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Tytuł:
Analiza wydajności biblioteki TensorFlow z wykorzystaniem różnych algorytmów optymalizacji
Performance analysis of the TensorFlow library with different optimisation algorithms
Autorzy:
Wadas, Maciej
Smołka, Jakub
Powiązania:
https://bibliotekanauki.pl/articles/2055131.pdf
Data publikacji:
2021
Wydawca:
Politechnika Lubelska. Instytut Informatyki
Tematy:
uczenie maszynowe
sieci neuronowe
machine learning
neural networks
Opis:
W artykule zaprezentowano wyniki analizy wydajności biblioteki TensorFlow wykorzystywanej w uczeniu maszyno-wym i głębokich sieciach neuronowych. Analiza skupia się na porównaniu parametrów otrzymanych podczas treningu modelu sieci neuronowej dla algorytmów optymalizacji: Adam, Nadam, AdaMax, AdaDelta, AdaGrad. Zwrócono szczególną uwagę na różnice pomiędzy efektywnością treningu na zadaniach wykorzystujących mikroprocesor i kartę graficzną. Do przeprowadzenia badań utworzono modele sieci neuronowej, której zadaniem było rozpoznawanie znaków języka polskiego pisanych odręcznie. Otrzymane wyniki wykazały, że najwydajniejszym algorytmem jest AdaMax, zaś podzespół komputera wykorzystywany podczas badań wpływa jedynie na czas treningu wykorzystanego modelu sieci neuronowej.
This paper presents the results of performance analysis of the Tensorflow library used in machine learning and deep neural networks. The analysis focuses on comparing the parameters obtained when training the neural network model for optimization algorithms: Adam, Nadam, AdaMax, AdaDelta, AdaGrad. Special attention has been paid to the differences between the training efficiency on tasks using microprocessor and graphics card. For the study, neural network models were created in order to recognise Polish handwritten characters. The results obtained showed that the most efficient algorithm is AdaMax, while the computer component used during the research only affects the training time of the neural network model used.
Źródło:
Journal of Computer Sciences Institute; 2021, 21; 330--335
2544-0764
Pojawia się w:
Journal of Computer Sciences Institute
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimization of Machine Learning Process Using Parallel Computing
Autorzy:
Grzeszczyk, Michał K.
Powiązania:
https://bibliotekanauki.pl/articles/102525.pdf
Data publikacji:
2018
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
parallel computing
machine learning
perceptron
neural networks
OpenMP
Opis:
The aim of this paper is to discuss the use of parallel computing in the supervised machine learning processes in order to reduce the computation time. This way of computing has gained popularity because sequential computing is often insufficient for large scale problems like complex simulations or real time tasks. After presenting the foundations of machine learning and neural network algorithms as well as three types of parallel models, the author briefly characterized the development of the experiments carried out and the results obtained. The experiments on image recognition, ran on five sets of empirical data, prove a significant reduction in calculation time compared to classical algorithms. At the end, possible directions of further research concerning parallel optimization of calculation time in the supervised perceptron learning processes were shortly outlined.
Źródło:
Advances in Science and Technology. Research Journal; 2018, 12, 4; 81-87
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Recognition of sports exercises using inertial sensor technology
Autorzy:
Krutz, Pascal
Rehm, Matthias
Schlegel, Holger
Dix, Martin
Powiązania:
https://bibliotekanauki.pl/articles/30148258.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
human activity recognition
machine learning
neural networks
classifier
Opis:
Supervised learning as a sub-discipline of machine learning enables the recognition of correlations between input variables (features) and associated outputs (classes) and the application of these to previously unknown data sets. In addition to typical areas of application such as speech and image recognition, fields of applications are also being developed in the sports and fitness sector. The purpose of this work is to implement a workflow for the automated recognition of sports exercises in the Matlab® programming environment and to carry out a comparison of different model structures. First, the acquisition of the sensor signals provided in the local network and their processing is implemented. Realised functionalities include the interpolation of lossy time series, the labelling of the activity intervals performed and, in part, the generation of sliding windows with statistical parameters. The preprocessed data are used for the training of classifiers and artificial neural networks (ANN). These are iteratively optimised in their corresponding hyper parameters for the data structure to be learned. The most reliable models are finally trained with an increased data set, validated and compared with regard to the achieved performance. In addition to the usual evaluation metrics such as F1 score and accuracy, the temporal behaviour of the assignments is also displayed graphically, allowing statements to be made about potential causes of incorrect assignments. In this context, especially the transition areas between the classes are detected as erroneous assignments as well as exercises with insufficient or clearly deviating execution. The best overall accuracy achieved with ANN and the increased dataset was 93.7 %.
