Wyniki wyszukiwania - Biblioteka Nauki

1

Using Singular Value Decomposition (SVD) as a solution for search result clustering

100%

Abdulla H. D. , Abdelrahman A. S. , Snasel V.

Poznan University of Technology Academic Journals. Electrical Engineering

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2014

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tom No. 80

71--78

EN

There are many search engines in the web, but they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Singular Value Decomposition (SVD) as a very good solution for search results clustering. Results are presented by visualizing neural network. Neural network is responsive for reducing result dimension to two dimensional space and we are able to present result as a picture that we are able to analyze.

2

Extension of hyperspatial helical codes for visualization and correlative analysis of attribute-referenced multi-dimensional data

100%

Zaremba M. B. , Varma H. , St-Laurent L.

Machine Graphics and Vision

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2000

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tom Vol. 9, No. 1/2

331-340

EN

In order to address the growing data management and analysis needs of spatial applications, more research is required in the field of spatial data indexing to support efficient data access and visualization, as well as in the field of new processing strategies that will include topological relationships between the data. This paper introduces hyperspetial helical codes (HHCode), presents the visualization capabilities offered by the codes, and describes the software that has been developed to support viewing, editing and updating of spatial data sets in 3D. Integration of the HHCode with a pre-processing scheme is then proposed, based on self-organizing maps. This enhancement allows the user to discover and visualize topological relationship hidden in the data, and provides a tool for indexing generic multidimensional attribute-referenced data.

3

Cluster analysis on the example of work data of the National Power System. Part 1, Comparative study of methods and conditions

88%

Tchórzewski J. , Kania T.

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tom Vol. 1-2(23)

25--41

EN

The paper presents the results of the research on the comparative study of the methods of cluster analysis and conditions, which was carried out from the point of view of their use, on the example of data concerning the operation of the National Power System. Two algorithms were used for the clustering analysis, i.e. the Ward algorithm and the algorithm of self-organizing two-dimensional maps. Cluster analysis was preceded by a review of hierarchical and non-hierarchical methods of data analysis and a description of the prepared experiment. The obtained results were interpreted. The work consists of two parts published under the same main title with different subtitles. This part 1 presents the results of the conducted review of selected methods of cluster analysis and the research conditions resulting from the adopted data on the operation of the National Power System. Part 2 presents the cluster analysis process and selected research results and their discussion.

4

Building a cognitive map using an SOM2

88%

Tokunaga K. , Furukawa T.

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2010

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tom Vol. 4, No. 2

39-47

EN

In this paper, we propose a new method for building an environmental map in a self-organizing manner using visual information from a mobile robot. This method is based on a Higher Rank of Self-Organizing Map (SOM ), in which Kohonen’s SOM is extended to create a map of data distributions (set of manifolds). It is expected that the “SOM” is capable of creating an environmental map in a self-organizing manner from visual information, since the set of visual information obtained from each position in the environment forms a manifold at every position. We also show the effectiveness of the proposed method.

5

Programmable, Asynchronous, Triangular Neighborhood Function for Self-Organizing Maps Realized on Transistor Level

88%

Kolasa M. , Długosz R. , Bieliński K.

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tom Vol. 56, No. 4

367-373

EN

A new hardware implementation of the triangular neighborhood function (TNF) for ultra-low power, Kohonen self-organizing maps (SOM) realized in the CMOS 0.18žm technology is presented. Simulations carried out by means of the software model of the SOM show that even low signal resolution at the output of the TNF block of 3-6 bits (depending on input data set) does not lead to significant disturbance of the learning process of the neural network. On the other hand, the signal resolution has a dominant influence on the overall circuit complexity i.e. the chip area and the energy consumption. The proposed neighborhood mechanism is very fast. For an example neighborhood range of 15 a delay between the first and the last neighboring neuron does not exceed 20 ns. This in practice means that the adaptation process starts in all neighboring neurons almost at the same time. As a result, data rates of 10-20 MHz are achievable, independently on the number of neurons in the map. The proposed SOM dissipates the power in-between 100 mW and 1 W, depending on the number of neurons in the map. For the comparison, the same network realized on PC achieves in simulations data rates in-between 10 Hz and 1 kHz. Data rate is in this case linearly dependend on the number of neurons.

