Application of EMD-AR and MTS for hydraulic pump fault diagnosis

A real-time diagnosis of hydraulic pumps is very crucial for the reliable operation of hydraulic systems. The main purpose of this study is to propose a fault diagnosis approach for hydraulic systems based on the empirical mode decomposition (EMD), autoregressive (AR) model, singular value decomposition (SVD), and Mahalanobis–Taguchi system (MTS). The AR model effectively extracts the fault feature of vibration signals. However, it can only be applied to stationary signals; the fault vibration signals of hydraulic pumps are non-stationary. To address this problem, the EMD method is used as a pretreatment step to decompose the non-stationary vibration signals of hydraulic pumps. First, the vibration signals of hydraulic pumps are decomposed into a finite number of stationary intrinsic mode functions (IMF). The AR model of each IMF component is established. The AR parameters and the remnant’s variance are regarded as the initial feature vector matrices. Third, the singular values are obtained by applying the SVD to the initial feature vector matrices. Finally, these values serve as the fault feature vectors to be entered to the MTS, thereby classifying the fault pattern of the hydraulic pumps. The Taguchi methods are employed to reduce the redundant features and extract the principal components. Experimental analysis results indicate that this method can effectively accomplish the fault diagnosis of hydraulic pumps

Rolling Bearing Fault Diagnosis Based on EMD-TEO and Mahalanobis Distance

A intelligent rolling bearing fault diagnosis method is proposed on Empirical Mode Decomposition (EMD) – Teager Energy Operator (TEO) and Mahalanobis distance. EMD can adaptively decompose vibration signal into a series of Intrinsic Mode Functions (IMFs), that is, zero mean mono-component AM-FM signal. TEO can estimate the total mechanical energy required to generate signals, so it has good time resolution and self-adaptive ability to the transient of the signal, which shows the advantage to detect the signal impact characteristics. With regards to the impulse feature of the bearing fault vibration signals, TEO can be used to detect cyclical impulse characteristic caused by bearing failure, gain the instantaneous amplitude spectrum of each IMF component, then identify the characteristic frequency of the interesting and single IMF component in bearing faults by means of Teager energy spectrum. The amplitude of the Teager energy spectrum in inner race fault frequency, outer fault frequency and the ratio of the energy of the resonance frequency to the total energy were extracted as the feature vectors, which were used as training samples and test samples separately for fault diagnosis. Then the Mahalanobis distances between the real measure and different type overalls of fault sample are calculated to classify the real condition of rolling bearing. Experimental results was concluded that this method can accurately identify and diagnose different fault types of rolling bearing

Waveform-Domain Adaptive Matched Filtering: A Novel Approach to Suppressing Interrupted-Sampling Repeater Jamming

The inadequate adaptability to flexible interference scenarios remains an unresolved challenge in the majority of techniques utilized for mitigating interrupted-sampling repeater jamming (ISRJ). Matched filtering system based methods is desirable to incorporate anti-ISRJ measures based on prior ISRJ modeling, either preceding or succeeding the matched filtering. Due to the partial matching nature of ISRJ, its characteristics are revealed during the process of matched filtering. Therefore, this paper introduces an extended domain called the waveform domain within the matched filtering process. On this domain, a novel matched filtering model, known as the waveform-domain adaptive matched filtering (WD-AMF), is established to tackle the problem of ISRJ suppression without relying on a pre-existing ISRJ model. The output of the WD-AMF encompasses an adaptive filtering term and a compensation term. The adaptive filtering term encompasses the adaptive integration outcomes in the waveform domain, which are determined by an adaptive weighted function. This function, akin to a collection of bandpass filters, decomposes the integrated function into multiple components, some of which contain interference while others do not. The compensation term adheres to an integrated guideline for discerning the presence of signal components or noise within the integrated function. The integration results are then concatenated to reconstruct a compensated matched filter signal output. Simulations are conducted to showcase the exceptional capability of the proposed method in suppressing ISRJ in diverse interference scenarios, even in the absence of a pre-existing ISRJ model

An Integrated Approach for Assessing Aquatic Ecological Carrying Capacity: A Case Study of Wujin District in the Tai Lake Basin, China