Źródło:
Applied Computer Science; 2023, 19, 1; 152-163
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
MIMO Beam Selection in 5G Using Neural Networks
Autorzy:
Ruseckas, Julius
Molis, Gediminas
Bogucka, Hanna
Powiązania:
https://bibliotekanauki.pl/articles/2055220.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
5G
context information
MIMO beam orientation
machine learning
neural networks
Opis:
In this paper, we consider cell-discovery problem in 5G millimeter-wave (mmWave) communication systems using multiple input, multiple output (MIMO) beam-forming technique. Specifically, we aim at the proper beam selection method using context-awareness of the user-equipment to reduce latency in beam/cell identification. Due to high path-loss in mmWave systems, beam-forming technique is extensively used to increase Signal-to-Noise Ratio (SNR). When seeking to increase user discovery distance, narrow beam must be formed. Thus, a number of possible beam orientations and consequently time needed for the discovery increases significantly when random scanning approach is used. The idea presented here is to reduce latency by employing artificial intelligence (AI) or machine learning (ML) algorithms to guess the best beam orientation using context information from the Global Navigation Satellite System (GNSS), lidars and cameras, and use the knowledge to swiftly initiate communication with the base station. To this end, here, we propose a simple neural network to predict beam orientation from GNSS and lidar data. Results show that using only GNSS data one can get acceptable performance for practical applications. This finding can be useful for user devices with limited processing power.
Źródło:
International Journal of Electronics and Telecommunications; 2021, 67, 4; 693--698
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of long short term memory neural networks for GPS satellite clock bias prediction
Autorzy:
Gnyś, Piotr
Przestrzelski, Paweł
Powiązania:
https://bibliotekanauki.pl/articles/1987078.pdf
Data publikacji:
2021-12-30
Wydawca:
Politechnika Gdańska
Tematy:
neural networks
LSTM
time series prediction
clock bias
GNSS
machine learning
Opis:
Satellite-based localization systems like GPS or Galileo are one of the most commonly used tools in outdoor navigation. While for most applications, like car navigation or hiking, the level of precision provided by commercial solutions is satisfactory it is not always the case for mobile robots. In the case of long-time autonomy and robots that operate in remote areas battery usage and access to synchronization data becomes a problem. In this paper, a solution providing a real-time onboard clock synchronization is presented. Results achieved are better than the current state-of-the-art solution in real-time clock bias prediction for most satellites.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2021, 25, 4; 381-395
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On graph mining with deep learning: introducing model r for link weight prediction
Autorzy:
Hou, Yuchen
Holder, Lawrence B.
Powiązania:
https://bibliotekanauki.pl/articles/91884.pdf
Data publikacji:
2019
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
deep learning
neural networks
machine learning
graph mining
link weight prediction
predictive models
node embeddings
Opis:
Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to the link weight prediction problem. This model uses a node embedding technique that extracts node embeddings (knowledge of nodes) from the known links’ weights (relations between nodes) and uses this knowledge to predict the unknown links’ weights. We demonstrate the power of Model R through experiments and compare it with the stochastic block model and its derivatives. Model R shows that deep learning can be successfully applied to link weight prediction and it outperforms stochastic block model and its derivatives by up to 73% in terms of prediction accuracy. We analyze the node embeddings to confirm that closeness in embedding space correlates with stronger relationships as measured by the link weight. We anticipate this new approach will provide effective solutions to more graph mining tasks
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 1; 21-40
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Indoor localization based on visible light communication and machine learning algorithms
Autorzy:
Ghonim, Alzahraa M.
Salama, Wessam M.
Khalaf, Ashraf A. M.
Shalaby, Hossam M. H.
