Research on the Construction of English Autonomous Learning Monitoring Mode under the Background of Big Data

The era of big data is coming,the combination of big data and traditional teaching can provide more and more accurate services for students'self-learning,and it is a good way to teach students according to their aptitude.In this background,a learning society is coming,which aiming at learning,autonomous learning and lifelong learning.Learning society emphasize the ability of learning autonomy for students unprecedentedly.Learning is no longer limited to the campus.Learning ability will accompany learners'social life and become an active and healthy lifelong activity.Autonomous learning is a learning theory that goes with the requirements of The Times and has a broad development prospect.The study of Autonomous learning not only has a very important guiding significance for the educational and teaching practice in China,but also plays an important role in the life development of every student.The subject of learning is gradually transferred from the classroom,teachers and textbooks to the students themselves.Teachers should not only impart knowledge and answer questions,but also,most importantly,teach students how to exert their autonomy in autonomous learning.After investigating and researching the existing monitoring model of autonomous English learning in colleges and universities,our group found that in practice,there is a lack of corresponding monitoring mechanisms and means,and autonomous learning has gradually become formalized.Therefore,according to the actual situation of autonomous English learning in our country's universities,the monitoring model of autonomous English learning has been reconstructed,and an effective comprehensive evaluation system has been established to effectively improve students'English learning ability.

Application Research of “3+1” Mode for Birth Defects Monitoring

Objective To explore the ＂3＋1＂ monitoring mode for birth defects and quality control measures based on the population, and to obtain the related information data for birth defects.Methods With the community population as the basis, adopting the unified monitoring scheme dominant by the leadership and administration of government, with districts （counties） as the monitoring sites, the ＂3＋1 ＂ monitoring mode for birth defects was based on a complete monitoring team with the combination of villages/residents＇ committees, townships （towns）, counties （districts） and the municipality. Demonstration research was carried out in the pilot districts/counties in Chongqing City.Results Birth defects population monitoring system based on population and family planning management and service network was established, and during 2005 and 2006.application research was carried out for the monitoring methods among birth defects population in the pilot districts （counties）, obtaining the relevant information in regional birth defects, with a monitoring coverage of over 99%. Conclusion Fully utilizing the birth management functions of Population and Famlty Planning System and the advantages of service networks, long term, dynamic birth defects monitoring system based on community population was established, with the integration of birth defects monitoring and regular reproductive health services, obtaining overall birth defects occurrence information in details, providing scientific basis for the government to formulate scientific, practical, economic and effective birth defects intervention policy, so as to improve the quality of the population.

Variational Mode Decomposition for Rotating Machinery Condition Monitoring Using Vibration Signals

The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process.Variational mode decomposition(VMD)is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions(VMFs)adaptively and non-recursively.The VMD method offers improved performance for the condition monitoring of rotating machinery applications.However,determining an accurate number of modes for the VMD method is still considered an open research problem.Therefore,a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF.Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method.The statistical parameters of the signals are extracted from the original signals,VMFs and intrinsic mode functions(IMFs)and have been fed into machine learning algorithms to validate the performance of the VMD method.The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery.Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Real-time State Monitoring During Switching Mode Transitions in High Power Three-level Inverters

大功率多电平逆变器近年来在实际工业生产中得到越来越广泛的应用。多电平逆变器由于结构复杂,采用元器件较多,因此在设计和实验中,实现各个工作状态下运行参数的同步监测和分析较为困难。本文针对大功率三电平逆变器,实现开关动态特性的在线测试,在此基础上,进一步研究三电平逆变器在开关状态变化时理论与实际负载运行工况下电路拓扑的转换变化规律。通过全电路电气参数和元器件状态的实时监测,发现在三电平逆变器非正常运行状态下开关转换时额外电应力,同时,深入研究在实际工况运行条件非正常状态下该额外电应力出现的机理和原因,为三电平逆变器的故障诊断提供了参考,对于设计高可靠性的多电平逆变器系统有一定的理论和现实意义。

Constructing a Monitoring Mode over English Autonomous Learning in the Networking Age

In the Networking age,the monitoring from teachers and classmates over one's English autonomous learning is weakened.The relevance among learning resources can't be measured,and the learning efficiency can't be ensured,either.In order to solve the problems,a monitoring mode over English autonomous learning process is constructed,which involves the application of the internal and external monitoring strategies.

