An investigation of customer delight during product evaluation: Implications for the development of desirable products

This paper reports findings from three research methods used to study customer delight during product evaluation. The results are framed in terms of existing models, high-lighting inadequacies in the assumptions these models make. Implications for product development are proposed in the form of practical strategies for understanding and delighting customers. © IMechE 2007.

Electricity Storage Using a Thermal Storage Scheme

The increasing use of renewable energy technologies for electricity generation, many of which have an unpredictably intermittent nature, will inevitably lead to a greater demand for large-scale electricity storage schemes. For example, the expanding fraction of electricity produced by wind turbines will require either backup or storage capacity to cover extended periods of wind lull. This paper describes a recently proposed storage scheme, referred to here as Pumped Thermal Storage (PTS), and which is based on "sensible heat" storage in large thermal reservoirs. During the charging phase, the system effectively operates as a high temperature-ratio heat pump, extracting heat from a cold reservoir and delivering heat to a hot one. In the discharge phase the processes are reversed and it operates as a heat engine. The round- trip efficiency is limited only by process irreversibilities (as opposed to Second Law limitations on the coefficient of performance and the thermal efficiency of the heat pump and heat engine respectively). PTS is currently being developed in both France and England. In both cases, the schemes operate on the Joule-Brayton (gas turbine) cycle, using argon as the working fluid. However, the French scheme proposes the use of turbomachinery for compression and expansion, whereas for that being developed in England reciprocating devices are proposed. The current paper focuses on the impact of the various process irreversibilities on the thermodynamic round-trip efficiency of the scheme. Consideration is given to compression and expansion losses and pressure losses (in pipe-work, valves and thermal reservoirs); heat transfer related irreversibility in the thermal reservoirs is discussed but not included in the analysis. Results are presented demonstrating how the various loss parameters and operating conditions influence the overall performance.

Thermodynamic analysis of pumped thermal electricity storage

The increasing use of renewable energy technologies for electricity generation, many of which have an unpredictably intermittent nature, will inevitably lead to a greater need for electricity storage. Although there are many existing and emerging storage technologies, most have limitations in terms of geographical constraints, high capital cost or low cycle life, and few are of sufficient scale (in terms of both power and storage capacity) for integration at the transmission and distribution levels. This paper is concerned with a relatively new concept which will be referred to here as Pumped Thermal Electricity Storage (PTES), and which may be able to make a significant contribution towards future storage needs. During charge, PTES makes use of a high temperature-ratio heat pump to convert electrical energy into thermal energy which is stored as ‘sensible heat’ in two thermal reservoirs, one hot and one cold. When required, the thermal energy is then converted back to electricity by effectively running the heat pump backwards as a heat engine. The paper focuses on thermodynamic aspects of PTES, including energy and power density, and the various sources of irreversibility and their impact on round-trip efficiency.

Spatio-temporal clustering of probabilistic region trajectories

We propose a novel model for the spatio-temporal clustering of trajectories based on motion, which applies to challenging street-view video sequences of pedestrians captured by a mobile camera. A key contribution of our work is the introduction of novel probabilistic region trajectories, motivated by the non-repeatability of segmentation of frames in a video sequence. Hierarchical image segments are obtained by using a state-of-the-art hierarchical segmentation algorithm, and connected from adjacent frames in a directed acyclic graph. The region trajectories and measures of confidence are extracted from this graph using a dynamic programming-based optimisation. Our second main contribution is a Bayesian framework with a twofold goal: to learn the optimal, in a maximum likelihood sense, Random Forests classifier of motion patterns based on video features, and construct a unique graph from region trajectories of different frames, lengths and hierarchical levels. Finally, we demonstrate the use of Isomap for effective spatio-temporal clustering of the region trajectories of pedestrians. We support our claims with experimental results on new and existing challenging video sequences. © 2011 IEEE.

Clustering user data for user modelling in the GUIDE multi-modal set-top box

Chapter 20 Clustering User Data for User Modelling in the GUIDE Multi-modal Set- top Box PM Langdon and P. Biswas 20.1 ... It utilises advanced user modelling and simulation in conjunction with a single layer interface that permits a ...

MCBoost: Multiple classifier boosting for perceptual co-clustering of images and visual features

We present a new co-clustering problem of images and visual features. The problem involves a set of non-object images in addition to a set of object images and features to be co-clustered. Co-clustering is performed in a way that maximises discrimination of object images from non-object images, thus emphasizing discriminative features. This provides a way of obtaining perceptual joint-clusters of object images and features. We tackle the problem by simultaneously boosting multiple strong classifiers which compete for images by their expertise. Each boosting classifier is an aggregation of weak-learners, i.e. simple visual features. The obtained classifiers are useful for object detection tasks which exhibit multimodalities, e.g. multi-category and multi-view object detection tasks. Experiments on a set of pedestrian images and a face data set demonstrate that the method yields intuitive image clusters with associated features and is much superior to conventional boosting classifiers in object detection tasks.

Integrated online speaker clustering and adaptation

For many applications, it is necessary to produce speech transcriptions in a causal fashion. To produce high quality transcripts, speaker adaptation is often used. This requires online speaker clustering and incremental adaptation techniques to be developed. This paper presents an integrated approach to online speaker clustering and adaptation which allows efficient clustering of speakers using the same accumulated statistics that are normally used for adaptation. Using a consistent criterion for both clustering and adaptation should yield gains for both stages. The proposed approach is evaluated on a meetings transcription task using audio from multiple distant microphones. Consistent gains over standard clustering and adaptation were obtained. Copyright © 2011 ISCA.