Objective. Transcranial magnetic stimulation in combination with electroencephalography (TMS-EEG) has been widely used to study the reactivity and connectivity of brain regions. In order to efficiently and fast solve ...
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Objective. Transcranial magnetic stimulation in combination with electroencephalography (TMS-EEG) has been widely used to study the reactivity and connectivity of brain regions. In order to efficiently and fast solve the pulse artifacts problem caused by TMS electromagnetic pulses, a three-dimensional adaptive rational quadratic Hermite interpolation algorithm is proposed. Approach. Firstly, a three-dimensional signal matrix is obtained by a signal recombination algorithm, where the removed window is automatically obtained by a derivative threshold. Secondly, the adaptive rational quartic Hermite interpolation algorithm is used to interpolate the removed window. Finally, the performance of the algorithm is verified using simulated and public database data. Main results. The simulation results show that the proposed algorithm improves the SNR by 23.88%-47.60%, reduces the RMSE by 46.52%-81.11%, reduces the average MAE by 47.83%-58.33%, and reduces the time consumption of the proposed algorithm by 45.90% compared with the piecewise cubic Hermite interpolation algorithm. Significance. Therefore, TMS-EEG pulse artifacts can be removed effectively and quickly with the proposed algorithm.
In the field of silicon photonic device design, this paper uses a genetic algorithm and adaptive genetic algorithm to optimize the BP neural network. For the first time, an optimized interlayer coupling structure neur...
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In the field of silicon photonic device design, this paper uses a genetic algorithm and adaptive genetic algorithm to optimize the BP neural network. For the first time, an optimized interlayer coupling structure neural network model was established. The methods used include backpropagation (BP) neural network, genetic algorithm optimized BP (GA-BP) neural network and adaptive genetic algorithm optimized BP (AGA-BP) neural network. The results indicate that the AGA-BP neural network exhibits the best performance. The mean square error (MSE) of optimizing single-layer coupled structures with neural networks can reach 5.77E-05, and the MSE of double-layer coupled structures can reach 3.13E-05. This indicates that neural networks can effectively replace lengthy full wave simulations to optimize interlayer coupler structures.
This paper investigates the problem of parameter estimation for DC-DC buck converter without current sensors. For the circuit, all the parameters of capacitance, inductance, resistance, and input voltage are unknown. ...
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This paper investigates the problem of parameter estimation for DC-DC buck converter without current sensors. For the circuit, all the parameters of capacitance, inductance, resistance, and input voltage are unknown. A novel Volterra operator-based fixed-time adaptive algorithm is proposed by using only the output voltage and control input signals. By selecting proper kernel function for the Volterra integral operator, the influence of the system initial values can be eliminated, and the calculation of the derivative of the system output can also be avoided. Strict analysis shows that the proposed estimation algorithm can ensure the estimation errors converge to zero in a fixed time independent of the initial error values. Finally, simulation results with different initial values verify the advantages of the proposed algorithm.
With the rapid development of the exhibition industry, high-quality imaging technology plays an increasingly important role in enhancing the audience experience and transmitting exhibition information. However, due to...
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With the rapid development of the exhibition industry, high-quality imaging technology plays an increasingly important role in enhancing the audience experience and transmitting exhibition information. However, due to the complex and changeable exhibition environment, traditional imaging methods are often difficult to obtain satisfactory image quality. Therefore, an adaptive feature enhancement method for exhibition imaging based on a Multi-scale Generative Adversarial Network (MS-GAN) is proposed in this paper. By constructing an MS-GAN model, this method realizes feature extraction and fusion of exhibition images at different scales. The generator part adopts a multi-scale convolution structure, which can capture the local details and global structure information of the image and improve the quality of the generated image through residual connection and attention mechanism. In the discriminator part, the multi-scale feature discriminant strategy is used to evaluate the generated image to guide the optimization direction of the generator. The experimental results show that this method can significantly improve the brightness, clarity and color saturation of exhibition images and effectively improve the image quality. Especially under complex lighting conditions and noise interference, the method can still maintain good performance stability and enhancement effect. In addition, the method also has strong adaptive ability, which can automatically adjust the enhancement parameters according to different exhibition environments and imaging conditions to achieve a personalized image enhancement effect.
Aiming at the problem of "energy hole" caused by random distribution of nodes in large-scale wireless sensor networks (WSNs), this paper proposes an adaptive energy -efficient balanced uneven clustering rout...
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Aiming at the problem of "energy hole" caused by random distribution of nodes in large-scale wireless sensor networks (WSNs), this paper proposes an adaptive energy -efficient balanced uneven clustering routing protocol (AEBUC) for WSNs. The competition radius is adaptively adjusted based on the node density and the distance from candidate cluster head (CH) to base station (BS) to achieve scale -controlled adaptive optimal clustering;in candidate CHs, the energy relative density and candidate CH relative density are comprehensively considered to achieve dynamic CH selection. In the inter -cluster communication, based on the principle of energy balance, the relay communication cost function is established and combined with the minimum spanning tree method to realize the optimized inter -cluster multi -hop routing, forming an efficient communication routing tree. The experimental results show that the protocol effectively saves network energy, significantly extends network lifetime, and better solves the "energy hole" problem.
While time series forecasting models are generally trained by optimising certain forms of error, the end-user's forecasting needs in a multi-objective setting can be broader, and often mutually conflicting. A prod...
