The influence of project complexity on estimating accuracy

With the rapid development in technology over recent years, construction, in common with many areas of industry, has become increasingly complex. It would, therefore, seem to be important to develop and extend the understanding of complexity so that industry in general and in this case the construction industry can work with greater accuracy and efficiency to provide clients with a better service. This paper aims to generate a definition of complexity and a method for its measurement in order to assess its influence upon the accuracy of the quantity surveying profession in UK new build office construction. Quantitative data came from an analysis of twenty projects of varying size and value and qualitative data came from interviews with professional quantity surveyors. The findings highlight the difficulty in defining and measuring project complexity. The correlation between accuracy and complexity was not straightforward, being subjected to many extraneous variables, particularly the impact of project size. Further research is required to develop a better measure of complexity. This is in order to improve the response of quantity surveyors, so that an appropriate level of effort can be applied to individual projects, permitting greater accuracy and enabling better resource planning within the profession.

Statistical methods for ordered categorical data based on a constrained odds model

The proportional odds model provides a powerful tool for analysing ordered categorical data and setting sample size, although for many clinical trials its validity is questionable. The purpose of this paper is to present a new class of constrained odds models which includes the proportional odds model. The efficient score and Fisher's information are derived from the profile likelihood for the constrained odds model. These results are new even for the special case of proportional odds where the resulting statistics define the Mann-Whitney test. A strategy is described involving selecting one of these models in advance, requiring assumptions as strong as those underlying proportional odds, but allowing a choice of such models. The accuracy of the new procedure and its power are evaluated.

The relationship between phonological awareness and word reading accuracy in Oriya and English: A Study of Oriya-speaking fifth-graders

This study investigated the relationships between phonological awareness and reading in Oriya and English. Oriya is the official language of Orissa, an eastern state of India. The writing system is an alphasyllabary. Ninety-nine fifth grade children (mean age 9 years 7 months) were assessed on measures of phonological awareness, word reading and pseudo-word reading in both languages. Forty-eight of the children attended Oriya-medium schools where they received literacy instruction in Oriya from grade 1 and learned English from grade 2. Fifty-one children attended English-medium schools where they received literacy instruction in English from grade 1 and in Oriya from grade 2. The results showed that phonological awareness in Oriya contributed significantly to reading Oriya and English words and pseudo-words for the children in the Oriya-medium schools. However, it only contributed to Oriya pseudo-word reading and English word reading for children in the English-medium schools. Phonological awareness in English contributed to English word and pseudo-word reading for both groups. Further analyses investigated the contribution of awareness of large phonological units (syllable, onsets and rimes) and small phonological units (phonemes) to reading in each language. The data suggest that cross-language transfer and facilitation of phonological awareness to word reading is not symmetrical across languages and may depend both on the characteristics of the different orthographies of the languages being learned and whether the first literacy language is also the first spoken language.

ExtSym: a program to aid space-group determination from powder diffraction data

Once unit-cell dimensions have been determined from a powder diffraction data set and therefore the crystal system is known (e.g. orthorhombic), the method presented by Markvardsen, David, Johnson & Shankland [Acta Cryst. (2001), A57, 47-54] can be used to generate a table ranking the extinction symbols of the given crystal system according to probability. Markvardsen et al. tested a computer program (ExtSym) implementing the method against Pawley refinement outputs generated using the TF12LS program [David, Ibberson & Matthewman (1992). Report RAL-92-032. Rutherford Appleton Laboratory, Chilton, Didcot, Oxon, UK]. Here, it is shown that ExtSym can be used successfully with many well known powder diffraction analysis packages, namely DASH [David, Shankland, van de Streek, Pidcock, Motherwell & Cole (2006). J. Appl. Cryst. 39, 910-915], FullProf [Rodriguez-Carvajal (1993). Physica B, 192, 55-69], GSAS [Larson & Von Dreele (1994). Report LAUR 86-748. Los Alamos National Laboratory, New Mexico, USA], PRODD [Wright (2004). Z. Kristallogr. 219, 1-11] and TOPAS [Coelho (2003). Bruker AXS GmbH, Karlsruhe, Germany]. In addition, a precise description of the optimal input for ExtSym is given to enable other software packages to interface with ExtSym and to allow the improvement/modification of existing interfacing scripts. ExtSym takes as input the powder data in the form of integrated intensities and error estimates for these intensities. The output returned by ExtSym is demonstrated to be strongly dependent on the accuracy of these error estimates and the reason for this is explained. ExtSym is tested against a wide range of data sets, confirming the algorithm to be very successful at ranking the published extinction symbol as the most likely. (C) 2008 International Union of Crystallography Printed in Singapore - all rights reserved.

