scholarly journals IMPROVING SCENARIO-TECHNIQUE BY A SEMI-AUTOMATIZED CONSISTENCY ASSESSMENT BASED ON PATTERN RECOGNITION BY ARTIFICIAL NEURAL NETWORKS

2020 ◽  
Vol 1 ◽  
pp. 147-156
Author(s):  
I. Gräßler ◽  
P. Scholle ◽  
H. Thiele
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Case Study Approach ◽  
Study Approach ◽  
Depth Analysis ◽  
Artificial Neural ◽  
Scenario Technique

AbstractTo enhance the success of innovations, various methods for foresight have been developed. Automatization yields the potential of shifting effort away from the process to the actual in-depth analysis of resulting scenarios in scenario-technique. Within this paper, an approach based on a user-specific classification of input factors (consistency values) is presented. Generic consistency patterns used for a semi-automatized consistency assessment based on artificial neural networks are identified using a case study approach. Hereby, the effort for scenario-technique can be reduced significantly.

Artificial neural networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae)

Biologia ◽  
2007 ◽  
Vol 62 (4) ◽  
Author(s):  
Jaromír Vaňhara ◽  
Natália Muráriková ◽  
Igor Malenovský ◽  
Josef Havel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Correct Identification ◽  
Training Set ◽  
Image Analyser ◽  
Artificial Neural ◽  
Reliable Classification ◽  
The Right

AbstractThe classification methodology based on morphometric data and supervised artificial neural networks (ANN) was tested on five fly species of the parasitoid genera Tachina and Ectophasia (Diptera, Tachinidae). Objects were initially photographed, then digitalized; consequently the picture was scaled and measured by means of an image analyser. The 16 variables used for classification included length of different wing veins or their parts and width of antennal segments. The sex was found to have some influence on the data and was included in the study as another input variable. Better and reliable classification was obtained when data from both the right and left wings were entered, the data from one wing were however found to be sufficient. The prediction success (correct identification of unknown test samples) varied from 88 to 100% throughout the study depending especially on the number of specimens in the training set. Classification of the studied Diptera species using ANN is possible assuming a sufficiently high number (tens) of specimens of each species is available for the ANN training. The methodology proposed is quite general and can be applied for all biological objects where it is possible to define adequate diagnostic characters and create the appropriate database.

scholarly journals Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting

2009 ◽  
Vol 6 (1) ◽  
pp. 897-919 ◽  
Author(s):  
E. Toth
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Specific Model ◽  
Meteorological Conditions ◽  
Rainfall Runoff ◽  
Streamflow Forecasting ◽  
Streamflow Forecasts ◽  
Artificial Neural

Abstract. This paper presents the application of a modular approach for real-time streamflow forecasting, that uses different system-theoretic rainfall-runoff models according to the situation characterising the forecast instant. For each forecast instant, a specific model is applied, parameterised on the basis of the data of the similar hydrological and meteorological conditions observed in the past. In particular, the hydro-meteorological conditions are here classified with a clustering technique based on Self-Organising Maps (SOM) and, in correspondence of each specific case, different feed-forward artificial neural networks issue the streamflow forecasts one to six hours ahead, for a mid-sized case study watershed. The SOM method allows a consistent identification of the different parts of the hydrograph, corresponding to current and future hydrological conditions, on the basis of the only information available in the forecast instant. The results show that an adequate distinction of the hydro-meteorological conditions characterising the basin, hence including additional knowledge on the forthcoming dominant hydrological processes, may considerably improve the rainfall-runoff modelling performance.

Methods and tools of ensuring the operational reliability of complex industrial facilities.

2020 ◽  
Vol 14 (1) ◽  
pp. 34-42
Author(s):  
A. VAZHYNSKYI ◽  
◽  
S. ZHUKOV ◽  
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Expert Systems ◽  
Residual Life ◽  
Full Potential ◽  
Scientific Publications ◽  
Specific Element ◽  
Industrial Facilities ◽  
Artificial Neural

Approaches and algorithms for processing experimental data and data obtained as a result of using modern means of measuring equipment, selecting diagnostic parameters, pattern recognition, which constitute the methodological basis for developing methods and designing tools for creating a service system for complex industrial facilities based on predicting their performance and residual life are described in submitted article. Along with classical methods, methods based on using the full potential of the modern elemental base of microprocessor technology and the use of artificial neural networks, machine learning, and "big data" are discovered. The given examples can serve as the basis for constructing a methodology for the application of the considered approaches for organizing predictive maintenance of complex industrial equipment. An analytical review of a number of scientific publications showed that the creation of new automated diagnostic systems that can increase fault tolerance and extend the life of sophisticated modern power equipment is extremely relevant. For this, various approaches are applied, based on mathematical models, expert systems, artificial neural networks and other algorithms. Summarizing the results of scientific publications, it can be argued that the implementation of a systematic approach to the organization of repair service at the enterprise requires a comprehensive solution to the following urgent problems: • monitoring is formulated as the task of interrogating sensors and collecting information necessary for further analysis; • diagnostics, it is solved as tasks of identifying informative signs with further detection and classification of failures and anomalies in data sets; • improving the accuracy of algorithms aimed at pattern recognition; • condition forecasting is the task of assessing the current and accumulated readings of monitoring systems for making decisions regarding either a specific element of the complex or the facilities. Thus, modern technology make it possible to arrange arbitrarily complex algorithms. However, to use the full potential that artificial neural networks, expert systems, and classical methods for identifying and diagnosing equipment it is necessary to have a conceptual development of the foundations of building systems for organizing maintenance and repair of complex energy equipment

Sign in / Sign up

Export Citation Format

Share Document