In this paper, we propose a parameter-free algorithm to calculate epsilon, a parameter of small quantity initially introduced into the nonlinear weights of weighted essentially nonoscillatory (WENO) scheme to avoid de...
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In this paper, we propose a parameter-free algorithm to calculate epsilon, a parameter of small quantity initially introduced into the nonlinear weights of weighted essentially nonoscillatory (WENO) scheme to avoid denominator becoming zero. The new algorithm, based on local smoothness indicators of fifth-order weighted compact nonlinear scheme (WCNS), is designed in a manner to adaptively increase epsilon in smooth areas to reduce numerical dissipation and obtain high-order accuracy, and decrease epsilon in discontinuous areas to increase numerical dissipation and suppress spurious numerical oscillations. We discuss the relation between critical points and discontinuities and illustrate that, when large gradient areas caused by high-order critical points are not well resolved with sufficiently small grid spacing, numerical oscillations arise. The new algorithm treats high-order critical points as discontinuities to suppress numerical oscillations. Canonical numerical tests are carried out, and computational results indicate that the new adaptive algorithm can help improve resolution of small scale flow structures, suppress numerical oscillations near discontinuities, and lessen susceptibility to flux functions and interpolation variables for fifth-order WCNS. The new adaptive algorithm can be conveniently generalized to WENO/WCNS with different orders.
In this paper, a probabilistic modification of the minimal element algorithm for solving the axial three-index assignment problem is suggested. Its general idea is to extend the basic greedy-type algorithmic schemes u...
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In this paper, a probabilistic modification of the minimal element algorithm for solving the axial three-index assignment problem is suggested. Its general idea is to extend the basic greedy-type algorithmic schemes using transition to a probabilistic setup based on variables randomization. The minimization of an objective function is replaced by the minimization of its expectation. The algorithm is implemented in three stages as follows. At the first stage, a motion in the set of random variables is defined. At the second stage, an inequality that expresses the local improvement condition is solved. At the third stage, the probabilities are recalculated, which represents an adaptation process. The second stage reveals a feature of the algorithm: the resulting solution depends on the qualities of the element itself and also on possible losses of its choice.
Functional near-infrared spectroscopy (fNIRS) signals are prone to problems caused by motion artifacts and physiological noises. These noises unfortunately reduce the fNIRS sensitivity in detecting the evoked brain ac...
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Functional near-infrared spectroscopy (fNIRS) signals are prone to problems caused by motion artifacts and physiological noises. These noises unfortunately reduce the fNIRS sensitivity in detecting the evoked brain activation while increasing the risk of statistical error. In fNIRS measurements, the repetitive resting-stimulus cycle (so-called block-design analysis) is commonly adapted to increase the sample number. However, these blocks are often affected by noises. Therefore, we developed an adaptive algorithm to identify, reject, and select the noise-free and/or least noisy blocks in accordance with the preset acceptance rate. The main features of this algorithm are personalized evaluation for individual data and controlled rejection to maintain the sample number. Three typical noise criteria (sudden amplitude change, shifted baseline, and minimum intertrial correlation) were adopted. Depending on the quality of the dataset used, the algorithm may require some or all noise criteria with distinct parameters. Aiming for real applications in a pediatric study, we applied this algorithm to fNIRS datasets obtained from attention deficit/hyperactivity disorder (ADHD) children as had been studied previously. These datasets were divided for training and validation purposes. A validation process was done to examine the feasibility of the algorithm regardless of the types of datasets, including those obtained under sample population (ADHD or typical developing children), intervention (nonmedication and drug/placebo administration), and measurement (task paradigm) conditions. The algorithm was optimized so as to enhance reproducibility of previous inferences. The optimum algorithm design involved all criteria ordered sequentially (0.047 mM mm of amplitude change, 0.029 mM mm / s of baseline slope, and 0.6 interquartile range of outlier threshold for each criterion, respectively) and presented complete reproducibility in both training and validation datasets. Compared
The aim of this article is to design parareal algorithms in the context of thermostated molecular dynamics. In its original setup, the fine and coarse propagators used in the parareal algorithm solve the same dynamics...
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The aim of this article is to design parareal algorithms in the context of thermostated molecular dynamics. In its original setup, the fine and coarse propagators used in the parareal algorithm solve the same dynamics with different time-steps, with the goal of achieving accuracy in the limit of small time-step of the integrators involved. This is typically not useful in molecular dynamics, where one is interested in extremely long trajectories and where the time-step of the fine propagator is in practice chosen as large as possible, that is, close to the limit of stability of the numerical scheme. In this article, we consider a version of the parareal algorithm which is better suited to molecular dynamics simulations, and wherein the propagators involved use the same time step while employing different potential energy landscapes to drive the dynamics. Although the parareal algorithm always converges, it suffers from various limitations in this context: intermediate blow-up of the trajectory (which makes it impossible to postprocess) may be observed;in certain cases the trajectory encounters undefined values before converging (the way the algorithm handles them might depend on the computer architecture);the algorithm does not provide any computational gain in terms of wall-clock time (compared to a standard sequential integration) in the limit of increasingly long time horizons. We highlight these issues with numerical experiments and provide some elements of theoretical analysis. We then present a modified version of the parareal algorithm wherein the algorithmadaptively divides the entire time horizon into smaller time-slabs where the aforementioned issues are circumvented. Using numerical experiments on toy examples, we show that the adaptive algorithm overcomes the various limitations of the standard parareal algorithm, thereby allowing for significantly improved gains.
