Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

The W transform and its improved methods for time-frequency analysis of seismic data

The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.

Study on the Application of Refined Nursing Care in The Care for Elderly Patients with Reflux Esophagitis

Objective: To investigate the application effect of refined nursing care in the care for elderly patients with reflux esophagitis. Methods: Following the difference in nursing style, 84 cases of elderly patients with reflux esophagitis admitted to our hospital from May 2022 to May 2023 were randomly grouped into a control group and a research group, with 42 cases each. The control group was given conventional nursing care and the research group was given refined nursing care. The psychological state and treatment adherence of the two groups of patients after the nursing intervention were compared. Results: After the nursing intervention, the self-rating anxiety scale (SAS) and self-rating depression scale (SDS) scores of the research group were lower than those of the control group (P < 0.05). The treatment compliance of the research group was better than the control group (P < 0.05). Conclusion: The implementation of refined nursing care for elderly patients with reflux esophagitis exhibited a significant effect on improving the patient’s psychological state, treatment compliance, and rehabilitation.

Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning

Recent developments in deep learning techniques have provided alternative and complementary approaches to the traditional matched-filtering methods for identifying gravitational wave(GW)signals.The rapid and accurate identification of GW signals is crucial to the advancement of GW physics and multi-messenger astronomy,particularly considering the upcoming fourth and fifth observing runs of LIGO-Virgo-KAGRA.In this study,we used the 2D U-Net algorithm to identify time-frequency domain GW signals from stellar-mass binary black hole(BBH)mergers.We simulated BBH mergers with component masses ranging from 7 to 50 M_(⊙)and accounted for the LIGO detector noise.We found that the GW events in the first and second observation runs could all be clearly and rapidly identified.For the third observing run,approximately 80% of the GW events could be identified.In contrast to traditional convolutional neural networks,the U-Net algorithm can output time-frequency domain signal images corresponding to probabilities,providing a more intuitive analysis.In conclusion,the U-Net algorithm can rapidly identify the time-frequency domain GW signals from BBH mergers.

Evaluation of the Impact of Refined Quality Control Management Model on the Qualified Rate of Disinfection and Sterilization of Surgical Instruments in the Sterilization Supply Center

Objective:To evaluate the application value of a refined quality control management model for a sterilization supply center.Methods:A retrospective analysis was conducted on the work situation of the sterilization supply center from January 2021 to January 2023.The work situation before January 31,2022,was classified as the control group;a routine quality control management model was implemented,and the work situation after January 31,2022,was classified as the observation group.The quality of medical device management and department satisfaction between the two groups were compared.Results:The timely recovery and supply rate,classification and cleaning pass rate,disinfection pass rate,packaging pass rate,sterilization pass rate,and department satisfaction score in the observation group were all higher than those of the control group(P<0.05).Conclusion:Implementing a refined quality control management model in the sterilization supply center can improve the quality management level of medical devices and department satisfaction and is worthy of promotion.

Refined Anam-Net:Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis

In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of Glaucoma.In this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images.Our DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding block.To reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens.This modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained applications.We evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and Drishti-GS.The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation.We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,respectively.For OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,respectively.Similarly,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.

Long-term Development Prospects and Suggestions for Domestic Refined Oil Market

Over the past decade,China’s refined oil market has experienced considerable growth and fluctuations.Gasoline consumption has generally followed the growth rate of vehicle equipment,with fluctuations influenced by travel frequency;aviation fuel consumption has seen stable growth following the end of COVID-19,while diesel consumption has been affected by multiple factors including demand and policy.With the rapid development of new energy vehicles and alternative fuels,the gasoline and diesel market has essentially peaked,yet domestic production of refined oil continues to grow,leading to an increasingly prominent oversupply issue.To achieve the dual carbon goals,the Chinese government has introduced a series of policies that have a profound impact on the refined oil market.Facing resource surplus and market demand changes,the refining industry needs to optimize production capacity structure,and oil products retail companies face transformation pressure.The article aims to provide market analysis and recommendations,serving as a reference for relevant enterprises and policymakers.

