Motion planning and stochastic control with experimental validation on a planetary rover

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This model is used to construct a control policy for navigation to a goal region in a terrain map built using an on-board RGB-D camera. The terrain includes flat ground, small rocks, and non-traversable rocks. We report the results of 200 simulated and 35 experimental trials that validate the approach and demonstrate the value of considering control uncertainty in maintaining platform safety.

The impact of social media features on print media firms’ online business models

Many newspapers and magazines have added “social media features” to their web-based information services in order to allow users to participate in the production of content. This study examines the specific impact of the firm’s investment in social media features on their online business models. We make a comparative case study of four Scandinavian print media firms that have added social media features to their online services. We show how social media features lead to online business model innovation, particularly linked to the firms’ value propositions. The paper discusses the repercussions of this transformation on firms’ relationship with consumers and with traditional content contributors. The modified value proposition also requires firms to acquire new competences in order to reap full benefit of their social media investments. We show that the firms have been unable to do so since they have not allowed the social media features to affect their online revenue models.

A typology of music distribution models

A typology of music distribution models is proposed consisting of the ownership model, the access model, and the context model. These models are not substitutes for each other and may co‐exist serving different market niches. The paper argues that increasingly the economic value created from recorded music is based on con‐text rather than on ownership. During this process, access‐based services temporarily generate economic value, but such services are destined to eventually become commoditised.

Towards training-free appearance-based localization : probabilistic models for whole-image descriptors

Whole image descriptors have recently been shown to be remarkably robust to perceptual change especially compared to local features. However, whole-image-based localization systems typically rely on heuristic methods for determining appropriate matching thresholds in a particular environment. These environment-specific tuning requirements and the lack of a meaningful interpretation of these arbitrary thresholds limits the general applicability of these systems. In this paper we present a Bayesian model of probability for whole-image descriptors that can be seamlessly integrated into localization systems designed for probabilistic visual input. We demonstrate this method using CAT-Graph, an appearance-based visual localization system originally designed for a FAB-MAP-style probabilistic input. We show that using whole-image descriptors as visual input extends CAT-Graph’s functionality to environments that experience a greater amount of perceptual change. We also present a method of estimating whole-image probability models in an online manner, removing the need for a prior training phase. We show that this online, automated training method can perform comparably to pre-trained, manually tuned local descriptor methods.

A deconvolution method for deriving the transit time spectrum for ultrasound propagation through cancellous bone replica models

The acceptance of broadband ultrasound attenuation for the assessment of osteoporosis suffers from a limited understanding of ultrasound wave propagation through cancellous bone. It has recently been proposed that the ultrasound wave propagation can be described by a concept of parallel sonic rays. This concept approximates the detected transmission signal to be the superposition of all sonic rays that travel directly from transmitting to receiving transducer. The transit time of each ray is defined by the proportion of bone and marrow propagated. An ultrasound transit time spectrum describes the proportion of sonic rays having a particular transit time, effectively describing lateral inhomogeneity of transit times over the surface of the receiving ultrasound transducer. The aim of this study was to provide a proof of concept that a transit time spectrum may be derived from digital deconvolution of input and output ultrasound signals. We have applied the active-set method deconvolution algorithm to determine the ultrasound transit time spectra in the three orthogonal directions of four cancellous bone replica samples and have compared experimental data with the prediction from the computer simulation. The agreement between experimental and predicted ultrasound transit time spectrum analyses derived from Bland–Altman analysis ranged from 92% to 99%, thereby supporting the concept of parallel sonic rays for ultrasound propagation in cancellous bone. In addition to further validation of the parallel sonic ray concept, this technique offers the opportunity to consider quantitative characterisation of the material and structural properties of cancellous bone, not previously available utilising ultrasound.

Identification of reduced models of power system areas using phasor measurement units

In this paper an approach is presented for identification of a reduced model for coherent areas in power systems using phasor measurement units to represent the inter-area oscillations of the system. The generators which are coherent in a wide range of operating conditions form the areas in power systems and the reduced model is obtained by representing each area by an equivalent machine. The reduced nonlinear model is then identified based on the data obtained from measurement units. The simulation is performed on three test systems and the obtained results show high accuracy of identification process.

Exact series solutions of reactive transport models with general initial conditions

Exact solutions of partial differential equation models describing the transport and decay of single and coupled multispecies problems can provide insight into the fate and transport of solutes in saturated aquifers. Most previous analytical solutions are based on integral transform techniques, meaning that the initial condition is restricted in the sense that the choice of initial condition has an important impact on whether or not the inverse transform can be calculated exactly. In this work we describe and implement a technique that produces exact solutions for single and multispecies reactive transport problems with more general, smooth initial conditions. We achieve this by using a different method to invert a Laplace transform which produces a power series solution. To demonstrate the utility of this technique, we apply it to two example problems with initial conditions that cannot be solved exactly using traditional transform techniques.

Self-exciting hurdle models for terrorist activity

A predictive model of terrorist activity is developed by examining the daily number of terrorist attacks in Indonesia from 1994 through 2007. The dynamic model employs a shot noise process to explain the self-exciting nature of the terrorist activities. This estimates the probability of future attacks as a function of the times since the past attacks. In addition, the excess of nonattack days coupled with the presence of multiple coordinated attacks on the same day compelled the use of hurdle models to jointly model the probability of an attack day and corresponding number of attacks. A power law distribution with a shot noise driven parameter best modeled the number of attacks on an attack day. Interpretation of the model parameters is discussed and predictive performance of the models is evaluated.

