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EN
This work presents an original model for detecting machine tool anomalies and emergency states through operation data processing. The paper is focused on an elastic hierarchical system for effective data reduction and classification, which encompasses several modules. Firstly, principal component analysis (PCA) is used to perform data reduction of many input signals from big data tree topology structures into two signals representing all of them. Then the technique for segmentation of operating machine data based on dynamic time distortion and hierarchical clustering is used to calculate signal accident characteristics using classifiers such as the maximum level change, a signal trend, the variance of residuals, and others. Data segmentation and analysis techniques enable effective and robust detection of operating machine tool anomalies and emergency states due to almost real-time data collection from strategically placed sensors and results collected from previous production cycles. The emergency state detection model described in this paper could be beneficial for improving the production process, increasing production efficiency by detecting and minimizing machine tool error conditions, as well as improving product quality and overall equipment productivity. The proposed model was tested on H-630 and H-50 machine tools in a real production environment of the Tajmac-ZPS company.
2
Content available IoT sensing networks for gait velocity measurement
EN
Gait velocity has been considered the sixth vital sign. It can be used not only to estimate the survival rate of the elderly, but also to predict the tendency of falling. Unfortunately, gait velocity is usually measured on a specially designed walk path, which has to be done at clinics or health institutes. Wearable tracking services using an accelerometer or an inertial measurement unit can measure the velocity for a certain time interval, but not all the time, due to the lack of a sustainable energy source. To tackle the shortcomings of wearable sensors, this work develops a framework to measure gait velocity using distributed tracking services deployed indoors. Two major challenges are tackled in this paper. The first is to minimize the sensing errors caused by thermal noise and overlapping sensing regions. The second is to minimize the data volume to be stored or transmitted. Given numerous errors caused by remote sensing, the framework takes into account the temporal and spatial relationship among tracking services to calibrate the services systematically. Consequently, gait velocity can be measured without wearable sensors and with higher accuracy. The developed method is built on top of WuKong, which is an intelligent IoT middleware, to enable location and temporal-aware data collection. In this work, we present an iterative method to reduce the data volume collected by thermal sensors. The evaluation results show that the file size is up to 25% of that of the JPEG format when the RMSE is limited to 0.5º.
EN
Extracting useful information from astronomical observations represents one of the most challenging tasks of data exploration. This is largely due to the volume of the data acquired using advanced observational tools. While other challenges typical for the class of big data problems (like data variety) are also present, the size of datasets represents the most significant obstacle in visualization and subsequent analysis. This paper studies an efficient data condensation algorithm aimed at providing its compact representation. It is based on fast nearest neighbor calculation using tree structures and parallel processing. In addition to that, the possibility of using approximate identification of neighbors, to even further improve the algorithm time performance, is also evaluated. The properties of the proposed approach, both in terms of performance and condensation quality, are experimentally assessed on astronomical datasets related to the GAIA mission. It is concluded that the introduced technique might serve as a scalable method of alleviating the problem of the dataset size.
EN
In the era of big data, solutions are desired that would be capable of efficient data reduction. This paper presents a summary of research on an algorithm for complementation of a Boolean function which is fundamental for logic synthesis and data mining. Successively, the existing problems and their proposed solutions are examined, including the analysis of current implementations of the algorithm. Then, methods to speed up the computation process and efficient parallel implementation of the algorithm are shown; they include optimization of data representation, recursive decomposition, merging, and removal of redundant data. Besides the discussion of computational complexity, the paper compares the processing times of the proposed solution with those for the well-known analysis and data mining systems. Although the presented idea is focused on searching for all possible solutions, it can be restricted to finding just those of the smallest size. Both approaches are of great application potential, including proving mathematical theorems, logic synthesis, especially index generation functions, or data processing and mining such as feature selection, data discretization, rule generation, etc. The problem considered is NP-hard, and it is easy to point to examples that are not solvable within the expected amount of time. However, the solution allows the barrier of computations to be moved one step further. For example, the unique algorithm can calculate, as the only one at the moment, all minimal sets of features for few standard benchmarks. Unlike many existing methods, the algorithm additionally works with undetermined values. The result of this research is an easily extendable experimental software that is the fastest among the tested solutions and the data mining systems.