Powiązania:
https://bibliotekanauki.pl/articles/2063908.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Stowarzyszenie Elektryków Polskich
Tematy:
free-space optical communication
visible light communication
neural networks
random forests
machine learning
Opis:
An indoor localization system is proposed based on visible light communications, received signal strength, and machine learning algorithms. To acquire an accurate localization system, first, a dataset is collected. The dataset is then used with various machine learning algorithms for training purpose. Several evaluation metrics are used to estimate the robustness of the proposed system. Specifically, authors’ evaluation parameters are based on training time, testing time, classification accuracy, area under curve, F1-score, precision, recall, logloss, and specificity. It turned out that the proposed system is featured with high accuracy. The authors are able to achieve 99.5% for area under curve, 99.4% for classification accuracy, precision, F1, and recall. The logloss and precision are 4% and 99.7%, respectively. Moreover, root mean square error is used as an additional performance evaluation averaged to 0.136 cm.
Źródło:
Opto-Electronics Review; 2022, 30, 2; art. no. e140858
1230-3402
Pojawia się w:
Opto-Electronics Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A comparison of conventional and deep learning methods of image classification
Porównanie metod klasycznego i głębokiego uczenia maszynowego w klasyfikacji obrazów
Autorzy:
Dovbnych, Maryna
Plechawska-Wójcik, Małgorzata
Powiązania:
https://bibliotekanauki.pl/articles/2055127.pdf
Data publikacji:
2021
Wydawca:
Politechnika Lubelska. Instytut Informatyki
Tematy:
image classification
machine learning
deep learning
neural networks
klasyfikacja obrazów
uczenie maszynowe
uczenie głębokie
sieci neuronowe
Opis:
The aim of the research is to compare traditional and deep learning methods in image classification tasks. The conducted research experiment covers the analysis of five different models of neural networks: two models of multi–layer perceptron architecture: MLP with two hidden layers, MLP with three hidden layers; and three models of convolutional architecture: the three VGG blocks model, AlexNet and GoogLeNet. The models were tested on two different datasets: CIFAR–10 and MNIST and have been applied to the task of image classification. They were tested for classification performance, training speed, and the effect of the complexity of the dataset on the training outcome.
Celem badań jest porównanie metod klasycznego i głębokiego uczenia w zadaniach klasyfikacji obrazów. Przeprowa-dzony eksperyment badawczy obejmuje analizę pięciu różnych modeli sieci neuronowych: dwóch modeli wielowar-stwowej architektury perceptronowej: MLP z dwiema warstwami ukrytymi, MLP z trzema warstwami ukrytymi; oraz trzy modele architektury konwolucyjnej: model z trzema VGG blokami, AlexNet i GoogLeNet. Modele przetrenowano na dwóch różnych zbiorach danych: CIFAR–10 i MNIST i zastosowano w zadaniu klasyfikacji obrazów. Zostały one zbadane pod kątem wydajności klasyfikacji, szybkości trenowania i wpływu złożoności zbioru danych na wynik trenowania.
Źródło:
Journal of Computer Sciences Institute; 2021, 21; 303--308
2544-0764
Pojawia się w:
Journal of Computer Sciences Institute
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Significance of Manufacturing Process Parameters in a Glassworks
Autorzy:
Paśko, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/175653.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neural networks
glass industry
glass packaging
significance of variables
sensitivity analysis
machine learning
Opis:
The article presents the use of artificial neural networks (multilayer perceptrons) to examine the significance of production process parameters. The considered problem relates to the occurrence of production periods with an increased number of defective products. The research aims to determine which of the 69 parameters of the manufacturing process most affect the number of defects. Two ways of expressing the parameters significance were used: using the sensitivity analysis and exploring the weights of connections between neurons. The results were determined using both single neural networks and a set of networks. The outcome from the research is the rankings of significance of the manufacturing process parameters. The analyzed data were obtained from a glassworks producing glass packaging.
