Productivity performance of Singapore’s retail sector : a two-stage non-parametric approach

This paper seeks to identify and quantify sources of the lagging productivity in Singapore’s retail sector as reported in the Economic Strategies Committee 2010 report. A two-stage analysis is adopted. In the first stage, the Malmquist productivity index is employed which provides measures of productivity change, technological change and efficiency change. In the second stage, technical efficiency estimates are regressed against explanatory variables based on a truncated regression model. Sources of technical efficiency were attributed to quality of workers while product assortment and competition negatively impacted on efficiency.

Semi-parametric forecasting of realized volatility

Forecasts generated by time series models traditionally place greater weight on more recent observations. This paper develops an alternative semi-parametric method for forecasting that does not rely on this convention and applies it to the problem of forecasting asset return volatility. In this approach, a forecast is a weighted average of historical volatility, with the greatest weight given to periods that exhibit similar market conditions to the time at which the forecast is being formed. Weighting is determined by comparing short-term trends in volatility across time (as a measure of market conditions) by means of a multivariate kernel scheme. It is found that the semi-parametric method produces forecasts that are significantly more accurate than a number of competing approaches at both short and long forecast horizons.

Semi-parametric modelling of ultrafine particle number concentration

Quantifying spatial and/or temporal trends in environmental modelling data requires that measurements be taken at multiple sites. The number of sites and duration of measurement at each site must be balanced against costs of equipment and availability of trained staff. The split panel design comprises short measurement campaigns at multiple locations and continuous monitoring at reference sites [2]. Here we present a modelling approach for a spatio-temporal model of ultrafine particle number concentration (PNC) recorded according to a split panel design. The model describes the temporal trends and background levels at each site. The data were measured as part of the “Ultrafine Particles from Transport Emissions and Child Health” (UPTECH) project which aims to link air quality measurements, child health outcomes and a questionnaire on the child’s history and demographics. The UPTECH project involves measuring aerosol and particle counts and local meteorology at each of 25 primary schools for two weeks and at three long term monitoring stations, and health outcomes for a cohort of students at each school [3].

Non-parametric techniques for fast and robust stereo matching

The mining environment presents a challenging prospect for stereo vision. Our objective is to produce a stereo vision sensor suited to close-range scenes consisting mostly of rocks. This sensor should produce a dense depth map within real-time constraints. Speed and robustness are of foremost importance for this application. This paper compares a number of stereo matching algorithms in terms of robustness and suitability to fast implementation. These include traditional area-based algorithms, and algorithms based on non-parametric transforms, notably the rank and census transforms. Our experimental results show that the rank and census transforms are robust with respect to radiometric distortion and introduce less computational complexity than conventional area-based matching techniques.

Suitability of non-parametric stereo matching algorithms for mining automation

The mining environment, being complex, irregular and time varying, presents a challenging prospect for stereo vision. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This paper assesses the suitability of a number of matching techniques for use in a stereo vision sensor for close range scenes consisting primarily of rocks. These include traditional area-based matching metrics, and non-parametric transforms, in particular, the rank and census transforms. Experimental results show that the rank and census transforms exhibit a number of clear advantages over area-based matching metrics, including their low computational complexity, and robustness to certain types of distortion.

Frame-rate stereopsis using non-parametric transforms and programmable logic

A frame-rate stereo vision system, based on non-parametric matching metrics, is described. Traditional metrics, such as normalized cross-correlation, are expensive in terms of logic. Non-parametric measures require only simple, parallelizable, functions such as comparators, counters and exclusive-or, and are thus very well suited to implementation in reprogrammable logic.

Parametric and generative methods with building information modelling : connecting BIM with explorative design modelling

Parametric and generative modelling methods are ways in which computer models are made more flexible, and of formalising domain-specific knowledge. At present, no open standard exists for the interchange of parametric and generative information. The Industry Foundation Classes (IFC) which are an open standard for interoperability in building information models is presented as the base for an open standard in parametric modelling. The advantage of allowing parametric and generative representations are that the early design process can allow for more iteration and changes can be implemented quicker than with traditional models. This paper begins with a formal definition of what constitutes to be parametric and generative modelling methods and then proceeds to describe an open standard in which the interchange of components could be implemented. As an illustrative example of generative design, Frazer’s ‘Reptiles’ project from 1968 is reinterpreted.