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While time series forecasting models are generally trained by optimising certain forms of error, the end-user's forecasting needs in a multi-objective setting can be broader, and often mutually conflicting. A production manager may prioritise high product fill rates and low average inventory resulting from a forecast over just low error. The conflict among multiple objectives is notably worrisome in intermittent demand forecasting, where error-minimising approaches can devalue the practitioner's objectives. To address such forecasting problems, we propose an adaptive Multi-objective Optimal Combination (AMOC) of forecasts which incorporates the end-user's preferences across multiple objectives. We demonstrate the use of AMOC in a real-life application of intermittent demand forecasting for optimising four distinct inventory management objectives using five specialised forecasting methods across single-period and multi-period inventory handling scenarios. Additionally, we conduct a comprehensive experiment on a subset of M5 competition data to exhibit the robustness of the AMOC using 13 diverse forecasting methods and four statistical objectives.
This work is motivated by the need of efficient numerical simulations of gas flows in the serpentine channels used in proton-exchange membrane fuel cells. In particular, we consider the Poisson problem in a 2D domain ...
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This work is motivated by the need of efficient numerical simulations of gas flows in the serpentine channels used in proton-exchange membrane fuel cells. In particular, we consider the Poisson problem in a 2D domain composed of several long straight rectangular sections and of several bended corners. In order to speed up the resolution, we propose a 0D model in the rectangular parts of the channel and a finite element resolution in the bends. To find a good compromise between precision and time consuming, the challenge is double: how to choose a suitable position of the interface between the 0D and the 2D models and how to control the discretization error in the bends. We shall present an a posteriori error estimator based on an equilibrated flux reconstruction in the sub domains where the finite element method is applied. The estimates give a global upper bound on the error measured in the energy norm of the difference between the exact and approximate solutions on the whole domain. They are guaranteed, meaning that they feature no undetermined constants. (Global) lower bounds for the error are also derived. An adaptive algorithm is proposed to use smartly the estimator for the aforementioned double challenge. A numerical validation of the estimator and the algorithm completes the work.
Model-based fault detection and isolation (FDI) methods allow to infer the health status of complex aerospace systems through a large quantity of data acquired in flight and evaluations of numerical models of the equi...
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Model-based fault detection and isolation (FDI) methods allow to infer the health status of complex aerospace systems through a large quantity of data acquired in flight and evaluations of numerical models of the equipment. This results in an intensive computational procedure that can be addressed only grounding the aircraft. We introduce an original methodology to sensitively accelerate FDI by reducing the computational demand to identify the health status of the aircraft. Our scheme FREEDOM (Fast Reliability Estimate and Incipient Fault Detection of Multiphysics Aerospace Systems) proposes an original combination of a novel two-step compression strategy to compute offline a synthesized representation of the dynamical response of the system and uses an inverse Bayesian optimization approach to infer online the level of damage determined by multiple fault modes affecting the equipment. We demonstrate and validate FREEDOM against numerical and physical experiments for the case of an electromechanical actuator employed for secondary flight controls. Particular attention is dedicated to simultaneous incipient mechanical and electrical faults considering different experimental settings. The outcomes validate our FDI strategy, which permits to achieve the accurate identification of complex damages outperforming the computational time of state-of-the-art algorithms by two orders of magnitude.
The public transportation system is now dealing with a number of problems brought on by the sharp increase in automobile ownership in cities as well as the buildup of vehicles as a result of events and accidents. Howe...
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The public transportation system is now dealing with a number of problems brought on by the sharp increase in automobile ownership in cities as well as the buildup of vehicles as a result of events and accidents. However, the city's limited road network capacity cannot keep up with the increasing traffic demand, which further worsens travel conditions and results in a waste of time and money. Given that it is challenging to enhance the capacity of the road network in practice, efficient vehicle travel and evacuation using algorithms has emerged as a recent study focus. It is crucial to learn how to manage urban traffic issues during emergencies and maintain smooth and safe traffic flow. The existing studies only consider the optimized route selection for individual vehicles, signal cycle of traffic lights and deploy historical data to disperse the vehicles on alternative routes. However, such works do not consider the conflict of routes between vehicles, the customized traffic demand of each vehicle and uncertain traffic conditions. Therefore, this paper proposes a novel approach to facilitate the user to select the optimal route with real-time traffic scenario. Furthermore, the Nash equilibrium is established by mutual information swapping and self-adaptive learning method. Simulation results show that the proposed algorithm has better route selection capability in real-time personalized road traffic as compared with existing algorithms.
In this article, we propose a new adaptive time filter algorithm for the unsteady Stokes/Darcy model. Firstly, we present a first order 0-scheme with variable time step which is one parameter family of Linear Multi-st...
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In this article, we propose a new adaptive time filter algorithm for the unsteady Stokes/Darcy model. Firstly, we present a first order 0-scheme with variable time step which is one parameter family of Linear Multi-step method. Furthermore, using time filter scheme, the convergence order is increased to the second order without almost increasing the extra computation. Moreover, we construct decoupled and coupled adaptive algorithms to improve the computational efficiency. Theoretically, we analyze stability and the second-order accuracy of Linear Multi-step method plus time filter with non-increasing variable time step, respectively. Finally, numerical experiments are given to verify theoretical results, including effectiveness, convergence and efficiency.
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