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1
Kravets, Petro. "Gaming Method of Ontology Clusterization." Webology 16, no. 1 (June 30, 2019): 55–76. http://dx.doi.org/10.14704/web/v16i1/a179.
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Mkrtchian, Oleksandr. "Automatic landscape-ecological regionalization by the application of clustering and segmentation." Visnyk of the Lviv University. Series Geography, no. 47 (November 27, 2014): 177–84. http://dx.doi.org/10.30970/vgg.2014.47.950.
Повний текст джерелаАнотація:
The paper deals with the principles and methods of automatic landscape-ecological regionalization by the clusterization and segmentation methods. The employment of ecological morphometric indices as criteria for clusterization and segmentation has been justified. The method of the quantification of spatial dependencies between typological and regional spatial units based on information theory has been suggested. Key words: regionalization, clusterization, segmentation.
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Litvinenko, Natalya, Orken Mamyrbayev, Assem Shayakhmetova, and Mussa Turdalyuly. "Clusterization by the K-means method when K is unknown." ITM Web of Conferences 24 (2019): 01013. http://dx.doi.org/10.1051/itmconf/20192401013.
Повний текст джерелаАнотація:
There are various methods of objects’ clusterization used in different areas of machine learning. Among the vast amount of clusterization methods, the K-means method is one of the most popular. Such a method has as pros as cons. Speaking about the advantages of this method, we can mention the rather high speed of objects clusterization. The main disadvantage is a necessity to know the number of clusters before the experiment. This paper describes the new way and the new method of clusterization, based on the K-means method. The method we suggest is also quite fast in terms of processing speed, however, it does not require the user to know in advance the exact number of clusters to be processed. The user only has to define the range within which the number of clusters is located. Besides, using suggested method there is a possibility to limit the radius of clusters, which would allow finding objects that express the criteria of one cluster in the most distinctive and accurate way, and it would also allow limiting the number of objects in each cluster within the certain range.
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Сірий, Олексій Олександрович. "Message clusterization method based on archive transformation." ScienceRise 6, no. 2(11) (June 21, 2015): 76. http://dx.doi.org/10.15587/2313-8416.2015.44364.
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Sadovsky, Michael G., Eugene Yu Bushmelev, and Anatoly N. Ostylovsky. "New Clusterization Method Based on Graph Connectivity Search." Journal of Siberian Federal University. Mathematics & Physics 10, no. 4 (December 2017): 443–49. http://dx.doi.org/10.17516/1997-1397-2017-10-4-443-449.
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Savina, N. P., N. A. Galstyan, O. V. Litvishko, and E. A. Zakrevskaya. "Using Methods of Intellectual Analysis to Step up Profitability of Network Business." Vestnik of the Plekhanov Russian University of Economics, no. 2 (April 13, 2022): 176–85. http://dx.doi.org/10.21686/2413-2829-2022-2-176-185.
Повний текст джерелаАнотація:
The article studies the use of drug-store chain clusterization and determines strategy of work with each of the clusters, which is necessary because key work parameters of drug-store chains, such as selling speed, the number of brands of the company being investigated in the chain, the condition of stores, availability of strong competitors in the chain selling must be verified. Another reason is complexity of company resource distribution to build communications with drug-store chains (representatives’ calls) and products themselves among drug-store chains. To resolve the problem of drug-store clusterization k-means method based on the assessment of the company share and the share of a concrete brand in the drug-store chain was chosen for the research. As metrics for identifying the quality of obtained results ‘silhouette’ was chosen, i.e. the form of a cluster set representation. Its factor is equal to 0.514, which testifies to rather high accuracy of results and possibility to introduce this algorithm into real business practice. By cluster analyzing the multitude of drug-store chains a set of three clusters was identified. On the basis of these results of clusterization and current business requirements for each cluster a number of recommendations were put forward aimed at interaction between the pharmaceutical company and drug-store chains in the aspect of assessing the competition environment, analyzing selling speed and re-filling stocks of the chain, frequency of pharmaceutical representatives’ calls to drug-store chains. The findings of the research allowed us to draw a conclusion that any method of clusterization of drug-store data should be renewed in view of the quality of result interpretation, topicality of initial criteria of clusterization and should be corrected proceeding from business requirements, which arrive from marketer teams and managers on medicine categories and separate brands.
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Yuryev, G. A., E. K. Verkhovskaya, and N. E. Yuryeva. "Stochastic swarm clusterization method in natural language data processing." Experimental Psychology (Russia) 11, no. 3 (2018): 5–18. http://dx.doi.org/10.17759/exppsy.2018110301.
Повний текст джерелаАнотація:
Consider natural language data processing technology based on non-linear dimensionality reduction method which takes into account the discriminating power of the solution found for given values of the categorical variable associated with each observation. Stochastic optimization method known as the “Particle swarm optimization” is proposed to found characteristics that ensure the best separation of observations in terms of a given quality functional. The basis for evaluating the quality of the solution lies in the purity of the clusters obtained with the k-means method, or with using self-organizing Kohonen feature maps.
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Tumino, A., C. Spitaleri, S. Cherubini, G. D’Agata, L. Guardo, M. Gulino, I. Indelicato, et al. "Clusterization of light nuclei and the Trojan Horse Method." Journal of Physics: Conference Series 863 (June 2017): 012072. http://dx.doi.org/10.1088/1742-6596/863/1/012072.
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Gorobchenko, O. "DEVELOPMENT OF THE METHOD OF CLUSTERIZATION OF TRAIN SITUATIONS." Collection of scientific works of the State University of Infrastructure and Technologies series "Transport Systems and Technologies" 1, no. 37 (June 29, 2021): 187–95. http://dx.doi.org/10.32703/2617-9040-2021-37-18.
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The introduction of intelligent locomotive control systems requires better approaches to assessing and monitoring the current train situation than those used in modern traction rolling stock. Automatic detection of complex abnormal situations is currently not provided. For example, determining the inefficiency of the brakes, speeding, the presence of obstacles or people on the track, the deterioration of the traction properties of rolling stock, etc. relies solely on the driver of the locomotive. Given the important impact of these factors on traffic safety, it is proposed to include in the functions of automated and intelligent traffic control systems recognition of abnormal situations and notification of its occurrence. When driving a train, all objects of classification (train situations) are divided into a finite number of classes. A finite number of precedent objects are known and studied for each class. The task of pattern recognition is to assign a new recognizable situation to a class. The classifier or decisive rule is the rule of assigning the image of a train situation to one of the classes on the basis of its vector of features. An order of classification of train situations has been developed, which allows to allocate clusters of any complex shape, provided that different parts of such clusters are connected by chains of close to each other elements. The measure of difference is the square of the Euclidean distance.
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PRONYAYEVA, Lyudmila I., Ol'ga A. FEDOTENKOVA, and Anna V. PAVLOVA. "Analyzing the state and development trends in economic clusterization processes in foreign countries." National Interests: Priorities and Security 17, no. 5 (May 14, 2021): 808–37. http://dx.doi.org/10.24891/ni.17.5.808.
Повний текст джерелаАнотація:
Subject. The article discusses the existing trends and national approaches to clustering in foreign economies, which concurrently determine the socio-economic parity of strategic growth indicators. Objectives. We determine the most suitable conditions for cluster structures to emerge in national economies and macroregions, and look for methods to optimize and use how economic clusterization in Russia can be developed. Methods. The study is based on general scientific methods, such as the generalization, synthesis and analysis, and special ones, which serve for evaluating the trajectory of clusterization processes in national economics across the globe, and point out their specifics. Furthermore, we applied the comprehensive approach to evaluating the development trajectory of cluster structures, and involved classification and identification techniques and the method of grouping and graphic representation. Results. We grouped countries by purpose of national economic clusterization, and performed the comparative analysis of clustering model through indicative points. The article presents approaches to describing key clusterization centers at the macroregional development level. The article spotlights the most frequent specialization of clusters abroad. We analyzed how cluster structures develop in the European Union, and concurrently assessed the volume of funds allocated to cluster structures there and the existing strategic partnerships of clusters in Europe. Conclusions and Relevance. The study allows to optimize the existing approaches to clustering of the Russian economy through the analysis of global practices of cluster structures’ operation, which can expand opportunities for making national integrated entities more competitive.