6

Rozpoznawanie wzorców emisji spalin w pomiarach PEMS za pomocą sieci Kohonena (SOM)

75%

Merkisz J. , Barczak A. , Pielecha J.

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tom nr 4

PL

Rozwój technik pomiarowych PEMS sprzyja badaniom ekologiczności pojazdów w rzeczywistych warunkach pracy. Interpretacja wyników pomiarów, na przykład emisji zanieczyszczeń, reprezentowanych bardzo licznymi zbiorami różnorodnych danych wymaga przeprowadzenia złożonych analiz numerycznych. Wyrafinowane metody statystyczne są skuteczne, lecz interpretacja wyników wymaga udziału eksperta o bardzo specjalistycznej wiedzy. Stosowanie metod data mining stwarza szerokie perspektywy i zwiększa zdecydowanie możliwości w zakresie analizy i interpretacji wyników eksperymentu. W artykule przeprowadzono badania transformacji wielolicznego zestawu uczącego uzyskanego z pomiarów do mało licznego zestawu neuronów sieci Kohonena. Dla wyuczonej sieci Kohonena przeprowadzono badania dotyczące rozpoznawania zadanych wzorców stanu pojazdu pomierzonych w trakcie eksperymentu drogowego.

EN

The improvement of PEMS enables better estimation of ecological quality of vehicles. However, interpretation of divers sort of data, e.g. exhaust emissions, gathered during on road testing requires complex numerical analyses. Advanced statistical analyses have already proven their applicability in this domain but the interpretation of the results requires the participation of experts with extended knowledge. The usage of the data mining methods brings the perspective in the ease of interpretation of obtained results. In this article, the Self-Organizing Maps were used to transform the multiplicity of data gathered during the on road experiment into the reduced set of representative data. Trained SOM was tested in the recognition of the vehicle states measured during the on road experiment.

7

A comparison of the neural gas and self organizing map methods for next day load curve forecasting

75%

Dudek G.

Przegląd Elektrotechniczny

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2009

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tom R. 85, nr 3

153-156

EN

Both the neural gas and self organizing map clustering methods are used in the short-term load forecasting. Two approaches, based on the similarity of the load sequence patterns, are presented in the paper. Patterns preceding the forecast moment and the patterns of forecast, which are concatenated and then divided into clusters, are used in the first model. The empirical probabilities, that the forecast pattern is associated to cluster j while the corresponding input pattern is associated to cluster i, are computed and applied to the forecast construction in the second approach.

PL

Gaz neuronowy i samoorganizujące się odwzorowanie jako metod grupowania użyto do krótkoterminowego prognozowania obciążeń elektroenergetycznych. Zaprezentowano dwa podejścia oparte na podobieństwie obrazów sekwencji obciążeń. Pierwszy model używa obrazów poprzedzających moment prognozy i obrazów prognoz, które są połączone i pogrupowane. W drugim podejściu obliczane są empiryczne prawdopodobieństwa, że obraz prognozy należy do grupy j gdy skojarzony z nim obraz wejściowy należy do grupy i. Prawdopodobieństwa te wykorzystuje się do konstrukcji prognozy.

8

Evaluation of the diference in water quality between urban and suburban rivers based on self organizing map

75%

Jiang Y. , Gui H. , Li C. , Chen J. , Chen C. , Wang C. , Zhao H. , Guo Y. , Xu J. , Li J. , Qiu H.

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tom Vol. 69, no. 5

1855--1864

EN

Urban rivers play an important role in maintaining the urban aquatic ecological environment, and there are bound to be differences in the water environment quality and pollution sources due to different locations of urban rivers. Therefore, this paper selects the urban river (Tuo River) and the suburban river (Bian River) in Suzhou City, Anhui, China, as the research objects. Based on the understanding of the hydrogeochemical characteristics of these two rivers, the self-organizing map is used to identify the main control factors that affect the water quality of the two rivers. The results showed that both the Bian river and Tuo river were weakly alkaline. The average content of conventional ions in Tuo river is less than that of Bian river (except HCO3 −); the water of Bian river was of Na–SO4–Cl type, and the water of Tuo river was mainly of Na–HCO3 type, with the minority was of Na–SO4–Cl type; Silicate weathering is an important source of conventional ions in the water of these two rivers; agricultural non-point source pollution is the main source of pollutants in Bian river, while Tuo river was mainly affected by natural factors, and human activities had little impact.

