展望专家系统在水利工程施工中的应用

本文介绍了专家系统发展状况、原理方法和基本结构。文中首次提出在我国把专家系统的理论方法用于水利工程的设想,阐述水利工程施工专家系统应用的方法、内容及建立过程。     

海上平台配电柜智能数据采集技术研究与应用

本文通过对海上平台配电柜智能化应用的改造方案,进行深入探讨与应用分析研究。     

Deep learning diffusion by infusion into preexisting technologies – Implications for users and society at large

Artificial Intelligence (AI) in the form of Deep Learning (DL) technology has diffused in the consumer domain in a unique way as compared to previous general-purpose technologies. DL has often spread by infusion, i.e., by being added to preexisting technologies that are already in use. We find that DL-algorithms for recommendations or ranking have been infused into all the 15 most popular mobile applications (apps) in the U.S. (as of May 2019). DL-infusion enables fast and vast diffusion. For example, when a DL-system was infused into YouTube, it almost immediately reached a third of the world's population. We argue that existing theories of innovation diffusion and adoption have limited relevance for DL-infusion, because it is a process that is driven by enterprises rather than individuals. We also discuss its social and ethical implications. First, consumers have a limited ability to detect and evaluate an infused technology. DL-infusion may thus help to explain why AI's presence in society has not been challenged by many. Second, the DL-providers are likely to face conflicts of interest, since consumer and supplier goals are not always aligned. Third, infusion is likely to be a particularly important diffusion process for DL-technologies as compared to other innovations, because they need large data sets to function well, which can be drawn from preexisting users. Related, it seems that larger technology companies comparatively benefit more from DL-infusion, because they already have many users. This suggests that the value drawn from DL is likely to follow a Matthew Effect of accumulated advantage online: many preexisting users provide a lot of behavioral data, which bring about better DL-driven features, which attract even more users, etc. Such a self-reinforcing process could limit the possibilities for new companies to compete. This way, the notion of DL-infusion may put light on the power shift that comes with the presence of AI in society.     

Does the method for Military Utility Assessment of Future Technologies provide utility?

The Military Utility Assessment of Future Technologies (MUAFT) method was developed as a cost-efficient alternative to methods such as NATO's Disruptive Technology Assessment Games, to be used as a part of the Swedish Armed Forces' long-term capability development process. The question addressed in this study is whether MUAFT can be considered to have validity in its context and thus if it has potential to be useful to other small to medium size states. The analysis was based on an operationalization of Clark's framework for science and technology intelligence analysis, combined with a military capability centric view of military utility. MUAFT reports from 2012 to 2018 were reviewed in terms of how they satisfy five key criteria. The study shows that MUAFT provides utility, if used by a suitably composed group of experts, who are aware of the method's limitations. The limitations mainly originate from a lack of explicit support for assessing the impact of forces for change, other than technological forces, on military capability development. The expert group serves as the synthesizing bridge between technology forecasts and military utility assessments. Therefore, comprehensive expertise is needed in various military technology specialisations, in the sponsor's military capabilities and in subjects necessary to master in order to assess other influential societal forces for change.     

NMS2000系列网络型智能控制调度模拟屏在县级调度中的应用

随着电网的不断发展和无人值守变电站的实现，调度工作日益重要，调度模拟控制系统作为调度员日常工作的重要工具尤为突出，三水供电分公司于2001年对原有调度模拟屏进行了改造，使用的是NMS—2000系列网络型智能控制调度模拟屏控制系统，为此，本文探讨了网络型智能控制调度模拟屏系统的原理．系统结构和功能特点。NMS—2000系统投入运行后，大大提高了佛山三水区电网调度的自动化水平。     

基于遗传算法的高考志愿填报排序问题的研究

针对高考志愿填报录取最优化、最佳匹配问题,提出了基于遗传算法搜索最优解的解决方案。该方案模拟物种自然选择和遗传进化过程,将不同考生的高考志愿按录取结果利益最大化进行排序。在可选择院校数量相同的情况下,对不同考生考试成绩的数据通过程序不断进行智能优化和迭代,志愿排序结果趋于稳定,且达到最佳匹配。其不但满足考生的实际需求,而且达到志愿填报利益最大化的目的。该方法采用涵盖985、211、普通院校的10所高校的实际数据进行测试,结果表明,遗传算法可以用来求解最优高考志愿填报排序问题,且具有很高的准确率和适应度。