Integrated model-driven dashboard development

Business performance modeling and model-driven business transformation are two research directions that are attracting much attention lately. In this study, we propose an approach for dashboard development that is model-driven and can be integrated with the business performance models. We adopt the business performance modeling framework, and we extend it in order to capture the reporting aspect of the business operation. We describe models that can effectively represent all the elements necessary for the business performance reporting process, and the interactions among them. We demonstrate how all these models can be combined and automatically generate the final solution. We further extend the proposed framework with mechanisms that can detect changes in the models and incrementally update the deployed solutions. Finally, we discuss our experience from the application of our technique in a real-world scenario. This case study shows that our technique can be efficiently applied to and handle changes in the underlying business models, delivering significant benefits in terms of both development time and flexibility.     

Distributed Learning for Planning Under Uncertainty Problems with Heterogeneous Teams

This paper considers the problem of multiagent sequential decision making under uncertainty and incomplete knowledge of the state transition model. A distributed learning framework, where each agent learns an individual model and shares the results with the team, is proposed. The challenges associated with this approach include choosing the model representation for each agent and how to effectively share these representations under limited communication. A decentralized extension of the model learning scheme based on the Incremental Feature Dependency Discovery (Dec-iFDD) is presented to address the distributed learning problem. The representation selection problem is solved by leveraging iFDD’s property of adjusting the model complexity based on the observed data. The model sharing problem is addressed by having each agent rank the features of their representation based on the model reduction error and broadcast the most relevant features to their teammates. The algorithm is tested on the multi-agent block building and the persistent search and track missions. The results show that the proposed distributed learning scheme is particularly useful in heterogeneous learning setting, where each agent learns significantly different models. We show through large-scale planning under uncertainty simulations and flight experiments with state-dependent actuator and fuel-burn- rate uncertainty that our planning approach can outperform planners that do not account for heterogeneity between agents.     

Exponential parameter and tracking error convergence guarantees for adaptive controllers without persistency of excitation

In model reference adaptive control (MRAC) the modelling uncertainty is often assumed to be parameterised with time-invariant unknown ideal parameters. The convergence of parameters of the adaptive element to these ideal parameters is beneficial, as it guarantees exponential stability, and makes an online learned model of the system available. Most MRAC methods, however, require persistent excitation of the states to guarantee that the adaptive parameters converge to the ideal values. Enforcing PE may be resource intensive and often infeasible in practice. This paper presents theoretical analysis and illustrative examples of an adaptive control method that leverages the increasing ability to record and process data online by using specifically selected and online recorded data concurrently with instantaneous data for adaptation. It is shown that when the system uncertainty can be modelled as a combination of known nonlinear bases, simultaneous exponential tracking and parameter error convergence can be guaranteed if the system states are exciting over finite intervals such that rich data can be recorded online; PE is not required. Furthermore, the rate of convergence is directly proportional to the minimum singular value of the matrix containing online recorded data. Consequently, an online algorithm to record and forget data is presented and its effects on the resulting switched closed-loop dynamics are analysed. It is also shown that when radial basis function neural networks (NNs) are used as adaptive elements, the method guarantees exponential convergence of the NN parameters to a compact neighbourhood of their ideal values without requiring PE. Flight test results on a fixed-wing unmanned aerial vehicle demonstrate the effectiveness of the method.     

Ferrofluid Lubrication of a Journal Bearing Considering Cavitation

Ferrohydrodynamic lubrication in journal bearings by considering cavitation boundary conditions is studied. It is assumed that the magnetization vector is not parallel to the magnetic field vector. Various bearing characteristics have been studied and it is seen that qualitative behavior of these characteristics remains similar to that in the case of non-ferromagnetic fluid. However, a significant quantitative enhancement of these characteristics is observed.     

Efficient Resource Allocation in Fog Computing Using QTCS Model

Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be shared. The embedded devices deployed with the Internet of Things (IoT) technology have increased since the past few years, and these devices generate huge amount of data. The devices in IoT can be remotely connected and might be placed in different locations which add to the network delay. Real time applications require high bandwidth with reduced latency to ensure Quality of Service (QoS). To achieve this, fog computing plays a vital role in processing the request locally with the nearest available resources by reduced latency. One of the major issues to focus on in a fog service is managing and allocating resources. Queuing theory is one of the most popular mechanisms for task allocation. In this work, an efficient model is designed to improve QoS with the efficacy of resource allocation based on a Queuing Theory based Cuckoo Search (QTCS) model which will optimize the overall resource management process.     