9

Towards Assessment of Innovativeness Economy Determinant Correlation: the Double Self-Organizing Feature Map Approach

75%

Czyżewska M. , Szkoła J. , Pancerz K.

Fundamenta Informaticae

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2014

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tom Vol. 129, nr 1/2

37--48

EN

In the paper, we attempt to identify the crucial determinants of innovativeness economy and the correlations between the determinants. We based our research on the Innovativeness Union Scoreboard (IUS) dataset. In order to solve the problem, we propose to use the Double Self-Organizing Feature Map (SOM) approach. In the first step, countries, described by determinants of innovativeness economy, are clustered using SOMs according to five year time series for each determinant separately. In the second step, results of the first step are clustered again using SOM to obtain the final correlation represented in the form of a minimal spanning tree. We propose some modifications of the clustering process using SOMs to improve classification results and efficiency of the learning process.

10

Response of the rotifer community to human-induced changes in the trophic state of a reservoir

75%

Kim S.-K. , Kim J.-C. , Joo J.-C. , Choi J.-Y.

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tom Vol. 49, No. 4

329--344

EN

Human disturbance and nutrient runoff lead to water pollution, particularly in downstream waters and reservoirs. We hypothesized that increased human activity in summer would affect the trophic state of downstream reservoirs, affecting the interannual species composition of rotifers. We used long-term data for the Unmun Reservoir in South Korea (2009–2015), which is increasingly affected by human activity. The interannual variation of nitrogen and phosphorus levels was higher in summer and autumn, resulting in eutrophication. This led to a change in species composition of rotifers. Anuraeopsis fissa, Brachionus calyciflorus and Trichocerca gracilis were abundant in the most eutrophic state, while high densities of Ascomorpha ovalis and Ploesoma hudsoni were observed when nutrient concentrations were lower. The trophic state changes in the Unmun Reservoir were largely attributed to summer human activity in tributary streams. Our study location is typical of the stream network in South Korea and we assume that similar trophic state changes in reservoirs will be common. Changes in the density and species diversity of rotifers due to eutrophication indicate the need for active management and conservation, including the restriction of human activity around streams.

11

Image Retrieval Based on Text and Visual Content Using Neural Networks

63%

Castro D. A. , Seijas L. M.

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2010

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tom Vol. 2 No. 1

21--39

EN

In the last few years there has been a dramatic increase in the amount of visual data to be searched and retrieved. Typically, images are described by their textual content (TBIR) or by their visual features (CBIR). However, these approaches still present many problems. The hybrid approach was recently introduced, combining both characteristics to improve the benefits of using text and visual content separately. In this work we examine the use of the Self Organizing Maps for content-based image indexing and retrieval. We propose a scoring function which eliminates irrelevant images from the results and we also introduce a SOM variant (ParBSOM) that reduces training and retrieval times. The application of these techniques to the hybrid approach improved computational results.

12

Classification of Different-Sized Aerosol Monitoring Data

63%

Tsakovski S. , Simeonov V.

Ecological Chemistry and Engineering. A

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2011

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tom Vol. 18, nr 2

275-288

EN

The present study deals with the application of self-organizing maps (SOM) of Kohonen for the classification of aerosol monitoring data sets from two sampling points (Arnoldstein and Unterloibach) located close to the border between Austria and Slovenia. The goal of the chemometric data treatment was to find some specific patterns in the classification maps for five different aerosol fractions collected in four different seasons of the year. The results obtained indicated a distinct separation of the ultrafine particles (PM 0.01–PM 0.4) from the other fractions which underlines their specific effect on human health. Seasonal separation but only between summer and winter sampling is also observed.

