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Wyświetlanie 1-15 z 17
Tytuł:
Adaptive controller design for electric drive with variable parameters by Reinforcement Learning method
Autorzy:
Pajchrowski, T.
Siwek, P.
Wójcik, A.
Powiązania:
https://bibliotekanauki.pl/articles/201068.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Reinforcement Learning
adaptive control
electric drive
machine learning
Opis:
The paper presents a method for designing a neural speed controller with use of Reinforcement Learning method. The controlled object is an electric drive with a synchronous motor with permanent magnets, having a complex mechanical structure and changeable parameters. Several research cases of the control system with a neural controller are presented, focusing on the change of object parameters. Also, the influence of the system critic behaviour is researched, where the critic is a function of control error and energy cost. It ensures long term performance stability without the need of switching off the adaptation algorithm. Numerous simulation tests were carried out and confirmed on a real stand.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 5; 1019-1030
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Stationary supercapacitor energy storage operation algorithm based on neural network learning system
Autorzy:
Jefimowski, W.
Nikitenko, A.
Drążek, Z.
Wieczorek, M.
Powiązania:
https://bibliotekanauki.pl/articles/200935.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
stationary energy storage
operation algorithms
machine learning
supervised learning
prediction
Opis:
The paper proposes to apply an algorithm for predicting the minimum level of the state of charge (SoC) of stationary supercapacitor energy storage system operating in a DC traction substation, and for changing it over time. This is done to insure maximum energy recovery for trains while braking. The model of a supercapacitor energy storage system, its algorithms of operation and prediction of the minimum state of charge are described in detail; the main formulae, graphs and results of simulation are also provided. It is proposed to divide the SoC curve into equal periods of time during which the minimum states of charge remain constant. To predict the SoC level for the subsequent period, the learning algorithm based on the neural network could be used. Then, the minimum SoC could be based on two basic types of data: the first one is the time profile of the energy storage load during the previous period with the constant minimum SoC retained, while the second one relies on the trains’ locations and speed values in the previous period. It is proved that the use of variable minimum SoC ensures an increase of the energy volume recovered by approximately 10%. Optimum architecture and activation function of the neural network are also found.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 4; 733-738
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning methods for optimal compatibility of materials in ecodesign
Autorzy:
Rojek, I.
Dostatni, E.
Powiązania:
https://bibliotekanauki.pl/articles/202203.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
machine learning methods
classification models
ecodesign
selection of materials
compatibility
Opis:
Machine learning (ML) methods facilitate automated data mining. The authors compare the effectiveness of selected ML methods (RBF networks, Kohonen networks, and random forest) as modelling tools supporting the selection of materials in ecodesign. Applied in the design process, ML methods help benefit from the knowledge, experience and creativity of designers stored in historical data in databases. Implemented into a decision support system, the knowledge can be utilized – in the case under analysis – in the process of design of environmentally friendly products. The study was initiated with an analysis of input data for the selection of materials. The input data, specified in cooperation with designers, include both technological and environmental parameters which guarantee the desired compatibility of materials. Next, models were developed using selected ML methods. The models were assessed and implemented into an expert system. The authors show which models best fit their purpose and why. Models supporting the selection of materials, connections and disassembly methods help boost the recycling properties of designed products.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 2; 199-206
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An enhanced performance evaluation of workflow computing and scheduling using hybrid classification approach in the cloud environment
Autorzy:
Tharani, P.
Kalpana, A. M.
Powiązania:
https://bibliotekanauki.pl/articles/2086824.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
cloud
workflow scheduling
machine learning
CNN
AlexNet
chmura
planowanie przepływu pracy
nauczanie maszynowe
Opis:
Workflow scheduling is the major problem in cloud computing consists of a set of interdependent tasks which is used to solve the various scientific and healthcare issues. In this research work, the cloud based workflow scheduling between different tasks in medical imaging datasets using Machine Learning (ML) and Deep Learning (DL) methods (hybrid classification approach) is proposed for healthcare applications. The main objective of this research work is to develop a system which is used for both workflow computing and scheduling in order to minimize the makespan, execution cost and to segment the cancer region in the classified abnormal images. The workflow computing is performed using different Machine Learning classifiers and the workflow scheduling is carried out using Deep Learning algorithm. The conventional AlexNet Convolutional Neural Networks (CNN) architecture is modified and used for workflow scheduling between different tasks in order to improve the accuracy level. The AlexNet architecture is analyzed and tested on different cloud services Amazon Elastic Compute Cloud- EC2 and Amazon Lightsail with respect to Makespan (MS) and Execution Cost (EC).
