Classification of water masses in the area investigated during the 1981 FIBEX Expedition and two winter expeditions at the "H. Arctowski" Station using the method of Empirical Orthogonal Functions (EOF) is presented. Four basic water masses (warm and cold Bellinghausen Sea surface waters, surface Weddell Sea waters, Circumpolar Warm Deep Water (CWDW) and the transitional zone) were observed in the area and a significant dependence of water masses distribution ón depth was found. A strong winter increase in the Weddell Sea waters influence was recorded.
This paper contains the results of phytosociological studies carried out on the model fragment of Spitsbergen tundra at Bellsund. In the area of 4800 m2 19 plant communities have been distinguished through association analysis and these communities, in turn, have been compared according to cluster analysis. Also, ecological groups of species have been distinguished.
Definition of a composite  describes an ideal composite material with perfect structure. In real composite materials, structure is usually imperfect – composites contain various types of defects [2, 3–5], especially as the casted composites are of concern. The reason for this is a specific structure of castings, related to course of the manufacturing process. In case of metal matrix composite castings, especially regarding these manufactured by saturation, there is no classification of these defects [2, 4]. Classification of defects in castings of classic materials (cast iron, cast steel, non-ferrous alloys) is insufficient and requires completion of specific defects of mentioned materials. This problem (noted during manufacturing metal matrix composite castings with saturated reinforcement in Institute of Basic Technical Sciences of Maritime University Szczecin) has become a reason of starting work aimed at creating such classification. As a result, this paper was prepared. It can contribute to improvement of quality of studied materials and, as a consequence, improve the environment protection level.
This investigation focuses on the modern Ukrainian surnames derived from a person’s appearance. The author analyzed different approaches to the systematization of such surnames that were applied in onomastic research, finding some differences in these classifications. For example, there is some controversy as to the scope (content) of this group. For example, some investigators do not include surnames derived from proper names of persons on the basis of their strength, health, clothing and so on as a part of this group surnames. On the other hand, some other researchers believe that it is necessary. Doubts have also been expressed as to the appropriacy of including some surnames which are derived from the names that described the gait of the person or their gender. Developing our approach to the classification of surnames derived from the proper names of persons on the basis of appearance, the author used the achievements of gabitology that uses characteristics of human appearance for the identification of the person. Fixed surnames were distributed according to the semantics of lexemes in the following subgroups: general physical (they can characterize the appearance of the person in general), anatomical (they can characterize body type, organs of the human body) and functional (characteristics of human appearance that are seen in motion). Surnames were also differentiated within each subgroup. For example, the author identified the surnames derived from the proper names whose meaning are connected with the features of human growth, body type (skinny build, slim build, chunky, fat), unusual shape, size, and color of organs of the human body. The important thing to note is that the namegivers focused on those characteristics of human appearance that were original, relatively constant, and helped to identify a person.
Traffic classification is an important tool for network management. It reveals the source of observed network traffic and has many potential applications e.g. in Quality of Service, network security and traffic visualization. In the last decade, traffic classification evolved quickly due to the raise of peer-to-peer traffic. Nowadays, researchers still find new methods in order to withstand the rapid changes of the Internet. In this paper, we review 13 publications on traffic classification and related topics that were published during 2009-2012. We show diversity in recent algorithms and we highlight possible directions for the future research on traffic classification: relevance of multi-level classification, importance of experimental validation, and the need for common traffic datasets.
The paper presents the results of a study of cyanobacteria and green algae assemblages occurring in various tundra types determined on the basis of mosses and vascular plants and habitat conditions. The research was carried out during summer in the years 2009–2013 on the north sea−coast of Hornsund fjord (West Spitsbergen, Svalbard Archipelago). 58 sites were studied in various tundra types differing in composition of vascular plants, mosses and in trophy and humidity. 141 cyanobacteria and green algae were noted in the research area in total. Cyanobacteria and green algae flora is a significant element of many tundra types and sometimes even dominate there. Despite its importance, it has not been hitherto taken into account in the description and classification of tundra. The aim of the present study was to demonstrate the legitimacy of using phycoflora in supplementing the descriptions of hitherto described tundra and distinguishing new tundra types. Numeric hierarchical−accumulative classification (MVSP 3.1 software) methods were used to analyze the cyanobacterial and algal assemblages and their co−relations with particular tundra types. The analysis determined dominant and distinctive species in the communities in concordance with ecologically diverse types of tundra. The results show the importance of these organisms in the composition of the vegetation of tundra types and their role in the ecosystems of this part of the Arctic.
