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.

Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled Smart Healthcare System

The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorporating key techniques like AI and IoT.The convergence of AI and IoT provides distinct opportunities in the medical field.Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population.Therefore,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support.Lately,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home care.This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system.Initially,the presented TWODL-FDSHS technique exploits IoT devices for the data collection process.Next,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature extraction.At last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS technique.The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.

An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.

Group Work Learning In English Learning And Teaching

Group work learning is one of the hot topics in English learning and teaching today. This discourse will probe the meaning and the advantages of group work learning, as well as its implementation. Also, the discourse discusses the proper time for group work learning. In addition to that, problems of group work learning are enclosed.

Alternative Assessment in an EFL Project-based Learning Context: Perceptions and Correlations

This study aims to explore Chinese university EFL learners'perceptions toward alternative assessment in a context of a project-based learning digital storytelling presentation in Speaking Course.It also seeks to compare the relationship between alternative assessment and teacher assessment.The findings showed that a strong correlation between alternative assessment and teacher assessment occurred.Alternative assessment activities are viewed by students as"authentic"assessments,as they mimic how the student will be using their knowledge in the future.Alternative assessment as a form of formative assessment can be a powerful day-to-day tool for teachers and students.Alternative assessment is an enabler of process of learning.The study suggests that alternative assessment can encourage learners to become more fully responsible for their learning and can result in more and better learning.Alternative assessment can thus be used as a golden key to the"deaf and dumb"phenomenon for Chinese university EFL learners.

Application of Linearized Alternating Direction Multiplier Method in Dictionary Learning

The Alternating Direction Multiplier Method (ADMM) is widely used in various fields, and different variables are customized in the literature for different application scenarios [1] [2] [3] [4]. Among them, the linearized alternating direction multiplier method (LADMM) has received extensive attention because of its effectiveness and ease of implementation. This paper mainly discusses the application of ADMM in dictionary learning (non-convex problem). Many numerical experiments show that to achieve higher convergence accuracy, the convergence speed of ADMM is slower, especially near the optimal solution. Therefore, we introduce the linearized alternating direction multiplier method (LADMM) to accelerate the convergence speed of ADMM. Specifically, the problem is solved by linearizing the quadratic term of the subproblem, and the convergence of the algorithm is proved. Finally, there is a brief summary of the full text.

Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold

Data-driven computing in elasticity attempts to directly use experimental data on material,without constructing an empirical model of the constitutive relation,to predict an equilibrium state of a structure subjected to a specified external load.Provided that a data set comprising stress-strain pairs of material is available,a data-driven method using the kernel method and the regularized least-squares was developed to extract a manifold on which the points in the data set approximately lie(Kanno 2021,Jpn.J.Ind.Appl.Math.).From the perspective of physical experiments,stress field cannot be directly measured,while displacement and force fields are measurable.In this study,we extend the previous kernel method to the situation that pairs of displacement and force,instead of pairs of stress and strain,are available as an input data set.A new regularized least-squares problem is formulated in this problem setting,and an alternating minimization algorithm is proposed to solve the problem.

Communication-Censored Distributed Learning for Stochastic Configuration Networks

This paper aims to reduce the communication cost of the distributed learning algorithm for stochastic configuration networks (SCNs), in which information exchange between the learning agents is conducted only at a trigger time. For this purpose, we propose the communication-censored distributed learning algorithm for SCN, namely ADMMM-SCN-ET, by introducing the event-triggered communication mechanism to the alternating direction method of multipliers (ADMM). To avoid unnecessary information transmissions, each learning agent is equipped with a trigger function. Only if the event-trigger error exceeds a specified threshold and meets the trigger condition, the agent will transmit the variable information to its neighbors and update its state in time. The simulation results show that the proposed algorithm can effectively reduce the communication cost for training decentralized SCNs and save communication resources.

基于e-learning平台的“工学结合”教学模式探索

解决身处异地学生的继续学习问题,是"工学结合"教学模式改革成功的保障。通过e-learning教学平台作用的描述,介绍了e-learning教学平台的构建,阐述了通过e-learning教学平台实施教学。

基于E-Learning的“工学交替”教学及管理模式

介绍对开源E-Learning平台进行修改并应用到"工学交替"管理中的方法和实施措施.讨论了采用E-Learning平台对实施"工学交替"学生进行指导、日常管理和综合评价的方案,为职业院校的实践管理提供新的思路.

