Modeling Rumor Diffusion Process With the Consideration of Individual Heterogeneity: Take the Imported Food Safety Issue as an Example During the COVID-19 Pandemic

Rice blast, rice sheath blight, and rice brown spot have become the most popular diseases in the cold areas of northern China. In order to further improve the accuracy and efficiency of rice disease diagnosis, a framework for automatic classification and recognition of rice diseases is proposed in this study. First, we constructed a training and testing data set including 1,500 images of rice blast, 1,500 images of rice sheath blight, and 1,500 images of rice brown spot, and 1,100 healthy images were collected from the rice experimental field. Second, the deep belief network (DBN) model is designed to include 15 hidden restricted Boltzmann machine layers and a support vector machine (SVM) optimized with switching particle swarm (SPSO). It is noted that the developed DBN and SPSO-SVM can simultaneously learn three proposed features including color, texture, and shape to recognize the disease type from the region of interest obtained by preprocessing the disease images. The proposed model leads to a hit rate of 91.37%, accuracy of 94.03%, and a false measurement rate of 8.63%, with the 10-fold cross-validation strategy. The value of the area under the receiver operating characteristic curve (AUC) is 0.97, whose accuracy is much higher than that of the conventional machine learning model. The simulation results show that the DBN and SPSO-SVM models can effectively extract the image features of rice diseases during recognition, and have good anti-interference and robustness.

Section: Discussionmentioning

confidence: 77%

Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm

Lü

Liu

et al. 2022

“…Thus, developing an approach to evaluate the relative importance of a performance criterion deserves further strives. Since the global and local search capabilities of PSO heavily affect its optimization performance and convergence speed, developing more advanced updating strategies by mixing PSO with some other approaches such as Faster RCNN ( Jiang, et al., 2021 ) or rumor diffusion process ( Chen et al, 2022 ) to enhance such two abilities of PSO could be the second significant issue in the near future to further improve and optimize the performance and the computation time of the upper level optimizer. Moreover, we are considering the possibility of comparing the developed approach with some other evolutionary algorithms by adding several more performance criteria in the lower level cost function in our shortcoming studies.…”

Section: Discussionmentioning

confidence: 99%

Cost Function Determination for Human Lifting Motion via the Bilevel Optimization Technology

Tang

Peng

Luo

et al. 2022

Investigating the optimal control strategy involved in human lifting motion can provide meritorious insights on designing and controlling wearable robotic devices to release human low-back pain and fatigue. However, determining the latent cost function regarding this motion remains challenging due to the complexities of the human central nervous system. Recently, it has been discovered that the underlying cost function of a biological motion can be identified from an inverse optimization control (IOC) issue, which can be handled via the bilevel optimization technology. Inspired by this discovery, this work is dedicated to studying the underlying cost function of human lifting tasks through the bilevel optimization technology. To this end, a nested bilevel optimization approach is developed by integrating particle swarm optimization (PSO) with the direction collocation (DC) method. The upper level optimizer leverages particle swarm optimization to optimize weighting parameters among different predefined performance criteria in the cost function while minimizing the kinematic error between the experimental data and the result predicted by the lower level optimizer. The lower level optimizer implements the direction collocation method to predict human kinematic and dynamic information based on the human musculoskeletal model inserted into OpenSim. Following after a benchmark study, the developed method is evaluated by experimental tests on different subjects. The experimental results reveal that the proposed method is effective at finding the cost function of human lifting tasks. Thus, the proposed method could be regarded as a paramount alternative in the predictive simulation of human lifting motion.

“…MOGA is widely used in various fields: scientific field, economic field, engineering field, etc ( Jiang et al, 2019a ; Sun and tian, 2022a ). And has also made good progress, such as robot path planning, optimal control ( Ma and Zhang, 2020 ; Qi and Jiang, 2020 ), mechanical structure case optimization design, mobile network planning, humanoid robot hub Design of neural motion controller, optimal design of solid rocket motor ( Jiang et al, 2019b ; Sun and Zhao, 2022b ), optimal design of postman path, optimal design of multi-sensor multi-object tracking data association problem, flexible multi-objective scheduling problem ( Chen and peng, 2021b ; Chen and Rong, 2022b ), vehicle scheduling, etc.…”

Section: Related Workmentioning

confidence: 99%

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

Sun

Huang

Cao³

et al. 2022

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.