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Wyświetlanie 1-15 z 44
Tytuł:
Decentralized fault location, isolation and self restoration (FLISR) logic implementation using IEC 61850 GOOSE signals
Autorzy:
Bielenica, Paweł
Widzińska, Joanna
Łukaszewski, Artur
Nogal, Łukasz
Łukaszewski, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/2173702.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
smart grids
distributed grid automation
IEC 61850
GOOSE signal
FLISR
FDIR
FLIR
inteligentne sieci
automatyzacja sieci rozproszona
sygnał GOOSE
Opis:
Fault location, isolation and self-restoration (FLISR) automation is an essential component of smart grids concept. It consists of a high level of comprehensive automation and monitoring of the distribution grid improving the quality of energy supplied to customers. This paper presents an algorithm for decentralized FLISR architecture with peer-to-peer communication using IEC 61860 GOOSE messages. An analysis of short circuit detection was presented due to the method of the grid earthing system. The proposed automation model was built based on communication logic between configured intelligent electronic devices (IED) from ABB and Siemens. The laboratory tests were conducted in a half-loop grid model with a bilateral power supply (typical urban grid). The laboratory research concerned three locations of short circuits: between substation and section point, between two section points and between section point and normally open point (NOP). The logic implementation was developed using State Sequencer software offered by Test Universe.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 5; art. no. e143101
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Monitoring of water distribution system effectiveness using fractal geometry
Autorzy:
Kowalski, D.
Kowalska, B.
Kwietniewski, M.
Powiązania:
https://bibliotekanauki.pl/articles/201524.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
water supply networks
monitoring
effectiveness
sieci wodociągowe
monitorowanie
skuteczność
Opis:
The paper discusses issues related to monitoring quality and pressure of water transmitted using water supply networks. Special attention was paid to methods of determining location of measuring points, which to a large extent influence effectiveness of the monitoring system. The purpose of the paper is to present authors’ own method of determining location of points of measuring quality and pressure of transmitted water. The basis for considerations was a real water supply network in a city of about 10.000 residents. The presented method is based on existence of self-similarity properties of the set of fractals formed by the geometrical structure of the water supply network. It is a rank-ordered method involving 3 basic stages - reduction of the number of potential measuring points, providing more details of a target location and checking usefulness of selected points for monitoring purposes. At the preparatory stage, existence of fractal properties of the examined network structure is required to be demonstrated as well as the construction of its numerical model. The ranking is based on two indicators referring by analogy to human circulatory system monitoring and elements of the risk theory. This theory was also used to evaluate usefulness of selected measuring points for monitoring purposes.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 1; 155--161
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep learning: theory and practice
Autorzy:
Cichocki, A.
Poggio, T.
Osowski, S.
Lempitsky, V.
Powiązania:
https://bibliotekanauki.pl/articles/202346.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep learning
networks
theory
practice
uczenie głębokie
sieci
teoria
praktyka
Opis:
This Special Section of the Bulletin of the Polish Academy of Sciences on Technical Sciences is devoted to theoretical aspects of deep machine learning as well as practical applications in some areas of signal and image processing, particularly in bioengineering.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 757-759
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Some aspects of modeling and analysis of complex biological systems using time Petri nets
Autorzy:
Olszak, J.
Radom, M.
Formanowicz, P.
Powiązania:
https://bibliotekanauki.pl/articles/199770.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
time Petri nets
t-invariants
biological system
system biologiczny
czasowe sieci Petriego
inwariant
Opis:
Models of complex biological systems can be built using different types of Petri nets. Qualitative nets, for example, can be successfully used to obtain a model of such a system and on its basis a structure-based analysis can be performed. Time is an important factor influencing a whole biological system behaviour and in many cases it should be considered during building a model of such a system. In this paper various types of time Petri nets have been described and methods for studying corresponding models have been discussed. In particular, an algorithm using time parameters to enhance t-invariants based analysis is proposed. This algorithm allows for calculation of the minimal and maximal numbers of tokens (respectively, for an optimistic and pessimistic case) in particular places necessary to assure that all transitions from a given t-invariant support will be able to fire. Additionally, to address the problem of the proper assignment of time values to transitions, the known methods for calculation and evaluation of such time parameters based on the net structure have also been discussed.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 1; 67-78
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection of driver fatigue symptoms using transfer learning
Autorzy:
Jakubowski, J.
Chmielińska, J.
