Rigid-flexible coupling fluid-structure interaction systems are expected to be future solutions for reducing energy lost in water. The dynamics of these systems is usually investigated via numerical simulations. Howev...
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Rigid-flexible coupling fluid-structure interaction systems are expected to be future solutions for reducing energy lost in water. The dynamics of these systems is usually investigated via numerical simulations. However, in existing numerical works there is no accurate algorithm for the initialization of the flexible filament, which ensures both the length and area constraints, leading to inaccurate results or even severe numerical instabilities. We propose two alternative initialization algorithms, respectively, the "Trapezoidal arrangement" and the "Quartic curve arrangement". The performances of both of these two algorithms are investigated in numerical simulations by using the immersed boundary method. The motion responses and force characteristics of the flexible filament are analyzed carefully, verifying the capability of the proposed algorithms. Specifically, "Quartic curve arrangement" is further recommended due to its good property of convergence.
Recently, deep learning-based intelligent fault diagnosis methods have been developed rapidly, which rely on massive data to train the diagnosis model. However, it is usually difficult to collect sufficient failure da...
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Recently, deep learning-based intelligent fault diagnosis methods have been developed rapidly, which rely on massive data to train the diagnosis model. However, it is usually difficult to collect sufficient failure data in practical industrial production, thus limits the application of intelligent diagnosis methods. To address the few-shot fault diagnosis problem, a task-sequencing meta-learning method is proposed in this article. First, the meta-learning model is trained over a series of learning tasks to obtain knowledge about how to diagnosis. Thus, the learned knowledge can help adapt and generalize with a few examples when dealing with new tasks that have never been encountered. Then, considering the difference and connection between different failures and diagnosis tasks, a task-sequencing algorithm is proposed to sort meta training tasks from easy to difficult, which followed the way human acquire knowledge. After evaluating the difficulty of each task, the proposed method learns simple tasks first and generalizes the learned knowledge to complex tasks. Better knowledge adaptability is obtained by gradually increasing the task difficulty. Finally, utilizing gradient-based meta learning, the initialization parameters are trained by a small number of gradient steps. The effectiveness of the proposed method is validated by a practice rolling bearing dataset and a power system dataset. The experiment results illustrate that the proposed method can identify new categories with only several samples. In addition, it also shows advantages in fault diagnosis when the categories are fine-grained according to the working conditions. Therefore, the proposed method is suitable for solving the few-shot problem in practice and complicated fault diagnosis.
In the field of unsupervised learning, Self-Organizing Map (SOM) has attracted the attention of many researchers. SOM is a popular algorithm in the area of data clustering;in this paper, new algorithms are developed t...
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In the field of unsupervised learning, Self-Organizing Map (SOM) has attracted the attention of many researchers. SOM is a popular algorithm in the area of data clustering;in this paper, new algorithms are developed to find the initial weights, to assign those initial weights to the SOM grid and a new way to determine the number of clusters in the SOM algorithm. Also, a new performance measure MSRI (Modified Semantic Relevant Index) has been introduced for the SOM algorithm. For the class label datasets, the performance criteria like Classification Accuracy (CA), Quantization error (QE) and Convergence time (CT) are used to compare the proposed SOM algorithm with existing SOM algorithms. Here the existing SOM algorithms like Enhanced SOM (ESOM), SOM Particle Swarm Optimization (SOMPSO), ESOMPSO and conventional SOM are used. In addition, MSRI is used to compare the proposed SOM with the existing SOM algorithms. We have also used different image classification datasets to compare our proposed SOM and existing SOM algorithm with CA, QE, CT, and MSRI. For the non-class label dataset, the criteria like QE, CT and MSRI are employed to analyze the performance of proposed SOM with the conventional SOM algorithm. The gene index is also used to validate the number of clusters obtained by the proposed SOM algorithm. It is found that our proposed SOM algorithm has shown better performance in all cases.
For any population-based algorithm, the initialization of the population is a very important step. In Genetic Programming (GP), in particular, initialization is known to play a crucial role - traditionally, a wide var...
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ISBN:
(纸本)9783319992532;9783319992525
For any population-based algorithm, the initialization of the population is a very important step. In Genetic Programming (GP), in particular, initialization is known to play a crucial role - traditionally, a wide variety of trees of various sizes and shapes are desirable. In this paper, we propose an advancement of a previously conceived Evolutionary Demes Despeciation algorithm (EDDA), inspired by the biological phenomenon of demes despeciation. In the pioneer design of EDDA, the initial population is generated using the best individuals obtained from a set of independent subpopulations (demes), which are evolved for a few generations, by means of conceptually different evolutionary algorithms - some use standard syntax-based GP and others use a semantics-based GP system. The new technique we propose here (EDDA-V2), imposes more diverse evolutionary conditions - each deme evolves using a distinct random sample of training data instances and input features. Experimental results show that EDDA-V2 is a feasible initialization technique: populations converge towards solutions with comparable or even better generalization ability with respect to the ones initialized with EDDA, by using significantly reduced computational time.
