This study uses Baidu News data and introduces a novel proxy for the rate of information flow to examine its relationship with return volatility in Chinese commodity futures and to test two competing hypotheses.We exa...This study uses Baidu News data and introduces a novel proxy for the rate of information flow to examine its relationship with return volatility in Chinese commodity futures and to test two competing hypotheses.We examine the contemporaneous relationships using correlation coefficient analysis,and find apparent differences between the information flow-return volatility relationship and the information flowtrading volume relationship.The empirical evidence contradicts the mixture of distribution hypothesis(MDH)and suggests that the rate of information flow distinctly affects trading volume and volatility.We conducted linear and nonlinear Granger causality tests to explore the sequential information arrival hypothesis(SIAH).The empirical results prove that a lead-lag linear and nonlinear causality exists between the information flow and return volatility of commodity futures,which is consistent with SIAH.In other words,a partial equilibrium exists before reaching the ultimate equilibrium when the new information arrives in the market.Finally,these findings are robust to alternative measurement of return volatility and subperiod analysis.Our findings reject the MDH and support the SIAH in the context of Chinese commodity futures.展开更多
In this paper,we introduce and investigate the mutual information and relative entropy on the sequentialeffect algebra,we also give a comparison of these mutual information and relative entropy with the classical ones...In this paper,we introduce and investigate the mutual information and relative entropy on the sequentialeffect algebra,we also give a comparison of these mutual information and relative entropy with the classical ones by thevenn diagrams.Finally,a nice example shows that the entropies of sequential effect algebra depend extremely on theorder of its sequential product.展开更多
The sequential rock remote sensing information is a group of rocks that are correlative in space or in space and time. For the sake of plottiug them, someone had brought forward the optimization segn.entotion metkod. ...The sequential rock remote sensing information is a group of rocks that are correlative in space or in space and time. For the sake of plottiug them, someone had brought forward the optimization segn.entotion metkod. We have ased this method to plot the sequential rock remote sensing information at tbe remote sensing hyperspetral test field of Daqing mountain, Inner Mongolia Autonomous Region, China, and found some disadvantages of this method. Therefore, we put forward the optimization dichotomy to plot them, and get better results. Finally we make a conclusion.展开更多
At present,the process of digital image information fusion has the problems of low data cleaning unaccuracy and more repeated data omission,resulting in the unideal information fusion.In this regard,a visualized multi...At present,the process of digital image information fusion has the problems of low data cleaning unaccuracy and more repeated data omission,resulting in the unideal information fusion.In this regard,a visualized multicomponent information fusion method for big data based on radar map is proposed in this paper.The data model of perceptual digital image is constructed by using the linear regression analysis method.The ID tag of the collected image data as Transactin Identification(TID)is compared.If the TID of two data is the same,the repeated data detection is carried out.After the test,the data set is processed many times in accordance with the method process to improve the precision of data cleaning and reduce the omission.Based on the radar images,hierarchical visualization of processed multi-level information fusion is realized.The experiments show that the method can clean the redundant data accurately and achieve the efficient fusion of multi-level information of big data in the digital image.展开更多
