一种基于人工智能的电梯群目的层控制方法（1）

本文从概述电梯工业采用的人工智能技术出发．分析了人工智能技术在电梯工业中不断发展的原因。回顾和总结了相关的文献．这些文献在普通的人工智能文献领域是不易见到的。第2部分论述了AI规划和多智能体系统用于电梯群控制的新近发展项目。     

国内外电梯标准比较研究（3）——中国电梯标准体系概述

电梯标准是电梯设计、制造、安装、维修、施工验收的技术依据，由全国电梯标准化技术委员归口管理。由于我国电梯工业发展较晚，在20世纪80年代前虽有一些标准，但水平较低也不成体系。80年代后随着电梯工业的快速发展，一些合资企业先后成立，从国外引进了较先进的电梯和技术，也促使我国的电梯标准逐渐完善，并向国际靠拢和接轨。20世纪90年代中期后，又根据我国电梯发展的实际情况，对电梯标准进行了大规模的修订与补充，使电梯标准体系得到了进一步完善。     

西部电梯业路在何方

1 电梯业东强西弱。我国电梯工业的整体结构存在着严重的地域分布失衡，即东强西弱。自从改革开放以来，东部沿海地区率先走上快速发展的道路，作为一个与建筑业密切相关的行业，大规模的经济建设带动东部电梯工业走上了快速发展的道路。同时，国外大量先进生产技术和管理经验也涌人东部地区，极大地推动和提高了东部地区电梯工业的发展进程，使东部成为我国电梯工业最大的亮点。     

电梯速度提高以后的思考

随着国民经济的发展，人民生活水平的提高，高层住宅区的涌现以及电梯工业水平的提高和变频调速技术在电梯上的广泛应用，国内中小电梯企业已从生产速度1．00m／s以下的电梯逐步向1．50～2．00m／s进军。这显然是市场的需要和技术的促进，给予了中小电梯企业一个提高和发展的良好时机。     

电梯速度提高以后的思考

随着国民经济的发展，人民生活水平的提高，高层住宅区的涌现以及电梯工业水平的提高和变频调速技术在电梯上的广泛应用，国内中小电梯企业已从生产1m／s速度以下的电梯逐步向1．5m／s～2m／s进军。这显然是市场的需要和技术进步的促进，给予了中小电梯企业上台阶的一个提高和发展的良好时机。     

电梯群控系统的控制算法

研究了国内外电梯群控制算法的发展现状，对专家系统、模糊逻辑和神经网络及遗传算法等人工智能方法在电梯群控系统中的应用情况作以评述。提出了把神经网络和遗传算法相结合的混合算法应用到电梯的群控系统中的设想，可以达到更好的控制效果。     

电梯群控问题的数学模型及智能群控技术

电梯群控问题本质上是一类调度优化问题。描述了电梯群控问题的数学模型，分析了电梯群控问题的特点及其与其它调度问题的不同；对人工智能技术在电梯群控问题研究中的应用情况进行了分析、总结，简要提出并分析了当前电梯群控研究领域可进一步深入研究的几个方向。     

未来中国电梯市场格局之遐想

电梯作为垂直运输的交通工具，是现代高层建筑和公共场所不可或缺的重要设备。电梯和高层建筑珠联璧合，相得益彰。高层建筑鳞次栉比，电梯技术日新月异。有了电梯，摩天大楼才得以崛起，现代城市才得以长高。 1中国电梯市场概述 我国电梯工业起步较晚，但是发展迅猛。1949年之前，我国电梯几乎是清一色的泊来之“洋玩意儿”；     

安全与节能是电梯人长期的追求

30年前，上海电梯厂、天津电梯厂等八大电梯企业奠定了中国电梯工业的基础；改革开放30年，中国电梯工业逐步发展和壮大。中国电梯杂志伴随我们电梯界同仁亲历与见证了中国电梯工业的成长史。     

分布式电梯故障诊断系统

基于分布式人工智能的思想，将多智能技术引入电梯故障诊断领域，分析了基于多智能系统的分布式智能故障诊断方法和过程；建立了一种分布式多智能故障诊断系统结构及其原型系统。通过在电梯故障诊断的应用仿真，提高了故障诊断的实时性、准确性和整体性能。     

人工智能如何改变指南的未来

近年来,人工智能的快速发展和进步对医学领域的影响日益加深。科技部发布的人工智能指南明确提出要加大人工智能技术在医药卫生领域应用的支持力度。临床实践指南作为医疗实践的重要指导性文件,可以规范医务人员的诊疗行为,缩小最佳研究证据与当前实践之间的差距,具有重要意义和价值。如何利用人工智能技术加速指南的制订流程,提高其制订效率,创新其传播与实施模式,乃至改变指南的未来发展,已引起国内外学者的广泛关注。本文就人工智能在临床实践指南领域的应用现状和发展前景进行分析和讨论,并在此基础上提出促进人工智能与临床实践指南结合的思考与建议。     

The attribute impact concept: Applications in case-based reasoning and parametric cost estimation

The success of every construction project depends on the satisfactory achievement of a client's needs relating to cost, duration, and quality. Among them, costs must be managed with special awareness. In an effort to improve the estimate accuracy of cost during the initial stages of a building project, this research introduces the concept of ‘attribute impact’ (AI), which can measure the weights of attributes quantitatively and prioritize them. This study will also explain AI development, which adopts the impulse–momentum theorem of physics. For a case study, the project analyzes 163 public apartment buildings from 15 housing complex projects in Korea. To examine the validity of the proposed AI, the case study carries out two types of validation in terms of estimate accuracy using the parametric method and the case-based reasoning (CBR) applicability test. The validation results support the acceptable use of the suggested AI in measuring the weights of attributes and its estimate accuracy when combined with parametric or CBR estimation.     

