Enforcing RFID data visibility restrictions using XACML security policies

Radio Frequency Identification (RFID) technology allows automatic data capture from tagged objects moving in a supply chain. This data can be very useful if it is used to answer traceability queries, however it is distributed across many different repositories, owned by different companies. © 2012 IEEE.

Optimal constraint tightening policies for robust variable horizon model predictive control

This paper develops a technique for improving the region of attraction of a robust variable horizon model predictive controller. It considers a constrained discrete-time linear system acted upon by a bounded, but unknown time-varying state disturbance. Using constraint tightening for robustness, it is shown how the tightening policy, parameterised as direct feedback on the disturbance, can be optimised to increase the volume of an inner approximation to the controller's true region of attraction. Numerical examples demonstrate the benefits of the policy in increasing region of attraction volume and decreasing the maximum prediction horizon length. © 2012 IEEE.

State estimation schemes for independent component coupled hidden Markov models

Conventional Hidden Markov models generally consist of a Markov chain observed through a linear map corrupted by additive noise. This general class of model has enjoyed a huge and diverse range of applications, for example, speech processing, biomedical signal processing and more recently quantitative finance. However, a lesser known extension of this general class of model is the so-called Factorial Hidden Markov Model (FHMM). FHMMs also have diverse applications, notably in machine learning, artificial intelligence and speech recognition [13, 17]. FHMMs extend the usual class of HMMs, by supposing the partially observed state process is a finite collection of distinct Markov chains, either statistically independent or dependent. There is also considerable current activity in applying collections of partially observed Markov chains to complex action recognition problems, see, for example, [6]. In this article we consider the Maximum Likelihood (ML) parameter estimation problem for FHMMs. Much of the extant literature concerning this problem presents parameter estimation schemes based on full data log-likelihood EM algorithms. This approach can be slow to converge and often imposes heavy demands on computer memory. The latter point is particularly relevant for the class of FHMMs where state space dimensions are relatively large. The contribution in this article is to develop new recursive formulae for a filter-based EM algorithm that can be implemented online. Our new formulae are equivalent ML estimators, however, these formulae are purely recursive and so, significantly reduce numerical complexity and memory requirements. A computer simulation is included to demonstrate the performance of our results. © Taylor & Francis Group, LLC.

Bayesian Learning of Noisy Markov Decision Processes

We consider the inverse reinforcement learning problem, that is, the problem of learning from, and then predicting or mimicking a controller based on state/action data. We propose a statistical model for such data, derived from the structure of a Markov decision process. Adopting a Bayesian approach to inference, we show how latent variables of the model can be estimated, and how predictions about actions can be made, in a unified framework. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from the posterior distribution. This step includes a parameter expansion step, which is shown to be essential for good convergence properties of the MCMC sampler. As an illustration, the method is applied to learning a human controller.

Expressive RFID data access policies for the Pharmaceuticals supply chain

The Pharma(ceuticals) industry is at a cross-roads. There are growing concerns that illegitimate products are penetrating the supply chain. There are proposals in many countries to apply RFID and other traceability technologies to solve this problem. However there are several trade-offs and one of the most crucial is between data visibility and confidentiality. In this paper, we use the TrakChain assessment framework tools to study the US Pharma supply chain and to compare candidate solutions to achieve traceability data security: Point-of-Dispense Authentication, Network-based electronic Pedigree, and Document-based electronic Pedigree. We also propose extensions to a supply chain authorization language that is able to capture expressive data sharing conditions considered necessary by the industry's trading partners. © 2013 IEEE.

Markov过程在森林资源结构动态预测中的应用──以福建省南平地区的树种结构为例

根据马尔可夫决策过程理论和森林资源连续清查的固定样地调查资料，对南平地区的树种结构进行了预测与调整，结果表明，按现状发展，针阔比例将日趋严重，并且毛竹、经济林的占有率呈下降趋势，最终达到以杉木２８．０５％、马尾松１６．６３％、阔叶树１９．０１％、毛竹５．４３％、经济林２．２６％、其它类２８．７１％的树种结构．经调整后稳定状态的树种结构基本趋于合理，即各树种的占有率分别为杉木１８．７２％、马尾松１３．２４％、阔叶树２６．９８％、毛竹１０．８４％、经济林５．４５％、其它类２４．７７％．

Markov和灰色模型在土地利用预测中的应用

马尔柯夫和灰色模型都适用于土地利用变化预测,根据同一套土地利用数据分别用两种模型预测,将所得结果相互验证、对比分析,提高预测可信度。以江西省新建县为例,两种预测方法的预测结果都表明,若继续保持1996-2000年的变化速度,耕地和未利用地将持续减少,林地和建设用地呈增长趋势,而草地和水域相对较稳定,草地有下降趋势,水域呈缓慢上升趋向。预测结果可为土地利用规划管理及政策的制定提供科学依据,研究方法为土地利用变化预测研究提供一种思路。