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Tytuł:
Vehicles Classification Using the HRBF Neural Network
Klasyfikacja pojazdów z wykorzystaniem sieci neuronowej HRBF
Autorzy:
Wantoch-Rekowski, R.
Powiązania:
https://bibliotekanauki.pl/articles/305921.pdf
Data publikacji:
2011
Wydawca:
Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
Tematy:
sieci neuronowe
klasyfikacja sieci
zbiór uczący
Hyper Radial Basis Function network HRBF
neural networks
networks classification
learning set
HRBF
Opis:
The paper presents the problem of using a neural network for military vehicle classification on the basis of ground vibration. One of the main elements of the system is a unit called the geophone. This unit allows to measure the amplitude of ground vibration in each direction for a certain period of time. The value of the amplitude is used to fix the characteristic frequencies of each vehicle. If we want to fix the main frequency it is necessary to use the Fourier transform. In this case the fast Fourier transform FFT was used. Since the neural network (Hyper Radial Basis Function network) was used, a learning set has to be prepared. Please find the attached results of using the HRBF neural network, which include: examples of learning, validation and test sets, the structure of the networks and the learning algorithm, learning and testing results.
W opracowaniu przedstawiono zagadnienie wykorzystania sieci neuronowej do klasyfikacji określonych typów pojazdów na podstawie analizy amplitudy drgań gruntu. Jednym z elementów systemu do pomiaru amplitudy drgań gruntu jest geofon. Umożliwia on pomiar amplitudy drgań gruntu w wybranym kierunku dla określonego przedziału czasu. Wartość wyznaczonej amplitudy wykorzystywana jest do wyznaczenia charakterystycznych częstotliwości drgań dla poszczególnych pojazdów. Do wyznaczenia charakterystycznych częstotliwości wykorzystywana jest transformata Fouriera FFT. Do klasyfikacji wykorzystana została sieć neuronowa z radialną funkcją aktywacji, dlatego też wymagane jest przygotowanie odpowiedniego zbioru uczącego. W opracowaniu przedstawiono wyniki użycia sieci HRBF. Przedstawiono strukturę oraz zawartość zbioru uczącego.
Źródło:
Biuletyn Instytutu Systemów Informatycznych; 2011, 7; 47-52
1508-4183
Pojawia się w:
Biuletyn Instytutu Systemów Informatycznych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Object classification with artificial neural networks : A comparative analysis
Autorzy:
Domeradzki, Kornel
Niewiadomski, Artur
Powiązania:
https://bibliotekanauki.pl/articles/1819259.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
object classification
neural networks
convolutional neural networks
residual neural networks
Opis:
Object classification is a problem which has attracted a lot of research attention in recent years. Traditional approach to this problem is built on a shallow trainable architecture that was meant to detect handcrafted features. That approach works poorly and introduces many complications in situations where one is to work with more than a couple types of objects in an image with a large resolution. That is why in the past few years convolutional and residual neural networks have experienced a tremendous rise in popularity. In this paper, we provide a review on topics related to artificial neural networks and a brief overview of our research. Our review begins with a short introduction to the topic of computer vision. Afterwards we cover briefly the concepts of neural networks, convolutional and residual neural networks and their commonly used models. Then we provide a comparative performance analysis of the previously mentioned models in a binary and multi-label classification problem. Finally, multiple conclusions are drawn, which are to serve as guidelines for future computer vision systems implementations.
Źródło:
Studia Informatica : systems and information technology; 2019, 1-2(23); 43--56
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of Social Network Inferred Data to Churn Modeling in Telecoms
Autorzy:
Gruszczyński, W.
Arabas, P.
Powiązania:
https://bibliotekanauki.pl/articles/308695.pdf
Data publikacji:
2016
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
churn reduction
classification
social networks
Opis:
The subject of this work is the use of social network analysis to increase the effectiveness of methods used to predict churn of telephony network subscribers. The social network is created on the basis of operational data (CDR records). The result of the analysis is customer segmentation and additional predictor variables. Proposed hybrid predictor employs set of regression models tuned to specific customer segments. The verification was performed on data obtained from one of the Polish operators.
Źródło:
Journal of Telecommunications and Information Technology; 2016, 2; 77-86
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A simple vehicle classification framework for wireless audio-sensor networks
Autorzy:
Malhotra, B.
Nikolaidis, I.
Harms, J.
