Enhanced supply chain tracking based on the EPC network: A Bayesian approach

Supply chain tracking information is one of the main levers for achieving operational efficiency. RFID technology and the EPC Network can deliver serial-level product information that was never before available. However, these technologies still fail to meet the managers' visibility requirements in full, since they provide information about product location at specific time instances only. This paper proposes a model that uses the data provided by the EPC Network to deliver enhanced tracking information to the final user. Following a Bayesian approach, the model produces realistic ongoing estimates about the current and future location of products across a supply network, taking into account the characteristics of the product behavior and the configuration of the data collection points. These estimates can then be used to optimize operational decisions that depend on product availability at different locations. The enhancement of tracking information quality is highlighted through an example. © 2009 IFAC.

A vertically aligned carbon nanotube/fiber based electrode for economic hydrogen production by water electrolysis

Electrolysis is the most mature form of hydrogen production. Unfortunately, water electrolysis has not yet achieved the efficiency and the cost levels required for any practical application. In order to enhance the current density, modification of the electrolyte and the electrode morphology are the most popular approaches. Recently there have been numerous reports on how to improve the efficiency of hydrogen production by water splitting [1-3]. On the electrode side, the use of non-platinum high efficiency electrode materials for water splitting will provide a promising future for the hydrogen economy. An ideal electrode for water electrolysis should have good permeability to water and gas. It should also offer good electrical properties with a long life. A porous graphite plate, when coated with titania, for example, is known to provide a simple and economical electrode for water electrolysis [4]. © 2010 IEEE.

Towards robust part tracking in an automated manufacturing process using RFID

Automated Identification and in particular, Radio Frequency Identification (RFID) promises to assist with the automation of mass customised production processes. RFID has long been used to gather a history or trace of part movements, but the use of it as an integral part of the control process is yet to be fully exploited. Such use places stringent demands on the quality of the sensor data and the method used to interpret that data. in particular, this paper focuses on the issue of correctly identifying, tracking and dealing with aggregated objects with the use of RFID. The presented approach is evaluated in the context of a laboratory manufacturing system that produces customised gift boxes. Copyright © 2005 IFAC.

Screening of healthcare workers for tuberculosis: development and validation of a new health economic model to inform practice

Methods for determining cost-effectiveness of different treatments are well established, unlike appraisal of non-drug interventions, including novel diagnostics and biomarkers.

MCMC-based tracking and identification of leaders in groups

We present a novel framework for identifying and tracking dominant agents in groups. Our proposed approach relies on a causality detection scheme that is capable of ranking agents with respect to their contribution in shaping the system's collective behaviour based exclusively on the agents' observed trajectories. Further, the reasoning paradigm is made robust to multiple emissions and clutter by employing a class of recently introduced Markov chain Monte Carlo-based group tracking methods. Examples are provided that demonstrate the strong potential of the proposed scheme in identifying actual leaders in swarms of interacting agents and moving crowds. © 2011 IEEE.

Reinforcement learning with reference tracking control in continuous state spaces

The contribution described in this paper is an algorithm for learning nonlinear, reference tracking, control policies given no prior knowledge of the dynamical system and limited interaction with the system through the learning process. Concepts from the field of reinforcement learning, Bayesian statistics and classical control have been brought together in the formulation of this algorithm which can be viewed as a form of indirect self tuning regulator. On the task of reference tracking using a simulated inverted pendulum it was shown to yield generally improved performance on the best controller derived from the standard linear quadratic method using only 30 s of total interaction with the system. Finally, the algorithm was shown to work on the simulated double pendulum proving its ability to solve nontrivial control tasks. © 2011 IEEE.