Development of a web-based insulin decision aid for the elderly: usability barriers and guidelines

Universal Access in the Information Society - In recent years, researchers have attempted to shift patient decision aids (PDAs) from paper-based to web-based to increase its accessibility. Insulin...     

Analysis of zero-inflated clustered count data: A marginalized model approach

Min and Agresti (2005) proposed random effect hurdle models for zero-inflated clustered count data with two-part random effects for a binary component and a truncated count component. In this paper, we propose new marginalized models for zero-inflated clustered count data using random effects. The marginalized models are similar to Dobbie and Welsh’s (2001) model in which generalized estimating equations were exploited to find estimates. However, our proposed models are based on a likelihood-based approach. A Quasi-Newton algorithm is developed for estimation. We use these methods to carefully analyze two real datasets.     

Contour matching using epipolar geometry

Matching features computed in images is an important process in multiview image analysis. When the motion between two images is large, the matching problem becomes very difficult. In this paper, we propose a contour matching algorithm based on geometric constraints. With the assumption that the contours are obtained from images taken from a moving camera with static scenes, we apply the epipolar constraint between two sets of contours and compute the corresponding points on the contours. From the initial epipolar constraints obtained from corner point matching, candidate contours are selected according to the epipolar geometry, contour end point constraints, and contour distance measures. In order to reduce the possibility of false matches, the number of match points on a contour is also used as a selection measure. The initial epipolar constraint is refined from the matched sets of contours. The algorithm can be applied to a pair or two pairs of images. All of the processes are fully automatic and successfully implemented and tested with various real images     

Architectural Methodology Based on Intentional Configuration of Behaviors

Intelligence has been an object of study for a long time. Different architectures try to capture and reproduce these aspects into artificial systems (or agents), but there is still no agreement on how to integrate them into a general framework. With this objective in mind, we propose an architectural methodology based on the idea of intentional configuration of behaviors. Behavior‐producing modules are used as basic control components that are selected and modified dynamically according to the intentions of the agent. These intentions are influenced by the situation perceived, knowledge about the world, and internal variables that monitor the state of the agent. The architectural methodology preserves the emergence of functionality associated with the behavior‐based paradigm in the more abstract levels involved in configuring the behaviors. Validation of this architecture is done using a simulated world for mobile robots, in which the agent must deal with various goals such as managing its energy and its well‐being, finding targets, and acquiring knowledge about its environment. Fuzzy logic, a topologic map learning algorithm, and activation variables with a propagation mechanism are used to implement the architecture for this agent.     

SUPPRESSION OF PERIOD DOUBLING CHATTER IN HIGH-SPEED MILLING BY SPINDLE SPEED VARIATION

Spindle speed variation is a well known technique to suppress regenerative machine tool vibrations, but it is usually considered to be effective only for low spindle speeds. In the current paper, spindle speed variation is applied to the high speed milling process, at the spindle speeds where the constant speed cutting results in period doubling chatter. The stability analysis of triangular and sinusoidal shape variations is made numerically with the semi-discretization method. It is shown that the milling process can be stabilized by increasing the amplitude of the spindle speed variation, while the frequency of the variation has no significant effect on the dynamic behaviour. The results are validated by experiments. Based on the analysis of the machined workpieces, it is shown that the surface roughness can also be decreased by the spindle speed variation technique.     

Numerical assessment of turbulent models at a critical regime on unstructured meshes

The present study mainly aims to investigate the performances of different turbulent models for the flow simulation around a circular cylinder at a critical Reynolds number regime (Re = 8.5×10⁵, Tu = 0.7%). A hybrid RANS/LES model (SAS model), a correlation-based transition model ( $\gamma - \widetilde{\operatorname{Re} }_{\theta t} $ model), and a fully turbulent RANS model (SST model) were used to simulate various flow features, such as laminar-turbulence transition inside the boundary layer and the unsteady vortex shedding in the wake region, and their feasibilities for the flow simulation at a critical Reynolds number regime were demonstrated. A vertex-centered finite-volume method was used to discretize the incompressible Navier-Stokes equations, and an unstructured mesh technique was used to discretize the computational domain. The inviscid fluxes were evaluated using 2^nd-order Roe??s flux difference splitting, and the viscous fluxes were computed based on central differencing. A dual time-stepping method and the Gauss-Seidel iteration were used for unsteady time integration. The parallelization strategy using METIS and MPI libraries was used to reduce computational costs. The unsteady characteristics and the time-averaged quantities of the flow fields were compared between turbulent models. The numerical results were also compared with experimental results. The turbulent models showed quite different results at the critical regime because of the different abilities of each model to predict various flow features, such as laminar-turbulence transition and unsteady vortex shedding.     

Disturbance decoupled fault reconstruction using cascaded sliding mode observers

This paper presents a disturbance decoupled fault reconstruction (DDFR) scheme using cascaded sliding mode observers (SMOs). The processed signals from a SMO are found to be the output of a fictitious system which treats the faults and disturbances as inputs; the ‘outputs’ are then fed into the next SMO. This process is repeated until the attainment of a fictitious system which satisfies the conditions that guarantee DDFR. It is found that this scheme is less restrictive and enables DDFR for a wider class of systems compared to previous work when only one or two SMOs were used. This paper also presents a systematic routine to check for the feasibility of the scheme and to calculate the required number of SMOs from the outset and also to design the DDFR scheme. A design example verifies its effectiveness.     

Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting

This study considers real estate appraisal forecasting problem. While there is a great deal of literature about use of artificial intelligence and multiple linear regression for the problem, there has been always controversy about which one performs better. Noting that this controversy is due to difficulty finding proper predictor variables in real estate appraisal, we propose a modified version of ridge regression, i.e., ridge regression coupled with genetic algorithm (GA-Ridge). In order to examine the performance of the proposed method, experimental study is done for Korean real estate market, which verifies that GA-Ridge is effective in forecasting real estate appraisal. This study addresses two critical issues regarding the use of ridge regression, i.e., when to use it and how to improve it.