In order to deal with the particle degeneracy and impov- erishment problems existed in particle filters, a modified sequential importance resampling （MSIR） filter is proposed. In this filter, the resampling is trans...In order to deal with the particle degeneracy and impov- erishment problems existed in particle filters, a modified sequential importance resampling （MSIR） filter is proposed. In this filter, the resampling is translated into an evolutional process just like the biological evolution. A particle generator is constructed, which introduces the current measurement information （CMI） into the resampled particles. In the evolution, new particles are first pro- duced through the particle generator, each of which is essentially an unbiased estimation of the current true state. Then, new and old particles are recombined for the sake of raising the diversity among the particles. Finally, those particles who have low quality are eliminated. Through the evolution, all the particles retained are regarded as the optimal ones, and these particles are utilized to update the current state. By using the proposed resampling approach, not only the CMI is incorporated into each resampled particle, but also the particle degeneracy and the loss of diver- sity among the particles are mitigated, resulting in the improved estimation accuracy. Simulation results show the superiorities of the proposed filter over the standard sequential importance re- sampling （SIR） filter, auxiliary particle filter and unscented Kalman particle filter.展开更多
Spectrum access approach and power allocation scheme are important techniques in cognitive radio(CR) system,which not only affect communication performance of CR user(secondary user,SU) but also play decisive role for...Spectrum access approach and power allocation scheme are important techniques in cognitive radio(CR) system,which not only affect communication performance of CR user(secondary user,SU) but also play decisive role for protection of primary user(PU).In this study,we propose a power allocation scheme for SU based on the status sensing of PU in a single-input single-output(SISO) CR network.Instead of the conventional binary primary transmit power strategy,namely the sensed PU has only present or absent status,we consider a more practical scenario when PU transmits with multiple levels of power and quantized side information known by SU in advance as a primary quantized codebook.The secondary power allocation scheme to maximize the average throughput under the rate loss constraint(RLC) of PU is parameterized by the sensing results for PU,the primary quantized codebook and the channel state information(CSI) of SU.Furthermore,Differential Evolution(DE) algorithm is used to solve this non-convex power allocation problem.Simulation results show the performance and effectiveness of our proposed scheme under more practical communication conditions.展开更多
In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is e...In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.展开更多
In this paper, we focus on the design of irregular QC-LDPC code based multi-level coded modulation(MLCM) scheme by jointly optimizing the component code rate and the degree distribution of the irregular QC-LDPC compon...In this paper, we focus on the design of irregular QC-LDPC code based multi-level coded modulation(MLCM) scheme by jointly optimizing the component code rate and the degree distribution of the irregular QC-LDPC component code. Firstly, the sub-channel capacities of MLCM systems is analyzed and discussed, based on which the optimal component code rate can be obtained. Secondly, an extrinsic information transfer chart based two-stage searching algorithm is proposed to find the good irregular QC-LDPC code ensembles with optimal component code rates for their corresponding sub-channels. Finally, by constructing the irregular QC-LDPC component codes from the designed ensembles with the aim of possibly enlarging the girth and reducing the number of the shortest cycles, the designed irregular QC-LDPC code based 16QAM and 64QAM MLCM systems can achieve 0.4 dB and 1.2 dB net coding gain, respectively, compared with the recently proposed regular QC-LDPC code based 16QAM and 64QAM MLCM systems.展开更多
