Study on the Interactivity of Controlled and Automatic Information Processing in L2 Reading

L2 reading is not only an important channel for people to obtain information and knowledge,but also the main way for people to learn a foreign language.Reading information processing can be divided into controlled processing and automatic processing.Controlled information processing is a conscious and resource-intensive processing model,while automatic information processing is an unconscious and automatic processing model.This study investigates the characteristics and interactivity of controlled and automatic information processing in L2 reading,and explores the roles of controlled and automatic information processing strategies in improving L2 reading ability.The findings are as follows:(a)controlled and automatic information processing is interactive in L2 reading;and(b)the uses of controlled and automatic information processing strategies are beneficial to the improvement of the reading ability of L2 learners.This study has important theoretical and practical value in improving the efficiency of L2 reading teaching and learning.

Exploring the Potentialities of Automatic Extraction of University Webometric Information

Purpose:The main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities’websites.The information automatically extracted can be potentially updated with a frequency higher than once per year,and be safe from manipulations or misinterpretations.Moreover,this approach allows us flexibility in collecting indicators about the efficiency of universities’websites and their effectiveness in disseminating key contents.These new indicators can complement traditional indicators of scientific research(e.g.number of articles and number of citations)and teaching(e.g.number of students and graduates)by introducing further dimensions to allow new insights for“profiling”the analyzed universities.Design/methodology/approach:Webometrics relies on web mining methods and techniques to perform quantitative analyses of the web.This study implements an advanced application of the webometric approach,exploiting all the three categories of web mining:web content mining;web structure mining;web usage mining.The information to compute our indicators has been extracted from the universities’websites by using web scraping and text mining techniques.The scraped information has been stored in a NoSQL DB according to a semistructured form to allow for retrieving information efficiently by text mining techniques.This provides increased flexibility in the design of new indicators,opening the door to new types of analyses.Some data have also been collected by means of batch interrogations of search engines(Bing,www.bing.com)or from a leading provider of Web analytics(SimilarWeb,http://www.similarweb.com).The information extracted from the Web has been combined with the University structural information taken from the European Tertiary Education Register(https://eter.joanneum.at/#/home),a database collecting information on Higher Education Institutions(HEIs)at European level.All the above was used to perform a clusterization of 79 Italian universities based on structural and digital indicators.Findings:The main findings of this study concern the evaluation of the potential in digitalization of universities,in particular by presenting techniques for the automatic extraction of information from the web to build indicators of quality and impact of universities’websites.These indicators can complement traditional indicators and can be used to identify groups of universities with common features using clustering techniques working with the above indicators.Research limitations:The results reported in this study refers to Italian universities only,but the approach could be extended to other university systems abroad.Practical implications:The approach proposed in this study and its illustration on Italian universities show the usefulness of recently introduced automatic data extraction and web scraping approaches and its practical relevance for characterizing and profiling the activities of universities on the basis of their websites.The approach could be applied to other university systems.Originality/value:This work applies for the first time to university websites some recently introduced techniques for automatic knowledge extraction based on web scraping,optical character recognition and nontrivial text mining operations(Bruni&Bianchi,2020).

System Supporting Automatic Generation of Finite Element Using Image Information

A mesh generating system has been developed in orde r to prepare large amounts of input data which are needed for easy implementation of a finite element analysis. This system consists of a Pre-Mesh Generator, an Automatic Mesh Generator and a Mesh Modifier. Pre-Mesh Generator produces the shape and sub-block information as input data of Automatic Mesh Generator by c arrying out various image processing with respect to the image information of th e drawing input using scanner. Automatic Mesh Generator generates mesh of trian gular elements in the arbitrarily shaped and multiple connected planar domain by using minimum necessary information. This generator has 3 methods of mesh gene ration for each sub-block, A) Regular Mesh Generation, B) Semi-Regular Mesh Ge neration and C) Irregular Mesh Generation. Any of them can be selected automati cally according to the external form of sub-block or the state of domain. Mesh Modifier projects and modifies the pattern of generated mesh by Automatic Mesh Generator as required. This system simplifies the user’s task while saving manp ower in carrying out the finite element analysis.

