Finding the electromagnetic （EM） counterpart of binary compact star merger, especially the binary neutron star （BNS） merger, is critically important for gravitational wave （GW） astronomy, cosmology and fundament...Finding the electromagnetic （EM） counterpart of binary compact star merger, especially the binary neutron star （BNS） merger, is critically important for gravitational wave （GW） astronomy, cosmology and fundamental physics. On Aug. 17, 2017, Advanced LIGO and Fermi/GBM independently triggered the first BNS merger, GW170817, and its high energy EM counterpart, GRB 170817A, respectively, resulting in a global observation campaign covering gamma-ray, X-ray, UV, optical, IR, radio as well as neutrinos. The High Energy X-ray telescope （HE） onboard Insight-HXMT （Hard X-ray Modulation Telescope） is the unique high-energy gamma-ray telescope that monitored the entire GW localization area and especially the optical counterpart （SSS17a/AT2017gfo） with very large collection area （M000 cm2） and microsecond time resolution in 0.2-5 MeV. In addition, Insight-HXMT quickly implemented a Target of Opportunity （TOO） observation to scan the GW localization area for potential X-ray emission from the GW source. Although Insight-HXMT did not detect any significant high energy （0.2-5 MeV） radiation from GW170817, its observation helped to confirm the unexpected weak and soft nature of GRB 170817A. Meanwhile, Insight-HXMT/HE provides one of the most stringent constraints （-10-7 to 104 erg/cm2/s） for both GRB170817A and any other possible precursor or extended emissions in 0.2-5 MeV, which help us to better understand the properties of EM radiation from this BNS merger. Therefore the observation of Insight-HXMT constitutes an important chapter in the full context of multi-wavelength and multi-messenger observation of this historical GW event.展开更多
The interaction between the heat source location, its intensity, thermal expansion coefficient, the machine system configuration and the running environment creates complex thermal behavior of a machine tool, and also...The interaction between the heat source location, its intensity, thermal expansion coefficient, the machine system configuration and the running environment creates complex thermal behavior of a machine tool, and also makes thermal error prediction difficult. To address this issue, a novel prediction method for machine tool thermal error based on Bayesian networks (BNs) was presented. The method described causal relationships of factors inducing thermal deformation by graph theory and estimated the thermal error by Bayesian statistical techniques. Due to the effective combination of domain knowledge and sampled data, the BN method could adapt to the change of running state of machine, and obtain satisfactory prediction accuracy. Ex- periments on spindle thermal deformation were conducted to evaluate the modeling performance. Experimental results indicate that the BN method performs far better than the least squares (LS) analysis in terms of modeling estimation accuracy.展开更多
Bayesian networks (BNs) have become increasingly popular in recent years due to their wide-ranging applications in modeling uncertain knowledge. An essential problem about discrete BNs is learning conditional probabil...Bayesian networks (BNs) have become increasingly popular in recent years due to their wide-ranging applications in modeling uncertain knowledge. An essential problem about discrete BNs is learning conditional probability table (CPT) parameters. If training data are sparse, purely data-driven methods often fail to learn accurate parameters. Then, expert judgments can be introduced to overcome this challenge. Parameter constraints deduced from expert judgments can cause parameter estimates to be consistent with domain knowledge. In addition, Dirichlet priors contain information that helps improve learning accuracy. This paper proposes a constrained Bayesian estimation approach to learn CPTs by incorporating constraints and Dirichlet priors. First, a posterior