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Ramadhan, Audi, Kinanti Prawita, M. Andik Izzudin, and Gitta Amandha. "Analisis strategi dan klasterisasi ketahanan pangan nasional dalam menghadapi pandemi covid-19." Teknologi Pangan : Media Informasi dan Komunikasi Ilmiah Teknologi Pertanian 12, no. 1 (March 9, 2021): 110–22. http://dx.doi.org/10.35891/tp.v12i1.2179.
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Covid-19 outbreak that scours the world nowadays is affecting all sectors, including food security. Therefore it needs to restructuring the food security policies to ensure that every people obtains adequate and nutritious food. However, the society in each province have different conditions. Thus the clusterization of food security level per province is indispensable to support strategic and policy decision in order to face the Covid-19 pandemic. This research aimed to clustering food security level of each province in Indonesia. Furthernore, this research also compare several clustering methods. The clustering method that used as a comparison in this study is K-means, DBSCAN, Louvain and Self organizing maps methods. Method with the highest silhouette coefficient value in this research will represent the results of food security clustering. The resul of the research show that K-means achieve highest silhouette coefficient value (0.568). Therefore the clusterization result of K-means chosen to represent the level of food security in this research. Further, it followed by self organizing maps with silhouette coefficient 0.559, louvain 0.312 and DBSCAN 0.15. The clusterization result show there are 7 provinces with high food security index, 24 provinces with medium food security index and 3 provinces with low food security index. This research also propose policies strategy and recommendation related to regional food security condition in order to face the Covid-19 pandemic. This research is expected to be a consideration of Indonesian government in making policies on national food security.
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Zarvin, A. E., N. G. Korobeishchikov, V. Zh Madirbaev, G. G. Gartvich, V. V. Kalyada, and R. G. Sharafutdinov. "A Method for Studying Clusterization Processes in a Free Impulse Jet." Instruments and Experimental Techniques 48, no. 6 (November 2005): 817–25. http://dx.doi.org/10.1007/s10786-005-0145-4.
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Scherer, Magdalena, and Piotr Milczarski. "Machine-Learning-Based Carbon Footprint Management in the Frozen Vegetable Processing Industry." Energies 14, no. 22 (November 19, 2021): 7778. http://dx.doi.org/10.3390/en14227778.
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In the paper, we present a method of automatic evaluation and optimization of production processes towards low-carbon-emissions products. The method supports the management of production lines and is based on unsupervised machine learning methods, i.e., canopy, k-means, and expectation-maximization clusterization algorithms. For different production processes, a different clustering method may be optimal. Hence, they are validated by classification methods (k-nearest neighbors (kNN), multilayer perceptron (MLP), binary tree C4.5, random forest (RF), and support vector machine (SVM)) that identify the optimal clusterization method. Using the proposed method with real-time production parameters for a given process, we can classify the process as optimal or non-optimal on an ongoing basis. The production manager can react appropriately to sub-optimal production processes. If the process is not optimal, then during the process the manager or production technologist may change the production parameters, e.g., speed up or slow down certain batches, so that the process returns to the optimal path. This path is determined by a model trained via the proposed method based on the selected clustering method. The method is verified on an onion production line with more than a hundred processes and then applied to production lines with a smaller number of cases. We use data from real-world measurements from a frozen food production plant. Our research demonstrates that proper process management using machine learning can result in a lower carbon footprint per ton of the final product.
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Janiga, Damian, Robert Czarnota, Jerzy Stopa, and Paweł Wojnarowski. "Self-adapt reservoir clusterization method to enhance robustness of well placement optimization." Journal of Petroleum Science and Engineering 173 (February 2019): 37–52. http://dx.doi.org/10.1016/j.petrol.2018.10.005.
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Supatman, Supatman, and Sri Ayem. "UMKM Clusterization with Unsupervised Neural Networks Method for Accounting by Business Capital." TAMANSISWA INTERNATIONAL JOURNAL IN EDUCATION AND SCIENCE 2, no. 1 (October 27, 2020): 33–39. http://dx.doi.org/10.30738/tijes.v2i1.7698.
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UMKM menurut pasal (6) UU nomor 20 tahun 2008 berdasarkan asset dan omset dibagi menjadi tiga kriteria yaitu UMi (Usaha Mikro), UK (Usaha Kecil) dan UM (Usaha Menengah). Sementara itu variabel dalam laporan BPS terkait UMKM meliputi Unit Usaha, Tenaga Kerja, PDB atas usaha yang berlaku, PDB atas dasar harga konstan 2000, Total Ekspor Non Migas, Investasi atas dasar harga berlaku, Investasi atas dasar harga konstan 2000. Sehingga pendekatan untuk melakukan kriteria berdasarkan asset dan omset relatif lemah mengingat secara rinci terdapat 7 variabel pendukung kriteria (berdasarkan data BPS).Unsupervised Neural Networks merupakan metode klusterisasi pembelajaran mandiri yang dapat melakukan klaterisasi data berdasarkan jarak eucledian data. SOM-Kohonen merupakan salah satu jenis Unsupervised Neural Networks yang digunakan untuk klasterisasi UMKM pada penelitian ini. Berdasarkan pengujian menggunakan data UMKM tahun 2010 – 2018, dengan parameter pelatihan alfa : 0.1, decalfa 0.2, iterasi 500 diperoleh hasil bahwa kluster UMKM terkluster menjadi 2 dengan perincian Umi tetap sebagai kluster Umi, sedangkan UK dan UM menggabung menjadi satu kluster.Berdasarkan hasil klusterisasi menggunakan unsupervised neural networks dengan SOM-Kohonen yaitu dua klaster, maka direkomendasikan pemodalan dibagi menjadi dua sesuai dengan klusternya. Keywords: Accounting, Business, Clusterization, UMKM, Unsupervised, Neural Networks, SOM-Kohonen.
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Cha, Myoung Jun, Woo Seok Song, Yoo Seok Kim, In Kyung Song, Dae Sung Jung, Suil Lee, Sung Hwan Kim, Sang Eun Park, and Chong Yun Park. "MeV Electron-Beam Induced Clusterization of Platinum Chloride on Graphene for Transparent Conductive Electrodes." Advanced Materials Research 677 (March 2013): 25–30. http://dx.doi.org/10.4028/www.scientific.net/amr.677.25.
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The use of graphene-based transparent conductive electrodes critically depends upon the enhancement of electrical conductivity with a negligible loss of optical transmittance of graphene. Hence, the hybridization of graphene and metal nanostructures has been intensively investigated to improve electrical conductivity. Here we demonstrate clusterization of PtCl2 on graphene by a facile method, MeV electron-beam irradiation (MEBI) under ambient conditions, as characterized by scanning electron microscopy, transmittance electron microscopy, and resonant Raman spectroscopy. The workfunction difference between PtCl2 nanoclusters and graphene results in p-type doping of graphene, to achieve a reduced sheet resistance of 69.1 % with respect to that of pristine graphene while maintaining transmittance of 91.7 %. The mechanism of formation of PtCl2 nanoclusters on graphene is likely to be defect-mediated clusterization due to the high energy electron-beam.
