High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff...High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.展开更多
To meet the future internet traffic challenges, enhancement of hardware architectures related to network security has vital role where software security algorithms are incompatible with high speed in terms of Giga bit...To meet the future internet traffic challenges, enhancement of hardware architectures related to network security has vital role where software security algorithms are incompatible with high speed in terms of Giga bits per second (Gbps). In this paper, we discuss signature detection technique (SDT) used in network intrusion detection system (NIDS). Design of most commonly used hardware based techniques for signature detection such as finite automata, discrete comparators, Knuth-Morris-Pratt (KMP) algorithm, content addressable memory (CAM) and Bloom filter are discussed. Two novel architectures, XOR based pre computation CAM (XPCAM) and multi stage look up technique (MSLT) Bloom filter architectures are proposed and implemented in third party field programmable gate array (FPGA), and area and power consumptions are compared. 10Gbps network traffic generator (TNTG) is used to test the functionality and ensure the reliability of the proposed architectures. Our approach involves a unique combination of algorithmic and architectural techniques that outperform some of the current techniques in terms of performance, speed and powerefficiency.展开更多
In recent decades, log system management has been widely studied fordata security management. System abnormalities or illegal operations can befound in time by analyzing the log and provide evidence for intrusions. In...In recent decades, log system management has been widely studied fordata security management. System abnormalities or illegal operations can befound in time by analyzing the log and provide evidence for intrusions. In orderto ensure the integrity of the log in the current system, many researchers havedesigned it based on blockchain. However, the emerging blockchain is facing significant security challenges with the increment of quantum computers. An attackerequipped with a quantum computer can extract the user's private key from thepublic key to generate a forged signature, destroy the structure of the blockchain,and threaten the security of the log system. Thus, blind signature on the lattice inpost-quantum blockchain brings new security features for log systems. In ourpaper, to address these, firstly, we propose a novel log system based on post-quantum blockchain that can resist quantum computing attacks. Secondly, we utilize apost-quantum blind signature on the lattice to ensure both security and blindnessof log system, which makes the privacy of log information to a large extent.Lastly, we enhance the security level of lattice-based blind signature under therandom oracle model, and the signature size grows slowly compared with others.We also implement our protocol and conduct an extensive analysis to prove theideas. The results show that our scheme signature size edges up subtly comparedwith others with the improvement of security level.展开更多
The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep le...The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.展开更多
将Flex与Web服务相结合构建RIA(Rich Internet Application,富互联网应用)系统集成,利用Web服务将应用系统中业务流程逻辑封装为标准服务,通过服务的发布与发现机制,实现企业数据资源共享;利用加密算法对传输报文数字签名和加密;利用Fle...将Flex与Web服务相结合构建RIA(Rich Internet Application,富互联网应用)系统集成,利用Web服务将应用系统中业务流程逻辑封装为标准服务,通过服务的发布与发现机制,实现企业数据资源共享;利用加密算法对传输报文数字签名和加密;利用Flex为用户提供统一的富客户端用户界面,实现高度互动性和响应性的客户端,丰富用户体验;利用模块Modules方式、导航方式、动态加载组件方式,组件重用方式等重构Flex表示层,提高系统性能,并与J2EE相整合,验证Flex与Web服务相结合构建RIA系统集成的可行性,合理性。展开更多
针对嵌入式设备固件更新的安全问题,文中提出了一种基于哈希、对称、非对称加密算法的多重校验固件安全更新方案。通过主密钥对、临时密钥对、共享密钥以及哈希链等设计,从身份认证、数据加密、完整性校验等多个方面对固件更新进行安全...针对嵌入式设备固件更新的安全问题,文中提出了一种基于哈希、对称、非对称加密算法的多重校验固件安全更新方案。通过主密钥对、临时密钥对、共享密钥以及哈希链等设计,从身份认证、数据加密、完整性校验等多个方面对固件更新进行安全防护,可以有效预防非法用户、固件篡改、固件数据泄露、重放攻击、固件回滚等攻击。文中对此安全更新方案进行了具体实现,实验结果显示该方案相较于无任何安全防护的ISP(In System Programming)和IAP(In Application Pragramming)技术,在时间成本方面分别增加约7%和11%的情况下实现了对固件更新的全流程安全防护,为嵌入式设备的固件更新提供了一种安全、可靠的更新方法。展开更多
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
文摘To meet the future internet traffic challenges, enhancement of hardware architectures related to network security has vital role where software security algorithms are incompatible with high speed in terms of Giga bits per second (Gbps). In this paper, we discuss signature detection technique (SDT) used in network intrusion detection system (NIDS). Design of most commonly used hardware based techniques for signature detection such as finite automata, discrete comparators, Knuth-Morris-Pratt (KMP) algorithm, content addressable memory (CAM) and Bloom filter are discussed. Two novel architectures, XOR based pre computation CAM (XPCAM) and multi stage look up technique (MSLT) Bloom filter architectures are proposed and implemented in third party field programmable gate array (FPGA), and area and power consumptions are compared. 10Gbps network traffic generator (TNTG) is used to test the functionality and ensure the reliability of the proposed architectures. Our approach involves a unique combination of algorithmic and architectural techniques that outperform some of the current techniques in terms of performance, speed and powerefficiency.
