Dynamics modeling and optimal control for multi-information diffusion in Social Internet of Things

As an ingenious convergence between the Internet of Things and social networks,the Social Internet of Things(SIoT)can provide effective and intelligent information services and has become one of the main platforms for people to spread and share information.Nevertheless,SIoT is characterized by high openness and autonomy,multiple kinds of information can spread rapidly,freely and cooperatively in SIoT,which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its diffusion.To this end,with the aim of exploring multi-information cooperative diffusion processes in SIoT,we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this paper.Subsequently,the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated,and the diffusion trend is predicted.On this basis,to further control the multi-information cooperative diffusion process efficiently,we propose two control strategies for information diffusion with control objectives,develop an optimal control system for the multi-information cooperative diffusion process,and propose the corresponding optimal control method.The optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control theory.Finally,extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model,strategy and method.

INTERPOLATION TECHNIQUE FOR SPARSE DATA BASED ON INFORMATION DIFFUSION PRINCIPLE-ELLIPSE MODEL

Addressing the difficulties of scattered and sparse observational data in ocean science,a new interpolation technique based on information diffusion is proposed in this paper.Based on a fuzzy mapping idea,sparse data samples are diffused and mapped into corresponding fuzzy sets in the form of probability in an interpolation ellipse model.To avoid the shortcoming of normal diffusion function on the asymmetric structure,a kind of asymmetric information diffusion function is developed and a corresponding algorithm-ellipse model for diffusion of asymmetric information is established.Through interpolation experiments and contrast analysis of the sea surface temperature data with ARGO data,the rationality and validity of the ellipse model are assessed.

Bayesian Network Model of Product Information Diffusion and Reasoning of Influence

Information diffusion on social media has become a key strategy in people’s daily interactions. This paper studies consumers’ participation in the product information diffusion, and analyzes the complexity of information diffusion which is affected by many factors. Prior investigations of information diffusion have primarily focused on the composition of diffusion networks with independent factors and the intricacy of the process has not been completely evaluated. The majority of prior investigations have focused on strategies and the moving forces in social media processes and the determination of influential seed nodes, with few evaluations conducted about the factors affecting consumers’ choices in information diffusion. In this study, a Bayesian network model of product information diffusion was created to examine the links between factors and consumer deportment. It revealed how those factors had an impact on each other and on consumer deportment choice. The innovation of the thesis is reflected in the exploration and analysis of the specific communication path of product information diffusion, which provides a better marketing idea and practical method for the development of mobile e-commerce. The research findings can help identify the quantitative relationships between the factors affecting the process of product information diffusion and user behavior.

A Survey of Information Diffusion Prediction in Online Social Networks

With the development of Web 2.0 and the gradual maturity of online social media, social network has become an indispensable platform for people＇ s social interaction, information sharing and news transmission.The study of social networks has become the consensus and urgent need of the academic and industrial circles. The prediction of information communication is the core function of social network, and it is also the core content.To be specific, this paper briefly introduces the concept of social network, then summarizes the diffusion prediction models and methods from five aspects, namely, user attributes-based, information characteristics-based,user groups-based, statistic and inference-based and network topology -based models. Finally, the directions for the future research are discussed.

Design Efficacy Evaluation of a Landscape Information Modeling-Stable Diffusion(LIM-SD)-based Approach for Ecological Engineered Landscaping Design:A Case Study of an Urban River Wetland

This study introduces a Landscape Information Modeling±Stable Diffusion(LIM±SD)-based digital workflow for ecological engineered landscaping(EEL)design,focusing on urban river wetlands.It explores how students from diverse academic backgrounds perform EEL tasks using the LIM±SD approach.A total of 30 participants,including industrial design postgraduates and landscape architecture undergraduates and postgraduates,completed the design tasks.The efficacy of their designs was assessed through expert evaluations on site appropriateness,aesthetics,spatial layout,and eco-engineering techniques of the design proposals,as well as the parametric simulation which calculated the vegetation coverage rate and proportion of riparian areas for each design.Moreover,evaluation of participants’subjective design experiences was conducted via questionnaires.Results indicated that landscape architecture postgraduates outperformed others applying ecological engineering principles.The study also elucidated discrepancies between LIM models and SD-generated renderings,as well as the uncertainty of SDgenerated renderings,suggesting improvements are needed to align digital outputs with ecological design criteria.

