Numerical integration for the real time production of fundamental ephemerides over a wide time span

Typical challenges that manufacturing enterprises are facing now are compounded by lack of timely, accurate, and consistent information of manufacturing resources. As a result, it is difficult to analyze the real-time production performance for the shop-floor. In this paper, the definition and overall architecture of the internet of manufacturing things is presented to provide a new paradigm by extending the techniques of internet of things (IoT) to manufacturing field. Under this architecture, the real-time primitive events which occurred at different manufacturing things such as operators, machines, pallets, key materials, and so forth can be easily sensed. Based on these distributed primitive events, a critical event model is established to automatically analyze the real-time production performance. Here, the up-level production performance analysis is regarded as a series of critical events, and the real-time value of each critical event can be easily calculated according to the logical and sequence relationships among these multilevel events. Finally, a case study is used to illustrate how to apply the designed methods to analyze the real-time production performance.

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Attention dynamics on the Chinese social media Sina Weibo during the COVID-19 pandemic

EPJ Data Science ◽

10.1140/epjds/s13688-021-00263-0 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Hao Cui ◽

János Kertész

Keyword(s):

Social Media ◽

Real Time ◽

Time Span ◽

Sina Weibo ◽

The Real ◽

Period 2 ◽

Temporal Placement ◽

Higher Rank ◽

Clustering Pattern ◽

Chinese Social Media

AbstractUnderstanding attention dynamics on social media during pandemics could help governments minimize the effects. We focus on how COVID-19 has influenced the attention dynamics on the biggest Chinese microblogging website Sina Weibo during the first four months of the pandemic. We study the real-time Hot Search List (HSL), which provides the ranking of the most popular 50 hashtags based on the amount of Sina Weibo searches. We show how the specific events, measures and developments during the epidemic affected the emergence of different kinds of hashtags and the ranking on the HSL. A significant increase of COVID-19 related hashtags started to occur on HSL around January 20, 2020, when the transmission of the disease between humans was announced. Then very rapidly a situation was reached where COVID-related hashtags occupied 30–70% of the HSL, however, with changing content. We give an analysis of how the hashtag topics changed during the investigated time span and conclude that there are three periods separated by February 12 and March 12. In period 1, we see strong topical correlations and clustering of hashtags; in period 2, the correlations are weakened, without clustering pattern; in period 3, we see a potential of clustering while not as strong as in period 1. We further explore the dynamics of HSL by measuring the ranking dynamics and the lifetimes of hashtags on the list. This way we can obtain information about the decay of attention, which is important for decisions about the temporal placement of governmental measures to achieve permanent awareness. Furthermore, our observations indicate abnormally higher rank diversity in the top 15 ranks on HSL due to the COVID-19 related hashtags, revealing the possibility of algorithmic intervention from the platform provider.

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Study on Production Optimization Method of Fractured Reservoir Based on Connectivity Model

Frontiers in Earth Science ◽

10.3389/feart.2021.767738 ◽

2021 ◽

Vol 9 ◽

Author(s):

Dajian Li ◽

Zhenfeng Zhao ◽

Bai Wang ◽

Haitao Yang ◽

Wenhao Cui ◽

...

Keyword(s):

Real Time ◽

Net Present Value ◽

Fractured Reservoirs ◽

Production Optimization ◽

Production Performance ◽

Time Production ◽

The Real ◽

Production Scheme ◽

Connectivity Model ◽

Simulation Time

Due to the extensive development of fractures and serious heterogeneity in fractured reservoirs, it is difficult for the traditional numerical simulation method to invert its geology, which greatly limits the efficiency and accuracy of simulation and cannot realize the real-time optimization of production scheme. The connectivity model can only consider the two characteristic parameters of conductivity and connectivity volume, which does not involve complex and rigorous geological modeling. It can quickly and accurately reflect the state of the real reservoir, greatly reducing the simulation time, and is suitable for real-time production performance prediction of the reservoir. Due to the large difference in conductivity of fractured reservoirs, the difficulty of fitting increases. In this paper, the connectivity model is first applied to fractured reservoirs to realize the production dynamic simulation of fractured reservoirs. The optimization principle is used to optimize the injection-production scheme with the economic net present value as the objective function. In order to verify the method, the connectivity model is applied to Mu 30 of Changqing Oilfield in this paper. The results show that this method can effectively reflect the real production situation of the oilfield and the connectivity of the reservoir, and the simulation time is relatively fast. After optimization, the cumulative oil production of the reservoir increases by 8.1%, the cumulative water injection decreases by 2.3%, and the rising rate of water cut decreases by 58.8%, indicating that the connectivity model can realize the real-time production optimization of the reservoir.

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Perencanaan Scheduling dan Rescheduling dengan Metode Heuristic SPT Guna Meminimumkan Mean Tardiness dan Makespan

Jurnal Teknik Industri ◽

10.22219/jtiumm.vol3.no1.55-65 ◽

2010 ◽

Vol 3 (1) ◽

pp. 55

Author(s):

Eti Kristinawati

Keyword(s):

Real Time ◽

Processing Time ◽

Shop Floor ◽

Dispatching Rule ◽

Production Schedule ◽

Time Production ◽

Floor Level ◽

The Real ◽

Mean Tardiness

To complete customer's demand so company must make a plan with using production schedule. Some diffrculties usually happen in shop floor level is a new job which is put in a production schedule that have arranged before. So that the old production schedule have to revised by reschedulling. The procedure that be used to schedule the real time production consist of n job m machine is algoritm of Hueristik Dispatching Rule Shorted Processing Time (SPT) method to minimize mean tardiness and makespan.

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Syndrome and Outbreak Detection Using Chief-Complaint Data - Experience of the Real-Time Outbreak and Disease Surveillance Project

PsycEXTRA Dataset ◽

10.1037/e307182005-005 ◽

2004 ◽

Cited By ~ 3

Author(s):

Michael M. Wagner ◽

J. Espino ◽

F-C. Tsui ◽

P. Gesteland ◽

W. Chapman ◽

...

Keyword(s):

Real Time ◽

Disease Surveillance ◽

Chief Complaint ◽

Outbreak Detection ◽

The Real

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The Real-Time Neural Genesis of a Punchline

PsycEXTRA Dataset ◽

10.1037/e528942014-019 ◽

2014 ◽

Author(s):

Irving Biederman ◽

Ori Amir

Keyword(s):

Real Time ◽

The Real

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Real-Time System for Sending Data from Three Clients to One Server with MATLAB

ACMIT Proceedings ◽

10.33555/acmit.v2i1.7 ◽

2015 ◽

Vol 2 (1) ◽

pp. 35-41

Author(s):

Rivan Risdaryanto ◽

Houtman P. Siregar ◽

Dedy Loebis

Keyword(s):

Information System ◽

Real Time ◽

Time System ◽

Real Time System ◽

The Real ◽

Performance Time ◽

Data Process ◽

Effectiveness And Efficiency

The real-time system is now used on many fields, such as telecommunication, military, information system, evenmedical to get information quickly, on time and accurate. Needless to say, a real-time system will always considerthe performance time. In our application, we define the time target/deadline, so that the system should execute thewhole tasks under predefined deadline. However, if the system failed to finish the tasks, it will lead to fatal failure.In other words, if the system cannot be executed on time, it will affect the subsequent tasks. In this paper, wepropose a real-time system for sending data to find effectiveness and efficiency. Sending data process will beconstructed in MATLAB and sending data process has a time target as when data will send.

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