An approach for the impact feature extraction method based on improved modal decomposition and singular value analysis

For non-stationary vibration useful information of the impact feature tends to be overwhelmed with strong routine components, which make it difficult to implement pattern recognition. This paper proposes improved signal processing methods of variational mode decomposition (VMD) and singular value decomposition (SVD) for non-stationary impact feature extraction in application to condition monitoring of reciprocating machinery. The impact feature is firstly simulated with the dynamics' analysis of the driving mechanism of a reciprocating pump. Through comparison the merit of the improved VMD method is demonstrated. The singular value of the decomposed modes is extracted with the SVD method. The support vector machine method is used as the classifier for the extracted set of features. The performance of the proposed VMD-based method is validated practically through a set of measured data from the reciprocating pump setup

Connecting spatial and frequency domains for the quaternion Fourier transform

The quaternion Fourier transform (qFT) is an important tool in multi-dimensional data analysis, in particular for the study of color images. An important problem when applying the qFT is the mismatch between the spatial and frequency domains: the convolution of two quaternion signals does not map to the pointwise product of their qFT images. The recently defined ‘Mustard’ convolution behaves nicely in the frequency domain, but complicates the corresponding spatial domain analysis. The present paper analyses in detail the correspondence between classical convolution and the new Mustard convolution. In particular, an expression is derived that allows one to write classical convolution as a finite linear combination of suitable Mustard convolutions. This result is expected to play a major role in the further development of quaternion image processing, as it yields a formula for the qFT spectrum of the classical convolution

Surgical treatment of congenital biliary duct cyst

<p>Abstract</p> <p>Background</p> <p>It is acknowledged that total cyst excision is a safe and ideal surgical treatment for congenital biliary duct cyst, compared to simple internal drainage. The aim of this study was to determine the optimal operation occasion and the effect of laparoscopy on congenital biliary duct cyst based upon total cyst excision.</p> <p>Methods</p> <p>From January 2002 to January 2011, 217 patients were admitted to Southwest Hospital for congenital biliary duct cyst. To determine the optimal surgery occasion, we divided these subjects into three groups, the infant group (age ≤ 3 years), the immaturity group (3 < age ≤ 18 years), and the maturity group (age > 18 years), and then evaluated the feasibility, risk and long-term outcome after surgery in the three groups. To analyze the effect of laparoscopic technique on congenital biliary duct cyst, we divided the patients into the laparoscopy and the open surgery groups.</p> <p>Results</p> <p>Among the three groups, the morbidity from cholangiolithiasis before surgical treatment had obvious discrepancy (p < 0.05) (lowest in the infant group), and intraoperative blood loss also had apparent diversity (p < 0.05). Furthermore, long-term outcomes (secondary cholangiolithiasis, stoma stenosis and cholangiocarcinoma) showed no significant difference between different groups (p > 0.05).</p> <p>Similarly, no significant discrepancy was observed in the morbidity from postoperative complications or long-term postoperative complications (p > 0.05) between the laparoscopic and the open surgery groups.</p> <p>Conclusions</p> <p>We conclude that total cyst excision should be performed as early as possible. The optimal treatment occasion is the infant period, and laparoscopic resection may be a new safe and feasible minimally invasive surgery for this disease.</p

Genome Sequencing and Comparative Transcriptomics of the Model Entomopathogenic Fungi Metarhizium anisopliae and M. acridum

Metarhizium spp. are being used as environmentally friendly alternatives to chemical insecticides, as model systems for studying insect-fungus interactions, and as a resource of genes for biotechnology. We present a comparative analysis of the genome sequences of the broad-spectrum insect pathogen Metarhizium anisopliae and the acridid-specific M. acridum. Whole-genome analyses indicate that the genome structures of these two species are highly syntenic and suggest that the genus Metarhizium evolved from plant endophytes or pathogens. Both M. anisopliae and M. acridum have a strikingly larger proportion of genes encoding secreted proteins than other fungi, while ∼30% of these have no functionally characterized homologs, suggesting hitherto unsuspected interactions between fungal pathogens and insects. The analysis of transposase genes provided evidence of repeat-induced point mutations occurring in M. acridum but not in M. anisopliae. With the help of pathogen-host interaction gene database, ∼16% of Metarhizium genes were identified that are similar to experimentally verified genes involved in pathogenicity in other fungi, particularly plant pathogens. However, relative to M. acridum, M. anisopliae has evolved with many expanded gene families of proteases, chitinases, cytochrome P450s, polyketide synthases, and nonribosomal peptide synthetases for cuticle-degradation, detoxification, and toxin biosynthesis that may facilitate its ability to adapt to heterogenous environments. Transcriptional analysis of both fungi during early infection processes provided further insights into the genes and pathways involved in infectivity and specificity. Of particular note, M. acridum transcribed distinct G-protein coupled receptors on cuticles from locusts (the natural hosts) and cockroaches, whereas M. anisopliae transcribed the same receptor on both hosts. This study will facilitate the identification of virulence genes and the development of improved biocontrol strains with customized properties

Pattern Analysis of Driver’s “Pressure-State-Response” in Traffic Congestion

Traffic congestion, which has a direct impact on the driver’s mood and action, has become a serious problem in rush hours in most cities of China. Currently, the study about driver’s mood and action in traffic congestion is scarce, so it is necessary to work on the relationship among driver’s mood and action and traffic congestion. And the PSR (pressure-state-response) framework is established to describe that relationship. Here, PSR framework is composed of a three-level logical structure, which is composed of traffic congestion environment, drivers’ physiology change, and drivers’ behavior change. Based on the PSR framework, various styles of drivers have been chosen to drive on the congested roads, and then traffic stream state, drivers’ physiology, and behavior characters have been measured via the appropriative equipment. Further, driver’s visual characteristics and lane changing characteristics are analyzed to determine the parameters of PSR framework. According to the PSR framework, the changing law of drivers’ characteristics in traffic congestion has been obtained to offer necessary logical space and systematic framework for traffic congestion management

Kinetics of Reed Black Liquor (RBL) Pyrolysis from Thermogravimetric Data

The pyrolysis of reed black liquor (RBL) was studied in nitrogen atmosphere by thermogravimetric analysis at six different heating rates of 5, 10, 20, 30, 40, and 50 ˚C•min-1 from ambient temperature (25 ˚C) to 800 ˚C. Thermogravimetric (TG) and differential thermogravimetric (DTG) curves were obtained. The results show that there are three main weight-loss stages in the temperature ranges of 180 to 350, 350 to 560, and 560 to 800 ˚C, for which the error is about ± 10 ˚C. The kinetic parameters were determined by the Coats-Redfern method. A kinetic compensation effect (KCE) between activation energy (E) and pre-exponential (A) factor also was found

Chance-Constrained Real-Time Dispatch with Renewable Uncertainty Based on Dynamic Load Flow

In this paper, a comprehensive real-time dispatch model considering renewable uncertainty based on dynamic load flow (DLF) is proposed. Through DLF, the primary and secondary frequency regulation amount caused by the variation of renewable energy as well as the line flow when primary and secondary regulation are deployed can be obtained easily. Not only the frequency constraints, but also the regular constraints like generator production limits and line flow limits are respected under both primary and secondary frequency regulation. To solve the dispatch problem with renewable uncertainty, chance-constrained programming based on cumulants and Cornish-fisher expansions (CCP-CMCF) is adopted to get the probability of holding the chance constraints and then the real-time dispatch model can be transformed into a quadratic programming. The simulation results show that the dispatch model proposed in this paper can deal with both primary and secondary regulation well and has a fast computation speed