Lateral impact response and parametric studies of axially loaded square concrete filled steel tube columns

This paper presents a numerical study on the response of axially loaded slender square concrete filled steel tube (CFST) columns under low velocity lateral impact loading. A finite element analysis (FEA) model was developed using the explicit dynamic nonlinear finite element code LS -DYNA in which the strain rate effects of both steel and concrete, contact between steel tube and concrete and confinement effect provided by the steel tube for the concrete were considered. The model also benefited from a relatively recent feature of LS-DYNA for applying a pre-loading in the explicit solver. The developed numerical model was verified for its accuracy and adequacy by comparing the results with experimental results available in the literature. The verified model was then employed to conduct a parametric study to investigate the influence of axial load level, impact location, support conditions, and slenderness ratio on the response of the CFST columns. A good agreement between the numerical and experimental results was achieved. The model could reasonably predict the impact load-deflection history and deformed shape of the column at the end of the impact event. The results of the parametric study showed that whilst impact location, axial load level and slenderness ratio can have a significant effect on the peak impact force, residual lateral deflection and maximum lateral deflection, the influence of support fixity is minimal. With an increase of axial load to up to a certain level, the peak force increases; however, a further increase in the axial load causes a decrease in the peak force. Both residual lateral deflection and maximum lateral deflection increase as axial load level increases. Shifting the impact location towards the supports increases the peak force and reduces both residual and maximum lateral deflections. A rise in slenderness ratio decreases the peak force and increases the residual and maximum lateral deflections.

A parametric analysis technique for design of fuel cell and hybrid-electric vehicles

This paper presents a new simplified parametric analysis technique for the design of fuel cell and hybrid-electric vehicles. The technique utilizes a comprehensive set of ∼30 parameters to fully characterize the vehicle platform, powertrain components, vehicle performance requirements and driving conditions. It is best applied to the sizing of powertrain components and prediction of energy consumption in a vehicle. This new parametric technique makes a good complement to existing vehicle simulation software packages and therefore represents a potentially valuable tool for the hybrid vehicle designer.

Bayesian indirect inference using a parametric auxiliary model

Indirect inference (II) is a methodology for estimating the parameters of an intractable (generative) model on the basis of an alternative parametric (auxiliary) model that is both analytically and computationally easier to deal with. Such an approach has been well explored in the classical literature but has received substantially less attention in the Bayesian paradigm. The purpose of this paper is to compare and contrast a collection of what we call parametric Bayesian indirect inference (pBII) methods. One class of pBII methods uses approximate Bayesian computation (referred to here as ABC II) where the summary statistic is formed on the basis of the auxiliary model, using ideas from II. Another approach proposed in the literature, referred to here as parametric Bayesian indirect likelihood (pBIL), we show to be a fundamentally different approach to ABC II. We devise new theoretical results for pBIL to give extra insights into its behaviour and also its differences with ABC II. Furthermore, we examine in more detail the assumptions required to use each pBII method. The results, insights and comparisons developed in this paper are illustrated on simple examples and two other substantive applications. The first of the substantive examples involves performing inference for complex quantile distributions based on simulated data while the second is for estimating the parameters of a trivariate stochastic process describing the evolution of macroparasites within a host based on real data. We create a novel framework called Bayesian indirect likelihood (BIL) which encompasses pBII as well as general ABC methods so that the connections between the methods can be established.