Quantitative analysis of accuracy of an inertial/acoustic 6DOF tracking system in motion

An increasing number of neuroscience experiments are using virtual reality to provide a more immersive and less artificial experimental environment. This is particularly useful to navigation and three-dimensional scene perception experiments. Such experiments require accurate real-time tracking of the observer's head in order to render the virtual scene. Here, we present data on the accuracy of a commonly used six degrees of freedom tracker (Intersense IS900) when it is moved in ways typical of virtual reality applications. We compared the reported location of the tracker with its location computed by an optical tracking method. When the tracker was stationary, the root mean square error in spatial accuracy was 0.64 mm. However, we found that errors increased over ten-fold (up to 17 mm) when the tracker moved at speeds common in virtual reality applications. We demonstrate that the errors we report here are predominantly due to inaccuracies of the IS900 system rather than the optical tracking against which it was compared. (c) 2006 Elsevier B.V. All rights reserved.

A cost-effective high-throughput digital system for observation and acquisition of animal behavioral data

We have designed and implemented a low-cost digital system using closed-circuit television cameras coupled to a digital acquisition system for the recording of in vivo behavioral data in rodents and for allowing observation and recording of more than 10 animals simultaneously at a reduced cost, as compared with commercially available solutions. This system has been validated using two experimental rodent models: one involving chemically induced seizures and one assessing appetite and feeding. We present observational results showing comparable or improved levels of accuracy and observer consistency between this new system and traditional methods in these experimental models, discuss advantages of the presented system over conventional analog systems and commercially available digital systems, and propose possible extensions to the system and applications to non-rodent studies.

Optimization algorithms in FMRF model-based segmentation for LIDAR data and co-registered bands

In this paper, a fuzzy Markov random field (FMRF) model is used to segment land-objects into free, grass, building, and road regions by fusing remotely, sensed LIDAR data and co-registered color bands, i.e. scanned aerial color (RGB) photo and near infra-red (NIR) photo. An FMRF model is defined as a Markov random field (MRF) model in a fuzzy domain. Three optimization algorithms in the FMRF model, i.e. Lagrange multiplier (LM), iterated conditional mode (ICM), and simulated annealing (SA), are compared with respect to the computational cost and segmentation accuracy. The results have shown that the FMRF model-based ICM algorithm balances the computational cost and segmentation accuracy in land-cover segmentation from LIDAR data and co-registered bands.

A kernel-based two-class classifier for imbalanced data sets

Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm.

Threshold-free object and ground point separation in LIDAR data

Light Detection And Ranging (LIDAR) is an important modality in terrain and land surveying for many environmental, engineering and civil applications. This paper presents the framework for a recently developed unsupervised classification algorithm called Skewness Balancing for object and ground point separation in airborne LIDAR data. The main advantages of the algorithm are threshold-freedom and independence from LIDAR data format and resolution, while preserving object and terrain details. The framework for Skewness Balancing has been built in this contribution with a prediction model in which unknown LIDAR tiles can be categorised as “hilly” or “moderate” terrains. Accuracy assessment of the model is carried out using cross-validation with an overall accuracy of 95%. An extension to the algorithm is developed to address the overclassification issue for hilly terrain. For moderate terrain, the results show that from the classified tiles detached objects (buildings and vegetation) and attached objects (bridges and motorway junctions) are separated from bare earth (ground, roads and yards) which makes Skewness Balancing ideal to be integrated into geographic information system (GIS) software packages.

Land-cover classification from airborne LIDAR data fused with aerial optical images

Airborne LIght Detection And Ranging (LIDAR) provides accurate height information for objects on the earth, which makes LIDAR become more and more popular in terrain and land surveying. In particular, LIDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. In this paper, an unsupervised approach based on an improved fuzzy Markov random field (FMRF) model is developed, by which the LIDAR data, its co-registered images acquired by optical sensors, i.e. aerial color image and near infrared image, and other derived features are fused effectively to improve the ability of the LIDAR system for the accurate land-cover classification. In the proposed FMRF model-based approach, the spatial contextual information is applied by modeling the image as a Markov random field (MRF), with which the fuzzy logic is introduced simultaneously to reduce the errors caused by the hard classification. Moreover, a Lagrange-Multiplier (LM) algorithm is employed to calculate a maximum A posteriori (MAP) estimate for the classification. The experimental results have proved that fusing the height data and optical images is particularly suited for the land-cover classification. The proposed approach works very well for the classification from airborne LIDAR data fused with its coregistered optical images and the average accuracy is improved to 88.9%.

Physical interpretation of the correlation between multi-angle spectral data and canopy height

Recent empirical studies have shown that multi-angle spectral data can be useful for predicting canopy height, but the physical reason for this correlation was not understood. We follow the concept of canopy spectral invariants, specifically escape probability, to gain insight into the observed correlation. Airborne Multi-Angle Imaging Spectrometer (AirMISR) and airborne Laser Vegetation Imaging Sensor (LVIS) data acquired during a NASA Terrestrial Ecology Program aircraft campaign underlie our analysis. Two multivariate linear regression models were developed to estimate LVIS height measures from 28 AirMISR multi-angle spectral reflectances and from the spectrally invariant escape probability at 7 AirMISR view angles. Both models achieved nearly the same accuracy, suggesting that canopy spectral invariant theory can explain the observed correlation. We hypothesize that the escape probability is sensitive to the aspect ratio (crown diameter to crown height). The multi-angle spectral data alone therefore may not provide enough information to retrieve canopy height globally.

