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1
Content available An intelligent approach to short-term wind power prediction using deep neural networks
Niksa-Rynkiewicz Tacjana, Stomma Piotr, Witkowska Anna, Rutkowska Danuta, Słowik Adam, Cpałka Krzysztof, Jaworek-Korjakowska Joanna, Kolendo Piotr
Journal of Artificial Intelligence and Soft Computing Research
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2023
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Vol. 13. No. 3
197--210
EN
In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.
2
Content available Monitoring the gas turbine start-up phase on a platform using a hierarchical model based on multi-layer perceptron networks
Niksa-Rynkiewicz Tacjana, Witkowska Anna, Głuch Jerzy, Adamowicz Marcin
Polish Maritime Research
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2022
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nr 4
123--131
EN
Very often, the operation of diagnostic systems is related to the evaluation of process functionality, where the diagnostics is carried out using reference models prepared on the basis of the process description in the nominal state. The main goal of the work is to develop a hierarchical gas turbine reference model for the estimation of start-up parameters based on multi-layer perceptron neural networks. A functional decomposition of the gas turbine start-up process was proposed, enabling a modular analysis of selected parameters of the process. Real data sets obtained from observations of the turbo-generator set located on a North Sea platform were used.
3
Content available Evolutionary algorithm for selecting dynamic signatures partitioning approach
Zalasiński Marcin, Laskowski Łukasz, Niksa-Rynkiewicz Tacjana, Cpałka Krzysztof, Byrski Aleksander, Przybyszewski Krzysztof, Trippner Paweł, Dong Shi
Journal of Artificial Intelligence and Soft Computing Research
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2022
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Vol. 12, No. 4
267--279
EN
In the verification of identity, the aim is to increase effectiveness and reduce involvement of verified users. A good compromise between these issues is ensured by dynamic signature verification. The dynamic signature is represented by signals describing the position of the stylus in time. They can be used to determine the velocity or acceleration signal. Values of these signals can be analyzed, interpreted, selected, and compared. In this paper, we propose an approach that: (a) uses an evolutionary algorithm to create signature partitions in the time and velocity domains; (b) selects the most characteristic partitions in terms of matching with reference signatures; and (c) works individually for each user, eliminating the need of using skilled forgeries. The proposed approach was tested using Biosecure DS2 database which is a part of the DeepSignDB, a database with genuine dynamic signatures. Our simulations confirmed the correctness of the adopted assumptions.
4
Content available Monitoring regenerative heat exchanger in steam power plant by making use of the recurrent neural network
Niksa-Rynkiewicz Tacjana, Szewczuk-Krypa Natalia, Witkowska Anna, Cpałka Krzysztof, Zalasiński Marcin, Cader Andrzej
Journal of Artificial Intelligence and Soft Computing Research
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2021
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Vol. 11, No. 2
143--155
EN
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.
5
Content available Application of apriori algorithm in the lamination process in yacht production
Niksa-Rynkiewicz Tacjana, Landowski Michal, Szalewski Paweł
Polish Maritime Research
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2020
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nr 3
59--70
EN
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.
6
Content available Application of Intuitionistic Fuzzy Sets to the assessment of technical university students
Niksa-Rynkiewicz Tacjana
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej
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2019
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Nr 65
101--104
EN
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.
PL
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.
7
Tworzenie modelu pojęciowego dla zarządzania ryzykiem na przykładzie procesu laminowania w stoczni jachtowej
Dembicka Anna, Niksa-Rynkiewicz Tacjana, Mołas Katarzyna Anna, Szalewski Paweł
Problemy Jakości
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2019
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R. 51, nr 5
25--30
PL
Celem artykułu jest zaproponowanie modelu pojęciowego normalizującego kryteria oceny ryzyka w systemie zarządzania jakością w stoczni jachtowej. Zamiarem jest przeprowadzenie analizy danych, pozwalającej wytyczyć zbiory o wysokim, średnim i niskim poziomie istotności dla wszystkich błędów powstających w procesie laminowania. W artykule skoncentrowano się na najważniejszych pojęciach związanych z ryzykiem i kryteriami jego oceny. Opracowany model pojęciowy został poddany analizie i posłużył do badań, uwzględniając 4 typy jachtów, zawierających łącznie 1450 próbek.
EN
The aim of the article is to propose a conceptual model that normalizes risk assessment criteria in a quality management system at a yacht shipyard. The intention is to perform a data analysis that allows to determine collections with high, medium and low level of significance for all errors arising in the laminating process. The article focuses on the most important concepts related to risk and the criteria for its evaluation. The conceptual model developed was analyzed and used for the study taking into account four types of yachts, containing a total of 1,450 samples.
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