Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

摩托车单向器自动装配机气动系统设计

针对当前摩托车单向器组件主要采用手工装配、装配效率低、随机性大、无法对装配过程实现有效管理控制的情况,为了易于对零件装配过程进行实时监测和提高生产效率,开发了一种智能摩托车单向器自动装配机。介绍了摩托车单向器自动装配机的上料系统、装配系统、检测系统和落料系统的机械结构,重点阐述了设备的气动系统设计,并进行了实验研究。结果表明,该设备可实现组成单向器的5种零件自动上料和装配,同时降低了操作人员的劳动强度,生产效率提高约50%。其气动系统噪音小、工作可靠,可应用于摩托车单向器的大批量生产。研究结果可为其他相似异形零件自动装配机的开发提供参考。     

基于案例学习的知识管理系统

简单了介绍知识管理的特点以及知识管理系统具有的功能,说明将基于案例的推理技术(CBR)和其它机器学习技术结合起来应用于知识管理系统的必要性和有效性,并在此基础上提出一种基于案例学习的知识管理系统的实现框架。     

盾构机自动控制技术现状与展望

随着我国社会主义市场经济的迅速发展以及科技水平的不断提升,交通运输行业得到了蓬勃发展。当前,随着我国地下隧道等各项工程建设数量的不断上升,盾构机的重要性不言而喻。加强对盾构机自动控制技术的研究工作,不断解决该技术应用过程中存在的问题,才能有效地促进技术应用,为提升我国地下工程施工安全性能奠定技术支持。     

我国港机CAD的应用与发展

港口装卸机械产品计算机辅助设计简称港机ＣＡＤ，本文介绍了港机ＣＡＤ１５年来发展的历程和取得的成绩，指出了港机ＣＡＤ今后的研究方向与课题。     

广州-茂名铁路换碴施工技术

文章介绍了有关在广州-茂名铁路运用大型养路机械进行更换石碴施工的技术和经验。     

论我国物流机械制造企业技术真空的形成及解决途径

阐述了当前我国物流机械制造业所面临的问题和现状,分析并提出了物流机械制造企业今后努力及其发展方向。     

昌黎缝纫机零配件产业集群发展策略研究

产业集群作为一种新兴的经济发展模式和区域经济的基础特征,已经成为区域竞争优势的重要支点。本文介绍了昌黎缝纫机零配件产业集群的基本情况,指出了其发展过程中的一些问题,就此提出了发展策略。     

基于模糊控制的三相异步电动机的软起动系统

本系统主要通过对模糊控制器的设计 ,将异步电动机的起动电流既限制在了一定的范围内 ,又可得到较大的起动力矩。由于又采用了参数自调整作为辅助控制 ,不但改善了起动时的动态性能 ,而且改善了稳定运行时的静态性能