一种基于多属性决策的防空阵地选择排序方法

针对防空阵地选择排序定量研究方法较少的现状,提出了一种基于OWA算子的离差最大化区间数多属性决策方法,对防空阵地选择问题进行建模分析,结合具体算例对所建模型进行了验证。     

电气控制系统的故障诊断及显示方法

本文探讨了在机械制造行业中自动控制系统的故障诊断及其显示的方法及设想 ,采用个人计算机与可编程序控制器应用技术进行实现 ,便于及时排除故障。     

船维修器材收发管理系统集成化研究

数据安全保密、系统简便可靠、调度快速反应3个问题是器材保障信息化的关键。文章利用多媒体技术、移动通讯技术、嵌入式数据库技术，实现器材收发管理系统的软硬件集成来解决这3个问题。对提高船维修器材机动保障工作的效率和质量，以及船维修器材机动保障的信息化水平具有重要意义。     

如何从设计、招标、施工及监理四环节保证高等级公路的工程质量

就目前高等级公路建设中存在的问题 ,提出要从设计、招标、施工、监理四个环节上进行强化管理 ,提高人员素质 ,引入竞争机制 ,加强内容管理 ,确保工程质量。     

论人身保险合同订立中保险人的答复义务

人身保险合同订立中,作为要约书面形式的投保书通常未标明有效期限,为合理分担合同未成立前的风险,保证交易的安全,实现当事人间利益的平衡和社会正义,应当强制保险人在法定时间或合理期限内答复, 而无论承诺与否;否则,即构成默示承诺。     

对运输业属性认识的理论综述

人们对运输业属性的认识基本上是围绕运输业的经济属性(含物质属性、产业属性与市场属性)与社会属性来展开的。在20世纪中叶前,提出了运输业的物质生产部门(物质属性)、第三产业(产业属性)和基础设施(社会属性)等基本属性;自20世纪初开始,人们提出并论证了运输业的市场属性———自然垄断性,并在20世纪中后期深化了对运输业物质属性和社会属性的认识,提出了运输业的俱乐部物品特性与外部性观念;20世纪80年代后,人们对运输业产业属性和市场属性的认识进一步深化,提出了运输业的网络经济性与可竞争性理论。     

基于二次误差测度的带属性三角网格简化算法

给出一种基于边折叠和二次误差测度的快速简便的算法来简化带属性的网格模型。该算法通过分别建立几何和颜色属性二次误差测度来计算几何和颜色属性误差,用几何与颜色属性误差的总和来控制网格简化的顺序和精度。边折叠是根据某种误差测度将候选的边按照折叠代价排序,每次取代价最小的边进行折叠操作,直至满足给定的终止条件。二次误差测度采用点到平面距离的平方作为误差测度。应用实例表明,该算法既能保证简化模型同初始模型在几何上尽可能相似,又能较好地保留初始模型的颜色、纹理等属性信息。     

多生物特征识别系统的关键技术

单生物识别技术一直存在着很多问题，它们在准确率，用户接受程度，成本等方面都有不同的缺点，并且适应于各自的应用场合。这些问题一般可以由多生物识别技术来克服。首先对各种不同生物特征识别技术作简要的介绍，然后分析多生物特征提出的背景，并着重阐述多生物特征识别技术系统的模型和各种数据融合技术及其相应的研究发展情况，提出了它的应用前景。     

Multimode trip information detection using personal trajectory data

Handheld global positioning system (GPS) devices can serve as a new tool to collect an individual's trip information with advantages of low cost, accurate data, and intensive spatial coverage. Various machine learning algorithms have been explored to detected trip train information in previous studies; however, few of them focused on the evaluation and comparison of the performance and applicability of different models. Meanwhile, according to previous studies, car and bus mode detection is a thorny issue due to their similar travel characteristics, and algorithms still need to be well explored and improved to solve this problem. In this article, an innovative method is proposed to detect trip information, including trip modes, mode-changing time and location, and other attributes, from personal trajectory data. The method is a two-step process. A machine learning algorith-based module (including artificial neural network, support vector machine, random forests, and Bayesian network) is firstly used to identify walk, bicycle, and motorized trip modes (bus or car); we thoroughly compared the performance of these four algorithms. Then a second module, using critical points on the GPS trajectories, is further developed to distinguish car and bus mode, incorporated with GIS map information. Field test results show that the proposed machine learning models can all be applied for walk, bicycle, and motorized mode detection with high detection rates exceeding 90%; however, the algorithms work relatively poorly for bus and car mode detection, with results mostly below 75%. The proposed two-step method can greatly improve bus and car mode detection accuracy by 14–30%. As a result, the average mode detection rates for all the four modes are above 90%. Compared with mode detection results by using only the machine learning algorithm, the proposed two-step method has much better performance in both accuracy and consistency.