Type WYZ-97 Eighteen information Noninsulate Frequency shift Automatic Blocking System

Type WYZ 97 eighteen information noninsulate frequency shift automatic blocking system is the most advanced railroad signaling system in China nowadays.The article introduced its principles,technical targets,features and the applications.

Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques

One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.

On String Stable Control of Platoon of Automated Vehicles with Predecessor-successor Information Framework

For the constant distance spacing policy,the existing researches of the string stability focus on the single-predecessor information framework（SPIF） and predecessor-successor information framework（PSIF）.The research results demonstrated that the string stability could not be guaranteed with the SPIF,and then the PSIF was proposed to resolve this string instability.But the issue,whether the string stability can be guaranteed when applying the PSIF,is still controversial.Meanwhile,most of the previous researches on the string stability were conducted without consideration of the parasitic time delays and lags.In this paper,the practical longitudinal vehicle dynamics model is built with consideration of the parasitic time delays and lags existing in the actuators,sensors or the communication systems.Secondly,the detailed theoretical analysis of string stability in frequency domain is conducted to demonstrate that the classical linear control laws can not ensure the string stability when applying both the symmetrical PSIF（SPSIF） and asymmetrical PSIF（APSIF）.Thirdly,a control law,which adds the position and velocity information of the leading vehicle,is proposed to guarantee string stability for small/medium platoon,and the other control law,which adds the acceleration information of the controlled vehicle,is proposed to guarantee string stability for large platoon as well as small/medium platoon.Finally,the comparative simulation is conducted to confirm the conducted analysis and the proposed control laws.The conducted research completes the means to analyze the string stability in frequency domain,provides the parameters＇ reference for the design and implementation of the practical automatic following controllers,and improves the reliability and stability of the platoon of automatic vehicles.

BASIC THEORY AND APPLICATIONS OF WELDING ARC SPECTRAL INFORMATION

Welding arc spectral information is a rising welding information source. In some occasion, it can reflect many physical phenomena of welding process and solve many problems that cannot be done with arc electric information, acoustic information and other arc information. It is of important significance in developing automatic control technique of welding process and other similar process. Many years study work on welding arc spectral information of the anthor are discussed from three aspects of theory, method and application. Basic theory, view and testing methods of welding arc spectral information has been put forward. In application aspects, many applied examples, for example, monitoring of harmful gases in arc （such as hydrogen and nitrogen） with the method of welding arc spectral information; welding arc spectral imaging of the welding pool which is used in automatic seam tracking; controlling of welding droplet transfer with welding arc spectral information and so on, are introduced. Especially, the successful application in real time controlling of welding droplet transfer in pulsed GMAW is introduced too. These application examples show that the welding arc spectral information has great applied significance and development potentialities. These .content will play an important role in applying and spreading welding arc spectral informarion technology.

MAS Based Distributed Automatic Generation Control for Cyber-Physical Microgrid System

The microgrid is a typical cyber-physical microgrid system(CPMS). The physical unconventional distributed generators(DGs) are intermittent and inverter-interfaced which makes them very different to control. The cyber components,such as the embedded computer and communication network,are equipped with DGs, to process and transmit the necessary information for the controllers. In order to ensure system-wide observability, controllability and stabilization for the microgrid,the cyber and physical component need to be integrated. For the physical component of CPMS, the droop-control method is popular as it can be applied in both modes of operation to improve the grid transient performance. Traditional droop control methods have the drawback of the inherent trade-off between power sharing and voltage and frequency regulation. In this paper, the global information(such as the average voltage and the output active power of the microgrid and so on) are acquired distributedly based on multi-agent system(MAS). Based on the global information from cyber components of CPMS, automatic generation control(AGC) and automatic voltage control(AVC)are proposed to deal with the drawback of traditional droop control. Simulation studies in PSCAD demonstrate the effectiveness of the proposed control methods.

Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies

For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.