This study addresses the problem of magnetic field emission produced by the laptop computers. Although, the magnetic field is spread over the entire frequency spectrum, the most dangerous part of it to the laptop users is the frequency range from 50 to 500 Hz, commonly called the extremely low frequency magnetic field. In this frequency region the magnetic field is characterized by high peak values. To examine the influence of laptop’s magnetic field emission in the office, a specific experiment is proposed. It includes the measurement of the magnetic field at six laptop’s positions, which are in close contact to its user. The results obtained from ten different laptop computers show the extremely high emission at some positions, which are dependent on the power dissipation or bad ergonomics. Eventually, the experiment extracts these dangerous positions of magnetic field emission and suggests possible solutions.
In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides the capabilities of MLP NNs, it uses Back Propagation (BP) and Gradient Descent (GD) for training; therefore, MLP NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well as lowconvergence speed. To lift defections, this study uses Adaptive Best Mass Gravitational Search Algorithm (ABGSA) to train MLP NN. This algorithm develops marginal disadvantage of the GSA using the bestcollected masses within iterations and expediting exploitation phase. To test the proposed classifier, this algorithm along with the GSA, GD, GA, PSO and compound method (PSOGSA) via three datasets in various dimensions will be assessed. Assessed metrics include convergence speed, fail probability in local minimum and classification accuracy. Finally, as a practical application assumed network classifies sonar dataset. This dataset consists of the backscattered echoes from six different objects: four targets and two non-targets. Results indicate that the new classifier proposes better output in terms of aforementioned criteria than whole proposed benchmarks.
The aim of this study was to investigate the possible use of geoinformatics tools and generally available geodata for mapping land cover/use on the reclaimed areas. The choice of subject was dictated by the growing number of such areas and the related problem of their restoration. Modern technology, including GIS, photogrammetry and remote sensing are relevant in assessing the reclamation effects and monitoring of changes taking place on such sites. The LULC classes mapping, supported with thorough knowledge of the operator, is useful tool for the proper reclamation process evaluation. The study was performed for two post-mine sites: reclaimed external spoil heap of the sulfur mine Machów and areas after exploitation of sulfur mine Jeziórko, which are located in the Tarnobrzeski district. The research materials consisted of aerial orthophotos, which were the basis of on-screen vectorization; LANDSAT satellite images, which were used in the pixel and object based classification; and the CORINE Land Cover database as a general reference to the global maps of land cover and land use.
The systematic position of Sorbus population occurring in the Pieniny Mts. is controversial. To verify its taxonomic status we studied the ITS sequence of closely related species of the S. aria group: Sorbus sp. from the Pieniny Mts., S. aria from the Tatra Mts., S. graeca from the Balkans, and other well-distinguished native Polish Sorbus species (S. aria, S. aucuparia, S. intermedia and S. torminalis). As a reference we examined Sorbus populations closest to the Pieniny Mts. where S. graeca was reported to occur, in Slovakia. The results indicate that the Sorbus plants found in the Pieniny Mts. differ genetically from those in the Tatra Mts. but are identical to those collected from the Vihorlat Mts. in Slovakia and are closely related to S. graeca from the Balkans
In the article the affiliation of Kujarke in genealogical classification is discussed. The Kujarke language is an isolate from Chad-Sudan neighborhood, described by the anthropologist Doornbos in 1981 (partially published in 1983). The present study operates with all c. 200 lexemes collected by Doornbos and evaluates their affinities in neighboring languages classified as Chadic and Nilo-Saharan. It is possible to conclude that Kujarke probably represents an independent group of East Chadic branch. From the neighboring Nilo-Saharan languages the strongest influence was identified from the Fur family.
