Co-evolutionary competitive swarm optimizer with three-phase for large-scale complex optimization problem

Huang, Chen; Zhou, Xiangbing; Ran, Xiaojuan; Liu, Yi; Deng, Wuquan; Deng, Wu

doi:10.1016/j.ins.2022.11.019

Cited by 105 publications

(41 citation statements)

References 43 publications

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“…The results indicate that English advertisements mainly use psychological processes to express their ecological attributes, emphasizing the establishment of an emotional connection between man and nature, while Chinese advertisements mainly use action processes to express ecological attributes, reflecting the connotation of man to nature. Huang et al (2022) [ 13 ] pointed out that the competitive group optimizer is an effective variant of the Particle Swarm Optimization (PSO) algorithm, which has been widely applied to deal with various practical large-scale optimization problems. They introduced a new multi-stage co-evolution technique and proposed a new three-phase co-evolution method to improve the convergence and search capabilities of the PSO algorithm.…”

Section: Related Workmentioning

confidence: 99%

The ecological discourse analysis of news discourse based on deep learning from the perspective of ecological philosophy

2023

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Recently, ecological damage and environmental pollution have become increasingly serious. Experts in various fields have started to study related issues from diverse points of view. To prevent the accelerated deterioration of the ecological environment, ecolinguistics emerged. Eco-critical discourse analysis is one of the important parts of ecolinguistics research, that is, it is a critical discourse analysis of the use of language from the perspective of the language’s ecological environment. Firstly, an ecological tone and modality system are constructed from an ecological perspective. Under the guidance of the ecological philosophy of "equality, harmony, and symbiosis", this study conducts an ecological discourse analysis on the Sino-US trade friction reports, aiming to present the similarities and differences between the two newspapers’ trade friction discourses and to reveal the ecological significance of international ecological factors in the discourse. Secondly, this method establishes a vector expression of abstract words based on emotion dictionary resources and introduces emotion polarity and part-of-speech features of words. Then the word vector is formed into the text feature matrix, which is used as the input of the Convolutional Neural Network (CNN) model, and the Back Propagation algorithm is adopted to train the model. Finally, in the light of the trained CNN model, the unlabeled news is predicted, and the experimental results are analyzed. The results reveal that during the training process of Chinese and English datasets, the accuracy of the training set can reach nearly 100%, and the loss rate can be reduced to 0. On the test set, the classification accuracy of Chinese text can reach 83%, while that of English text can reach 90%, and the experimental results are ideal. This study provides an explanatory approach for ecological discourse analysis on the news reports of Sino-US trade frictions and has certain guiding significance for the comparative research on political news reports under different ideologies between China and the United States.

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Section: Related Workmentioning

confidence: 99%

The ecological discourse analysis of news discourse based on deep learning from the perspective of ecological philosophy

2023

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“…Based on [ 29 ], ref. [ 30 ] proposed a three-phase large-scale optimizer. The BFM we proposed achieves a two-stage enhancement of the FPN, which weakens the interference and confounding effect of the low-level semantic information on the key text information, and smoothly fuses the high-level and low-level semantic information.…”

Section: Related Workmentioning

confidence: 99%

Attention-Based Scene Text Detection on Dual Feature Fusion

Silamu

Wang

et al. 2022

Sensors

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The segmentation-based scene text detection algorithm has advantages in scene text detection scenarios with arbitrary shape and extreme aspect ratio, depending on its pixel-level description and fine post-processing. However, the insufficient use of semantic and spatial information in the network limits the classification and positioning capabilities of the network. Existing scene text detection methods have the problem of losing important feature information in the process of extracting features from each network layer. To solve this problem, the Attention-based Dual Feature Fusion Model (ADFM) is proposed. The Bi-directional Feature Fusion Pyramid Module (BFM) first adds stronger semantic information to the higher-resolution feature maps through a top-down process and then reduces the aliasing effects generated by the previous process through a bottom-up process to enhance the representation of multi-scale text semantic information. Meanwhile, a position-sensitive Spatial Attention Module (SAM) is introduced in the intermediate process of two-stage feature fusion. It focuses on the one feature map with the highest resolution and strongest semantic features generated in the top-down process and weighs the spatial position weight by the relevance of text features, thus improving the sensitivity of the text detection network to text regions. The effectiveness of each module of ADFM was verified by ablation experiments and the model was compared with recent scene text detection methods on several publicly available datasets.

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“…It is a vital part of safety helmet detection. Hence, the optimization of monitoring systems is widely studied [ 4 , 5 ]. Traditional video surveillance is mainly used for continuous monitoring.…”

Section: Introductionmentioning

confidence: 99%

Safety Helmet Detection Based on YOLOv5 Driven by Super-Resolution Reconstruction

Han

Liu

et al. 2023

Sensors

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High-resolution image transmission is required in safety helmet detection problems in the construction industry, which makes it difficult for existing image detection methods to achieve high-speed detection. To overcome this problem, a novel super-resolution (SR) reconstruction module is designed to improve the resolution of images before the detection module. In the super-resolution reconstruction module, the multichannel attention mechanism module is used to improve the breadth of feature capture. Furthermore, a novel CSP (Cross Stage Partial) module of YOLO (You Only Look Once) v5 is presented to reduce information loss and gradient confusion. Experiments are performed to validate the proposed algorithm. The PSNR (peak signal-to-noise ratio) of the proposed module is 29.420, and the SSIM (structural similarity) reaches 0.855. These results show that the proposed model works well for safety helmet detection in construction industries.

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Co-evolutionary competitive swarm optimizer with three-phase for large-scale complex optimization problem

Cited by 105 publications

References 43 publications

The ecological discourse analysis of news discourse based on deep learning from the perspective of ecological philosophy

The ecological discourse analysis of news discourse based on deep learning from the perspective of ecological philosophy

Attention-Based Scene Text Detection on Dual Feature Fusion

Safety Helmet Detection Based on YOLOv5 Driven by Super-Resolution Reconstruction

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