Self-Organising (Kohonen) Maps for the Vietnam Banking Industry

This is the first study to use the self-organisation (Kohonen) map technique, an artificial neural network based on a non-supervised learning algorithm, to categorise Vietnamese banks into super-class groups. Drawing on unbalanced yearly data from 2008 to 2017, this study identifies two super-class groups (one and two). While group one consists of joint stock banks, group two consists of commercial state and joint stock banks. Using the non-structural indicator, the Lerner index, to capture market power, and the data enveloped analysis technique to measure bank performance, our result shows significant differences in Lerner scores (which represent bank market power) of the two groups of banks. Differences in the Lerner scores provide evidence of a group of strong banks that is isolated from other banks. This implies that this strong bank group has the potential to be monopolist and impairs Vietnam’s competitive banking environment. The reason is that group two banks may be more profitable due to greater market power, whereas group one banks may struggle to cut costs to remain viable. These findings provide a better understanding for bank executives, policymakers and regulators of the Vietnam banking industry, and ensure an efficient and competitive Vietnam banking environment.

Identification of acoustic emission sources under cyclic loading of the polymer composite material used in shipbuilding

Данная работа посвящена исследованию механизмов накопления повреждений в полимерном композиционном материале (ПКМ) в ходе усталостного нагружения. Механическое испытание образца стеклопластика проводили циклическим растяжением в сопровождении регистрации акустической эмиссии (АЭ). Для зарегистрированных сигналов АЭ рассчитывались спектры Фурье и использовались для кластеризации самоорганизующейся картой Кохонена (SOM). Полученные центроиды, для снижения количества анализируемых кластеров, разделяли на кластеры методом k-средних. Кластеры второго этапа кластеризации соотносились с типами повреждений в структуре ПКМ. Рассчитывались периоды критической интенсивности регистрации различных типов образующихся повреждений. Дополнительно проведён анализ пиковых частот уровней вейвлет декомпозиции Добеши 14 сигналов АЭ. На основании проведенных методов анализа данных АЭ описаны протекающие процессы разрушения в образце ПКМ. This work is aimed the study the mechanisms of damage accumulation in a polymer composite material (PCM) during fatigue loading. Mechanical test of a fiberglass sample was done by cyclic tension with acoustic emission (AE) registration. The Fourier spectra were calculated for the recorded AE signals and used for clustering with a self-organizing Kohonen map (SOM). The obtained centroids, in order to reduce the number of analyzed clusters, were divided into clusters by the k-means method. Clusters of the second stage clustering correlated with the types of damage in the structure of the PCM. The periods of the critical intensity of registration of various types of formed damages were calculated. Additionally, the peak frequencies of the levels of Daubechies 14 wavelet decomposition of AE signals was analyzed. Based on the methods for analyzing the AE data, the processes of destruction in the PCM sample are described.

ABOUT IMPLEMENTATION OF NEUFAL NETWORKS IN SCILAB

An example of the implementation of the Kohonen map in SCILAB is given.

Application of Data Mining Methods in the Design and Creation of New Products and Services

The purpose of research is to substantiate the need to use knowledge extraction methods in the design and creation of new products and services and the feasibility of using the Kohonen self-organizing map method through its formation. Such a map helps to identify previously unknown groups, in particular, as in the case of this article – consumer groups, and their analysis will make it possible to form new tariffs for the services of the mobile operator’s billing system. The main reason for the research is to show organizations the ability to design and create innovative products.Research methods are empirical in nature, based on the collection and accumulation of data on consumer behavior in the market and their subsequent analysis. In order to analyze the collected data, Data Mining methods are used, in particular, the Kohonen self-organizing map method, which allows to obtain automatic clustering of consumers in the market by various characteristics. Clustering was performed using the Kohonen self-organizing map algorithm implemented in the BaseGroup Labs Deductor Studio analytical platform. The choice of this software product is explained by a clear interface and the availability of the required functionality. The study was based on data provided by the mobile operator’s billing system. This is a fairly large amount of data showing the completed operations of mobile operator subscribers.Results. The article provides an overview of sources that offer possible methods for extracting knowledge and ways to process it. The Kohonen map is also built, which allows you to get information about the current situation for mobile subscribers from various independently selected areas. After analyzing this information, the revealed knowledge is applied in the formation of new tariffs and services of the mobile operator. This method of extracting knowledge can also be applied to other large volumes of data from various fields of activity. However, there is a limitation when using this type of knowledge extraction, which is that the data must be structured. If you use unstructured data, you can consider other methods for extracting knowledge described in this article.Conclusion. The article considers the stage of knowledge extraction when designing and creating new products and services based on Data Mining methods, in particular the self-organizing Kohonen map. Innovation in the design and creation of products and services is emphasized by the variability of data in accordance with the dynamic behavior of consumers in the market, which causes the need to periodically review the requirements and concepts of products and services brought to the market.

Application of Neural Networks and Evolutionary Algorithms to Solve Energy Optimization and Unit Commitment for a Smart City

The optimization problem of two or more special-purpose functions of the energy system is subjected to an analysis. Based on experience of our research and general knowledge of partial solutions of energy system optimization at the level of control of production and power energy supply by energy companies in the Czech Republic, a special-purpose (cost) function has been defined. By analysing the special-purpose function, penalty and limitations have been defined. Using the fuzzy logic, a set of suitable solutions for the special-purpose function is accepted. An optimum of the special-purpose function is looked for using the simulated annealing method. The history of electricity consumption is sorted by day and by hour, representing the multidimensional data. When using the cluster analysis, type daytime diagrams of consumption are defined. Type daytime diagrams form prototypes of identified clusters. The so-called self-organizing neural network with Kohonen map attached is used to perform the cluster analysis. The result of our research is presented by an experiment.

QoS to QoE Mapping Function for Iptv Quality Assessement Based on Kohonen Map: A Pilot Study

This paper deals with an analysis of Kohonen map usage possibility for real-time evaluation of end-user video quality perception. The Quality of Service framework (QoS) describes how the network impairments (network utilization or packet loss) influence the picture quality, but it does not reflect precisely on customer subjective perceived quality of received video stream. There are several objective video assessment metrics based on mathematical models trying to simulate human visual system but each of them has its own evaluation scale. This causes a serious problem for service providers to identify a critical point when intervention into the network behaviour is needed. On the other hand, subjective tests (Quality of Experience concept) are time-consuming and costly and of course, cannot be performed in real-time. Therefore, we proposed a mapping function able to predict subjective end-user quality perception based on the situation in a network, video stream features and results obtained from the objective video assessment method.

The influence of the internally displaced persons forced migration on the dynamics of regional social and economic security indicators

The processes of forced internal migration, which became significant in 2014 as a result of the armed conflict in the east of the country, caused significant demographic and social changes in the regions performance. Particularly large changes have been taken place in the areas directly adjacent to the joint forces operation zone. The study is devoted to the research of the impact of the described processes on certain aspects of social and economic security of the regions. Impact assessment was performed on the basis of cluster analysis. In particular, the author constructed a neural network such as the Kohonen map. The model divided the neural sample from 25 regions (24 regions and the city of Kyiv) into six clusters according to the level of four indicators of social and economic security. This allowed assessing the impact of forced internal migration on some aspects of social and economic security of the regions. Based on the obtained map, it has been depicted that Donetsk and Luhansk regions, which directly border the joint forces operation zone, had a dramatic increase in the demographic burden and unemployment rate during the study period. The obtained results allowed assessing the impact of the processes of forced internal migration on the dynamics of certain indicators of social and economic security of the territories.