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.

Reasons for Elective Surgery Cancellations in a Senegalese Pediatric Surgery Department

Introduction: Cancellation of elective surgery is common in developing countries. This decision is difficult to make as it generates economic and organizational consequences for the healthcare facility and an additional source of stress for patients and their caregivers. This study aimed to analyze the various aspects of this medical problem. Patients and Methods: We conducted a prospective and descriptive study over six months (from January 1st, 2017, to June 30th, 2017) at the pediatric surgery department of Aristide Le Dantec University Teaching Hospital in Senegal. Results: Ninety-one cases were collected. The cancellation rate was 20.8%. Infants were affected in 36.3% of cases. Among anesthesiologists, 83.5% were residents, and 16.5% were specialists. Cancellation in nephroblastoma children with an indication for extended nephrectomy represented 15.4% of cases. Concerning reasons for cancellation, comorbidities, dominated by respiratory infections, accounted for 28.5% of cases, patient absences for 24.2%, and issues related to the anesthesiologist for 17.6%. Cancellations were avoidable in 33% of cases. Patients were responsible for cancellation in 37.4% of cases, the healthcare system in 33%, and medical reasons in 29.7%. Conclusion: Our findings suggest that one-third of cancellations could have been avoided with improvements in the healthcare system. Actions should be taken to reduce the cancellation rate in our context.

Digital cancellation of multi-band passive inter-modulation based on Wiener-Hammerstein model

Utilizing multi-band and multi-carrier techniques enhances throughput and capacity in Long-Term Evolution(LTE)-Advanced and 5G New Radio(NR)mobile networks.However,these techniques introduce Passive Inter-Modulation(PIM)interference in Frequency-Division Duplexing(FDD)systems.In this paper,a novel multi-band Wiener-Hammerstein model is presented to digitally reconstruct PIM interference signals,thereby achieving effective PIM Cancellation(PIMC)in multi-band scenarios.In the model,transmitted signals are independently processed to simulate Inter-Modulation Distortions(IMDs)and Cross-Modulation Distortions(CMDs).Furthermore,the Finite Impulse Response(FIR)filter,basis function generation,and B-spline function are applied for precise PIM product estimation and generation in multi-band scenarios.Simulations involving 4 carrier components from diverse NR frequency bands at varying transmitting powers validate the feasibility of the model for multi-band PIMC,achieving up to 19 dB in PIMC performance.Compared to other models,this approach offers superior PIMC performance,exceeding them by more than 5 dB in high transmitting power scenarios.Additionally,its lower sampling rate requirement reduces the hardware complexity associated with implementing multi-band PIMC.

A new adaptive co-site broadband interference cancellation method with auxiliary channel

With the boom of the communication systems on some independent platforms(such as satellites,space stations,airplanes,and vessels),co-site interference is becoming prominent.The adaptive interference cancellation method has been adopted to solve the co-site interference problem.But the broadband interference cancellation performance of traditional Adaptive Co-site Interference Cancellation System(ACICS)with large delay mismatching and antenna sway is relatively poor.This study put forward an Adaptive Co-site Broadband Interference Cancellation System With Two Auxiliary Channels(ACBICS-2A).The system model was established,and the steady state weights and Interference Cancellation Ratio(ICR)were deduced by solving a time-varying differential equation.The relationship of ICR,system gain,modulation factor,interference signal bandwidth and delay mismatching degree was acquired through an in-depth analysis.Compared with traditional adaptive interference cancellation system,the proposed ACBICS-2A can improve broadband interference cancellation ability remarkably with large delay mismatching and antenna sway for the effect of auxiliary channel.The maximum improved ICR is more than 25 dB.Finally,the theoretical and simulation results were verified by experiments.

Multitap RF Canceller with Single LMS Loop for Adaptive Co-Site Broadband Interference Cancellation

With the development of wireless communication technology,an urgent problem to be solved is co-site broadband interference on independent communication platforms such as satellites,space stations,aircrafts and ships.Also,the problem of strong selfinterference rejection should be solved in the co-time co-frequency full duplex mode which realizes spectrum multiplication in 5G communication technology.In the research of such interference rejection,interference cancellation technology has been applied.In order to reject multipath interference,multitap double LMS(Least Mean Square)loop interference cancellation system is often used for cancelling RF(Radio Frequency)domain interference cancelling.However,more taps will lead to a more complex structure of the cancellation system.A novel tap single LMS loop adaptive interference cancellation system was proposed to improve the system compactness and reduce the cost.In addition,a mathematical model was built for the proposed cancellation system,the correlation function of CP2FSK(Continuous Phase Binary Frequency Shift Keying)signal was derived,and the quantitative relationship was established between the correlation function and the interference signal bandwidth and tap delay differential.The steadystate weights and the expression of the average interference cancellation ratio(ICR)were deduced in the scenes of LOS(Line of Sight)interference with antenna swaying on an independent communication platform and indoor multipath interference.The quantitative relationship was deeply analyzed between the interference cancellation performance and the parameters such as antenna swing,LMS loop gain,and interference signal bandwidth,which was verified by simulation experiment.And the performance of the proposed interference cancellation system was compared with that of the traditional double LMS loop cancellation system.The results showed that the compact single LMS loop cancellation system can achieve an average interference rejection capability comparable to the double LMS loop cancellation system.

