Intelligent technology-based control of motion and vibration using MR dampers

Due to their intrinsically nonlinear characteristics, development of control strategies that are implementable and can fully utilize the capabilities of semiactive control devices is an important and challenging task. In this study, two control strategies are proposed for protecting buildings against dynamic hazards, such as severe earthquakes and strong winds, using one of the most promising semiactive control devices, the magnetorheological (MR) damper. The first control strategy is implemented by introducing an inverse neural network (NN) model of the MR damper. These NN models provide direct estimation of the voltage that is required to produce a target control force calculated from some optimal control algorithms. The major objective of this research is to provide an effective means for implementation of the MR damper with existing control algorithms. The second control strategy involves the design of a fuzzy controller and an adaptation law. The control objective is to minimize the difference between some desirable responses and the response of the combined system by adaptively adjusting the MR damper. The use of the adaptation law eliminates the need to acquire characteristics of the combined system in advance. Because the control strategy based on the combination of the fuzzy controller and the adaptation law doesn’t require a prior knowledge of the combined building-damper system, this approach provides a robust control strategy that can be used to protect nonlinear or uncertain structures subjected to random loads. Supported by: Hong Kong Research Grant Council Competitive Earmarked Research Grant HKUST 6218 / 99E and by the National Science Foundation under grant CMS 99-00234.     

九寨沟地质灾害预测的空间分析模型

九寨沟是世界自然遗产,由于其活跃的新构造背景条件,因此是地质灾害的高发区。此次研究,应用了地理信息系统的空间分析工具,提取模型因子,采用模糊综合评价方法,建立九寨沟地区的地质灾害预测空间分析模型,划分地质灾害等级,对九寨沟地区的灾害进行预测,取得较好的效果。     

大连市近岸海域环境的模糊模式识别及动态分析

21世纪是人类开发利用、保护海洋的新世纪。海洋是大连的最大优势，大连的建置和发展得益于海，海洋生态环境保护是大连城市经济与社会可持续发展的前提。首次采用模糊模式识别模型对大连市近岸海域水质环境进行动态分析，结果表明，模糊模式识别模型能够全面反映水质环境的真实状态，大连湾近岸海域水质较差，大连南部海域水质逐渐变好，大窑湾海域水质较好。文章最好提出了海洋环境保护的措施。     

云南省金沙江流域低温霜冻灾害区划研究

选取5个指标,运用模糊聚类方法进行了云南金沙江流域低温霜冻灾害区划,将该流域划分为3个低温霜冻灾害区、9个低温霜冻灾害亚区,揭示了该流域低温霜冻灾害的地域差异性,为因地制宜地制定低温霜冻灾害防治规划及减灾防灾措施提供了科学依据。     

云南省金沙江流域作物病虫灾害区划研究

选取5个指标,运用模糊聚类方法进行了云南金沙江流域作物病虫灾害区划,将该流域划分为3个病虫灾害 区、8个病虫灾害亚区,揭示了该流域病虫灾害的地域差异性,为因地制宜地制定病虫灾害防治规划及减灾防灾措施 提供了科学依据。     

云南省金沙江流域水土流失灾害区划研究

水土流失是云南金沙江流域的头号环境问题和重要自然灾害。选取8个指标,运用模糊聚类方法进行了云 南金沙江流域水土流失灾害区划,将该流域划分为3个水土流失灾害区、9个水土流失灾害亚区,揭示了该流域水土 流失灾害状况的地域差异性,为因地制宜地制定水土流失灾害防治规划及措施提供了科学依据。     

基于模糊评价模型的民用机场无人机监测系统评估方法

论文结合民用机场净空保护需求,针对无人机监测系统在民用机场的应用可行性和有效性进行定量评估。首先,深入分析了无人机监测系统的多维度评价指标,从系统的功能、性能、可靠性、安全性、可部署性5个评价维度设计了10个方面的系统特征,包括监测覆盖区域、有效探测距离、空间定位精度、俯仰精度、目标跟踪能力、即时性度量、报警提示置信程度、目标识别能力、可靠性度量、信息安全和可部署性度量等。然后,结合这10个系统评价特征,建立基于模糊集和模糊测度的综合评估模型,设计多类无人机监测设备在机场真实场景的实验验证。实验结果表明,该方法可有效度量无人机监测系统对民用机场的适用性和有效性等,对不同类型的技术具有较好的兼容性,评价结果对民用机场无人机监测系统选择具有较高的参考价值。     

拟稳平差在测量数据处理中的应用

拟稳平差是一种顾及基准点稳定性的平差方法,在变形监测领域有着广泛的应用。介绍了拟稳平差的基本原理,说明了拟稳平差在沉降观测中的应用;拟稳平差的关键是选取符合实际的“拟稳点”,讨论了拟口点选择的两种方法：假设检验法和模糊聚类法,并给出算例进行说明。     

地图印刷质量检测与评定

本文论述了印刷品的质量内容及影响印刷品质量的主要因素,结合地图印刷的特殊性,阐述了质量检测评定的原理,给出了模糊综合评价法和可拓综合评价法两种质量评定数学模型,并对质量检测系统的应用现状与未来发展进行了展望。     

空间数据模糊聚类的有效性(英文)

The validity measurement of fuzzy clustering is a key problem. If clustering is formed, it needs a kind of machine to verify its validity. To make mining more accountable, comprehensible and with a usable spatial pattern, it is necessary to first detect whether the data set has a clustered structure or not before clustering. This paper discusses a detection method for clustered patterns and a fuzzy clustering algorithm, and studies the validity function of the result produced by fuzzy clustering based on two aspects, which reflect the uncertainty of classification during fuzzy partition and spatial location features of spatial data, and proposes a new validity function of fuzzy clustering for spatial data. The experimental result indicates that the new validity function can accurately measure the validity of the results of fuzzy clustering. Especially, for the result of fuzzy clustering of spatial data, it is robust and its classification result is better when compared to other indices.