一种多点拟合的恒定立体角纤维重建模型

基于弥散磁共振成像DTI的纤维追踪技术是非侵入性活体脑神经研究的关键技术.恒定立体角重建模型CSA是基于DTI发展而来的一种纤维重建模型,能够根据采样球壳上的数据对弥散方向分布函数进行线性径向投影计算,从而进行纤维重建.目前,恒定立体角纤维重建模型存在鲁棒性较差,重建纤维过于杂乱以及弥散方向分布偏差的问题.针对上述问题,本文提出MCSA(Multipoint Constant Solid Angle)模型,首先引入可以使弥散方向分布函数更加准确的最小二乘法,接着通过自适应高斯函数引入多点拟合弥散信息提高模型鲁棒性和抗噪性.最后,本文分别使用Fibercup、ISMRM2015年模拟数据以及Stanford HARDI真实影像数据对传统CSA模型以及本文提出MCSA模型进行对比分析,结果表明,利用本文提出MCSA模型重建的纤维更加符合客观规律,并且在一定程度上减少了假阳性纤维的生成.

湖南省极端气候时空特征分析及风险评估

为探究湖南省极端气候的时空变化特征及风险发生概率,基于湖南省32个气象站点1961—2017年逐日气象观测资料,选取12个极端降水指数和16个极端气温指数,利用线性倾向法、M-K趋势检验法、克里金插值等方法,对湖南省极端气候的时空变化特征进行探究,并采用信息扩散模型对湖南省极端气候风险进行评估,研究表明:近57年来湖南省极端降水指数总体变化趋势较为平缓,大多数极端降水指数在空间上呈东高西低、南高北低的分布。极端气温指数总体变化趋势呈显著变暖趋势,表征高温的指数在空间上呈东南向西北递减的趋势。对湖南省极端降水的风险评估显示,湘西北发生极端降水的风险更大,而湘东南的降水总量更多。对湖南省极端气温的风险评估显示,湘西北为持续高温的高风险地区,常德市发生极端高温的风险概率较大,为2~3 a一遇。

参数化物理内嵌神经网络求解稳态单能中子扩散解集

物理内嵌神经网络(PINN)是一种结合了物理学知识的深度学习方法,近年开始被用于计算核工程领域中的堆芯中子问题。然而,PINN在求解不同问题时的网络不可复用性极大地影响了该方法的使用价值和效率。为了解决网络不可复用性问题,提出了一种基于参数化PINN的方法,开发了一种新型的中子扩散物理方程的代理模型。该代理模型具有很高的灵活性和效率,可以在无样本的前提下,快速给出任意给定参数中子扩散问题的解。此外,比较了边界软约束和硬约束下参数化PINN的代理模型预测精度。最后,验证了硬约束PINN代理模型相较于有限元计算软件的加速能力。结果表明,硬约束下的PINN代理模型具有更高的预测精度,且加速比在1000以上。

基于信息扩散的隧道突水岩层安全厚度概率模型研究

通过虚功原理和莫尔库伦强度准则建立隧道最小安全岩层厚度力学模型,并推导计算公式,从公式看出最小安全岩层厚度受隧道半径、岩石内摩擦角度、掌子面前溶腔水压和黏聚力等因素影响。针对小样本信息不完备特点,基于信息扩散原理建立岩石力学参数(黏聚力、内摩擦角、弹性模量)和最小安全岩层厚度概率估计模型,以宜万铁路5个隧道的样本数据为基础,获取了岩石力学参数和最小安全厚度概率密度分布函数,并且绘制了图形。依据信息扩散原理建立的隧道最小安全岩层厚度概率模型能很好地利用样本数据信息。建立的最小安全岩层厚度概率模型可以应用到岩溶隧道突涌水灾害的风险评价中,对设计和施工有一定参考价值。

国内外图情领域的知识扩散特征研究

【目的/意义】知识经济时代,知识扩散的路径研究是掌握知识发展动向、把握新机遇的重要途径。摸清知识扩散的规律与机制,有助于准确预测知识扩散的未来动向,推动知识的共享与发展。【方法/过程】以CNKI和Webof Science两个数据源近五年的图情学科研究型文献为例,采用文献计量的分析方法,结合SATI分析软件,从知识扩散的广度、速度、强度三个指标,以及知识扩散的主题演化方面,比较分析我国图情学者在国际和国内平台的知识扩散特征差异。【结果/结论】国内和国外图情学科的知识扩散广度在逐渐增加,但国外的增速更加明显;国内图情学科的知识扩散速度明显高于国外;国内向本学科的知识渗透率最高,在50%以上,国外更多渗透于其他学科,且与环境科学的交叉融合在逐年加深。

