Small, medium and micro enterprises are an important force in my country's economic and social development. On the one hand, development creates regional GDP and on the other hand provides a large number of employ...
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Small, medium and micro enterprises are an important force in my country's economic and social development. On the one hand, development creates regional GDP and on the other hand provides a large number of employment opportunities. At present, most of the small, medium and micro enterprises have their own imperfect development, and there are problems such as small business scale, irregular management, and information asymmetry. To avoid risks, commercial banks do not actively provide loans for small, medium and micro enterprises. Small, medium and micro enterprises have long faced financing difficulties. The problem. This article first uses data processing software to classify and summarize the credit data of 123 companies with credit records, establishes 10 quantitative indicators represented by reputation risk level and default risk level, and classifies and visualizes each impact indicator, to find out the correlation between the indicators. Furthermore, based on the analysis results of the indicators, a mathematical model of the multidimensional scaling algorithm among the 10 indicators is established, and the deep correlation classification of the indicators among the enterprises is analyzed.
In this paper, we analyze the nontrivial zeros of the Riemann zeta-function using the mul-tidimensional scaling (MDS) algorithm and computational visualization features. The non -trivial zeros of the Riemann zeta-func...
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In this paper, we analyze the nontrivial zeros of the Riemann zeta-function using the mul-tidimensional scaling (MDS) algorithm and computational visualization features. The non -trivial zeros of the Riemann zeta-function as well as the vectors with several neighboring zeros are interpreted as the basic elements (points or objects) of a data set. Then we em-ploy a variety of different metrics, such as the Jeffreys and Lorentzian ones, to calculate the distances between the objects. The set of the calculated distances is then processed by the MDS algorithm that produces the loci, organized according to the objects features. Then they are analyzed from the perspective of the emerging patterns. Surprisingly, in the case of the Lorentzian metric, this procedure leads to the very clear periodical structures both in the case of the objects in form of the single nontrivial zeros of the Riemann zeta-function and in the case of the vectors with a given number of neighboring zeros. The other tested metrics do not produce such periodical structures, but rather chaotic ones. In this paper, we restrict ourselves to numerical experiments and the visualization of the produced results. An analytical explanation of the obtained periodical structures is an open problem worth for investigation by the experts in the analytical number theory. (c) 2021 Elsevier B.V. All rights reserved.
A validation of the graininess attribute was made by means of a psychophysical experiment and the multidimensional scaling algorithm. A visual experiment was designed to obtain graininess differences to be used like t...
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A validation of the graininess attribute was made by means of a psychophysical experiment and the multidimensional scaling algorithm. A visual experiment was designed to obtain graininess differences to be used like the dissimilarity matrix in the MDS algorithm. The results revealed that two dimensions are needed to characterize the graininess effect. The BYK-mac-i instrument and a gonio-hyperspectral imaging system were employed to evaluate the statistical dimensions. On one hand, the first dimension correlated well with the graininess value provided by the BYK-mac-i (r(2) = 0.9566). However, we were unable to find a relationship with dimension 2 and any parameter measured by this instrument. Furthermore, the images captured by the gonio-hyperspectral imaging system were processed. A good relationship with the correlation parameter was observed (r(2) = 0.8958). However, no relationship was established with dimension 2. Based on these conclusions, further research is necessary that focuses on the new imaging processes and a new visual experiment.
Mobile station (MS) localisation that plays an important role in the process of target continuous localisation has received considerable attention. In this study, a new framework based on subspace approach for positio...
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Mobile station (MS) localisation that plays an important role in the process of target continuous localisation has received considerable attention. In this study, a new framework based on subspace approach for positioning an MS at minimum localisation system with the use of time-of-arrival measurements is introduced. Unlike ordinary multidimensional scaling algorithm using eigendcomposition or inverse computation to estimate the MS position, a computationally simple weighting estimator is proposed by introducing Lagrange multiplier and mean-square error weighting matrix. Computer simulations are included to corroborate the theoretical development and to contrast the estimator performance with several conventional algorithms as well as the Cramer-Rao lower bound (CRLB). It is shown that the new method with low computational complexity attains the CRLB for zero-mean white Gaussian range error at moderate noise level.
Wireless sensor networks have been successfully applied in a wide range of application domains. However, because of the properties of wireless signals, Wireless sensor networks applications in underground environments...
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Wireless sensor networks have been successfully applied in a wide range of application domains. However, because of the properties of wireless signals, Wireless sensor networks applications in underground environments have been limited. In this paper, we present a Kalman-filter-based localisation algorithm for use in a Wireless sensor networks deployed in a sub-surface mine for environmental monitoring to identify the positions of a large number of miners, each carrying a wireless mobile node. To improve the positioning accuracy even when current measurements are not available, we enhance the estimates of the received signal strength indication (RSSI) signal intensity and range obtained from the Kalman filter by adjusting them using the elastic particle model. Then, we obtain the distance matrix of the WSN based on arrival of angle and the cosine theorem. Finally, we determine the final positions of all mobile nodes using a multidimensional scaling algorithm.
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