The parameter identification problem of linear discrete period time‐varying (LDPV) systems is studied in this paper and a two‐step identification algorithm based on the recursive least squares principle is proposed. The recursive principle‐based identification algorithm is mostly used to deal with the parameter identification of time‐invariant systems. Combine with the cyclic reconstruction method, the LDPV system is converted to a linear time‐invariant system. Then, based on this and combined with recursive least squares identification, a discrimination algorithm is proposed for the parameter estimation of the LDPV system. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed algorithm.
As a trusted decentralized application, smart contracts manage a large number of digital assets on the blockchain. Vulnerability detection of smart contracts is an important part of ensuring the security of digital assets. At present, many researchers extract features of smart contract source code for vulnerability detection based on deep learning methods. However, the current research mainly focuses on the single representation form of the source code, which cannot fully obtain the rich semantic and structural information contained in the source code, so it is not conducive to the detection of various and complex smart contract vulnerabilities. Aiming at this problem, this paper proposes a vulnerability detection model based on the fusion of syntax and semantic features. The syntactic and semantic representation of the source code is obtained from the abstract syntax tree and control flow graph of the smart contract through TextCNN and Graph Neural Network. The syntactic and semantic features are fused, and the fused features are used to detect vulnerabilities. Experiments show that the detection accuracy and recall rate of this model have been improved on the detection tasks of five types of vulnerabilities, with an average precision of 96% and a recall rate of 90%, which can effectively identify smart contract vulnerabilities.
The problem of solving a class of Sylvester-conjugate periodic matrix equations is investigated in this paper. Utilising conjugate gradient method, an iterative algorithm is provided, from which a matrix sequence can be generated to approximate the unknown matrix of the equation to be solved. Theoretical derivation proves that the proposed algorithm is convergent starting from any initial value, and simulation examples show the effectiveness of the proposed algorithm.
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