PL

Przedstawiono wyniki badań monitoringowych próbek aerozolu atmosferycznego pobranych z dwóch punktów pomiarowych (Arnoldstein i Unterloibach) z pobliża granicy między Austrią i Słowenią. Dane zinterpretowano z wykorzystaniem samoorganizujących się map (SOM) Kohonena. Celem chemometrycznej interpretacji danych było znalezienie charakterystycznych struktur na mapach klasyfikacji dla pięciu różnych frakcji aerozoli, zebranych w czterech różnych porach roku. Uzyskane wyniki wskazują na wyraźne oddzielenie najdrobniejszych cząstek (PM 0,01 – PM 0,4) od innych frakcji, co wskazuje na ich specyficzne działanie na zdrowie człowieka. Obserwuje się również zmiany sezonowe, ale tylko między próbkami pobranymi latem i zimą.

13

Zastosowanie sieci samoorganizującej do identyﬁkacji struktur odmienionych węgla kamiennego

63%

Skiba M. , Młynarczuk M.

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2016

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tom T. 18, nr 3

61--66

PL

Wewnętrzna budowa węgla, możliwa do obserwacji wyłącznie pod mikroskopem, może wykazywać pewne cechy (takie jak: obecność spękań, struktury kataklastyczne czy mylonityczne), które wpływają na zwiększoną pojemność gazową oraz wskazują na pokład szczególnie zagrożony wyrzutami gazów i skał. Problematyka ta była przedmiotem zainteresowania licznych badaczy, którzy dokonali klasyfikacji węgla odmienionego, wyróżniając różne typu strukturalne takiego węgla. W pracy do identyfikacji poszczególnych struktur zastosowano mapę samoorganizującą (SOM). Może ona posłużyć do ujawnienia takich cech w zbiorze danych, które są często niedostrzegalne w wypadku zastosowania sieci neuronowej uczonej z nauczycielem. Badania wykonane zostały na zdjęciach mikroskopowych, a każdą z analizowanych klas opisano za pomocą 7-wymiarowej przestrzeni cech. Zastosowanie sieci samoorganizującej skutkowało klasyfikacją badanych struktur na poziomie 82% skuteczności.

EN

The internal structure of the coal, observable microscopically only, may have certain features (such as the presence of cracks, cataclastic or mylonitic structures) that affect the increased gas capacity and point to the seams particularly endangered by gas and rock outbursts. The issue was the subject of interest for many researchers who have made a classifi cation of structurally altered coal, distinguishing different types of such coal structure. In this paper, individual structures were identified using self-organizing map (SOM). It can be used to reveal such features in the data set, which are often invisible in the case of the use of neural network learning with a teacher. Tests were performed on microscopic photographs, each of the analyzed grades were described using a 7-dimensional feature space. The use of a self-organizing map resulted in the effectiveness of the classification of these structures at the level of 82%.

14

Zastosowanie samoorganizujących się map cech w diagnostyce silników o zapłonie samoczynnym

63%

Klimkiewicz M.

Inżynieria Rolnicza

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2008

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tom R. 12, nr 7(105)

101-108

PL

Wykorzystano właściwości samoorganizujących się map cech w wykrywaniu uszkodzeń silników z zapłonem samoczynnym. Zbudowano model, w którym zmiennymi wejściowymi są symptomy zaobserwowane przez użytkownika wskazujące na niewłaściwą pracę silnika oraz sprawdzenia i pomiary wykonane przez mechanika. Za pomocą mapy topologicznej zlokalizowano podobne skupienia przypadków. Neuronom radialnym mapy nadano etykiety zgodne z nazwami mogących się pojawić usterek.

EN

The researchers made use of self-organizing properties of maps of characteristics in detecting defects of self-ignition engines. A model was developed with the following input variables: the symptoms observed by user that indicate abnormal engine work, and checks and measurements carried out by a mechanic. Similar concentrations of clusters were located using a topological map. Radial neurons in the map were marked with labels consistent with names of defects, which may possibly occur.

15

Application of self-organizing maps in visualization of multidimensional pareto fronts

63%

Schlieter T.

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tom Vol. 15, Nr 1

75--87

EN

The aim of this study is to show the application of Kohonen’s self-organizing maps for the purpose of visualization. Multi-dimensional sets of solutions of test functions for optimization and real multiobjective optimization problems are presented using self-organizing maps and traditional methods for comparison.