Multi-source Fuzzy Information Fusion Method Based on Bayesian Optimal Classifier

为了做常规贝叶斯的最佳的分类器，拥有处理模糊信息并且认识到推理过程的自动化的能力，一个新贝叶斯的最佳的分类器被建议，模糊信息嵌入。它不能仅仅有效地处理模糊信息，而且保留贝叶斯的最佳的分类器的学习性质。另外根据模糊集合理论的进化，含糊的集合也是嵌入的进它产生含糊的贝叶斯的最佳的分类器。它能同时从积极、反向的方向模仿模糊信息的双重的特征。进一步，贝叶斯的最佳的分类器也是的集合对从积极、反向、不确定的方面就模糊信息的三方面的特征而言求婚了。最后，一个知识库的人工的神经网络(KBANN ) 被介绍认识到贝叶斯的最佳的分类器的自动推理。它不仅减少贝叶斯的最佳的分类器的计算费用而且改进它学习质量的分类。

BASIC THEORY AND METHOD OF WELDING ARC SPECTRAL INFORMATION

Arc spectral information is a rising information source which can solve manyproblems that can not be done with arc electric information and other arc information. It is ofimportant significance to develop automatic control technique of welding process. The basic theoryand methods on it play an important role in expounding and applying arc spectral information. Usingconcerned equation in plasma physics and spectrum theory, a system of equations including 12equations which serve as basic theory of arc spectral information is set up. Through analyzing ofthe 12 equations, a basic view that arc spectral information is the reflection of arc state andstate variation, and is the most abundant information resource reflecting welding arc process isdrawn. Furthermore, based on the basic theory, the basic methods of test and control of arc spectralinformation and points out some applications of it are discussesed.

Automatic quality improvement reports in the intensive care unit: One step closer toward meaningful use

AIM: To examine the feasibility and validity of electronic generation of quality metrics in the intensive care unit(ICU).METHODS: This minimal risk observational study was performed at an academic tertiary hospital. The Critical Care Independent Multidisciplinary Program at Mayo Clinic identified and defined 11 key quality metrics. These metrics were automatically calculated using ICU Data Mart, a near-real time copy of all ICU electronic medical record(EMR) data. The automatic report was compared with data from a comprehensive EMR review by a trained investigator. Data was collected for 93 randomly selected patients admitted to the ICU during April 2012(10% of admitted adult population). This study was approved by the Mayo Clinic Institution Review Board.RESULTS: All types of variables needed for metric calculations were found to be available for manual and electronic abstraction, except information for availability of free beds for patient-specific time-frames. There was 100% agreement between electronic and manual data abstraction for ICU admission source, admission service, and discharge disposition. The agreement between electronic and manual data abstraction of the time of ICU admission and discharge were 99% and 89%. The time of hospital admission and discharge were similar for both the electronically and manually abstracted datasets. The specificity of the electronically-generated report was 93% and 94% for invasive and non-invasive ventilation use in the ICU. One false-positive result for each type of ventilation was present. The specificity for ICU and in-hospital mortality was 100%. Sensitivity was 100% for all metrics.CONCLUSION: Our study demonstrates excellent accuracy of electronically-generated key ICU quality metrics. This validates the feasibility of automatic metric generation.

自动驾驶基础地理信息数据质量检查初探

自动驾驶基础地理信息数据质量关乎自动驾驶地图乃至自动驾驶产业的发展。其质量检查是自动驾驶产业的质量保障,也可加快自动驾驶地图规范化进程。基于自动驾驶基础地理信息数据的生产流程,分析了自动驾驶基础地理信息数据的特点;参考现行测绘成果质量检查规范,介绍了自动驾驶基础地理信息数据质量检查的内容与方法,并从7个方面阐述了检查的要求和指标;以自动驾驶基础地理信息生产道路数据成果质检项目为例,探讨了自动驾驶基础地理信息数据的质量控制方法和检查重点。

自动出院知情告知制度之完善

医务人员在患者自动出院时的告知若有不当,可能会引发医疗纠纷,医疗机构也会因此担责;医务人员与法医鉴定人当从防范医疗纠纷的角度来认知自动出院告知义务,明晰“自动出院”概念及其分类与知情告知的相关规定。再由分析医疗纠纷鉴定的案例与某三甲医院的告知书为范本,总结出在患者坚持自动出院情况下医务人员履行告知义务时的注意事项,并提出完善相关法律制度的建议。

