Fuzzy Clustering Using the Convex Hull as Geometrical Model

A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Ergonomic risk factors associated with muscuslokeletal disorders in computer workstation

Ergonomics Risk Factors (ERFs) at computer works are commonly related to Musculoskeletal Disorders (MSDs) such as repetitive movements, doing work in awkward postures and static postures while prolonged seating at works. The main objective of this study was to investigate the ergonomic risk factors associated with MSDs among employees in computer workstation. In this study, the data were obtained by structured interview using self-reported questionnaire and direct observation. The results show that there is significant association between neck and stress score with musculoskeletal symptoms and among office workers. As a conclusion, by assessing ERFs at workplace, the effectiveness of workplace interventions can be evaluated without waiting for changes in the prevalence of MSDs

Improvements and critique on Sugeno's and Yasukawa's qualitative modeling

Investigates Sugeno's and Yasukawa's (1993) qualitative fuzzy modeling approach. We propose some easily implementable solutions for the unclear details of the original paper, such as trapezoid approximation of membership functions, rule creation from sample data points, and selection of important variables. We further suggest an improved parameter identification algorithm to be applied instead of the original one. These details are crucial concerning the method's performance as it is shown in a comparative analysis and helps to improve the accuracy of the built-up model. Finally, we propose a possible further rule base reduction which can be applied successfully in certain cases. This improvement reduces the time requirement of the method by up to 16% in our experiments

A State Observer Design for Simultaneous Estimation of Charge State and Crossover in Self-Discharging Disproportionation Redox Flow Batteries

This paper presents an augmented state observer design for the simultaneous estimation of charge state and crossover flux in disproportionation redox flow batteries, which exhibits exponential estimation error convergence to a bounded residual set. The crossover flux of vanadium through the porous separator is considered as an unknown function of the battery states, model-approximated as the output of a persistently excited linear system. This parametric model and the simple isothermal lumped parameter model of the battery are combined to form an augmented space state representation suitable for the observer design, which is carried out via Lyapunov stability theory including the error-uncertainty involved in the approximation of the crossover flux. The observer gain is calculated by solving a polytopic linear matrix inequality problem via convex optimization. The performance of this design is evaluated with a laboratory flow battery prototype undergoing self-discharge.Comment: arXiv admin note: text overlap with arXiv:1903.0407

Comparison of Alternative Strategies for Invasive Species Distribution Modeling

Species distribution models (SDMs) can provide useful information for managing biological invasions,such as identification of priority areas for early detection or for determining containment boundaries.However, prediction of invasive species using SDMs can be challenging because they typically violatethe core assumption of being at equilibrium with their environment, which may lead to poorly guidedmanagement resulting from high levels of omission. Our goal was to provide a suite of potential decisionstrategies (DSs) that were not reliant on the equilibrium assumption but rather could be chosento better match the management application, which in this case was to ensure containment throughadequate surveillance. We used presence-only data and expert knowledge for model calibration andpresence/absence data to evaluate the potential distribution of an introduced mesquite (Leguminoseae:Prosopis) invasion located in the Pilbara Region of northwest Western Australia. Five different DSs withvarying levels of conservatism/risk were derived from a multi-criteria evaluation model using orderedweighted averaging. The performance of DSs over all possible thresholds was examined using receiveroperating characteristic (ROC) analysis. DSs not on the convex hull of the ROC curves were discarded. Twothreshold determination methods (TDMs) were compared on the two remaining DSs, one that assumedequilibrium (by maximizing overall prediction success) and another that assumed the invasion was ongoing(using a 95% threshold for true positives). The most conservative DS fitted the validation data mostclosely but could only predict 75% of the presence data. A more risk-taking DS could predict 95% of thepresence data, which identified 8.5 times more area for surveillance, and better highlighted known populationsthat are still rapidly invading. ThisDSandTDMcoupling was considered to be the most appropriatefor our management application. Our results show that predictive niche modeling was highly sensitiveto risk levels, but that these can be tailored to match specified management objectives. The methodsimplemented can be readily adapted to other invasive species or for conservation purposes