基金supported by the NSFC(Grant Nos.92046001,61962009)JSPS KAKENHI Grant Number JP20F20080+3 种基金the Natural Science Foundation of Inner Mongolia(2021MS06006)Baotou Kundulun District Science and technology plan project(YF2020013)Inner Mongolia discipline inspection and supervision big data laboratory open project fund(IMDBD2020020)the Scientific Research Foundation of North China University of Technology.
文摘In recent decades, log system management has been widely studied fordata security management. System abnormalities or illegal operations can befound in time by analyzing the log and provide evidence for intrusions. In orderto ensure the integrity of the log in the current system, many researchers havedesigned it based on blockchain. However, the emerging blockchain is facing significant security challenges with the increment of quantum computers. An attackerequipped with a quantum computer can extract the user's private key from thepublic key to generate a forged signature, destroy the structure of the blockchain,and threaten the security of the log system. Thus, blind signature on the lattice inpost-quantum blockchain brings new security features for log systems. In ourpaper, to address these, firstly, we propose a novel log system based on post-quantum blockchain that can resist quantum computing attacks. Secondly, we utilize apost-quantum blind signature on the lattice to ensure both security and blindnessof log system, which makes the privacy of log information to a large extent.Lastly, we enhance the security level of lattice-based blind signature under therandom oracle model, and the signature size grows slowly compared with others.We also implement our protocol and conduct an extensive analysis to prove theideas. The results show that our scheme signature size edges up subtly comparedwith others with the improvement of security level.
基金supported in part by the National Natural Science Foundation of China under Grant U1908212,62203432 and 92067205in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03 and 2023-Z15in part by the Natural Science Foundation of Liaoning Province under Grant 2020-KF-11-02.
文摘The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.
文摘将Flex与Web服务相结合构建RIA(Rich Internet Application,富互联网应用)系统集成,利用Web服务将应用系统中业务流程逻辑封装为标准服务,通过服务的发布与发现机制,实现企业数据资源共享;利用加密算法对传输报文数字签名和加密;利用Flex为用户提供统一的富客户端用户界面,实现高度互动性和响应性的客户端,丰富用户体验;利用模块Modules方式、导航方式、动态加载组件方式,组件重用方式等重构Flex表示层,提高系统性能,并与J2EE相整合,验证Flex与Web服务相结合构建RIA系统集成的可行性,合理性。
文摘针对嵌入式设备固件更新的安全问题,文中提出了一种基于哈希、对称、非对称加密算法的多重校验固件安全更新方案。通过主密钥对、临时密钥对、共享密钥以及哈希链等设计,从身份认证、数据加密、完整性校验等多个方面对固件更新进行安全防护,可以有效预防非法用户、固件篡改、固件数据泄露、重放攻击、固件回滚等攻击。文中对此安全更新方案进行了具体实现,实验结果显示该方案相较于无任何安全防护的ISP(In System Programming)和IAP(In Application Pragramming)技术,在时间成本方面分别增加约7%和11%的情况下实现了对固件更新的全流程安全防护,为嵌入式设备的固件更新提供了一种安全、可靠的更新方法。