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Чуканов, Андрей, and Andrey Chukanov. "Clusterization of Russian Regions by the Level of Mortgage Developing." Scientific Research and Development. Economics 7, no. 1 (March 4, 2019): 31–36. http://dx.doi.org/10.12737/article_5c59831d0d88f2.82305663.
Повний текст джерелаАнотація:
In this article, in order to optimize the economic policy in the field of mortgagehousing lending, the clustering of Russian regions by the most optimal method was carried out and analyzed. The main limitations arising from the application of the most popular k-means clustering algorithm for analyzing mortgages are considered and ways to correct them are suggested. The regions were grouped using clustering algorithms using medians and medoids that are more resistant to outliers. A comparison was made of the results of the k-means, k-medians and k-medoids algorithms, and the optimal number of groups of regions with similar indicators in the field of mortgage lending and their relevant regions representatives were found. A hierarchical clustering algorithm based on the Ward method was used, the result of which was the use of five mortgage clusters in Russia. The study of the characteristics of these groups of regions will help in creating a mortgage policy that takes into account the peculiarities of the regions of Russia. All calculations were made in the R programming language; graphics were created in the Rstudio development environment.
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Priadana, Adri, and Aris Wahyu Murdiyanto. "Shrimps clusterization by size using digital image processing with CCA and DBSCAN." Jurnal Teknologi dan Sistem Komputer 8, no. 2 (February 14, 2020): 106–12. http://dx.doi.org/10.14710/jtsiskom.8.2.2020.106-112.
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The quality of farmed shrimps has several criteria, one of which is shrimp size. The shrimp selection was carried out by the contractor at the harvest time by grouping the shrimp based on their size. This study aims to apply digital image processing for shrimp clustering based on size using the connected component analysis (CCA) and density-based spatial clustering of applications with noise (DBSCAN) methods. Shrimp group images were taken with a digital camera at a light intensity of 1200-3200 lux. The clustering results were compared with clustering from direct observation by two experts, each of which obtained an accuracy of 79.81 % and 72.99 % so that the average accuracy of the method was 76.4 %.
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Bodrunova, Svetlana S., Ivan Blekanov, Anna Smoliarova, and Anna Litvinenko. "Beyond Left and Right: Real-World Political Polarization in Twitter Discussions on Inter-Ethnic Conflicts." Media and Communication 7, no. 3 (August 9, 2019): 119–32. http://dx.doi.org/10.17645/mac.v7i3.1934.
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Studies of political polarization in social media demonstrate mixed evidence for whether discussions necessarily evolve into left and right ideological echo chambers. Recent research shows that, for political and issue-based discussions, patterns of user clusterization may differ significantly, but that cross-cultural evidence of the polarization of users on certain issues is close to non-existent. Furthermore, most of the studies developed network proxies to detect users’ grouping, rarely taking into account the content of the Tweets themselves. Our contribution to this scholarly discussion is founded upon the detection of polarization based on attitudes towards political actors expressed by users in Germany, the USA and Russia within discussions on inter-ethnic conflicts. For this exploratory study, we develop a mixed-method approach to detecting user grouping that includes: crawling for data collection; expert coding of Tweets; user clusterization based on user attitudes; construction of word frequency vocabularies; and graph visualization. Our results show that, in all the three cases, the groups detected are far from being conventionally left or right, but rather that their views combine anti-institutionalism, nationalism, and pro- and anti-minority views in varying degrees. In addition to this, more than two threads of political debate may co-exist in the same discussion. Thus, we show that the debate that sees Twitter as either a platform of ‘echo chambering’ or ‘opinion crossroads’ may be misleading. In our opinion, the role of local political context in shaping (and explaining) user clusterization should not be under-estimated.
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Sayapina, K. V. "INNOVATION CLUSTERS FORMATION AS AN INSTRUMENT OF EFFECTIVE ECONOMIC MANAGEMENT." Strategic decisions and risk management, no. 6 (October 25, 2014): 88–95. http://dx.doi.org/10.17747/2078-8886-2013-6-88-95.
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The nature of innovation cluster and its distinction from regional cluster on base of leading cluster theories’ analysis is explained. Foreign and Russian models of forming innovation clusters’ description is presented. Types of Russian innovation clusters with their appropriate problems and disadvantages are analyzed in comparison with international experience. Tendencies of innovation clusterization policy in states as an effective method of managing economy are underlined.
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Archakov, Alexander I., Andrey V. Lisitsa, Victor G. Zgoda, Marina S. Ivanova, and Luc Koymans. "Clusterization of P450 Superfamily Using the Objective Pair Alignment Method and the UPGMA Program." Journal of Molecular Modeling 4, no. 7 (July 30, 1998): 234–38. http://dx.doi.org/10.1007/s008940050080.
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Sanchez-Mondragon, Joel, and Alberto Omar Vazquez-Hernandez. "Solitary wave collisions by double-dam-broken simulations with the MPS method." Engineering Computations 35, no. 1 (March 5, 2018): 53–70. http://dx.doi.org/10.1108/ec-04-2016-0142.
Повний текст джерелаАнотація:
Purpose The paper aims to apply a modified version of the MPS method to a double-dam-breaking test in which high dispersion zones and high natural clusterization zones are present, such as when the water column collapses into two sides and the two solitary waves collide, respectively. Design/methodology/approach The work takes advantage of the mixed source term from the cheaper computational version of the moving particle semi-implicit (MPS) method to reduce one step from the MPS classical algorithm. The proposed test can be successfully simulated by applying modifications to the variance parameter in the Laplacian operator and gradient model. Findings The results show stable behavior in dispersion and clusterization zones. Also, the collision and merging produced by solitary waves was successfully simulated. Research limitations/implications The main limitation in this work was the development of a comparison between the obtained results and the simulations with the original cheaper computational version of the MPS, this limitation is due to the overestimation of inter particle repulsive forces from its gradient model. Practical implications The application of solitary waves is of paramount importance in a number of applications, and this stems from the fact that the interaction of solitary waves with ships and other floating structures could generate highly deformed and complex free surface flows. Social implications For future work, the modified version of the MPS method can be applied in flow over sill base simulations, in close and open channels, and in simulating breaking waves to determine impact pressures by using solitary wave propagation. Originality/value The simulation of interaction of large groups of particles as in the case when two solitary waves collide could cause severe instability problems in pressure, causing the computer analysis to stop. MPS classical algorithm takes into account an explicit step that, in this case, may provoke the problem. For this reason, the cheaper version of MPS method is used to correctly simulate solitary wave interactions.
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Buldaev, Alexander Alexandrovich, Larisa Vladimirovna Naykhanova, and Inga Sergeevna Evdokimova. "Model of decision support system in educational process of a university on the basis of learning analytics." Программные системы и вычислительные методы, no. 4 (April 2020): 42–52. http://dx.doi.org/10.7256/2454-0714.2020.4.34286.
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In recent decades, the potential of analytics and data mining – the methodologies that extract valuable information from big data, transformed multiple fields of scientific research. Analytics has become a trend. With regards to education, these methodologies are called the learning analytics (LA) and educational data mining (EDM). Latterly, the use of learning analytics has proliferated due to four main factors: a significant increase in data quantity, improved data formats, achievements in the area of computer science, and higher complexity of available analytical tools. This article is dedicated to the description of building the model of decision support system (DSS) of a university based on educational data acquired from digital information and educational environment. The subject of this research is the development of DSS with application of learning analytics methods. The article provides a conceptual model of decision-making system in the educational process, as well as a conceptual model of the components of DSS component – forecasting subsystem. The peculiarity of forecasting subsystem model implies usage of learning analytics methods with regards to data sets of a higher educational institution, which contain the results of work of the digital information and educational environment, and include the characteristics of student activity. The main results of the conducted research is the examined and selected methods of clusterization and classification (KNN), the testing of which demonstrated palatable results. The author examined various methods of clusterization, among which k-prototypes method showed best results. The conclusion is made on favorable potential of application of the methods of learning analytics in Russian universities.
