Zastosowanie Intuicjonistycznych Zbiorów Rozmytych do oceny studentów technicznych uczelni

The article proposes application of artificial intelligence methods to assess students of technical universities. The level of achieved educational goals can be assessed using measurements based on the idea of Fuzzy Intuitionistic Sets (IFS). A classification algorithm was developed and an exemplary distribution of the criteria values using IFS was presented. The application of the proposed approach in online education can enrich the student evaluation process with additional information related to the uncertainty or lack of data.W artykule proponuje się do oceny studenta uczelni technicznych użycie intuicjonistycznych zbiorów rozmytych, które znajdują zastosowanie w metodach sztucznej inteligencji. Poziom osiąganych celów edukacyjnych można ocenić za pomocą miar opartych na idei rozmytych zbiorów intuicjonistycznych (IFS). Opracowano algorytm klasyfikacji oraz zaprezentowano przykładowy rozkład wartości kryteriów z wykorzystaniem IFS. Zastosowanie proponowanego podejścia w kształceniu online może wzbogacić proces oceny studenta o dodatkowe informacje związane z niepewnością lub brakiem danych

Dynamically Positioned Ship Steering Making Use of Backstepping Method and Artificial Neural Networks

The article discusses the issue of designing a dynamic ship positioning system making use of the adaptive vectorial backstepping method and RBF type artificial neural networks. In the article, the backstepping controller is used to determine control laws and neural network weight adaptation laws. The artificial neural network is applied at each time instant to approximate nonlinear functions containing parametric uncertainties. The proposed control system does not require precise knowledge of the model of ship dynamics and external disturbances, it also eliminates the problem of analytical determination of the regression matrix when designing the control law with the aid of the adaptive backstepping procedure

Analysis of Impact of Ship Model Parameters on Changes of Control Quality Index in Ship Dynamic Positioning System

In this work there is presented an analysis of impact of ship model parameters on changes of control quality index in a ship dynamic positioning system designed with the use of a backstepping adaptive controller. Assessment of the impact of ship model parameters was performed on the basis of Pareto-Lorentz curves and ABC method in order to determine sets of the parameters which have either crucial, moderate or low impact on objective function. Simulation investigations were carried out with taking into account integral control quality indices

Analysis of Impact of Ship Model Parameters on Changes of Control Quality Index in Ship Dynamic Positioning System

In this work there is presented an analysis of impact of ship model parameters on changes of control quality index in a ship dynamic positioning system designed with the use of a backstepping adaptive controller. Assessment of the impact of ship model parameters was performed on the basis of Pareto-Lorentz curves and ABC method in order to determine sets of the parameters which have either crucial, moderate or low impact on objective function. Simulation investigations were carried out with taking into account integral control quality indices

A Framework of A Ship Domain-Based Near-Miss Detection Method Using Mamdani Neuro-Fuzzy Classification

Safety analysis of navigation over a given area may cover application of various risk measures for ship collisions. One of them is percentage of the so called near-miss situations (potential collision situations). In this article a method of automatic detection of such situations based on the data from Automatic Identification System (AIS), is proposed. The method utilizes input parameters such as: collision risk measure based on ship’s domain concept, relative speed between ships as well as their course difference. For classification of ships encounters, there is used a neuro-fuzzy network which estimates a degree of collision hazard on the basis of a set of rules. The worked out method makes it possibile to apply an arbitrary ship’s domain as well as to learn the classifier on the basis of opinions of experts interpreting the data from the AIS

