An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

Spectral observation of symmetry-protected selection rules for dynamical high-dimensional parity in alignment magnetic resonance

Multidimensional Floquet-driven alignment systems with dynamical symmetry present various exotic phenomena and applications.However,there are challenges in directly characterizing large-spin dynamical symmetry from spectra.Here,we first observe the symmetry-protected selection rules of dynamical high-dimensional parity in a large-spin(F=4)system.We theoretically construct a Floquet-driven alignment system that can be used to reveal high-dimensional spatiotemporal symmetry.In the experiment,the system is implemented in Cs atomic gas subjected to two-dimensional Floquet-modulated magnetic resonance driving.By developing Floquet detection protocols of alignment double-sided spectra,we directly verify symmetry-protected selection rules of dynamical high-dimensional parity for large-spin systems.This work advances the exploration of dynamical symmetry to large spins,and unravels a universal Floquet scheme for the investigation of symmetry-protected selection rules.

A forced alignment approach to detect Chinese repetitive stuttering

A forced alignment based algorithms to detect Chinese repetitive stuttering is studied. According to the features of repetitions in Chinese stuttered speech,improvement solutions are provided based on the previous research findings.First,a multi-span looping forced alignment decoding networks is designed to detect multi-syllable repetitions in Chinese stuttered speech.Second,branch penalty factor is added in the networks to adjust decoding trend using recursive search in order to reduce the error from the complexity of the decoding networks. Finally,we re-judge the detected stutters by calculating confidence to improve the reliability of the detection result.The experimental results show that compared to previous algorithm,the proposed algorithm can improve system performance significantly,about 18%average detection error rate relatively.