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NOVIKOV, D. I., and H. E. JØRGENSEN. "THE TOPOLOGY OF THE COSMIC MICROWAVE BACKGROUND ANISOTROPY ON THE SCALE ~1°." International Journal of Modern Physics D 05, no. 04 (August 1996): 319–62. http://dx.doi.org/10.1142/s0218271896000229.
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In this paper we develop the theory of clusterization of peaks in a Gaussian random field. We have obtained new mathematical results from this theory and the theory of percolation and have proposed a topological method of analysis of sky maps based on these results. We have simulated 10°×10° sky maps of the cosmic microwave background anisotropy expected from different cosmological models with 0.5°–1° resolution in order to demonstrate how this method can be used for detection of non-Gaussian noise in the maps and detection of the Doppler-peak in the spectrum of perturbation of ΔT/T.
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Serga, E. N. "Peculiarities of homogeneous areas distribution within the fields of hydrometeorological characteristics in the Northern Pacific during the cold season." Ukrainian hydrometeorological journal, no. 17 (October 29, 2017): 49–60. http://dx.doi.org/10.31481/uhmj.17.2016.06.
Повний текст джерелаАнотація:
In order to investigate horizontal distribution of hydro-meteorological characteristics, statistical analysis techniques, including multidimensional statistical analysis techniques (for example, factor, cluster analysis etc.) are usually applied. These techniques allow obtaining not only fields of particular characteristic by means of plotting appropriate isolines, but determining entire homogeneous areas with typical representative point which helps to compress information considerably and to reveal boundaries of distribution of certain characteristic within the entire spatial aggregation.
Schemes of zoning of fields of difference for monthly average temperatures “underlying surface-air” at 2 m height, of surface flows of latent heat, of zonal aspects of wind speed in the Northern Pacific obtained by means of the Universal Iterative Method of Data Clusterization are offered. The obtained clusterization schemes underwent both physical and statistical analyses having good scientific justification. It is shown that distribution of zonal aspect of wind speed has latitudinal direction, and distribution of flows of latent heat and temperature differences has mainly a focal nature. Analysis of variability of boundaries of homogeneous areas, average values of representative vectors, dispersions, mean-square deviations during future time intervals will allow identifying the specific features of climate variability through the example of the fields of hydrometeorological characteristics under study.
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de la Puente-Gil, Álvaro, Alberto González-Martínez, David Borge-Diez, Jorge Blanes-Peiró, and Miguel de Simón-Martín. "Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study." Environments 5, no. 12 (December 6, 2018): 133. http://dx.doi.org/10.3390/environments5120133.
Повний текст джерелаАнотація:
Health Services building stock is usually the top energy consumer in the Administrative sector, by a considerable margin. Therefore, energy consumption supervision, prediction, and improvement should be carried out for this group in a preferential manner. Most prior studies in this field have characterized the energy consumption of buildings based on complex simulations, which tend to be limited by modelisation restrictions and assumptions. In this paper, an improved method for the clusterization of buildings based on their electrical energy consumption is proposed and, then, reference profiles are determined by examining the variation of energy consumption over the typical yearly consumption period. The temporary variation has been analyzed by evaluating the temporary evolution of the area consumption index through data mining and statistical clusterization techniques. The proposed methodology has been applied to building stock of the Health Services in the Castilla y León region in Spain, based on three years of historical monthly electrical energy consumption data for over 250 buildings. This building stock consists of hospitals, health centers (with and without emergency services) and a miscellaneous set of administrative and residential buildings. Results reveal five distinct electrical consumption profiles that have been associated with five reference buildings, permitting significant improvement in the demand estimation as compared to merely using the classical energy consumption indicators.
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Gružauskas, Valentas, Dalia Čalnerytė, Tautvydas Fyleris, and Andrius Kriščiūnas. "Application of Multivariate Time Series Cluster Analysis to Regional Socioeconomic Indicators of Municipalities." Real Estate Management and Valuation 29, no. 3 (August 13, 2021): 39–51. http://dx.doi.org/10.2478/remav-2021-0020.
Повний текст джерелаАнотація:
Abstract The socio-economic development of municipalities is defined by a set of indicators in a period of interest and can be analyzed as a multivariate time series. It is important to know which municipalities have similar socio-economic development trends when recommendations for policy makers are provided or datasets for real estate and insurance price evaluations are expanded. Usually, key indicators are derived from expert experience, however this publication implements a statistical approach to identify key trends. Unsupervised machine learning was performed by employing K-means clusterization and principal component analysis for a dataset of multivariate time series. After 100 runs, the result with minimal summing error was analyzed as the final clusterization. The dataset represented various socio-economic indicators in municipalities of Lithuania in the period from 2006 to 2018. The significant differences were noticed for the indicators of municipalities in the cluster which contained the 4 largest cities of Lithuania, and another one containing 3 districts of the 3 largest cities. A robust approach is proposed in this article, when identifying socio-economic differences between regions where real estate is allocated. For example, the evaluated distance matrix can be used for adjustment coefficients when applying the comparative method for real estate valuation.
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Denisov, A. B., and S. A. Panfilov. "Analysis of the effect of acupuncture on the regeneration of rat submaxillary salivary gland using the clusterization method." Bulletin of Experimental Biology and Medicine 119, no. 6 (June 1995): 637–39. http://dx.doi.org/10.1007/bf02443712.
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Gómez-Rubio, Álvaro, Ricardo Soto, Broderick Crawford, Adrián Jaramillo, David Mancilla, Carlos Castro, and Rodrigo Olivares. "Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method." Mathematics 10, no. 2 (January 16, 2022): 274. http://dx.doi.org/10.3390/math10020274.
Повний текст джерелаАнотація:
In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms.
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Zalisko, Olga. "MODERN MNEs FINANCIAL LEVERAGE AND CAPITAL STRUCTURE: THE COMPARATIVE ANALYSIS OF CLUSTER MODELS." ACTUAL PROBLEMS OF INTERNATIONAL RELATIONS 1, no. 127 (2016): 149–62. http://dx.doi.org/10.17721/apmv.2016.127.1.149-162.
Повний текст джерелаАнотація:
The cluster analysis applying the k-means method has been carried out. The analysis allowed discovering statistical features on financial leverage and capital structure of explored MNEs considering their industrial specificity as well. Clusterization was performed by thee criteria: the level of debt ratio; the level of direct financial leverage by means of the pair linear regression factor reflecting the influence of debt ratio on ROI; the level of indirect financial leverage by means of the pair linear regression factor reflecting the influence of short-term debt ratio on current market stock price. Three persistent models of financial leverage have been revealed and quantitatively identified. The model of reverse financial leverage implies the existence of reverse effect either from direct or indirect financial leverage as well as the low share of debt in capital structure. This model is typical for oil industry, mostly typical for pharmaceutical and mining industries and partially typical for electronics and food industries. The highest direct financial leverage model can be distinguished by the second highest among clusters (but-positive) average value of indirect financial leverage effect and the highest direct financial leverage effect among clusters. The level of debt usage in this model is relatively high. It describes only MNEs having not typical values of clusterization criteria. This did not allow to make sectoral generalizations. The highest debt model implies the use of the largest share of debt in their capital structure. This model shows the average positive effect of indirect financial leverage and the minimal reverse effect of direct financial leverage. It is typical for wholesale trade MNEs and mostly typical for utilities, telecommunications and motor vehicles corporations.
