Research on Feature Extraction of Indicator Card Data for Sucker-Rod Pump Working Condition Diagnosis

Three feature extraction methods of sucker-rod pump indicator card data have been studied, simulated, and compared in this paper, which are based on Fourier Descriptors (FD), Geometric Moment Vector (GMV), and Gray Level Matrix Statistics (GLMX), respectively. Numerical experiments show that the Fourier Descriptors algorithm requires less running time and less memory space with possible loss of information due to nonoptimal numbers of Fourier Descriptors, the Geometric Moment Vector algorithm is more time-consuming and requires more memory space, while the Gray Level Matrix Statistics algorithm provides low-dimension feature vectors with more time consumption and more memory space. Furthermore, the characteristic of rotational invariance, both in the Fourier Descriptors algorithm and the Geometric Moment Vector algorithm, may result in improper pattern recognition of indicator card data when used for sucker-rod pump working condition diagnosis

A novel target detection approach based on adaptive radar waveform design

AbstractTo resolve problems of complicated clutter, fast-varying scenes, and low signal-clutter-ratio (SCR) in application of target detection on sea for space-based radar (SBR), a target detection approach based on adaptive waveform design is proposed in this paper. Firstly, complicated sea clutter is modeled as compound Gaussian process, and a target is modeled as some scatterers with Gaussian reflectivity. Secondly, every dwell duration of radar is divided into several sub-dwells. Regular linear frequency modulated pulses are transmitted at Sub-dwell 1, and the received signal at this sub-dwell is used to estimate clutter covariance matrices and pre-detection. Estimated matrices are updated at every following sub-dwell by multiple particle filtering to cope with fast-varying clutter scenes of SBR. Furthermore, waveform of every following sub-dwell is designed adaptively according to mean square optimization technique. Finally, principal component analysis and generalized likelihood ratio test is used for mitigation of colored interference and property of constant false alarm rate, respectively. Simulation results show that, considering configuration of SBR and condition of complicated clutter, 9 dB is reduced for SCR which reliable detection requires by this target detection approach. Therefore, the work in this paper can markedly improve radar detection performance for weak targets

Analysis of weak faults of planetary gears based on frequency domain information exchange method

This paper focuses on solving a series of problems, in particular, the extraction of planetary gear fault characteristics for cracked and broken teeth, using the frequency domain information exchange method. First, we discuss deficiencies in classical stochastic resonance fault feature extraction method. A number of issues are associated with adaptive stochastic resonance based on the re-scaling frequency method used during the small parameter issues, such as sampling frequency ratio constraints and easily induced aliasing of the target frequency band. Second, to overcome the above-mentioned problems, this paper proposes a frequency domain information exchange optimization method. Simulations were carried out used the proposed method and results were compared to those obtained using previously presented adaptive stochastic resonance based on the re-scaling frequency method. Finally, tests were performed on an experimental planetary gearbox failure platform to further verify the frequency domain information exchange method for effectively extracting planetary gear crack and missing tooth fault features

Self-training with dual uncertainty for semi-supervised medical image segmentation

In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem. How to effectively learn image features from unlabeled images to improve segmentation accuracy is the main research direction in this field. Traditional self-training methods can partially solve the problem of insufficient labeled data by generating pseudo labels for iterative training. However, noise generated due to the model's uncertainty during training directly affects the segmentation results. Therefore, we added sample-level and pixel-level uncertainty to stabilize the training process based on the self-training framework. Specifically, we saved several moments of the model during pre-training, and used the difference between their predictions on unlabeled samples as the sample-level uncertainty estimate for that sample. Then, we gradually add unlabeled samples from easy to hard during training. At the same time, we added a decoder with different upsampling methods to the segmentation network and used the difference between the outputs of the two decoders as pixel-level uncertainty. In short, we selectively retrained unlabeled samples and assigned pixel-level uncertainty to pseudo labels to optimize the self-training process. We compared the segmentation results of our model with five semi-supervised approaches on the public 2017 ACDC dataset and 2018 Prostate dataset. Our proposed method achieves better segmentation performance on both datasets under the same settings, demonstrating its effectiveness, robustness, and potential transferability to other medical image segmentation tasks. Keywords: Medical image segmentation, semi-supervised learning, self-training, uncertainty estimatio