Źródło:
Advances in Manufacturing Science and Technology; 2020, 44, 2; 39-45
0137-4478
Pojawia się w:
Advances in Manufacturing Science and Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wound image segmentation using clustering based algorithms
Autorzy:
Farmaha, Ihor
Banaś, Marian
Savchyn, Vasyl
Lukashchuk, Bohdan
Farmaha, Taras
Powiązania:
https://bibliotekanauki.pl/articles/2064381.pdf
Data publikacji:
2019
Wydawca:
STE GROUP
Tematy:
clustering
Segmentation
machine learning
neural networks
wounds
segmentacja
nauczanie maszynowe
sieci neuronowe
rany
klastrowanie
Opis:
Classic methods of measurement and analysis of the wounds on the images are very time consuming and inaccurate. Automation of this process will improve measurement accuracy and speed up the process. Research is aimed to create an algorithm based on machine learning for automated segmentation based on clustering algorithms Methods. Algorithms used: SLIC (Simple Linear Iterative Clustering), Deep Embedded Clustering (that is based on artificial neural networks and k-means). Because of insufficient amount of labeled data, classification with artificial neural networks can`t reach good results. Clustering, on the other hand is an unsupervised learning technique and doesn`t need human interaction. Combination of traditional clustering methods for image segmentation with artificial neural networks leads to combination of advantages of both of them. Preliminary step to adapt Deep Embedded Clustering to work with bio-medical images is introduced and is based on SLIC algorithm for image segmentation. Segmentation with this method, after model training, leads to better results than with traditional SLIC.
Źródło:
New Trends in Production Engineering; 2019, 2, 1; 570--578
2545-2843
Pojawia się w:
New Trends in Production Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Utilisation of the artificial neural network in the strategy for the allocation of storage space
Autorzy:
Janke, Piotr
Jończyk, Paweł
Powiązania:
https://bibliotekanauki.pl/articles/1883695.pdf
Data publikacji:
2020
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
logistics
machine learning
artificial intelligence
neural networks
logistyka
uczenie maszynowe
sztuczna inteligencja
sieci neuronowe
Opis:
Purpose: The main goal of the article is to develop a method that automatically allocates the warehouse zones of the product range of the studied enterprise for the selected machine learning algorithm. Design/methodology/approach: The problem of the studied issue is presented in the context of a specific company. The research used the double ABC method for the initial classification of zones. Input data were prepared according to the developed methodology. Selected machine learning algorithms were tested for the same data. Findings: Machine learning methods can be used to classify storage zones in that specific warehouse. Especially Boosted Trees and Neural Networks gives small errors at training stage witch our methodology. There may be differences in errors at the stage of learning the algorithm and the stage of implementing it with completely new data. Originality/value: Machine learning is a new solution that is increasingly used in various areas of logistics. The article draws attention to some problems in implementing this solution for enterprises.
Źródło:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2020, 145; 197-209
1641-3466
Pojawia się w:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatyczne rozpoznawanie treści nielegalnych filmów typu CSAM za pomocą klasyfikatora częściowo splatającego kolejne klatki materiału wideo
Autorzy:
Laskowska, Barbara
Powiązania:
https://bibliotekanauki.pl/articles/20311657.pdf
Data publikacji:
2023-10-31
Wydawca:
Akademia Sztuki Wojennej
Tematy:
cybersecurity
computer video analysis
machine learning
neural networks
deep neural networks
transfer learning
Child Sexual Abuse Material
CSAM
Opis:
The paper describes one of the methods of automatic recognition of CSAM materials, which was tested during the research under the APAKT project. The proposed solution is based on Temporal Shift Module (TSM), a model of a deep neural network created for efficient human activities rocognition in video. We applied transfer learning method for training the model with a relatively small number of training data to succesfully rocognize films with pornografic and illegal content. We conducted some tests of classification of films from three categories: neutral films, legal pornography and illegal pornografic videos (CSAM). In this paper we present problems that are connected with this research topic that come from the characteristic of the data. We also show that further works are needed to keep children safe in cyberspace.
Źródło:
Cybersecurity and Law; 2023, 10, 2; 195-201
2658-1493
Pojawia się w:
Cybersecurity and Law
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning versus human-developed algorithms in image analysis of microstructures
Autorzy:
Piwowarczyk, Adam
Wojnar, Leszek
Powiązania:
https://bibliotekanauki.pl/articles/103967.pdf
Data publikacji:
2019
Wydawca:
Stowarzyszenie Menedżerów Jakości i Produkcji
Tematy:
image analysis
object detection
neural networks
machine learning
analiza obrazu
detekcja obiektów
sieci neuronowe
uczenie maszynowe
Opis:
Automatic image analysis is nowadays a standard method in quality control of metallic materials, especially in grain size, graphite shape and non-metallic content evaluation. Automatically prepared solutions, based on machine learning, constitute an effective and sufficiently precise tool for classification. Human-developed algorithms, on the other hand, require much more experience in preparation, but allow better control of factors affecting the final result. Both attempts were described and compared.