5
Content available Analiza czynnikowa zdjęć wielospektralnych
PL
Analiza zdjęć wielospektralnych sprowadza się często do modelowania matematycznego opartego o wielowymiarowe przestrzenie metryczne, w których umieszcza się pozyskane za pomocą sensorów dane. Tego typu bardzo intuicyjne, łatwe do zaaplikowania w algorytmice analizy obrazu postępowanie może skutkować zupełnie niepotrzebnym wzrostem niezbędnej do analiz zdjęć mocy obliczeniowej. Jedną z ogólnie przyjętych grup metod analizy zbiorów danych tego typu są metody analizy czynnikowej. Wpracy tej prezentujemy dwie z nich: Principal Component Analysis (PCA) oraz Simplex Shrink-Wrapping (SSW). Użyte jednocześnie obniżają znacząco wymiar zadanej przestrzeni metrycznej pozwalając odnaleźć w danych wielospektralnych charakterystyczne składowe, czyli przeprowadzić cały proces detekcji fotografowanych obiektów. W roku 2014 w Pracowni Przetwarzania Danych Instytutu Lotnictwa oraz Zakładzie Ochrony Lasu Instytutu Badawczego Leśnictwa metodykę tą równie skutecznie przyjęto dla analizy dwóch niezwykle różnych serii zdjęć wielospektralnych: detekcji głównych składowych powierzchni Marsa (na podstawie zdjęć wielospektralnych pozyskanych w ramach misji EPOXI, NASA) oraz oszacowania bioróżnorodności jednej z leśnych powierzchni badawczych projektu HESOFF.
EN
Mostly, analysis of multispectral images employs mathematical modeling based on multidimensional metric spaces that includes collected by the sensors data. Such an intuitive approach easily applicable to image analysis applications can result in unnecessary computing power increase required by this analysis. One of the groups of generally accepted methods of analysis of data sets are factor analysis methods. Two such factor analysis methods are presented in this paper, i.e. Principal Component Analysis (PCA ) and Simplex Shrink - Wrapping (SSW). If they are used together dimensions of a metric space can be reduced significantly allowing characteristic components to be found in multispectral data, i.e. to carry out the whole detection process of investigated images. In 2014 such methodology was adopted by Data Processing Department of the Institute of Aviation and Division of Forest Protection of Forest Research Institute for the analysis of the two very different series of multispectral images: detection of major components of the Mars surface (based on multispectral images obtained from the epoxy mission, NASA) and biodiversity estimation of one of the investigated in the HESOFF project forest complexes.
EN
The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning process executed by a team of agents (A-Team). Several A-Team architectures with agents executing the simulated annealing and tabu search procedures are proposed and investigated. The paper includes a detailed description of the proposed approach and discusses the results of a validating experiment.
EN
The paper presents a powerful algorithm useful to solve the problem of experimental data values finding don't occur within the registered experimental data set by using the one-dimensional polynomial interpolation procedure via a cubic spline method. Described free algorithm is presented in the form of Matlab routine with short author's comments.
8
Content available Redukcja danych w diagnostycznych bazach danych
PL
W artykule przedstawiono problematykę związaną ze wspomaganiem procesu wnioskowania o stanie obiektu rzeczywistego. Głównym zagadnieniem jest redukcja olbrzymiej ilości danych dostarczanych do systemu monitorowania. Wyróżniono trzy grupy metod: ograniczania liczby rozpatrywanych cech, ograniczania w zbiorze rozróżnianych wartości oraz ograniczania liczby elementów wykresu wartości. W wyniku przeprowadzonej analizy danych zaproponowano metodę kwantowania z histerezą w celu redukcji liczby rozróżnianych wartości. Ponadto zaproponowano metodę optymalizacji szerokości pasma kwantowania z histerezą z użyciem testu statystycznego.