Multi-dimensional database design and implementation of dam safety monitoring system

To improve the effectiveness of dam safety monitoring database systems, the development process of a multi-dimensional conceptual data model was analyzed and a logic design wasachieved in multi-dimensional database mode. The optimal data model was confirmed by identifying data objects, defining relations and reviewing entities. The conversion of relations among entities to external keys and entities and physical attributes to tables and fields was interpreted completely. On this basis, a multi-dimensional database that reflects the management and analysis of a dam safety monitoring system on monitoring data information has been established, for which factual tables and dimensional tables have been designed. Finally, based on service design and user interface design, the dam safety monitoring system has been developed with Delphi as the development tool. This development project shows that the multi-dimensional database can simplify the development process and minimize hidden dangers in the database structure design. It is superior to other dam safety monitoring system development models and can provide a new research direction for system developers.

老采空区地表沉降预测合理监测模式分析

为研究老采空区沉降监测数据时间间隔对预测精度的影响,本文利用某老采空区地表沉降监测点实测沉降数据,在等时间间隔、非等时间间隔两种情况下建立两种方案、六种沉降预测模式,采用长短期记忆神经网络(LSTM)预测模型对老采空区地表沉降进行预测,以平均绝对误差(MAE)和平均绝对百分比误差(MAPE)作为评价指标,分析不同时间间隔监测数据对预测精度的影响。结果表明,在总监测时长不变的情况下,预测精度随平均监测间隔时长的增长呈先增高后降低的趋势,即并非监测间隔越短,预测精度越高,而是在相应监测间隔范围内存在预测精度最优值。研究成果可为老采空区监测方案设计及沉降预测模式提供借鉴和指导。

Local measurement for structural health monitoring

Localized nature of damage in structures requires local measurements for structural health monitoring. The local measurement means to measure the local, usually higher modes of the vibration in a structure. Three fundamental issues about the local measurement for structural health monitoring including (1) the necessity of making local measurement, (2) the difficulty of making local measurement and (3) how to make local measurement are addressed in this paper. The results from both the analysis and the tests show that the local measurement can successfully monitor the structural health status as long as the local modes are excited. Unfortunately, the results also illustrate that it is difficult to excite local modes in a structure. Therefore, in order to carry structural health monitoring into effect, we must (1) ensure that the local modes are excited, and (2) deploy enough sensors in a structure so that the local modes can be monitored.

Wind-induced instabilities and monitoring of wind turbine

This paper presents real-time monitoring data and analysis results of the non-stationary vibrations of an operational wind turbine. The advanced time-frequency spectrum analysis reveals varied non-stationary vibrations with timevarying frequencies, which are correlated with certain system natural modes characterized by finite element analysis. Under the effects of strong wind load, the wind turbine system exhibits certain resonances due to blade passing excitations. The system also exhibits certain instabilities due to the coupling of the tower bending modes and blade flapwise mode with blade passing excitations under the variation of wind speed. An analytical model is used to elaborate the non-stationary and instability phenomena observed in experimental results. The properties of the nonlinear instabilities are evaluated by using Lyapunov exponent estimation.

深部条带老采空区覆岩破坏多参量监测研究

针对深部条带老采空区覆岩移动变形问题,确定了裂隙发育、拉压应变、岩层物性等主要参量指标,采用钻孔电视法、分布式光纤感测法、井间电法CT、微震法构建了采空区覆岩“点-线-面-体”多参量监测模式;运用该监测模式对工程实例进行实测分析,获取了采空区覆岩全方位、多层次的移动破坏特征,得出覆岩裂隙多集中在465 m以下,少量空洞位于垮落带及煤柱上方靠近采空区侧,煤柱塑性区范围约8 m的结论,综合判定采空区覆岩结构可在长时间内保持相对稳定。结果表明:“点-线-面-体”多参量监测模式在内容上能够有效涵盖条带采空区覆岩移动变形的各参量指标,时空上可实现点(静态)、线(动态)、面(静态)、体(动态)相互结合的全过程监测,监测结果间能相互验证,可满足深部条带采空区覆岩移动变形监测要求。