Źródło:
Quality Production Improvement - QPI; 2019, 1, 1; 412-416
2657-8603
Pojawia się w:
Quality Production Improvement - QPI
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Performance comparison of machine learning algotihms for predictive maintenance
Porównanie skuteczności algorytmów uczenia maszynowego dla konserwacji predykcyjnej
Autorzy:
Gęca, Jakub
Powiązania:
https://bibliotekanauki.pl/articles/1841332.pdf
Data publikacji:
2020
Wydawca:
Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
Tematy:
machine learning
random forest
predictive maintenance
neural networks
uczenie maszynowe
las losowy
konserwacja predykcyjna
sieci neuronowe
Opis:
The consequences of failures and unscheduled maintenance are the reasons why engineers have been trying to increase the reliability of industrial equipment for years. In modern solutions, predictive maintenance is a frequently used method. It allows to forecast failures and alert about their possibility. This paper presents a summary of the machine learning algorithms that can be used in predictive maintenance and comparison of their performance. The analysis was made on the basis of data set from Microsoft Azure AI Gallery. The paper presents a comprehensive approach to the issue including feature engineering, preprocessing, dimensionality reduction techniques, as well as tuning of model parameters in order to obtain the highest possible performance. The conducted research allowed to conclude that in the analysed case, the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records. In conclusion, predictive maintenance based on machine learning represents the future of machine reliability in industry.
Skutki związane z awariami oraz niezaplanowaną konserwacją to powody, dla których od lat inżynierowie próbują zwiększyć niezawodność osprzętu przemysłowego. W nowoczesnych rozwiązaniach obok tradycyjnych metod stosowana jest również tzw. konserwacja predykcyjna, która pozwala przewidywać awarie i alarmować o możliwości ich powstawania. W niniejszej pracy przedstawiono zestawienie algorytmów uczenia maszynowego, które można zastosować w konserwacji predykcyjnej oraz porównanie ich skuteczności. Analizy dokonano na podstawie zbioru danych Azure AI Gallery udostępnionych przez firmę Microsoft. Praca przedstawia kompleksowe podejście do analizowanego zagadnienia uwzględniające wydobywanie cech charakterystycznych, wstępne przygotowanie danych, zastosowanie technik redukcji wymiarowości, a także dostrajanie parametrów poszczególnych modeli w celu uzyskania najwyższej możliwej skuteczności. Przeprowadzone badania pozwoliły wskazać najlepszy algorytm, który uzyskał dokładność na poziomie 99,92%, spośród ponad 122 tys. rekordów danych testowych. Na podstawie tego można stwierdzić, że konserwacja predykcyjna prowadzona w oparciu o uczenie maszynowe stanowi przyszłość w zakresie podniesienia niezawodności maszyn w przemyśle.
Źródło:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2020, 10, 3; 32-35
2083-0157
2391-6761
Pojawia się w:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Terrain classification using static and dynamic texture features by uav downwash effect
Autorzy:
Carvalho, João Pedro
Fonseca, José Manuel
Mora, André Damas
Powiązania:
https://bibliotekanauki.pl/articles/384711.pdf
Data publikacji:
2019
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
image processing
texture
machine learning
terrain classificafion
neural networks
UAV
przetwarzanie obrazu
tekstura
nauczanie maszynowe
sieci neuronowe
Opis:
Knowing how to identify terrain types is especially important in the autonomous navigation, mapping, decision making and emergency landings areas. For example, an unmanned aerial vehicle (UAV) can use it to find a suitable landing position or to cooperate with other robots to navigate across an unknown region. Previous works on terrain classification from RGB images taken onboard of UAVs shown that only static pixel-based features were tested with a considerable classification error. This paper presents a computer vision algorithm capable of identifying the terrain from RGB images with improved accuracy. The algorithm complement the static image features and dynamic texture patterns produced by UAVs rotors downwash effect (visible at lower altitudes) and machine learning methods to classify the underlying terrain. The system is validated using videos acquired onboard of a UAV with a RGB camera.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2019, 13, 1; 84-93
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
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