A Mechanism for Collecting and Feedbacking the Real-Time Quality of Web Service

2009 ◽  
Author(s):  
Cheng Zhou ◽  
Haopeng Chen
Keyword(s):  
Web Service ◽  
Real Time ◽  
The Real ◽  
Quality Of Web Service

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

Ontological Support of Human-Computer Interactions

2017 ◽  
pp. 203-243
Keyword(s):  
Real Time ◽  
Design Thinking ◽  
Human Computer Interactions ◽  
Important Place ◽  
The Real ◽  
Analytical Reasoning ◽  
Effective Models ◽  
Computer Interactions

In the real-time design, conceptual solving any new task is impossible without analytical reasoning of designers who interact with natural experience and its models among which important place occupies models of precedents. Moreover, the work with new tasks is a source of such useful models. The quality of applied reasoning essentially depends on the constructive use of appropriate language and its effective models. In the version of conceptual activity described in this book, the use of language means is realized as an ontological support of design thinking that is aimed at solving a new task and creating a model of corresponding precedent. The ontological support provides controlled using the lexis, extracting the questions for managing the analysis, revealing the cause-and effects regularities and achieving the sufficient understanding. Designers fulfill all these actions in interactions with the project ontology that can be developed by manual or programmed way in work with the task.

Design of High Accuracy Robot Welding Machine for a Cone Bottom

2014 ◽  
Vol 496-500 ◽  
pp. 1289-1292
Author(s):  
De Huan Tang ◽  
De Yang Luo
Keyword(s):  
Real Time ◽  
Tracking System ◽  
High Accuracy ◽  
Welding Robot ◽  
Robotic Welding ◽  
High Quality ◽  
The Real ◽  
Laser Tracking ◽  
Welding Machine

This paper designed a special welding machine for an aluminum cone bottom workpiece. This machine contains highly accurate positioner system, laser tracking system, and robotic welding devices. It is used to weld the transverse seams and the longitudinal seams of the workpiece. The interaction of welding robot with positioner and the real-time seam correcting can ensure high quality of welding.

Juan Downey's Ethnographic Present

ARTMargins ◽  
2017 ◽  
Vol 6 (3) ◽  
pp. 28-49
Author(s):  
Benjamin Murphy
Keyword(s):  
Real Time ◽  
Video Feedback ◽  
Closed Circuit ◽  
Complex Relationship ◽  
Historical Time ◽  
Video Technology ◽  
Close Attention ◽  
The Real

Recorded between 1976 and 77, Juan Downey's video experiments with the Yanomami people have been widely celebrated as offering a critique of traditional anthropology through their use of feedback technology. This article argues, however, that close attention to the different feedback situations the artist constructs with the group reveal a more complex relationship between Downey and that discipline. In the enthusiasm he manifests for synchronous, closed-circuit video feedback in many of his statements about his Yanomami project, Downey in fact tacitly affirms some of the most problematic principles of traditional anthropology. In his emphasis on the real-time quality of this particular form of feedback, the artist puts forth a view of Yanomami society as itself synchronous, as a type of homeostatic, changeless system outside of historical time. As such he participates in a synchronic bias that anthropologists of his own time had begun to seriously critique. By focusing on one individual video from the Yanomami project, The Laughing Alligator of 1979, this essay argues that Downey's critical contribution to anthropological debates of his time does not come in the form of synchronous feedback, but rather through a different procedure unique to video technology based on temporal lag, delay, and spacing.

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