Wavelet texture analysis of on-line acquired images for paper formation assessment and monitoring

http://www.sciencedirect.com/science/article/B6TFP-4TN82BP-2/2/0f2b38841a24101a06bac25004632e0

A qualitative assessment by primary and secondary school teachers

UIDB/04097/2020 UIDP/04097/2020Successful inclusive education requires school transformations and changes to the education system. In Portugal new legislation passed in 2018 (Decree-law 54/2018) brought a new perspective in inclusive education for all educational agents. Three years later, it is essential that the legislation is evaluated by the teachers implementing it. Forty-three primary and secondary school teachers and two coordinators of multidisciplinary teams providing inclusive education support were the participants of our study. The data collection instruments were a questionnaire and interviews. This study values the participants' narratives, highlighting the new concepts and attitudes required for the implementation of the new inclusive education legal framework in Portugal. The results of the thematic analysis were organized around four key themes: 1. Theoretical representations; 2. Practices; 3. Challenges; and 4. Training. It is also worth noting the notions of collaborative work, the need for reflective teachers and the development of learning communities to support the implementation of the new legislation.publishersversionpublishe

Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR)

Current multivariate control charts for monitoring large scale industrial processes are typically based on latent variable models, such as principal component analysis (PCA) or its dynamic counterpart when variables present auto-correlation (DPCA). In fact, it is usually considered that, under such conditions, DPCA is capable to effectively deal with both the cross- and auto-correlated nature of data. However, it can easily be verified that the resulting monitoring statistics (T2 and Q, also referred by SPE) still present significant auto-correlation. To handle this issue, a set of multivariate statistics based on DPCA and on the generation of decorrelated residuals were developed, that present low auto-correlation levels, and therefore are better positioned to implement SPC in a more consistent and stable way (DPCA-DR). The monitoring performance of these statistics was compared with that from other alternative methodologies for the well-known Tennessee Eastman process benchmark. From this study, we conclude that the proposed statistics had the highest detection rates on 19 out of the 21 faults, and are statistically superior to their PCA and DPCA counterparts. DPCA-DR statistics also presented lower auto-correlation, which simplifies their implementation and improves their reliability

Heteroscedastic latent variable modelling with applications to multivariate statistical process control

We present an approach for conducting multivariate statistical process control (MSPC) in noisy environments, i.e., when the signal to noise ratio is low, and, furthermore, noise standard deviation (uncertainty) affecting each collected value can vary over time, and is assumingly known. This approach is based upon a latent variable model structure, HLV (standing for heteroscedastic latent variable model), that explicitly integrates information regarding data uncertainty. Moderate amounts of missing data can also be handled in a coherent and fully integrated way through HLV. Several examples show the added value achieved under noisy conditions by adopting such an approach and a case study illustrates its application to a real industrial context of pulp and paper product quality data analysis.http://www.sciencedirect.com/science/article/B6TFP-4GX1HVW-2/1/c5e6b0a181b2fb4ffd7803ff38c9dac

Multiscale statistical process control with multiresolution data

An approach is presented for conducting multiscale statistical process control that adequately integrates data at different resolutions (multiresolution data), called MR-MSSPC. Its general structure is based on Bakshi's MSSPC framework designed to handle data at a single resolution. Significant modifications were introduced in order to process multiresolution information. The main MR-MSSPC features are presented and illustrated through three examples. Issues related to real world implementations and with the interpretation of the multiscale covariance structure are addressed in a fourth example, where a CSTR system under feedback control is simulated. Our approach proved to be able to provide a clearer definition of the regions where significant events occur and a more sensitive response when the process is brought back to normal operation, when it is compared with previous approaches based on single resolution data. © 2006 American Institute of Chemical Engineers AIChE J, 200