Stochastic nonlinear systems are a specific class of nonlinear systems where unknown disturbances affect the system's output through a nonlinear trans- formation. In general, the identification of parametric model...
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Stochastic nonlinear systems are a specific class of nonlinear systems where unknown disturbances affect the system's output through a nonlinear trans- formation. In general, the identification of parametric models for this kind of systems can be very challenging. A main statistical inference technique for parameter estimation is the Maximum Likelihood estimator. The central object of this technique is the likelihood function, i. e. a mathematical expres- sion describing the probability of obtaining certain observations for given values of the parameter. For many stochastic nonlinear systems, however, the likelihood function is not available in closed-form. Several methods have been developed to obtain approximate solutions to the Maximum Likelihood problem, mainly based on the Monte Carlo method. However, one of the main difficulties of these methods is that they can be computationally ex- pensive, especially when they are combined with numerical optimization techniques for likelihood maximisation. This thesis can be divided in three parts. In the first part, a background on the main statistical techniques for parameter estimation is presented. In particular, two iterative methods for finding the Maximum Likelihood estimator are introduced. They are the gradient-based and the Expectation- Maximisation algorithms. In the second part, the main Monte Carlo methods for approximating the Maximum Likelihood problem are analysed. Their combination with gradient-based and Expectation-Maximisation algorithms is considered. For ensuring convergence, these algorithms require the use of enormous Monte Carlo effort, i. e. the number of random samples used to build the Monte Carlo estimates. In order to reduce this effort and make the algorithms usable in practice, iterative solutions alternating local Monte Carlo estimates and maximisation steps are derived. In particular, a procedure implementing an efficient samples simulation across the steps of a Newton's method is develope
In this paper, we propose a new look-up-table (LUT) based adaptive baseband predistorter which corrects signal distortions caused by the high power amplifier (HPA) and other analog processing modules in transmitters. ...
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ISBN:
(纸本)9781467311830
In this paper, we propose a new look-up-table (LUT) based adaptive baseband predistorter which corrects signal distortions caused by the high power amplifier (HPA) and other analog processing modules in transmitters. For the fast initialization of the LUT, a corresponding initialization algorithm is proposed. An adaptive updating algorithm is also proposed to compensate for the time-varying property of the HPA. The simulation results demonstrate that nonlinear distortions can be greatly reduced with this algorithm.
A newly deployed sensor network is unstructured and sensor nodes at the beginning are in the "blind" state, which have almost no knowledge about the topology of the network. The most urgent and important thi...
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ISBN:
(纸本)9781424405169
A newly deployed sensor network is unstructured and sensor nodes at the beginning are in the "blind" state, which have almost no knowledge about the topology of the network. The most urgent and important thing for deployment of sensor networks is to set up a coarse structure. In this paper, a practical algorithm to initialize the network and select cluster leader nodes is presented. The algorithm is analyzed on SNIR model, which addressed the specific characteristics in the deployment of wireless sensor network, such as nodes do not feature a reliable collision detection mechanism and may wake up asynchronously, and especially co-channel interference effect on transmission during initialization phase. A novel concept of interference-tolerant neighbor nodes is proposed to guarantee efficiency of the algorithm. The simulation result shows even in the restrict applicable model, clustering can be completed successfully.
In nuclear magnetic resonance (NMR) quantum computing systems, it is very important to prepare an effective pure state rather than a true pure state. Our main objective is to present a general algorithm that provides ...
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In nuclear magnetic resonance (NMR) quantum computing systems, it is very important to prepare an effective pure state rather than a true pure state. Our main objective is to present a general algorithm that provides quantum circuits for preparing an effective pure state from any mixed state, consisting only of controlled-not (CNOT) quantum gates for homonuclear spin systems. Our algorithm is based on the product operator approach and could be applicable to NMR quantum computing systems with more than eight qubits. Optimization of the number of controlled-not quantum gates is discussed. (c) 2005 Wiley Periodicals, Inc.
In nuclear magnetic resonance (NMR) quantum computing systems, it is very important to prepare an effective pure state rather than a true pure state. Our main objective is to present a general algorithm that provides ...
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In nuclear magnetic resonance (NMR) quantum computing systems, it is very important to prepare an effective pure state rather than a true pure state. Our main objective is to present a general algorithm that provides quantum circuits for preparing an effective pure state from any mixed state, consisting only of controlled-not (CNOT) quantum gates for homonuclear spin systems. Our algorithm is based on the product operator approach and could be applicable to NMR quantum computing systems with more than eight qubits. Optimization of the number of controlled-not quantum gates is discussed. (c) 2005 Wiley Periodicals, Inc.
The initialization of states is of importance in nuclear magnetic resonance quantum computing systems. In this article it is shown that the initialization of n-qubits states can be realized via a sequence of controlle...
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The initialization of states is of importance in nuclear magnetic resonance quantum computing systems. In this article it is shown that the initialization of n-qubits states can be realized via a sequence of controlled-not quantum gates from any mixed state. We present a general algorithm and its optimization to construct a circuit of controlled-not quantum gates that realizes the initialization using the concept of Gray Codes. (C) 2003 Wiley Periodicals, Inc.
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