提出了一种基于CAE_ViT网络模型和顺序层状耦合信息框架(sequential hierarchical coupled information framework,SHCIF)的多粒度多缺陷图像分类识别方法,并结合模糊综合评价(FCE)方法,以桥梁设施为例,对其表面缺陷进行细致的分类及安...提出了一种基于CAE_ViT网络模型和顺序层状耦合信息框架(sequential hierarchical coupled information framework,SHCIF)的多粒度多缺陷图像分类识别方法,并结合模糊综合评价(FCE)方法,以桥梁设施为例,对其表面缺陷进行细致的分类及安全评价。首先,研究提出了SHCIF及对应3个层次粒度的识别模型,并构建和增强了对应不同粒度的数据集。SHCIF框架和跨粒度分类决策旨在通过利用桥梁组件和缺陷类型这两个粒度的信息和准确性,提升对缺陷严重程度的识别。其次,使用迁移学习对CAE_ViT预训练模型进行微调,以满足桥梁缺陷检测的具体需求,并通过跨粒度分类决策进一步提升分类的准确性。最后,基于层次分析法与熵权法(AHP⁃EWM)的权重体系,通过模糊综合评价,综合考虑桥梁部位、桥梁组件、缺陷类型及其严重程度,实现了基于表观健康状态对桥梁安全状态等级的定量评价。实验结果显示,在3个层次粒度的识别模型中的宏平均F1⁃Score分数分别达到94.1%、81.6%和75.3%,而跨粒度分类决策的准确率为82%。最终通过一个桥梁的安全评价案例证明了方法的有效性、系统性和可拓展性。展开更多
现有序列推荐方法未能充分挖掘项目属性语义信息且存在语义空间迁移不匹配的问题,导致对长尾物品推荐能力不足.为此,文中提出融合大模型语义增强信息和协同信息融合的序列推荐方法(Large Language Model Enhancement and Collaborative ...现有序列推荐方法未能充分挖掘项目属性语义信息且存在语义空间迁移不匹配的问题,导致对长尾物品推荐能力不足.为此,文中提出融合大模型语义增强信息和协同信息融合的序列推荐方法(Large Language Model Enhancement and Collaborative Information Fusion for Sequential Recommendation,LLM-CFSR).首先,通过属性级数据增强与对比微调技术,利用大语言模型生成细粒度语义嵌入,捕捉长尾物品的深层语义关联.然后,设计双视图融合机制,分别从语义视图与协同视图两方面对用户偏好进行联合建模.最后,引入交叉注意力机制,实现嵌入层、序列层与预测层的多层次信息融合,促进语义信息与协同信号的深度交互.在Yelp、Amazon Fashion、Amazon Beauty数据集上的实验表明,LLM-CFSR对于整体推荐性能和长尾物品推荐性能都有所提升.展开更多
碳排放连续在线监测法作为一种高效、可溯源的方法,在我国碳计量领域中逐渐应用。然而,由于烟囱管道的大直径、复杂烟气流场,以及流量计检修维护、粉尘堵塞导致的监测数据中断与异常,烟气流量的准确监测成为一大挑战。为此,提出一种融...碳排放连续在线监测法作为一种高效、可溯源的方法,在我国碳计量领域中逐渐应用。然而,由于烟囱管道的大直径、复杂烟气流场,以及流量计检修维护、粉尘堵塞导致的监测数据中断与异常,烟气流量的准确监测成为一大挑战。为此,提出一种融合变量投影重要性分析(variable importance in projection,VIP)、最大信息系数(maximal information coefficient,MIC)及后向搜索(sequential backward selection,SBS)算法的联合筛选方法,结合支持向量机(support vector machine,SVM)构建烟气流量软测量模型。基于某F级燃气-蒸汽联合循环发电机组,通过VIP值评估辅助变量显著性,并结合MIC和SBS算法,进行变量冗余消除与优化选择,从而提升模型的预测精度和泛化能力。实验结果显示:SVM的表现优于长短时间记忆网络模型,与反向传播神经网络相比具有较好的泛化能力;当辅助变量数量为12时,模型性能最佳,测试集的均方根误差和平均绝对百分比误差均较低,验证了变量筛选方法的有效性;在稳态和非稳态工况下,模型预测值的平均绝对百分比误差小于0.7%,并有一定的滤波作用。展开更多
基金supported by the National Social Science Fund of China(24CGL027)the National Natural Science Foundation of China(72101009,72141304,72201122)National Key Research and Development Program of China(2022YFC3303304).
文摘This study uses Baidu News data and introduces a novel proxy for the rate of information flow to examine its relationship with return volatility in Chinese commodity futures and to test two competing hypotheses.We examine the contemporaneous relationships using correlation coefficient analysis,and find apparent differences between the information flow-return volatility relationship and the information flowtrading volume relationship.The empirical evidence contradicts the mixture of distribution hypothesis(MDH)and suggests that the rate of information flow distinctly affects trading volume and volatility.We conducted linear and nonlinear Granger causality tests to explore the sequential information arrival hypothesis(SIAH).The empirical results prove that a lead-lag linear and nonlinear causality exists between the information flow and return volatility of commodity futures,which is consistent with SIAH.In other words,a partial equilibrium exists before reaching the ultimate equilibrium when the new information arrives in the market.Finally,these findings are robust to alternative measurement of return volatility and subperiod analysis.Our findings reject the MDH and support the SIAH in the context of Chinese commodity futures.
基金Supported by Research Foundation of Kumoh National Institute of Technology
文摘In this paper,we introduce and investigate the mutual information and relative entropy on the sequentialeffect algebra,we also give a comparison of these mutual information and relative entropy with the classical ones by thevenn diagrams.Finally,a nice example shows that the entropies of sequential effect algebra depend extremely on theorder of its sequential product.
文摘The sequential rock remote sensing information is a group of rocks that are correlative in space or in space and time. For the sake of plottiug them, someone had brought forward the optimization segn.entotion metkod. We have ased this method to plot the sequential rock remote sensing information at tbe remote sensing hyperspetral test field of Daqing mountain, Inner Mongolia Autonomous Region, China, and found some disadvantages of this method. Therefore, we put forward the optimization dichotomy to plot them, and get better results. Finally we make a conclusion.
