The development of real time data driving multi-axis linkage and synergic movement control system of 3D variable cross-section roll forming machine

The three dimensional variable cross-section roll forming is a kind of new metal forming technol- ogy which combines large forming force, multi-axis linkage movement and space synergic movement, and the sequential synergic movement of the ganged roller group is used to complete the metal sheet forming according to the shape of the complicated and variable forming part data. The control system should meet the demands of quick response to the test requirements of the product part. A new kind of real time data driving multi-axis linkage and synergic movement control strategy of 3D roll forming is put forward in the paper. In the new control strategy, the forming data are automatically generated according to the shape of the parts, and the multi-axis linkage movement together with cooperative motion among the six stands of the 3D roll forming machine is driven by the real-time information, and the control nodes are also driven by the forming data. The new control strategy is applied to a 48 axis 3D roll forming machine developed by our research center, and the control servo period is less than 10ms. A forming experiment of variable cross section part is carried out, and the forming preci- sion is better than ＋ 0.5mm by the control strategy. The result of the experiment proves that the control strategy has significant potentiality for the development of 3D roll forming production line with large scale, multi-axis ganged and svner^ic movement

Real Time Data Analysis of West Rapti River Basin of Nepal

Floods are the most widespread climate-related hazards in the world, and they impact more people globally than any other type of natural disasters. It causes over one third of the total economic loss from natural catastrophes and is responsible for two thirds of people affected by natural disasters. On the other hand, studies and analysis have shown that damage reductions due to forecasts improvements can range from a few percentage points to as much as 35% of annual flood damages. About 300 people lose their lives each year due to floods and landslides in Nepal with property damage exceeding 626 million NPR on average. The West Rapti River basin is one of the most flood prone river basins in Nepal. The real-time flood early warning system together with the development of water management and flood protection schemes plays a crucial role in reducing the loss of lives and properties and in overall development of the basin. The non-structural mitigating measure places people away from flood. This method is designed to reduce the impact of flooding to society and economy. This paper presents an overview of flood problems in the West Rapti River basin, causes and consequences of recent floods and the applicability and effectiveness of the real time data to flood early warning in Nepal.

Sentiment Drift Detection and Analysis in Real Time Twitter Data Streams

Handling sentiment drifts in real time twitter data streams are a challen-ging task while performing sentiment classifications,because of the changes that occur in the sentiments of twitter users,with respect to time.The growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real time.This work proposes an adap-tive learning algorithm-based framework,Twitter Sentiment Drift Analysis-Bidir-ectional Encoder Representations from Transformers(TSDA-BERT),which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real time.The framework also works on static data by converting them to data streams using the Kafka tool.The experiments conducted on real time and simulated tweets of sports,health care andfinancial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model,with accuracies of 91%,87%and 90%,respectively.Though the results have been provided only for a few topics,as a proof of concept,this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.

Quality control of marine big data——a case study of real-time observation station data in Qingdao

Offshore waters provide resources for human beings,while on the other hand,threaten them because of marine disasters.Ocean stations are part of offshore observation networks,and the quality of their data is of great significance for exploiting and protecting the ocean.We used hourly mean wave height,temperature,and pressure real-time observation data taken in the Xiaomaidao station(in Qingdao,China)from June 1,2017,to May 31,2018,to explore the data quality using eight quality control methods,and to discriminate the most effective method for Xiaomaidao station.After using the eight quality control methods,the percentages of the mean wave height,temperature,and pressure data that passed the tests were 89.6%,88.3%,and 98.6%,respectively.With the marine disaster(wave alarm report)data,the values failed in the test mainly due to the influence of aging observation equipment and missing data transmissions.The mean wave height is often affected by dynamic marine disasters,so the continuity test method is not effective.The correlation test with other related parameters would be more useful for the mean wave height.

Screening and reconstruction of real-time traffic data

The quality of real time traffic information is of the great importance, therefore the factors having effect on traffic characteristics are analyzed in general, and the necessities of real time data processing are summarized. The identification and reconstruction of real time traffic data are analyzed using Kalman filter equation and statistical approach. Four methods for ITS (Intelligent transportation system) detector data screening in traffic management systems are discussed in detail. Meanwhile traffic data examinations are discussed with solutions formulated through analysis, and recommendations are made for information collection and data management in future.

Web-based GIS System for Real-time Field Data Collection Using Personal Mobile Phone

Recently, use of mobile communicational devices in field data collection is increasing such as smart phones and cellular phones due to emergence of embedded Global Position System GPS and Wi-Fi Internet access. Accurate timely and handy field data collection is required for disaster management and emergency quick responses. In this article, we introduce web-based GIS system to collect the field data by personal mobile phone through Post Office Protocol POP3 mail server. The main objective of this work is to demonstrate real-time field data collection method to the students using their mobile phone to collect field data by timely and handy manners, either individual or group survey in local or global scale research.