Application of Apriori Algorithm in the Lamination Process in Yacht Production

The article specifies the dependence of defects occurring in the lamination process in the production of yachts. Despite great knowledge about their genesis, they cannot be completely eliminated. Authentic data obtained through cooperation with one of the Polish yacht shipyards during the years 2013–2017 were used for the analysis. To perform a simulation, the sample size was observed in 1450 samples, consisting of 6 models of yachts with closed and open deck. Finding the dependence of the occurrence of specific defects will allow for faster procedures and more effective quality control, which will contribute to lower costs. The use of new methods based on artificial intelligence related to Big Data allows for easier observation of dependencies in the complex structure of data from yacht production. The association rules were defined using the algorithm Apriori and define interdependent defects. A number of dependencies were found for the occurrence of production defects not obvious to technologists, but occurring with a high probability of coexistence. The presented research results may allow the planning process of production tasks to be improved

Application of apriori algorithm in the lamination process in yacht production

The article specifies the dependence of defects occurring in the lamination process in the production of yachts. Despite great knowledge about their genesis, they cannot be completely eliminated. Authentic data obtained through cooperation with one of the Polish yacht shipyards during the years 2013–2017 were used for the analysis. To perform a simulation, the sample size was observed in 1450 samples, consisting of 6 models of yachts with closed and open deck. Finding the dependence of the occurrence of specific defects will allow for faster procedures and more effective quality control, which will contribute to lower costs. The use of new methods based on artificial intelligence related to Big Data allows for easier observation of dependencies in the complex structure of data from yacht production. The association rules were defined using the algorithm Apriori and define interdependent defects. A number of dependencies were found for the occurrence of production defects not obvious to technologists, but occurring with a high probability of coexistence. The presented research results may allow the planning process of production tasks to be improved

The study on the appearance of deformation defects in the yacht lamination process using an AI algorithm and expert knowledge

Abstract This article describes the application of the A-priori algorithm for defining the rule-based relationships between individual defects caused during the lamination process, affecting the deformation defect of the yacht shell. The data from 542 yachts were collected and evaluated. For the proper development of the algorithm, a technological process of the yacht lamination supported by expert decisions was described. The laminating technology is a complex process of a sequential application of individual laminates according to a special strategy. The A-priori algorithm allowed for obtaining the set of association rules defining the relationships between the defects resulting from the lamination process and influencing the deformation defect of the yacht shell, which is one of the most common errors in yacht production. The obtained aggregated rules were compared with the expert knowledge of the employees of the production, quality control, mould regeneration, and technology departments of the yacht yard. The use of the proposed A-priori algorithm allowed for the generation of relationship rules consistent with the general opinion of experts. Associative rules additionally took into account detailed causes of a specific error, which were not always noticed by employees of specific departments. The assessment of the lamination process using an artificial intelligence algorithm turned out to be more objective, which made it possible to gradually reduce the total number of errors occurring in the yacht shell lamination process, and thus shorten the time needed to repair errors and the total time of producing the yacht

Application of Apriori Algorithm in the Lamination Process in Yacht Production

The article specifies the dependence of defects occurring in the lamination process in the production of yachts. Despite great knowledge about their genesis, they cannot be completely eliminated. Authentic data obtained through cooperation with one of the Polish yacht shipyards during the years 2013–2017 were used for the analysis. To perform a simulation, the sample size was observed in 1450 samples, consisting of 6 models of yachts with closed and open deck. Finding the dependence of the occurrence of specific defects will allow for faster procedures and more effective quality control, which will contribute to lower costs. The use of new methods based on artificial intelligence related to Big Data allows for easier observation of dependencies in the complex structure of data from yacht production. The association rules were defined using the algorithm Apriori and define interdependent defects. A number of dependencies were found for the occurrence of production defects not obvious to technologists, but occurring with a high probability of coexistence. The presented research results may allow the planning process of production tasks to be improved

Monitoring regenerative heat exchanger in steam power plant by making use of the recurrent neural network

Artificial Intelligence algorithms are being increasingly used in industrial applications. Their important function is to support operation of diagnostic systems. This paper presents a new approach to the monitoring of a regenerative heat exchanger in a steam power plant, which is based on a specific use of the Recurrent Neural Network (RNN). The proposed approach was tested using real data. This approach can be easily adapted to similar monitoring applications of other industrial dynamic objects