Powiązania:
https://bibliotekanauki.pl/articles/308519.pdf
Data publikacji:
2008
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
sensor networks
vehicle classification
acoustic signals
Opis:
Vehicle tracking is one of the important applications of wireless sensor networks. We consider an aspect of tracking: the classification of targets based on the acoustic signals produced by vehicles. In this paper, we present a naive classifier and simple distributed schemes for vehicle classification based on the features extracted from the acoustic signals. We demonstrate a novel way of using Aura matrices to create a new feature derived from the power spectral density (PSD) of a signal, which performs at par with other existing features. To benefit from the distributed environment of the sensor networks we also propose efficient dynamic acoustic features that are low on dimension, yet effective for classification. An experimental study has been conducted using real acoustic signals of different vehicles in an urban setting. Our proposed schemes using a na¨ive classifier achieved highly accurate results in classifying different vehicles into two classes. Communication and computational costs were also computed to capture their trade-off with the classification quality.
Źródło:
Journal of Telecommunications and Information Technology; 2008, 1; 43-50
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Kohonen self-organizing maps for symbolic objects
Samoorganizujące się mapy Kohonena dla obiektów symbolicznych
Autorzy:
Dudek, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/907030.pdf
Data publikacji:
2008
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
Classification
visualization
symbolic data
neural networks
Opis:
Visualizing data in the form of illustrative diagrams and searching, in these diagrams, for structures, clusters, trends, dependencies etc. is one of the main aims of multivariate statistical analysis. In the case of symbolic data (e.g. data in form of: single quantitative value, categorical values, intervals, multi-valued variables, multi-valued variables with weights), some well-known methods are provided by suitable 'symbolic' adaptations of classical methods such as principal component analysis or factor analysis. An alternative visualization of symbolic data is obtained by constructing a Kohonen map. Instead of displaying the individual items k = 1,..., n by n points or rectangles in a two dimensional space, the n items are first clustered into a number m of mini-clusters and then these mini-clusters are assigned to the vertices of a rectangular lattice of points in the plane such that 'similar' clusters are represented by neighbouring vertices in the lattice.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 2008, 216
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Approach to classifying data with highly localized unmarked features using neural networks
Autorzy:
Grzeszczuk, Rafał
Powiązania:
https://bibliotekanauki.pl/articles/305688.pdf
Data publikacji:
2019
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
classification
neural networks
medical image analysis
Opis:
To face the increasing demand of quality healthcare, cutting-edge automation technology is being applied in demanding areas such as medical imaging. This paper proposes a novel approach to classification problems on datasets with sparse highly localized features. It is based on the use of a saliency map in the amplification of features. Unlike previous efforts, this approach does not use any prior information about feature localization. We present an experimental study based on the Diabetic Retinopathy classification problem, in which our method has shown to achieve an over 20%-higher accuracy in solving a two-class Diabetic Retinopathy classification problem than a naive approach based solely on residual neural networks. The dataset consists of 35,120 images of various qualities, inconsistent resolutions, and aspect ratios.
Źródło:
Computer Science; 2019, 20 (3); 329-342
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic Fault Classification for Journal Bearings Using ANN and DNN
Autorzy:
Narendiranath Babu, T.
Aravind, A.
Rakesh, A.
Jahzan, M.
Rama Prabha, D.
Ramalinga Viswanathan, M.
Powiązania:
https://bibliotekanauki.pl/articles/177579.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
journal bearing
fault classification
artificial neural networks
deep neural networks
Opis:
Journal bearings are the most common type of bearings in which a shaft freely rotates in a metallic sleeve. They find a lot of applications in industry, especially where extremely high loads are involved. Proper analysis of the various bearing faults and predicting the modes of failure beforehand are Essentials to increase the working life of the bearing. In the current study, the vibration data of a journal Bering in the healthy condition and in five different fault conditions are collected. A feature extraction metod is employed to classify the different fault conditions. Automatic fault classification is performed using artificial neural networks (ANN). As the probability of a correct prediction goes down for a higher number of faults in ANN, the method is made more robust by incorporating deep neural networks (DNN) with the help of autoencoders. Training was done using the scaled conjugate gradient algorithm and the performance was calculated by the cross entropy method. Due to the increased number of hidden layers in DNN, it is possible to achieve a high efficiency of 100% with the feature extraction method.