基金2018 National Grade Innovation and Entrepreneurship Training Program for College Students,China(No.201811562005)Research Project of Gansu University,China(No.2016A-105)Innovation and Entrepreneurship Education Project of Gansu Province in 2019,China(No.2019024)。
文摘At present,the process of digital image information fusion has the problems of low data cleaning unaccuracy and more repeated data omission,resulting in the unideal information fusion.In this regard,a visualized multicomponent information fusion method for big data based on radar map is proposed in this paper.The data model of perceptual digital image is constructed by using the linear regression analysis method.The ID tag of the collected image data as Transactin Identification(TID)is compared.If the TID of two data is the same,the repeated data detection is carried out.After the test,the data set is processed many times in accordance with the method process to improve the precision of data cleaning and reduce the omission.Based on the radar images,hierarchical visualization of processed multi-level information fusion is realized.The experiments show that the method can clean the redundant data accurately and achieve the efficient fusion of multi-level information of big data in the digital image.
基金supported by the National Natural Science Foundation of China(61372136)
文摘In order to deal with the particle degeneracy and impov- erishment problems existed in particle filters, a modified sequential importance resampling （MSIR） filter is proposed. In this filter, the resampling is translated into an evolutional process just like the biological evolution. A particle generator is constructed, which introduces the current measurement information （CMI） into the resampled particles. In the evolution, new particles are first pro- duced through the particle generator, each of which is essentially an unbiased estimation of the current true state. Then, new and old particles are recombined for the sake of raising the diversity among the particles. Finally, those particles who have low quality are eliminated. Through the evolution, all the particles retained are regarded as the optimal ones, and these particles are utilized to update the current state. By using the proposed resampling approach, not only the CMI is incorporated into each resampled particle, but also the particle degeneracy and the loss of diver- sity among the particles are mitigated, resulting in the improved estimation accuracy. Simulation results show the superiorities of the proposed filter over the standard sequential importance re- sampling （SIR） filter, auxiliary particle filter and unscented Kalman particle filter.
基金supported by the National Natural Science Foundation of China(Grant No.61571209)
文摘Spectrum access approach and power allocation scheme are important techniques in cognitive radio(CR) system,which not only affect communication performance of CR user(secondary user,SU) but also play decisive role for protection of primary user(PU).In this study,we propose a power allocation scheme for SU based on the status sensing of PU in a single-input single-output(SISO) CR network.Instead of the conventional binary primary transmit power strategy,namely the sensed PU has only present or absent status,we consider a more practical scenario when PU transmits with multiple levels of power and quantized side information known by SU in advance as a primary quantized codebook.The secondary power allocation scheme to maximize the average throughput under the rate loss constraint(RLC) of PU is parameterized by the sensing results for PU,the primary quantized codebook and the channel state information(CSI) of SU.Furthermore,Differential Evolution(DE) algorithm is used to solve this non-convex power allocation problem.Simulation results show the performance and effectiveness of our proposed scheme under more practical communication conditions.
基金Supported by the National Natural Science Foundation of China(No.62172352,61871465,42002138)the Natural Science Foundation of Hebei Province(No.F2019203157)the Science and Technology Research Project of Hebei(No.ZD2019004)。
文摘In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.
基金supported by National Natural Science Foundation of China(No.61571061)
文摘In this paper, we focus on the design of irregular QC-LDPC code based multi-level coded modulation(MLCM) scheme by jointly optimizing the component code rate and the degree distribution of the irregular QC-LDPC component code. Firstly, the sub-channel capacities of MLCM systems is analyzed and discussed, based on which the optimal component code rate can be obtained. Secondly, an extrinsic information transfer chart based two-stage searching algorithm is proposed to find the good irregular QC-LDPC code ensembles with optimal component code rates for their corresponding sub-channels. Finally, by constructing the irregular QC-LDPC component codes from the designed ensembles with the aim of possibly enlarging the girth and reducing the number of the shortest cycles, the designed irregular QC-LDPC code based 16QAM and 64QAM MLCM systems can achieve 0.4 dB and 1.2 dB net coding gain, respectively, compared with the recently proposed regular QC-LDPC code based 16QAM and 64QAM MLCM systems.
