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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

自监督学习结合对抗迁移的跨工况轴承故障诊断

轴承智能故障诊断应用中,由于实际工况复杂多变,极难获得足够的真实故障数据,且目标域和源域信号存在较大差异,导致深度模型的跨工况迁移识别也出现特征提取及分类困难、模型泛化性弱。考虑到目标域存在大量无标签数据,引入无监督思想,提出基于自监督学习结合对抗迁移的改进方法。首先根据信号本身特点创建辅助任务,对大量无标签数据学习,建立源域与目标域故障类别之间的内在联系;再通过对抗域适应和联合最大平均差异将源域知识迁移到目标域中,结合辅助任务优化两域差异,最终实现目标域准确的故障分类。用2个公开的轴承数据集上验证了所提方法的性能,实验结果表明,所提方法的故障诊断识别准确率在多数情况下均高于98%。

不合规任务对酒店员工退缩行为的影响机制:一个被调节的链式中介模型

不合规任务在酒店企业中的普遍性,以及对员工的消极影响已得到初步验证。然而,现有研究大多探讨不合规任务对酒店员工绩效表现的影响,缺乏对员工消极工作行为及其整合性作用机制的关注。本研究基于资源保存理论和认知-情感人格系统理论,通过411份酒店一线员工4个时点的有效问卷数据,构建了不合规任务对员工工作退缩行为的链式中介模型,并探讨了员工学习目标导向的多路径调节作用。结果表明:不合规任务对工作退缩行为存在正向影响;工作意义感与和谐型激情在不合规任务与工作退缩行为之间起链式中介作用;学习目标导向分别削弱了不合规任务对工作意义感的负向影响,以及不合规任务对和谐型激情的负向影响,进而负向调节整个链式中介作用。研究结果揭示了不合规任务对酒店员工消极行为的多路径作用机制及其边界条件,对酒店等旅游企业应对不合规任务的负面影响具有指导意义。

工学结合视角下高职院校校外实践教学存在的问题及其解决对策——以清远职业技术学院旅游管理专业为例

随着我国高职教育的快速发展,“工学结合”的“实践教学”已成为我国高职教育教学领域研究的热点,社会实用型人才与高素质应用型人才需求量有明显提升,工学结合模式应运而生。这也为高职院校人才培养工作指明发展方向、带来创新发展动力。清远职业技术学院旅游管理专业在高技能人才培养期间引入工学结合模式,从其在工学结合中的问题入手,分析其原因,提出了优化实习时间安排,建立专兼结合的“双师”教学队伍,创新校企合作机制,创新旅游“产学研”的实践教学新模式的实践策略,实现学生与企业无缝对接的人才培养模式。这一论题的探索对其他类似的高等职业院校校外实践教学起到一定的抛砖引玉作用。

基于随机邻域嵌入的无监督复杂工况识别

现代工业生产设备通常结构复杂并交替运行于不同工况,基于监测数据进行准确的工况识别是对系统进行健康监测的基础,但系统的监测数据通常维度较高、数据量较大。针对设备复杂工况的识别问题,提出了一种基于随机邻域嵌入的无监督工况识别方法。采用随机邻域嵌入算法,能够保留数据的局部和全局结构特性;计算了高维和低维空间中数据点的概率相似性,可实现设备高维监测数据的降维和无监督聚类,在不建立系统模型的基础上达成准确识别系统工况的目的。结果表明:该方法可有效实现高维监测数据的复杂工况识别,是一种有效的无监督聚类学习方法。

信息化冲击、数字化政府与出口结构升级

本文利用中国各地级市政府工作报告和机器学习方法测度了数字化政府建设指数,并考察了数字化政府建设对出口结构升级的影响。研究表明,数字化政府建设有利于推动出口结构升级,系列因果推断及稳健性检验均验证了该结论可靠。异质性分析表明,数字化政府建设能够跨越“数字鸿沟”助力出口结构升级,且对中西部地区和人口红利薄弱地区的影响更大。机制分析表明,数字化政府建设通过创新驱动效应、进口中间品种类扩张效应和全国统一大市场效应推动出口结构升级。此外,数字化政府建设能够通过促进资源在城市间再配置及产品新增消亡推动出口结构升级。拓展分析表明,智慧城市建设能够与数字化政府实现有效联动,共同助力中国出口结构升级。

基于多源域迁移学习的带式输送机剩余寿命预测方法

煤矿开采过程中,带式输送机运行环境恶劣、工况复杂,致使获得的传感监测数据量有限且存在大量噪声干扰,严重限制了其剩余寿命预测的准确度。针对该问题,提出了一种多源域迁移学习剩余寿命预测方法,充分利用煤矿运输过程中积累的带式输送机多工况数据,以达到准确预测其关键零部件托辊轴承剩余寿命的目的。首先构建集成多尺度卷积神经网络和双向门控循环单元(MCNN-BiGRU)的设备退化特征提取模型,对单工况数据进行特征提取挖掘,并使用PSO算法确定模型超参数。在此基础上,加入多源域迁移学习(MDT)方法,利用多个工况数据进行剩余寿命预测,通过最大均值差异(MMD)与相互关系对齐(CORAL)联合损失拉近各源域数据分布差异,解决因数据量少导致的模型训练精度不高的问题。最后以煤矿实际生产数据集为例进行实验,结果表明:MDT-MCNN-BiGRU模型的预测效果较好,Savitzky-Golay滤波去噪后模型性能得以进一步提升;使用IMS数据集与现有方法进行比较,发现所提方法预测准确度较高,对煤矿运输设备健康管理具有一定的指导意义。

社会工作专业课程思政的体系建构:理念、内容与实践

课程思政强调对社会主流思想价值的传播、实践和再生产。社会工作是一门以价值为本的实践性专业,其专业教育既注重价值传播更强调价值实践和价值生产,富含课程思政的教育品质和优势资源。在价值理念上,体现了思想政治教育社会性和个体性并重的发展意蕴;从内容层次看,兼具国家政治教育、社会道德教育、个体生命教育等意义内涵;在实践路径上,应注重立足于日常生活的场域,通过服务学习的实践模式,推动服务性学习和反思性实践的螺旋发展。社会工作专业课程思政体系实现了理念、内容和实践等方面的整合,能够不断增强专业课程思政的现实性和有效性,促进思想政治教育的全面性和主体性发展。