Off-Grid Compressed Channel Estimation with Parallel Interference Cancellation for Millimeter Wave Massive MIMO

Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capacity.However,channel estimation has become very challenging due to the use of massive MIMO antenna array.Fortunately,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid.Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity.However,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid,thus leading to the problem of power leakage.Moreover,multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel estimation.To address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)algorithm in this paper.The proposed algorithm consists of three stages,including coarse estimation,refined estimation,and inter-path cyclic iterative inter-ference cancellation.More specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interference is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation accuracy.Simulation results show that compared with the traditional orthogonal matching pursuit(OMP)algorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.

Iterative Signal Detection Based Soft Interference Cancellation for Satellite-Terrestrial Downlink NOMA Systems

To improve the bit error rate(BER)performance of multi-user signal detection in satelliteterrestrial downlink non-orthogonal multiple access(NOMA)systems,an iterative signal detection algorithm based on soft interference cancellation with optimal power allocation is proposed.Given that power allocation has a significant impact on BER performance,the optimal power allocation is obtained by minimizing the average BER of NOMA users.According to the allocated powers,successive interference cancellation(SIC)between NOMA users is performed in descending power order.For each user,an iterative soft interference cancellation is performed,and soft symbol probabilities are calculated for soft decision.To improve detection accuracy and without increasing the complexity,the aforementioned algorithm is optimized by adding minimum mean square error(MMSE)signal estimation before detection,and in each iteration soft symbol probabilities are utilized for soft-decision of the current user and also for the update of soft interference of the previous user.Simulation results illustrate that the optimized algorithm i.e.MMSE-IDBSIC significantly outperforms joint multi-user detection and SIC detection by 7.57dB and 8.03dB in terms of BER performance.

A deep multimodal fusion and multitasking trajectory prediction model for typhoon trajectory prediction to reduce flight scheduling cancellation

Natural events have had a significant impact on overall flight activity,and the aviation industry plays a vital role in helping society cope with the impact of these events.As one of the most impactful weather typhoon seasons appears and continues,airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms.This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation.The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules,and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction.With more dependable data accuracy,problems can be analysed rapidly and more efficiently,enabling better decision-making with a proactive versus reactive posture.When multiple modalities coexist,features can be extracted from them simultaneously to supplement each other’s information.An actual case study,the typhoon Lichma that swept China in 2019,has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.

Turbo Message Passing Based Burst Interference Cancellation for Data Detection in Massive MIMO-OFDM Systems

This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.

On Normalized Least Mean Square Based Interference Cancellation Algorithm for Integrated Sensing and Communication Systems

Integrated sensing and communication(ISAC)technology is a promising candidate for next-generation communication systems.However,severe co-site interference in existing ISAC systems limits the communication and sensing performance,posing significant challenges for ISAC interference management.In this work,we propose a novel interference management scheme based on the normalized least mean square(NLMS)algorithm,which mitigates the impact of co-site interference by reconstructing the interference from the local transmitter and canceling it from the received signal.Simulation results demonstrate that,compared to typical adaptive interference management schemes based on recursive least square(RLS)and stochastic gradient descent(SGD)algorithms,the proposed NLMS algorithm effectively cancels co-site interference and achieves a good balance between computational complexity and convergence performance.

The Effects of Infrastructure Projects on House Prices and Rents:Evidence from the HS2 Extension Cancellation in the UK

This paper uses the HS2 extension cancellation in November 2021 as a quasi-experiment to study its impact on house prices and rents in Leeds.Using a DiD approach on repeat sales and monthly rents,I compare property values near the HS2 station and proposed construction site before and after the announcement.Results show a 3.6%decrease in house prices and a 3.9%decline in rents near the station,while properties near the construction site experienced a 2.4%increase in prices and a 2.1%rise in rents.This is the first paper to analyse the HS2 cancellation effect using panel data methods.

