The effect of shoe sole tread groove depth on the friction coefficient with different tread groove widths, floors and contaminants

Slipping and falling are common phenomena in both workplaces and our daily activities. The risks associated with slipping and falling are related to the materials of footwear/floor, contamination condition, and geometric design of the sole. Shoe soles of various tread design are very common. Tread pattern of the shoe affects friction especially under liquid-contaminated conditions. Verification of the effects of tread groove depth is significant in assisting designers in designing proper footwear for workers exposed to slippery floor conditions. In this study, we measured the friction coefficients using the Neolite footwear pads on the terrazzo, steel, and vinyl floors under three liquid-contaminated conditions. A Brungraber Mark II slipmeter was used. The footwear pads had tread grooves with a width of either 3 or 9 mm. The depth of the tread grooves ranged from 1 to 5 mm. The results showed that tread groove depth affected the friction coefficients significantly. Higher friction values were recorded for footwear pads with deeper tread grooves on wet and water–detergent-contaminated floors. The averaged coefficient of friction (COF) gain per tread groove depth increase in millimeter under these two surface conditions ranged from 0.018 to 0.108, depending on the tread groove width, floor, and contaminant.     

Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care

Hyperglycaemia is prevalent in critical illness and increases the risk of further complications and mortality, while tight control can reduce mortality up to 43%. Adaptive control methods are capable of highly accurate, targeted blood glucose regulation using limited numbers of manual measurements due to patient discomfort and labour intensity. Therefore, the option to obtain greater data density using emerging continuous glucose sensing devices is attractive. However, the few such systems currently available can have errors in excess of 20-30%. In contrast, typical bedside testing kits have errors of approximately 7-10%. Despite greater measurement frequency larger errors significantly impact the resulting glucose and patient specific parameter estimates, and thus the control actions determined creating an important safety and performance issue. This paper models the impact of the continuous glucose monitoring system (CGMS, Medtronic, Northridge, CA) on model-based parameter identification and glucose prediction. An integral-based fitting and filtering method is developed to reduce the effect of these errors. A noise model is developed based on CGMS data reported in the literature, and is slightly conservative with a mean Clarke Error Grid (CEG) correlation of R=0.81 (range: 0.68-0.88) as compared to a reported value of R=0.82 in a critical care study. Using 17 virtual patient profiles developed from retrospective clinical data, this noise model was used to test the methods developed. Monte-Carlo simulation for each patient resulted in an average absolute 1-h glucose prediction error of 6.20% (range: 4.97-8.06%) with an average standard deviation per patient of 5.22% (range: 3.26-8.55%). Note that all the methods and results are generalizable to similar applications outside of critical care, such as less acute wards and eventually ambulatory individuals. Clinically, the results show one possible computational method for managing the larger errors encountered in emerging continuous blood glucose sensors, thus enabling their more effective use in clinical glucose regulation studies.     

Fast parametric imaging algorithm for dual-input biomedical system parameter estimation

Medical parametric imaging with dynamic positron emission tomography (PET) plays an increasingly potential role in modern biomedical research and clinical diagnosis. The key issue in parametric imaging is to estimate parameters based on sampled data at the pixel-by-pixel level from certain dynamic processes described by valid mathematical models. Classic nonlinear least squares (NLS) algorithm requires a "good" initial guess and the computational time-complexity is high, which is impractical for image-wide parameter estimation. Although a variety of fast parametric imaging techniques have been developed, most of them focus on single input systems, which do not provide an optimal solution for dual-input biomedical system parameter estimation, which is the case of liver metabolism. In this study, a dual-input-generalized linear least squares (D-I-GLLS) algorithm was proposed to identify the model parameters including the parameter in the dual-input function. Monte Carlo simulation was conducted to examine this novel fast algorithm. The results of the quantitative analysis suggested that the proposed technique could provide comparable reliability of the parameter estimation with NLS fitting and accurately identify the parameter in the dual-input function. This method may be potentially applicable to other dual-input biomedical system parameter estimation as well.     

Computation of madalines' sensitivity to input and weight perturbations

The sensitivity of a neural network's output to its input and weight perturbations is an important measure for evaluating the network's performance. In this letter, we propose an approach to quantify the sensitivity of Madalines. The sensitivity is defined as the probability of output deviation due to input and weight perturbations with respect to overall input patterns. Based on the structural characteristics of Madalines, a bottom-up strategy is followed, along which the sensitivity of single neurons, that is, Adalines, is considered first and then the sensitivity of the entire Madaline network. By means of probability theory, an analytical formula is derived for the calculation of Adalines' sensitivity, and an algorithm is designed for the computation of Madalines' sensitivity. Computer simulations are run to verify the effectiveness of the formula and algorithm. The simulation results are in good agreement with the theoretical results.     

不确定时滞系统的稳定条件

利用比较定理、矩阵范数和矩阵测度的有关性质,提出了简单不确定时滞系统及对称组合不确定时滞系统的稳定条件.指出若带有时滞的系统稳定,则去掉时滞时系统一定稳定.在组合系统的情况下给出了时滞相关型的稳定条件.所研究对象的不确定矩阵的范数有一定限制.     

具有遍历特性的移动机器人规划方法的研究

本文提出了遍历规划的概念和数学描述,以及完全遍历规划的设计方法,给出了常用 的几种性能评价函数．在此基础上,设计了基于带有综合性能评价指标的基本遍历运动序列 的完全遍历规划器．     

面向搜索引擎的自然语言理解的设计与实现*

提出了一种针对智能检索的自然语言理解的实现模型。该模型通过句模分析、分词和概念扩展的方法来理解问句,在一定程度上提高理解自然语言的能力。详细介绍了其系统架构、实现思想和原理。最后通过一系列的实例来对普通搜索引擎和加载了本模型的搜索引擎进行测试。实验结果表明,提出的模型能有效地分析自然语言提问,提高信息检索的准确性和智能性。     

基于COM与N层B/S结构的SNMP网管研究与实现*

分析了传统单一C/S结构的不足以及新型N层B/S结构的优越性,阐述了基于Web技术的网络管理是未来网络管理的发展趋势,结合现代软件开发中的软件复用理论,提出并设计实现了一种基于COM技术与N层B/S结构的SNMP网管系统模型,着重叙述了该系统中所使用的关键技术与设计要点。     

Stream segregation algorithm for pattern matching in polyphonic music databases

As music can be represented symbolically, most of the existing methods extend some string matching algorithms to retrieve musical patterns in a music database. However, not all retrieved patterns are perceptually significant because some of them are, in fact, inaudible. Music is perceived in groupings of musical notes called streams. The process of grouping musical notes into streams is called stream segregation. Stream-crossing musical patterns are perceptually insignificant and should be pruned from the retrieval results. This can be done if all musical notes in a music database are segregated into streams and musical patterns are retrieved from the streams. Findings in auditory psychology are utilized in this paper, in which stream segregation is modelled as a clustering process and an adapted single-link clustering algorithm is proposed. Supported by experiments on real music data, streams are identified by the proposed algorithm with considerable accuracy.

甚小孔径卫星（VSAT）的数据传输应用

简单分析了甚小孔径卫星通信的技术特点和优缺点。重点讨论用它来解决数据传输问题,通过举例对目前采用ＶＳＡＴ卫星通信解决数据传输问题的技术做进一步的讨论。