Impact of early refined nursing program on prognosis of middleaged and elderly patients with cognitive dysfunction combined with cerebral infarction

BACKGROUND Cerebral infarction is a local or extensive necrosis of brain tissue.Subsequently,the corresponding neurological deficits appear.The incidence of cerebrovascular diseases in China is increasing gradually.After the onset of cerebrovascular disease,the most common sequelae include movement disorders,language disorders,and cognitive dysfunction.AIM To investigate the effect of early refined nursing program on the prognosis of middle-aged and elderly patients with cerebral infarction combined with cognitive dysfunction.METHODS A retrospective study was conducted to divide 60 patients with cerebral infarction and cognitive impairment into an experimental group(n=32)and a control group(n=28).The experimental group received early intensive care every day,and the control group received daily routine care.The scores of the Mini-Mental State Examination(MMSE)and the Trail Making Test(TMT),as well as the latency and amplitude of the event-related potential P300,were used as main indicators to evaluate changes in cognitive function,and changes in BDNF,TGF-β,and GDNF expression were used as secondary indicators.RESULTS Both groups experienced notable enhancements in MMSE scores,with the experi-mental group demonstrating higher scores than the control group(experimental:28.75±2.31;control:25.84±2.87).Moreover,reductions in TMT-A and TMT-B scores were observed in both groups(experimental:TMT-A 52.36±6.18,TMT-B 98.47±10.23;control:TMT-A 61.48±7.92,TMT-B 112.63±12.55),with the experimental group displaying lower scores.P300 Latency decreased(experimental:270.63 ms±14.28 ms;control:285.72 ms±16.45 ms),while amplitude increased(experimental:7.82μV±1.05μV;control:6.35μV±0.98μV)significantly in both groups,with superior outcomes in the experimental cohort.Additionally,the levels of the growth factors BDNF,TGF-β1,and GDNF surged(experimental:BDNF 48.37 ng/mL±5.62 ng/mL,TGF-β152.14 pg/mL±4.28 pg/mL,GDNF 34.76 ng/mL±3.89 ng/mL;control:BDNF 42.58 ng/mL±4.73 ng/mL,TGF-β146.23 pg/mL±3.94 pg/mL,GDNF 30.25 ng/mL±2.98 ng/mL)in both groups,with higher levels in the experimental group.CONCLUSION For middle-aged and elderly patients with cerebral infarction and cognitive dysfunction,early refined nursing can significantly improve their cognitive function and prognosis.

From“Car Priority”to“People Priority”: Exploring New Ideas for Refined Road Space Design

Roads are crucial public spaces in cities and serve as a window to showcase the city’s characteristics.They serve not only as a means of urban transportation but also as a crucial spatial carrier for urban communication activities and a significant location for meeting the increasing demands of people for a better quality of life.The current road infrastructure prioritizes the right of way for cars,neglecting the design of sidewalks and green belts within the road’s boundaries,and extension spaces between buildings and boundary lines of roads.There is a pressing need to improve street space in response to the demand for development transformation and the creation of a warmer city.This paper summarizes common problems in current road spaces,draws on the experience of excellent urban road spaces in foreign countries,discusses new ideas for the refined design of road spaces based on the transformation of road planning concepts,and suggests a reference standard for guiding detailed design.Simultaneously,the review of road construction will incorporate the detailed design of road space to enhance the role of planning in guiding and controlling the construction of road works.

