Guest Editorial Special Section on Advanced Signal and Image Processing Techniques for Electric Machines and Drives Fault Diagnosis and Prognosis

© 2017 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works[EN] With the expansion of the use of electrical drive sys- tems to more critical applications, the issue of reliability and fault mitigation and condition-based maintenance have consequently taken an increasing importance: it has become a crucial one that cannot be neglected or dealt with in an ad-hoc way. As a result research activity has increased in this area, and new methods are used, some based on a continuation and improvement of previous accomplishments, while others are applying theory and techniques in related areas. This Special Section of the IEEE Transactions on Industrial Informatics attracted a number of papers dealing with Advanced Signal and Image Processing Techniques for Electric Machine and Drives Fault Diagnosis and Prognosis. This editorial aims to put these contributions in context, and highlight the new ideas and directions therein.Antonino-Daviu, J.; Lee, SB.; Strangas, E. (2017). Guest Editorial Special Section on Advanced Signal and Image Processing Techniques for Electric Machines and Drives Fault Diagnosis and Prognosis. IEEE Transactions on Industrial Informatics. 13(3):1257-1260. doi:10.1109/TII.2017.2690464S1257126013

Advanced Rotor Fault Diagnosis for Medium-Voltage Induction Motors Via Continuous Transforms

[EN] Anumber of field case studies for rotor fault diagnosis on medium-voltage induction motors operating in a petrochemical plant are presented in this paper. The methodology employed is based on analyzing the induction motor startup current with advanced signal processing tools (continuous transforms) that enable a capture of a complete picture of the rotor condition. Indeed, unlike the classical tools that often rely on the detection of few fault frequencies, these new tools allow extraction of the evolution of a wide range of fault components during the startup transient and steady-state evolutions, which enables improved reliability. This is crucial in medium-high-voltage motors, where a false diagnosis may result in significant expense due to inspection, repair, or forced outage. An additional contribution of the study is its immunity to external voltage supply disturbances, which introduce components that are not related to the failure and which are difficult to detect with classical tools. The results of this study prove how the advanced continuous tools enable an improved visualization of the fault components, distinguishing them from the other components that are not linked to the failure.This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology under Grant NRF-2013R1A1A2010370, and in part by the Human Resources Development Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) funded by the Korea Government Ministry of Trade, Industry, and Energy under Grant 20134030200340Antonino-Daviu, J.; Pons Llinares, J.; Lee, SB. (2016). Advanced Rotor Fault Diagnosis for Medium-Voltage Induction Motors Via Continuous Transforms. IEEE Transactions on Industry Applications. 52(5):4503-4509. https://doi.org/10.1109/TIA.2016.2582720S4503450952

HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech Synthesis

Large language models (LLM)-based speech synthesis has been widely adopted in zero-shot speech synthesis. However, they require a large-scale data and possess the same limitations as previous autoregressive speech models, including slow inference speed and lack of robustness. This paper proposes HierSpeech++, a fast and strong zero-shot speech synthesizer for text-to-speech (TTS) and voice conversion (VC). We verified that hierarchical speech synthesis frameworks could significantly improve the robustness and expressiveness of the synthetic speech. Furthermore, we significantly improve the naturalness and speaker similarity of synthetic speech even in zero-shot speech synthesis scenarios. For text-to-speech, we adopt the text-to-vec framework, which generates a self-supervised speech representation and an F0 representation based on text representations and prosody prompts. Then, HierSpeech++ generates speech from the generated vector, F0, and voice prompt. We further introduce a high-efficient speech super-resolution framework from 16 kHz to 48 kHz. The experimental results demonstrated that the hierarchical variational autoencoder could be a strong zero-shot speech synthesizer given that it outperforms LLM-based and diffusion-based models. Moreover, we achieved the first human-level quality zero-shot speech synthesis. Audio samples and source code are available at https://github.com/sh-lee-prml/HierSpeechpp.Comment: 16 pages, 9 figures, 12 table

Antagonism of PPARγ signaling expands human hematopoietic stem and progenitor cells by enhancing glycolysis

Hematopoietic stem cells (HSCs) quiescently reside in bone marrow niches and have the capacity to self-renew or differentiate to form all blood cells throughout the lifespan of an animal–. Allogeneic HSC transplantation is a life-saving treatment for malignant and non-malignant disorders,. HSCs isolated from umbilical cord blood (CB) are used for hematopoietic cell transplantation (HCT)–, but due to limited numbers of HSCs in single units of umbilical CB, a number of methods have been proposed for ex vivo expansion of human HSCs,,. We show here that antagonism of the nuclear hormone receptor PPARγ promotes ex vivo expansion of phenotypically and functionally-defined subsets of human CB HSCs and hematopoietic progenitor cells (HSPCs). PPARγ antagonism in CB HSPCs strongly downregulated expression of several differentiation associated genes, as well as fructose 1, 6-bisphosphatase (FBP1), a negative regulator of glycolysis, and enhanced glycolysis without compromising mitochondrial metabolism. The expansion of CB HSPCs by PPARγ antagonism was completely suppressed by removal of glucose or inhibition of glycolysis. Moreover, knockdown of FBP1 expression promoted glycolysis and ex vivo expansion of long-term repopulating CB HSPCs, whereas overexpression of FBP1 suppressed the expansion of CB HSPCs induced by PPARγ antagonism. Our study suggests the possibility for a new and simple means for metabolic reprogramming of CB HSPCs to improve the efficacy of HCT

A machine-learning approach to predict postprandial hypoglycemia

Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.11Ysciescopu