Simultaneous Denoising and Interpolation of Seismic Data via the Deep Learning Method

Utilizing data from controlled seismic sources to image the subsurface structures and invert the physical properties of the subsurface media is a major effort in exploration geophysics. Dense seismic records with high signal-to-noise ratio(SNR) and high fidelity helps in producing high quality imaging results. Therefore, seismic data denoising and missing traces reconstruction are significant for seismic data processing. Traditional denoising and interpolation methods rarely occasioned rely on noise level estimations, thus requiring heavy manual work to deal with records and the selection of optimal parameters. We propose a simultaneous denoising and interpolation method based on deep learning. For noisy records with missing traces, we adopt an iterative alternating optimization strategy and separate the objective function of the data restoring problem into two sub-problems. The seismic records can be reconstructed by solving a least-square problem and applying a set of pre-trained denoising models alternatively and iteratively.We demonstrate this method with synthetic and field data.

Low-level lead exposure effects on spatial reference memory and working memory in rats

BACKGROUND： Studies have demonstrated that lead exposure can result in cognitive dysfunction and behavior disorders. However, lead exposure impairments vary under different experimental conditions. OBJECTIVE： To detect changes in spatial learning and memory following low-level lead exposure in rats, in Morris water maze test under the same experimental condition used to analyze lead exposure effects on various memory types and learning processes. DESIGN AND SETTING： The experiment was conducted at the Animal Laboratory, Institute of Psychology, Chinese Academy of Science between February 2005 and March 2006. One-way analysis of variance （ANOVA） and behavioral observations were performed. MATERIALS： Sixteen male, healthy, adult, Sprague Dawley rats were randomized into normal con-trol and lead exposure groups （n = 8）. METHODS： Rats in the normal control group were fed distilled water, and those in the lead exposure group were fed 250 mL of 0.05% lead acetate once per day. At day 28, all rats performed the Morris water maze test, consisting of four phases： space navigation, probe test, working memory test, and visual cue test. MAIN OUTCOME MEASURES： Place navigation in the Morris water maze was used to evaluate spatial learning and memory, probe trials for spatial reference memory, working memory test for spatial working memory, and visual cue test for non-spatial cognitive function. Perkin-Elmer Model 300 Atomic Absorption Spectrometer was utilized to determine blood lead levels in rats. RESULTS： （1） In the working memory test, the time to reach the platform remained unchanged between the control and lead exposure groups （F（1,1） = 0.007, P = 0.935）. A visible decrease in escape latencies was observed in each group （P = 0.028）. However, there was no significant difference between the two groups （F（1,1） = 1.869, P = 0.193）. The working memory probe test demonstrated no change between the two groups in the time spent in the target quadrant during the working memory probe test （F（1,1） = 1.869, P = 0.193）. However, by day 4, differences were observed in the working memory test （P 〈 0.01）. （2） Multivariate repetitive measure and ANOVA in place navigation presented no significant difference between the two groups （F（1,1） = 0.579, P = 0.459）. （3） Spatial probe test demonstrated that the time to reach the platform was significantly different between the two groups （F（1,1） = 4.587, P = 0.048）, and one-way ANOVA showed no significant difference in swimming speed between the two groups （F（1,1） = 1.528, P = 0.237）. （4） In the visual cue test, all rats reached the platform within 15 seconds, with no significant difference （F（1,1） = 0.579, P = 0.459）. （5） During experimentation, all rats increased in body mass, but there was no difference between the two groups （F（1,1） = 0.05, P = 0.943）. At day 28 of 0.05% lead exposure, the blood lead level was 29.72 μg/L in the lead exposure group and 5.86 μg/L in the control group （P 〈 0.01）. CONCLUSION： The present results revealed low-level lead exposure significantly impaired spatial reference memory and spatial working memory, but had no effect on spatial learning.

Review on Video Object Tracking Based on Deep Learning

Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracking algorithms have beenproposed, there are still many difficulties in the actual tracking process, such asillumination change, occlusion, motion blurring, scale change, self-change and so on.Therefore, the development of object tracking technology is still challenging. Theemergence of deep learning theory and method provides a new opportunity for theresearch of object tracking, and it is also the main theoretical framework for the researchof moving object tracking algorithm in this paper. In this paper, the existing deeptracking-based target tracking algorithms are classified and sorted out. Based on theprevious knowledge and my own understanding, several solutions are proposed for theexisting methods. In addition, the existing deep learning target tracking method is stilldifficult to meet the requirements of real-time, how to design the network and trackingprocess to achieve speed and effect improvement, there is still a lot of research space.

