Degradation feature extraction method for piezoelectric ceramic of ultrasonic motor based on DCT-SV cross entropy

Crack on piezoelectric ceramic is the main reason leading to failure of ultrasonic motors. A novel degradation feature extraction method based on discrete cosine transform (DCT) -singular value (SV) cross entropy was proposed in this paper. In order to improve the correlation with the crack, the DCT coefficients with the property of energy aggregation, were used to extract fault information. To avoid the influence of human factors in traditional DCT de-noising method, a matrix composed of DCT coefficients was constructed, and the SV cross entropy of the matrix was taken as the degradation feature for ultrasonic motor. A numerical simulated noise was added to the measured signal to verify the anti-noise performance of the feature. Analysis of the experimental results demonstrates that the proposed DCT-SV cross entropy is feasible and effective in indicating the degradation of piezoelectric ceramic in ultrasonic motor

Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction

Developing link prediction models to automatically complete knowledge graphs has recently been the focus of significant research interest. The current methods for the link prediction taskhavetwonaturalproblems:1)the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations. These two problems limit the training effectiveness and practical applications of the existing link prediction models. We advocate a holistic understanding of KGs and we propose in this work a unified Generalized Relation Learning framework GRL to address the above two problems, which can be plugged into existing link prediction models. GRL conducts a generalized relation learning, which is aware of semantic correlations between relations that serve as a bridge to connect semantically similar relations. After training with GRL, the closeness of semantically similar relations in vector space and the discrimination of dissimilar relations are improved. We perform comprehensive experiments on six benchmarks to demonstrate the superior capability of GRL in the link prediction task. In particular, GRL is found to enhance the existing link prediction models making them insensitive to unbalanced relation distributions and capable of learning unseen relations.Comment: Preprint of accepted AAAI2021 pape

Alterations in microRNA-124 and AMPA receptors contribute to social behavioral deficits in frontotemporal dementia

Many neurodegenerative diseases, such as frontotemporal dementia (FTD), are associated with behavioral deficits, but the anatomical and molecular bases remain poorly understood. Here we show that forebrain-specific expression of FTD-associated mutant CHMP2B causes several age-dependent neurodegenerative phenotypes, including social behavioral impairments. The social deficits were accompanied by a change in AMPA receptor (AMPAR) composition, leading to imbalance between Ca2+-permeable and -impermeable AMPARs. Expression of most AMPAR subunits was regulated by the brain-enriched microRNA (miR-124), whose abundance was markedly decreased in the superficial layers of cerebral cortex of FTD mice. We found similar changes in miR-124 and AMPAR levels in the frontal cortex and iPSC-derived neurons of subjects with behavioral variant FTD. Moreover, miR-124 expression in the medial prefrontal cortex decreased AMPAR levels and partially rescued behavioral deficits. Knockdown of Gria2 also alleviated social impairments in FTD mice. Our results identify a novel mechanism involving miR-124 and AMAPRs in regulating social behavior in FTD and suggest a potential therapeutic avenue

Degradation feature extraction of the hydraulic pump based on high-frequency harmonic local characteristic-scale decomposition sub-signal separation and discrete cosine transform high-order singular entropy

Hydraulic pump degradation feature extraction is a key step of condition-based maintenance. In this article, a novel method based on high-frequency harmonic local characteristic-scale decomposition sub-signal separation and discrete cosine transform high-order singular entropy is proposed. In order to reduce noises and other disturbances, the vibration signal is processed by the local characteristic-scale decomposition modified by the high-frequency harmonic. Sub-signal with sensitive information is obtained by blind source separation of the selected intrinsic scale components. Furthermore, the discrete cosine transform high-order spectral analysis algorithm is proposed to extract singular entropies of Shannon and Tsallis to be the degradation features of the hydraulic pump. Analysis of the hydraulic pump experiment demonstrates that the proposed method is feasible and effective in indicating the performance degradation of the hydraulic pump

Soil respiration at different time scales from 2000 to 2018 in forest ecosystems across China

The related studies on soil respiration (Rs) are increasing year by year in China, amounts of Rs data were published, especially in the form of monthly dynamics figures. Here, we compiled a comprehensive and uniform Rs database in China's forests from 568 literatures published up to 2018, including Rs and the concurrently measured soil temperature (N=8317), mean monthly Rs (N=5003), and annual Rs (N=634). Besides the Rs data directly given in the original papers, the monthly patterns of Rs and the concurrently measured soil temperature at 5 cm and/or 10 cm depth in the figures were digitized. These Rs data derived from the undisturbed forest ecosystems. The common measurement methods were selected, i.e. infrared gas analyzers (model Li-6400, Li-8100, Li-8150 (LI-COR Inc., Lincoln, Nebraska, USA)) and gas chromatography. Meanwhile, the associated information was recorded, e.g. geographical location (province, study site, latitude, longitude and elevation), climate factors (mean annual temperature and mean annual precipitation), stand description (forest type, origin, age, density, mean tree height and diameter at breast height), measurement regime (method, time, frequency, collar area, height and numbers). We hope the database will be used by the science community to provide a better understanding of carbon cycle in China's forests and reduce the uncertainty in evaluating of carbon budget at the large scale