scholarly journals Design for intelligent prediction system of oilfield development index based on pattern recognition

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1757-1764
Author(s):  
Yihua Zhong ◽  
Xiaodie Lv ◽  
Min Bao ◽  
Lina Li ◽  
Yan Yang
Keyword(s):  
Pattern Recognition ◽  
Knowledge Base ◽  
Oil Field ◽  
Computer Interface ◽  
Prediction System ◽  
Human Computer Interface ◽  
Optimal Prediction ◽  
Recognition Method ◽  
Inference Problem ◽  
Oilfield Development

In order to realize Digital Oil Field, some key problems need to be improved, esp. accurate and automatic prediction of oilfield development indexes which may be resolved by designing of intelligent prediction system. With the shortcoming of inference of system designed by us, automatic inference problem for a complicated intelligent prediction system was improved using pattern recognition method. First, intelligent prediction system and the methods as well as principles of pattern recognition were introduced. Then the framework of intelligent prediction system based on pattern recognition was formulated by using technologies and methods of human-computer interface, fuzzy processing and pattern recognition. Secondly, the knowledge base was extended as augmented knowledge base with introducing credibility to measure uncertainty of knowledge. Particularly, the methods and principles of pattern recognition were used to design two recognizers and one inferring machine. Moreover, the method of selecting predictive model based on reasoning of pattern recognition was presented by coupling them and intelligent prediction system. Finally, the design of improving intelligent prediction system of oilfield development indexes was simulated. Simulation result shows that improved system may automatically realize to select optimal prediction model by computer according to different reservoirs and different development stages. The results obtained in this thesis will helpful to design for intelligent prediction system.

Design of an Accelerometer-Controlled Myoelectric Human Computer Interface

2011 ◽  
Vol 403-408 ◽  
pp. 3973-3979
Author(s):  
Anusha Lalitha ◽  
Nitish V. Thakor
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Position Control ◽  
Computer Interface ◽  
Input Device ◽  
Two Dimensional ◽  
Human Computer Interface ◽  
Finger Movements ◽  
Mouse Cursor

The purpose of this study is to develop an alternate in-air input device which is intended to make interaction with computers easier for amputees. This paper proposes the design and utility of accelerometer controlled Myoelectric Human Computer Interface (HCI). This device can function as a PC mouse. The two dimensional position control of the mouse cursor is done by an accelerometer-based method. The left click and right click and other extra functions of this device are controlled by the Electromyographic (EMG) signals. Artificial Neural Networks (ANNs) are used to decode the intended movements during run-time. ANN is a pattern recognition based classification. An amputee can control it using phantom wrist gestures or finger movements.

EXPERT MANAGEMENT SYSTEMS OF AGROTECHNOLOGIES IN CLOUD INFORMATION TECHNOLOGY

1970 ◽  
pp. 12-17
Author(s):  
I. М. Mikhaylenko ◽  
V. N. Timoshin
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Information System ◽  
Knowledge Base ◽  
Information Technologies ◽  
Agricultural Sector ◽  
Perennial Grasses ◽  
Management Decision ◽  
Agricultural Technologies ◽  
Automation Systems

The transition to "intellectual" agriculture is the main vector of modernization of the agricultural sector of the economy. It is based on integrated automation and robotization of production, the use of automated decision-making systems. This is inevitably accompanied by a significant increase in data flow from sensors, monitoring systems, meteorological stations, drones, satellites and other external systems. Farm management has the opportunity to use various online applications for accurate recommendations and making various kinds of management decisions. In this regard, the most effective use of cloud information technologies, allowing implementing the most complex information and technical level of automation systems for management of agricultural technologies. The purpose of this work is to test the approach to creating expert management decision support systems (DSS) through the knowledge base (KB), formed in the cloud information system. For this, we consider an example of constructing a DSS for choosing the optimal date for preparing forage from perennial grasses. A complete theoretical and algorithmic database of the analytical DSS implemented in the data processing center of the cloud information system is given. On its basis, a KB is formed for a variety of different decision-making conditions. This knowledge base is transmitted to the local DSS. To make decisions about the optimal dates for the preparation of the local DSS, two variants of algorithms are used. The first option is based on management models, and the second uses the pattern recognition method. The approbation of the algorithms was carried out according to the BZ from 50 cases. According to the results of testing, the method of pattern recognition proved to be more accurate, which provides a more flexible adjustment of the situation on the local DSS to a similar situation in the KB. The considered technique can be extended to other crops.

Sign in / Sign up

Export Citation Format

Share Document