基于扩散模型和双向长短期记忆网络的锂电池SOH估计

【目的】锂电池健康状态(state of health, SOH)的精确预测评估可以提高电池设备的安全性,降低故障的发生率。针对数据驱动方法在模型训练过程中需要大量标签样本数据的问题,提出了一种新的基于扩散模型和双向长短期记忆网络的锂电池SOH估计方法。【方法】首先,建立电池充电时间、电压和温度三者间的长期依赖关系云图;其次,设计一个时空信息捕捉模块,将该模块捕获的长期依赖信息作为扩散模型的生成条件,赋予扩散模型电池SOH数据生成能力;最后,利用双向长短期记忆网络(Bi-LSTM)对部分由原始数据和生成数据混合而成的电池数据集进行训练,并利用剩余的原始数据作为测试集对所提方法进行验证。【结果】验证结果表明,该方法不仅可以减少收集电池数据类型的周期和成本,而且能够有效预测电池SOH。【结论】该方法在电池SOH估计上具备良好的精度,可进一步探索其他电池数据集组合,优化模型结构,提高电池管理系统。

Storm surge disaster evaluation model based on an artificial neural network

Back propagation is employed to forecast the current of a storm with various characteristics of storm surge; the technique is thus important in disaster forecasting. One of the most fuzzy types of information in the prediction of geological calamity is handled employing the information diffusion method. First, a single-step prediction model and neural network prediction model are employed to collect influential information used to predict the extreme tide level. Second, information is obtained using the information diffusion method, which improves the precision of risk recognition when there is insufficient information. Experiments demonstrate that the method proposed in this paper is simple and effective and provides better forecast results than other methods. Future work will focus on a more precise forecast model.

A study on the effects of diffusion of information on epidemic spread

In this work,the spread of a contagious disease on a society where the individuals may take precautions is modeled.The primary assumption is that the infected individuals transmit the infection to the susceptible members of the community through direct contact interactions.In the meantime,the susceptibles gather information from the adjacent sites which may lead to taking precautions.The SIR model is used for the diffusion of infection while the Bass equation models the information diffusion.The sociological classification of the individuals indicates that a small percentage of the population takes action immediately after being informed,while the majority expects to see some real advantage of taking action.The individuals are assumed to take two different precautions.The precursory measures are getting vaccinated or trying to avoid direct contact with the neighbors.A weighted average of states of the neighbors leads to the choice of action.The fully connected and scale-free Networks are employed as the underlying network of interactions.The comparison between the simple contagion diffusion and the diffusion of infection in a responsive society showed that a very limited precaution makes a considerable difference in the speed and the size of the spread of illness.Particularly,highly connected hub nodes play an essential role in the reduction of the spread of disease.

人际互动作用下集群行为的演化逻辑与治理

中国社会正经历全面转型阶段,社会利益主体的阶层分化和利益冲突使阶层关系日趋复杂,由此引发的集群行为对中国公共安全造成严重威胁。针对不同社会情景下集群行为演化逻辑进行科学分析,进而为有效控制和预防恶性集群事件(行为)提供参考,具有重要的社会意义。集群行为是公共管理经典研究问题,传统的社会科学研究范式难以从微观视角属性理解集群行为的涌现特征以及整体结构,进而难以对机制进行精确剖析。在已有集群行为理论与研究基础上,创新性地构建了“理性与非理性动因—影响与独立场域”的综合解释框架,结合信息接收与处理机制的探讨对集群行为的演化逻辑进行理论上的再构建,形成了全局完备、局部完备、全局不完备与局部不完备信息传播机制下的演化链条;进一步引入社会计算领域中的复杂网络理论与信息传播模型,对集群行为演化传递过程建模。在总体框架指导下,依据不同情景与传播机制下的分析框架与演化模型对演化过程进行数学分析,发现集群行为的演化存在自校正性,较大的社会影响以及有限的时间是阻止集群行为自校正的环境制约,导致系统不可预测性与路径依赖性。在结构化的人群中,发现稀疏网络有利于集群行为的消散,但无标度网络增强了系统中社会影响的自强化性。通过计算机模拟以及基于农民工实证数据的仿真实验,对集群行为的一般化与特殊化机制进行讨论,发现农民工的正式网络结构与无标度网络结构在集群行为的演化上呈现相似特征,处在正式网络关键位置的农民工对于集群行为的发动具有重大影响。

基于智能体建模的医疗众筹信息扩散模型与平台策略研究

基于扩散理论,从微观层面分析了医疗个体转发与捐赠行为的决策机制,并通过仿真实验探究医疗众筹信息扩散的影响因素和平台广播策略。结果表明,项目信息因素、口碑因素对医疗众筹信息的扩散速率有影响,但两者均对筹款率无明显影响;发起者节点度对传播速率和筹款率均有积极影响;利用平台广播策略,可以有效扩大潜在决策者的比例,使发起者不再局限于自身的社交圈中,促进筹资公平;平台广播周期越长、广播人群比例越大,捐赠者比例和传播速率的效果越好,但当周期超过自然扩散周期的50%、广播比例超过80%时,增大策略成本投入的边际利润变低。