Powiązania:
https://bibliotekanauki.pl/articles/201238.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
driver fatigue
convolutional neural networks
transfer learning
AlexNet
zmęczenie kierowcy
splotowe sieci neuronowe
Opis:
This paper presents the results of the scientific investigations which aimed at developing the detectors of the selected driver fatigue symptoms based on face images. The presented approach assumed using convolutional neural networks and transfer learning technique. In the conducted research the pretrained model of AlexNet was used. The net underwent slight modification of the structure and then the fine-tuning procedure was applied with the use of an appropriate dataset. In this way all detectors of the selected fatigue symptoms were created. The results of conducted computations indicate that it is potentially possible to apply such an approach to the problem of fatigue symptom detection. The values of the overall misclassification rates for the most troublesome symptom are less than 5.5%, which seems to be a quite satisfactory result.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 869-874
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Time-slot based architecture for power beam-assisted relay techniques in CR-WSNs with transceiver hardware inadequacies
Autorzy:
Umer, Mushtaq Muhammad
Jiang, Hong
Zhang, Qiuyun
Manlu, Liu
Muhammad, Owais
Powiązania:
https://bibliotekanauki.pl/articles/27311438.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
cognitive radio WSNs
energy harvesting
DF relaying
relay selection schemes in WSNs
hardware inadequacy in WSNs
wireless sensor networks
zbieranie energii
bezprzewodowa sieć czujników
WSN
sieci radia kognitywnego
przekaz DF
bezprzewodowe sieci sensoryczne
Opis:
Over the past two decades, numerous research projects have concentrated on cognitive radio wireless sensor networks (CR-WSNs) and their benefits. To tackle the problem of energy and spectrum shortfall in CR-WSNs, this research proposes an underpinning decode-&-forward (DF) relaying technique. Using the suggested time-slot architecture (TSA), this technique harvests energy from a multi-antenna power beam (PB) and delivers source information to the target utilizing energy-constrained secondary source and relay nodes. The study considers three proposed relay selection schemes: enhanced hybrid partial relay selection (E-HPRS), conventional opportunistic relay selection (C-ORS), and leading opportunistic relay selection (L-ORS). We present evidence for the sustainability of the suggested methods by examining the outage probability (OP) and throughput (TPT) under multiple primary users (PUs). These systems leverage time switching (TS) receiver design to increase end-to-end performance while taking into account the maximum interference constraint and transceiver hardware inadequacies. In order to assess the efficacy of the proposed methods, we derive the exact and asymptotic closed-form equations for OP and TPT & develop an understanding to learn how they affect the overall performance all across the Rayleigh fading channel. The results show that OP of the L-ORS protocol is 16% better than C-ORS and 75% better than E-HPRS in terms of transmitting SNR. The OP of L-ORS is 30% better than C-ORS and 55% better than E-HPRS in terms of hardware inadequacies at the destination. The L-ORS technique outperforms C-ORS and E-HPRS in terms of TPT by 4% and 11%, respectively.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 5; art. no. e146620
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Speeding-up convolutional neural networks: A survey
Autorzy:
Lebedev, V.
Lempitsky, V.
Powiązania:
https://bibliotekanauki.pl/articles/201708.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
convolutional neural networks
resource-efficient computation
algorithm optimization
splotowe sieci neuronowe
efektywne zasoby obliczeniowe
optymalizacja algorytmu
Opis:
Convolutional neural networks (CNN) have become ubiquitous in computer vision as well as several other domains, but the sheer size of the modern CNNs means that for the majority of practical applications, a significant speed up and compression are often required. Speeding-up CNNs therefore have become a very active area of research with multiple diverse research directions pursued by many groups in academia and industry. In this short survey, we cover several research directions for speeding up CNNs that have become popular recently. Specifically, we cover approaches based on tensor decompositions, weight quantization, weight pruning, and teacher-student approaches. We also review CNN architectures designed for optimal speed and briefly consider automatic architecture search.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 799-811
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Models of multimodal networks and transport processes
Autorzy:
Bocewicz, G.
Muszyński, W.
Banaszak, Z.
