In many batch processes, there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independen...In many batch processes, there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independent method, and in the feature extraction there is not fully taken into account the characterization of fault information, it will make the process monitoring ineffective, so a fault monitoring method based on WGNPE(weighted global neighborhood preserving embedding)–GSVDD(greedy support vector data description) related and independent variables is proposed. First, mutual information method is used to separate the related variables and independent variables. Secondly, WGNPE method is used to extract the local and global structures of the related variables in batch process and highlight the fault information, GSVDD method is used to extract the process information of the independent variables quickly and effectively. Finally, the statistical monitoring model is established to achieve process monitoring based on WGNPE and GSVDD. The effectiveness of the proposed method was verified by the penicillin fermentation process.展开更多
In this paper,a case study is carried out in comparison of pipes-and-filters architecture and batch sequential architecture.Concepts on a data flow system and the two mentioned architectures are presented.A Java templ...In this paper,a case study is carried out in comparison of pipes-and-filters architecture and batch sequential architecture.Concepts on a data flow system and the two mentioned architectures are presented.A Java template class design in implementing the "pipes" and "filters" in the pipes-and-filters architecture is given at the design level.Finally,this paper uses a concrete example to show how to use Java to implement the pipesand-filters architecture.Using varied amount of data from text files,performance and memory usage of the two architectures are illustrated.展开更多
In the scenario of large-scale data ownership transactions,existing data integrity auditing schemes are faced with security risks from malicious third-party auditors and are inefficient in both calculation and communi...In the scenario of large-scale data ownership transactions,existing data integrity auditing schemes are faced with security risks from malicious third-party auditors and are inefficient in both calculation and communication,which greatly affects their practicability.This paper proposes a data integrity audit scheme based on blockchain where data ownership can be traded in batches.A data tag structure which supports data ownership batch transaction is adopted in our scheme.The update process of data tag does not involve the unique information of each data,so that any user can complete ownership transactions of multiple data in a single transaction through a single transaction auxiliary information.At the same time,smart contract is introduced into our scheme to perform data integrity audit belongs to third-party auditors,therefore our scheme can free from potential security risks of malicious third-party auditors.Safety analysis shows that our scheme is proved to be safe under the stochastic prediction model and k-CEIDH hypothesis.Compared with similar schemes,the experiment shows that communication overhead and computing time of data ownership transaction in our scheme is lower.Meanwhile,the communication overhead and computing time of our scheme is similar to that of similar schemes in data integrity audit.展开更多
针对作业车间环境具有能力受限的批量调度问题(capacitated lot-sizing and scheduling problem,CLSP),提出基于改进蜜獾算法和神经网络的混合优化算法,以此来应对需求和处理时间的不确定性。首先,考虑需求和处理时间受到不确定性影响,...针对作业车间环境具有能力受限的批量调度问题(capacitated lot-sizing and scheduling problem,CLSP),提出基于改进蜜獾算法和神经网络的混合优化算法,以此来应对需求和处理时间的不确定性。首先,考虑需求和处理时间受到不确定性影响,构建基于可满足性模理论的确定性模型,引入安全库存和安全松弛两个弹性参数,以运营总成本最低为优化目标,建立需求不确定下的鲁棒优化模型;其次,提出基于改进蜜獾算法和神经网络的混合算法,利用混沌理论生成伪随机值,估计安全参数的标称值和变化幅度,提高算法速度;最后,进行示例验证,结果表明所提算法可优化调度准则,减小最优性差距,有效解决具有延期订单许可的问题,降低平均短缺成本。展开更多
基金Supported by the National Natural Science Foundation of China(No.61763029)the Natural Science Foundation of Gansu Province(1610RJZA016)
文摘In many batch processes, there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independent method, and in the feature extraction there is not fully taken into account the characterization of fault information, it will make the process monitoring ineffective, so a fault monitoring method based on WGNPE(weighted global neighborhood preserving embedding)–GSVDD(greedy support vector data description) related and independent variables is proposed. First, mutual information method is used to separate the related variables and independent variables. Secondly, WGNPE method is used to extract the local and global structures of the related variables in batch process and highlight the fault information, GSVDD method is used to extract the process information of the independent variables quickly and effectively. Finally, the statistical monitoring model is established to achieve process monitoring based on WGNPE and GSVDD. The effectiveness of the proposed method was verified by the penicillin fermentation process.
文摘In this paper,a case study is carried out in comparison of pipes-and-filters architecture and batch sequential architecture.Concepts on a data flow system and the two mentioned architectures are presented.A Java template class design in implementing the "pipes" and "filters" in the pipes-and-filters architecture is given at the design level.Finally,this paper uses a concrete example to show how to use Java to implement the pipesand-filters architecture.Using varied amount of data from text files,performance and memory usage of the two architectures are illustrated.
基金supported by National Key R&D Program of China(2020YFB1005900)the National Natural Science Foundation of China(62072051).
文摘In the scenario of large-scale data ownership transactions,existing data integrity auditing schemes are faced with security risks from malicious third-party auditors and are inefficient in both calculation and communication,which greatly affects their practicability.This paper proposes a data integrity audit scheme based on blockchain where data ownership can be traded in batches.A data tag structure which supports data ownership batch transaction is adopted in our scheme.The update process of data tag does not involve the unique information of each data,so that any user can complete ownership transactions of multiple data in a single transaction through a single transaction auxiliary information.At the same time,smart contract is introduced into our scheme to perform data integrity audit belongs to third-party auditors,therefore our scheme can free from potential security risks of malicious third-party auditors.Safety analysis shows that our scheme is proved to be safe under the stochastic prediction model and k-CEIDH hypothesis.Compared with similar schemes,the experiment shows that communication overhead and computing time of data ownership transaction in our scheme is lower.Meanwhile,the communication overhead and computing time of our scheme is similar to that of similar schemes in data integrity audit.
文摘针对作业车间环境具有能力受限的批量调度问题(capacitated lot-sizing and scheduling problem,CLSP),提出基于改进蜜獾算法和神经网络的混合优化算法,以此来应对需求和处理时间的不确定性。首先,考虑需求和处理时间受到不确定性影响,构建基于可满足性模理论的确定性模型,引入安全库存和安全松弛两个弹性参数,以运营总成本最低为优化目标,建立需求不确定下的鲁棒优化模型;其次,提出基于改进蜜獾算法和神经网络的混合算法,利用混沌理论生成伪随机值,估计安全参数的标称值和变化幅度,提高算法速度;最后,进行示例验证,结果表明所提算法可优化调度准则,减小最优性差距,有效解决具有延期订单许可的问题,降低平均短缺成本。