Group-Based Successive Interference Cancellation for Multi-Antenna NOMA System with Error Propagation

Non-orthogonal multiple access(NOMA)is viewed as a key technique to improve the spectrum efficiency and solve the issue of massive connectivity.However,for power domain NOMA,the required overall transmit power should be increased rapidly with the increasing number of users in order to ensure that the signal-to-interference-plus-noise ratio reaches a predefined threshold.In addition,since the successive interference cancellation(SIC)is adopted,the error propagation would become more serious as the order of SIC increases.Aiming at minimizing the total transmit power and satisfying each user’s service requirement,this paper proposes a novel framework with group-based SIC for the deep integration between power domain NOMA and multi-antenna technology.Based on the proposed framework,a joint optimization of power control and equalizer design is investigated to minimize transmit power consumption for uplink multi-antenna NOMA system with error propagations.Based on the relationship between the equalizer and the transmit power coefficients,the original problem is transformed to a transmit power optimization problem,which is further addressed by a parallel iteration algorithm.It is shown by simulations that,in terms of the total power consumption,the proposed scheme outperforms the conventional OMA and the existing cluster-based NOMA schemes.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

Frequency-Domain Wideband Acoustic Noise Cancellation System

When building an adaptive noise cancellation system for wideband acoustic signals, one can meet some difficulties in practical implementation of such a system. The major problem is related to the necessity of using real-time signal generation and processing. In this paper the active noise control system which utilizes adaptation in frequency domain is considered. It is shown that the proposed algorithms simplify practical implementation of a noise cancellation system. The results of computer simulations and prototype experiments show the effectiveness of the proposed methods. .

Standardization of apple cancellation test for neglect patients in Korea:An observational study

BACKGROUND Neglect can be divided into two types using apple cancellation test(apple test):Egocentric neglect(EN)and allocentric neglect(AN).However,in South Korea,apple test results and decision criteria are still largely dependent on tests by foreign countries.AIM To establish a new South Korea standard and improve the accuracy of neglect assessment,the apple experiment was standardized in this study.METHODS This study was conducted on 223 healthy subjects for a total of 7 mo from August 2021 to February 2022.Standardization was carried out using the original apple test developed by Bickerton in 2011.In scoring for the apple test,total omission error refers to the number of missed targets(full apple)in the entire test sheet(left,middle,and right area).The score for EN is the difference between the correct number of right area and the correct number of left area(excluding the middle area).For AN,the score is difference between the number of left opening apples and number of right opening apples(including the middle area).Linear regression analysis was used for standardization using the general characteristics of subjects and the results of the apple test.RESULTS The cut-off score,which is the standard value indicating the pathological condition by combining the results of all subjects,is as follows:Total omission error(5),error for EN(2),and error for AN(2).Also,differences in cut-off score according to age were found.CONCLUSION This study will be helpful in facilitating a more accurate differential diagnosis of neglect.

Advanced surface coil layout with intrinsic noise cancellation properties for surface-NMR applications

A recent study demonstrated that in small-scale prepolarized surface nuclear magnetic resonance(SNMR-PP)measurements with a footprint of a few square meters,customized PP switch-off ramps can serve as an efficient excitation mechanism,eliminating the requirement for a conventional oscillating excitation pulse.This approach enables the detection of short relaxation signals from the unsaturated soil zone and can,therefore,be used to directly provide soil moisture and pore geometry information.Because ultimately such small-scale SNMR-PP setups are intended for a mobile application,it is necessary to develop strategies that allow for speedy measurement progress and do not require noise cancellation protocols based on reference stations.Hence,we developed a new concentric figure-of-eight(cFOE)loop layout that combines the direction independence of a circular loop with the intrinsic noise cancellation properties of a classical FOE-loop.This approach significantly decreases the measurement time because suitable signal-to-noise ratios are reached much faster compared to a classical circular loop and will bring us one step further toward fast and non-invasive soil moisture mapping applications.

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

Practical Considerations for Implementing Adaptive Acoustic Noise Cancellation in Commercial Earbuds

Active noise cancellation has become a prominent feature in contemporary in-ear personal audio devices.However,due to constraints related to component arrangement,power consumption,and manufacturing costs,most commercial products utilize fixed-type controller systems as the basis for their active noise control algorithms.These systems offer robust performance and a straightforward structure,which is achievable with cost-effective digital signal processors.Nonetheless,a major drawback of fixed-type controllers is their inability to adapt to changes in acoustic transfer paths,such as variations in earpiece fitting conditions.Therefore,adaptive-type active noise control systems that employ adaptive digital filters are considered as the alternative.To address the increasing system complexity,design concepts and implementation strategies are discussed with respect to actual hardware limitations.To illustrate these considerations,a case study showcasing the implementation of a filtered-x least mean square-based active noise control algorithm is presented.A commercial evaluation board accommodating a low-cost,fixed-point digital signal processor is used to simplify operation and provide programming access.The earbuds are obtained from a commercial product designed for noise cancellation.This study underscores the importance of addressing hardware constraints when implementing adaptive active noise cancellation,providing valuable insights for real-world applications.

Active Cancelation of Acoustic Noise in Waveguides by Standard Control Task Decision Usage

Cybernetic decision variants were analyzed in order to use for physical task of active noise cancelation. 10 dB mean active noise cancellation is demonstrated in two decades frequency band by usage of cybernetic decision for acoustical duct physical scale model. The used decision was found on minimization of acoustical field power transfer function from the beginning of waveguide to their end.