Źródło:
Archives of Acoustics; 2018, 43, 4; 727-738
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of EMD ANN and DNN for Self-Aligning Bearing Fault Diagnosis
Autorzy:
Narendiranath, B. T.
Aravind, A.
Rakesh, A.
Jahzan, M.
Rama, P. D.
Powiązania:
https://bibliotekanauki.pl/articles/176889.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
self-aligning bearing
fault classification
artificial neural networks
deep neural networks
Opis:
Self-aligning roller bearings are an integral part of the industrial machinery. The proper analysis and prediction of the various faults that may happen to the bearing beforehand contributes to an increase in the working life of the bearing. This study aims at developing a novel method for the analysis of the various faults in self-aligning bearings as well as the automatic classification of faults using artificial neural network (ANN) and deep neural network (DNN). The vibration data is collected for six different faults as well as for the healthy bearing. Empirical mode decomposition (EMD) followed by Hilbert Huang transform is used to extract instantaneous frequency peaks which are used for fault analysis. Time domain and time-frequency domain features are then extracted which are used to implement the neural networks through the pattern recognition tool in MATLAB. A comparative study of the outputs from the two neural networks is also performed. From the confusion matrix, the efficiency of the ANN has been found to be 95.7% and using DNN has been found to be 100%.
Źródło:
Archives of Acoustics; 2018, 43, 2; 163-175
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Proposition of Applying k-Means Classification Method and the SOM Type Neural Network to Improve the Efficiency of Small Domains Estimation in a Representative Study of Small and Medium-Sized Enterprises
Propozycja zastosowania metody klasyfikacji k-średnich oraz sieci neuronowej typu SOM do poprawy efektywności estymacji dla małych domen w reprezentacyjnym badaniu małych i średnich przedsiębiorstw
Autorzy:
Jurkiewicz, Tomasz
Najman, Krzysztof
Powiązania:
https://bibliotekanauki.pl/articles/904708.pdf
Data publikacji:
2005
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
small domain estimation
classification methods
neural networks
Opis:
Problem zbyt małej liczby obserwacji w próbie, reprezentującej określoną domenę populacji, może być rozwiązany między innymi poprzez zastosowanie takich estymatorów, które do szacowania parametrów w określonej supopulacji (małym obszarze, domenie) mogłyby wykorzystać informacje o innych jednostkach w próbie, które pochodzą spoza określonej części populacji. Jedna z metod estymacji dla małych domen zwana estymacją syntetyczną zakłada, że rozkład w badanej małej domenie jest identyczny z rozkładem całej populacji. Założenie to pozostaje zazwyczaj niespełnione, zwłaszcza w przypadku specyficznych domen, co skutkuje dużymi błędami estymacji. Autorzy przedstawiają propozycję dwuetapowego procesu estymacji. W pierwszym etapie za pomocą sieci neuronowych typu SOM oraz za pomocą metody klasyfikacji k-średnich określa się podobieństwa jednostek należących do małej domeny do jednostek z pozostałej części próby. Drugim krokiem jest wykorzystanie w estymacji, za pomocą odpowiednio skonstruowanych wag, informacji tylko z tych domen, które są podobne do badanej małej domeny. Autorzy przedstawiają rezultaty zastosowania podanej procedury w analizie branży budowlanej na podstawie wyników reprezentacyjnego badania małych i średnich przedsiębiorstw. Podjęli także próbę oszacowania błędów tak zmodyfikowanej metody estymacji syntetycznej.
The problem of a too small number of observations of a sample, representing a defined domain of a population may be solved inter alia thanks to the application of estimators which would use information about other components of the sample (derived from outside the defined part of the population) to estimate parameters in a given subpopulation (small area, domain). One of estimation methods for small domains - the synthetic estimation - assumes, that the distribution of the studied small domain is identical with the distribution of the whole population. This assumption remains usually unfulfilled, in particular in case of specific domains, what results in large estimation errors. The authors present a proposition of two-stage estimation process. In the first stage, using the SOM-type neural networks and using the k-means classification method the similarity of components belonging to the small domain with the components belonging to the remaining part of the sample is determined. The second step consists in using the information only from those domains, which are similar to the studied small domain with the help of appropriately construed weights. Authors present the results of the above procedure in the analysis of the building industry on the basis of a representative study of small and medium-sized enterprises. They have also undertaken an attempt to estimate the errors of the synthetic estimation method modified in such a way.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 2005, 194
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modified neuro-fuzzy TSK network and its application in electronic nose
Autorzy:
Osowski, S.