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 4; e137728, 1--9
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bayesian optimization for solving high-frequency passive component design problems
Autorzy:
Baranowski, Michal
Fotyga, Grzegorz
Lamecki, Adam
Mrozowski, Michal
Powiązania:
https://bibliotekanauki.pl/articles/2173688.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
high-frequency design
machine learning
Bayesian optimization
optymalizacja bayesowska
konstrukcja o wysokiej częstotliwości
nauczanie maszynowe
Opis:
In this paper, the performance of the Bayesian Optimization (BO) technique applied to various problems of microwave engineering is studied. Bayesian optimization is a novel, non-deterministic, global optimization scheme that uses machine learning to solve complex optimization problems. However, each new optimization scheme needs to be evaluated to find its best application niche, as there is no universal technique that suits all problems. Here, BO was applied to different types of microwave and antenna engineering problems, including matching circuit design, multiband antenna and antenna array design, or microwave filter design. Since each of the presented problems has a different nature and characteristics such as different scales (i.e. number of design variables), we try to address the question about the generality of BO and identify the problem areas for which the technique is or is not recommended.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 4; art. no. e141595
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic speech based emotion recognition using paralinguistics features
Autorzy:
Hook, J.
Noroozi, F.
Toygar, O.
Anbarjafari, G.
Powiązania:
https://bibliotekanauki.pl/articles/200261.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
random forests
speech emotion recognition
machine learning
support vector machines
lasy
rozpoznawanie emocji mowy
nauczanie maszynowe
Opis:
Affective computing studies and develops systems capable of detecting humans affects. The search for universal well-performing features for speech-based emotion recognition is ongoing. In this paper, a?small set of features with support vector machines as the classifier is evaluated on Surrey Audio-Visual Expressed Emotion database, Berlin Database of Emotional Speech, Polish Emotional Speech database and Serbian emotional speech database. It is shown that a?set of 87 features can offer results on-par with state-of-the-art, yielding 80.21, 88.6, 75.42 and 93.41% average emotion recognition rate, respectively. In addition, an experiment is conducted to explore the significance of gender in emotion recognition using random forests. Two models, trained on the first and second database, respectively, and four speakers were used to determine the effects. It is seen that the feature set used in this work performs well for both male and female speakers, yielding approximately 27% average emotion recognition in both models. In addition, the emotions for female speakers were recognized 18% of the time in the first model and 29% in the second. A?similar effect is seen with male speakers: the first model yields 36%, the second 28% a?verage emotion recognition rate. This illustrates the relationship between the constitution of training data and emotion recognition accuracy.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 3; 479-488
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A machine learning-based mobile robot visual homing approach
Autorzy:
Zhu, Q.
Ji, X.
Wang, J.
Cai, C.
Powiązania:
https://bibliotekanauki.pl/articles/201706.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
robot navigation
visual homing
panoramic vision sensors
machine learning
homing performance
nawigacja robotów
panoramiczny czujnik wizyjny
uczenie maszynowe
Opis:
Visual homing enables mobile robots to move towards a previously visited location solely based on panoramic vision sensors. In this paper, a SIFT-based visual homing approach incorporating machine learning is presented. The proposed approach can reduce the impact of inaccurate landmarks on the performance, and generate more precise home direction with simple model. The effectiveness of the proposed approach is verified on both panoramic image databases and actual mobile robot, experimental results reveal that compared to some traditional visual homing methods, the proposed approach exhibits better homing performance and adaptability in both static and dynamic environments.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 5; 621-634
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hierarchical Bi-LSTM based emotion analysis of textual data
Autorzy:
Mahto, Dashrath
Yadav, Subhash Chandra
Powiązania:
https://bibliotekanauki.pl/articles/2173676.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
emotion analysis
machine learning
emotion detection
deep learning
hierarchical Bi-LSTM
analiza emocji
uczenie maszynowe
wykrywanie emocji
głęboka nauka
hierarchiczna dwukierunkowa pamięć krótkotrwała
Opis:
Nowadays, Twitter is one of the most popular microblogging sites that is generating a massive amount of textual data. Such textual data is intended to incorporate human feelings and opinions with related events like tweets, posts, and status updates. It then becomes difficult to identify and classify the emotions from the tweets due to their restricted word length and data diversity. In contrast, emotion analysis identifies and classifies different emotions based on the text data generated from social media platforms. The underlying work anticipates an efficient category and prediction technique for analyzing different emotions from textual data collected from Twitter. The proposed research work deliberates an enhanced deep neural network (EDNN) based hierarchical Bi-LSTM model for emotion analysis from textual data; that classifies the six emotions mainly sadness, love, joy, surprise, fear, and anger. Furthermore, the emotion analysis result obtained by the proposed hierarchical Bi-LSTM model is being compared and validated with the traditional hybrid CNN-LSTM approach regarding the accuracy, recall, precision, and F1-Score. It can be observed from the results that the proposed hierarchical Bi-LSTM achieves an average accuracy of 89% for emotion analysis, whereas the existing CNN-LSTM model achieved an overall accuracy of 75%. This result shows that the proposed hierarchical Bi-LSTM approach achieves desired performance compared to the CNN-LSTM model.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e141001
