Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO) is easy to fall into the local op...
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Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO) is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO). This paper presents an improved MPSCO algorithm (IMPSCO) firstly and then integrates it with Newmark's algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA). The results show threefold: (1) the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance;(2) the damage location can be accurately detected using the damage threshold proposed in this paper;and (3) compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.
Artificial neural networks (ANNs) method is widely used in reliability analysis. However, the performance of ANNs cannot be guaranteed due to the fitting problems because there is no efficient constructive method for ...
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Artificial neural networks (ANNs) method is widely used in reliability analysis. However, the performance of ANNs cannot be guaranteed due to the fitting problems because there is no efficient constructive method for choosing the structure and the learning parameters of the network. To mitigate these difficulties, this article presents a new adaptive wavelet frame neural network method for reliability analysis of structures. The new method uses the single-scaling multidimensional wavelet frame as the activation function in the network to deal with the multidimensional problems in reliability analysis. Because the wavelet frame is highly redundant, the time-frequency localization and matching pursuit algorithm are respectively utilized to eliminate the superfluous wavelets, thus the obtained wavelet frame neural network can be implemented efficiently. Five examples are given to demonstrate the application and effectiveness of the proposed method. Comparisons of the new method and the classical radial basis function network method are made.
Factorable Laplace operators of the form L = a, (x) a, (y) + aa, (x) + ba, (y) + c, where the coefficients a, b, c are not necessarily constants, are considered. For these operators, the Darboux transformations , M a ...
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Factorable Laplace operators of the form L = a, (x) a, (y) + aa, (x) + ba, (y) + c, where the coefficients a, b, c are not necessarily constants, are considered. For these operators, the Darboux transformations , M a K[a, (x) ], defined by the intertwining relation NL = L (1) M are considered. It is shown that only the following cases are possible: either (1) M a (c) kera, (x) + b = {0} and L (1) is also factorable or (2) M a (c) kera, (x) + b contains a nonzero element. We prove that, in both cases, the Darboux transformation can be represented as a product of first-order Darboux transformations. For case (2), the proof is based on the fact that the Darboux transformation of operator L can be reduced to Darboux transformations of first-order operators.
Under partial shading conditions (e. g., due to buildings, trees, and clouds), multiple peaks may exist on the power-voltage (P-V) characteristic curve of photovoltaic (PV) array, leading to the conventional maximum p...
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Under partial shading conditions (e. g., due to buildings, trees, and clouds), multiple peaks may exist on the power-voltage (P-V) characteristic curve of photovoltaic (PV) array, leading to the conventional maximum power point tracking methods fail to extract the global maximum power point (GMPP). In this paper, a mathematical model of PV array under partial shading conditions with a voltage calculated principle is established. The presented model is implemented with an m-file in MATLAB software, which is used as a tool to study the non-linear characteristics of current-voltage (I-V) and P-V curves of PV array, as well as quickly develop the GMPP tracking controllers. Besides, an adaptive random particle swarm optimization (ARPSO) algorithm is presented to accurately extract the GMPP under partial shading conditions. Five simulation cases with different partial shading patterns are used to evaluate the performance of the presented approach by comparing with the conventional PSO, perturbation and observation (P&O), incremental conductance (INC), and genetic algorithm (GA) methods. Simulation results show that the ARPSO algorithm can rapidly find the GMPP under different shading conditions compared with the conventional PSO algorithm. Furthermore, the presented algorithm can accurately extract the GMPP when the shading condition sharply changes, while the P&O and INC algorithms fail to track the GMPP, but only detect the rightmost MPP encountered either local or global and regardless of the course. Besides, the ARPSO can rapidly and accurately converge to the GMPP with smaller population size and higher convergence speed compared with the GA. (C) 2014 AIP Publishing LLC.
The ten-year anniversary of TOPLAP presents a unique opportunity for reflection and introspection. In this essay we ask the question, what is the meaning of live coding? Our goal is not to answer this question, in abs...
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The ten-year anniversary of TOPLAP presents a unique opportunity for reflection and introspection. In this essay we ask the question, what is the meaning of live coding? Our goal is not to answer this question, in absolute terms, but rather to attempt to unpack some of live coding's many meanings. Our hope is that by exploring some of the formal, embodied, and cultural meanings surrounding live-coding practice, we may help to stimulate a conversation that will resonate within the live-coding community for the next ten years.
Many algorithms for computing minimal coverability sets for Petri nets prune futures. That is, if a new marking strictly covers an old one, then not just the old marking but also some subset of its successor markings ...