Serum HBV RNA: a New Potential Biomarker for Chronic Hepatitis B Virus Infection

Chronic hepatitis B (CHB) is one of the major etiological causes of liver failure, cirrhosis, and hepatocellular carcinoma worldwide, and it cannot be completely cured by currently available drugs due to the persistent existence of hepatitis B virus (HBV) covalently closed circular DNA (cccDNA), the bona fide transcription template for HBV RNAs, in the infected hepatocytes. Since quantifying cccDNA per se requires an invasive procedure, serum biomarkers reflecting the intrahepatic cccDNA activity are warranted. Recently, a growing body of research suggests that the circulating HBV RNA may serve as a new serum biomarker for HBV infection, treatment and prognosis. In order to delineate the molecular and clinical characteristics of serum HBV RNA, we systematically reviewed the available literature on serum HBV RNA dating back to early 1990s. In this review, we will summarize the reported serum HBV RNA quantification methods and discuss the potential HBV RNA species in patient serum, and compare the reported correlations of serum HBV RNA with other serological markers, including HBV DNA, hepatitis B surface antigen (HBsAg), e antigen (HBeAg), and core‐related antigen (HBcrAg), as well as their correlations with the intrahepatic cccDNA, to assess its potential in clinical applications. The future directions for serum HBV RNA research will also be discussed

Partial masquerading and background matching in two Asian box turtle species (<i>Cuora</i> spp.)

Animals living in heterogeneous natural environments adopt different camouflage strategies against different backgrounds, and behavioral adaptation is crucial for their survival. However, studies of camouflage strategies have not always quantified the effect of multiple strategies used together. In the present study, we used a human visual model to quantify similarities in color and shape between the carapace patterns of two Cuora species and their preferred habitats. Our results showed that the color of the middle stripe on the carapace of Cuora galbinifrons (Indochinese box turtle) was significantly similar to the color of their preferred substrates. Meanwhile, the middle stripe on the carapace of C. mouhotii (keeled box turtle) contrasted more with their preferred substrates, and the side stripe matched most closely with the environment. Furthermore, the carapace side stripe of C. galbinifrons and the carapace middle stripe of C. mouhotii highly contrasted with their preferred substrates. We quantified the similarity in shape between the high-contrast stripes of both Cuora species and leaves from their habitats. The carapace middle stripe of C. mouhotii was most similar in shape to leaves from the broad-leaves substrate, and the carapace side stripe of C. galbinifrons was the most similar in shape to leaves from the bamboo-leaves substrate. We determined that these species adopt partial masquerading when their entire carapace is exposed and partially match their background when they semi-cover themselves in leaf litter. To the best of our knowledge, this is the first study to demonstrate that partial masquerading and background matching improve the camouflage effect of Asian box turtles in their preferred habitats. This is a novel study focusing on the influence of the shape and color of individual carapace segments on reducing detectability and recognition.</p

Structural and colored disruption as camouflage strategies in two sympatric Asian box turtle species (<i>Cuora</i> spp.)

Disruptive coloration is a common camouflage strategy that breaks body outlines and ostensibly blends into complex backgrounds. However, the contrasting false edge caused by the animal's structure can also break the outline, and there is no empirical evidence to support this strategy. Here, we examined the Gabor edge disruption ratio (GabRat) of two species with divergent carapaces, the keeled box turtle (Cuora mouhotii) and the Indochinese box turtle (C. galbinifrons), on preferred (e.g., deciduous leaves) and non-preferred (i.e., grass) substrates. We quantified edge disruption in different substrates to compare between-species differences in the GabRat of disruptive coloration among the turtles’ preferred and non-preferred (control) substrates. We found that both species exhibited higher GabRat on preferred substrates, but interestingly, the keeled box turtle, with a uniformly colored carapace containing flat scutes and two keels, had a higher GabRat than the Indochinese box turtle, characterized by two yellow stripes on its carapace. Our results indicated that the strong brightness gradients caused by the directional illumination of the flatted and keeled carapace creates disruptive coloration in the keeled box turtle, whereas a high chroma contrast creates disruptive coloration in the Indochinese box turtle. For these turtles, the structural modifications result in variations in brightness that lead to higher levels of disruption than the chromatic disruption of the Indochinese box turtle. Our study provides, to our knowledge, the first evidence of disruptive camouflage in turtles and the first comprehensive test of structural and colored disruption in vertebrates