循环多特征信息融合法:一种基于深度学习的车道线检测方法

车道线检测是辅助驾驶和自动驾驶的核心技术之一。为了进一步增强车道线特征的提取能力,提出了一种基于深度学习的循环多特征信息融合车道线识别算法。针对模型计算效率问题,该算法将车道线检测问题视为基于行选择单元格的分类问题;针对图像中车道信息聚合问题,提出了一种新的循环多特征信息聚合(recurrent multi-feature information aggregator,RMFA)方法,并将该方法与残差神经网络(residual neural network,ResNet)相结合提出融合上下文及多通道信息的车道线识别网络ResNet-RMFA。将该网络模型在Tusimple和CULane公开数据集上进行了性能测试,实验结果表明该模型单帧图像的推理时间可达4.8 ms,在Tusimple数据集上的精确度为96.07%,在CULane数据集上的F_(1)(IoU=0.5)评分为69.3%,达到了速度与精度的良好平衡。

基于多传感器信息融合的机床测量数据自动补偿系统

机床测量数据自动补偿能够减少加工误差,为提高机床加工精度,设计基于多传感器信息融合的机床测量数据自动补偿系统。首先设计温度传感器、单片机主控与补偿脉冲发生装置,获取机床运动数据,然后采用多传感器融合算法融合采集信息,对信息中误差处理,采用粗糙集理论寻找最佳补偿值,实现机床测量数据自动补偿。实验结果表明,该系统能够有效减少加工误差,可以满足实际应用要求。

基于卷积神经网络的燃气表信息自动识别方法研究

提出了一种基于改进的LeNet-5卷积神经网络的识别方法。通过加装摄像头和通信线路的方式,实时采集图像信息,并对图像进行部分预处理。引入Gabor滤波器、ReLU-Softplus函数、SVM分类器等优化传统LeNet-5模型,并根据图像数据的不均衡性,运用Grid Loss函数优化CNN网络,由此,实现燃气表自动化识别方法的构建。在Caffe深度学习框架下进行实验测评,结果表明该方法整体的识别准确性高达99.60%、整个样本集及单幅字码的训练总时间均优于其他识别方法,且对于不完整表码字的识别准确率也达到了99.21%。

融合风貌特征的上海城市框架模型生产更新方法

城市三维(3D)模型的快速构建和及时更新是实景3D中国建设的重点之一。本文针对上海历史风貌建筑与地标性异形建筑较多的特点进行深入分析,结合城市风貌特征和使用效能,提出了一种融合简易模型和去纹理精细模型的上海城市框架模型构建方法,阐述了其概念、构建规则和生产方法,解决了传统简易模型难以表现城市特征而导致应用场景受限的问题。在此基础上提出了基于二维(2D)建筑底面和3D精细模型生产更新的城市框架模型动态更新方法,并通过试验对所提出方法进行了验证。目前该方法已应用于上海城市“一张图”数据生产更新中,服务于市国土空间信息平台、城市运行管理等场景,证明了方法的有效性。

基于光纤传感的铁路牵引网故障自动识别方法

由于现有的铁路牵引网故障识别方法在识别过程中易受到周围环境影响,故障识别准确率较低,因此,提出基于分布式光纤传感的铁路牵引网故障自动识别方法。构建三种牵引网故障自动识别规则,通过自动识别规则,使用分布式光纤传感对故障特征进行提取;使用变量分析对故障损伤数据进行处理,筛选符合传输的故障损伤数据进行传输,计算故障损伤数据,运用网络拓扑结构对故障特征信息进行传输;通过融合层次的诊断,进行数据的融合,得到诊断结果,实现基于分布式光纤传感的铁路牵引网故障自动识别。搭建仿真模拟模型进行实验,通过实验结果表明:在10种不同的故障类型条件下,本文设计方法在对牵引网故障损伤自动识别中,准确率最高可达到93.35%,故障识别效果较好,可满足对铁路牵引网故障自动准确识别的需求,具有一定的实际应用价值。