A novel method of embedding distributed optical fiber sensor for structural health monitoring

A distributed optical fiber sensor based on Brillouin scattering (BOTDR or BOTDA) can measure and monitor strain and temperature generated along optical fiber. Because it can measure in real-time with high precision and stability, it is quite suitable for health monitoring of large-scale civil infrastructures. However, the main challenge of applying it to structural health monitoring is to ensure it is robust and can be repaired by adopting a suitable embedding method. In this paper, a novel method based on air-blowing and vacuum grouting techniques for embedding long-distance optical fiber sensors was developed. This method had no interference with normal concrete construction during its installation, and it could easily replace the long-distance embedded optical fiber sensor (LEOFS). Two stages of static loading tests were applied to investigate the performance of the LEOFS. The precision and the repeatability of the LEOFS were studied through an overloading test. The durability and the stability of the LEOFS were confirmed by a corrosion test. The strains of the LEOFS were used to evaluate the reinforcing effect of carbon fiber reinforced polymer and thereby the health state of the beams.

Electrical energy storage and Smart Grid technologies to integrate the next generation of Renewable Power Systems

The growth of renewable power sources, distributed generation and the potential for alternative fuelled modes of transport such as electric vehicles has led to concerns over the ability of existing grid systems to facilitate such diverse portfolio mixes in already congested power systems. Internationally the growth in renewable energy sources is driven by government policy targets associated with the uncertainties of fossil fuel supplies, environmental issues and a move towards energy independence. Power grids were traditionally designed as vertically integrated centrally managed entities with fully dispatchable generating plant. Renewable power sources, distributed generation and alternative fuelled vehicles will place these power systems under additional stresses and strains due to their different operational characteristics. Energy storage and smart grid technologies are widely proposed as the tools to integrate these future diverse portfolio mixes within the more conventional power systems. The choice in these technologies is determined not only by their location on the grid system, but by the diversification in the power portfolio mix, the electricity market and the operational demands. This paper presents a high level technical and economic overview of the role and relevance of electrical energy storage and smart grid technologies in the next generation of renewable power systems.

Using smart people to form future mobile wireless networks

The concept of a body-to-body network, where smart communicating devices carried or worn by a person are used to form a wireless network with devices situated on other nearby persons. New innovations in this area will see the form factor of smart devices being modified, so that they may be worn on the human body or integrated into clothing, in the process creating a new generation of smart people. Applications of body-to-body networking will extend well beyond the support of cellular and Wi-Fi networks. They will also be used in short-range covert military applications, first responder applications, team sports and used to interconnect body area networks (BAN). Security will be a major issue as routing between multiple nodes will increase the risk of unauthorized access and compromise sensitive data. This will add complexity to the medium access layer (MAC) and network management. Antennas designed to operate in body centric communications systems may be broadly categorized as on- or off-body radiators, according to their radiation pattern characteristics when mounted on the human body.

ARE ARTIFICIAL NEURAL NETS AS SMART AS A RAT

The origins of artificial neural networks are related to animal conditioning theory: both are forms of connectionist theory, which in turn derives from the empiricist philosophers' principle of association. The parallel between animal learning and neural nets suggests that interaction between them should benefit both sides.

An orientation free adaptive step detection algorithm using a smart phone in physical activity monitoring

In this paper we present an Orientation Free Adaptive Step Detection (OFASD) algorithm for deployment in a smart phone for the purposes of physical activity monitoring. The OFASD algorithm detects individual steps and measures a user’s step counts using the smart phone’s in-built accelerometer. The algorithm considers both the variance of an individual’s walking pattern and the orientation of the smart phone. Experimental validation of the algorithm involved the collection of data from 10 participants using five phones (worn at five different body positions) whilst walking on a treadmill at a controlled speed for periods of 5 min. Results indicated that, for steps detected by the OFASD algorithm, there were no significant differences between where the phones were placed on the body (p > 0.05). The mean step detection accuracies ranged from 93.4 % to 96.4 %. Compared to measurements acquired using existing dedicated commercial devices, the results demonstrated that using a smart phone for monitoring physical activity is promising, as it adds value to an accepted everyday accessory, whilst imposing minimum interaction from the user. The algorithm can be used as the underlying component within an application deployed within a smart phone designed to promote self-management of chronic disease where activity measurement is a significant factor, as it provides a practical solution, with minimal requirements for user intervention and less constraints than current solutions.

Smart meter devices and the effect of feedback on residential electricity consumption: Evidence from a natural experiment in Northern Ireland

Using a unique set of data and exploiting a large-scale natural experiment, we estimate the effect of real-time usage information on residential electricity consumption in Northern Ireland. Starting in April 2002, the utility replaced prepayment meters with advanced meters that allow the consumer to track usage in real-time. We rely on this event, account for the endogeneity of price and payment plan with consumption through a plan selection correction term, and find that the provision of information is associated with a decline in electricity consumption of 11-17%. We find that the reduction is robust to different specifications, selection-bias correction methods and subsamples of the original data. The advanced metering program delivers reasonably cost-effective reductions in carbon dioxide emissions, even under the most conservative usage reduction scenarios.