The tendering process and performance analysis of a public building project in Ghana

A case study on the tendering process and cost/time performance of a public building project in Ghana is conducted. Competitive bids submitted by five contractors for the project, in which contractors were required to prepare their own quantities, were analyzed to compare differences in their pricing levels and risk/requirement perceptions. Queries sent to the consultants at the tender stage were also analyzed to identify the significant areas of concern to contractors in relation to the tender documentation. The five bidding prices were significantly different. The queries submitted for clarifications were significantly different, although a few were similar. Using a before-and-after experiment, the expected cost/time estimate at the start of the project was compared to the actual cost/time values, i.e. what happened in the actual construction phase. The analysis showed that the project exceeded its expected cost by 18% and its planned time by 210%. Variations and inadequate design were the major reasons. Following an exploration of these issues, an alternative tendering mechanism is recommended to clients. A shift away from the conventional approach of awarding work based on price, and serious consideration of alternative procurement routes can help clients in Ghana obtain better value for money on their projects.

Solving complex optimal control problems at no cost with PSOPT

This paper introduces PSOPT, an open source optimal control solver written in C++. PSOPT uses pseudospectral and local discretizations, sparse nonlinear programming, automatic differentiation, and it incorporates automatic scaling and mesh refinement facilities. The software is able to solve complex optimal control problems including multiple phases, delayed differential equations, nonlinear path constraints, interior point constraints, integral constraints, and free initial and/or final times. The software does not require any non-free platform to run, not even the operating system, as it is able to run under Linux. Additionally, the software generates plots as well as LATEX code so that its results can easily be included in publications. An illustrative example is provided.

Neural networks for linear control: an analysis

This paper considers the use of radial basis function and multi-layer perceptron networks for linear or linearizable, adaptive feedback control schemes in a discrete-time environment. A close look is taken at the model structure selected and the extent of the resulting parameterization. A comparison is made with standard, nonneural network algorithms, e.g. self-tuning control.

Multivariable cluster analysis for high-speed industrial machinery

The overall operation and internal complexity of a particular production machinery can be depicted in terms of clusters of multidimensional points which describe the process states, the value in each point dimension representing a measured variable from the machinery. The paper describes a new cluster analysis technique for use with manufacturing processes, to illustrate how machine behaviour can be categorised and how regions of good and poor machine behaviour can be identified. The cluster algorithm presented is the novel mean-tracking algorithm, capable of locating N-dimensional clusters in a large data space in which a considerable amount of noise is present. Implementation of the algorithm on a real-world high-speed machinery application is described, with clusters being formed from machinery data to indicate machinery error regions and error-free regions. This analysis is seen to provide a promising step ahead in the field of multivariable control of manufacturing systems.

Performance analysis of the unconstrained FxLMS algorithm for active noise control

The paper analyzes the performance of the unconstrained filtered-x LMS (FxLMS) algorithm for active noise control (ANC), where we remove the constraints on the controller that it must be causal and has finite impulse response. It is shown that the unconstrained FxLMS algorithm always converges to, if stable, the true optimum filter, even if the estimation of the secondary path is not perfect, and its final mean square error is independent of the secondary path. Moreover, we show that the sufficient and necessary stability condition for the feedforward unconstrained FxLMS is that the maximum phase error of the secondary path estimation must be within 90°, which is the only necessary condition for the feedback unconstrained FxLMS. The significance of the analysis on a practical system is also discussed. Finally we show how the obtained results can guide us to design a robust feedback ANC headset.

A value chain approach to animal diseases risk management: technical foundations and practical framework for field application

Classical risk assessment approaches for animal diseases are influenced by the probability of release, exposure and consequences of a hazard affecting a livestock population. Once a pathogen enters into domestic livestock, potential risks of exposure and infection both to animals and people extend through a chain of economic activities related to producing, buying and selling of animals and products. Therefore, in order to understand economic drivers of animal diseases in different ecosystems and to come up with effective and efficient measures to manage disease risks from a country or region, the entire value chain and related markets for animal and product needs to be analysed to come out with practical and cost effective risk management options agreed by actors and players on those value chains. Value chain analysis enriches disease risk assessment providing a framework for interdisciplinary collaboration, which seems to be in increasing demand for problems concerning infectious livestock diseases. The best way to achieve this is to ensure that veterinary epidemiologists and social scientists work together throughout the process at all levels.

Cluster analysis for multivariable process control

This paper describes the novel use of cluster analysis in the field of industrial process control. The severe multivariable process problems encountered in manufacturing have often led to machine shutdowns, where the need for corrective actions arises in order to resume operation. Production faults which are caused by processes running in less efficient regions may be prevented or diagnosed using a reasoning based on cluster analysis. Indeed the intemal complexity of a production machinery may be depicted in clusters of multidimensional data points which characterise the manufacturing process. The application of a Mean-Tracking cluster algorithm (developed in Reading) to field data acquired from a high-speed machinery will be discussed. The objective of such an application is to illustrate how machine behaviour can be studied, in particular how regions of erroneous and stable running behaviour can be identified.