Active disturbance rejection controller (ADRC) has achieved soaring success in motion controls featured by rapid dynamics. However, it turns obstreperous to implement it in the power plant process ...with considerable time-delay, largely because of the tuning difficulty. To this end, this article proposes a quantitative tuning rule for the time-delayed ADRC (TD-ADRC) structure based on the typical first order plus time delay (FOPTD) model. By compensating the FOPTD process as an integrator plus time delay in low frequencies, the gain parameter of TD-ADRC can be related to a scaled time constant which shapes the closed-loop tracking performance. Bandwidth parameter of extended state observer is scaled as a dimensionless parameter. A sufficient stability condition of TD-ADRC is theoretically derived in terms of the scaled parameter pair, the range of which falls within the practical interest. Relative delay margin is revealed as a critical robustness metric among others, a default pair of scaled parameter setting is recommended as well as an explicit retuning guideline according to the user's preference for performance or robustness. Simulation and laboratory water tank experiment validate the tuning efficacy and a coal mill temperature control test depicts a promising prospective of the proposed method in process control practice.
This paper introduces a new evidential clustering method based on the notion of "belief peaks" in the framework of belief functions. The basic idea is that all data objects in the neighborhood of ...each sample provide pieces of evidence that induce belief on the possibility of such sample to become a cluster center. A sample having higher belief than its neighbors and located far away from the other local maxima is then characterized as cluster center. Finally, a credal partition is created by minimizing an objective function with the fixed cluster centers. An adaptive distance metric is used to fit for unknown shapes of data structures. We show that the proposed evidential clustering procedure has very good performance with an ability to reveal the data structure in the form of a credal partition, from which hard, fuzzy, possibilistic, and rough partitions can be derived. Simulations on synthetic and real-world datasets validate our conclusions.
For a class of industrial processes described by a second-order plus time delay model, this paper investigates the design of active disturbance rejection control (ADRC) incorporating model ...information. By using the low-frequency approximation, the lumped plant consisting of all internal loops can shape the closed-loop tracking dynamic to the expected response, allowing the feedback gains to be formulated by the desired tracking factor. In this framework, a rigorous stability criterion is first depicted intuitively in terms of dimensionless parameters. An explicit upper bound on the observer bandwidth is then analytically derived through an in-depth analysis of the decaying behavior in the magnitude of the loop function, which guarantees a more satisfactory margin for mismatched delays than existing related studies. Based on this constraint, the regulable tracking factor can further endow the ADRC system with comprehensive robustness to cope with other uncertainties. Comparative simulations and a water tank experiment reveal the merits of the proposed configuration. Furthermore, the field application of this method in the wet flue gas desulfurization process demonstrates its promising practical prospects.
Active disturbance rejection control (ADRC) shows great potential in mitigating various uncertainties of both motion and process control. However, the controller tuning is generally time-consuming, ...and the resulting performance highly depends on the individual experience. This brief aims to develop a standard robust tuning rule for a time-delayed ADRC structure based on a class of time-delay systems represented by a second-order plus time delay (SOPTD) model. The bandwidth parameters of the controller and the observer are, respectively, scaled in terms of SOPTD parameters. The resulting dimensionless parameters enable exhaustive evaluation of the robustness. Compared to the traditional metrics, it reveals that delay margin is the dominant robustness index for the controller tuning. A dominated form of the robust tuning rule is recommended, and the stability condition of a closed-loop system is given. Simulations and experiments are conducted on a factual electrostatic precipitator in the coal-fired power plant to validate the efficacy of the proposed tuning rule.
Capacitive power transfer (CPT) systems based on high-frequency electric field coupling have attracted much attention recently due to their simplicity and low eddy-current losses. This paper proposes ...a mixed-resonant topology consisted of a Π-CLC resonant circuit on the primary side and a T-CLC circuit on the secondary side for multiple pickups constant current output applications. The voltage gain, current gain, and zero phase angle frequency at different operating modes of Π-CLC and T-CLC circuits are analyzed by fundamental frequency approximation, and the conditions leading to a constant output current independent of loads are determined. Based on the analysis, a design method to determine the resonant network parameters is proposed according to the required output current of each pickup. A prototype with three pickups has been designed and built, and both simulation and experimental results have demonstrated that the proposed multiple-pickup CPT system can output a constant current at each operating power pickup against the load variations of itself and others.
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•A mathematical model of an ultra-supercritical unit is developed.•The analysis of open-loop experiments is described.•Parameter identification based on immune genetic algorithm is ...presented.•The closed-loop validation is performed and compared with the previous study.
