With the increase of communication devices and demands, the problems of high power consumption, tight spectrum resources, and low energy efficiency in the two-layer heterogeneous network are the popular topics, which ...
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With the increase of communication devices and demands, the problems of high power consumption, tight spectrum resources, and low energy efficiency in the two-layer heterogeneous network are the popular topics, which need to be solved urgently. For the purpose of solving these problems in a two-layer heterogeneous network consisting of femtocell base stations in randomly distributed a macrocell base station, which can also be called the Macrocell/Femtocell two-layer heterogeneous network, the hierarchical clustering algorithm is firstly used to cluster femtocell base stations in accordance with a distance threshold, the spectrum partitioning mechanism and non-orthogonal multiple access technique are combined to obtain spectrum allocation schemes for different users. Then, the modified social network search algorithm is used to simulate the power allocation problem in the two-layer heterogeneous network with system energy efficiency as the objective function. By comparing with the previous algorithms, the proposed algorithm's superior performance is verified on the test functions. The results show that the proposed method can effectively improve spectrum utilization and reduce interference. The modified social network search algorithm is more robust and widely applicable regarding energy and computational efficiency.
作者：
Xu, ShangWuhan Univ Technol
Sch Navel Architecture Ocean & Energy Power Engn Wuhan 430063 Hubei Peoples R China
Swift and precise fault diagnosis is a significant category to guarantee machinery operates reliably and avoid major failures. Conventional methods for monitoring bearing health rely on large datasets of labeled fault...
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Swift and precise fault diagnosis is a significant category to guarantee machinery operates reliably and avoid major failures. Conventional methods for monitoring bearing health rely on large datasets of labeled faulty samples, which can be time-consuming and costly to gain. Intelligent fault identification techniques are limited by a lack of defective samples. Although Convolutional Neural networks (CNNs) are effective tools for diagnosing mechanical faults, they may perform poorly on unseen data due to overfitting when trained with few faulty samples. The small sample problem poses a significant difficulty in mechanical fault diagnosis, as insufficient faulty samples can induce overfitting in models such as CNNs that leads to inadequate generalization. For instance, conventional CNNs may extremely adapt to the unique traits of limited training data, whereas SCNNs, characterized by their sparse connectivity, reduce this concern. Additionally, optimizing the hyperparameters of SCNNs can be complex. However, the M-SNS algorithm, using Levy flight and self-adjusting population mechanisms proficiently solves this issue by enhancing exploitation and exploration. This study suggests a novel approach to solve the small sample problem Sparsely Connected Neural networks (SCNNs) enhanced by optimizing its hyperparameters based on an improved version of socialnetworksearch (M-SNS). While standard SNS-based optimizers struggle with local optima, the improved version incorporates Levy flight to meaningly improve global search performance, guaranteeing better generalization even in small sample scenarios. The proposed SCNN/M-SNS is employed to use a tool for the fault diagnosis. To guarantee the efficiency of the model, its results are applied to a benchmark, called Case Western Reserve University (CWRU) Bearing Dataset which includes a flexible test rig with a 2 hp motor to simulate various load conditions (0, 1, 2, and 3 hp) and controlled fault i
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