This paper describes the model-based design and the experimental validation of a control system which suppresses the bouncing behavior of Compressed Natural Gas (CNG) fuel injectors. First a detailed model of the syst...
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This paper describes the model-based design and the experimental validation of a control system which suppresses the bouncing behavior of Compressed Natural Gas (CNG) fuel injectors. First a detailed model of the system is developed, including temperature and supply-voltage variation effects. Using an optical position sensor this model is experimentally validated in a second step. Based on this model a feed-forward controller is developed and tested which minimizes the bouncing energy of the system. Since in series applications position sensing would be too expensive to use, an observer-based iterative control algorithm is derived which uses coil current measurements instead of the position information to asymptotically suppress bouncing.
This article presents the development and application of a fault-tolerant control technology, its online implementation, and results from several tests conducted for a large-sized HVAC system. By integrating model-bas...
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This article presents the development and application of a fault-tolerant control technology, its online implementation, and results from several tests conducted for a large-sized HVAC system. By integrating model-based model predictive control and data-driven fault detection and diagnosis algorithms, the technology automatically adapts the HVAC control laws to a set of subsystem faults and can therefore reach and maintain the largest energy consumption reduction levels that are achievable at any point throughout a building lifecycle. The model predictive control algorithm generates optimal set-points that minimize energy consumption for the HVAC actuator loops while meeting equipment operational constraints and occupant thermal comfort constraints. The fault detection and diagnosis algorithm uses probabilistic graphical models to detect and classify in real time potential faults of the HVAC actuators based on data from multiple sensors. The fault-tolerant control system is realized by executing the two algorithms on the same platform, within the same framework, and by using the fault detection and diagnosis algorithm's output to continuously update the model predictive control algorithm constraints. The proposed integrated technology is executed at the supervisory level in a hierarchical control architecture as an extension of a baseline building management system. The performance and limitations of the fault detection and diagnosis, model predictive control, and fault-tolerant control algorithms are illustrated and discussed using measurement data recorded from multiple tests.
This study proposes a passivity-based controller for sensorless synchronous reluctance motor drive systems. By using the proposed controller, the drive systems can achieve fast transient responses, good load disturban...
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This study proposes a passivity-based controller for sensorless synchronous reluctance motor drive systems. By using the proposed controller, the drive systems can achieve fast transient responses, good load disturbance responses and good tracking responses. The parameters of the controller can be derived according to the required specification of the drive systems. In addition, a new sensorless technique based on an extended flux estimator is proposed as well. The estimator provides good performance in transient and steady-state conditions. The details are discussed. A digital signal processor TMS-320F-28335 is used to execute the rotor position/speed estimation, the passivity-based control algorithm, the d-q to a-b-c coordinate transformation, and the pulse width modulation switching strategy. As a result, the hardware is very simple. Experimental results show that the proposed system can perform in a wide adjustable speed range from 1 to 2000 r/min and a precise position control with satisfactory performance.
The stereo matching problem takes two images captured by nearby cameras and attempts to recover quantitative disparity information. Most of the existing stereo matching algorithms find it difficult to estimate dispari...
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The stereo matching problem takes two images captured by nearby cameras and attempts to recover quantitative disparity information. Most of the existing stereo matching algorithms find it difficult to estimate disparity in the occlusion, discontinuities and textureless regions in the images. In the last few decades, a number of stereo matching methods have been proposed to overcome some of these problems. In the same line of thought, the authors propose a new feature-based stereo matching method, which consists of four basic steps - feature-based stereo correspondence, two-pass cost aggregation, disparity computation using winner-takes-all selection and finally, the disparity refinement. In the proposed method, local features of Gabor wavelet in spatial domain are used for matching cost computation and subsequently a cost aggregation step is implemented by combined use of the Kuwahara filter and the median filter. Experimental results on the Middlebury benchmark database shows that the proposed method outperforms many existing local stereo matching methods.
A technique is presented for three-dimensional (3-D) reconstruction of the left-ventricular endocardial surface from multiplanar transesophageal echocardiograms, using both commercial software and investigator written...