Contribution of water-leaving radiances to multiangle, multispectral polarimetric observations over the open ocean: bio-optical model results for case 1 waters

Multiangle, multispectral photopolarimetry of atmosphere-ocean systems provides the fullest set of remote sensing information possible on the scattering properties of aerosols and on the color of the ocean. Recent studies have shown that inverting such data allows for the potential of separating the retrieval of aerosol properties from ocean color monitoring in the visible part of the spectrum. However, the data in these studies were limited to those principal plane observations where the polarization of water-leaving radiances could be ignored. Examining similar potentials for off-principal plane observations requires the ability to assess realistic variations in both the reflectance for and bidirectionality of polarized water-leaving radiances for such viewing geometries. We provide hydrosol models for use in underwater light scattering computations to study such variations. The model consists of two components whose refractive indices resemble those of detritus-minerallike and planktonlike particles, whose size distributions are constrained by underwater light linear polarization signatures, and whose mixing ratios change as a function of particulate backscattering efficiency. Multiple scattering computations show that these models are capable of reproducing realistic underwater light albedos for wavelengths ranging from 400 to 600 nm, and for chlorophyll a concentrations ranging from 0.03 to 3.0 mg/m(3). Numerical results for spaceborne observations of the reflectance for total and polarized water-leaving radiances are provided as a function of polar angles, and the change in these reflectances with wavelength, chlorophyll a concentration, and hydrosol model are discussed in detail for case 1 (open ocean) waters.     

Self-perceived somatotypes and clothing-related behavior of older men and women

507 older men and women (65 yr. or over) participated in an investigation of relationship between clothing-related behavior of older men and women and self-perceived somatotypes. Analysis indicated that body-type was significantly related to significance of apparel, self-esteem, and chronological age. No sex differences were found in perception of body-types. Several implications are discussed and suggestions for further research are made.     

Under canopy light detection and ranging‐based autonomous navigation

This paper describes a light detection and ranging (LiDAR)‐based autonomous navigation system for an ultralightweight ground robot in agricultural fields. The system is designed for reliable navigation under cluttered canopies using only a 2D Hokuyo UTM‐30LX LiDAR sensor as the single source for perception. Its purpose is to ensure that the robot can navigate through rows of crops without damaging the plants in narrow row‐based and high‐leaf‐cover semistructured crop plantations, such as corn (Zea mays) and sorghum ( Sorghum bicolor). The key contribution of our work is a LiDAR‐based navigation algorithm capable of rejecting outlying measurements in the point cloud due to plants in adjacent rows, low‐hanging leaf cover or weeds. The algorithm addresses this challenge using a set of heuristics that are designed to filter out outlying measurements in a computationally efficient manner, and linear least squares are applied to estimate within‐row distance using the filtered data. Moreover, a crucial step is the estimate validation, which is achieved through a heuristic that grades and validates the fitted row‐lines based on current and previous information. The proposed LiDAR‐based perception subsystem has been extensively tested in production/breeding corn and sorghum fields. In such variety of highly cluttered real field environments, the robot logged more than 6 km of autonomous run in straight rows. These results demonstrate highly promising advances to LiDAR‐based navigation in realistic field environments for small under‐canopy robots.     

Concurrent learning adaptive control of linear systems with exponentially convergent bounds

Concurrent learning adaptive controllers, which use recorded and current data concurrently for adaptation, are developed for model reference adaptive control of uncertain linear dynamical systems. We show that a verifiable condition on the linear independence of the recorded data is sufficient to guarantee global exponential stability. We use this fact to develop exponentially decaying bounds on the tracking error and weight error, and estimate upper bounds on the control signal. These results allow the development of adaptive controllers that ensure good tracking without relying on high adaptation gains, and can be designed to avoid actuator saturation. Simulations and hardware experiments show improved performance. Copyright © 2012 John Wiley & Sons, Ltd.