信息扩散函数在半参数地壳形变分析中的应用

提出一种利用信息扩散函数构建正则矩阵的新方法。首先,仅保留半参数模型的参数化模型部分,利用最小二乘估计获取参数模型的最小二乘残差;然后,将此残差向量作为信号,计算信号的信息扩散函数估值;最后,将信息扩散估计值作为正则矩阵的主对角元素构建正则矩阵。与时间序列法相比,新方法淡化了对信号连续性和光滑性的要求,且信息扩散函数估值本质上是残差的概率分布,相当于事先合理地确定了信号权阵,使得参数估计具有良好的稳健性。中国大陆华北块体的半参数地壳形变分析结果表明,新方法具有明显优势。

内部信息者的最优效用

在现有跳扩散模型和对数效用函数的基础上,考虑了内部信息市场中的期望效用最大问题和信息效用比较问题。采用一个到期日可测的随机变量来刻画现实金融市场中处处存在的内部信息,并在此基础上构建了内部信息市场模型,定义了一般信息者和内部信息人士。利用多维跳扩散模型描述风险资产的变化,由此提出两类不同信息投资者的期望效用最大问题。利用扩大信息流方法和随机分析理论,风险资产在内部信息模型下的动态被刻画,两类不同信息者的期望效用最大策略和相应的最大期望效用被明确给出。市场信息影响着投资策略,投资者为获取信息往往需要花费一定的信息成本,不同信息给投资者带来的效用也不相同。在付出信息成本相同的条件下,理性的投资者总会选取能够给自己带来更大效用的信息。为比较信息的效用大小,将不同信息下为得到相同的最大效用而必须投入的初始投资成本之比定义为信息效用比,通过分析给出它的五个性质。最后导出了一般信息与内部信息的效用比的具体表示式。

Identifying Influential Communities Using IID for a Multilayer Networks

In online social networks(OSN),they generate several specific user activities daily,corresponding to the billions of data points shared.However,although users exhibit significant interest in social media,they are uninterested in the content,discussions,or opinions available on certain sites.Therefore,this study aims to identify influential communities and understand user behavior across networks in the information diffusion process.Social media platforms,such as Facebook and Twitter,extract data to analyze the information diffusion process,based on which they cascade information among the individuals in the network.Therefore,this study proposes an influential information diffusion model that identifies influential communities across these two social media sites.More-over,it addresses site migration by visualizing a set of overlapping communities using hyper-edge detection.Thus,the overlapping community structure is used to identify similar communities with identical user interests.Furthermore,the com-munity structure helps in determining the node activation and user influence from the information cascade model.Finally,the Fraction of Intra/Inter-Layer(FIL)dif-fusion score is used to evaluate the efficiency of the influential information diffu-sion model by analyzing the trending influential communities in a multilayer network.However,from the experimental result,it observes that the FIL diffusion score for the proposed method achieves better results in terms of accuracy,preci-sion,recall and efficiency of community detection than the existing methods.

数据与物理信息混合驱动的固定床吸附穿透深度学习模型

提出一种基于数据与物理信息混合驱动的固定床吸附穿透深度学习模型(PINN_MOD),采用基于残差自适应网格加密策略联合惩罚因子增强外部数据约束方法,通过最小化损失函数逐步调整神经网络参数逼近固定床双组分气体动态吸附过程偏微分方程(PDE)的解。嵌入物理信息神经网络(PINN)模型可以高保真地求解一维单组分对流-扩散模型和一维单组分固定床吸附模型PDE的正向解和逆向解,但在求解长时间尺度一维双组分固定床吸附模型PDE时存在收敛困难。利用传统有限差分方法(FDM)首先计算一维双组分固定床吸附穿透PDE问题,然后将FDM模拟获得的时空区域内组分浓度数据作为外部约束,联合PINN模型一起求解一维双组分固定床吸附穿透PDE。以填充CALF-20和UTSA-16两种MOF材料的固定床吸附CO_(2)/N_(2)(摩尔比30∶70)混合物为例,采用PINN_MOD模型计算出组分CO_(2)固定床出口穿透曲线,能够较好地复制FDM计算结果,证实了该模型仅依赖于少量外部数据就能有效地获得PDE高保真解。PINN_MOD模型有望在开发面向气体分离应用的新型金属有机骨架(MOF)材料吸附剂方面发挥重要作用。

A Specification Test of Stochastic Diffusion Models

In this paper, we propose a hypothesis testing approach to checking model mis-specification in continuous-time stochastic diffusion model. The key idea behind the development of our test statistic is rooted in the generalized information equality in the context of martingale estimating equations. We propose a bootstrap resampling method to implement numerically the proposed diagnostic procedure. Through intensive simulation studies, we show that our approach is well performed in the aspects of type I error control, power improvement as well as computational efficiency.