Five models and methodology are discussed in this paper for constructing classifiers capable of recognizing in real time the type of fuel injected into a diesel engine cylinder to accuracy acceptable in practical technical applications. Experimental research was carried out on the dynamic engine test facility. The signal of in-cylinder and in-injection line pressure in an internal combustion engine powered by mineral fuel, biodiesel or blends of these two fuel types was evaluated using the vibro-acoustic method. Computational intelligence methods such as classification trees, particle swarm optimization and random forest were applied.
Land use/land cover (LULC) maps are important datasets in various environmental projects. Our aim was to demonstrate how GEOBIA framework can be used for integrating different data sources and classification methods in context of LULC mapping.We presented multi-stage semi-automated GEOBIA classification workflow created for LULC mapping of Tuszyma Forestry Management area based on multi-source, multi-temporal and multi-resolution input data, such as 4 bands- aerial orthophoto, LiDAR-derived nDSM, Sentinel-2 multispectral satellite images and ancillary vector data. Various classification methods were applied, i.e. rule-based and Random Forest supervised classification. This approach allowed us to focus on classification of each class ‘individually’ by taking advantage from all useful information from various input data, expert knowledge, and advanced machine-learning tools. In the first step, twelve classes were assigned in two-steps rule-based classification approach either vector-based, ortho- and vector-based or orthoand Lidar-based. Then, supervised classification was performed with use of Random Forest algorithm. Three agriculture-related LULC classes with vegetation alternating conditions were assigned based on aerial orthophoto and Sentinel-2 information. For classification of 15 LULC classes we obtained 81.3% overall accuracy and kappa coefficient of 0.78. The visual evaluation and class coverage comparison showed that the generated LULC layer differs from the existing land cover maps especially in relative cover of agriculture-related classes. Generally, the created map can be considered as superior to the existing data in terms of the level of details and correspondence to actual environmental and vegetation conditions that can be observed in RS images.
We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R 2 . These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for five percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifier. For the threshold of relevant change set as ten percent all approaches performed satisfactory.
In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.
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 essential to increase the working life of the bearing. In the current study, the vibration data of a journal bearing in the healthy condition and in five different fault conditions are collected. A feature extraction method 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.
The research was aimed at examining the impact of the petrographic composition of coal from the Janina mine on the gasification process and petrographic composition of the resulting char. The coal was subjected to fluidized bed gasification at a temperature below 1000°C in oxygen and CO2 atmosphere. The rank of coal is borderline subbituminous to bituminous coal. The petrographic composition is as follows: macerals from the vitrinite (61.0% vol.); liptinite (4.8% vol.) and inertinite groups (29.0% vol.). The petrofactor in coal from the Janina deposit is 6.9. The high content of macerals of the inertinite group, which can be considered inert during the gasification, naturally affects the process. The content of non-reactive macerals is around 27% vol. The petrographic analysis of char was carried out based on the classification of International Committee for Coal and Organic Petrology. Both inertoid (34.7% vol.) and crassinetwork (25.1% vol.) have a dominant share in chars resulting from the above-mentioned process. In addition, the examined char contained 3.1% vol. of mineroids and 4.3% vol. of fusinoids and solids. The calculated aromaticity factor increases from 0.75 in coal to 0.98 in char. The carbon conversion is 30.3%. Approximately 40% vol. of the low porosity components in the residues after the gasification process indicate a low degree of carbon conversion. The ash content in coal amounted to 13.8% and increased to 24.10% in char. Based on the petrographic composition of the starting coal and the degree of conversion of macerals in the char, it can be stated that the coal from the Janina deposit is moderately suitable for the gasification process.
A genetic subgrouping of 16 East Chadic languages is proposed in this paper. Contrary to the popular lexicostatistical approach, and in order to take into account potentially different rates of lexical evolution in the individual languages, it is attempted here to rely on the identification of common innovations. A practical method is presented how to apply the notion of common innovation when working with lexical isoglosses. This new method can also serve as a model for the subgrouping of language families other than East Chadic.
The primary evaluation of the economic losses caused by water pollution in Shanghai in the year 2009 is made by classification approach in order to provide basis for decision of the relative water management policy. The result shows that the portion of water pollution losses in GDP of Shanghai was 2.7%, which was still lower than the average level of whole China despite of the local high population density and the scale of industry, suggesting to some extent the continuous attention in water protection paid by Shanghai government.
