Geometric shape errors in forging: developing a metric and an inverse model

The complexity of the forging process ensures that there is inherent variability in the geometric shape of a forged part. While knowledge of shape error, comparing the desired versus the measured shape, is significant in measuring part quality the question of more interest is what can this error suggest about the forging process set-up? The first contribution of this paper is to develop a shape error metric which identifies geometric shape differences that occur from a desired forged part. This metric is based on the point distribution deformable model developed in pattern recognition research. The second contribution of this paper is to propose an inverse model that identifies changes in process set-up parameter values by analysing the proposed shape error metric. The metric and inverse models are developed using two sets of simulated hot-forged parts created using two different die pairs (simple and 'M'-shaped die pairs). A neural network is used to classify the shape data into three arbitrarily chosen levels for each parameter and it is accurate to at least 77 per cent in the worst case for the simple die pair data and has an average accuracy of approximately 80 per cent when classifying the more complex 'M'-shaped die pair data.

Integrating errors into developmental assessment: 'time' for ages 8-13

It is widely agreed that measurement is of paramount importance to students’ overall development in mathematics. This paper describes a developmental ‘map’ of students’ understanding and skills in measurement, focussed on the topic of Time, that integrates correct and incorrect student ideas. The map is based on a Rasch analysis of data from a large-scale UK national survey for standardising assessment for children from 5 to 14 years of age. It is demonstrated how a partial credit strategy enables a developmental map to be constructed to show students’ strengths and weaknesses in a meaningful and useful summative and formative manner. This map provides evidence, of both a summative and a formative nature, which may enable teachers to craft appropriate and successful learning experiences for children.

Risky driving habits and motor vehicle driver injury

Risky driving is an important cause of motor vehicle injury, but there is a lack of good epidemiological data in this field, particularly data comparing risky driving in younger drivers to those of other age groups. We examined the relationship between risky driving habits, prior traffic convictions and motor vehicle injury using cross-sectional data amongst 21,893 individuals in New Zealand, including 8029 who were aged 16–24 years. Those who reported frequently racing a motor vehicle for excitement or driving at 20 km/h or more over the speed limit, and those who had received traffic convictions over the past 12 months, were between two and four times more likely to have been injured while driving over the same time period. Driving unlicensed was a risk factor for older but not younger drivers, and driving at 20 km/h or more above the speed limits was a stronger risk factor for younger (<25 years) than older drivers. These results confirm the need for interventions targeting risky driving and suggest that different strategies may be required for different high-risk groups.

The ethics and practical importance of defining, distinguishing and disclosing nursing errors : a discussion paper

Nurses globally are required and expected to report nursing errors. As is clearly demonstrated in the international literature, fulfilling this requirement is not, however, without risks. In this discussion paper, the notion of ‘nursing error’, the practical and moral importance of defining, distinguishing and disclosing nursing errors and how a distinct definition of ‘nursing error’ fits with the new ‘system approach’ to human-error management in health care are critiqued. Drawing on international literature and two key case exemplars from the USA and Australia, arguments are advanced to support the view that although it is ‘right’ for nurses to report nursing errors, it will be very difficult for them to do so unless a non-punitive approach to nursing-error management is adopted.

The feasibility of a sustainable energy, driver-only electric commuter vehicle for New Zealand

This paper investigates whether low technology driver-only, battery electric commuter vehicles are feasible for New Zealand. Personal passenger transport faces several challenges in the coming decades: depletion of cheap oil reserves, increasing congestion, localised pollution, the need for reduced carbon emissions and the long term goal of sustainability. One way of solving some of these problems could be to introduce low cost, comfortable, energy efficient, driver-only electric vehicles. These would still give the driver a weatherproof, safe and comfortable means of commuting, but at a fraction of the energy and running costs of conventional petrol/diesel cars. To help assess their viability, the performance and energy use of the E-POD electric commuter vehicle is used as a benchmark. The work shows that such a vehicle could be made cheaply, using readily available technology with a range of 180km and a top speed of over 90km/h. The chassis could be made from natural fibre composite materials that might reduce significantly the embedded energy required for its manufacture. The electricity taken from the grid to charge the batteries could be replaced by electricity generated from grid connected photovoltaic panels mounted on the garage roof of the vehicle owner.

Analysis of children's merging errors

Array

Driver recognition based on dynamic handgrip pattern on steeling wheel

This paper proposes a novel biometric authentication method based on the recognition of drivers' dynamic handgrip on steering wheel. A pressure sensitive mat mounted on a steering wheel is employed to collect handgrip data exerted by the hands of drivers who intend to start the vehicle. Then, the likelihood-ratio-based classifier is designed to distinguish rightful driver of a car after analyzing their inherent dynamic features of grasping. The experimental results obtained in this study show that mean acceptance rates of 85.4% for the trained subjects and mean rejection rates of 82.65% for the un-trained ones are achieved by the classifier in the two batches of testing. It can be concluded that the driver verification approach based on dynamic handgrip recognition on steering wheel is a promising biometric technology and will be further explored in the near future in smart car design.

Driver verification based on handgrip recognition on steering wheel

This paper presents a novel driver verification algorithm based on the recognition of handgrip patterns on steering wheel. A pressure sensitive mat mounted on a steering wheel is employed to collect a series of pressure images exerted by the hands of the drivers who intend to start the vehicle. Then, feature extraction from those images is carried out through two major steps: Quad-Tree-based multi-resolution decomposition on the images and Principle Component Analysis (PCA)-based dimension reduction, followed by implementing a likelihood-ratio classifier to distinguish drivers into known or unknown ones. The experimental results obtained in this study show that the mean acceptance rates of 78.15% and 78.22% for the trained subjects and the mean rejection rates of 93.92% and 90.93% to the un-trained ones are achieved in two trials, respectively. It can be concluded that the driver verification approach based on the handgrip recognition on steering wheel is promising and will be further explored in the near future.