Research of the Reliability of the Electrical Supply System of Airports and Aerodromes Using Neural Networks

2020 ◽  
pp. 279-305
Author(s):  
Serhii Mykolaiovych Boiko ◽  
Yurii Shmelev ◽  
Viktoriia Chorna ◽  
Marina Nozhnova
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
High Precision ◽  
Supply System ◽  
Electricity Supply ◽  
Diagnostic Data ◽  
Number Of Factors ◽  
Standard Situation ◽  
Electrical Supply

The system of supplying airports and airfields is subject to high requirements for the degree of reliability. This is due to the existence of a large number of factors that affect the work of airports and airfields. In this regard, the control systems for these complexes must, as soon as possible, adopt the most optimal criteria for the reliability and quality of the solution. This complicates the structure of the electricity supply complex quite a lot and necessitates the use of modern, reliable, and high-precision technologies for the management of these complexes. One of them is artificial intelligence, which allows you to make decisions in a non-standard situation, to give recommendations to the operator to perform actions based on analysis of diagnostic data.

scholarly journals Demand Modelling in Telecommunications

10.14311/1121 ◽  
2009 ◽  
Vol 49 (2) ◽  
Author(s):  
M. Chvalina
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Term Forecast ◽  
Artificial Intelligence Methods ◽  
Demand Modelling

This article analyses the existing possibilities for using Standard Statistical Methods and Artificial Intelligence Methods for a short-term forecast and simulation of demand in the field of telecommunications. The most widespread methods are based on Time Series Analysis. Nowadays, approaches based on Artificial Intelligence Methods, including Neural Networks, are booming. Separate approaches will be used in the study of Demand Modelling in Telecommunications, and the results of these models will be compared with actual guaranteed values. Then we will examine the quality of Neural Network models. 

scholarly journals Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring

2020 ◽  
Vol 10 (20) ◽  
pp. 7157
Author(s):  
Bernardino Chiaia ◽  
Valerio De Biagi
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Damage Detection ◽  
Working Life ◽  
Research Topic ◽  
Structural Monitoring ◽  
The Sixties ◽  
Relevant Role ◽  
Measurement Devices

Structural monitoring is a research topic that is receiving more and more attention, especially in light of the fact that a large part our infrastructural heritage was built in the Sixties and is aging and approaching the end of its design working life. The detection of damage is usually performed through artificial intelligence techniques. In contrast, tools for the localization and the estimation of the extent of the damage are limited, mainly due to the complete datasets of damages needed for training the system. The proposed approach consists in numerically generating datasets of damaged structures on the basis of random variables representing the actions and the possible damages. Neural networks were trained to perform the main structural monitoring tasks: damage detection, localization, and estimation. The artificial intelligence tool interpreted the measurements on a real structure. To simulate real measurements more accurately, noise was added to the synthetic dataset. The results indicate that the accuracy of the measurement devices plays a relevant role in the quality of the monitoring.

scholarly journals Energy-efficient memcapacitor devices for neuromorphic computing

2021 ◽  
Author(s):  
Kai-Uwe Demasius ◽  
Aron Kirschen ◽  
Stuart Parkin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Energy Efficiency ◽  
High Precision ◽  
Energy Efficient ◽  
Lateral Size ◽  
Data Intensive Computing ◽  
Data Intensive ◽  
Specialized Hardware

AbstractData-intensive computing operations, such as training neural networks, are essential for applications in artificial intelligence but are energy intensive. One solution is to develop specialized hardware onto which neural networks can be directly mapped, and arrays of memristive devices can, for example, be trained to enable parallel multiply–accumulate operations. Here we show that memcapacitive devices that exploit the principle of charge shielding can offer a highly energy-efficient approach for implementing parallel multiply–accumulate operations. We fabricate a crossbar array of 156 microscale memcapacitor devices and use it to train a neural network that could distinguish the letters ‘M’, ‘P’ and ‘I’. Modelling these arrays suggests that this approach could offer an energy efficiency of 29,600 tera-operations per second per watt, while ensuring high precision (6–8 bits). Simulations also show that the devices could potentially be scaled down to a lateral size of around 45 nm.