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Vylkova, Elena S., Natalia G. Victorova, Vladimir N. Naumov, and Natalia V. Pokrovskaia. "Tax Clusterization of Regions of the Russian Federation to Identify Territories-Drivers of Sustainable Development." Vestnik Tomskogo gosudarstvennogo universiteta. Ekonomika, no. 53 (2021): 138–57. http://dx.doi.org/10.17223/19988648/53/11.
Повний текст джерелаАнотація:
The relevance of tax clustering is due to the need for a competent scientifically grounded definition of territories that are drivers of economic growth. The aim of the study was to identify, on the basis of econometric methods, clusters of the regions of the Russian Federation by a set of indicators reflecting their tax status, tax administration, informatization of the tax environment. The Russian regions were grouped into clusters by a set of tax indicators based on official statistical data for 2018 using SPSS, Rstudio, Anaconda Navigator software. As a result of the anomalous values, five federal subjects were excluded from the analysis: Moscow, Sevastopol, Ingushetia, Khanty-Mansi and Yamalo-Nenets Autonomous Okrugs. Econometric analysis made it possible to conclude that there are three clusters of regions according to the analyzed parameters: 1) the least functionally proportional (7 regions), which have the lowest tax intensity of the gross regional product, the highest debt intensity of the gross regional product and the highest level of tax debt of the employed population, companies, and individual entrepreneurs; 2) medium functionally proportional (50 regions) with the lowest efficiency of tax administration, the highest coefficient of tax collection, the lowest level of taxation of the employed population and individual entrepreneurs (but not companies), the lowest level of tax debt of all analyzed subjects, and the lowest additional tax charges and sanctions for violation of tax legislation from tax audits, 3) the most comprehensively successful (22 regions), which are characterized by the highest tax intensity of the gross regional product and the highest level of tax revenues generated by the employed population, companies, and individual entrepreneurs. The regions of this cluster have the most effective taxation of value added and financial results of organizations. Among the regions of the third group, the leaders in terms of digital indicators are: Tyumen Oblast, Murmansk Oblast, Republic of Tatarstan, Leningrad Oblast. The study can develop in the following promising directions: 1) inclusion in the cluster analysis of indicators, not typical for the characteristics of the tax environment, that most fully reflect the influence of external diverse factors on the tax state of the regions; 2) extrapolation of the results to assess the tax status of the territories of other states; 3) the need to improve the tax clustering method based on artificial intelligence technology.
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Kiseleva, Natalia Stanislavovna. "APPLICATION OF CLUSTERIZATION METHOD IN THE SYSTEM-COGNITIVE ANALYSIS AND INTELLECTUAL SYSTEM «EIDOS» IN PEAR BREEDING ON THE SET SIGNS." Fruit growing and viticulture of South Russia 1, no. 61 (January 15, 2020): 16–32. http://dx.doi.org/10.30679/2219-5335-2020-1-61-16-32.
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Leal, Esmeide, German Sanchez-Torres, John W. Branch-Bedoya, Francisco Abad, and Nallig Leal. "A Saliency-Based Sparse Representation Method for Point Cloud Simplification." Sensors 21, no. 13 (June 23, 2021): 4279. http://dx.doi.org/10.3390/s21134279.
Повний текст джерелаАнотація:
High-resolution 3D scanning devices produce high-density point clouds, which require a large capacity of storage and time-consuming processing algorithms. In order to reduce both needs, it is common to apply surface simplification algorithms as a preprocessing stage. The goal of point cloud simplification algorithms is to reduce the volume of data while preserving the most relevant features of the original point cloud. In this paper, we present a new point cloud feature-preserving simplification algorithm. We use a global approach to detect saliencies on a given point cloud. Our method estimates a feature vector for each point in the cloud. The components of the feature vector are the normal vector coordinates, the point coordinates, and the surface curvature at each point. Feature vectors are used as basis signals to carry out a dictionary learning process, producing a trained dictionary. We perform the corresponding sparse coding process to produce a sparse matrix. To detect the saliencies, the proposed method uses two measures, the first of which takes into account the quantity of nonzero elements in each column vector of the sparse matrix and the second the reconstruction error of each signal. These measures are then combined to produce the final saliency value for each point in the cloud. Next, we proceed with the simplification of the point cloud, guided by the detected saliency and using the saliency values of each point as a dynamic clusterization radius. We validate the proposed method by comparing it with a set of state-of-the-art methods, demonstrating the effectiveness of the simplification method.
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Mishchuk, Nataliia, and Oleksandr Zavada. "STATISTICAL ANALYSIS OF THE LEVEL OF ECONOMIC ACTIVITY AND LEVEL OF UNEMPLOYMENT IN UKRAINE." Economic Analysis, no. 29(1) (2019): 29–35. http://dx.doi.org/10.35774/econa2019.01.029.
Повний текст джерелаАнотація:
Introduction. Economically active population causes both direct growth of the gross domestic product of the country and the creation of progressive labour relations. It is the basis for the formation of the middle class. Instead, high unemployment (underutilization of labour potential) is a major economic and social problem for the country. Therefore, the study of factors that affect the level of economic activity of the population and the level of unemployment is an actual scientific task. It is also important to study the economic activity and unemployment of the population of Ukraine in the regional context and a comparative analysis of regions by the size of these indicators. Purpose. The article aims to obtain analytical dependencies of unemployment rates and economic activity of the population of Ukraine on a number of factors, as well as clusterization of regions of Ukraine according to unemployment rates. Method (methodology). To achieve the goal, official statistical information on the labour market of Ukraine has been used. The following statistical methods such as correlation analysis, least squares method and hierarchical cluster analysis are used. Results. The analytical dependence of the level of economic activity and the unemployment rate of Ukrainian population on the duration of studies is constructed. Economic activity, depending on the duration of studies, has been received in the form of a logistic function with saturation of 92%. The unemployment rate is respectively a downline linear function. The educational levels, which are already sufficient to ensure high economic activity of the population, have been identified. A correlation analysis of the interdependence of a number of factors that influence the level of unemployment has been established. It has been performed the clusterization of the regions of Ukraine according to the percentage of unemployment among the economically active population, the unemployment rate for one vacancy and the proportion of the urban population. Five key clusters have been identified. On the basis if use of statistical methods, we have concluded that the most important factor in reducing unemployment is the increase in the economic activity of the population. It is determined that in order to increase the competitiveness of labour force in Ukraine it is necessary to stimulate the population to improve its level of education, in particular, to increase the duration of studies.
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Krzyściak, Wirginia, Dorota Kościelniak, Monika Papież, Anna Jurczak, and Palina Vyhouskaya. "Methods of Biotyping of Streptococcus mutans Species with the Routine Test as a Prognostic Value in Early Childhood Caries." Evidence-Based Complementary and Alternative Medicine 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/6859543.
Повний текст джерелаАнотація:
Purpose. In order to investigate the suitability of Streptococcus mutans species biotyping by measuring the activity of selected enzymes from a commercial test, criteria were established for biotyping clinical strains from children with dental caries. In addition, the relationships between the selected biotypes, sensitivity to commonly used antibiotics, and early childhood caries were determined. Methods. A total of 142 S. mutans isolates from dental plaque of children with caries were divided into different biotypes. Patients were divided into two groups: noncavitated (1-2 in ICDAS) and cavitated (5-6 in ICDAS) lesions. Biotyping criteria were determined based on both the arbitrary method and the clusterization method. The susceptibility of the strains to amoxicillin, cefazolin, erythromycin, and teicoplanin was studied by diluting a solid medium. Results. Biotype I was the most common. Mean MIC values showed that the strains belonging to biotypes II and IV were the most sensitive to amoxicillin. For predetermined biotypes, observed differences were dependent on the severity of dental caries. Conclusions. The proposed method of S. mutans strains biotyping is relatively quick and simple to use, provided the application of suitable biotyping criteria, and may contribute to the effective prevention of dental caries induced by S. mutans.