In order to avoid the flight dysfunctions caused by the failure and damage of the actuator, the sensor, and the structure of the unmanned aerial vehicle (UAV) for the UAV could return safely or continue to complete th...
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ISBN:
(纸本)9781728144634
In order to avoid the flight dysfunctions caused by the failure and damage of the actuator, the sensor, and the structure of the unmanned aerial vehicle (UAV) for the UAV could return safely or continue to complete the missions, the adaptive control technology, the PID control technology, and other related technologies have been combined to study the fault tolerant control of UAV. At the same time, in order to ensure the speed and stability of the aircraft, a robust H-infinity based multi-model adaptive control system is designed for the attitude control of the quadrotor UAV rotor aircraft;in addition, the effectiveness of the above method is verified through simulation experiments. The results have shown that if the flight control system can detect and identify the system faults in time and take certain fault tolerant controls, the survivability of the UAV would be greatly improved.
The fast optimisation of transient stability emergency control (TSEC) strategy is a key factor of online safeguard for AC-DC interconnected systems. An engineering applicable TSEC algorithm has to obtain the disturbed...
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The fast optimisation of transient stability emergency control (TSEC) strategy is a key factor of online safeguard for AC-DC interconnected systems. An engineering applicable TSEC algorithm has to obtain the disturbed trajectories of the system by numerical integration, and then do quantised stability knowledge mining. Theoretical analysis and simulation verification reveal that the premise is to identify the unstable mode correctly, and then emergency control decision can be optimised according to the cost performance of the actions. Small integral step is usually needed for accuracy. In order to coordinate the accuracy and computational burden intelligently, this study proposes an algorithm for optimising generator-tripping control, which can adaptively reduce computational burden. If the unstable mode is insensitive to the change of fault parameters (e.g. fault clearing time), this case's time-varying property is more likely to be weak. Only for such cases, large-step Taylor series expansions (LSTSE) can be taken to replace small-step numerical integration (SSNI) and search for the optimal result. Based on its time-varying property, each case can get disturbed trajectories using LSTSE or SSNI adaptively and obtain optimal solution. The excellent performance of this algorithm is verified by simulation of nine Chinese regional power systems under various operating conditions.
this paper presents a comparative study of two adaptive algorithms, Linear Constraint Minimum Variance (LMCV) and the Minimum Variance Distortionless Response (MVDR) applied to the smart antenna system. These algorith...
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ISBN:
(纸本)9781538662205
this paper presents a comparative study of two adaptive algorithms, Linear Constraint Minimum Variance (LMCV) and the Minimum Variance Distortionless Response (MVDR) applied to the smart antenna system. These algorithms are studied in terms of power while tracking the desired signal. In this case, we used a system with three upcoming signals containing the desired signal, whose position changes constantly, and two signals considered as interference. The simulation result shows that the MVDR technique provides better results than LMCV by creating a beam forming that follows the desired signal through its movement preserving an adequate power.
Atomistic/continuum coupling methods aim to achieve optimal balance between accuracy and efficiency. Adaptivity is the key for the efficient implementation of such methods. In this paper, we carry out a rigorous a pos...
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Atomistic/continuum coupling methods aim to achieve optimal balance between accuracy and efficiency. Adaptivity is the key for the efficient implementation of such methods. In this paper, we carry out a rigorous a posteriori analysis of the residual, the stability constant, and the error bound for a consistent atomistic/continuum coupling method in two dimensions. We design and implement the corresponding adaptive mesh refinement algorithm, and the convergence rate with respect to degrees of freedom is optimal compared with a priori error estimates.
In order to facilitate the user recommendation service system of library access, predict the future number of users and hobbies, and provide decision-making basis for e-commerce enterprises, this paper designs an adap...
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In order to facilitate the user recommendation service system of library access, predict the future number of users and hobbies, and provide decision-making basis for e-commerce enterprises, this paper designs an adaptive Web Recommendation System Framework Self-adaptive Websites Recommendation System. It is divided into two parts: offline parts and online components. The former includes data collection, preprocessing and frequent access pattern mining. The latter generates recommendation sets based on existing mining rules and user's current access behaviour of offline components, and realizes adaptive online recommendation service. Taking the university library as an example, this paper uses the sliding window method to obtain the current user access path, then use association rule algorithm based on aggregation tree to generate association rule set. After getting association rule set, we use recommendation set generation algorithm to generate recommendation set. The results show that both theoretical analysis and experiment indicate that the method is effective and feasible.
One of the main problems facing beamforming in smart antenna system is the alpha-stable noise. To address this problem, a novel algorithm, named the recursive continuous logarithmic mixed p-norm (RCLMP) algorithm, whi...
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One of the main problems facing beamforming in smart antenna system is the alpha-stable noise. To address this problem, a novel algorithm, named the recursive continuous logarithmic mixed p-norm (RCLMP) algorithm, which employs a logarithmic cost, is proposed in this brief. The proposed algorithm combines the logarithmic p-norms 1 <= p <= 2 which does not need the parameter selection and prior knowledge of astable noise, and exhibits good robustness against alpha-stable noise. Moreover, we show some H-infinity norm bounds for the proposed algorithm. Simulation results show that the RCLMP algorithm outperforms the existing algorithms in terms of interference rejection capability and estimation accuracy.
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