Real-time crash prediction on freeways using data mining and emerging techniques

Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.

Study on Rear-end Real-time Data Quality Control Method of Regional Automatic Weather Station

[Objective] The aim was to study the rear-end real-time data quality control method of regional automatic weather station. [Method] The basic content and steps of rear-end real-time data quality control of regional automatic weather station were introduced. Each element was treated with systematic quality control procedure. The existence of rear-end real time data of regional meteorological station in Guangxi was expounded. Combining with relevant elements and linear changes, improvement based on traditional quality control method was made. By dint of evaluation and relevant check of element, the quality of temperature and pressure was controlled. [Result] The method was optimized based on traditional quality control method, and it narrowed the effectiveness of real-time data quality control. The quality check of hourly precipitation applied relevant check of hourly minimum temperature, vertical consistency check of radar data, which can effectively improve the accuracy and credibility of hourly precipitation quality control. [Conclusion] The method was on trial for one year in the quality control of real-time data in the regional automatic meteorological station in Guangxi and had gained good outcome.

Integrated Real-Time Big Data Stream Sentiment Analysis Service

Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with opportunities to discover valuable intelligence from the massive user generated text streams. However, the traditional content analysis frameworks are inefficient to handle the unprecedentedly big volume of unstructured text streams and the complexity of text analysis tasks for the real time opinion analysis on the big data streams. In this paper, we propose a parallel real time sentiment analysis system: Social Media Data Stream Sentiment Analysis Service (SMDSSAS) that performs multiple phases of sentiment analysis of social media text streams effectively in real time with two fully analytic opinion mining models to combat the scale of text data streams and the complexity of sentiment analysis processing on unstructured text streams. We propose two aspect based opinion mining models: Deterministic and Probabilistic sentiment models for a real time sentiment analysis on the user given topic related data streams. Experiments on the social media Twitter stream traffic captured during the pre-election weeks of the 2016 Presidential election for real-time analysis of public opinions toward two presidential candidates showed that the proposed system was able to predict correctly Donald Trump as the winner of the 2016 Presidential election. The cross validation results showed that the proposed sentiment models with the real-time streaming components in our proposed framework delivered effectively the analysis of the opinions on two presidential candidates with average 81% accuracy for the Deterministic model and 80% for the Probabilistic model, which are 1% - 22% improvements from the results of the existing literature.

Real-time performance of periodic data transmission in EPA industrial Ethernet

To evaluate and improve the real-time performance of Ethernet for plant automation（EPA） industrial Ethernet,the real-time performance of EPA periodic data transmission was theoretically and experimentally studied.By analyzing information transmission regularity and EPA deterministic scheduling mechanism,periodic messages were categorized as different modes according to their entering-queue time.The scheduling characteristics and delivery time of each mode and their interacting relations were studied,during which the models of real-time performance of periodic information transmission in EPA system were established.On this basis,an experimental platform is developed to test the delivery time of periodic messages transmission in EPA system.According to the analysis and the experiment,the main factors that limit the real-time performance of EPA periodic data transmission and the improvement methods were proposed.

Quality Control System of Meteorological Real-time Data from Automatic Weather Stations in Shandong

The quality control system for meteorological real-time data from automatic weather stations in Shandong realized integration of communi- cation system and provincial quality control system, and an interaction platform which was mainly created by Web was set up. The system not only was fully guaranteed for the funning of basic business, also improved the reliability of the data.

Alternate Data Acquisition and Real-time Monitoring System on HT-7 Tokamak

A new system called alternate data acquisition and real-time monitoring system has been developed for long-time discharge in tokamak operation. It can support continuous on-line data acquisition at a high sampling rate and a graphic display of the plasma parameters during the discharge. Thus operators can monitor and control the plasma state in real time. An application of this system has been demonstrated on the HT-7 tokamak.

Data Transfer Over the Internet for Real Time Applications

Efficient real time data exchange over the Internet plays a crucial role in the successful application of web-based systems. In this paper, a data transfer mechanism over the Internet is proposed for real time web based applications. The mechanism incorporates the eXtensible Markup Language （XML） and Hierarchical Data Format （HDF） to provide a flexible and efficient data format. Heterogeneous transfer data is classified into light and heavy data, which are stored using XML and HDF respectively; the HDF data format is then mapped to Java Document Object Model （JDOM） objects in XML in the Java environment. These JDOM data objects are sent across computer networks with the support of the Java Remote Method Invocation （RMI） data transfer infrastructure. Client＇s defined data priority levels are implemented in RMI, which guides a server to transfer data objects at different priorities. A remote monitoring system for an industrial reactor process simulator is used as a case study to illustrate the proposed data transfer mechanism.