High-resolution seismic inversion method based on joint data-driven in the time-frequency domain

Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydomain inversion.Frequency domain inversion has stronger ability to identify small-scale bodies and higher inversion resolution.Therefore,the research on the joint inversion method in the time-frequency domain is of great significance for improving the inversion resolution,stability,and noise resistance.The introduction of prior information constraints can effectively reduce ambiguity in the inversion process.However,the existing modeldriven time-frequency joint inversion assumes a specific prior distribution of the reservoir.These methods do not consider the original features of the data and are difficult to describe the relationship between time-domain features and frequency-domain features.Therefore,this paper proposes a high-resolution seismic inversion method based on joint data-driven in the time-frequency domain.The method is based on the impedance and reflectivity samples from logging,using joint dictionary learning to obtain adaptive feature information of the reservoir,and using sparse coefficients to capture the intrinsic relationship between impedance and reflectivity.The optimization result of the inversion is achieved through the regularization term of the joint dictionary sparse representation.We have finally achieved an inversion method that combines constraints on time-domain features and frequency features.By testing the model data and field data,the method has higher resolution in the inversion results and good noise resistance.

Inhibition of Propionibacterium acnes by refined bamboo vinegar and preparation of the slow-release system with bamboo charcoal as the carrier

Background:Acne vulgaris is one of the most common skin diseases that can significantly impact a considerable proportion of individuals over their lifetime.Objective:This study focuses on the exploration of the application potential of bamboo vinegar in cosmetics.Materials and Methods:The stock solution of bamboo vinegar is subjected to reduced-pressure distillation at different temperatures to obtain refined bamboo vinegar.Then,inhibition of Propionibacterium acnes(P.acnes)by refined bamboo vinegar is observed.Moreover,the refined bamboo vinegar is adsorbed and released with activated bamboo charcoal as the carrier.In all,this study aims to probe into the mechanism of the controlledrelease system of bamboo vinegar.Results:The results shows that the harmful substances(tar)in bamboo vinegar distilled at 70℃decreased by 94.44%,which is a more notable decrease compared with that in the stock solution.The total organic acid content in bamboo vinegar after reduced-pressure distillation is 11.840%,reaching the national standard for refined bamboo vinegar(GB/T 31734–2015).Additionally,the minimum inhibitory concentration of refined bamboo vinegar against P.acnes using the punch method is 7.90 mg/mL.This indicates that refined bamboo vinegar has the potential as a prospective raw material for formulations in anti-acne cosmetic products.Furthermore,the release rate of bamboo charcoal/bamboo vinegar in water for 15 min reaches 70.57%,which then slows down to a plateau.The slow-release behavior is agreed with the Ritger-Peppas model and is beneficial to relieve the irritation of bamboo vinegar to the skin and lengthen its bacteriostatic duration.Conclusion:The foregoing conclusions can serve as the theoretical foundation for the application of bamboo vinegar in anti-acne cosmetics.

Exploring the Application of Refined Management in University Financial Management

The refined management of university finances primarily involves optimizing management methods and continuously improving financial management levels.This process helps enhance fund utilization efficiency,optimize resource allocation,ensure the rational use of educational funds,and provide solid financial support for the development of teaching,research,and other university undertakings.This paper explores the application of refined management in university financial management.

Application of sparse time-frequency decomposition to seismic data

The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time and frequency domains. To improve the resolution of the time-frequency decomposition results, we use the instantaneous frequency distribution function（IFDF） to express the seismic signal. When the instantaneous frequencies of the nonstationary signal satisfy the requirements of the uncertainty principle, the support of IFDF is just the support of the amplitude ridges in the signal obtained using the short-time Fourier transform. Based on this feature, we propose a new iteration algorithm to achieve the sparse time-frequency decomposition of the signal. The iteration algorithm uses the support of the amplitude ridges of the residual signal obtained with the short-time Fourier transform to update the time-frequency components of the signal. The summation of the updated time-frequency components in each iteration is the result of the sparse timefrequency decomposition. Numerical examples show that the proposed method improves the resolution of the time-frequency decomposition results and the accuracy of the analysis of the nonstationary signal. We also use the proposed method to attenuate the ground roll of field seismic data with good results.

TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft

Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.

Performance characterization, of rigid polyurethane foam with refined alkali lignin and modified alkali lignin

The two kinds of rigid polyurethane （PU） foams were prepared with respectively adding the refined alkali lignin and alkali lignin modified by 3-chloro-1,2-epoxypropane to be instead of 15% of the polyether glycol in weight. The indexes of mechanical performance, apparent density, thermal stability and aging resistance were separately tested for the prepared PU foams. The results show that the mechanical property, thermal insulation and thermal stability for PU foam with modified alkali lignin are excellent among two kinds of PU foams and control samples. The additions of the refined alkali lignin and modified alkali lignin to PU foam have little effect on the natural aging or heat aging resistance except for decreasing hot alkali resistance apparently. Additionally, the thermal conductivity of modified alkali lignin PU foam is lowest among two kinds of PU foams and control samples. The alkali lignin PU foam modified by 3-chloro-1,2-epoxypropane could be applied in the heat preservation field.

Boxcar滤波器和极化Refined Lee滤波器对极化SAR分类精度影响的评估

本文阐述了Boxcar滤波器和极化Refined Lee滤波器的滤波原理,比较了两种滤波器的滤波效果。通过滤波后的极化图像分类精度的比较发现,滤波能显著提高监督分类的精度,滤波是提高分类精度必不可少的技术环节,Boxcar滤波在提高分类精度方面要优于Refined Lee滤波,此外滤波窗口的大小对分类精度也有重要的影响。

Intelligibility evaluation of enhanced whisper in joint time-frequency domain

Some factors influencing the intelligibility of the enhanced whisper in the joint time-frequency domain are evaluated. Specifically, both the spectrum density and different regions of the enhanced spectrum are analyzed. Experimental results show that for a spectrum of some density, the joint time-frequency gain-modification based speech enhancement algorithm achieves significant improvement in intelligibility. Additionally, the spectrum region where the estimated spectrum is smaller than the clean spectrum, is the most important region contributing to intelligibility improvement for the enhanced whisper. The spectrum region where the estimated spectrum is larger than twice the size of the clean spectrum is detrimental to speech intelligibility perception within the whisper context.

River-Net:面向河道提取的Refined-Lee Kernel深度神经网络模型

高精度提取合成孔径雷达(SAR)图像中的河流边界,对河流水势监测具有重要意义。以检测郑州7·20暴雨后黄河的健康状况为实施例,该文融合精致Lee滤波思想与卷积操作的滤波特性,提出了基于河道几何特性的优化内部权值卷积核Refined-Lee Kernel,进而提出了一种新型河道提取深度神经网络模型,即River-Net。为验证所提模型的有效性,该文获取了郑州7·20暴雨前后两景欧空局Sentinel-1卫星20 m分辨率干涉宽幅(IW)影像数据,利用暴雨前的影像对模型进行训练,用于提取暴雨后的黄河河道,分析黄河在暴雨后的涨势情况。实验结果表明,相比主流语义分割模型,所提模型能够更精确地在SAR图像中提取河道,对洪水灾害的检测与评估有重要应用价值。

Effects of Gabor transform parameters on signa time-frequency resolution

In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given.

REFINED HYBRID MINDLIN PLATE ELEMENT FOR GEOMETRICALLY NON LINEAR ANALYSIS

Based upon a generalized variational principle, which relaxed the inter element continuity requirements, a novel refined hybrid Mindlin plate element is developed, its non linear element stiffness matrices are decomposed into a series of matrices with respect to the assumed strain modes. The formulation presented in this paper is different from any other non linear mixed/hybrid element formulation all successful experience of linear hybrid formulation is absorbed into the formulation(adding non conforming modes and realizing orthogonalization) Numerical results show that the present approach is more effective than any other non linear hybrid element formulation over the accuracy and computational efficiency. In addition, non conforming modes can also overcome the shear locking effect.