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It is very difficult to control the distributed parameter system (DPS) for consistent spatial performance. In this paper, a dual scale spatio‐temporal control is designed for the DPS to achieve a consistent performance across the entire workspace under unknown exogenous disturbances. First, the generalized "spatial observer" should be designed under spectral decomposition/synthesis, to act as the nominal model of the DPS, through which the spatial mismatch between the model and the process could be estimated. Then a distributed disturbance compensator can be constructed to suppress all the undesirable spatial disturbance through the inner loop, and the compensated system will become closer to the nominal model and easier to control. Third, a dual controller will be designed in the outer loop for the final consistent spatial performance. Since the spatial performance is mainly affected by the dominant dynamics on the slow scale, a convex optimization algorithm can be effectively established in terms of nonlinear matrix inequalities for the dual controller. In this way, the nonlinear optimization problem is solved by converting it into the problem of a linear matrix inequalities with constraints. Besides, the spatio‐temporal state variable of the controlled plant is demonstrated to converge in Hilbert space. The feasibility and effectiveness of the proposed control method are verified by temperature control of a catalytic rod, a benchmark in chemical reactors. [ABSTRACT FROM AUTHOR]

Compared to the fault detection problem, fault localization for spatially distributed processes is physically intuitive but was seldom studied. The main challenge lies in the contradiction between the spatially distributed nature of the processes and a limited number of sensors that can be placed in the space for applications. In this article, we make the first attempt to systematically study the fault localization problem for a class of nonlinear spatially distributed processes without knowing any prior information of the spatial distribution function of the spatio‐temporal fault. The main contribution is that a spatial mapping filter is first designed to generate a distributed residual signal which contains spatial information while using limited sensor measurements. The residual evaluation procedure is also conducted in a distributed manner. With these two parts, a systematic methodology framework is constructed and analyzed rigorously. Finally, the performance of the proposed method is evaluated on a faulty catalytic rod, which widely exists in chemical engineering. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an H∞ sampled‐data control problem is addressed for semilinear parabolic partial differential equation (PDE) systems. By using a time‐dependent Lyapunov functional and vector Poincare's inequality, a sampled‐data controller under spatially averaged measurements is developed to stabilize exponentially the PDE system with an H∞ control performance. The stabilization condition is presented in terms of a set of linear matrix inequalities. Finally, simulation results on the control of the diffusion equation and the FitzHugh‐Nagumo equation are given to illustrate the effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]

As a representative paradigm of evolutionary algorithms, particle swarm optimization (PSO) has been combined with partial least square (PLS) (called PSO-PLS) to select informative descriptors in quantitative structure-activity/property relationship (QSAR/QSPR). However, one of the main limitations of PSO-PLS is that it ignores PLS model information. In this paper, by incorporating the PLS model information into PSO-PLS, we present a novel weighted sampling method (called WS-PSO-PLS) to choose the optimal descriptor subset. Due to the fact that the regression coefficients of the PLS model reflect the importance of descriptors in the model development, we firstly obtain the normalized regression coefficients by establishing the PLS model with all the descriptors. Afterward, weighted sampling is used to generate some individuals according to the aforementioned normalized regression coefficients. Finally, we employ some dimensions of the generated individuals to replace the corresponding dimensions of the individuals with poor quality in the population at each generation. WS-PSO-PLS has been assessed through three QSAR/QSPR datasets and the experimental results suggest that WS-PSO-PLS has the capability to effectively guide the search process by introducing the PLS model coefficients into PSO during the evolution and, therefore, performs better than PSO-PLS. WS-PSO-PLS could be considered as a general and promising mechanism to introduce extra information to improve the performance of PSO for descriptor selection in QSAR/QSPR. [ABSTRACT FROM AUTHOR]

Purpose To investigate the use of a newly designed machine learning-based classifier in the automatic identification of myelopathic levels in cervical spondylotic myelopathy (CSM). Materials and Methods In all, 58 normal volunteers and 16 subjects with CSM were recruited for diffusion tensor imaging (DTI) acquisition. The eigenvalues were extracted as the selected features from DTI images. Three classifiers, naive Bayesian, support vector machine, and support tensor machine, and fractional anisotropy (FA) were employed to identify myelopathic levels. The results were compared with clinical level diagnosis results and accuracy, sensitivity, and specificity were calculated to evaluate the performance of the developed classifiers. Results The accuracy by support tensor machine was the highest (93.62%) among the three classifiers. The support tensor machine also showed excellent capacity to identify true positives (sensitivity: 84.62%) and true negatives (specificity: 97.06%). The accuracy by FA value was the lowest (76%) in all the methods. Conclusion The classifiers-based method using eigenvalues had a better performance in identifying the levels of CSM than the diagnosis using FA values. The support tensor machine was the best among three classifiers. J. Magn. Reson. Imaging 2015;41:1682-1688. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The guaranteed cost distributed fuzzy ( GCDF) observer-based control design is proposed for a class of nonlinear spatially distributed processes described by first-order hyperbolic partial differential equations ( PDEs). Initially, a T-S fuzzy hyperbolic PDE model is proposed to accurately represent the nonlinear PDE system. Then, based on the fuzzy PDE model, the GCDF observer-based control design is developed in terms of a set of space-dependent linear matrix inequalities. In the proposed control scheme, a distributed fuzzy observer is used to estimate the state of the PDE system. The designed fuzzy controller can not only ensure the exponential stability of the closed-loop PDE system but also provide an upper bound of quadratic cost function. Moreover, a suboptimal fuzzy control design is addressed in the sense of minimizing an upper bound of the cost function. The finite difference method in space and the existing linear matrix inequality optimization techniques are used to approximately solve the suboptimal control design problem. Finally, the proposed design method is applied to the control of a nonisothermal plug-flow reactor. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2366-2378, 2013 [ABSTRACT FROM AUTHOR]