Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area

This study aims to provide a rapid screening tool for assessment of sustainable flood retention basins (SFRBs) to predict corresponding dam failure risks. A rapid expert-based assessment method for dam failure of SFRB supported by an artificial neural network (ANN) model has been presented. Flood storage was assessed for 110 SFRB and the corresponding Dam Failure Risk was evaluated for all dams across the wider Greater Manchester study area. The results show that Dam Failure Risk can be estimated by using the variables Dam Height, Dam Length, Maximum Flood Water Volume, Flood Water Surface Area, Mean Annual Rainfall (based on Met Office data), Altitude, Catchment Size, Urban Catchment Proportion, Forest Catchment Proportion and Managed Maximum Flood Water Volume. A cross-validation R² value of 0.70 for the ANN model signifies that the tool is likely to predict variables well for new data sets. Traditionally, dams are considered safe because they have been built according to high technical standards. However, many dams that were constructed decades ago do not meet the current state-of-the-art dam design guidelines. Spatial distribution maps show that dam failure risks of SFRB located near cities are higher than those situated in rural locations. The proposed tool could be used as an early warning system in times of heavy rainfall.     

Evaluation of a workplace technology for mental health assessment: A meaning-making process

This paper describes the results of a summative evaluation conducted to explore the usability and acceptability of an automated telephone-based technology, designed and developed as a screening tool for mental health disorders in the workplace. The system screens employees for symptoms and subsequently provides educational information and referrals to improve mental health and enhance productivity. The qualitative evaluation was a sub-study of a larger randomized controlled trial (RCT). We conducted in-depth interviews with 15 individuals who participated in the RCT and had used the system. A phenomenological theoretical framework was utilized to analyze the narrative data. Results indicated that participants had mostly positive experience with the system and perceived it as a confidential program that allowed them to reflect on their symptoms without inhibition, fear of judgment or embarrassment. We also learned that asking mere questions about mental health symptoms can be a positive, instructive and possibly a restorative experience. There is a likelihood that for many participants, exposure to questions about their emotional and mental distress validated their feelings about their symptoms and was sufficiently compelling to promote help-seeking behavior. This evaluation study demonstrates that computers can be utilized in the workplace as effective screening and educational tools to improve employees’ mental health.     

An adaptive neural network switching control approach of robotic manipulators for trajectory tracking

In this paper, an adaptive neural network (NN) switching control strategy is proposed for the trajectory tracking problem of robotic manipulators. The proposed system comprises an adaptive switching neural controller and the associated robust compensation control law. Based on the Lyapunov stability theorem and average dwell-time approach, it is shown that the proposed control scheme can guarantee tracking performance of the robotic manipulators system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance and approximate error of radical basis function (RBF) NNs on the tracking error can be converged to zero in an infinite time. Finally, simulation results on a two-link robotic manipulator show the feasibility and validity of the proposed control scheme.     

多个大股东并存下的公司治理研究综述

在不同国家的企业中股权结构有很大差异.多个大股东并存下的股权制衡模式作为一种特殊的股权结构,产生了其特有的公司治理问题.文中主要从多个大股东并存的公司治理效应、对于公司决策和业绩的影响、与公司价值的关系三个方面对国外研究进行了综述,较为全面的总结了前人对于多个大股东并存下的股权制衡的公司治理问题的研究.     

基于多最小支持度的多层模糊关联规则挖掘

为了在事务数据库中发现关联规则,在现实挖掘应用中,经常采用不同的标准去判断不同项目的重要性,管理项目之间的分类关系和处理定量数据集这3个方法去处理问题,因此提出一个在定量事务数据库中采用多最小支持度,在项目集中获取隐含知识的多层模糊关联规则挖掘算法。该挖掘算法使用两种支持度约束和至上而下逐步细化的方法推导出频繁项集,同时可以发现交叉层次的模糊关联规则。通过实例证明了该挖掘算法在多最小支持度约束下推导出的多层模糊关联规则是易于理解和有意义的,具有很好的效率和伸缩性。     

热电偶毫伏信号发生器的设计与实现

设计了一种热电偶毫伏信号发生器,利用上位机软件LabVIEW的计算优势,将所需的温差转化为相应的热电动势大小,并通过USB或串口发送到单片机,由单片机控制12位DAC转换电路输出相应的电压。测试表明,采用此种方法可以方便地实现多种类型的热电偶信号产生,精度可达±0.1℃,适用于一般的热电偶测量仪表的校准,并且可以根据需要对温度范围进行调节,同时支持多路独立输出,提高了校准效率。     

基于IMM多传感器顺序粒子滤波跟踪机动目标算法

针对交互式多模型粒子滤波在跟踪机动目标时精度受限问题,提出一种基于交互式多模型(IMM)的多传感器顺序粒子滤波算法。采用IMM机制实现目标运动模式的确认;在合理利用单传感器量测和多传感器量测中冗余和互补信息的基础上,引入顺序重抽样方法改善粒子分布,并将改善后的粒子应用于IMM粒子滤波算法框架。仿真实验结果表明:新算法能够估计出强机动目标状态,且精度明显优于标准IMM粒子滤波算法。     

基于权值范围设置的多模型稳定切换控制研究

针对线性加权多模型切换过程中权值难以确定问题,提出了具有Lyapunov函数的多模型稳定切换的充分条件,依据该充分条件可以确定加权多模型的权值范围和快速获取加权因子的取值,实现多模型的平滑切换.所提出的充分条件克服了多模型子系统必须稳定的苛刻条件,通过对自主水下机器人多运动模型切换过程的仿真实验表明,依据权值范围所给定的加权因子能够保证加权多模型切换过程中的渐近稳定性.     

New approaches for determining a common set of weights for a voting system

Data Envelopment Analysis (DEA) is one of the methods that have been proposed to determine the weights in a ranked voting system. DEA solves one model corresponding to each candidate and evaluates candidates with their own weights. However, sometimes solving only one model, and evaluating the candidates based on a common set of weights, is preferred. In this paper, some drawbacks of existing models are explained and new approaches for determining a common set of weights are proposed. Numerical examples are utilized to illustrate the content of the paper.