrated intensities W of the three components in s1449. The running median is shown in orange, with the error bars showing the standard deviation of W in each radius bin. The Gaussian fit to the running median is shown in red, and the offset in blue. The value of the Gaussian amplitude and its uncertainty are shown at the top of each frame. The resolutions are 18, 25, and 37″ (0.07, 0.09, and 0.14 pc) for the 250, 350, and 500 μm bands of SPIRE, and 7.7 and 12″ (0.03 and 0.04 pc) for the 100 and 160 μm bands of PACS.

2 The MSICA Fault Detection Strategy

This thought also drove the political leaders to encourage the simulation-based design concepts. Multiscale modeling or multiscale mathematics is the field of solving problems that have important features at multiple scales of time and/or space. Important problems include multiscale modeling of fluids, solids, polymers, proteins, nucleic acids as well as various physical and chemical phenomena .

multi-scale product analysis

The experimentalists provided detailed reports of recording techniques and neural behavior across multiple brain areas at different levels of abstraction. Mechanisms for coding and generation of stimulus responses were examined. A novel aspect that emerged from the presentations of the experimentalists is that the representation of the external world in the brain may not be as highly compartmentalized into specific functional areas as is widely thought. The theoreticians and computational modelers presented models of cortical function where the interactions between neural units were determined by properties of the networks that connected these units. Techniques for the computation of causal relationships from multivariate neural data were also discussed as were the dynamics of a network of interconnected neurons.

Deterministic and Stochastic Material Models

The HOTFGM theory and code are designed to model the thermal and mechanical response of structures with arbitrary cross-sections composed of functionally graded materials. These new technologies enable the HyperSizer analysis to localize beyond its traditional stiffened panel multi-scale analysis and laminate ply scale. Equipped with the MAC/GMC and HOTFGM capabilities, HyperSizer can now design on the microscale, considering the individual fiber and matrix phases, their arrangements, and their likelihood to initiate failure of the global aerospace structure.

multi-scale product analysis

To quantify randomness on the meso-scale level, the previously described experiment is repeated several times, and the averaged stored elastic energies per experiment are collected. In particular, we observe realisations of the meso-scale elastic material described by randomly placed inclusions with a volume fraction of 40%. Initially, the stored energy is identified given the observed data by using the variational Bayesian inference method as described in “Bayesian upscaling of random meso-structures” section, resp.


Few data-driven FD methods assume that the data is gaussian in nature and the effectiveness reduces as the data deviates from gaussianity. However, multi-scale decomposition using wavelets causes data to be gaussian at multiple levels and this unique property of wavelets can improve the performance of FD methods when non-gaussian errors are present in the data. The presence of auto-correlated measurement noise in the data degrades the detection capability of an FD strategy. The wavelet-based data representation involves decomposing the data at multiple scales and wavelet coefficients at each scale L would decorrelate the noise component present in the original data. In operations research, multiscale modeling addresses challenges for decision-makers that come from multiscale phenomena across organizational, temporal, and spatial scales.

  • The target audience consists of neuroscientists, graduate students, decision-makers in funding agencies, and those scientists outside the field who are looking for significant problems to address.
  • In offline monitoring stage, once the data under normal process operation is acquired from a process, it undergoes pre-treatment of scaling to zero-mean.
  • The task of fault detection is divided into offline monitoring and online monitoring stages.
  • The microstructure is determined by complex molecular interactions and depends mainly on the organization of molecular compounds in the food matrix (Verboven et al., 2018).
  • This thought also drove the political leaders to encourage the simulation-based design concepts.
  • Therefore, the log of measurements are modelled as copula Gaussian mixtures with the unknown number of components.

Please complete this reCAPTCHA to demonstrate that it’s you making the requests and not a robot. If you are having trouble seeing or completing this challenge, this page may help. A Ti-6Al-4V lattice structure sample produced by selective laser melting was provided by the University of Kassel for analysis. The cross and plusshow the locations of the 2MASS and Spitzer (as reported by Renaud et al. 2013) point sources, respectively. After searching the SPIRE 250 μm map for a minimum in surface brightness. Pointing corrections and focus corrections were performed every 1.5 h and 3 h, respectively, leading to a pointing accuracy measured to be ≲5″.

The multi-scale analysis workflow offered by Thermo Fisher Scientific integrating software and hardware. In the second stage the predicted micellar and emulsion structure file is processed in COSMOtherm to determine the Gibbs free energy profile and so the partition coefficient of the whole structure of the aggregate. At first a molecular dynamic simulation (MD’s) is performed to determine the micelle and emulsion structure of the simulated system.

multi-scale product analysis

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