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A technique is presented for three-dimensional (3-D) reconstruction of the left-ventricular endocardial surface from multiplanar transesophageal echocardiograms, using both commercial software and investigator written Fortran programs for Intel 80286 and 80386 microcomputers. The approach provides quantitative global and regional cardiac performance measures and allows viewing the endocardial surface, at end-diastole and end-systole, from chosen perspectives. Anatomical landmarks are incorporated to aid in orientation. For regional calculation, the surface is divided into equal angular elements with each conceptually connected to the left-ventricular end-diastole centroid, forming a pyramidal volume element. This angular division automatically normalizes for heart size. The fractional change of these elements over the cardiac cycle provides a regional ejection fraction measure which is color-coded on the reconstructed endocardial surface. Composite perspective views, regional ejection fraction histograms and calculations of global end-diastolic, end-systolic, and stroke volumes, are all performed by the method.
Most currently available active control algorithms target noise sources with relatively singular characteristics such as tonal or wideband noise. However, noise in some practical environments is a mixture of different...
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Most currently available active control algorithms target noise sources with relatively singular characteristics such as tonal or wideband noise. However, noise in some practical environments is a mixture of different sounds generated by different sources, where the existing algorithms designed for single noise sources may not be optimal. This kind of noise is generally referred to as mixed noise, and this paper develops a specific active control algorithm for mixed noise based on the recursive least square structure. By minimizing the weighted summation of the logarithmic transformation of posterior errors and taking the commutation error into consideration, the proposed algorithm not only reduces broadband, narrovvbancl and impulse noise successfully, but also mixtures of them. Simulation results demonstrate the superiority of the proposed algorithm over existing algorithms such as the filtered-x least mean square, filtered-x logarithmic least mean square, filtered-x normalized least mean square and filtered weight filtered-x normalized least mean square algorithms in terms of convergence rate and noise reduction. (C) 2014 Elsevier Ltd. All rights reserved.
This paper presents an algorithm for estimating the performance of high-power station systems connected in series, parallel, and mixed series-parallel with collective factor failures caused by any part of the system e...
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This paper presents an algorithm for estimating the performance of high-power station systems connected in series, parallel, and mixed series-parallel with collective factor failures caused by any part of the system equipment. Failures that occur frequently can induce a selective effect, which means that the failures generated from different equipment parts can cause failures in various subsets of the system elements. The objectives of this study are to increase the lifetime of the station and reduce sudden station failures. The case study data was collected from an electricity distribution company in Baghdad, Iraq. Data analysis was performed using the most valid distribution of the Weibull distribution with scale parameter alpha = 1.3137 and shape parameter beta = 94.618. Our analysis revealed that the reliability value decreased by 2.82% in 30 days. The highest critical value was obtained for components T-1, CBF5, CBF7, CBF8, CBF9, and CBF10 and must be changed by a new item as soon as possible. We believe that the results of this research can be used for the maintenance of power systems models and preventive maintenance models for power systems.
This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of...
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This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs) firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label non-DRs. The validation of the clustering procedure of the first-phase is analyzed theoretically. A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier to work with the informed information. A fast training approach for DRs' extraction is provided to bring more efficiency. Experimental results on synthetic and real datasets verify that the proposed idea is of correctness and performance and SDSN exhibits higher popularity in practice over the traditional pure clustering procedure.
In this research we address the problem of discriminant subband selection for texture classification. A novel Effective Information based Subband Selection (EISS) algorithm is proposed which utilizes the intro-class a...
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In this research we address the problem of discriminant subband selection for texture classification. A novel Effective Information based Subband Selection (EISS) algorithm is proposed which utilizes the intro-class and inter-class distributions. Essentially these distributions are used to calculate the class-based entropy for a given subband. This class-based information is incorporated in the total information content of the training images to develop a robust Effective Information (El) criterion. Only the subbands with the top El criteria are allowed to participate in the classification process. The proposed EISS algorithm is evaluated on Brodatz texture database and has shown to outperform the most relevant method based on mutual information criterion. (C) 2012 Elsevier Ltd. All rights reserved.
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