Coventry University

About: Coventry University is a education organization based out in Coventry, United Kingdom. It is known for research contribution in the topics: Context (language use) & Population. The organization has 4964 authors who have published 12700 publications receiving 255898 citations. The organization is also known as: Lanchester Polytechnic & Coventry Polytechnic.

Developing a BPI framework and PAM for SMEs

Abstract: Purpose – The last decade has seen much interest in small and medium‐sized enterprises (SMEs) from successive UK Governments highlighting the importance of this sector to the wealth‐creating process of the UK economy. World‐class manufacturing (WCM) is a set of methodologies that are used by organisations to compete globally and continuously improve their competitiveness. Original equipment manufacturers (OEMs) are now competing at a global level and many are world‐class. The majority of the companies that make up the OEM's supply chains are SMEs. It is, therefore, imperative that SMEs also improve their competitiveness to a world‐class level. This paper aims to address these issues.Design/methodology/approach – The paper uses a triangulation methodology consisting of a literature review, analysis of a 150‐company survey and semi‐structured interviews in the development of the business process improvement (BPI) framework and performance assessment methodology (PAM) tool.Findings – This study advocates a p...

Retrenchment: An Engineering Variation on Refinement

Abstract: It is argued that refinement, in which I/O signatures stay the same, preconditions are weakened and postconditions strengthened, is too restrictive to describe all but a fraction of many realistic developments. An alternative notion is proposed called retrenchment, which allows information to migrate between I/O and state aspects of operations at different levels of abstraction, and which allows only a fraction of the high level behaviour to be captured at the low level. This permits more of the informal aspects of design to be formally captured and checked. The details are worked out for the B-Method.

The value of values: Resourcing co-design of ubiquitous computing

Abstract: The importance of values in design work is gaining increasing attention. However, some of the work to date takes an approach which starts with generic values, or assumes values are constant. Throug...

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection.

Abstract: The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k-nearest neighbor (k-NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.