The harmony search algorithm has been proven to be an effective optimization method for solving diverse optimization problems. However, due to its slow convergence, the performance of HSA over constrained optimization...
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The harmony search algorithm has been proven to be an effective optimization method for solving diverse optimization problems. However, due to its slow convergence, the performance of HSA over constrained optimization problems is not very competitive. Therefore, many researchers have hybridized HSA with local search algorithms. However, it's very difficult to known in advance which local search should be hybridized with HSA as it depends heavily on the problem characteristics. The question is how to design an effective selection mechanism to adaptively select a suitable local search to be combined with HSA during the search process. Therefore, this work proposes an adaptive HSA that embeds an adaptive selection mechanism to adaptively select a suitable local search algorithm to be applied. This work hybridizes HSA with five local search algorithms: hill climbing, simulated annealing, record to record, reactive tabu search and great deluge. We use the Solomon's vehicle routing problem with time windows benchmark to examine the effectiveness of the proposed algorithm. The obtained results are compared with basic HSA, the local search algorithms and existing methods. The results demonstrate that the proposed adaptive HSA achieves very good results compared other methods. This demonstrates that the selection mechanism can effectively assist HSA to adaptively select a suitable local search during the problem solving process.
The paper discusses an adaptive algorithm for processing hydroacoustic signals of antenna arrays in real time. We perform mathematical analysis of the adaptation algorithm. The algorithm is based on an iterative proce...
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ISBN:
(纸本)9781509048663
The paper discusses an adaptive algorithm for processing hydroacoustic signals of antenna arrays in real time. We perform mathematical analysis of the adaptation algorithm. The algorithm is based on an iterative procedure for finding weight coefficients. We provide recommendations for algorithm application under different conditions.
The purpose of the software metrics is to use software metrics to scientifically evaluate software quality,to more effectively control and manage the software development process,to rationally organize and allocate re...
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The purpose of the software metrics is to use software metrics to scientifically evaluate software quality,to more effectively control and manage the software development process,to rationally organize and allocate resources,to develop practical software development plans,and to reduce costs and achieve high *** metrics are divided into two major processes:measurement planning and also the measurement *** measurement implementation includes data collection,data analysis,feedback and *** can be seen that data analysis is an important part of software measurement ***,data analysis in software metrics has many problems in *** strategic significance of the era of big data lies not only in mastering large amounts of general data,but also in discovering and understanding the relationship between information content and information and *** data analysis is one of the core contents of big data *** analysis is the decisive factor in the decision-making process,and it is also the most critical link in the big data era to play the value of data.
Clarity and intelligibility in speech signal demands removal of noise and interference associated with the signal at the source. This poses further challenge when the speech signal is colored with human emotions. In t...
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ISBN:
(纸本)9781509012770
Clarity and intelligibility in speech signal demands removal of noise and interference associated with the signal at the source. This poses further challenge when the speech signal is colored with human emotions. In this work, the authors have taken a novel step to enhance the emotional speech signal adaptively before classification. Most popular adaptive algorithm such as Least mean square (LMS), Normalized least mean squares (NLMS) and Recursive least square (RLS) has been put to test to obtain enhanced speech emotions. Neural network based Multilayer perceptron (MLP) classifier is used to recognize fear speech emotion as against neutral voices using effective Linear Prediction coefficients (LPCs). The accuracy has improved to approximately 77% with enhanced signal. The increased accuracy of this signal has been witnessed with RLS algorithm as against the noisy signal with corresponding algorithm.
The number of crossband filters controls convergence rate and steady-state MSE of LMS algorithm in subband in the short-time Fourier domain,which necessitates a compromise between them due to its fixed ***,a decision ...
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ISBN:
(纸本)9781538629185
The number of crossband filters controls convergence rate and steady-state MSE of LMS algorithm in subband in the short-time Fourier domain,which necessitates a compromise between them due to its fixed ***,a decision to adaptively control the number of crossband filters is proposed to provide both fast convergence rate and small steady-state *** advantage of the proposed algorithm is verified by the simulation.
Within this article an adaptive approach for parallel simulation of SystemC RTL Models on future many-core architectures like the Single-chip Cloud Computer (SCC) from Intel is presented. It is based on a configurable...
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Within this article an adaptive approach for parallel simulation of SystemC RTL Models on future many-core architectures like the Single-chip Cloud Computer (SCC) from Intel is presented. It is based on a configurable parallel SystemC kernel that preserves the partial order defined by the SystemC delta cycles while avoiding global synchronization as far as possible. The underlying algorithm relies on a classification of existing communication relations between parallel processes. The type and topology of communication relations determines the type and number of causality conditions that need to be fulfilled during runtime. The parallel kernel is complemented by an automated tool flow that allows detecting relevant model-specific properties, performing a fine-grained model partitioning, classifying communication relations and configuring the kernel. Experiments by means of a MPSoC model show that pure local synchronization can provide significant performance gains compared to global synchronization. Furthermore, the combination of local synchronization with fine-grained partitioning provides additional degrees of freedom for optimization. (c) 2015 Elsevier B.V. All rights reserved.
