DEGREE OF APPROXIMATION ASSOCIATED WITH SOME ELLIPTIC OPERATORS AND ITS APPLICATIONS

The Jackson-type estimates by using some elliptic operators will be achieved. These results will be used to characterize the regularity of some elliptic operators by means of the approximation degree and the saturation class of the multivariate Bernstein operators as well.

The Degree of Association between Concepts Focusing on the Viewpoint

For a computer to perform intelligent information processing requires functions that can extract concepts from words, as humans do, and then associate those concepts with related concepts. In order to implement this association function, it is necessary to quantify the degree of association between two concepts. In the present paper, we propose a method for quantifying degree of association focusing on the viewpoint that uses a concept base （a knowledge base that expresses concepts as a collection of pairs, each pair consisting of an attribute word used to describe the concept and a weighting that expresses the word＇s importance）. Here, ＂Viewpoint＂ is the perspective from which a concept is viewed; for example, consider the degree of association between ＂airplane＂ and ＂automobile＂, and the degree of association between ＂airplane＂ and ＂bird＂. From the viewpoint of ＂vehicle＂, ＂airplane＂ and ＂automobile＂ are highly related, while from the viewpoint of ＂flight＂, ＂airplane＂ and ＂bird＂ are highly related. We present herein a comparison of two methods for calculating degree of association focusing on the viewpoint, and demonstrate that the method involving modulation of attribute weightings based on viewpoint results in degree of association calculations that are closer to human senses.

Spatiotemporal characteristics and trend assessment of Ms≥5.6 earthquakes in the Tianshan area of China based on co-occurrence analysis and commensurability

Based on the complete seismic data of the Tianshan area in the past 118 years,the co-occurrence rate of earthquakes in different fault zones is calculated,the fault zone with the highest associative degree in the Tianshan area is determined as the research object,extracted the earthquake time series information of the strong associative fault zone and the commensurability method is used to obtain the commensurability expression,the year of strongest earthquake signal is calculated and predicted,then the butterfly structure diagram and commensurability structure system are drawn.Combined the butterfly structure diagram and commensurability structure system to analyze the seismic spatiotemporal structure characteristics of the Tianshan area,judge the trend of future earthquakes,and predict the time of future earthquakes.The results demonstrate that Ms≥5.6 earthquake signals are strong in 2020 and 2021.In order to determine the location of future earthquakes,the historical epicenter migration law of the strongly correlated fault zone in the Tianshan area is analyzed.It is revealed that the epicenter distribution is symmetric in both the longitude and latitude directions.Analyzing the spatial distribution of epicenters,the five successive earthquakes in the strong associative fault zone in the Tianshan area present a pentagonal symmetrical structure in space.It is judged that the next earthquake will migrate toward the northeast.Additionally,the sunspot has a strong seismic correlation with the strong associative fault zone in the Tianshan area.Specifi cally,on the 11-year cycle scale of sunspot activity,64.71% of earthquakes occurred in the fluctuation descending range;on the monthly scale of sunspot activity.Hence,it has been verified that the proposed disaster prediction method based on commensurability theory is scientifi c and has a broad application prospect.

Emotion Judgment System by EEG Based on Concept Base of EEG Features

This paper proposes an emotion judgment system by using an electroencephalogram(EEG)feature concept base with premise of noises included.This method references the word concept association system,which associates one word with other plural words and decides the relationship between several words.In this proposed emotion judgment system,the source EEG is input and 42 EEG features are constructed by EEG data;the data are then calculated by spectrum analysis and normalization.All 2945 EEG data of 4 emotions in the EEG data emotion knowledge base are calculated by the degree of association for getting the nearest EEG data from the EEG feature concept base constructed by 2844 concepts.From the experiment,the accuracy of the proposed system was 55.9%,which was higher than the support vector machine(SVM)method.As this result,the chain structured feature of the EEG feature concept base and the efficiency by the calculation of degree of association for EEG data help reduce the influence of the noise.

Environmental stress level evaluation approach based on physical model and interval grey association degree

Associating environmental stresses （ESs） with built-in test （BIT） output is an important means to help diagnose intermittent faults （IFs）. Aiming at low efficiency in association of traditional time stress measurement device （TSMD）, an association model is built. Thereafter, a novel approach is given to evaluate the integrated environmental stress （IES） level. Firstly, the selection principle and approach of main environmental stresses （MESs） and key characteristic parameters （KCPs） are presented based on fault mode, mechanism, and ESs analysis （FMMEA）. Secondly, reference stress events （RSEs） are constructed by dividing IES into three stress levels according to its impact on faults; and then the association model between integrated environmental stress event （IESE） and BIT output is built. Thirdly, an interval grey association approach to evaluate IES level is proposed due to the interval number of IES value. Consequently, the association output can be obtained as well. Finally, a case study is presented to demonstrate the proposed approach. Results show the proposed model and approach are effective and feasible. This approach can be used to guide ESs measure, record, and association. It is well suited for on-line assistant diagnosis of faults, especially IFs.

Fuzzy Association Degree with Delayed Time in Temporal Data Model

This paper presents an expression of the semantic proximity. Based on the temporal data model, a method of the temporal approximation is given. Using these concepts, this paper provides an evaluated method of fuzzy and dynamic association degree with delayed time and a superposition method of association degrees. Particularly, by means of the fuzzy and dynamic association degree, the connection between the weather data of two regions can be discovered.