Translating Interdisciplinary Research on Language Learning into Identifying Specific Learning Disabilities in Verbally Gifted and Average Children and Youth

The current research was grounded in prior interdisciplinary research that showed cognitive ability (verbal ability for translating cognitions into oral language) and multiple-working memory endophenotypes (behavioral markers of genetic or brain bases of language learning) predict reading and writing achievement in students with and without specific learning disabilities in written language (SLDs-WL). Results largely replicated prior findings that verbally gifted with dyslexia score higher on reading and writing achievement than those with average verbal ability but not on endophenotypes. The current study extended that research by comparing those with and without SLDs-WL with assessed verbal ability held constant. The verbally gifted without SLDs-WL (n = 14) scored higher than the verbally gifted with SLDs-WL (n = 27) on six language skills (oral sentence construction, best and fastest handwriting in copying, single real word oral reading accuracy, oral pseudoword reading accuracy and rate) and four endophenotypes (orthographic and morphological coding, orthographic loop, and switching attention). The verbally average without SLDs-WL (n = 6) scored higher than the verbally average with SLDs-WL (n = 22) on four language skills (best and fastest hand-writing in copying, oral pseudoword reading accuracy and rate) and two endophenotypes (orthographic coding and orthographic loop). Implications of results for translating interdisciplinary research into flexible definitions for assessment and instruction to serve students with varying verbal abilities and language learning and endophenotype profiles are discussed along with directions for future research.

Dissociation between Performances in Water Maze and Spontaneous Alternation in BALB/C versus A/J Mice

Learning processes are extensively studied in behavioral neuroscience. As experimental models, Morris Water Maze (MWM) and Spontaneous Alternation (SA) represent two of the most frequently used laboratory tests to respectively address spatial vs non-spatial tasks. Several factors have been shown to impact on those learning, including strain, gender, apparatus, conditioning, vision, lighting conditions and stress level. In order to focus on the later, we compared the acquisition of two learning tasks (MWM and SA) in BALB/c and A/J mice, which are known as fearful and stress-sensitive strains. Here, we report that BALB/c mice exhibited higher performances than A/J mice in the MWM (i.e. spatial reference memory task), whereas A/J mice performed better in the SA (i.e. spatial working memory task). These results indicate dissociated processes in the acquisition of spatial vs non-spatial tasks, and emphasize a varying influence of emotional reactivity on different forms of cognition.

Child Labor: Prevalence, Reasons and Knowledge of Early Learning of Handicrafts in Couffo, Benin in 2018

Introduction: One form of child labor is early learning, which is a less worrying phenomenon in our communities in Benin. The objective of this study was to assess the practice of early learning for children in rural areas. Methods: This was a cross-sectional study combined with a qualitative component conducted in the Kissamey district of Benin with four targets: child apprentices (52), master craftsmen (41), parents and guardians (34), local authorities (9). The collection tools were a questionnaire and an interview guide. Results: The frequency of early learning among children was 32.07% with difficult socioeconomic conditions: polygamy (75%), strong siblings (79%), out of school (33%), unmet food needs (96%). The reasons for early learning according to parents were: refusal of the child to go to school (44%), financial difficulties (31%), school failure (22%), but 38% of these children did not know the reason for their learning. The actors had little knowledge of the regulatory texts. Conclusion: Early learning remains a societal problem related to out-of-school and difficult socioeconomic conditions.

Readdressing The Redundancy Effect: A Cognitive Strategy For E-learning Design

This study challenges understandings on the‘redundancy effect’of cognitive load theory and visual/verbal classifications of dual-coding theory.Current understandings assert that a multimedia mix of narration and text displayed during e-learning leads to cognitive overload,thus,impeding learning[1,2].Previous research suggests that for optimal learning to occur,the most effective multimedia mix for e-learning presentation is the use of graphics and narration[3-6].The current study was undertaken with 90 undergraduate students at a British University.Participants were allocated to one of three groups.Each group used a different multimedia mix of a music e-learning program.Participants received learning material electronically,which involved either a mix of narration and text,graphics and text,or graphics and narration.Learning was measured by differences in music knowledge scores obtained before and after receiving the learning material.Results indicate that the combination of text and narration is most effective for learning,compared to combinations of graphics and text and graphics and narration.These findings challenge the currently accepted stance on the redundancy effect in e-learning design.

On Collaborative Learning in English Classes

The current situation of English learners in class is not optimistic. Facing with such a situation, how to promote students' learning is a big problem in English teaching. Cooperative learning is a kind of effective learning method and teaching strategy,which is student-centered, group-centered, and aims at common learning goals. The paper focuses on the definition, advantages and effective implementation of cooperative learning.

Deep Neural Network-based Speaker-Aware Information Logging for Augmentative and Alternative Communication

People with complex communication needs can use a high-technology augmentative and alternative communication device to communicate with others.Currently,researchers and clinicians often use data logging from high-tech augmentative and alternative communication devices to analyze augmentative and alternative communication user performance.However,existing automated data logging systems cannot differentiate the authorship of the data log when more than one user accesses the device.This issue reduces the validity of the data logs and increases the difficulties of performance analysis.Therefore,this paper presents a solution using a deep neural network-based visual analysis approach to process videos to detect different augmentative and alternative communication users in practice sessions.This approach has significant potential to improve the validity of data logs and ultimately to enhance augmentative and alternative communication outcome measures.