Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper pr...
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Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.
Job-dependent tool switching is necessary in many batch processing systems (BPSs). Heterogeneous tool demand and extra time consumption for tool switches bring great challenges for high-performance production scheduli...
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This paper presents a method to estimate power system inertia in real-time using Phasor Measurement Unit (PMU) data and the swing equation. Inertia is essential for maintaining power system stability during disturbanc...
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This paper presents a novel approach for the online calculation of Linear Quadratic Regulator (LQR) gains using the Tabular Dyna-Q algorithm. By leveraging Q-learning, this technique enables the determination of gains...
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This work formulates the feedback control strategies for vehicles to reach a goal point amongst a field of dynamic risk regions. Whereas previous work has considered deterministic versions of this problem, we consider...
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In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the curr...
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In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.
AMC stands for automatic modulation classification, a significant tool in wireless communication for identification of the signal modulations of immense relevance to dynamic spectrum management, interference identific...
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ISBN:
(纸本)9798331529635
AMC stands for automatic modulation classification, a significant tool in wireless communication for identification of the signal modulations of immense relevance to dynamic spectrum management, interference identification and cognitive radio networks. Many of the previous AMC approaches include feature extraction and manual classification which are not particularly portable and accurate in other contexts of communications. Recently, with the development of deep leaning, attention based methods have brought obviously enhanced performance in perceiving comprehensive signal characteristics;but there are still problems in focusing to multi-aspect features and training at a large scale signal data with high computational complexity. In order to address this problem, this research introduces a Avg-TopK pooling layer along with a Multi-Aspect Graph Attention Network (MAGAN) for the purpose of the AMC while using the Osprey Optimization Algorithm for the optimization of the proposed model. The Avg-TopK pooling only keeps K most important feature representations and decreases noise levels for better signal modulation classification. MAGAN uses multi-aspect attention mechanism allowing the emphasize various properties of the signal while perceiving mutual connections between the features of the signal. Additionally, the proposed Osprey Optimization Algorithm improves the training time of the model through optimization of hyperparameters. The presented results obtained from experimental analysis of standard AMC performance metrics show that the proposed model provides better AMC classification capability and insensitivity to the noise and existence of multiple types of modulation modes in contrast to other AMC models. In light of this, this work aims at solving some of the major challenges facing AMC through presenting a highly adaptive, efficient and accurate solution for the ever evolving wireless communication systems. The introduced approach attains higher accuracy as 99%
Automation of ship maneuvering in limited sailing conditions usually requires 100% redundancy of thrusters (THRs) of various modifications and their locations in accordance with the matrix. The hierarchy of the motion...
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Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm that is used for solving multiple optimization tasks concurrently. Most MTO algorithms limit each individual to one task, and thus we...
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The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machin...
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The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.
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