The TAMORA algorithm: satellite rainfall estimates over West Africa using multi-spectral SEVIRI data

A multi-spectral rainfall estimation algorithm has been developed for the Sahel region of West Africa with the purpose of producing accumulated rainfall estimates for drought monitoring and food security. Radar data were used to calibrate multi-channel SEVIRI data from MSG, and a probability of rainfall at several different rain-rates was established for each combination of SEVIRI radiances. Radar calibrations from both Europe (the SatPrecip algorithm) and Niger (TAMORA algorithm) were used. 10 day estimates were accumulated from SatPrecip and TAMORA and compared with kriged gauge data and TAMSAT satellite rainfall estimates over West Africa. SatPrecip was found to produce large overestimates for the region, probably because of its non-local calibration. TAMORA was negatively biased for areas of West Africa with relatively high rainfall, but its skill was comparable to TAMSAT for the low-rainfall region climatologically similar to its calibration area around Niamey. These results confirm the high importance of local calibration for satellite-derived rainfall estimates. As TAMORA shows no improvement in skill over TAMSAT for dekadal estimates, the extra cloud-microphysical information provided by multi-spectral data may not be useful in determining rainfall accumulations at a ten day timescale. Work is ongoing to determine whether it shows improved accuracy at shorter timescales.

Assessing the accuracy of CME speed and trajectory estimates from STEREO observations through a comparison of independent methods

We have estimated the speed and direction of propagation of a number of Coronal Mass Ejections (CMEs) using single-spacecraft data from the STEREO Heliospheric Imager (HI) wide-field cameras. In general, these values are in good agreement with those predicted by Thernisien, Vourlidas, and Howard in Solar Phys. 256, 111 -aEuro parts per thousand 130 (2009) using a forward modelling method to fit CMEs imaged by the STEREO COR2 coronagraphs. The directions of the CMEs predicted by both techniques are in good agreement despite the fact that many of the CMEs under study travel in directions that cause them to fade rapidly in the HI images. The velocities estimated from both techniques are in general agreement although there are some interesting differences that may provide evidence for the influence of the ambient solar wind on the speed of CMEs. The majority of CMEs with a velocity estimated to be below 400 km s(-1) in the COR2 field of view have higher estimated velocities in the HI field of view, while, conversely, those with COR2 velocities estimated to be above 400 km s(-1) have lower estimated HI velocities. We interpret this as evidence for the deceleration of fast CMEs and the acceleration of slower CMEs by interaction with the ambient solar wind beyond the COR2 field of view. We also show that the uncertainties in our derived parameters are influenced by the range of elongations over which each CME can be tracked. In order to reduce the uncertainty in the predicted arrival time of a CME at 1 Astronomical Unit (AU) to within six hours, the CME needs to be tracked out to at least 30 degrees elongation. This is in good agreement with predictions of the accuracy of our technique based on Monte Carlo simulations.

A hybrid data assimilation scheme for model parameter estimation: application to morphodynamic modelling

We present a novel algorithm for joint state-parameter estimation using sequential three dimensional variational data assimilation (3D Var) and demonstrate its application in the context of morphodynamic modelling using an idealised two parameter 1D sediment transport model. The new scheme combines a static representation of the state background error covariances with a flow dependent approximation of the state-parameter cross-covariances. For the case presented here, this involves calculating a local finite difference approximation of the gradient of the model with respect to the parameters. The new method is easy to implement and computationally inexpensive to run. Experimental results are positive with the scheme able to recover the model parameters to a high level of accuracy. We expect that there is potential for successful application of this new methodology to larger, more realistic models with more complex parameterisations.

Valuation accuracy: reconciling the timing of the valuation and sale

Carsberg (2002) suggested that the periodic valuation accuracy studies undertaken by, amongst others, IPD/Drivers Jonas (2003) should be undertaken every year and be sponsored by the RICS, which acts as the self-regulating body for valuations in the UK. This paper does not address the wider issues concerning the nature of properties which are sold and whether the sale prices are influenced by prior valuations, but considers solely the technical issues concerning the timing of the valuation and sales data. This study uses valuations and sales data from the Investment Property Databank UK Monthly Index to attempt to identify the date that sale data is divulged to valuers. This information will inform accuracy studies that use a cut-off date as to the closeness of valuations to sales completion date as a yardstick for excluding data from the analysis. It will also, assuming valuers are informed quickly of any agreed sales, help to determine the actual sale agreed date rather than the completion date, which includes a period of due diligence between when the sale is agreed and its completion. Valuations should be updated to this date, rather than the formal completion date, if a reliable measure of valuation accuracy is to be determined. An accuracy study is then undertaken using a variety of updating periods and the differences between the results are examined. The paper concludes that the sale only becomes known to valuers in the month prior to the sale taking place and that this assumes either that sales due diligence procedures are shortening or valuers are not told quickly of agreed sale prices. Studies that adopt a four-month cut-off date for any valuations compared to sales completion dates are over cautious, and this could be reduced to two months without compromising the data.