Application of artificial intelligence to rock mechanics: An overview

Different artificial intelligence(AI)methods have been applied to various aspects of rock mechanics,but the fact that none of these methods have been used as a standard implies that doubt as to their generality and validity still exists.For this,a literature review of application of AI to the field of rock mechanics is presented.Comprehensive studies of the researches published in the top journals relative to the fields of rock mechanics,computer applications in engineering,and the textbooks were conducted.The performances of the AI methods that have been used in rock mechanics applications were evaluated.The literature review shows that AI methods have successfully been used to solve various problems in the rock mechanics field and they performed better than the traditional empirical,mathematical or statistical methods.However,their practical applicability is still an issue of concern as many of the existing AI models require some level of expertise before they can be used,because they are not in the form of tractable mathematical equations.Thus some advanced AI methods are still yet to be explored.The limited availability of dataset for the AI simulations is also identified as a major problem.The solutions to the identified problems and the possible future research focus were proposed in the study subsequently.     

Chemical aspects of coagulation using aluminum salts—II. coagulation of fulvic acid using alum and polyaluminum chloride

This is the second of a two-part series investigating chemical aspects of coagulation using AI salts. Part I of the series examined the hydrolytic reactions of AI. In this paper, the coagulation of fulvic acid (FA) by alum and polyaluminum chloride (PACI) is examined. An Al speciation methodology was used to examine complexation reactions between Al and FA for water treatment conditions. From pH 5 to 7 and at typical coagulant doses, hydrolysis and complexation of Al is described by a simple model based on the reaction stoichiometry between AI, FA and OH⁻. Model results show that when alum is used as a coagulant, Al complexed with FA is hydrolyzed to a ligand number, This is similar to the ligand number for the prehydrolyzed PACI and explains similarities in dose requirements for these coagulants. Effects of temperature on coagulation performance are shown to be largely chemical in nature. Chemical aspects of coagulation are discussed and the importance of complex formation in coagulation is examined.     

Artificial Intelligence in Bridge Engineering

Abstract: The idea to use artificial intelligence (AI) in civil engineering is as old as AI itself. Since the 1950s, studies on AI applications in civil or bridge engineering have proliferated. Most of these studies have dealt with specialized isolated engineering subtasks. Few of the applications have been delivered to practitioners and were used to advance their work. This paper presents a model of the life-cycle flow of information in bridge engineering as an organizing framework for reviewing previous work on AI applications in bridge engineering. Several common patterns that emerge from the studies are summarized. A subsequent analysis of the status of the bridge stock in many countries suggests that a more integrated approach to AI applications may have a larger practical impact. Several practical areas that can benefit significantly from AI techniques are identified, and several studies are proposed including the AI technology needed and the methodology according to which these applications should be developed.     

Advanced CAD Environments: A Knowledge-Based Foundation

Abstract: Current computer-aided design (CAD) systems are extremely powerful tools for the development of products in an industrial setting. However, they still leave a lot to be desired when it comes to fulfilling a designer's demands and dreams. We believe that knowledge engineering, or more broadly artificial intelligence (AI), is a promising candidate for providing advanced design environments. This paper proposes assorted techniques from AI—such as logical knowledge representation, naive physics, and commonsense reasoning—as effective means of obtaining such environments. In order to make adept, temporal comments, an architecture machine must have a certain basic understanding of qualities. Though at first primitive, this qualitative appreciation itself would evolve within a value system that is very personal, between a man and a machine.     

阿联酋AI Ain市规划

艾茵费达位于杰贝．哈费特山脚旁边，周围是艾茵市宁静的自然景观，是一处给人们保持身心健康和心境平和的地方。在那里，大自然和艾茵市的矿泉加上阿拉伯的当地传统和现代化的科技成为了和谐生命的根本元素。     

Artificial intelligence approaches to achieve strategic control over project cash flows

The ability over the course of a construction project to make reliable predictions regarding cash flows enhances project cost management. This paper uses artificial intelligence (AI) approaches to predict cash flow trends for such projects in order to develop appropriate strategies that apply factors such as float, process execution time, construction rate and resource demand to project cash flow control. AI approaches involved in this paper include K-means clustering, genetic algorithm (GA), fuzzy logic (FL), and neural network (NN). K-means clustering is employed to categorize similar projects, while the other approaches are used to develop the Evolutionary Fuzzy Neural Inference Model (EFNIM), a knowledge learning model. FL and NN are employed in the EFNIM to develop a neural-fuzzy model that can deal with uncertainties and knowledge mapping. GA is used to optimize the membership functions of FL and NN parameters globally. The major target of this AI learning is to address sequential cash flow trends. This trained result is furthermore applied to a strategic project cash flow control. This cash flow control affects project performance within the banana envelope of the S-curve for project management.     

On big data,artificial intelligence and smart cities

Cities are increasingly turning towards specialized technologies to address issues related to society, ecology, morphology and many others. The emerging concept of Smart Cities highly encourages this prospect by promoting the incorporation of sensors and Big Data through the Internet of Things (IoT). This surge of data brings new possibilities in the design and management of cities just as much as economic prospects. While Big Data processing through Artificial Intelligence (AI) can greatly contribute to the urban fabric, sustainability and liveability dimensions however must not be overlooked in favour of technological ones. This paper reviews the urban potential of AI and proposes a new framework binding AI technology and cities while ensuring the integration of key dimensions of Culture, Metabolism and Governance; which are known to be primordial in the successful integration of Smart Cities for the compliance to the Sustainable Development Goal 11 and the New Urban Agenda. This paper is aimed towards Policy Makers, Data Scientists and Engineers who are looking at enhancing the integration of Artificial Intelligence and Big Data in Smart Cities with an aim to increase the liveability of the urban fabric while boosting economic growth and opportunities.