EN
The article present problems about computer aided machinery state reasoning. The main task of this issue was reduction of huge quantity data sets provides to monitoring system. The methods can be divided into methods for reduction of features, methods for reduction of measured data sets and time-domain methods. On the basis of data analysis, was proposed data set reduction by sampling with hysteresis to reduction of measured data sets. Moreover the method for a tolerable degree fit was proposed and analyzed. The method is based upon statistical analysis.
9
Content available remote Rough Sets Methods in Feature Reduction and Classification
EN
The paper presents an application of rough sets and statistical methods to feature reduction and pattern recognition. The presented description of rough sets theory emphasizes the role of rough sets reducts in feature selection and data reduction in pattern recognition. The overview of methods of feature selection emphasizes feature selection criteria, including rough set-based methods. The paper also contains a description of the algorithm for feature selection and reduction based on the rough sets method proposed jointly with Principal Component Analysis. Finally, the paper presents numerical results of face recognition experiments using the learning vector quantization neural network, with feature selection based on the proposed principal components analysis and rough sets methods.
PL
Praca dotyczy zagadnień redukcji i selekcji informacji w danych podawanych na wejście układu klasyfikatora. Artykuł zawiera omówienie zagadnień związanych z wykorzystaniem funkcji bazowych jako narzędzia wstępnej analizy danych. Przedstawiono skrótowe omówienia własności transformacji falkowej WT oraz jej uogólnionej wersji w postaci pakietowego przekształcenia falkowego WPT. Wskazano na wady i zalety transformacji wykorzystujących funkcje bazowe z punktu widzenia zagadnienia klasyfikacji danych pomiarowych. Wnioski z tej analizy pozwoliły na zaproponowanie nowego algorytmu wstępnej analizy danych nazwanego algorytmem LDBFS. Jest to rozwiązanie hybrydowe, które posiada własności metod wydobywania cech (zagadnienie analizy informacji zawartej w zbiorze danych pomiarowych) jak i metod selekcji cech (zagadnienie redukcji wymiaru wektora cech). Algorytm LDBFS pozwala na dowolne definiowanie cech nowych zbiorów danych powstających przez przekształcenie pierwotnych zbiorów cech. Dobierając odpowiednią dla danego zagadnienia funkcję matkę w transformacji falkowej oraz funkcję wyróżniania informacji można uzyskać efekt uwypuklenia wyłącznie danych istotnych z punktu widzenia dalszej analizy. Praca zawiera szczegółowy opis algorytmu LDBFS. Własności nowego algorytmu zostały zweryfikowane poprzez porównanie wyników klasyfikacji danych pochodzących z sonaru. Wykorzystano w tym celu dwie metody klasyczne, tj. algorytm M1NERR (selekcja cech), algorytm PCA (wyszukiwanie cech), oraz opracowany przez autorów algorytm LDBFS.
EN
The primary goal of the paper is to present the new data preprocessing method which allows the improvement of the classification process. That method, which we called LDBFS (Local Discriminant Bases with Feature Selection) is based on the wavelet packet transform. The LDBFS algorithm is a hybrid approach which consists of two main parts: feature extraction block and feature selection block. It reduces the dimensionality of data sets and maximizes a class separability for classification problems. Thanks to possibility to define the information discriminant function related to the data characteristics, the most significant features can be easily separated while the others, less important from the classification point of view, can be rejected. We tested our method using sonar dataset. That example show the superiority of our approach over classical methods (M1NERR and PCA). The comparison, presented in the paper, proves that LDBFS algorithm provide us with better insight of relationships between the essential features of inputs signals and corresponding outputs what tends to enhances the performance of any classifier.
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