基于NI Real-Time Hypervisor的液压摇摆台实时测控系统设计

提出一种新的利用Real-Time Hypervisor在一台PXI控制器上实现双液压缸实时控制的方案,可完成摇摆台数据的实时采集和双液压缸的实时控制。建立液压摇摆台的数学模型,并用MATLAB软件进行仿真,得到实验条件下双液压缸的行程轨迹;依据Real-Time Hypervisor配置合适的PXI硬件系统,开发面向非实时系统(Windows XP)和实时系统(Lab-VIEW RT)的软件;在Real-Time Hypervisor的虚拟网络中建立共享变量实现实时软件和非实时软件之间的数据通信。

符合Real-time CORBA规范的传感器网络监控系统设计

传感器网络在解决底层数据源和I/O设备的异构性、处理连续查询的实时性、服务质量自适应等方面提出了许多新的挑战。提出一种基于实时CORBA的传感器网络数据流处理框架,说明了符合实时中间件规范的数据流处理系统设计的方法和特点,通过实验对该框架进行了性能测试,验证了其处理连续查询的实时性。

Development and Application of Real-time Bridge Scour Monitoring System

Because of the complex nature of the changes in the current and movement of the riverbeds by bridge scouring, it is impossible to understand or predict these changes. In order to have a reliable data, it is critical to have the current methods and equipment for measuring bridge scouring replaced with technology that could acquire real-time bridge scouring data. Despite the critical need for real-time data acquisition, the harsh environmental conditions have prevented the scientific community from acquiring real-time data. Harsh environmental conditions were addressed by the developmental of an automated, remote data collection system, allowing real-time data such as scour movement, scour depth, and scour trend to be viewed in a safe location. As a result, accurate sea-floor movements were seen for the first time, aiding the direction and future of bridge scour research, ultimately contributing greatly to the safety of bridges.

Patient Centered Real-Time Mobile Health Monitoring System

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

深部地层智能压井多解性分析与优化策略

开发深部地层油气资源时普遍存在地质条件复杂、钻井周期长和井筒压力控制困难等问题,采用智能压井方法结合多源实时信息反馈,可实现井筒内气液分布状态和压力变化规律的实时预测与更新,但不同修正系数组合可能得到相同的压力计算结果,导致模型存在多解性难题。为此,分析了不同历史时间节点解空间形态的演变规律,揭示了模型多解性的本质源于少量数据约束下模型训练的不完善性;并对应建立了基于实时信息序列的模型全局训练优化方法及动态随机种群训练优化方法,测试了其对于模型全局最优解的搜索能力及适用条件。测试结果表明,全局训练优化方法在压井初期能够实现精准调控,但计算耗时较长;而动态随机种群训练优化方法在压井初期与预期值略有差异,但计算耗时较少。根据可用计算资源情况选择合适的训练优化方法,可实现多源实时数据约束下模型关于井筒气液流动规律的深度学习。

深地工程多维信息感知与智能建造的发展与展望

随着大数据、云计算、人工智能等数字技术的加速演进,各领域智能化、信息化、数字化已成为未来的大势所趋。深地工程作为国家重大战略科技问题,必然面临智能化升级。然而,深部岩体“三高一扰动”的复杂特征给深地工程智能化转型带来严峻的挑战。为实现深地工程与数字技术的高效融合,研究基于“感知-传送-解译-分析-决策”的智能化实践路径,系统回顾了地下工程中智能感知、实时传输、信息解译、数据分析、智能决策等领域的代表性研究进展,并针对性提出了“多感知、快响应、大数据、优方法、精模型、强平台、易推广”的深地工程智能建造发展方向。研究表明:①前沿的深地工程感知技术包括:光纤传感器、MEMS传感器、计算机视觉、自动化机器人等,待数据采集完毕后,通过兼具配置简单、容错能力强、可移动性好等优点的无线通信协议完成数据的实时响应,以实现深地工程监测数据的精准感知与实时传输;②深地工程原位监测技术获取的数据类型主要包括图像、波、点云等,对原始数据解译及分析的模型众多,采用新一代的人工智能技术,如:人工神经网络和深度学习技术,可显著提高解译与分析的效率;③智能决策系统具备高效的学习能力,能够适应复杂环境下的不确定性,通过循环自主学习,以进行决策问题的智能解答。当前,我国深地工程智能建造的政策与产业体系已基本建立,大量智能建造系统已应用于实践。基于此,从智能感知与信息解译、围岩评价及安全评估、围岩控制与动态修复、平台开发及应用推广等4个方面展望了数智化深地工程的发展方向,进而构建了基于多源信息的深地工程围岩稳定性综合评价与分析系统构想。