Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition

Wang, Shudi; Huang, Li; Du, Jiang; Sun, Ying; Jiang, Guozhang; Li, Jun; Zou, Cejing; Fan, Hanwen; Xie, Yuanmin; Xiong, Hao; Chen, Baojia

doi:10.3389/fbioe.2022.909023

Cited by 29 publications

(26 citation statements)

References 97 publications

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“…Because P c and P m in the traditional genetic algorithm are fixed values, for crossover operation, if P c is large, the probability of the destruction of individuals with high fitness will also increase. If P c is small, the search speed will be slower (Wang et al, 2022). For mutation operation, if P m is large, the number of random mutation individual raises, which is not conducive to search.…”

Section: ) Adaptive Crossover and Mutation Probabilitymentioning

confidence: 99%

Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm

Shi

Huang²,

Du³

et al. 2022

Front. Bioeng. Biotechnol.

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Intelligent vehicles were widely used in logistics handling, agriculture, medical service, industrial production, and other industries, but they were often not smooth enough in planning the path, and the number of turns was large, resulting in high energy consumption. Aiming at the unsmooth path planning problem of four-wheel intelligent vehicle path planning algorithm, this article proposed an improved genetic and ant colony hybrid algorithm, and the physical model of intelligent vehicle was established. This article first improved ant colony optimization algorithm about heuristic function with the adaptive change of evaporation factor. Then, it improved the genetic algorithm on fitness function, adaptive adjustment of crossover factor, and mutation factor. Last, this article proposed the improved hybrid algorithm with the addition of a deletion operator, adoption of an elite retention strategy, and addition of suboptimal solutions obtained from the improved ant colony algorithm to improved genetic algorithm to obtain optimized new populations. The simulation environment for this article is windows 10, the processor is Intel Core i5-5257U, the running memory is 4GB, the compilation environment is MATLAB2018b, the number of ant samples is 50, the maximum number of iterations is 100, the initial population size of the genetic algorithm is 200, and the maximum number of iterations is 50. Simulation and physical experiments show that the improved hybrid algorithm is effective. Compared with the traditional hybrid algorithm, the improved hybrid algorithm reduced by 46% in the average number of iterations and 75% in the average number of turns in a simple grid. The improved hybrid algorithm reduced by 47% in the average number of iterations and 21% in the average number of turns in a complex grid. The improved hybrid algorithm works better to reduce the number of turns in simple maps.

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Section: ) Adaptive Crossover and Mutation Probabilitymentioning

confidence: 99%

Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm

Shi

Huang²,

Du³

et al. 2022

Front. Bioeng. Biotechnol.

Self Cite

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“…In practical engineering optimization problems, many design problems do not have only one design index requirement ( Li and Li 2016 ; Bai et al, 2022 ; Liu et al, 2022d ). For example, when designing a new type of ladle, it is usually hoped that the ladle has light weight, corrosion resistance, good thermal insulation performance, and long service life ( Tan and sun, 2020 ; Tao and Wang, 2022b ; Wang and Huang, 2022 ). This kind of problem that requires multiple design variables to achieve optimality is a multi-objective optimization problem ( Santos and Moreira, 2018 ; Srivastava and chattopathyay, 2021 ).…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm

Sun

Huang

Cao³

et al. 2022

Front. Bioeng. Biotechnol.

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Genetic algorithm is widely used in multi-objective mechanical structure optimization. In this paper, a genetic algorithm-based optimization method for ladle refractory lining structure is proposed. First, the parametric finite element model of the new ladle refractory lining is established by using ANSYS Workbench software. The refractory lining is mainly composed of insulating layer, permanent layer and working layer. Secondly, a mathematical model for multi-objective optimization is established to reveal the functional relationship between the maximum equivalent force on the ladle lining, the maximum temperature on the ladle shell, the total mass of the ladle and the structural parameters of the ladle refractory lining. Genetic algorithm translates the optimization process of ladle refractory lining into natural evolution and selection. The optimization results show that, compared with the unoptimized ladle refractory lining structure (insulation layer thickness of 0 mm, permanent layer thickness of 81 mm, and working layer thickness of 152 mm), the refractory lining with insulation layer thickness of 8.02 mm, permanent layer thickness of 76.20 mm, and working layer thickness of 148.61 mm has the best thermal insulation performance and longer service life within the variation of ladle refractory lining structure parameters. Finally, the results of the optimization are verified and analyzed in this paper. The study found that by optimizing the design of the ladle refractory lining, the maximum equivalent force on the ladle lining, the maximum temperature on the ladle shell and the ladle mass were reduced. The thermal insulation performance and the lightweight performance of the ladle are improved, which is very important for improving the service life of the ladle.

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“…In object detection, the object detection algorithm used not only needs to have high detection accuracy, but also has a certain detection speed and a small model memory, that is, to meet the requirements of real-time and small parameter volume. 89,90 The evaluation index of the detection speed of object detection is the frame rate per second, that is, the number of pictures per second or the time required for the algorithm to process a chapter of pictures under the condition that the external conditions are consistent. This article uses FPS to represent the number of pictures that have completed detection per second, and the larger the FPS, the faster the model detection speed.…”

Section: 13mentioning

confidence: 99%

Substation instrumentation target detection based on multi‐scale feature fusion

Feng

Huang

Sun

et al. 2022

Concurrency and Computation

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SUMMARY With the promotion of smart grid construction work, the use of high‐precision and high‐efficiency substation inspection robot has become the development trend of substation inspection. A multi‐scale feature fusion meter target detection algorithm is proposed to address the problems of low efficiency and susceptibility to surrounding environmental factors by the traditional manual meter reading method. Kinecct is used to acquire color images of substation meters with different backgrounds, light intensities, and angles to build a substation meter dataset. Based on the complementarity and correlation of multi‐scale features, an SSD target detection model with multi‐scale feature fusion is established, and the performance of the algorithm is tested on the constructed dataset, and comparative experiments are conducted to verify the effectiveness of the algorithm for target detection accuracy improvement.

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Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition

Cited by 29 publications

References 97 publications

Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm

Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm

Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm

Substation instrumentation target detection based on multi‐scale feature fusion

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