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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.

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Multi-population-based algorithm with an exchange of training plans based on population evaluation

Łapa Krystian, Cpałka Krzysztof, Kisiel-Dorohinicki Marek, Paszkowski Józef, Dębski Maciej, Le Van-Hung

Journal of Artificial Intelligence and Soft Computing Research

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2022

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Vol. 12, No. 4

239--253

EN

Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.

3

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

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

On-line signature partitioning using a population based algorithm

Zalasiński Marcin, Łapa Krystian, Cpałka Krzysztof, Przybyszewski Krzysztof, Yen Gary G.

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 1

5--13

EN

The on-line signature is a biometric attribute which can be used for identity verification. It is a very useful characteristic because it is commonly accepted in societies across the world. However, the verification process using this particular biometric feature is a rather difficult one. Researchers working on identity verification involving the on-line signature might face various problems, including the different discriminative power of signature descriptors, the problem of a large number of descriptors, the problem of descriptor generation, etc. However, population-based algorithms (PBAs) can prove very useful when resolving these problems. Hence, we propose a new method for on-line signature partitioning using a PBA in order to improve the verification process effectiveness. Our method uses the Differential Evolution algorithm with a properly defined evaluation function for creating the most characteristic partitions of the dynamic signature. We present simulation results of the proposed method for the BioSecure DS2 database distributed by the BioSecure Association.

6

Evolutionary algorithm with a configurable search mechanism

Łapa Krystian, Cpałka Krzysztof, Laskowski Łukasz, Cader Andrzej, Zeng Zhigang

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 3

151--171

EN

In this paper, we propose a new population-based evolutionary algorithm that automatically configures the used search mechanism during its operation, which consists in choosing for each individual of the population a single evolutionary operator from the pool. The pool of operators comes from various evolutionary algorithms. With this idea, a flexible balance between exploration and exploitation of the problem domain can be achieved. The approach proposed in this paper might offer an inspirational alternative in creating evolutionary algorithms and their modifications. Moreover, different strategies for mutating those parts of individuals that encode the used search operators are also taken into account. The effectiveness of the proposed algorithm has been tested using typical benchmarks used to test evolutionary algorithms.

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An algorithm for the evolutionary-fuzzy generation of on-line signature hybrid descriptors

Zalasiński Marcin, Cpałka Krzysztof, Laskowski Łukasz, Wunsch II Donald C., Przybyszewski Krzysztof

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 3

173--187

EN

In biometrics, methods which are able to precisely adapt to the biometric features of users are much sought after. They use various methods of artificial intelligence, in particular methods from the group of soft computing. In this paper, we focus on on-line signature verification. Such signatures are complex objects described not only by the shape but also by the dynamics of the signing process. In standard devices used for signature acquisition (with an LCD touch screen) this dynamics may include pen velocity, but sometimes other types of signals are also available, e.g. pen pressure on the screen surface (e.g. in graphic tablets), the angle between the pen and the screen surface, etc. The precision of the on-line signature dynamics processing has been a motivational springboard for developing methods that use signature partitioning. Partitioning uses a well-known principle of decomposing the problem into smaller ones. In this paper, we propose a new partitioning algorithm that uses capabilities of the algorithms based on populations and fuzzy systems. Evolutionary-fuzzy partitioning eliminates the need to average dynamic waveforms in created partitions because it replaces them. Evolutionary separation of partitions results in a better matching of partitions with reference signatures, eliminates disproportions between the number of points describing dynamics in partitions, eliminates the impact of random values, separates partitions related to the signing stage and its dynamics (e.g. high and low velocity of signing, where high and low are imprecise-fuzzy concepts). The operation of the presented algorithm has been tested using the well-known BioSecure DS2 database of real dynamic signatures.