Improved multi-local world model and the analysis of its topological properties

Shoukui, Si; Xi-jing, Sun; Cuiling, Ma; Sun, Huijing

doi:10.1109/icicip.2011.6008374

Cited by 5 publications

(8 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Newton's second law and rigid body rotation law, the motion model of the wave energy device is established, and the differential equation is solved by the four-step Runge-Kutta algorithm, and the known wave excitation force data is used to continuously change the value of the unknown quantity to determine its optimal value, and finally the best model solution can be obtained. This solution is the change law of displacement and velocity with respect to time when the wave excitation force and damping coefficient of the damper are known [1] .…”

Section: The Fourth-order Runge-kutta Methods Was Used To Solve the M...mentioning

confidence: 99%

“…The data in this paper is derived from Question A of the 2022 Higher Education Cup National College Students Mathematical Modeling Competition, in order to achieve the maximum output of wave energy power, the linear damper and rotational damper coefficients of the wave energy device are optimized and solved. In order to better analyze and solve the problem, the following hypotheses are proposed: (1) It is assumed that seawater is an ideal fluid. If seawater is not an ideal fluid, it will have problems such as viscosity and rotation [4] , which will affect the establishment of experimental models.…”

Section: Data Sources and Assumptionsmentioning

confidence: 99%

“…Step3 Algorithm iteration: Set the step length 0.0001 h  , the traversal length is 40 wave periods, and calculate the parameters corresponding to the four first-order differential equations according to the recurrence formula, and then calculate the corresponding next discrete value ( 1) y n  ;…”

Section: Model Solvingmentioning

confidence: 99%

“…Combine equations 21 and 22, and take the velocity and angular velocity within a stationary period in Figures 2 and 4. The simulated annealing algorithm is used to solve the bi-objective optimization model [1] . The annealing temperature is set to 100, the number of traverses is 20, and the number of new solutions is 100.…”

Section: Model Solvingmentioning

confidence: 99%

See 3 more Smart Citations

Research on the maximum output power of wave energy

Xie,

Lin,

et al. 2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

View full text Add to dashboard Cite

For the design problem of maximum output power of wave energy device, the motion law of the device is obtained by establishing the vertical and longitudinal motion model, and the motion law of the device is obtained by using the fourthorder Runge-Kutta method, and the simulation annealing algorithm is used to optimize and solve the problem of linear damper and rotational damper coefficient optimization. According to the experimental data, MATLAB is used to calculate the maximum output power of the wave energy device and the optimal damping coefficient of the linear damper and rotary damper.

show abstract

Section: The Fourth-order Runge-kutta Methods Was Used To Solve the M...mentioning

confidence: 99%

Section: Data Sources and Assumptionsmentioning

confidence: 99%

Section: Model Solvingmentioning

confidence: 99%

Section: Model Solvingmentioning

confidence: 99%

See 2 more Smart Citations

Research on the maximum output power of wave energy

Xie,

Lin,

et al. 2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

View full text Add to dashboard Cite

show abstract

“…In order to better dig and analyze the implicit relationship in the data, this paper adopts the One-Hot Encoding to encode the variables, and adopts the BP neural network model [5,6] for learning. The loss function and cost function are defined as follows:…”

Section: Glass Type Identificationmentioning

confidence: 99%

An Ancient Glass Component Analysis Identification Model Based on Correlation Analysis and Neural Network Algorithm

Jiang,

Yang,

Chen

2024

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

Identification models for glassware are important because the influence of the burial environment on the degree of weathering of old glass can lead to an inaccurate assessment of its category. This paper explores the relationship between glass surface weathering and decoration, color and type by Chi-square test and Fisher’s exact test. The experiment results show that the surface weathering degree of glass is not related to its pattern and color, but is significantly related to its type. This paper has introduced a new method to obtain important characteristic factors for distinguishing high potash glass and barium lead glass and for subclassification by Random Forest Model, so as to carry out R-type clustering to obtain classification results with 95% accuracy and high sensitivity. To identify the unknown category of cultural relics, the method of applying the BP neural network with the Bayesian regularization algorithm for training is proposed to improve the generalization ability of the model. In addition, based on the Spearman correlation coefficient, the differences in chemical composition correlation between different types of glass are compared in this paper. In conclusion, this paper explores a method for modeling and validation of glass component data and constructs an identification model that takes the accuracy of classification and prediction results and high robustness into account.

show abstract

Target threat assessment model based on PCA-BP neural network

Shang,

Chen,

Dou

2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

In this paper, the air threat received in modern naval warfare is taken as the background, and based on the threat assessment index, PCA and BP neural networks are used to establish a target threat assessment model. Through simulation and analysis, the threat values of different incoming targets are derived, and the data results of this model and other models are compared. It is concluded that the results of this model are basically consistent with the original values, and the error is significantly smaller than that of the other models, which realizes the real-time dynamic detection of threatening targets, and provides a strong support for the subsequent combat program.

show abstract

Improved multi-local world model and the analysis of its topological properties

Cited by 5 publications

References 1 publication

Research on the maximum output power of wave energy

Research on the maximum output power of wave energy

An Ancient Glass Component Analysis Identification Model Based on Correlation Analysis and Neural Network Algorithm

Target threat assessment model based on PCA-BP neural network

Contact Info

Product

Resources

About