基于跨模型模态应变能变化的结构损伤识别两阶段法

为提高结构损伤识别精度,采用跨模型模态应变能变化(Cross-model Modal Strain Energy Changes,CMSEC)代替传统的模态应变能变化(Modal Strain Energy Changes, MSEC)建立先定位后定量的结构损伤识别两阶段法.与传统模态应变能(Modal Strain Energy, MSE)计算时仅利用结构损伤后的振型不同,跨模型模态应变能(CMSE)的计算同时利用结构损伤前后的振型.先利用CMSEC构建CMSECI (Cross-model Modal Strain Energy Index)损伤指标来确定损伤位置,筛选出疑似损伤构件后,再利用CMSEC建立定量识别损伤程度的灵敏度方程.采用基于奇异值截断的迭代法求解该方程,以避免迭代过程中可能出现的病态方程问题.根据公式构成可以定性判断CMSEC比MSEC更难被噪声干扰.某简支梁算例表明,本文方法在损伤定位和损伤定量两个阶段均比传统方法具有更高的识别精度.为了深入考察本文方法的优越性,采用蒙特卡洛法从宏观的相对误差上界和具体的相对误差分布这两个视角解释了本文方法优于传统方法的原因.

基于动响应数据特征的桥梁结构损伤识别

介绍了本征正交分解(Proper Orthogonal Decomposition,POD)的基本原理,探讨了POD在桥梁结构损伤识别中的应用。提出了基于动响应数据特征的桥梁结构损伤识别方法,该识别方法基于POD技术对桥梁结构在不同位置、不同时刻收集到的位移快照矩阵(Snapshot Matrix)进行本征正交分解,得到结构的本征正交模态(POMs),进而构造出损伤指标来识别结构的损伤位置及程度,实现了对桥梁结构损伤的多工况识别。并以保定黄花沟桥为例,通过数值模拟试验,验证了该方法的有效性,结果表明POD能够从空心板桥结构的振动响应数据中提取出结构的本质特征,并且提取过程简单、快捷,可为桥梁结构提供一种有效的损伤识别方法。

爆破振动在岩质边坡坡面的放大机制研究

岩质边坡坡面的爆破振动放大现象,影响着边坡爆破振动监测及安全评价的准确性。采用数值模拟方法,研究了边坡坡面的振动放大现象,基于结构动力学原理,根据振型分析理论揭示了边坡坡面振动放大机制。数值模拟结果表明:边坡振动放大现象主要发生在台阶坡顶线附近区域,受台阶突出物几何尺寸及岩体物理力学参数的影响,随着平台宽度增大、台阶高度减小、台阶坡比减小以及岩体工程质量等级的降低,台阶坡顶线位置振动速度峰值相对于台阶坡底线位置的振动放大现象逐渐显著,但第一主应力分布与振动速度峰值分布规律相反,因此为了更准确地进行爆破振动安全评价,建议将监测点布置于台阶坡底线位置。振型分析结果表明:台阶突出物的低阶振型中坡底线位置与坡顶线位置的振动速度峰值之比随着台阶几何尺寸及物理力学参数的变化规律与数值模拟计算结果一致。由此可见:边坡坡面的振动放大效应主要由台阶突出物几何尺寸及岩体物理力学参数决定的低阶振型控制。

基于EMD-SVD的矿山微震信号降噪方法及其应用

为了提高微震监测技术对微震信号分析处理的准确性,充分提取微震信号波形中的有效信息,针对矿山微震信号非平稳、非线性的特点,提出了一种基于经验模态分解(EMD)和奇异值分解(SVD)的联合降噪方法。该方法首先通过EMD分解获得信号的IMF分量,利用相关系数、方差贡献率和相似度对IMF分量进行了优选;然后使用优选后的IMF分量重构一维微震信号时间序列的相空间数据,经过SVD分解后,利用奇异值能量百分比确立了SVD重构阶数,并根据SVD恢复原理得到了降噪后的一维微震时间序列;最后以山东某矿现场矿山爆破为例,采用不同降噪方法对3类典型微震信号进行了降噪处理,并对其降噪效果进行了对比分析。结果表明,EMD-SVD降噪方法与传统降噪方法相比,其平均信噪比提高了35%,平均均方根误差降低了50%,有效剔除了微震信号的噪声分量,保留了信号的特征信息。该研究对分析矿山微震信号、微震事件定位及煤矿动力灾害监测具有重要意义。