Aquatic ecological carrying capacity is an effective method for analyzing sustainable development in regional water management. In this paper, an integrated approach is employed for assessing the aquatic ecological carrying capacity of Wujin District in the Tai Lake Basin, China. An indicator system is established considering social and economic development as well as ecological resilience perspectives. While calculating the ecological index, the normalized difference vegetation index (NDVI) is extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) time-series images, followed by spatial and temporal analysis of vegetation cover. Finally, multi-index assessment of aquatic ecological carrying capacity is carried out for the period 2000 to 2008, including both static and dynamic variables. The results reveal that aquatic ecological carrying capacity presents a slight upward trend in the past decade and the intensity of human activities still exceeded the aquatic ecological carrying capacity in 2008. In terms of human activities, population has decreased, GDP has quadrupled, and fertilizer application and industrial wastewater discharge have declined greatly in the past decade. The indicators representing aquatic ecosystem conditions have the lowest scores, which are primarily attributed to the water eutrophication problem. Yet the terrestrial ecosystem is assessed to be in better condition since topographic backgrounds and landscape diversity are at higher levels. Based on the work carried out, it is suggested that pollutant emission be controlled to improve water quality and agricultural development around Ge Lake (the largest lake in Wujin District) be reduced

Outsourced decentralized multi-authority attribute based signature and its application in IoT

IoT devices often collect data and store the data in the cloud for sharing and further processing. A natural solution for secure access is directly using the device owner?s identity as the private key to generate a signature for data authentication. However this will simultaneously expose this identity. Attribute based signature (ABS), which takes the signer?s attributes instead of his/her identity as the private key, can realize data authentication while preserving the signer?s identity privacy. In ABS, there are multiple authorities that issue different private keys for signers based on their various attributes, and a central authority is usually established to manage all these attribute authorities. However, one security concern is that if the central authority is compromised, the whole system will be broken. In this paper, we present an outsourced decentralized multi-authority attribute based signature (ODMA-ABS) scheme. The proposed ODMAABS achieves attribute privacy and stronger authority-corruption resistance than existing multi-authority attribute based signature schemes. In addition, the overhead to generate a signature is further reduced by outsourcing expensive computation to a signing cloud server. We provide extensive security analysis and experimental simulation of the proposed scheme. We also propose an access control scheme that is based on ODMA-ABS

Confidentiality-preserving publicly verifiable computation schemes for polynomial evaluation and matrix-vector multiplication

With the development of cloud services, outsourcing computation tasks to a commercial cloud server has drawn attention of various communities, especially in the Big Data era. Public verifiability offers a flexible functionality in real circumstance where the cloud service provider (CSP) may be untrusted or some malicious users may slander the CSP on purpose. However, sometimes the computational result is sensitive and is supposed to remain undisclosed in the public verification phase, while existing works on publicly verifiable computation (PVC) fail to achieve this requirement. In this paper, we highlight the property of result confidentiality in publicly verifiable computation and present confidentiality-preserving public verifiable computation (CPPVC) schemes for multivariate polynomial evaluation and matrix-vector multiplication, respectively. The proposed schemes work efficiently under the amortized model and, compared with previous PVC schemes for these computations, achieve confidentiality of computational results, while maintaining the property of public verifiability. The proposed schemes proved to be secure, efficient, and result-confidential. In addition, we provide the algorithms and experimental simulation to show the performance of the proposed schemes, which indicates that our proposal is also acceptable in practice

ADEPT:A dataset for evaluating prosody transfer

Text-to-speech is now able to achieve near-human naturalness and research focus has shifted to increasing expressivity. One popular method is to transfer the prosody from a reference speech sample. There have been considerable advances in using prosody transfer to generate more expressive speech, but the field lacks a clear definition of what successful prosody transfer means and a method for measuring it. We introduce a dataset of prosodically-varied reference natural speech samples for evaluating prosody transfer. The samples include global variations reflecting emotion and interpersonal attitude, and local variations reflecting topical emphasis, propositional attitude, syntactic phrasing and marked tonicity. The corpus only includes prosodic variations that listeners are able to distinguish with reasonable accuracy, and we report these figures as a benchmark against which text-to-speech prosody transfer can be compared. We conclude the paper with a demonstration of our proposed evaluation methodology, using the corpus to evaluate two text-to-speech models that perform prosody transfer.Comment: 5 pages, 1 figure, accepted to Interspeech 202