STUDY OF THE INFLUENCE OF TRANSFORMERS ON THE QUALITY OF ELECTRIC ENERGY IN THE ELECTRICAL SUPPLY SYSTEM

2020 ◽  
pp. 24-30
Author(s):  
AZAT S. KHISMATULLIN ◽  
◽  
MARAT R. SURAKOV ◽  
ELMIRA M. BASHIROVA ◽  
◽  
...  
Keyword(s):  
Electric Energy ◽  
Supply System ◽  
Electrical Supply

Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence

2011 ◽  
pp. 290-313
Author(s):  
Christian Hillbrand
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Time Series ◽  
Forecasting Model ◽  
Empirical Validation ◽  
Functional Dependencies ◽  
Cause And Effect ◽  
Decision Supporting ◽  
Approximate Simulation

The motivation for this chapter is the observation that many companies build their strategy upon poorly validated hypotheses about cause and effect of certain business variables. However, the soundness of these cause-and-effect-relations as well as the knowledge of the approximate shape of the functional dependencies underlying these associations turns out to be the biggest issue for the quality of the results of decision supporting procedures. Since it is sufficiently clear that mere correlation of time series is not suitable to prove the causality of two business concepts, there seems to be a rather dogmatic perception of the inadmissibility of empirical validation mechanisms for causal models within the field of strategic management as well as management science. However, one can find proven causality techniques in other sciences like econometrics, mechanics, neuroscience, or philosophy. Therefore this chapter presents an approach which applies a combination of well-established statistical causal proofing methods to strategy models in order to validate them. These validated causal strategy models are then used as the basis for approximating the functional form of causal dependencies by the means of Artificial Neural Networks. This in turn can be employed to build an approximate simulation or forecasting model of the strategic system.

Comprehensive Analysis of the Efficiency of the Operator - Machine System Based on Artificial Intelligence Methods

2021 ◽  
pp. 40-46
Author(s):  
Syergyey Logvinov ◽  
S. Logvinov
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comprehensive Analysis ◽  
Self Organization ◽  
Multilayer Perceptrons ◽  
Number Of Factors ◽  
Methodological Approaches ◽  
Machine Systems ◽  
Low Sensitivity

Methodological approaches to the analysis of the effectiveness of complex human-machine systems (SMS) based on the use of methods of heuristic self-organization and artificial neural networks are considered. Modeling of the system taking into account a large number of factors and several output variables that characterize the MFM is based on obtaining multilayer perceptrons with the exception of factors with low sensitivity

scholarly journals Artificial intelligence-assisted analysis of endoscopic retrograde cholangiopancreatography image for identifying ampulla and difficulty of selective cannulation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Taesung Kim ◽  
Jinhee Kim ◽  
Hyuk Soon Choi ◽  
Eun Sun Kim ◽  
Bora Keum ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Clinical Significance ◽  
Endoscopic Retrograde Cholangiopancreatography ◽  
Endoscopic Retrograde ◽  
Centroid Distance ◽  
Selective Cannulation ◽  
Assisted Analysis ◽  
Difficult Cases

AbstractThe advancement of artificial intelligence (AI) has facilitated its application in medical fields. However, there has been little research for AI-assisted endoscopy, despite the clinical significance of the efficiency and safety of cannulation in the endoscopic retrograde cholangiopancreatography (ERCP). In this study, we aim to assist endoscopists performing ERCP through automatic detection of the ampulla and the identification of cannulation difficulty. We developed a novel AI-assisted system based on convolutional neural networks that predict the location of the ampulla and the difficulty of cannulation to the ampulla. ERCP data of 531 and 451 patients were utilized in the evaluation of our model for each task. Our model detected the ampulla with mean intersection-over-union 64.1%, precision 76.2%, recall 78.4%, and centroid distance 0.021. In classifying the cannulation difficulty, it achieved the recall of 71.9% for the class of easy cases and that of 61.1% for that of difficult cases. Remarkably, our model accurately detected AOV with varying morphological shape, size, and texture on par with the level of a human expert and showed promising results for recognizing cannulation difficulty. It demonstrated its potential to improve the quality of ERCP by assisting endoscopists.