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Melnikova, Nataliya V., Anna V. Kudryavtseva, Alexander V. Zelenin, Valentina A. Lakunina, Olga Yu Yurkevich, Anna S. Speranskaya, Alexey A. Dmitriev, et al. "Retrotransposon-Based Molecular Markers for Analysis of Genetic Diversity within the GenusLinum." BioMed Research International 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/231589.
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SSAP method was used to study the genetic diversity of 22Linumspecies from sectionsLinum,Adenolinum, Dasylinum, Stellerolinum, and 46 flax cultivars. All the studied flax varieties were distinguished using SSAP for retrotransposonsFL9andFL11. Thus, the validity of SSAP method was demonstrated for flax marking, identification of accessions in genebank collections, and control during propagation of flax varieties. Polymorphism ofFl1a, Fl1b, andCassandrainsertions were very low in flax varieties, but these retrotransposons were successfully used for the investigation ofLinumspecies. Species clusterization based on SSAP markers was in concordance with their taxonomic division into sectionsDasylinum, Stellerolinum, Adenolinum, andLinum. All species of sect.Adenolinumclustered apart from species of sect.Linum. The data confirmed the accuracy of the separation in these sections. Members of sectionLinumare not as closely related as members of other sections, so taxonomic revision of this section is desirable.L. usitatissimumaccessions genetically distant from modern flax cultivars were revealed in our work. These accessions are of utmost interest for flax breeding and introduction of new useful traits into flax cultivars. The chromosome localization ofCassandraretrotransposon inLinumspecies was determined.
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Al-Momani, Mohammad Musa. "Cluster mechanism for hot data storage in HBase system." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 3 (June 1, 2019): 1420. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1420-1424.
Повний текст джерелаАнотація:
The evolution in different areas like e-business contributed to increase the number of data. So, a scalable database is required to accommodate a large number of data. Moreover, this data could be important and the number of accessing for reading or writing operations can make a pressure in some servers more than others, this pressure called unbalanced accessing. This will be achieved if a system that guarantees the distribution of this important data in useful form is used, because the pressure can causes a delay. Accordingly, when we design a multi storage node to keep this data, the pressure will transfer from a server to another. So, the proposed system searched for a satisfied solution to distribute all the users on the servers in a useful way to detect any mistake or repeating updating operation data by using identification feature. This method is applied on Hbase and called Clusterization.
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Ramírez-Rojas, Alejandro, and Elsa Leticia Flores-Márquez. "Nonlinear Statistical Features of the Seismicity in the Subduction Zone of Tehuantepec Isthmus, Southern México." Entropy 24, no. 4 (March 30, 2022): 480. http://dx.doi.org/10.3390/e24040480.
Повний текст джерелаАнотація:
After the M8.2 main-shock occurred on 7 September 2017 at the Isthmus of Tehuantepec, Mexico, the spatial distribution of seismicity has showed a clear clusterization of earthquakes along the collision region of the Tehuantepec Transform/Ridge with the Middle America Trench off Chiapas. Furthermore, nowadays, the temporal rate of occurrence in the number of earthquakes has also showed a pronounced increase. On the basis of this behavior, we studied the sequence of magnitudes of the earthquakes which occurred within the Isthmus of Tehuantepec in southern Mexico from 2010 to 2020. Since big earthquakes are considered as a phase transition, after the M8.2 main-shock, one must expect changes in the Tehuantepec ridge dynamics, which can be observed considering that the b-value in the Gutenberg–Richter law, has also showed changes in time. The goal of this paper is to characterize the behavior of the seismic activity by using the Gutenberg–Richter law, multifractal detrended fluctuation analysis, visibility graph and nowcasting method. Those methods have showed important parameters in order to assess risk, the multifractality and connectivity. Our findings indicate, first that b-value shows a dependency on time, which is clearly described by our analyses based on nowcasting method, multifractality and visibility graph.
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Cherkashyna, T. "Clusterization of post-communist countries of the Central and Eastern Europe by income inequality level." Galic'kij ekonomičnij visnik 72, no. 5 (2021): 41–52. http://dx.doi.org/10.33108/galicianvisnyk_tntu2021.05.041.
Повний текст джерелаАнотація:
Using level of income inequality, the clustering of post-communist countries of the Central and Eastern Europe is carried out by the following indicators: Gini index, share in the national income of the second quintile group, share in the national income of the third quintile group, share in the national income of the fourth quintile group, share in the national income of 10% of the poorest, share in the national income of 20% of the richest.,Сluster analysis (k-means method), in the programming environment Statistica is used as analysis tool and five clusters are obtained. The first cluster includes 8 countries (Albania, Hungary, Poland, Bosnia and Herzegovina, Czech Republic, Сroatia, Russia, Slovakia) is characterized by sufficiently low level of income inequality and can be explained by flow of foreign investment and business transnationalization contributing to the increase of incomes of the main population groups of these countries. The second cluster includes 4 countries (Belarus, Slovenia, Ukraine, Moldova) and is characterized by comparatively low level of income inequality, but high level of property inequality due to heredity, аccumulated wealth та concentration of physical and financial capital by so called «oligarchic clans». The third cluster includes 5 countries (Bulgaria, Montenegro, Macedonia, Romania, Serbia) and is characterized by medium level of income inequality. The fourth and fifth clusters include so called «Baltic tigers» (Latvia, Lihuania, Estonia) and is characterized by high level of income inequality as the result of the occurrence of «excess profits» of financial assets owners. In order to decrease the income inequality in the investigated countries, the following measures are proposed: for the countries of the first cluster to accelerate deconcentration of capital ownership by «spaying» (redemption) of privatized enterprises shares by all categories on preferential terms (so called «ESOP programs»); for the countries of the second cluster to implement progressive tax scale where the tax rate for different groups of population vary depending on the income received and citizens with the lowest incomes (at the level of subsistence minimum or minimum wage) do not pay individual taxes at all; for the countries of the third cluster to cope with «shadow» economy and informal unemployment; for the counties of the fourth and fifth clusters to decrease tax burden on private entrepreneurs and thus stimulate self-employment.
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Santoso, Trio, and Fitria Saftarina. "Clusterization Of Paddy Field Farmers Condition In Kota Metro Lampung Province Indonesia Using K-Means Clustering Algorithm." Journal of Agribusiness and Community Empowerment 3, no. 1 (March 19, 2020): 37–43. http://dx.doi.org/10.32530/jace.v3i1.187.
Повний текст джерелаАнотація:
Metro City is one of the administrative cities in Lampung province, Indonesia and also one of important rice producing regions in Lampung. Application of intensive agriculture in trend of declining area, low quality of land and differences of farmers internal characteristics that requires special treatment as solutions. Clustering farmers using the K-Means Cluster algorithm and Elbow Method can be used to facilitate policy makers determine programs and activities must be taken. Results showed that farmers are ideally grouped into 5 clusters (C1, C2, C3, C4 and C5). C1 members having most family members (4,54 persons). C2 members are the oldest age (68 years old) with longest farming experience (52.00 years) but have lowest formal education (7.67 years), least family members (3.33 person) and lowest total area (0.37 hectare). C3 having highest formal education (14.60 years) and largest paddy fields (0.80 hectare) but don't use any pesticides in paddy cropping management. Whereas farmers in C4 have largest family members helped (2.00 people). Farmers in C5 are the youngest (45.50 years old) and having the shortest experience (29.50 years) but use the most types (4 brands) and amounts of pesticides (400.00 mm.hectare.rotation-1) in paddy field management practices in Metro City.
