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.

Research on Technology Early-Warning System Based on Dynamic Information Monitoring

Relying on the advanced information technologies, such as information monitoring, data mining, natural language processing etc., the dynamic technology early-warning system is constructed. The system consists of technology information automatic retrieval, technology information monitoring, technology threat evaluation, and crisis response and management subsystem, which implements uninterrupted dynamic monitoring, trace and crisis early-warning to the specific technology. Empirical study testifies that the system improves the accuracy, timeliness and reliability of technology early-warning.

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.

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.

Framework Design of Emergency Public Warning Information Release Platform in Hunan Province

Construction background of Hunan emergency public warning information release platform was introduced,and current status and demands of provincial information release were analyzed.Supported by GIS,Java,and Net,Hunan early warning information release platform of public emergency was constructed based on ORACLE database and Windows 2008 Server,achieving information collection,packaging,transmission,de-packaging,and releasing concentration,standard and processing.In addition,the framework and functional characters of the system were illustrated and the innovation points were explored.The system provides guarantee for meteorological services and disaster prevention or reducing of Hunan public emergencies.

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.

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。

Design and Application of the Automatic Monitoring System for Wastewater Source in Shandong Province

In order to realize real-time online monitoring of the wastewater source enterprises,manage and issue monitoring information,this paper comprehensively uses automatic control,embedded data acquisition and transmission,distributed computing and data processing,geographic information system,etc.to develop automatic monitoring system of the wastewater source in Shandong Province.This system incorporates automatic monitoring information acquisition,transmission and daily work as an organic whole.The system realizes not only the continuous online monitoring of wastewater source enterprise,but also the deep excavation and utilization on monitoring information.It provides scientific and objective basis for energy saving,consumption reduction,carbon emission reduction,total amount control and other environmental management works,and meets the requirements of environmental management and related departments to wastewater source management.

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.

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.

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. © 2014 Chinese Association of Automation.

Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams

Analyzing the service behavior of high dams and establishing early-warning systems for them have become increasingly important in ensuring their long-term service.Current analysis methods used to obtain safety monitoring data are suited only to single survey point data.Unreliable or even paradoxical results are inevitably obtained when processing large amounts of monitoring data,thereby causing difficulty in acquiring precise conclusions.Therefore,we have developed a new method based on multi-source information fusion for conducting a comprehensive analysis of prototype monitoring data of high dams.In addition,we propose the use of decision information entropy analysis for building a diagnosis and early-warning system for the long-term service of high dams.Data metrics reduction is achieved using information fusion at the data level.A Bayesian information fusion is then conducted at the decision level to obtain a comprehensive diagnosis.Early-warning outcomes can be released after sorting analysis results from multi-positions in the dam according to importance.A case study indicates that the new method can effectively handle large amounts of monitoring data from numerous survey points.It can likewise obtain precise real-time results and export comprehensive early-warning outcomes from multi-positions of high dams.

An automatic extraction method for individual tree crowns based on self-adaptive mutual information and tile computing

Forest data acquisition,which is of crucial importance for modeling global biogeochemical cycles and climate,makes a contribution to building the ecological Digital Earth(DE).Due to the complex calculations and large volumes of data associated with high-resolution images of large areas,accurate and effective extraction of individual tree crowns remains challenging.In this study,two GeoEye-1 panchromatic images of Beihai and Ningbo in China with areas of 5 and 25 km2,respectively,were used as experimental data to establish a novel method for the automatic extraction of individual tree crowns based on a self-adaptive mutual information(SMI)algorithm and tile computing technology(SMI-TCT).To evaluate the performance of the algorithm,four commonly used algorithms were also applied to extract the individual tree crowns.The overall accuracy of the proposed method for the two experimental areas was superior to that of the four other algorithms,with maximum extraction accuracies of 85.7%and 63.8%.Moreover,the results also indicated that the novel method was suitable for individual tree crowns extraction in sizeable areas because of the multithread parallel computing technology.

Automatic Video Segmentation Based on Information Centroid and Optimized SaliencyCut

We propose an automatic video segmentation method based on an optimized SaliencyCut equipped with information centroid(IC)detection according to level balance principle in physical theory.Unlike the existing methods,the image information of another dimension is provided by the IC to enhance the video segmentation accuracy.Specifically,our IC is implemented based on the information-level balance principle in the image,and denoted as the information pivot by aggregating all the image information to a point.To effectively enhance the saliency value of the target object and suppress the background area,we also combine the color and the coordinate information of the image in calculating the local IC and the global IC in the image.Then saliency maps for all frames in the video are calculated based on the detected IC.By applying IC smoothing to enhance the optimized saliency detection,we can further correct the unsatisfied saliency maps,where sharp variations of colors or motions may exist in complex videos.Finally,we obtain the segmentation results based on IC-based saliency maps and optimized SaliencyCut.Our method is evaluated on the DAVIS dataset,consisting of different kinds of challenging videos.Comparisons with the state-of-the-art methods are also conducted to evaluate our method.Convincing visual results and statistical comparisons demonstrate its advantages and robustness for automatic video segmentation.

茶叶生产装备自动化与智能化技术研究进展与展望

茶产业是我国传统特色优势产业,大数据、物联网、云计算等新一代信息技术与农业的融合促进了茶产业向智能化转型与升级,在茶叶全产业链赋能增效中发挥重要作用。本文在概述了茶产业智能化技术体系的基础上,围绕种植、加工、检测和销售等4方面总结了国内外信息技术在茶产业智能化中应用的研究成果,分析了茶产业实现智能化的关键技术。最后对茶产业智能化未来发展方向进行展望,建议增强茶产业信息技术基础设施建设、加强人机协作智能茶机设备研发、重视茶叶种植加工大模型开发、提升大数据分析助力茶叶销售能力,为更好利用信息技术进行茶产业升级奠定基础。

非天然地震信息专报自动产出系统的设计与实现

【目的】为提高非天然地震信息专报产出效率、简化流程,构建了地震事件多元自动识别模型。【方法】基于支持向量机OvO方法研发事件类型多元自动识别模块,并采用可视化编程开发非天然地震信息专报自动产出系统。【结果】该系统的应用提高了非天然地震监测的自动化、智能化水平,实现非天然地震信息可视化显示及专报快速、准确的自动化产出。【结论】非天然地震信息专报自动产出系统能快速识别事件类型、产出信息专报,为影响非天然地震的快速应急响应提供技术支撑,极大地提高工作效率。