Graph Neural Networks (GNNs) have gained widespread adoption across various fields due to their superior capability in processing graph-structured data. Nevertheless, these models are susceptible to unintentionally di...
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Graph Neural Networks (GNNs) have gained widespread adoption across various fields due to their superior capability in processing graph-structured data. Nevertheless, these models are susceptible to unintentionally disclosing sensitive user information. Current differential privacy algorithms for graph neural networks exhibit constrained adaptability and prolonged runtimes. To address these issues, this paper introduces an adaptive GNN protection algorithm grounded in differential privacy. The algorithm offers robust privacy safeguards at both node and edge levels, employing a bespoke normalization approach based on mean and variance to effectively manage data non-uniformity and outliers, thereby enhancing the model's adaptability to diverse data distributions. Furthermore, the implementation of an early stopping strategy markedly decreases runtime while exerting negligible influence on accuracy, thus enhancing computational efficiency. Experimental results indicate that this approach not only improves the model's predictive accuracy but also significantly reduces its computational time.
In this paper, a third-order time adaptive algorithm with less computation, low complexity is provided for shale reservoir model based on coupled fluid flow with porous media flow. This algorithm combines a method of ...
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In this paper, a third-order time adaptive algorithm with less computation, low complexity is provided for shale reservoir model based on coupled fluid flow with porous media flow. This algorithm combines a method of three-step linear time filters for simple post-processing and a second-order backward differential formula (BDF2), is third-order accurate in time, and provides no extra computational complexity. At the same time, the time filter method can also be used to damp non-physical oscillations inherent in the BDF2 method, ensuring stability. We prove the algorithm's stability of the constant stepsize second-order backward differential formula plus time filter (BDF2-TF) and the third-order convergence properties of the fluid velocity u and hydraulic head phi in the L2 norm. In numerical experiments, this adaptive algorithm automatically adjusts a time step in response to the varying characteristics of different models, ensuring that errors are maintained within acceptable limits. The algorithm addresses the issue that high-order algorithms may select inappropriate time steps, resulting in instability or reduced accuracy of a numerical solution, and thereby it enhances calculation accuracy and efficiency. We perform three-dimensional numerical tests to examine the BDF2-TF algorithm's effectiveness, stability, and third-order convergence. Simultaneously, a simplified model is employed to simulate the process of shale oil extraction from reservoirs, further demonstrating the algorithm's practical applicability.
To further improve the accuracy and expand the applicability of sensorless control, an improved scheme is proposed in this article which uses a sliding mode observer (SMO) and a phase-locked loop (PLL) for a dual thre...
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To further improve the accuracy and expand the applicability of sensorless control, an improved scheme is proposed in this article which uses a sliding mode observer (SMO) and a phase-locked loop (PLL) for a dual three-phase permanent magnet synchronous hub motor (DTP-PMSHM). First, the supertwisting algorithm (STA) is combined with the adaptive algorithm and applied to the observer to improve its accuracy and expand its applicable speed range. Second, an iterative algorithm based on the Secant method is introduced into the PLL designing an infinite position set (IPS) PLL (IPS-PLL) to avoid conventional defects and to improve the rotor position information estimation. Finally, a series of experiments are conducted to compare various performances of the proposed control scheme with the conventional control scheme. The superiority and feasibility of the proposed sensorless control scheme are demonstrated by experimental results on a DTP-PMSHM. The proposed scheme has fewer parameters and is portable, and improves the accuracy of control, which has practical significance in improving the control precision and practical application.
Solving tensor systems is a common task in scientific computing and artificial intelligence. In this paper, we propose a tensor randomized average Kaczmarz method with adaptive parameters that exponentially converges ...
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Solving tensor systems is a common task in scientific computing and artificial intelligence. In this paper, we propose a tensor randomized average Kaczmarz method with adaptive parameters that exponentially converges to the unique least Frobenius norm solution of a given consistent tensor system under the t-product structure. In order to accelerate convergence, a tensor average Kaczmarz method based on stochastic heavy ball momentum technique (tAKSHBM) is proposed. The tAKSHBM method utilizes iterative information to update parameters instead of relying on prior information, addressing the problem in the adaptive learning of parameters. Additionally, the tAKSHBM method based on Fourier transform is proposed, which can be effectively implemented in a distributed environment. It is proven that the iteration sequences generated by all the proposed methods are convergent for given consistent tensor systems. Finally, we conduct experiments on both synthetic data and practical applications to support our theoretical results and demonstrate the effectiveness of the proposed algorithms.
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