A limited ability to discriminate between different materials is the fundamental problem with all conventional eddy-current-based metal detectors. This paper presents the use, evaluation and classification of nontraditional excitation signals for eddy-current metal detectors to improve their detection and discrimination ability. The presented multi-frequency excitation signals are as follows: a step sweep sine wave, a linear frequency sweep and sin(x)/x signals. All signals are evaluated in the frequency domain. Amplitude and phase spectra and polar graphs of the detector output signal are used for classification and discrimination of the tested objects. Four different classifiers are presented. The classification results obtained with the use of poly-harmonic signals are compared with those obtained with a classical single-tone method. Multifrequency signals provide more detailed information, due to the response function – the frequency characteristic of a detected object, than standard single-tone methods. Based on the measurements and analysis, a metal object can be better distinguished than when using a single-tone method.
Population density varies sharply from place to place on the whole territory of Poland. The largest number of people per 1 km2 is 21,531, while uninhabited areas account for about 48% of the country. Such uneven, non-Gaussian distribution of the data causes some difficulty in choosing the classification method in geometric choropleth maps. A thorough evaluation of a geometric choropleth map of population data is not possible using only traditional indicators such as the Tabular Accuracy Index (TAI). That is why the aim of the article is to develop an innovative index based on distance analysis and neighbour analysis of grid cells. Two indexes have been suggested in this paper: the Spatial Distance Index (SDI) and the Spatial Contiguity Index (SCI). The paper discusses the use of five classification methods to evaluate choropleth maps of population data, like head-tail breaks, natural breaks, equal intervals, quantile, and geometrical intervals. A comprehensive assessment of such geometric choropleth maps is also done. The research was conducted for the whole territory of Poland, using data from the 2011 National Census of Population and Housing. Population data are presented in the 1km grid. The results of the analysis are shown on thematic maps. A compatibility of the choropleth maps with urban-rural typology of the OECD (Organisation for Economic Co-operation and Development) was also checked.
Classification techniques have been widely used in different remote sensing applications and correct classification of mixed pixels is a tedious task. Traditional approaches adopt various statistical parameters, however does not facilitate effective visualisation. Data mining tools are proving very helpful in the classification process. We propose a visual mining based frame work for accuracy assessment of classification techniques using open source tools such as WEKA and PREFUSE. These tools in integration can provide an efficient approach for getting information about improvements in the classification accuracy and helps in refining training data set. We have illustrated framework for investigating the effects of various resampling methods on classification accuracy and found that bilinear (BL) is best suited for preserving radiometric characteristics. We have also investigated the optimal number of folds required for effective analysis of LISS-IV images.
Qualitative and quantitative results of high terrain elevation effect on spectral radiance of optical satellite image which affect the accuracy in retrieving of land surface cover changes is given. The paper includes two main parts: correction model of spectral radiance of satellite image affected by high terrain elevation and assessment of impacts and variation of land cover changes before and after correcting influence of high terrain elevation to the spectral radiance of the image. Study has been carried out with SPOT 5 in Hoa Binh mountain area of two periods: 2007 and 2010. Results showed that appropriate correction model is the Meyer’s one. The impacts of correction spectral radiance to 7 classes of classified images fluctuate from 15% to 400%. The varying changes before and after correction of image radiation fluctuate over 7 classes from 5% to 100%.
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%.
In this paper the authors propose a decision support system for automatic blood smear analysis based on microscopic images. The images are pre-processed in order to remove irrelevant elements and to enhance the most important ones – the healthy blood cells (erythrocytes) and the pathologic ones (echinocytes). The separated blood cells are analysed in terms of their most important features by the eigenfaces method. The features are the basis for designing the neural network classifier, learned to distinguish between erythrocytes and echinocytes. As the result, the proposed system is able to analyse the smear blood images in a fully automatic way and to deliver information on the number and statistics of the red blood cells, both healthy and pathologic. The system was examined in two case studies, involving the canine and human blood, and then consulted with the experienced medicine specialists. The accuracy of classification of red blood cells into erythrocytes and echinocytes reaches 96%.