PL

Celem pracy jest przedstawienie zastosowania samoorganizujących map Kohonena do wizualizacji. Wielowymiarowe zbiory niezdominowanych rozwiązań testowych funkcji optymalizacji oraz rzeczywistych problemów optymalizacji wielokryterialnej są przedstawione z wykorzystaniem samoorganizujących map oraz tradycyjnych metod dla porównania.

16

Tailwater habitat stability after dam removal and return to a natural hydrological regime

63%

Tszydel M. , Kruk A.

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tom Vol. 44, No. 3

410--425

EN

The study was conducted from 2000 to 2003 in the tailwater of the Drzewieckie Lake, an artificial reservoir in Central Poland. Short-term peaks in water flow were generated for the purpose of the operation of a whitewater slalom canoeing track built just downstream of the dam. In 2002, the reservoir was drawn down. The patterns in habitat samples were recognized with a Kohonen’s unsupervised artificial neural network (SOM). The SOM spatial gradient was stronger than the SOM temporal gradient, which shows that the removal of the studied dam did not have a destructive impact on habitats’ features, as shown in other studies, and that the patchy nature of the riverbed has been maintained. The complete emptying of the Drzewieckie Lake took place at the beginning of the vegetation season, which allowed plants to cover the exposed bottom of the reservoir and, consequently, reduce the downstream flow of organic matter accumulated there. Patterns in the displacement of aquatic macrophytes, inorganic substratum and different fractions of particulate organic matter are discussed. The amount of dissolved oxygen decreased because of the lack of intensive water discharge from the reservoir into the river, which would result in high water turbulence. Results of this study are important for planning the ecologically sound dam removals.

17

Stick-slip modelling by SOM (self-organising map): towards the intelligent design and selection of friction surface and material in machine tool design

51%

Naude J. , Nel N. , Kruger J.

Prace Naukowe Instytutu Technologii Maszyn i Automatyzacji Politechniki Wrocławskiej. Konferencje

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2000

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tom Vol. 76, nr 35

69-80

EN

In the designing of machine tools the absence of a detailed design procedure for selecting friction material on slideways often results in the stick-slip phenomena causing tolerance defects and undesirable motion properties. The paper describes the use of self-organising maps (SOM) to capture the tendencies of the stick-slip phenomena, according to predefined parameters, from experimental data. These tendencies are presented graphically to the designer through which the designer can analyse and predict the stick-slip properties for a given slideway set-up. Due to the intelligent data grouping properties of the SOM the graphical interface produces areas of "acceptable" and "unacceptable" stick-slip properties according to the designer's stick-slip criteria for the current slideway design. The above results in the correct friction material selection and slideway surface finish for a certain stress on the friction material. The system tested in industry proved to be very successful and is currently under further development to include an expert system for more detailed design by various graphical interfaces.

18

Application of SOM in classification of EGG signals

51%

Świerczyński Z. , Guszkowski T.

Metrology and Measurement Systems

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2006

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tom Vol. 13, nr 3

279-287

EN

The report presents problems associated with computer aided gastric diagnosis. The subject of the study are electrogastrographic (EGG) signals (non-invasively measured electrical signals generated by the human stomach). The signals were digitally recorded and then parametrized, with linear autoregressive models (AR). The data and parametrization method used in the study were the same as used by the authors in the previous study; therefore here they are only shortly described. The sets of numbers, obtained by these means, were treated as information vectors, and classified with the Self Organizing Map (SOM) classifier. The structure and parameters of the algorithm used for classification of the parametrized EGG data are described. The final efficiency of the whole system (SOM classifier with the parametrization method applied), reaching 80%, is promising. It is similar to the results of other classifiers. The ways to improve the effectiveness are also outlined.

PL

Praca przedstawia problemy związane z komputerowo wspomaganym diagnozowaniem układu pokarmowego. Obiektem badań są tutaj sygnały elektrogastrograficzne - EGG (nieinwazyjnie mierzone sygnały elektryczne generowane przez żołądek człowieka). Sygnały te zostały zarejestrowane cyfrowo a następnie poddane parametryzacji przy pomocy liniowego modelu autoregresyjnego AR. Dane oraz metoda parametryzacji użyta w przedstawionych badaniach zostały opisane w poprzednich pracach autorów, więc tutaj ujęte są jedynie w zarysie. Zestawy liczb otrzymane w wyniku parametryzacji potraktowane zostały jako wektor parametrów i sklasyfikowane przy pomocy klasyfikatora opartego na samoorganizujących się mapach (SOM). W pracy przedstawiono strukturę i parametry użytego algorytmu. Ostateczna skuteczność całego systemu (tj. klasyfikatora SOM oraz zastosowanej metody parametryzacji) wyniosła 80%, co jest wynikiem obiecującym i bardzo podobnym do tych jakie osiągnięto przy zastosowaniu innych metod klasyfikacji. Praca przedstawia również zarys metod poprawy efektywności opisanego systemu.