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Lippolis, Vincenzo, Elisabetta De Angelis, Giuseppina Maria Fiorino, Annalisa Di Gioia, Marco Arlorio, Antonio Francesco Logrieco, and Linda Monaci. "Geographical Origin Discrimination of Monofloral Honeys by Direct Analysis in Real Time Ionization-High Resolution Mass Spectrometry (DART-HRMS)." Foods 9, no. 9 (September 1, 2020): 1205. http://dx.doi.org/10.3390/foods9091205.
Повний текст джерелаАнотація:
An untargeted method using direct analysis in real time and high resolution mass spectrometry (DART-HRMS) combined to multivariate statistical analysis was developed for the discrimination of two monofloral (chestnut and acacia) honeys for their geographical origins—i.e., Italy and Portugal for chestnut honey and Italy and China for acacia honey. Principal Component Analysis, used as an unsupervised approach, showed samples of clusterization for chestnut honey samples, while overlapping regions were observed for acacia honeys. Three supervised statistical approaches, such as Principal Components—Linear Discriminant Analysis, Partial Least Squares—Discriminant Analysis and k-nearest neighbors, were tested on the dataset gathered and relevant performances were compared. All tested statistical approaches provided comparable prediction abilities in cross-validation and external validation with mean values falling between 89.2–98.4% for chestnut and between 85.8–95.0% for acacia honey. The results obtained herein indicate the feasibility of the DART-HRMS approach in combination with chemometrics for the rapid authentication of honey’s geographical origin.
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Khaimovich, I. N., V. M. Ramzaev, and V. G. Chumak. "Multimodel clustering of social networks in social dampening applying BIG DATA (acquiring knowledge from data)." Information Technology and Nanotechnology, no. 2416 (2019): 376–86. http://dx.doi.org/10.18287/1613-0073-2019-2416-376-386.
Повний текст джерелаАнотація:
The developed methodology provides a solution to two essential tasks, thereby revealing the gnoseological potential of Big Data technology: social forecasting in the three most significant areas of the information society based on a model which identifies conditions for social resonance; successful implementation of the social dampening procedure based on the use of appropriate management options using multimodal clusterization of social networks based on Big Data technology. The article suggests the tool that helps to increase work efficiency in the sphere of social dampening in the region. The proposed method of regulation may be efficient when it comes to the control of the regional social dampening processes which have variety of forms and broad range of elements and factors, as well as growth dynamics and active transformation of life activities. At the same time using modern products make it possible to evaluate and show changes on a real-time basis which can be useful for local government authorities.
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Lopatin, Artem, Natalia Ishchenko, Olena Filimonova, and Natalia Rudenko. "Criteria for evaluating and selecting suppliers for maritime enterprises." MATEC Web of Conferences 339 (2021): 01009. http://dx.doi.org/10.1051/matecconf/202133901009.
Повний текст джерелаАнотація:
Supplier selection problem for maritime enterprises might be solved by using a combination of information processing methods. In this article we propose to use an integrated approach to problem analysis, specifically a multi-objectives analysis merged with analytical hierarchical process (AHP-method), k-meaning cluster analysis and Taboo Search as a way of sorting and clusterization of potential suppliers. The main purposes of this article are to provide academic researchers, maritime companies with an evaluation methodology of potential suppliers. In addition, it might be helpful for suppliers to evaluate themselves and direct their attention to the fields that matter the most for their customers. The article identifies supplier selection’s criteria and sub-criteria that fits the most for maritime enterprises such as (a) parts and stores companies or (b) bunkers and lubricators suppliers ‘selection accordingly. Also shows the way to gradate list of potential suppliers based on the compilation of their scores on certain sub-criteria and its weight for customer-company using analytical hierarchical process. In addition, suggested the combination of k-meaning cluster analysis with Taboo Search as a way to choose a group of suppliers for enterprises that prefer to work with couple suppliers.
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Asri, Yessy, Dwina Kuswardani, Efy Yosrita, and Ferdinand Hendrik Wullur. "Clusterization of customer energy usage to detect power shrinkage in an effort to increase the efficiency of electric energy consumption." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (April 1, 2021): 10. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp10-17.
Повний текст джерелаАнотація:
<span>Automatic meter reading (AMR) is a reading system result the measurement of electrical energy consumen, both locally and remotely. The problems faced is the high non-technical shrinkage of AMR customers due to installation, maintenance errors as well as dishonest actions some consumers, this has a major influence on electrical power losses. PT. PLN Disjaya currently faces difficulties having to choose which customers should be checked first, so the field can only find a little damage. The K-means method based on historical electric power usage and determine the most optimal number of groups the davies-bouldin index (DBI) method. Based on the results of testing with 2-6 sets of clusters, the cluster set results are the most optimal is set cluster 4 because it has the smallest DBI value 0.893. The set of 4 clusters has the best performance in data grouping of historical power usage of AMR customers the business class, each centroid of each cluster is used as an attribute and value of the AMR customer power usage business chart. The testing phase is customers who categorized as customers with un-normal usage electricity power. The test is, by determining the distance data testing each centroid in the cluster 4 set.</span>
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Lerner, I. M., V. V. Kondratyev, V. V. Kadushkin, D. V. Shushpanov, and I. V. Vishnyakova. "Information technologies in the formation of clusters of perception of information in students with hearing impairments." Informatics and education, no. 8 (November 10, 2019): 57–63. http://dx.doi.org/10.32517/0234-0453-2019-34-8-57-63.
Повний текст джерелаАнотація:
The article discusses issues with the peculiarity of using information technology to study the features of perception of information in students with hearing impairments. Thanks to the use of information technology, a method for clustering technical science students with hearing disabilities connected with so-called image thinking phenomenon into subgroups with different characteristics of the information perception in the learning process was created.Employed methods and research strategy of the issue are presented in section 1. Based on the methodology of R. Cattell (form C) and using the modification of the test questionnaire of A. Mehrabian non-metric learning assessment scale using novel clusterization algorithm was proposed. Approbation of the proposed scale was conducted during the survey among the university students of technical specialties. The processing of survey results was conducted in Matlab based software, which was developed as an express method that allows to form personal, intellectual characteristics of hearing impaired students, as well as their motivational basis for learning. Analysis of the results obtained by the software is provided in section 2.Section 3 of the article includes discussion of the results and recommendations to adopt the real-virtual environment of education, that allows to increase the efficiency of education for students with different types of hearing disabilities. A novel interaction model between teacher and students is also proposed.
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Dybo, Anna V., Lidia F. Abubakirova, Zukhra K. Aibazova, Oleg R. Hisamov, Evgeniya V. Korovina, Vera S. Maltseva, Oleg A. Mudrak та ін. "Новые результаты в генеалогической классификации тюркских диалектов («случаи с аффрикатами»)". Oriental Studies 13, № 3 (24 грудня 2020): 696–713. http://dx.doi.org/10.22162/2619-0990-2020-49-3-696-713.