Visualization of predictive distributions for discrete spatial-temporal log Cox processes approximated with MCMC

An important aspect of decision support systems involves applying sophisticated and flexible statistical models to real datasets and communicating these results to decision makers in interpretable ways. An important class of problem is the modelling of incidence such as fire, disease etc. Models of incidence known as point processes or Cox processes are particularly challenging as they are ‘doubly stochastic’ i.e. obtaining the probability mass function of incidents requires two integrals to be evaluated. Existing approaches to the problem either use simple models that obtain predictions using plug-in point estimates and do not distinguish between Cox processes and density estimation but do use sophisticated 3D visualization for interpretation. Alternatively other work employs sophisticated non-parametric Bayesian Cox process models, but do not use visualization to render interpretable complex spatial temporal forecasts. The contribution here is to fill this gap by inferring predictive distributions of Gaussian-log Cox processes and rendering them using state of the art 3D visualization techniques. This requires performing inference on an approximation of the model on a discretized grid of large scale and adapting an existing spatial-diurnal kernel to the log Gaussian Cox process context.

Autism : an introduction to behavioural therapy models used for autism and nursing the person with autism in the primary care setting

Childhood autism falls under the guise of autism spectrum disorders and is generally found in children over two years of age. There are of course variations in severity and clinical manifestations, however the most common features being disinterest in social interaction and engagement in ritualistic and repetitive behaviours. In Singapore the incidence of autism is on the rise as parents are becoming more aware of the early signs of autism and seek healthcare programmes to ensure the quality of life for their child is optimised. Two such programmes, Applied Behaiour Analysis and Floortime approach have proven successful in alleviating some of the behavioural and social skills problems associated with autism. Using positive behaviour reinforcement both Applied Behaviour Analysis and Floortime approach reward behaviour associated with positive social responses.

On the use of complex numbers in quantum models for information retrieval

Quantum-inspired models have recently attracted increasing attention in Information Retrieval. An intriguing characteristic of the mathematical framework of quantum theory is the presence of complex numbers. However, it is unclear what such numbers could or would actually represent or mean in Information Retrieval. The goal of this paper is to discuss the role of complex numbers within the context of Information Retrieval. First, we introduce how complex numbers are used in quantum probability theory. Then, we examine van Rijsbergen’s proposal of evoking complex valued representations of informations objects. We empirically show that such a representation is unlikely to be effective in practice (confuting its usefulness in Information Retrieval). We then explore alternative proposals which may be more successful at realising the power of complex numbers.

Quality Information System: Investigating the conception, role, models and suggesting conceptual and hierarchal models

In the current business world which companies’ competition is very compact in the business arena, quality in manufacturing and providing products and services can be considered as a means of seeking excellence and success of companies in this competition arena. Entering the era of e-commerce and emergence of new production systems and new organizational structures, traditional management and quality assurance systems have been challenged. Consequently, quality information system has been gained a special seat as one of the new tools of quality management. In this paper, quality information system has been studied with a review of the literature of the quality information system, and the role and position of quality Information System (QIS) among other information systems of a organization is investigated. The quality Information system models are analyzed and by analyzing and assessing presented models in quality information system a conceptual and hierarchical model of quality information system is suggested and studied. As a case study the hierarchical model of quality information system is developed by evaluating hierarchical models presented in the field of quality information system based on the Shetabkar Co.

Geometric sensitivity of patient-specific finite element models of the spine to variability in user-selected anatomical landmarks

Software to create individualised finite element (FE) models of the osseoligamentous spine using pre-operative computed tomography (CT) data-sets for spinal surgery patients has recently been developed. This study presents a geometric sensitivity analysis of this software to assess the effect of intra-observer variability in user-selected anatomical landmarks. User-selected landmarks on the osseous anatomy were defined from CT data-sets for three scoliosis patients and these landmarks were used to reconstruct patient-specific anatomy of the spine and ribcage using parametric descriptions. The intra-observer errors in landmark co-ordinates for these anatomical landmarks were calculated. FE models of the spine and ribcage were created using the reconstructed anatomy for each patient and these models were analysed for a loadcase simulating clinical flexibility assessment. The intra-observer error in the anatomical measurements was low in comparison to the initial dimensions, with the exception of the angular measurements for disc wedge and zygapophyseal joint (z-joint) orientation and disc height. This variability suggested that CT resolution may influence such angular measurements, particularly for small anatomical features, such as the z-joints, and may also affect disc height. The results of the FE analysis showed low variation in the model predictions for spinal curvature with the mean intra-observer variability substantially less than the accepted error in clinical measurement. These findings demonstrate that intra-observer variability in landmark point selection has minimal effect on the subsequent FE predictions for a clinical loadcase.

Fast re-parameterisation of Gaussian mixture models for robotics applications

Autonomous navigation and picture compilation tasks require robust feature descriptions or models. Given the non Gaussian nature of sensor observations, it will be shown that Gaussian mixture models provide a general probabilistic representation allowing analytical solutions to the update and prediction operations in the general Bayesian filtering problem. Each operation in the Bayesian filter for Gaussian mixture models multiplicatively increases the number of parameters in the representation leading to the need for a re-parameterisation step. A computationally efficient re-parameterisation step will be demonstrated resulting in a compact and accurate estimate of the true distribution.

Controlled automated discovery of collections of business process models

Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models – each one representing a variant of the business process – as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.