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PurposeThe purpose of this paper is to establish a grey cloud incidence clustering model to assess the drought disaster degree under 15 indexes in 18 cities of Henan province.Design/methodology/approachThe grey incidence degree between each index and ideal index is used to determine the index weight and combined with the subjective weight, the comprehensive weight is given; the traditional possibility function is transformed into grey cloud possibility function by using the principle of maximum deviation and maximum entropy, which fully reflects the coexistence of multiple decision-making conclusions and constructs the grey cloud incidence clustering model.FindingsThe drought disaster degree of Henan province is divided into four grades under the selected 15 indexes. The drought grades show obvious regional differences. The risk levels of the east and southwest are higher, and the risk levels of the north and southeast are lower. This result is consistent with the study of drought disaster grades in Henan province, which shows the practicability and usefulness of the model.Practical implicationsIt provides an effective method for the assessment of drought disaster grade and the basis for formulating disaster prevention and mitigation plan.Originality/valueBy studying the method of multiattribute and multistage decision-making with interval grey number information. The objective weight model of index value is designed, and the subjective weight is given by experts. On the basis of the two, the comprehensive weight of subjective and objective combination is proposed, which effectively weakens the randomness of subjective weight and reasonably reflects the practicality of index decision-making. The time weight reflects the dynamic of the index. The traditional possibility function is transformed into the grey cloud possibility function, which effectively takes advantage of the grey cloud model in dealing with the coexistence of fuzzy information, grey information and random information. Thus, the conflict between the decision-making results and the objective reality is effectively solved. The interval grey number can make full use of the effective information and improve the accuracy of the actual information.

Providing source privacy is a critical security service for sensor networks. However, privacy preserving in sensor networks is a challenging task, particularly due to the limited resources of sensor nodes and the threat of node capture attack. On the other hand, existing works use either random walk path or fake packets injection, both incurring tremendous overhead. In this work, we propose a new approach, which separates the sensor nodes into groups. The source packet is randomly forwarded within and between the groups with elaborate design to ensure communication anonymity; furthermore, members of each group exchange encrypted traffic of constant packet length to make it difficult for the adversary to trace back. One salient feature of the proposed scheme is its flexibility of trading transmission for higher anonymity requirement. We analyze the ability of our proposed scheme to withstand different attacks and demonstrate its efficiency in terms of overhead and functionality when compared to existing works.

PurposeWith the prosperity of grey extension models, the form and structure of grey forecasting models tend to be complicated. How to select the appropriate model structure according to the data characteristics has become an important topic. The purpose of this paper is to design a structure selection method for the grey multivariate model.Design/methodology/approachThe linear correction term is introduced into the grey model, then the nonhomogeneous grey multivariable model with convolution integral [NGMC(1,N)] is proposed. Then, by incorporating the least absolute shrinkage and selection operator (LASSO), the model parameters are compressed and estimated based on the least angle regression (LARS) algorithm.FindingsBy adjusting the values of the parameters, the NGMC(1,N) model can derive various structures of grey models, which shows the structural adaptability of the NGMC(1,N) model. Based on the geometric interpretation of the LASSO method, the structure selection of the grey model can be transformed into sparse parameter estimation, and the structure selection can be realized by LASSO estimation.Practical implicationsThis paper not only provides an effective method to identify the key factors of the agricultural drought vulnerability, but also presents a practical model to predict the agricultural drought vulnerability.Originality/valueBased on the LASSO method, a structure selection algorithm for the NGMC(1,N) model is designed, and the structure selection method is applied to the vulnerability prediction of agricultural drought in Puyang City, Henan Province.

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The focus of this paper is the improvement of the quality of texture image segmentation. We proposed a new unsupervised segmentation method based on Overcomplete Brushlet transform and Gaussian Markov Random Field. A texture image was transformed to Overcomplete Brushlet domain, in order to extract its high dimensional singularity information. In view of the influences of the spectral information and the spatial correlations between pixels to the segmentation result, Markov Random Filed model is used in the process of both feature extraction and region segmentation: Gauss Markov model is used to evaluate the arguments of the feature field; the probability of the marker field is calculated through Gibbs distribution function based on the second order neighborhood system of MRF. MAP criterion is adopted to obtain segmentation results. We did a lot of contrast experiments, using this paper's algorithm, Markov Random Field algorithm in wavelet domain and Markov Random Field algorithm in Brushlet domain. Those experiment results indicate that this paper's algorithm is an effective segmentation algorithm for it can detect better texture direction information and keep better regional consistency than other two traditional algorithms.