distribution of BN parameters is developed over a restricted parameter space based on training data and Dirichlet priors. Then, the expectation of the posterior distribution is taken as a parameter estimation. As it is difficult to directly compute the expectation for a continuous distribution with an irregular feasible domain, we apply the Monte Carlo method to approximate it. In the experiments on learning standard BNs, the proposed method outperforms competing methods. It suggests that the proposed method can facilitate solving real-world problems. Additionally, a case study of Wine data demonstrates that the proposed method achieves the highest classification accuracy.展开更多
域名系统(Domain Name System,DNS)是Web2.0时代互联网最基础的服务之一,其提供了将域名解析为真实IP地址的功能,从而使得网络互联互通成为可能。DNS有着非常多的优点,也有着相应的升级和改进,但是一些深层次的基本问题并没有得到根本...域名系统(Domain Name System,DNS)是Web2.0时代互联网最基础的服务之一,其提供了将域名解析为真实IP地址的功能,从而使得网络互联互通成为可能。DNS有着非常多的优点,也有着相应的升级和改进,但是一些深层次的基本问题并没有得到根本的解决,如网络中立性问题、匿名性问题、安全性问题等。区块链(Blockchain)技术是一个分布式数字账本,是通过密码学和计算机科学,按时间顺序排列和记录所有类型的交易的一种去中心化的账本。区块链是一种安全、可靠和稳定的技术,可以用于解决和确保数据、信息和交易安全性、真实性和唯一性。因此,基于区块链的域名系统(Blockchain Name System,BNS)可以作为一种有效的手段来解决现有DNS系统的问题。提出一种基于CNWW3公链网络的BNS系统--CNS(CNWW3 Name System),既兼容现有的DNS系统,同时又能解决现有其他BNS的不足,从而使得基于区块链的域名系统真正具有广泛的应用性。展开更多
【目的】本文主要根据当前网络发展存在的问题及基于区块链的网络空间标识系统的关键技术等方面展开介绍,为后续相关工作的开展提供参考。【方法】本文梳理了基于区块链的网络空间标识系统的研究现状,介绍了Namecoin、Blockstack、Hands...【目的】本文主要根据当前网络发展存在的问题及基于区块链的网络空间标识系统的关键技术等方面展开介绍,为后续相关工作的开展提供参考。【方法】本文梳理了基于区块链的网络空间标识系统的研究现状,介绍了Namecoin、Blockstack、Handshake、Ethereum Name Service等几种系统的设计架构、技术方案,并对不同项目进行了对比分析。【结果】相较于现有DNS系统,基于区块链的网络空间标识实现了安全平等、抗审查的网络基础架构,并且在数字认证、全球统一身份标识等领域积极探索展开合作,得到了一定程度的应用。【结论】基于区块链的网络空间标识服务为未来网络基础资源发展提供了一种新的思路与趋势,同时也面临着如何处理与现有系统的关系及广泛推广等问题。展开更多
采用废钢+70%~80%铁水-100 t UHP EAF-双工位120 t LF-VD-连铸-轧制工艺流程生产石油管用钢API5L BNS。通过电弧炉全程泡沫渣操作,EBT出钢合金化,连铸全程保护浇注,大压缩比、无扭微张力轧制,获得钢的良好的低倍组织、夹杂物、表面质量...采用废钢+70%~80%铁水-100 t UHP EAF-双工位120 t LF-VD-连铸-轧制工艺流程生产石油管用钢API5L BNS。通过电弧炉全程泡沫渣操作,EBT出钢合金化,连铸全程保护浇注,大压缩比、无扭微张力轧制,获得钢的良好的低倍组织、夹杂物、表面质量,满足极为严格的微量元素的控制目标,各项指标均满足用户协议要求。展开更多
基金supported by the National Program on Key Research and Development Project(Grant No.2016YFA0400800)from the Ministry of Science and Technology of China(MOST)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB23040400)the Hundred Talent Program of Chinese Academy of Sciences,the National Natural Science Foundation of China(Grant Nos.11233001,11503027,11403026,11473027,and11733009)
文摘Finding the electromagnetic （EM） counterpart of binary compact star merger, especially the binary neutron star （BNS） merger, is critically important for gravitational wave （GW） astronomy, cosmology and fundamental physics. On Aug. 17, 2017, Advanced LIGO and Fermi/GBM independently triggered the first BNS merger, GW170817, and its high energy EM counterpart, GRB 170817A, respectively, resulting in a global observation campaign covering gamma-ray, X-ray, UV, optical, IR, radio as well as neutrinos. The High Energy X-ray telescope （HE） onboard Insight-HXMT （Hard X-ray Modulation Telescope） is the unique high-energy gamma-ray telescope that monitored the entire GW localization area and especially the optical counterpart （SSS17a/AT2017gfo） with very large collection area （M000 cm2） and microsecond time resolution in 0.2-5 MeV. In addition, Insight-HXMT quickly implemented a Target of Opportunity （TOO） observation to scan the GW localization area for potential X-ray emission from the GW source. Although Insight-HXMT did not detect any significant high energy （0.2-5 MeV） radiation from GW170817, its observation helped to confirm the unexpected weak and soft nature of GRB 170817A. Meanwhile, Insight-HXMT/HE provides one of the most stringent constraints （-10-7 to 104 erg/cm2/s） for both GRB170817A and any other possible precursor or extended emissions in 0.2-5 MeV, which help us to better understand the properties of EM radiation from this BNS merger. Therefore the observation of Insight-HXMT constitutes an important chapter in the full context of multi-wavelength and multi-messenger observation of this historical GW event.