Brudzewski, K.
Tran-Hoai, L.
Powiązania:
https://bibliotekanauki.pl/articles/201226.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neuro-fuzzy TSK networks
fuzzy clusterization
regression
classification
Opis:
The paper develops the modified structure of the Takagi-Sugeno-Kang neuro-fuzzy network with a theoretical basis for its adaptation. The simplified structure follows from the basic theoretical considerations concerning the way of creating the inference rules. The important point of this solution is the application of the fuzzy clustering algorithm to the input data. The efficiency of the proposed solution has been checked on the examples of regression and classification problems concerning the electronic nose.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 3; 675-680
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Single-ended quality measurement of a music content via convolutional recurrent neural networks
Autorzy:
Organiściak, Kamila
Borkowski, Józef
Powiązania:
https://bibliotekanauki.pl/articles/1849158.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
audio data analysis
artefacts detection
convolutional neural networks
recurrent neural networks
classification model
Opis:
The paper examines the usage of Convolutional Bidirectional Recurrent Neural Network (CBRNN) for a problem of quality measurement in a music content. The key contribution in this approach, compared to the existing research, is that the examined model is evaluated in terms of detecting acoustic anomalies without the requirement to provide a reference (clean) signal. Since real music content may include some modes of instrumental sounds, speech and singing voice or different audio effects, it is more complex to analyze than clean speech or artificial signals, especially without a comparison to the known reference content. The presented results might be treated as a proof of concept, since some specific types of artefacts are covered in this paper (examples of quantization defect, missing sound, distortion of gain characteristics, extra noise sound). However, the described model can be easily expanded to detect other impairments or used as a pre-trained model for other transfer learning processes. To examine the model efficiency several experiments have been performed and reported in the paper. The raw audio samples were transformed into Mel-scaled spectrograms and transferred as input to the model, first independently, then along with additional features (Zero Crossing Rate, Spectral Contrast). According to the obtained results, there is a significant increase in overall accuracy (by 10.1%), if Spectral Contrast information is provided together with Mel-scaled spectrograms. The paper examines also the influence of recursive layers on effectiveness of the artefact classification task.
Źródło:
Metrology and Measurement Systems; 2020, 27, 4; 721-733
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Semiautomatic land cover mapping according to the 2nd level of the CORINE Land Cover legend
Autorzy:
Golenia, M.
Zagajewski, B.
Ochtyra, A.
Hościło, A.
Powiązania:
https://bibliotekanauki.pl/articles/92466.pdf
Data publikacji:
2015
Wydawca:
Oddział Kartograficzny Polskiego Towarzystwa Geograficznego
Tematy:
classification
Corine Land Cover
Landsat
artificial neural networks
Warsaw
Opis:
Actual land cover maps are a very good source of information on present human activities. It increases value of actual spatial databases and it is a key element for decision makers. Therefore, it is important to develop fast and cheap algorithms and procedures of spatial data updating. Every day, satellite remote sensing deliver vast amount of new data, which can be semi-automatically classified. The paper presents a method of land cover classification based on a fuzzy artificial neural network simulator and Landsat TM satellite images. The latest CORINE Land Cover 2012 polygons were used as reference data. Three satellite images acquired 21 April 2011, 5 June 2010, 27 August 2011 over Warsaw and surrounding areas were processed. As an outcome of classification procedure, the maps, error matrices and a set of overall, producer and user accuracies and a kappa coefficient were achieved. The classification accuracy oscillates around 76% and confirms that artificial neural networks can be successfully used for forest, urban fabric, arable land, pastures, inland waters and permanent crops mapping. Low accuracies were obtained in case of heterogenic land cover units.
Źródło:
Polish Cartographical Review; 2015, 47, 4; 203-212
2450-6974
Pojawia się w:
Polish Cartographical Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie sieci neuronowych do klasyfikacji uszkodzeń maszyn wirujących
Application of neural networks to classification of malfunctions of rotating machinery
Autorzy:
Barszcz, T.