Ctrl-P:Temporal control of prosodic variation for speech synthesis

Text does not fully specify the spoken form, so text-to-speech models must be able to learn from speech data that vary in ways not explained by the corresponding text. One way to reduce the amount of unexplained variation in training data is to provide acoustic information as an additional learning signal. When generating speech, modifying this acoustic information enables multiple distinct renditions of a text to be produced. Since much of the unexplained variation is in the prosody, we propose a model that generates speech explicitly conditioned on the three primary acoustic correlates of prosody: $F_{0}$, energy and duration. The model is flexible about how the values of these features are specified: they can be externally provided, or predicted from text, or predicted then subsequently modified. Compared to a model that employs a variational auto-encoder to learn unsupervised latent features, our model provides more interpretable, temporally-precise, and disentangled control. When automatically predicting the acoustic features from text, it generates speech that is more natural than that from a Tacotron 2 model with reference encoder. Subsequent human-in-the-loop modification of the predicted acoustic features can significantly further increase naturalness.Comment: To be published in Interspeech 2021. 5 pages, 4 figure

Recent Progress in Phage Therapy to Modulate Multidrug-Resistant Acinetobacter baumannii, Including in Human and Poultry

Acinetobacter baumannii is a multidrug-resistant and invasive pathogen associated with the etiopathology of both an increasing number of nosocomial infections and is of relevance to poultry production systems. Multidrug-resistant Acinetobacter baumannii has been reported in connection to severe challenges to clinical treatment, mostly due to an increased rate of resistance to carbapenems. Amid the possible strategies aiming to reduce the insurgence of antimicrobial resistance, phage therapy has gained particular importance for the treatment of bacterial infections. This review summarizes the different phage-therapy approaches currently in use for multiple-drug resistant Acinetobacter baumannii, including single phage therapy, phage cocktails, phage–antibiotic combination therapy, phage-derived enzymes active on Acinetobacter baumannii and some novel technologies based on phage interventions. Although phage therapy represents a potential treatment solution for multidrug-resistant Acinetobacter baumannii, further research is needed to unravel some unanswered questions, especially in regard to its in vivo applications, before possible routine clinical use

Genomic traits of multidrug resistant enterotoxigenic Escherichia coli isolates from diarrheic pigs

Diarrhea caused by enterotoxigenic Escherichia coli (ETEC) infections poses a significant challenge in global pig farming. To address this issue, the study was conducted to identify and characterize 19 ETEC isolates from fecal samples of diarrheic pigs sourced from large-scale farms in Sichuan Province, China. Whole-genome sequencing and bioinformatic analysis were utilized for identification and characterization. The isolates exhibited substantial resistance to cefotaxime, ceftriaxone, chloramphenicol, ciprofloxacin, gentamicin, ampicillin, tetracycline, florfenicol, and sulfadiazine, but were highly susceptible to amikacin, imipenem, and cefoxitin. Genetic diversity among the isolates was observed, with serotypes O22:H10, O163orOX21:H4, and O105:H8 being dominant. Further analysis revealed 53 resistance genes and 13 categories of 195 virulence factors. Of concern was the presence of tet(X4) in some isolates, indicating potential public health risks. The ETEC isolates demonstrated the ability to produce either heat-stable enterotoxin (ST) alone or both heat-labile enterotoxin (LT) and ST simultaneously, involving various virulence genes. Notably, STa were linked to human disease. Additionally, the presence of 4 hybrid ETEC/STEC isolates harboring Shiga-like toxin-related virulence factors, namely stx2a, stx2b, and stx2e-ONT-2771, was identified. IncF plasmids carrying multiple antimicrobial resistance genes were prevalent, and a hybrid ETEC/STEC plasmid was detected, highlighting the role of plasmids in hybrid pathotype emergence. These findings emphasized the multidrug resistance and pathogenicity of porcine-origin ETEC strains and the potential risk of epidemics through horizontal transmission of drug resistance, which is crucial for effective control strategies and interventions to mitigate the impact on animal and human health