Grey incidence clustering method based on multidimensional dynamic time warping distance

The traditional grey incidence degree is mainly based on the distance analysis methods, which is measured by the displacement difference between corresponding points between sequences. When some data of sequences are missing (inconsistency in the length of the sequences), the only way is to delete the longer sequences or to fill the shorter sequences. Therefore, some uncertainty is introduced. To solve this problem, by introducing three-dimensional grey incidence degree (3D-GID), a novel GID based on the multidimensional dynamic time warping distance (MDDTW distance-GID) is proposed. On the basis of it, the corresponding grey incidence clustering (MDDTW distance-GIC) method is constructed. It not only has the simpler computation process, but also can be applied to the incidence comparison between uncertain multidimensional sequences directly. The experiment shows that MDDTW distance-GIC is more accurate when dealing with the uncertain sequences. Compared with the traditional GIC method, the precision of the MDDTW distance-GIC method has increased nearly 30%.

Numerical simulation research on response characteristics of surrounding rock for deep super-large section chamber under dynamic and static combined loading condition

The stability control of surrounding rock for large or super-large section chamber is a difficult technical problem in deep mining condition.Based on the in-site geological conditions of Longgu coal mine,this paper used the dynamic module of FLAC3D to study the response characteristics of deep super-large section chamber under dynamic and static combined loading condition.Results showed that under the static loading condition,the maximum vertical stress,deformation and failure range are large,where the stress concentration coefficient is 1.64.The maximum roof-to-floor and two-sides deformations are 54.6 mm and 53.1 mm,respectively.Then,under the dynamic and static combined loading condition:(1)The influence of dynamic load frequency on the two-sides is more obvious;(2)The dynamic load amplitude has the greatest influence on the stress concentration degree,and the plastic failure tends to develop to the deeper;(3)With the dynamic load source distance increase,the response of surrounding rock is gradually attenuated.On this basis,empirical equations for each dynamic load conditions were obtained by using regression analysis method,and all correlation coefficients are greater than 0.99.This research provided reference for the supporting design of deep super-large section chamber under same or similar conditions.

3D cluster members and near-infrared distance of open cluster NGC 6819

In order to obtain clean members of the open cluster NGC 6819, the proper motions and radial velocities of 1691 stars are used to construct a three-dimensional （3D） velocity space. Based on the DBSCAN clustering algorithm, 537 3D cluster members are obtained. From the 537 3D cluster members, the average radial velocity and absolute proper motion of the cluster are Vr = ＋2.30 ±0.04 km s-1 and （PMRA, PMDec） = （-2.5 ±0.5, -4.3 ± 0.5） mas yr-1, respectively. The proper motions, radial velocities, spatial positions and color-magnitude diagram of the 537 3D members indicate that our membership determination is effective. Among the 537 3D cluster members, 15 red clump giants can be easily identified by eye and are used as reliable standard candles for the distance estimate of the cluster. The distance modulus of the cluster is determined to be （m - M）0 -- 11.86 ± 0.05 mag （2355 ±54 pc）, which is quite consistent with published values. The uncertainty of our distance mod- ulus is dominated by the intrinsic dispersion in the luminosities of red clump giants （--0.04 mag）.

SSDBA:the stretch shrink distance based algorithm for link prediction in social networks

In the field of social network analysis,Link Predic-tion is one of the hottest topics which has been attracted attentions in academia and industry.So far,literatures for solving link prediction can be roughly divided into two categories:similarity-based and learning-based methods.The learning-based methods have higher accuracy,but their time complexities are too high for complex networks.However,the similarity-based methods have the advantage of low time consumption,so improving their accuracy becomes a key issue.In this paper,we employ community structures of social networks to improve the prediction accuracy and propose the stretch shrink distance based algorithm(SSDBA),In SSDBA,we first detect communities of a social network and identify active nodes based on community average threshold(CAT)and node average threshold(NAT)in each community.Second,we propose the stretch shrink distance(SSD)model to iteratively calculate the changes of distances between active nodes and their local neighbors.Finally,we make predictions when these links'distances tend to converge.Furthermore,extensive parameters learning have been carried out in experiments.We compare our SSDBA with other popular approaches.Experimental results validate the effectiveness and efficiency of proposed algorithm.