基于BP人工神经网络中国光伏产业贸易摩擦预警研究

建立有效的贸易摩擦预警系统有利于中国新兴产业的健康发展,有利于中国实施制造强国战略。将警兆信号法与人工神经网络相结合,从宏观经济形势、产业供给能力、双边贸易状况、市场需求水平等四个维度切入,构建了中国光伏产品出口贸易摩擦预警系统模型。预警分析表明,模型的预警效果与现实拟合较好,具有可行性。从模型的预警结果来看,2019年~2020年中国输美光伏产品贸易摩擦警情均低于轻警级别,仍面临一定的贸易摩擦风险。     

人工智能技术变革对国际贸易的影响

人工智能技术的快速发展正催生第四次工业革命,可能引发全球价值链深度重构和世界经贸格局重大变革。世界主要经济强国将发展人工智能技术作为争夺新一轮产业竞争优势的重要战略抓手。本文基于全球价值链视角研究人工智能技术变革对国际贸易的影响,我们发现人工智能技术变革可能推动国际贸易规模扩大,提升服务贸易份额,并促进国际贸易交易模式平台化、小宗化,可为中小企业创造更多参与国际贸易的机会。然而,人工智能技术变革也可能通过降低企业劳动力需求从而对我国等发展中国家的出口拉动型增长模式造成严重的潜在威胁。为应对人工智能技术变革,我国应部署并强化对人工智能产业发展的政策支持,加快培育制造业国际竞争新优势,大力推动先进制造业与现代生产性服务业深度融合发展,全面促进"中国制造"攀升全球价值链中高端。     

人工智能在我国公共卫生建设中的应用与思考

新冠疫情的暴发以及长期防治对中国的公共卫生体系造成深远影响，给我国突发公共卫生事件应急与防控机制带来巨大的考验。在疫情防控的过程中，信息化技术为传统医疗机构带来瞩目的帮助与改变，人工智能、大数据、5G高速网络等新兴高科技的蓬勃发展在直接提升医疗水平方面表现出巨大潜力。本文就人工智能在疫情期间的医疗辅助功能展开讨论，并对未来我国公共卫生体系的建设进行了思考。     

Exploring the knowledge spillovers of a technology in an entrepreneurial ecosystem—The case of artificial intelligence in Sydney

New knowledge presents opportunities for commercial value and can hence be a critical asset for entrepreneurial ecosystems (EEs). In particular, general purpose technologies are major drivers of entrepreneurship. Thus, a nuanced understanding on technological knowledge and its spillovers among actors within an EE is warranted. Using knowledge‐spillover‐based strategic entrepreneurship theory, we propose to observe knowledge spillovers through the assessment of the knowledge bases of a technology in an EE. To do so, this article proposes to use three key sources of knowledge: publications reflecting the emerging knowledge base, patents representing the realized knowledge base, and startups showing the experimental knowledge base. This article uses secondary data sources such as Web of Science and applies the method of bibliometrics to illustrate how an assessment is carried out in practice by evaluating the artificial intelligence (AI) knowledge bases in Sydney from 2000 to 2018. The findings are summarized with an illustration of the evolution of the key actors and their activities over time in order to indicate the key strengths and weaknesses in Sydney's AI knowledge among the different bases. Contrary to expectations from the high potential of knowledge spillovers from a general purpose digital technology such as AI, the article shows that apparent knowledge spillovers are yet highly limited in Sydney. Even though Sydney has a strong emerging knowledge base, the realized knowledge base seems weak and the experimental knowledge base is slowly improving. That observation itself verifies the need to take strategic actions to facilitate knowledge spillovers within EEs. After the implications for theory and policy makers are discussed, suggestions for further studies are proposed.     

人工智能背景下会计人才教育模式改革研究

在人工智能高速发展的时代,会计人工智能应运而生。会计人工智能以其高效、精确、低成本的特点迅速取代基础会计工作人员的工作。在此背景下,国内会计人才教育该何去何从,文章将从会计教育模式现状入手,结合人工智能对会计的影响,以会计教育侧重点、会计人才的职业道德建设等方面为切入点,探讨会计人才教育模式改革。     

Modeling and supporting task-oriented group processes: Purposeful complex adaptive systems and Evolutionary Systems Design

Evolutionary Systems Design (ESD) is a universal general problem solving, formal modeling, design framework for purposeful complex adaptive systems (PCAS) and processes, i.e., task-oriented group processes. These processes constitute policy making, group decision, negotiation, and multiagent problem solving with human and/or artificial agents. ESD is also a framework for computer group support systems (GSS) that support these processes. The ESD general framework can be applied to define and solve specific problems. In this article the ESD framework is presented and illustrated by example. The article provides background for ESD computer implementations discussed in two other related articles (Lewis and Shakun 1996; Bui and Shakun 1996).     

Search reduction in hierarchical distributed problem solving

Knoblock and Korf have determined that abstraction can reduce search at a single agent from exponential to linear complexity (Knoblock 1991; Korf 1987). We extend their results by showing how concurrent problem solving among multiple agents using abstraction can further reduce search to logarithmic complexity. We empirically validate our formal analysis by showing that it correctly predicts performance for the Towers of Hanoi problem (which meets all of the assumptions of the analysis). Furthermore, a powerful form of abstraction for large multiagent systems is to group agents into teams, and teams of agents into larger teams, to form an organizational pyramid. We apply our analysis to such an organization of agents and demonstrate the results in a delivery task domain. Our predictions about abstraction's benefits can also be met in this more realistic domain, even though assumptions made in our analysis are violated. Our analytical results thus hold the promise for explaining in general terms many experimental observations made in specific distributed AI systems, and we demonstrate this ability with examples from prior research.This research has been sponsored, in part, by the National Science Foundation under grants IRI-9015423 and IRI-9010645, by the University of Michigan Rackham Graduate School, and by a Bell Northern Research Postgraduate Award.     

基于人工免疫算法的多无人机分布式任务分配方法

对多无人机任务分配问题进行了研究,在重量和路径负载均衡的基础上建立了带有攻击顺序的01决策变量的模型,运用分布式的人工免疫算法对该模型进行仿真实验,最终得到每个无人机攻击的目标顺序以及目标函数值,实验证明该方法在得到不劣于遗传算法结果的同时,提高了寻找最优解的收敛速度。     

基于BP人工神经网络的绿色施工模糊综合评价研究

发展绿色施工促进施工企业节约能源和资源、保护环境,是全球应对气候变化挑战、实现可持续发展的重要手段,而绿色施工评价对于建筑业落实可持续发展起到一定作用。本文在介绍BP人工神经网络基本原理的基础之上,分析了与其它评价方法相比,BP人工神经网络在绿色施工评价中的优越性。通过建立模糊评价矩阵,利用BP人工神经网络处理数据,较客观地对绿色施工进行评价。最后将此评价模型应用于实例,取得了令人满意的结果。     

基于BP神经网络的老工业基地企业核心竞争力的综合评价

我国对老工业基地进行调整和改造,作为老工业基地主体的企业,有必要了解自己和其他同行企业的核心竞争力水平和状况。为此,运用AHP法构建了老工业基地企业核心竞争力评价指标的递阶层次结构,应用BP神经网络理论对老工业基地企业核心竞争力进行综合评价。实证研究表明,评价结果与实际基本相符合,从而提供了一条对老工业基地企业核心竞争力进行定量评价的途径。