基金Project supported by National Natural Science Foundation of China(No. 50675199)the Science and Technology Project of Zhejiang Province (No. 2006C11067), China
文摘The interaction between the heat source location, its intensity, thermal expansion coefficient, the machine system configuration and the running environment creates complex thermal behavior of a machine tool, and also makes thermal error prediction difficult. To address this issue, a novel prediction method for machine tool thermal error based on Bayesian networks (BNs) was presented. The method described causal relationships of factors inducing thermal deformation by graph theory and estimated the thermal error by Bayesian statistical techniques. Due to the effective combination of domain knowledge and sampled data, the BN method could adapt to the change of running state of machine, and obtain satisfactory prediction accuracy. Ex- periments on spindle thermal deformation were conducted to evaluate the modeling performance. Experimental results indicate that the BN method performs far better than the least squares (LS) analysis in terms of modeling estimation accuracy.
基金supported by the National Natural Science Foundation of China(61573285)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China(CX201619)
文摘Bayesian networks (BNs) have become increasingly popular in recent years due to their wide-ranging applications in modeling uncertain knowledge. An essential problem about discrete BNs is learning conditional probability table (CPT) parameters. If training data are sparse, purely data-driven methods often fail to learn accurate parameters. Then, expert judgments can be introduced to overcome this challenge. Parameter constraints deduced from expert judgments can cause parameter estimates to be consistent with domain knowledge. In addition, Dirichlet priors contain information that helps improve learning accuracy. This paper proposes a constrained Bayesian estimation approach to learn CPTs by incorporating constraints and Dirichlet priors. First, a posterior distribution of BN parameters is developed over a restricted parameter space based on training data and Dirichlet priors. Then, the expectation of the posterior distribution is taken as a parameter estimation. As it is difficult to directly compute the expectation for a continuous distribution with an irregular feasible domain, we apply the Monte Carlo method to approximate it. In the experiments on learning standard BNs, the proposed method outperforms competing methods. It suggests that the proposed method can facilitate solving real-world problems. Additionally, a case study of Wine data demonstrates that the proposed method achieves the highest classification accuracy.
文摘域名系统(Domain Name System,DNS)是Web2.0时代互联网最基础的服务之一,其提供了将域名解析为真实IP地址的功能,从而使得网络互联互通成为可能。DNS有着非常多的优点,也有着相应的升级和改进,但是一些深层次的基本问题并没有得到根本的解决,如网络中立性问题、匿名性问题、安全性问题等。区块链(Blockchain)技术是一个分布式数字账本,是通过密码学和计算机科学,按时间顺序排列和记录所有类型的交易的一种去中心化的账本。区块链是一种安全、可靠和稳定的技术,可以用于解决和确保数据、信息和交易安全性、真实性和唯一性。因此,基于区块链的域名系统(Blockchain Name System,BNS)可以作为一种有效的手段来解决现有DNS系统的问题。提出一种基于CNWW3公链网络的BNS系统--CNS(CNWW3 Name System),既兼容现有的DNS系统,同时又能解决现有其他BNS的不足,从而使得基于区块链的域名系统真正具有广泛的应用性。
文摘【目的】本文主要根据当前网络发展存在的问题及基于区块链的网络空间标识系统的关键技术等方面展开介绍,为后续相关工作的开展提供参考。【方法】本文梳理了基于区块链的网络空间标识系统的研究现状,介绍了Namecoin、Blockstack、Handshake、Ethereum Name Service等几种系统的设计架构、技术方案,并对不同项目进行了对比分析。【结果】相较于现有DNS系统,基于区块链的网络空间标识实现了安全平等、抗审查的网络基础架构,并且在数字认证、全球统一身份标识等领域积极探索展开合作,得到了一定程度的应用。【结论】基于区块链的网络空间标识服务为未来网络基础资源发展提供了一种新的思路与趋势,同时也面临着如何处理与现有系统的关系及广泛推广等问题。
文摘采用废钢+70%~80%铁水-100 t UHP EAF-双工位120 t LF-VD-连铸-轧制工艺流程生产石油管用钢API5L BNS。通过电弧炉全程泡沫渣操作,EBT出钢合金化,连铸全程保护浇注,大压缩比、无扭微张力轧制,获得钢的良好的低倍组织、夹杂物、表面质量,满足极为严格的微量元素的控制目标,各项指标均满足用户协议要求。