scholarly journals The application of the artificial neural network in analysing the spread quality

2018 ◽  
Vol 226 ◽  
pp. 04042
Author(s):  
Marko Petkovic ◽  
Marija Blagojevic ◽  
Vladimir Mladenovic
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Food Processing ◽  
New Approach ◽  
Confectionery Products ◽  
Speed Up ◽  
Artificial Neural

In this paper, we introduce a new approach in food processing using an artificial intelligence. The main focus is simulation of production of spreads and chocolate as representative confectionery products. This approach aids to speed up, model, optimize, and predict the parameters of food processing trying to increase quality of final products. An artificial intelligence is used in field of neural networks and methods of decisions.

scholarly journals FEATURES OF ELECTRICITY SUPPLY OF SIDE-BASED EQUIPMENT IN IMPLEMENTATION OF RESTRICTED GENERATION

2019 ◽  
pp. 118-128
Author(s):  
Serhii Boiko ◽  
◽  
Andrey Nekrasov ◽  
Oleksiy Gorodny ◽  
Oksana Borysenko ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Distributed Generation ◽  
Power Supply ◽  
Iron Ore ◽  
Supply System ◽  
Electricity Supply ◽  
Dispersed Generation ◽  
Artificial Neural ◽  
Supply Systems

Urgency of the research. Ukraine has powerful dispersed generation resources. At the same time, one of the four measures proposed by the International Energy Agency for improving energy efficiency in the countries of the world is increasing the use of renewable energy sources in the total electricity production, including not the last role given to dispersed generation. Meanwhile, the consequence of the natural decrease in the levels of iron ore extraction in our country, at depths exceeding 1000-1500 m, that is, towards the projected ones, which already increases the energy intensity of its extraction and the decline of the con-capita ability on the world market. Target setting. The main purpose of this work is to synthesize the features of the electric power supply of iron ore enterprises, provided that they are introduced into the system of their electricity supply of distributed generation sources and analysis of the specifics of their operation. Actual scientific researches and issues analysis. Thus, the actual scientific-practical task is to study the peculiarities of the operation of systems of electricity supply of iron ore enterprises with the use of sources of dispersed generation in their distribution networks. Uninvestigated parts of general matters defining. Previously, it was proposed to install power plants at the crosscutting overheads, on dumps of quarries and other possible places of installation in the conditions of iron ore enterprises. However, the principles of the implementation of intelligent power supply management systems for the enterprises under consideration, especially when reconfiguring these systems, are not yet definitively defined. The research objective. The purpose of this work is to synthesize the features of the electricity supply of iron ore enterprises, provided that the sources of distributed generation are introduced into their electricity supply system. The statement of basic materials. In a number of previous studies, the authors justify the positive effect of the introduction of dispersed generation into the structure of the power supply systems of enterprises. The application of artificial neural networks in control systems and determination of electrical energy parameters of power supply systems of iron ore enterprises is proposed, which will allow multifactorial management and analysis of energy parameters in the implementation of distributed generation sources. The proposed approach for the implementation of artificial neural networks for modeling the reliability of the power supply system of iron ore enterprises with the introduction of dispersed generation can be presented with the help of artificial neural networks, which will improve predictability of generated electricity by dispersed generation in time. Conclusions. The model of reliability of the power supply system of the field-view enterprises in the implementation of dispersed generation can be represented using artificial neural networks, which will improve predictability of generated electricity by dispersed generation in time.

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