It is challenging and interesting to establish a precise dynamic model of an OTB (once-through boiler) power plant unit in order to meet large scale load demands from the power grid. This study proposes to establish such a dynamic mathematical model of an ultra-supercritical OTB unit under dry operating conditions. More precisely, the dynamic model structure was derived from mass and energy conservation laws as well as thermodynamic principles under some reasonable simplifications and assumptions. Then an IGA (immune genetic algorithm) was improved to identify the parameters, combined with running data. After this, to further enhance model performance, the dynamic mathematical model was extended to be the one with different sets of parameters and functions under different monotonous load ranges. Additionally, open- and closed-loop experiments were conducted in order to further validate the developed model. The experimental results show that the model outputs can approach the actual running data over a wide operating range with appropriate accuracy. More importantly, the dynamic model captures the essential dynamic characteristics of the unit. Therefore, the model can be feasible and applicable for simulation analysis and testing control algorithms.
In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful ...patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL.
Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section.
The Cytoscape plugin clusterMaker provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the clusterMaker plugin. clusterMaker is available via the Cytoscape plugin manager.
An important feature of an inductive power transfer (IPT) system is its power transfer efficiency and capability can be significantly affected by the load and the magnetic coupling variations. ...Therefore, identifying the load and the mutual inductance is essential to improve the system performance. This paper proposes a load and mutual inductance identification method for IPT systems with parallel-compensated power pickups based only on the information detected from the primary side. The proposed method can be implemented for primary resonant circuits whether they are series or parallel tuned, or with a hybrid compensation, such as an LCL configuration. An identification model is established according to the steady-state characteristics of the system. Identification results are obtained based on mathematical derivations and analyses. The proposed identification method is realized without any extra communication or control, and both the simulation and experimental results have verified its feasibility.
Background： Sacroiliac （SI） screw fixation is a demanding technique, with a high rate of screw malposition due to the complex pelvic anatomy. TiRobot- is an orthopedic surgery robot which can be used ...for SI screw fixation. This study aimed to evaluate the accuracy of robot-assisted placement of SI screws compared with a freehand technique. Methods：Thirty patients requiring posterior pelvic ring stabilization were randomized to receive freehand or robot-assisted SI screw fixation, between January 2016 and June 2016 at Beijing Jishuitan Hospital. Forty-five screws were placed at levels S1 and S2. In both methods, the primary end point screw position was assessed and classified using postoperative computed tomography. Fisher＇s exact probability test was used to analyze the screws＇positions. Secondary end points, such as duration of trajectory planning, surgical time after reduction of the pelvis, insertion time for guide wire, number of guide wire attempts, and radiation exposure without pelvic reduction, were also assessed. Results： Twenty-three screws were placed in the robot-assisted group and 22 screws in the freehand group; no postoperative complications or revisions were reported. The excellent and good rate of screw placement was 100% in the robot-assisted group and 95% in the freehand group. The P value （0.009） showed the same superiority in screw distribution. The fluoroscopy time after pelvic reduction in the robot-assisted group was significantly shorter than that in the freehand group （median Q1, Q3： 6.0 6.0, 9.0 s vs. median Q1, Q3： 36.0 21.5, 48.0 s; χ2 = 13.590, respectively, P 〈 0.001）; no difference in operation time after reduction of the pelvis was noted （χ2 = 1.990, P = 0.158）. Time for guide wire insertion was significantly shorter for the robot-assisted group than that for the freehand group （median Q1, Q3： 2.0 2.0, 2.7 min vs. median Q1, Q3： 19.0 15.5, 45.0 min; χ2 = 20.952, respectively, P 〈 0.001）. The number of guide wire attempts in the robot-assisted group was significantly less than that in the freehand group （median Q1, Q3： 1.0 1.0,1.0 time vs. median Q1, Q3： 7.0 1.0, 9.0 times; χ2 = 15.771, respectively, P 〈 0.001）. The instrumented SI levels did not differ between both groups （from S1 to S2, χ2 = 4.760, P = 0.093）. Conclusions： Accuracy of the robot-assisted technique was superior to that of the freehand technique. Robot-assisted navigation is safe for unstable posterior pelvic ring stabilization, especially in S1, but also in S2. SI screw insertion with robot-assisted navigation is clinically feasible.
The Evidential K -Nearest Neighbor (EK-NN) classification rule provides a global treatment of uncertainty and imprecision in class labels, and has been widely used in pattern recognition. ...Nevertheless, EK-NN still suffers from the fixed presupposition of hyper-parameter K without prior knowledge, due to the different spatial distribution of neighbors of each pattern in Euclidean space. More concretely, neighbors of some patterns may provide confusing information and then derive wrong classification results. To address this issue, we propose a sparse reconstructive evidential K -NN (SEK-NN) classifier, appropriately determining an individual K for each pattern and mapping the correlations between patterns from Euclidean space to a sparse reconstructed space. To match with this sparse reconstructed space, SEK-NN supersedes the Euclidean distance by correlation coefficients to measure the dissimilarities between patterns. When handling high-dimensional data, a parallel version of SEK-NN is implemented under the Apache Spark to speed up the parameter estimation. We respectively test SEK-NN and parallel SEK-NN over 19 middle dimensional datasets, 1 middle volume and 4 high-dimensional datasets that are up to 100 thousand of dimensions. Experimental results show that SEK-NN has great prediction performance and parallel SEK-NN is able to appropriately tackle high-dimensional datasets.
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