Powiązania:
https://bibliotekanauki.pl/articles/200255.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
multimodal processes
cyclic scheduling
constraint satisfaction problems
procesy multimodalne
planowanie cykliczne
procesy transportowe
modele sieci multimodalnych
Opis:
Models of multimodal cyclic processes, i.e. processes realized with synergic utilization of various local and cyclic acting processes, play a determining role in an evaluation of functioning efficiency inter alia in public transport systems, passengers movement, cargo transport, data and energy transmission etc. We assume that the structure of a system determines repertoire of its behaviors. The paper presents a constraints satisfaction problem, which solving enables an evaluation of potential behaviors of the system of concurrently interacting local cyclic processes. Consequently, it is possible to plan and schedule the multimodal processes realized in that system. The constraints satisfaction problem, enabling the search for the structure of inter-position transport system and guaranteeing realization of assumed schedule of multi-assortment production was formulated for a declarative model of the multimodal transportation processes system. The attached calculation example illustrates the computational efficiency of the proposed approach.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 3; 635-650
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of convolutional neural networks with anatomical knowledge for brain MRI analysis in MS patients
Autorzy:
Stasiak, B.
Tarasiuk, P.
Michalska, I.
Tomczyk, A.
Powiązania:
https://bibliotekanauki.pl/articles/200542.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
multiple sclerosis
convolutional neural networks
skull stripping
ventricular system
stwardnienie rozsiane
splotowe sieci neuronowe
system komorowy
Opis:
In this paper we consider the problem of automatic localization of multiple sclerosis (MS) lesions within brain tissue. We use a machine learning approach based on a convolutional neural network (CNN) which is trained to recognize the lesions in magnetic resonance images (MRI scans) of the patient’s brain. The training images are relatively small fragments clipped from the MRI scans so – in order to provide additional hints on location of a given clip within the brain structures – we include anatomical information in the training/testing process. Our research has shown that indicating the location of the ventricles and other structures, as well as performing brain tissue classification may enhance the results of the automatic localization of the MS-related demyelinating plaques in the MRI scans.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 857-868
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Local dynamic integration of ensemble in prediction of time series
Autorzy:
Osowski, S.
Siwek, K.
Powiązania:
https://bibliotekanauki.pl/articles/201557.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neural networks
ensemble of predictors
dynamic integration
time series prediction
sieci neuronowe
zespół predyktorów
dynamiczna integracja
Opis:
The paper presents local dynamic approach to integration of an ensemble of predictors. The classical fusing of many predictor results takes into account all units and takes the weighted average of the results of all units forming the ensemble. This paper proposes different approach. The prediction of time series for the next day is done here by only one member of an ensemble, which was the best in the learning stage for the input vector, closest to the input data actually applied. Thanks to such arrangement we avoid the situation in which the worst unit reduces the accuracy of the whole ensemble. This way we obtain an increased level of statistical forecasting accuracy, since each task is performed by the best suited predictor. Moreover, such arrangement of integration allows for using units of very different quality without decreasing the quality of final prediction. The numerical experiments performed for forecasting the next input, the average PM10 pollution and forecasting the 24-element vector of hourly load of the power system have confirmed the superiority of the presented approach. All quality measures of forecast have been significantly improved.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 3; 517-525
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Theory I: Deep networks and the curse of dimensionality
Autorzy:
Poggio, T.
Liao, Q.
Powiązania:
https://bibliotekanauki.pl/articles/200623.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep network
shallow network
convolutional neural network
function approximation
deep learning
sieci neuronowe
aproksymacja funkcji
uczenie głębokie
Opis:
We review recent work characterizing the classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these conditions, though weight sharing is not the main reason for their exponential advantage.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 761-773
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep learning vs feature engineering in the assessment of voice signals for diagnosis in Parkinson’s disease
Autorzy:
Majda-Zdancewicz, Ewelina
Potulska-Chromik, Anna
Jakubowski, Jacek
Nojszewska, Monika
Kostera-Pruszczyk, Anna
Powiązania:
https://bibliotekanauki.pl/articles/2173626.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
voice processing
Parkinson’s disease
non-linear analysis
convolutional network
przetwarzanie głosu
choroba Parkinsona
analiza nieliniowa
sieci konwolucyjne
Opis:
Voice acoustic analysis can be a valuable and objective tool supporting the diagnosis of many neurodegenerative diseases, especially in times of distant medical examination during the pandemic. The article compares the application of selected signal processing methods and machine learning algorithms for the taxonomy of acquired speech signals representing the vowel a with prolonged phonation in patients with Parkinson’s disease and healthy subjects. The study was conducted using three different feature engineering techniques for the generation of speech signal features as well as the deep learning approach based on the processing of images involving spectrograms of different time and frequency resolutions. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. The discriminatory ability of feature vectors was evaluated using the SVM technique. The spectrograms were processed by the popular AlexNet convolutional neural network adopted to the binary classification task according to the strategy of transfer learning. The results of numerical experiments have shown different efficiencies of the examined approaches; however, the sensitivity of the best test based on the selected features proposed with respect to biological grounds of voice articulation reached the value of 97% with the specificity no worse than 93%. The results could be further slightly improved thanks to the combination of the selected deep learning and feature engineering algorithms in one stacked ensemble model.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e137347
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep learning vs feature engineering in the assessment of voice signals for diagnosis in Parkinson’s disease
Autorzy:
Majda-Zdancewicz, Ewelina
Potulska-Chromik, Anna
Jakubowski, Jacek
Nojszewska, Monika
Kostera-Pruszczyk, Anna
Powiązania:
https://bibliotekanauki.pl/articles/2090742.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
voice processing
Parkinson’s disease
non-linear analysis
convolutional network
przetwarzanie głosu
choroba Parkinsona
analiza nieliniowa
sieci konwolucyjne
Opis:
Voice acoustic analysis can be a valuable and objective tool supporting the diagnosis of many neurodegenerative diseases, especially in times of distant medical examination during the pandemic. The article compares the application of selected signal processing methods and machine learning algorithms for the taxonomy of acquired speech signals representing the vowel a with prolonged phonation in patients with Parkinson’s disease and healthy subjects. The study was conducted using three different feature engineering techniques for the generation of speech signal features as well as the deep learning approach based on the processing of images involving spectrograms of different time and frequency resolutions. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. The discriminatory ability of feature vectors was evaluated using the SVM technique. The spectrograms were processed by the popular AlexNet convolutional neural network adopted to the binary classification task according to the strategy of transfer learning. The results of numerical experiments have shown different efficiencies of the examined approaches; however, the sensitivity of the best test based on the selected features proposed with respect to biological grounds of voice articulation reached the value of 97% with the specificity no worse than 93%. The results could be further slightly improved thanks to the combination of the selected deep learning and feature engineering algorithms in one stacked ensemble model.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e137347, 1--10
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Developing automatic recognition system of drill wear in standard laminated chipboard drilling process
Autorzy:
Kurek, J.
Kruk, M.
Osowski, S.
Hoser, P.
Wieczorek, G.
Jegorowa, A.
Górski, J.
Wilkowski, J.
Śmietańska, K.
Kossakowska, J.
Powiązania:
https://bibliotekanauki.pl/articles/200766.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
diagnostic expert systems
neural networks
wavelet packets
wear monitoring
diagnostyczny system ekspercki
sieci neuronowe
pakiety falkowe
monitorowanie zużycia
Opis:
The paper presents an automatic approach to recognition of the drill condition in a standard laminated chipboard drilling process. The state of the drill is classified into two classes: “useful” (sharp enough) and “useless” (worn out). The case “useless” indicates symptoms of excessive drill wear, unsatisfactory from the point of view of furniture processing quality. On the other hand the “useful” state identifies tools which are still able to drill holes acceptable due to the required processing quality. The main problem in this task is to choose an appropriate set of diagnostic features (variables), based on which the recognition of drill state (“useful” versus “useless”) can be made. The features have been generated based on 5 registered signals: feed force, cutting torque, noise, vibration and acoustic emission. Different statistical parameters describing these signals and also their Fourier and wavelet representations have been used for defining the features. Sequential feature selection is applied to detect the most class discriminative set of features. The final step of recognition is done by using three types of classifiers, including support vector machine, ensemble of decision trees and random forest. Six standard drills of 12 mm diameter with tungsten carbide tips were used in experiments. The results have confirmed good quality of the proposed diagnostic system.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2016, 64, 3; 633-640
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Type of modulation identification using Wavelet Transform and Neural Network
Autorzy:
Walenczykowska, M.
Kawalec, A.
Powiązania:
https://bibliotekanauki.pl/articles/201661.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
modulation identification
artificial neural networks
continuous wavelet transform (CWT)
identyfikacja modulacji
sztuczne sieci neuronowe (SSN)
transformacja falkowa (CWT)
Opis:
Automatic recognition of the signal modulation type turned out to be useful in many areas, including electronic warfare or surveillance. The wavelet transform is an effective way to extract signal features for identification purposes. In this paper there are M-ary ASK, M-ary PSK, M-ary FSK, M-ary QAM, OOK and MSK signals analysed. The mean value, variance and central moments up to five of continuous wavelet transform (CWT) are used as signal features. The principal component analysis (PCA) is applied to reduce a number of features. A multi-layer neural network trained with backpropagation learning algorithm is considered as a classifier. There are two research variants: interclass and intraclass recognition with a wide range of signal-to-noise ratio (SNR).
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2016, 64, 1; 257-261
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 44

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