Повний текст джерелаАнотація:
Introduction. As is well known, the three Turkic dialectal continua — Tatar-Bashkir, Shor-Khakass-Chulym, and Karachay-Balkar ones — have developed quite distinctive reflexes of proto-Turkic palatal *j- and *č-, *-č(-). While compiling the Dialectological Atlas of Russia’s Turkic Languages, the authors were able to compose exact isoglosses of *j- and *č change in members of the mentioned continua, which made it also possible to partially reevaluate genetic clusterization on the basis of this data. Materials and Methods. Apart from the available publications and archival sources on the three areas in question, the analysis is based on the authors’ extensive field work that involves the use of a set of lexical questionnaires compiled in accordance with known aspects of the Turkic linguistic history. The source recordings for every speaker were turned into idiolectal audio-dictionaries and are linked to an electronic etymological database of the Turkic languages, each elicitation analyzed both with the comprehension method and the software for experimental phonetics. Results. As it turns out, this methodology of field work and post-analysis provides information crucial for detailed linguistic clusterization of dialectal continua in particular and any dialectal system in general. Traditionally, subtle problems of divergence and convergence, problems of archaic and innovative phenomena receive their solutions. The results are as follows. Palatal *j- and *č in the languages of the Khakass-Shor-Chulym group have changed by a strict series of rules none of which could be simultaneous, nor could follow each other in a different order. Thus, the two Middle Chulym dialects — Melet and Tutal ones — prove to lack an immediate linguistic ancestor, the Tutal ‘dialect’ is an archaic version of Mrassu Shor, while Melet is closely related to Kyzyl Khakass. Reflexes of *j- and *č are also principal isoglosses for a previously undocumented Khakass dialect, which does not have any specific affinity with Saghai, Kyzyl and Kachin dialects. Areal analysis of KarachayBalkar shows that dz < proto-Turkic *j- is a secondary development, while, on the other hand, it is finally proven that reflexes *j- > dz~dʑ and *j- > ʑ~z form a more significant isogloss. And for the Tatar-Bashkir dialectal continuum, there were identified three main types of proto-Turkic *jreflexation; a chronology for these three types intermixing during the early period of the continuum is also proposed.
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Bodyanskiy, Ye V., A. Yu Shafronenko, and I. N. Klymova. "ONLINE FUZZY CLUSTERING OF INCOMPLETE DATA USING CREDIBILISTIC APPROACH AND SIMILARITY MEASURE OF SPECIAL TYPE." Radio Electronics, Computer Science, Control 1, no. 1 (March 27, 2021): 97–104. http://dx.doi.org/10.15588/1607-3274-2021-1-10.
Повний текст джерелаАнотація:
Context. In most clustering (classification without a teacher) tasks associated with real data processing, the initial information is usually distorted by abnormal outliers (noise) and gaps. It is clear that “classical” methods of artificial intelligence (both batch and online) are ineffective in this situation.The goal of the paper is to propose the procedure of fuzzy clustering of incomplete data using credibilistic approach and similarity measure of special type.
Objective. The goal of the work is credibilistic fuzzy clustering of distorted data, using of credibility theory.
Method. The procedure of fuzzy clustering of incomplete data using credibilistic approach and similarity measure of special type based on the use of both robust goal functions of a special type and similarity measures, insensitive to outliers and designed to work both in batch and its recurrent online version designed to solve Data Stream Mining problems when data are fed to processing sequentially in real time.
Results. The introduced methods are simple in numerical implementation and are free from the drawbacks inherent in traditional methods of probabilistic and possibilistic fuzzy clustering data distorted by abnormal outliers (noise) and gaps.
Conclusions. The conducted experiments have confirmed the effectiveness of proposed methods of credibilistic fuzzy clustering of distorted data operability and allow recommending it for use in practice for solving the problems of automatic clusterization of distorted data. The proposed method is intended for use in hybrid systems of computational intelligence and, above all, in the problems of learning artificial neural networks, neuro-fuzzy systems, as well as in the problems of clustering and classification.
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Svetukhin, Viacheslav, and Mikhail Tikhonchev. "Effective Atomic Displacements in Fe-9at.%Cr Alloy." Defect and Diffusion Forum 375 (May 2017): 139–49. http://dx.doi.org/10.4028/www.scientific.net/ddf.375.139.
Повний текст джерелаАнотація:
A computer simulation of atomic displacement cascades in Fe-9at.%Cr binary alloy has been performed by molecular dynamics method for temperature of 300 K and cascade energies from 100 eV to 20 keV. The average number of Frenkel pairs produced in cascade has been calculated. The data on point defect clusterization have been obtained. Obtained evaluations of effective fraction of surviving defects are well approximated by the sum of power and linear functions of cascade energy. Increased chromium fraction in the self-interstitial (SIA) configurations has been observed and has been explained by combination of two factors: positive binding energy of Cr atom with SIAs and mobility of SIA configuration. The diffusion coefficient of single SIA configuration in the matrix of pure bcc Fe has been evaluated for the temperature range of 300 – 1000 K. We have prepared 100 group neutron cross-sections of effective displacement generation in Fe-9at.%Cr binary alloy. It has been shown that effective dpa generation rate can be 2-3 times lower than corresponding rates of conventional dpa generation rate.
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Krak, Iurii, Anatoliy Kulias, Valentina Petrovych, and Vladyslav Kuznetsov. "About Methods for Classifying Hidden Language Concepts in Specialized Texts Involving Pseudoinverse, Clustering and Data Grouping." Cybernetics and Computer Technologies, no. 2 (June 30, 2021): 68–75. http://dx.doi.org/10.34229/2707-451x.21.2.7.
Повний текст джерелаАнотація:
This paper discusses the problems of analysis of hidden language concepts in scientific texts in the Ukrainian language, using methods of text mining, dimensionality reduction, grouping of features and linear classifiers. A corpus of scientific texts and dictionaries, as well as stop words and affixes, has been formed for processing specialized texts. The resulting texts were analyzed and converted into text frequency-inverse document frequency (TF-IDF) feature representation. In order to process the feature vector, we propose to use methods of dimensionality rteduction of the data, in particular, the algorithm for the synthesis of linear systems and Karunen – Loeve transform and grouping of features: T-stochastic grouping of nearest neighbors (T-SNE). A series of experiments were performed on test examples, in particular, for the determination of informational density in the text and classification by keywords in specialized texts using the method of random samples consensus (RANSAC). A method of classification of hidden language concepts was proposed, making use of clustering methods (K-means). As a result of the experiment, the structure of the classifier of hidden language concepts was obtained in structured texts was obtained, which gained a relatively high recognition accuracy (97 – 99 %) using such linear classification algorithms: decision trees and extreme gradient boost machine. The stability of the proposed method is investigated by using the perturbation of the original data by a variational autoencoder, test runs shown that sparse autocoder reduces the mean square error, but the separation band decreases, which affects the convergence of the classification algorithm. In further research, we propose to apply other methods of analysis of structured texts and ways to improve the separability of specialized texts with similar authorial styles and different topic using a proposed set of parameters. Keywords: text processing, language concepts, pseudoinverse, clusterization, methods of data groupings.
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Opromolla, Roberto, Giancarmine Fasano, Michele Grassi, Al Savvaris, and Antonio Moccia. "PCA-Based Line Detection from Range Data for Mapping and Localization-Aiding of UAVs." International Journal of Aerospace Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/4241651.
Повний текст джерелаАнотація:
This paper presents an original technique for robust detection of line features from range data, which is also the core element of an algorithm conceived for mapping 2D environments. A new approach is also discussed to improve the accuracy of position and attitude estimates of the localization by feeding back angular information extracted from the detected edges in the updating map. The innovative aspects of the line detection algorithm regard the proposed hierarchical clusterization method for segmentation. Instead, line fitting is carried out by exploiting the Principal Component Analysis, unlike traditional techniques relying on least squares linear regression. Numerical simulations are purposely conceived to compare these approaches for line fitting. Results demonstrate the applicability of the proposed technique as it provides comparable performance in terms of computational load and accuracy compared to the least squares method. Also, performance of the overall line detection architecture, as well as of the solutions proposed for line-based mapping and localization-aiding, is evaluated exploiting real range data acquired in indoor environments using an UTM-30LX-EW 2D LIDAR. This paper lies in the framework of autonomous navigation of unmanned vehicles moving in complex 2D areas, for example, being unexplored, full of obstacles, GPS-challenging, or denied.