19

Identification of "hot-spot' imission events reflected in rain precipitation chemistry basing on ion chromatography analysis and self-organizing maps classification

51%

Astel A.

Archives of Environmental Protection

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2008

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tom Vol. 34, no. 4

3-12

EN

The present study deals with the application of self-organizing maps (SOM) to identification of "hot-spot" imission events reflected in bulk precipitation chemical profiles basing on ion chromatography analysis. An experiment was conducted in the period between January 1999 and December 2003 at the Dupniański Stream catchment (Silesian Beskid) area to collect both analytical measurements (Cl,NO_3, SO_4^2, NH_4, Na^+,K*, Ca^2*, Mg^2+, Fe, Mn and Zn), pH and meteorological parameters (prevailing wind direction). A classification of rainwater samples according to identification of strong imission events was performed basing on Kohonen's algorithm. SOM approach allows to identify strong, temporal impact of remote pollution sources located in the vicinity of the Polish - Czech Republic border and indicates cyclical impact of remote pollution sources located in highly industrially developed Katowice and Bełchatów regions.

PL

W pracy opisano możliwości zastosowania algorytmu samoorganizujących się map (SOM) do identyfikacji przypadków wyjątkowo wysokiej imisji odzwierciedlonych w profilach chemicznych opadów atmosferycznych wyznaczonych techniką chromatografii jonowej. Badania prowadzono w okresie od stycznia 1999 do grudnia 2003 na terenie zlewni Potoku Dupniańskiego (Beskid Śląski) gromadząc wyniki oznaczeń analitycznych (Cl, NO_3, SO_4^2, NH_4, Na*, K*, Ca^2*, Mg2+, Fe, Mn i Zn), pH oraz parametrów meteorologicznych (przeważający kierunek wiatru). Klasyfikację próbek opadów atmosferycznych w celu identyfikacji wysokich wartości imisji wykonano poprzez zastosowanie algorytmu Kohonena. Technika SOM umożliwiła wykrycie silnego, incydentalnego oddziaływania odległego źródła zanieczyszczeń zlokalizowanego w pobliżu granicy polsko-czeskiej i wskazuje na występowanie cyklicznego oddziaływania odległych źródeł zanieczyszczeń zlokalizowanych na terenach silnie uprzemysłowionych Katowic i Bełchatowa.

20

Probabilistic morphological modeling of hydrographic networks from satellite imagery using Self-Organizing Maps

51%

Zaremba M. , Palenichka R.

Control and Cybernetics

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2002

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tom Vol. 31, no 2

343-369

EN

Adequate and concise representation of the shape of irregular objects from satellite imagery is a challenging problem in remote sensing. The conventional methods for cartographic shape representation are usually inaccurate and will provide only a rough shape description if the description process is to be fully automated. The method for automatic cartographic description of water basins presented in this paper is based on Self-Organizing Maps (SOM) - a class of neural networks with unsupervised learning. So-called structured SOM with local shape attributes such as scale and local connections of vertices are proposed for the description of object shape. The location of each vertex of piecewise linear generating curves that represent skeletons of the objects corresponds to the position of a particular SOM unit. The proposed method makes it possible to extract the object skeletons and to reconstruct the planar shapes of sparse objects based on the topological constraints of generating lines and the estimation of local scale. A context-dependent vertex connectivity test is proposed to enhance the skeletonization process. The test is based on the Markov random chain model of vertices belonging to the same generating line and the Bayesian decision-making principle. The experimental test results using Landsat-7 images demonstrate the accuracy of the proposed approach and its potential for fully automated mapping of hydrological objects.