Intelligent Diagnosis Using Continuous Wavelet Transform and Gauss Convolutional Deep Belief Network

Zhao, Huimin; Liu, Jie; Chen, Huayue; Chen, Jie; Li, Yang; Xu, Junjie; Deng, Wu

doi:10.1109/tr.2022.3180273

Cited by 139 publications

(82 citation statements)

References 29 publications

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“…However, MSACLPSO is still insufficient in solving large-scale parameter optimization problems, such as time complexity and easy stagnation, among others. In the future, these applications should be considered [ 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 ]. The algorithm should be deeply studied, and the parameter adaptability of MSACLPSO in different stages and scales should also be further explored in future works.…”

Section: Discussionmentioning

confidence: 99%

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

Zhang

Song

2022

Entropy

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In this paper, a multi-strategy adaptive comprehensive learning particle swarm optimization algorithm is proposed by introducing the comprehensive learning, multi-population parallel, and parameter adaptation. In the proposed algorithm, a multi-population parallel strategy is designed to improve population diversity and accelerate convergence. The population particle exchange and mutation are realized to ensure information sharing among the particles. Then, the global optimal value is added to velocity update to design a new velocity update strategy for improving the local search ability. The comprehensive learning strategy is employed to construct learning samples, so as to effectively promote the information exchange and avoid falling into local extrema. By linearly changing the learning factors, a new factor adjustment strategy is developed to enhance the global search ability, and a new adaptive inertia weight-adjustment strategy based on an S-shaped decreasing function is developed to balance the search ability. Finally, some benchmark functions and the parameter optimization of photovoltaics are selected. The proposed algorithm obtains the best performance on 6 out of 10 functions. The results show that the proposed algorithm has greatly improved diversity, solution accuracy, and search ability compared with some variants of particle swarm optimization and other algorithms. It provides a more effective parameter combination for the complex engineering problem of photovoltaics, so as to improve the energy conversion efficiency.

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Section: Discussionmentioning

confidence: 99%

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

Zhang

Song

2022

Entropy

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“…To consider the time resolution, this paper makes A = 0.7. Through multiple experimental analyses, to account for the accuracy and rapidity of fault identification simultaneously, the 0 to 300 Hz frequency band is selected as the characteristic frequency band for time‐spectrum analysis, and GST is performed every 10 consecutive experimental currents signal cycles [27].…”

Section: Extraction Of Series Arc Fault Featuresmentioning

confidence: 99%

A particle swarm optimization‐based support vector machine model to detect charging pile arc faults by using the pre‐treatment generalized S transform

Deng

Zeng

Huang

et al. 2022

The Journal of Engineering

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An arc fault is the most common cause of charging pile fire. The series arc fault current is usually lower than the short‐circuit fault current and is challenging to detect, resulting in the inaction of the circuit breaker. Therefore, a method for detecting series arc faults of charging piles based on generalized Stockwell transform (GST) is proposed in this paper. First, an real time digital simulation system (RTDS)‐based charging pile arc experimental platform is constructed, and the point contact and the carbonization path arc fault current are measured. Then, the fault information is extracted using GST, and the 250‐Hz component is selected as the arc fault identification feature. On this basis, an improved sampling point selection method is proposed. Finally, multiple feature quantities are constructed as feature vectors, and the trained particle swarm optimization _support vector machine (PSO_SVM) is used for arc fault detection. The experimental results show that the accuracy rate reaches 98.75%; thus, the scheme proposed by this paper could detect the series arc fault current accurately and avoid the incorrect action of the circuit breaker.

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“…Zhao et. al [30] proposed a novel vibration amplitude spectrum imaging feature extraction method using continuous wavelet transform and image conversion is proposed, which can extract the image features with two-dimensional and eliminate the effect of handcrafted features. To improve the fog resource provisioning performance of mobile devices, Chen et al [31] proposed a learning-based mobile fog scheme with deep deterministic policy gradient (DDPG) algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Video Cache Replacement Scheme based on Local Video Popularity and Video Size for MEC Servers

2022

KSII TIIS

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With the mobile traffic in the network increases exponentially, multi-access edge computing (MEC) develops rapidly. MEC servers are deployed geo-distribution, which serve many mobile terminals locally to improve users' QoE (Quality of Experience). When the cache space of a MEC server is full, how to replace the cached videos is an important problem. The problem is also called the cache replacement problem, which becomes more complex due to the dynamic video popularity and the varied video sizes. Therefore, we proposed a new cache replacement scheme based on local video popularity and video size to solve the cache replacement problem of MEC servers. First, we built a local video popularity model, which is composed of a popularity rise model and a popularity attenuation model. Furthermore, the popularity attenuation model incorporates a frequency-dependent attenuation model and a frequency-independent attenuation model. Second, we formulated a utility based on local video popularity and video size. Moreover, the weights of local video popularity and video size were quantitatively analyzed by using the information entropy. Finally, we conducted extensive simulation experiments based on the proposed scheme and some compared schemes. The simulation results showed that our proposed scheme performs better than the compared schemes in terms of hit rate, average delay, and server load under different network configurations.

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Intelligent Diagnosis Using Continuous Wavelet Transform and Gauss Convolutional Deep Belief Network

Cited by 139 publications

References 29 publications

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

A particle swarm optimization‐based support vector machine model to detect charging pile arc faults by using the pre‐treatment generalized S transform

A Video Cache Replacement Scheme based on Local Video Popularity and Video Size for MEC Servers

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