UAV rotor fault diagnosis based on interval sampling reconstruction of vibration signals and a 1D-CNN deep learning method

Aiming at the possible mechanical faults of UAV rotor in the working process, this paper proposes a UAV rotor fault identification method based on interval sampling reconstruction of vibration signals and one-dimensional convolutional neural network (1D-CNN) deep learning. Firstly, experiments were designed to collect the vibration acceleration signals of UAV working at high speed under three states (normal, rotor damage by varying degrees, and rotor crack by different degrees). Then considering the powerful feature extraction and complex data analysis abilities of 1D-CNN, an effective deep learning model for fault identification is established utilizing 1D-CNN. During analysis, it is found that the recognition effect of minor faults is not ideal, which causes by all states were identified as normal and then reduces the overall identification accuracy, when using conventional sequential sampling to construct learning. To this end, in order to make the sample data cover the whole process of data collection as much as possible, a learning sample processing method based on interval sampling reconstruction of vibration signal is proposed. And it is also verified that the sample set reconstructed can easily reflect the global information of mechanical operation. Finally, according to the comparison of analysis results, the recognition rate of deep learning model for different degrees of faults is greatly improved, and minor faults could also be accurately identified, through this method. The results show that, the 1D-CNN deep learning model, could diagnose and identify UAV rotor damage faults accurately, by combing the proposed method of interval sampling reconstruction.

Influences of Roughness Sampling Interval and Anisotropy on Shear Failure Mechanism of Rock Joint Surface

Roughness is an important factor affecting the engineering stability of jointed rock masses. The existing roughness evaluation methods are all based on a uniform sampling interval, which changes the geometrical morphology of the original profile and inevitably ignores the influence of secondary fluctuations on the roughness. Based on the point cloud data obtained by 3D laser scanning, a non-equal interval sampling method and an equation for determining the sampling frequency on the roughness profile are proposed. The results show that the non-equal interval sampling method can successfully maintain the morphological characteristics of the original profile and reduce the data processing cost. Additionally, direct shear tests under constant normal load (CNL) conditions are carried out to study the influence of roughness anisotropy on the shear failure mechanism of joint surfaces. It is found that with the increase in shear displacement, the variations in the shear stress are related to the failure mechanisms of dilatancy and shear fracture of the joint. Finally, the distributions of shear stress, dilatancy and fracture areas on the rough joint in different shear directions are calculated theoretically. Results show that the anisotropy and failure mechanism of rough joint can be well characterized by the modified root mean square parameter Z2′.

Finger sweat analysis enables short interval metabolic biomonitoring in humans

Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers.

Reproductive Biology of Oci Fish Selaroides leptolepis in Tomini Bay

Local people name Selaroides leptolepis distributed in Tomini Bay as Oci Fish. A study of the fish reproductive biology, which is one aspect of fisheries biology, is crucial to support the implementation of sustainable Oci Fish resource management policies. This study aims to determine the average length at first maturity, gonad maturity stages, and fecundity of the fish in Tomini Bay. Sampling was carried out using a stratified random sampling method from the catches of the fishermen landed in Fish Landing Base Kampung Tenda, Gorontalo City. The time interval sampling was conducted per month during April, May, and June 2020. Fish and egg samples preserved using ice cubes and a 10% formaldehyde solution correspondingly. The results of the study showed that the Oci Fish in Tomini Bay had a length range of the first maturity between 166 and 174 mm with a gonad maturity index ranging from 1.773 to 2.760%. The average fish fecundity was 16623 ± 4850 eggs.

Finger Sweat Analysis Enables Short Interval Metabolic Biomonitoring in Humans

Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues and are impractical for performing time-course studies. The analysis of the minute amounts of sweat sampled from the fingertip enables a solution to this challenge. Sweat sampling from the fingertip is non-invasive and robust and can be accomplished repeatedly by untrained personnel. This matrix represents a rich source for metabolomic phenotyping, which is exemplified by the detection of roughly 50’000 features per sample. Moreover, the determined limits of detection demonstrate that the ingestion of 200 μg of a xenobiotic may be sufficient for its detection in sweat from the fingertip. The feasibility of short interval sampling of sweat from the fingertips was confirmed in three time-course studies after coffee consumption or ingestion of a caffeine capsule, successfully monitoring all known caffeine metabolites. Fluctuations in the rate of sweat production were accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. Biomonitoring using sweat from the fingertip has far reaching implications for personalised medical diagnostics and biomarker discovery.

Some comments on sampling of ergodic process, an ergodic theorem and turbulent pressure fluctuations

We present a new advanced mathematics engineering proof for an ergodic theorem for finite time duration signals in the frequency domain (periodograms) which is free from Nyquist interval sampling restriction .We also point out the usefulness of such theorem in the context of a model of random vibrations transmissions (pressure fluctuations )

Temporal aggregation of random walk processes and implications for economic analysis

This paper examines the impact of time averaging and interval sampling data assuming that the data generating process for a given series follows a random walk with iid errors. We provide exact expressions for the corresponding variances, and covariances, for both levels and higher order differences of the aggregated series, as well as that for the variance ratio, demonstrating exactly how the degree of temporal aggregation impacts these properties. We empirically investigate this issue on exchange rates and find that the values of the variance ratios and autocorrelation coefficients at different frequencies are consistent with our theoretical results. We also conduct a simulation exercise that illustrates the potential effect that conditional heteroskedasticity and fat tails may have on the temporal aggregation of a random walk and of a highly persistent autoregressive process.

Sella Turcica Bridging as a Predicator of Dentofacial Anomalies: A Cephalometric Analysis

Introduction: Sellar changes are associated with several dentofacial anomalies. Clinicians should be aware of different morphological varaiants of sella turcica (ST).Objective: To find the prevalence of sella turcica bridging and to analyze the absence or presence of bridging with a spectrum of dentofacial anomalies.Materials & Method: 710 case records were selected from the database; out of which 473 subjects met the inclusion criteria. 280 lateral cephalogram revealed a normal shape of ST. Among them, 71 subjects were selected by interval sampling which were taken as the control group. Among initial 473 subjects, 78 samples showed ST bridging and were taken as the study group. Dental casts and radiographs (panoramic and cephalometric) were evaluated to find any dentofacial anomalies. Subjects were divided on the basis of dentofacial anomalies such as abnormal tooth position, size, shape, number, malocclusion, sagittal skeletal relationship. Chi-square test and binomial logistic regression analysis were done to find the association and correlation among the variables.Result: In the study group, 32 subjects showed a complete bridging and 46 subjects a partial bridging of the sella turcica. There was no significant association between ST bridging and age, gender or ethnic groups. However, there was significant association (p=0.001) between the presence of bridging and dentofacial anomalies when compared with the control group.Conclusion: The occurrence of ST bridging is 16.49% with no association to age, gender and ethnic groups in a Nepalese sample. Sella turcica bridging can be used as a diagnostic tool for early prediction of dentofacial anomalies