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Many algorithms for computing minimal coverability sets for Petri nets prune futures. That is, if a new marking strictly covers an old one, then not just the old marking but also some subset of its successor markings is discarded from search. In this publication, a simpler algorithm that lacks future pruning is presented and proven correct. Its performance is compared with future pruning. It is demonstrated, using examples, that neither approach is systematically better than the other. However, the simple algorithm has some attractive features. It never needs to re-construct pruned parts of the minimal coverability set. It automatically gives most of the advantage of future pruning, if the minimal coverability set is constructed in depth-first or most tokens first order, and if so-called history merging is applied. Some implementation aspects of minimal coverability set construction are also discussed. Some measurements are given to demonstrate the effect of construction order and other implementation aspects.
One of the challenges in image search is to learn with few labeled examples. Existing solutions mainly focus on leveraging either unlabeled data or query logs to address this issue, but little is known in taking both ...
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One of the challenges in image search is to learn with few labeled examples. Existing solutions mainly focus on leveraging either unlabeled data or query logs to address this issue, but little is known in taking both into account. This work presents a novel learning scheme that exploits both unlabeled data and query logs through a unified Manifold Ranking (MR) framework. In particular, we propose a local scaling technique to facilitate MR by self-tuning the scale parameter, and a soft label propagation strategy to enhance the robustness of MR against erroneous query logs. Further, within the proposed MR framework, a hybrid active learning method is developed, which is effective and efficient to select the informative and representative unlabeled examples, so as to maximally reduce users' labeling effort. An empirical study shows that the proposed scheme is significantly more effective than the state-of-the-art approaches. (C) 2013 Elsevier Ltd. All rights reserved.
A variable learning rate least mean-square (LMS) based on hyperbolic tangent function is used to enhance the rate of convergence and to suppress the existing noise in the system for the control of distribution static ...
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A variable learning rate least mean-square (LMS) based on hyperbolic tangent function is used to enhance the rate of convergence and to suppress the existing noise in the system for the control of distribution static compensator (DSTATCOM) consisting of a three-leg voltage source converter (VSC) with a zig-zag transformer to eliminate the significant neutral current caused by non-linear unbalanced loads in a three-phase four-wire supply system. Simulation is made for the proposed control algorithm in three-phase four-wire DSTATCOM system and its performance is compared with the conventional Adaline-based LMS control algorithm. It is also shown that the convergence rate of the proposed control algorithm is quite faster in comparison with the conventional control algorithm. A prototype of DSTATCOM is developed and the real-time implementation of the proposed control algorithm is performed on a digital signal processor. Test results have demonstrated the satisfactory performance of DSTATCOM with the proposed control algorithm under linear and non-linear loads.
A closed-loop throttle controller for a laboratory-scale N2O and hydroxyl-terminated polybutadiene hybrid rocket motor is presented. Closed-loop throttling was achieved using commercial off-the-shelf valve hardware an...
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A closed-loop throttle controller for a laboratory-scale N2O and hydroxyl-terminated polybutadiene hybrid rocket motor is presented. Closed-loop throttling was achieved using commercial off-the-shelf valve hardware and a commercially available motor case adapted for hybrid rocket testing. Multiple and open- and closed-loop tests were performed to demonstrate that closed-loop control can significantly reduce the run-to-run burn variability typical of hybrid rocket motors. Closed-loop proportional/integral control algorithms featuring thrust or pressure feedback were used to track prescribed step and linear ramp profiles. Because the relationship between the selected throttle control valve position and the effective valve flow area was highly nonlinear, the effect of valve position on motor thrust/chamber pressure was measured open loop and curve fit to allow direct command of either total thrust or chamber pressure. Control law gains were tuned a priori using a numerical model and then adjusted using the actual test hardware. Response profiles were optimized according the integral absolute error criterion. Control law tuning examples are presented. Test results indicate that, to a 95% confidence level, closed-loop throttling significantly reduces the mean run-to-run thrust variability from +/- 9.1% to less than +/- 3.9%. When effects of nozzle erosion are accounted for, the closed-loop thrust variability reduces to +/- 1.5%.
This paper proposes a new adaptive learning algorithm for Madalines based on a sensitivity measure that is established to investigate the effect of a Madaline weight adaptation on its output. The algorithm, following ...
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This paper proposes a new adaptive learning algorithm for Madalines based on a sensitivity measure that is established to investigate the effect of a Madaline weight adaptation on its output. The algorithm, following the basic idea of minimal disturbance as the MRII did, introduces an adaptation selection rule by means of the sensitivity measure to more accurately locate the weights in real need of adaptation. Experimental results on some benchmark data demonstrate that the proposed algorithm has much better learning performance than the MRII and the BP algorithms.
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