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Physics-guided neural networks (PGNNs) to solve differential equations for spatial analysis
Autorzy:
Borzyszkowski, Bartłomiej
Damaszke, Karol
Romankiewicz, Jakub
Świniarski, Marcin
Moszyński, Marek
Powiązania:
https://bibliotekanauki.pl/articles/2086847.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
physics-guided neural networks
spatial analysis
differential equations
machine learning
sieci neuronowe
fizyka
analiza przestrzenna
równania różniczkowe
uczenie maszynowe
Opis:
Numerous examples of physically unjustified neural networks, despite satisfactory performance, generate contradictions with logic and lead to many inaccuracies in the final applications. One of the methods to justify the typical black-box model already at the training stage involves extending its cost function by a relationship directly inspired by the physical formula. This publication explains the concept of Physics-guided neural networks (PGNN), makes an overview of already proposed solutions in the field and describes possibilities of implementing physics-based loss functions for spatial analysis. Our approach shows that the model predictions are not only optimal but also scientifically consistent with domain specific equations. Furthermore, we present two applications of PGNNs and illustrate their advantages in theory by solving Poisson’s and Burger’s partial differential equations. The proposed formulas describe various real-world processes and have numerous applications in the area of applied mathematics. Eventually, the usage of scientific knowledge contained in the tailored cost functions shows that our methods guarantee physics-consistent results as well as better generalizability of the model compared to classical, artificial neural networks.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 6; e139391, 1--10
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Discretization of data using Boolean transformations and information theory based evaluation criteria
Autorzy:
Jankowski, C.
Reda, D.
Mańkowski, M.
Borowik, G.
Powiązania:
https://bibliotekanauki.pl/articles/200750.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
machine learning
discretization
discernibility function
logic minimization
information theory
entropy
nauczanie maszynowe
dyskretyzacja
minimalizacja funkcji logicznych
teoria informacji
entropia
Opis:
Discretization is one of the most important parts of decision table preprocessing. Transforming continuous values of attributes into discrete intervals influences further analysis using data mining methods. In particular, the accuracy of generated predictions is highly dependent on the quality of discretization. The paper contains a description of three new heuristic algorithms for discretization of numeric data, based on Boolean reasoning. Additionally, an entropy-based evaluation of discretization is introduced to compare the results of the proposed algorithms with the results of leading university software for data analysis. Considering the discretization as a data compression method, the average compression ratio achieved for databases examined in the paper is 8.02 while maintaining the consistency of databases at 100%.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 4; 923-932
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Power quality analysis in electrical drives and a case study of artificial intelligence prediction algorithm for fault deterrent electrical drives
Autorzy:
Kumarasamy, Vishnu Murthy
Loganathan, Ashok Kumar
Powiązania:
https://bibliotekanauki.pl/articles/2173673.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
voltage sag
voltage swell
voltage imbalance
machine learning
inverter drives
artificial intelligence
wzrost napięcia
spadek napięcia
asymetria napięcia
uczenie maszynowe
napędy falownikowe
sztuczna inteligencja
Opis:
Since electrical drives have become an integral element of any industrial sector, power quality difficulties have been well expected, and delivering genuine quality of the same has proven to be a difficult challenge. Since power quality relies on load side non-linearity and high semiconductor technology consumption, it is a serious concern. The efficiency of the drive segment employed in the sector is increasingly becoming a topic of discussion in today’s market. Numerous reviews of available literature have found problems with the load side as well as with electrical drive proficiency, as a result of the issues listed above. A high level of power quality vulnerability is simply too much. Even the most advanced technology has its limits when it comes to drive operation. Research on the grid-side quality issues of electrical drives is the focus of this article. After field testing of grid power quality, each parametric analysis is performed to identify crucial parameters that can cause industrial drives to fail. Based on this discovery, a machine learning strategy was developed and an artificial intelligence technique was proposed to administer the fault deterrent prediction algorithm. An accurate forecast of anomalous behavior on the grid side ensures safe and dependable grid operation such that shutdown or failure probability is minimized to a greater extent by the results. Additional information gleaned from historical data will prove useful to equipment manufacturers in the future, providing a solution to this problem.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e141180
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Ensemble of feature extraction methods to improve the structural damage classification in a wind turbine foundation
Autorzy:
Leon-Medina, Jersson X.
Parés, Núria
Anaya, Maribel
Tibaduiza, Diego A.