Powiązania:
https://bibliotekanauki.pl/articles/328828.pdf
Data publikacji:
2006
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
wibrodiagnostyka
sieć neuronowa
NARX
klasyfikacja
vibrodiagnostics
neural networks
classification
Opis:
Maszyny wirujące są najczęściej modelowane z dobrą dokładnością metodami liniowymi. Niektóre uszkodzenia tych maszyn mają jednak naturę nieliniową. Typowymi przykładami takich uszkodzeń są luzy posadowienia lub przytarcia wirnika. Zjawiska te powinny być wykrywane przy uwzględnieniu istnienia nieliniowości. Dobrą metodą modelowania takich nieliniowości jest NARX - metoda identyfikacji systemów nieliniowych. Osobnym problemem jest przejście od problemu modelowania zjawisk nieliniowych do wykorzystania takiego modelu do diagnostyki. Niniejszy artykuł prezentuje kilka możliwych podejść, a następnie proponuje metodę klasyfikacji opartą na sieciach NARX. Zaletą metody jest możliwość jej zastosowania do wykrywania nowych uszkodzeń, które nie były znane na etapie trenowania sieci. W końcowej części opisano zastosowanie metody do klasyfikacji nieliniowych uszkodzeń mierzonych na stanowisku badawczym.
Rotating machinery are most often modeled with good accuracy using linear methods. Some malfunctions, however, are of non-linear nature. Typical examples of those malfunctions are loose bearings and rotor rub. These phenomena can be detected taking nonlinearities into account. Good method to detect it is NARX - identification of nonlinear systems. Separate problem is the transition from modeling of nonlinear phenomena to using such models in diagnostics. The following paper presents few approaches and then proposes the method of classification based on NARX networks. The advantage of the method is the possibility to detect new failures, which were not known at the time of training the network. The last part of the paper describes application of the proposed method to the data acquired on the test rig.
Źródło:
Diagnostyka; 2006, 1(37); 107-113
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Ensemble of Statistical Metadata and CNN Classification of Class Imbalanced Skin Lesion Data
Autorzy:
Nayak, Sachin
Vincent, Shweta
Sumathi, K.
Kumar, Om Prakash
Pathan, Sameena
Powiązania:
https://bibliotekanauki.pl/articles/2055258.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
classification
Convolutional Neural Networks
Ensemble Learning
machine learning
metadata
Opis:
Skin Cancer is one of the most widely present forms of cancer. The correct classification of skin lesions as malignant or benign is a complex process that has to be undertaken by experienced specialists. Another major issue of the class imbalance of data causes a bias in the results of classification. This article presents a novel approach to the usage of metadata of skin lesions images to classify them. The usage of techniques addresses the problem of class imbalance to nullify the imbalances. Further, the use of a convolutional neural network (CNN) is proposed to finetune the skin lesion data classification. Ultimately, it is proven that an ensemble of statistical metadata analysis and CNN usage would result in the highest accuracy of skin color classification instead of using the two techniques separately.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 2; 251--257
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The SVM method as an instrument for the classification of vertical displacements
Autorzy:
Mrówczyńska, M.
Powiązania:
https://bibliotekanauki.pl/articles/106759.pdf
Data publikacji:
2015
Wydawca:
Politechnika Warszawska. Wydział Geodezji i Kartografii
Tematy:
neural networks
classification
vertical displacements
sieci neuronowe
klasyfikacja
przemieszczenia pionowe
Opis:
The article presents the basic rules for constructing and training neural networks called the Support Vector Machine method as well as possible applications for this kind of network. SVM networks are mainly used for solving tasks of classifying linearly and non-linearly separable data and regression. However, in recent years more applications have been found for them. The networks also solve such problems as the recognition of signals and images as well as speech identification.In this paper, non-linear SVM networks have been used for classifying linearly separable and non-separable data with a view to formulating a model of displacements of points in a measurement-control network. The points of the measurement-control network were placed on a civil engineering object located on expansive soil (linearly separable data) and represented a mining exploitation area (linearly non-separable data). The task of training SVM networks requires the use of quadratic programming in search of an optimum point of the Lagrangian function in relation to the parameters being optimised. In the case of linearly non-separable data, the SVM method makes it possible to find a hyperplane which classifies objects as correctly as possible, and at the same time is located possibly far away from concentrations typical of each class.
Źródło:
Reports on Geodesy and Geoinformatics; 2015, 98; 18-27
2391-8365
2391-8152
Pojawia się w:
Reports on Geodesy and Geoinformatics
Dostawca treści:
Biblioteka Nauki
Artykuł
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