A parametric duration model of the reaction times of drivers distracted by mobile phone conversations

The use of mobile phones while driving is more prevalent among young drivers—a less experienced cohort with elevated crash risk. The objective of this study was to examine and better understand the reaction times of young drivers to a traffic event originating in their peripheral vision whilst engaged in a mobile phone conversation. The CARRS-Q Advanced Driving Simulator was used to test a sample of young drivers on various simulated driving tasks, including an event that originated within the driver’s peripheral vision, whereby a pedestrian enters a zebra crossing from a sidewalk. Thirty-two licensed drivers drove the simulator in three phone conditions: baseline (no phone conversation), hands-free and handheld. In addition to driving the simulator each participant completed questionnaires related to driver demographics, driving history, usage of mobile phones while driving, and general mobile phone usage history. The participants were 21 to 26 years old and split evenly by gender. Drivers’ reaction times to a pedestrian in the zebra crossing were modelled using a parametric accelerated failure time (AFT) duration model with a Weibull distribution. Also tested where two different model specifications to account for the structured heterogeneity arising from the repeated measures experimental design. The Weibull AFT model with gamma heterogeneity was found to be the best fitting model and identified four significant variables influencing the reaction times, including phone condition, driver’s age, license type (Provisional license holder or not), and self-reported frequency of usage of handheld phones while driving. The reaction times of drivers were more than 40% longer in the distracted condition compared to baseline (not distracted). Moreover, the impairment of reaction times due to mobile phone conversations was almost double for provisional compared to open license holders. A reduction in the ability to detect traffic events in the periphery whilst distracted presents a significant and measurable safety concern that will undoubtedly persist unless mitigated.

Parametric modelling for the efficient design of daylight strategies with complex geometries

Daylight devices are important components of any climate responsive façade system. But, the evolution of parametric CAD systems and digital fabrication has had an impact on architectural form so that regular forms are shifting to complex geometries. Architectural and engineering integration of daylight devices in envelopes with complex geometries is a challenge in terms of design and performance evaluation. The purpose of this paper is to assess daylight performance of a building with a climatic responsive envelope with complex geometry that integrates shading devices in the façade. The case study is based on the Esplanade buildings in Singapore. Climate-based day-light metrics such as Daylight Availability and Useful Daylight Illuminance are used. DIVA (daylight simulation), and Grasshopper (parametric analysis) plug-ins for Rhinoceros have been employed to examine the range of performance possibilities. Parameters such as dimension, inclination of the device, projected shadows and shape have been changed in order to maximize daylight availability and Useful Daylight Illuminance while minimizing glare probability. While orientation did not have a great impact on the results, aperture of the shading devices did, showing that shading devices with a projection of 1.75 m to 2.00 m performed best, achieving target lighting levels without issues of glare.

Semi-parametric forecasting of spikes in electricity prices

The occurrence of extreme movements in the spot price of electricity represents a significant source of risk to retailers. A range of approaches have been considered with respect to modelling electricity prices; these models, however, have relied on time-series approaches, which typically use restrictive decay schemes placing greater weight on more recent observations. This study develops an alternative, semi-parametric method for forecasting, which uses state-dependent weights derived from a kernel function. The forecasts that are obtained using this method are accurate and therefore potentially useful to electricity retailers in terms of risk management.

A near-to-far non-parametric learning approach for estimating traversability in deformable terrain

It is well recognized that many scientifically interesting sites on Mars are located in rough terrains. Therefore, to enable safe autonomous operation of a planetary rover during exploration, the ability to accurately estimate terrain traversability is critical. In particular, this estimate needs to account for terrain deformation, which significantly affects the vehicle attitude and configuration. This paper presents an approach to estimate vehicle configuration, as a measure of traversability, in deformable terrain by learning the correlation between exteroceptive and proprioceptive information in experiments. We first perform traversability estimation with rigid terrain assumptions, then correlate the output with experienced vehicle configuration and terrain deformation using a multi-task Gaussian Process (GP) framework. Experimental validation of the proposed approach was performed on a prototype planetary rover and the vehicle attitude and configuration estimate was compared with state-of-the-art techniques. We demonstrate the ability of the approach to accurately estimate traversability with uncertainty in deformable terrain.