scholarly journals Computational Intelligence in Marine Control Engineering Education

2021 ◽  
Vol 28 (1) ◽  
pp. 163-172
Author(s):  
Józef Lisowski
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Intelligence ◽  
Control Engineering ◽  
New Approach ◽  
Practical Education ◽  
Ship Control ◽  
The Cost ◽  
Ship Operation

Abstract This paper presents a new approach to the existing training of marine control engineering professionals using artificial intelligence. We use optimisation strategies, neural networks and game theory to support optimal, safe ship control by applying the latest scientific achievements to the current process of educating students as future marine officers. Recent advancements in shipbuilding, equipment for robotised ships, the high quality of shipboard game plans, the cost of overhauling, dependability, the fixing of the shipboard equipment and the requesting of the safe shipping and environmental protection, requires constant information on recent equipment and programming for computational intelligence by marine officers. We carry out an analysis to determine which methods of artificial intelligence can allow us to eliminate human subjectivity and uncertainty from real navigational situations involving manoeuvring decisions made by marine officers. Trainees learn by using computer simulation methods to calculate the optimal safe traverse of the ship in the event of a possible collision with other ships, which are mapped using neural networks that take into consideration the subjectivity of the navigator. The game-optimal safe trajectory for the ship also considers the uncertainty in the navigational situation, which is measured in terms of the risk of collision. The use of artificial intelligence methods in the final stage of training on ship automation can improve the practical education of marine officers and allow for safer and more effective ship operation.

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 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. 

Introduction

2013 ◽  
pp. 1-5
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Intelligence ◽  
Evolutionary Computing ◽  
Historical Background ◽  
Intelligent Computing ◽  
Biometric Security ◽  
Introductory Overview ◽  
Over Time

This chapter presents an introductory overview of the application of computational intelligence in biometrics. Starting with the historical background on artificial intelligence, the chapter proceeds to the evolutionary computing and neural networks. Evolutionary computing is an ability of a computer system to learn and evolve over time in a manner similar to humans. The chapter discusses swarm intelligence, which is an example of evolutionary computing, as well as chaotic neural network, which is another aspect of intelligent computing. At the end, special concentration is given to a particular application of computational intelligence—biometric security.

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 Particle-Based Fluid Surface Rendering with Neural Networks

2021 ◽  
Author(s):  
Viktória Burkus ◽  
Attila Kárpáti ◽  
László Szécsi
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Ground Truth ◽  
Fluid Simulation ◽  
Image Manipulation ◽  
Surface Rendering ◽  
Fluid Surface ◽  
Image Pairs ◽  
The Cost

Surface reconstruction for particle-based fluid simulation is a computational challenge on par with the simula- tion itself. In real-time applications, splatting-style rendering approaches based on forward rendering of particle impostors are prevalent, but they suffer from noticeable artifacts. In this paper, we present a technique that combines forward rendering simulated features with deep-learning image manipulation to improve the rendering quality of splatting-style approaches to be perceptually similar to ray tracing solutions, circumventing the cost, complexity, and limitations of exact fluid surface rendering by replacing it with the flat cost of a neural network pass. Our solution is based on the idea of training generative deep neural networks with image pairs consisting of cheap particle impostor renders and ground truth high quality ray-traced images.

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.

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.

Design of Fuzzy Logic Controller and its Distinctive Feature

2018 ◽  
Vol 22 (5) ◽  
pp. 740-746
Author(s):  
Takeshi Yamakawa ◽  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Deep Learning ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Linguistic Terms ◽  
New Approach ◽  
Distinctive Features ◽  
Logic Control

Prof. Lotfi A. Zadeh, who created a new approach to describe a knowledge of a human expert with a natural language, passed away on September 6, 2017. His significant accomplishment was to create a novel artificial intelligence (AI) which exhibits the knowledge of human experts in natural linguistic terms. This system is structured and clear in two points of why a result is obtained and how it is done. The system contrasts with AI systems based on neural networks or deep learning. In this paper, the design of a fuzzy logic controller and its application to controlling of the mouse-platform stabilization are described. In addition, the distinctive features of fuzzy logic control are discussed. The author wants to offer this paper on the altar of Prof. Zadeh.

scholarly journals Optimization of Extrusion-Based 3D Printing Process Using Neural Networks for Sustainable Development

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2737
Author(s):  
Izabela Rojek ◽  
Dariusz Mikołajewski ◽  
Marek Macko ◽  
Zbigniew Szczepański ◽  
Ewa Dostatni
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Risk Assessment ◽  
Sustainable Development ◽  
3D Printing ◽  
Computational Intelligence ◽  
Learning Program ◽  
Printing Process ◽  
Printing Processes ◽  
Self Learning

Technological and material issues in 3D printing technologies should take into account sustainable development, use of materials, energy, emitted particles, and waste. The aim of this paper is to investigate whether the sustainability of 3D printing processes can be supported by computational intelligence (CI) and artificial intelligence (AI) based solutions. We present a new AI-based software to evaluate the amount of pollution generated by 3D printing systems. We input the values: printing technology, material, print weight, etc., and the expected results (risk assessment) and determine if and what precautions should be taken. The study uses a self-learning program that will improve as more data are entered. This program does not replace but complements previously used 3D printing metrics and software.

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