Optimization Method of Residual Networks of Residual Networks for Image Classification

Zhang, Ke; Guo, Liru; Gao, Ce

doi:10.1007/978-3-319-95957-3_23

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The recurrent neural network (RNN) [21], long short-term memory (LSTM) [22,23], and gated recurrent unit (GRU) [24] are commonly used for one-dimensional signals such as time sequence signal [25] classification. In terms of two-dimensional image identification, the deep convolutional network [26,27], residual network (Resnet) [28,29], Densenet [30,31] and their improved structures have been widely used in image classification tasks. Ouahabi [32] used the deep-learning method to perform real-time semantic segmentation of ultrasonic acoustic images.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Signal Transmission Performance in Bolted Connections of Wood Structures under Different Preloads

Zhuang

Liu

et al. 2021

Forests

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In industrial applications, bolt connections are simple and economical, contributing to their popularity for use in wood packing boxes. However, they can easily fail when subjected to a continuous vibrational load under usual working conditions such as transportation and hoisting. Based on an ultrasonic technique, nondestructive evaluation can be used to quickly detect large-scale structures, but the complex propagation properties in wood limit its application. To solve this problem, a time-reversal method was adopted to predict the residual preload on bolted connections by focusing on the signals collected by wood structures, which helps to assess the structures’ reliability. In this study, the residual preload of bolted connections in wood structures was predicted using the deep-learning method, LSTM, one-dimensional Resnet and Densenet, and tree classification models. It was confirmed that the use of the time-reversal method for ultrasonic detection focused on the signals transmitted in bolted connections of wood structures and deep-learning methods are a feasible way to predict an ultrasonic transmission model.

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Section: Introductionmentioning

confidence: 99%

Ultrasonic Signal Transmission Performance in Bolted Connections of Wood Structures under Different Preloads

Zhuang

Liu

et al. 2021

Forests

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“…Valeryi et al [26] proposed the technique of automatic labeling the data set with the finite element model for training of artificial neural network in tomography. Lin et al [15] proposed a residual networks of residual networks (RoR) optimization method to avoid over-fitting and gradient vanish. Gomez et al [6] found in the ResNet network along with the increase of the depth in improving performance, but increased memory consumption, thus put forward each layer's activations can be reconstructed exactly from the next layer's.…”

Section: Introductionmentioning

confidence: 99%

An improved simulated annealing algorithm based on residual network for permutation flow shop scheduling

Wang

Gao

et al. 2020

Complex Intell. Syst.

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The permutation flow shop scheduling problem (PFSP), which is one of the most important scheduling types, is widespread in the modern industries. With the increase of scheduling scale, the difficulty and computation time of solving the problem will increase exponentially. Adding the knowledge to intelligent algorithms is a good way to solve the complex and difficult scheduling problems in reasonable time. To deal with the complex PFSPs, this paper proposes an improved simulated annealing (SA) algorithm based on residual network (SARes). First, this paper defines the neighborhood of the PFSP and divides its key blocks. Second, the Residual Network (ResNet) is used to extract and train the features of key blocks. And, the trained parameters are stored in the SA algorithm to improve its performance. Afterwards, some key operators, including the initial temperature setting and temperature attenuation function of SA algorithm, are also modified. After every new solution is generated, the parameters trained by the ResNet are used for fast ergodic search until the local optimal solution found in the current neighborhood. Finally, the most famous benchmarks including part of TA benchmark are selected to verify the performance of the proposed SARes algorithm, and the comparisons with the-state-of-art methods are also conducted. The experimental results show that the proposed method has achieved good results by comparing with other algorithms. This paper also conducts experiments on network structure design, algorithm parameter selection, CPU time and other problems, and verifies the advantages of SARes algorithm from the aspects of stability and efficiency.

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Correction to: Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images

Bakkouri

Afdel

2020

Multimed Tools Appl

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Optimization Method of Residual Networks of Residual Networks for Image Classification

Cited by 3 publications

References 14 publications

Ultrasonic Signal Transmission Performance in Bolted Connections of Wood Structures under Different Preloads

Ultrasonic Signal Transmission Performance in Bolted Connections of Wood Structures under Different Preloads

An improved simulated annealing algorithm based on residual network for permutation flow shop scheduling

Correction to: Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images

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