A belief-rule-based inventory control method under nonstationary and uncertain demand

This paper is devoted to investigating inventory control problems under nonstationary and uncertain demand. A belief-rule-based inventory control (BRB-IC) method is developed, which can be applied in situations where demand and demand-forecast-error (DFE) do not follow certain stochastic distribution and forecasting demand is given in single-point or interval styles. The method can assist decision-making through a belief-rule structure that can be constructed, initialized and adjusted using both manager’s knowledge and operational data. An extended optimal base stock (EOBS) policy is proved for initializing the belief-rule-base (BRB), and a BRB-IC inference approach with interval inputs is proposed. A numerical example and a case study are examined to demonstrate potential applications of the BRB-IC method. These studies show that the belief-rule-based expert system is flexible and valid for inventory control. The case study also shows that the BRB-IC method can compensate DFE by training BRB using historical demand data for generating reliable ordering policy.     

Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.     

Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopaedic screws

Optimizing the orthopaedic screws can greatly improve their biomechanical performances. However, a methodical design optimization approach requires a long time to search the best design. Thus, the surrogate objective functions of the orthopaedic screws should be accurately developed. To our knowledge, there is no study to evaluate the strengths and limitations of the surrogate methods in developing the objective functions of the orthopaedic screws. Three-dimensional finite element models for both the tibial locking screws and the spinal pedicle screws were constructed and analyzed. Then, the learning data were prepared according to the arrangement of the Taguchi orthogonal array, and the verification data were selected with use of a randomized selection. Finally, the surrogate objective functions were developed by using either the multiple linear regression or the artificial neural network. The applicability and accuracy of those surrogate methods were evaluated and discussed. The multiple linear regression method could successfully construct the objective function of the tibial locking screws, but it failed to develop the objective function of the spinal pedicle screws. The artificial neural network method showed a greater capacity of prediction in developing the objective functions for the tibial locking screws and the spinal pedicle screws than the multiple linear regression method. The artificial neural network method may be a useful option for developing the objective functions of the orthopaedic screws with a greater structural complexity. The surrogate objective functions of the orthopaedic screws could effectively decrease the time and effort required for the design optimization process.     

基于ZigBee技术的远端通信站监控系统及其实现

提出了一种基于ZigBee技术的远端通信站环境监控系统设计方案。包括系统结构、Zigbee通信接口、单片微处理器、温度采集等模块的软硬件设计。此方案设计的监测系统已在某远端无人小型频谱监测站使用。     

Linux下连续媒体文件系统研究

Linux是Internet时代最辉煌的成果之一,它已经成为Internet上最受欢迎的网络操作系统。人们通过Linux提供面向Internet的各种服务,范围从WWW到网上直播。但基于Linux的高质量视频点播服务（VOD）鲜见有人研究。文章正是在这种背景下提出了Linux下的连续媒体文件系统（LCMFS）,将其作为基于Linux的高质量视频点播服务系统的底层支撑。文章先介绍了与Linux相关的背景知识,接着对Linux的现有文件系统—Ext2文件系统的结构进行剖析,指出其在处理连续媒体文件时的不足之处,然后据此提出了LCMFS的设计思想和原则,并阐述了LCMFS的设计及实现方案,最后对LCMFS的性能进行了分析和测试。     

基于Web的企业信息管理系统安全方案

分析了传统企业信息管理系统存在的安全问题,应用SSL协议、网络型人侵检测系统、虚拟专用网等技术,提出了一种新的企业信息管理系统安全设计方案。理论分析和实验结果表明,新方案是保证企业信息管理系统安全运行的一种有效方案。