The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.De...The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.展开更多
The libration control problem of space tether system(STS)for post-capture of payload is studied.The process of payload capture will cause tether swing and deviation from the nominal position,resulting in the failure o...The libration control problem of space tether system(STS)for post-capture of payload is studied.The process of payload capture will cause tether swing and deviation from the nominal position,resulting in the failure of capture mission.Due to unknown inertial parameters after capturing the payload,an adaptive optimal control based on policy iteration is developed to stabilize the uncertain dynamic system in the post-capture phase.By introducing integral reinforcement learning(IRL)scheme,the algebraic Riccati equation(ARE)can be online solved without known dynamics.To avoid computational burden from iteration equations,the online implementation of policy iteration algorithm is provided by the least-squares solution method.Finally,the effectiveness of the algorithm is validated by numerical simulations.展开更多
基金supported by the National Key Research and Development Plan in China(Grant No.2020YFB1005500)。
文摘The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.
基金supported by the National Natural Science Foundation of China(No.62111530051)the Fundamental Research Funds for the Central Universities(No.3102017JC06002)the Shaanxi Science and Technology Program,China(No.2017KW-ZD-04).
文摘The libration control problem of space tether system(STS)for post-capture of payload is studied.The process of payload capture will cause tether swing and deviation from the nominal position,resulting in the failure of capture mission.Due to unknown inertial parameters after capturing the payload,an adaptive optimal control based on policy iteration is developed to stabilize the uncertain dynamic system in the post-capture phase.By introducing integral reinforcement learning(IRL)scheme,the algebraic Riccati equation(ARE)can be online solved without known dynamics.To avoid computational burden from iteration equations,the online implementation of policy iteration algorithm is provided by the least-squares solution method.Finally,the effectiveness of the algorithm is validated by numerical simulations.
