相对性区域创新指数与经济周期挖掘

提出了一类新的相对性区域创新指数,并采用世界专利申请数据对其进行了具体计算.基于区域创新同经济发展水平之间的超线性关系,该指数消除了经济发展水平对创新能力的影响,可以实现对不同发展水平的经济体之间进行有效的创新能力横纵对比.该创新指数尽管极其简单,却揭示出一系列迥异于传统认知的现象,例如中国大陆地区的技术创新能力在1980年代就已经位居世界前列.采用该指数,不但可以在较高水平上解释世界各国的经济增长,还发现它同经济增长率之间的相关性存在一个20年的经济周期.这些结果显示,该指数作为一个单一性指标,以极小的数据依赖就实现了较高程度的解释性,不但重新定位了世界各经济体的创新能力,对深入理解创新同经济发展之间的关系提供了新的角度,而且暗示着这类相对性经济指标的发展潜力与应用空间.     

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.     

A self‐adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application

The self‐adaptive intelligence gray predictive model (SAIGM) has an alterable‐flexible model structure, and it can build a dynamic structure to fit different external environments by adjusting the parameter values of SAIGM. However, the order number of the raw SAIGM model is not optimal, which is an integer. For this, a new SAIGM model with the fractional order accumulating operator (SAIGM_FO) was proposed in this paper. Specifically, the final restored expression of SAIGM_FO was deduced in detail, and the parameter estimation method of SAIGM_FO was studied. After that, the Particle Swarm Optimization algorithm was used to optimize the order number of SAIGM_FO, and some steps were provided. Finally, the SAIGM_FO model was applied to simulate China's electricity consumption from 2001 to 2008 and forecast it during 2009 to 2015, and the mean relative simulation and prediction percentage errors of the new model were only 0.860% and 2.661%, in comparison with the ones obtained from the raw SAIGM model, the GM(1, 1) model with the optimal fractional order accumulating operator and the GM(1, 1) model, which were (1.201%, 5.321%), (1.356%, 3.324%), and (2.013%, 23.944%), respectively. The findings showed both the simulation and the prediction performance of the proposed SAIGM_FO model were the best among the 4 models.     

面向信息化战争的广义兰切斯特战争模型

21世纪是一个信息化时代,为了更好的面向信息化战争.通过对传统的兰切斯特微分方程进行改进,考虑了信息化的诸多因素,引入了战场情报感知系数和信息优势系数.构建了数学模型,并对其进行差分处理求解,比较分析了交战双方对战场感知系数变化和信息优势系数变化情况下,对战争的影响.以海湾战争为例,检验了模型的可行性.     

结构形状优化与智能模型化

阻碍形状优化软件广泛应用的障碍之一是依据自然设计度量描述和建立设计模型、分析模型、优化模型以及实现三个模型之间的转换.本文称这一困难为结构形状优优设计软件的适用性.本文提出了一种方法用来动态地确定平面连续体结构形状优化过程中的边界,应用基于人工智能方法的启发式规则与技术,自动生成由设计单元法表示的几何模型,也就是将一个结构自动剖分成若干个大的四边形映射单元.这些大单元对于进一步的网络生成是必要的,同时也是向全自动计算机辅助形状优化系统前进的重要一步.     

Condition Monitoring of Ground Anchorages by Dynamic Impulses: GRANIT System

The GRANIT system operates by applying an impulse of known force by means of an impact device that is attached to the tendon of the anchorage. The vibration response signals resulting from this impulse are complex in nature and require analysis to be undertaken in order to extract information from the vibrational response signatures that is relevant to the condition of the anchorage. In the system, the complicated relationship that exists between characteristics of an anchorage and its response to an impulse is identified and learned by a novel artificial intelligence network based on artificial intelligence techniques.The results presented in this paper demonstrate the potential of the GRANIT system to diagnose the integrity of ground anchorages at a site near Stone, England, by using a trained neural network capable of diagnosing the post-tension level of the anchorage. This neural network was used for the diagnosis of load in a second ground anchorage adjacent to the original anchorage used for the training of the neural network. Further tests were taken with a different anchor head configuration of the anchorage and a different relationship between the signature response of the anchorage to an applied impulse and its post-tension level was found.Problems encountered during the diagnosis of this second set of test signatures by the trained neural network are investigated with the use of a lumped parameter dynamic model. This model is able to identify the parameters in the anchorage system that affect this change in response signature. The results from the investigation lead to a new form of classification for the installed anchorages, based on their anchor head configuration.Laboratory strand anchorage tests were undertaken in order to compare with and validate the results obtained from the field tests and the lumped parameter dynamic model.     

具有固定时滞的经济周期模型的动态分析

在经济活动中,投资行为和资本存量存在一定的时滞效应,这会影响经济周期模型的动态行为,进而使得投资政策对经济的稳定调整复杂化.考虑到资本存量的预期时间以及投资时滞对经济活动的影响,采用Hopf分岔理论,研究具有固定时滞的经济周期模型的均衡点的稳定性以及形成经济周期的条件.研究发现,投资过程中的投资时滞,以及投资决策中对于资本存量的预测时间构成经济周期产生的诱因;同时可通过政府投资政策调整达到预期均衡目标,这对保持经济周期稳定及经济政策制定有一定的指导作用.     

智能岩石力学及其内容

本文针对岩石力学研究中目前存在的问题和信息时代的要求, 提出了21世纪的智能岩石力学的新思想和研究内容。它是综合现有各种行之有效的方法, 系统研究岩石工程问题, 提高岩石力学解决复杂问题的准确性。     

在线社交网络控制实验的现状与展望

在线社交网络已发展成为一个独特的电子生态系统，其应用深刻影响着人们生活的方方面面。由于在线社交网络特性复杂，分析在线社交网络形成和变化中的规律成为当前计算机科学、社会学和物理学的一项挑战。传统上，在线社交网络实证研究主要采用计算机辅助的被动数据获取和分析方式。近年来，在真实大规模在线社交网络上直接进行控制实验从而主动获取数据并开展分析研究的方式广受关注。评述了这一领域的研究进展，包括：社交网络控制实验的主要研究模式；控制实验方法在社交网络结构、信息传播、行为和心理学等领域取得的主要成果以及主要实验工具的适用条件和局限性。最后，展望了人工智能技术在社交网络控制实验中的应用潜力，分析了智能算法对降低实验成本和提高实验效率的作用。     

State of the art and research development prospects of energy and resource-efficient environmentally safe chemical process systems engineering

In this focus overview, the main types and directions of engineering, methods and techniques of intensification of chemical process systems (CPS) and process optimization of energy- and resource-efficient processes for the representative production of titanium compounds, mining waste processing systems, electrochemical coating technologies, combined technologies for the treatment of industrial effluents and energy-and resource-efficient technologies for cleaning soils from petroleum and chemical pollution products are reviewed. The following issues have been discussed: methods of complex assessment of production energy efficiency and software and information support for automated synthesis of optimal energy-efficient regenerative heat exchange systems using pinch analysis; methods and algorithms for fractal-statistical characteristics analysis of nonstationary gas flows in complex gas pipelines; methods of ecological and economic optimization of production, infrastructure supply chains; methods for assessing and preventing the dangerous environmental impact assessment of chemical pollution; organization and logistics management of business processes engineering for improving the energy efficiency of plants; engineering of problem oriented computer systems, heuristic-computational models and algorithms for intelligent integrated logistics support of the equipment life cycle; engineering developments in the field of digital transformation of energy-efficient CPS and technological production systems; application of methods for optimizing reliability factors optimization, digitalized risk and safety management in the engineering of energy- and resource efficient CPS.