文摘提出了一种基于CAE_ViT网络模型和顺序层状耦合信息框架(sequential hierarchical coupled information framework,SHCIF)的多粒度多缺陷图像分类识别方法,并结合模糊综合评价(FCE)方法,以桥梁设施为例,对其表面缺陷进行细致的分类及安全评价。首先,研究提出了SHCIF及对应3个层次粒度的识别模型,并构建和增强了对应不同粒度的数据集。SHCIF框架和跨粒度分类决策旨在通过利用桥梁组件和缺陷类型这两个粒度的信息和准确性,提升对缺陷严重程度的识别。其次,使用迁移学习对CAE_ViT预训练模型进行微调,以满足桥梁缺陷检测的具体需求,并通过跨粒度分类决策进一步提升分类的准确性。最后,基于层次分析法与熵权法(AHP⁃EWM)的权重体系,通过模糊综合评价,综合考虑桥梁部位、桥梁组件、缺陷类型及其严重程度,实现了基于表观健康状态对桥梁安全状态等级的定量评价。实验结果显示,在3个层次粒度的识别模型中的宏平均F1⁃Score分数分别达到94.1%、81.6%和75.3%,而跨粒度分类决策的准确率为82%。最终通过一个桥梁的安全评价案例证明了方法的有效性、系统性和可拓展性。
文摘现有序列推荐方法未能充分挖掘项目属性语义信息且存在语义空间迁移不匹配的问题,导致对长尾物品推荐能力不足.为此,文中提出融合大模型语义增强信息和协同信息融合的序列推荐方法(Large Language Model Enhancement and Collaborative Information Fusion for Sequential Recommendation,LLM-CFSR).首先,通过属性级数据增强与对比微调技术,利用大语言模型生成细粒度语义嵌入,捕捉长尾物品的深层语义关联.然后,设计双视图融合机制,分别从语义视图与协同视图两方面对用户偏好进行联合建模.最后,引入交叉注意力机制,实现嵌入层、序列层与预测层的多层次信息融合,促进语义信息与协同信号的深度交互.在Yelp、Amazon Fashion、Amazon Beauty数据集上的实验表明,LLM-CFSR对于整体推荐性能和长尾物品推荐性能都有所提升.
文摘碳排放连续在线监测法作为一种高效、可溯源的方法,在我国碳计量领域中逐渐应用。然而,由于烟囱管道的大直径、复杂烟气流场,以及流量计检修维护、粉尘堵塞导致的监测数据中断与异常,烟气流量的准确监测成为一大挑战。为此,提出一种融合变量投影重要性分析(variable importance in projection,VIP)、最大信息系数(maximal information coefficient,MIC)及后向搜索(sequential backward selection,SBS)算法的联合筛选方法,结合支持向量机(support vector machine,SVM)构建烟气流量软测量模型。基于某F级燃气-蒸汽联合循环发电机组,通过VIP值评估辅助变量显著性,并结合MIC和SBS算法,进行变量冗余消除与优化选择,从而提升模型的预测精度和泛化能力。实验结果显示:SVM的表现优于长短时间记忆网络模型,与反向传播神经网络相比具有较好的泛化能力;当辅助变量数量为12时,模型性能最佳,测试集的均方根误差和平均绝对百分比误差均较低,验证了变量筛选方法的有效性;在稳态和非稳态工况下,模型预测值的平均绝对百分比误差小于0.7%,并有一定的滤波作用。
文摘在绿色能源革命背景下,以氢能为载体的含氢综合能源信息物理系统是能源行业低碳化转型的重要支撑。供能安全可靠性是对能源系统最基本的要求,综合能源信息物理系统(cyber physical system,CPS)中信息流与能量流耦合关系复杂,其可靠性评估变得更困难。为了评估含氢综合能源CPS的可靠性,首先针对综合能源系统(integrated energy system,IES)的信息传输过程建立可靠性模型,搭建了综合能源CPS模型;然后采用一种伪序贯蒙特卡洛和非序贯蒙特卡洛相结合的算法来评估综合能源CPS可靠性,从而提高评估效率;最后对比了常规运行策略、负荷跟随策略及季节性储氢3种运行策略下储氢子系统对系统可靠性的影响。通过仿真分析,结果表明季节性储氢策略能够提升系统的供能可靠性,同时验证了所提评估方法的有效性和快速性。