基于VMD-HT和深度学习的流噪环境腐蚀损伤声发射识别模型

对在役管道进行腐蚀声发射监测的过程中,管内介质流动产生的噪声同样会被传感器接收,导致腐蚀信号被覆盖从而引发误判。针对这一问题,提出了一种基于变分模态分解(VMD)、希尔伯特变换(HT)和深度双向门限循环单元神经网络(BiGRU)的流噪环境腐蚀损伤声发射识别模型。该模型能够将原始信号自适应地转化成多个本征模态分量,并提取各分量的瞬时频率及谱熵构建多维时序特征矩阵,进而建立原始信号与多维特征之间的映射关系。为验证该方法的有效性,对在役管道进行腐蚀声发射监测试验。结果表明,所提模型在流噪环境下具有良好的鲁棒性,监测数据的识别准确率达96.3%,可作为一种解决在役管道腐蚀声发射监测的新方案。

Damage Detection in Reinforced Concrete Berthing Jetty Using a Plasticity Model Approach

A conventional method of damage modeling by a reduction in stiffness is insufficient to model the complex non-linear damage characteristics of concrete material accurately.In this research,the concrete damage plasticity constitutive model is used to develop the numerical model of a deck beam on a berthing jetty in the Abaqus finite element package.The model constitutes a solid section of 3D hexahedral brick elements for concrete material embedded with 2D quadrilateral surface elements as reinforcements.The model was validated against experimental results of a beam of comparable dimensions in a cited literature.The validated beam model is then used in a three-point load test configuration to demonstrate its applicability for preliminary numerical evaluation of damage detection strategy in marine concrete structural health monitoring.The natural frequency was identified to detect the presence of damage and mode shape curvature was found sensitive to the location of damage.

基于多参数自适应VMD的GNSS形变监测序列分解

针对结构健康监测场景下,全球导航卫星系统(GNSS)形变监测序列中各类特征相互混叠,难以进行特征提取与独立分析的问题,提出一种基于多参数自适应变分模态分解(MA-VMD)的时间序列分解算法。首先对变分模态分解(VMD)算法中多项参数对分解结果的影响进行了综合分析;然后从原始序列以及分解结果的频域特性出发,自适应调整分解模态数、惩罚因子、初始中心频率及拉格朗日乘子四组参数,建立MA-VMD算法。仿真序列实验表明,MA-VMD算法的序列分解结果与真实值之间的互相关系数为98.77%、均方根误差为0.1365 mm,均接近全局最优,并显著优于经验模态分解、奇异谱分析、改进变分模态分解等算法。最后基于实测GNSS变形监测数据验证了所提算法在工程应用上的有效性。

中西药学专业治疗药物监测实习带教工作浅析

治疗药物监测(Therapeutic drug monitoring,TDM)是临床实现药物治疗个体化的重要手段。在医学教育中,TDM的实习对于培养专业人才和学科发展具有重要意义。文章通过分析安徽医科大学第一附属医院TDM实习带教中存在的问题、介绍相关工作并总结中西药学专业实习带教经验,认为基于多种教学模式的综合运用是加强TDM理论学习,帮助实习生梳理TDM全流程的关键点,有利于培养实习生实践能力、提升实习质量。

基于运动模式划分的工业机器人健康监测方法

由于工业机器人存在多运动模式耦合的问题,传统的健康监测方案需要在每个关节处单独安装传感器,难以满足实际工业现场需求。本文以6关节工业机器人为研究对象,基于振动信号研究了多运动模式切换场景下工业机器人的健康监测方法。首先,通过跳变点算法实现机器人运动模式划分,获取不同运动模式对应的信号区间。其次,对不同运动模式的信号分别提取监测指标。最后,基于控制图法实现工业机器人不同关节的健康监测。在工业机器人退化实验数据中验证了本文所提方法,表明本文所提方法能够在仅使用两个振动传感器的条件下实现机器人6个关节的健康状态监测。本文所提运动模式划分算法在对大量历史退化数据进行分析时,所需运行时间更短、单次精度更高且重复性更好。本文提出的方法能够在使用少量传感器的条件下,有效避免运动模式耦合和采样信号的差异可能导致的监测结果误判,使得监测结果更加精准可靠,适用于实际工业现场的工业机器人健康状态监测。