Pozo, Francesc
Powiązania:
https://bibliotekanauki.pl/articles/27311417.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
structural health monitoring
wind turbine foundation
damage classification
machine learning
feature extraction
XGBoost
monitorowanie stanu konstrukcji
fundament turbiny wiatrowej
klasyfikacja uszkodzeń
uczenie maszynowe
ekstrakcja cech
Opis:
The condition monitoring of offshore wind power plants is an important topic that remains open. This monitoring aims to lower the maintenance cost of these plants. One of the main components of the wind power plant is the wind turbine foundation. This study describes a data-driven structural damage classification methodology applied in a wind turbine foundation. A vibration response was captured in the structure using an accelerometer network. After arranging the obtained data, a feature vector of 58 008 features was obtained. An ensemble approach of feature extraction methods was applied to obtain a new set of features. Principal Component Analysis (PCA) and Laplacian eigenmaps were used as dimensionality reduction methods, each one separately. The union of these new features is used to create a reduced feature matrix. The reduced feature matrix is used as input to train an Extreme Gradient Boosting (XGBoost) machine learning-based classification model. Four different damage scenarios were applied in the structure. Therefore, considering the healthy structure, there were 5 classes in total that were correctly classified. Five-fold cross validation is used to obtain a final classification accuracy. As a result, 100% of classification accuracy was obtained after applying the developed damage classification methodology in a wind-turbine offshore jacket-type foundation benchmark structure.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 3; art. no. e144606
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The identification method of the coal mill motor power model with the use of machine learning techniques
Autorzy:
Łabęda-Grudziak, Zofia Magdalena
Lipiński, Mariusz
Powiązania:
https://bibliotekanauki.pl/articles/2090698.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
coal mill motor power
nonlinear model identification
machine learning
additive regression models
process monitoring
moc silnika młyna węglowego
identyfikacja modelu nieliniowa
nauczanie maszynowe
model regresji addytywny
monitorowanie procesu
Opis:
The article presents an identification method of the model of the ball-and-race coal mill motor power signal with the use of machine learning techniques. The stages of preparing training data for model parameters identification purposes are described, as well as these aimed at verifying the quality of the evaluated model. In order to meet the tasks of machine learning, additive regression model was applied. Identification of the additive model parameters was performed on the basis of iterative backfitting algorithm combined with nonparametric estimation techniques. The proposed models have predictive nature and are aimed at simulation of the motor power signal of a coal mill during its regular operation, startup and shutdown. A comparative analysis has been performed of the models structured differently in terms of identification quality and sensitivity to the existence of an exemplary disturbance in the form of overhangs in the coal bunker. Tests carried out on the basis of real measuring data registered in the Polish power unit with a capacity of 200 MW confirm the effectiveness of the method.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 1; e135842, 1--9
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The identification method of the coal mill motor power model with the use of machine learning techniques
Autorzy:
Łabęda-Grudziak, Zofia Magdalena
Lipiński, Mariusz
Powiązania:
https://bibliotekanauki.pl/articles/2086819.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
coal mill motor power
nonlinear model identification
machine learning
additive regression models
process monitoring
moc silnika młyna węglowego
nieliniowa identyfikacja modelu
nauczanie maszynowe
model regresji addytywny
monitorowanie procesu
Opis:
The article presents an identification method of the model of the ball-and-race coal mill motor power signal with the use of machine learning techniques. The stages of preparing training data for model parameters identification purposes are described, as well as these aimed at verifying the quality of the evaluated model. In order to meet the tasks of machine learning, additive regression model was applied. Identification of the additive model parameters was performed on the basis of iterative backfitting algorithm combined with nonparametric estimation techniques. The proposed models have predictive nature and are aimed at simulation of the motor power signal of a coal mill during its regular operation, startup and shutdown. A comparative analysis has been performed of the models structured differently in terms of identification quality and sensitivity to the existence of an exemplary disturbance in the form of overhangs in the coal bunker. Tests carried out on the basis of real measuring data registered in the Polish power unit with a capacity of 200 MW confirm the effectiveness of the method.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 1; art. no. e135842, 1--9
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Coherent structures and flow control: genesis and prospect
Autorzy:
Gad-El-Hak, M.
Powiązania:
https://bibliotekanauki.pl/articles/201471.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
smart wings
coherent structures
reactive flow control
adaptive control
machine-learning control
futuristic control systems
microsensors
microactuators
artificial intelligence
turbulent shear flows
history of flow control
history of coherent structures
inteligentne skrzydła
kontrola adaptacyjna
mikroczujniki
sztuczna inteligencja
Opis:
The genesis of both coherent structures and reactive flow control strategies is explored. Futuristic control systems that utilize microsensors and microactuators together with artificial intelligence to target specific coherent structures in a transitional or turbulent flow are considered. Of possible interest to the readers of this journal is the concept of smart wings, to be briefly discussed early in the article.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 3; 411-444
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
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