Research on Dynamic Graphic Features in Digital Media Interfaces

In digital media interfaces,dynamic graphics integrate elements of animation design,graphic design,and cinematic language,presenting in various forms and demonstrating significant inclusivity.From the perspective of visual cognition,by capturing dynamic graphics within the interface,three-dimensional information such as time and space is acquired,and the cognition process exhibits characteristics of spatial and temporal correlation.In terms of information cognition,dynamic representation fragments information,resulting in a sense of discontinuity and intermittent memory.The efficiency of information cognition and transmission is enhanced through the coordination of audio,video,and other elements.In aesthetic perception,with the rise of Internet culture,people’s aesthetic perceptions towards dynamic graphic design have evolved,showcasing a generalization of form and the instantaneous nature of content.Technically,the complexity of design and the diversity of applications have established dynamic graphics as a typical design style label in digital media interfaces.

Discrete-time dynamic graphical games:model-free reinforcement learning solution

This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from to make all the agents synchronize to the state of a command multi-agent dynamical systems, where pinning control is used generator or a leader agent. Novel coupled Bellman equations and Hamiltonian functions are developed for the dynamic graphical games. The Hamiltonian mechanics are used to derive the necessary conditions for optimality. The solution for the dynamic graphical game is given in terms of the solution to a set of coupled Hamilton-Jacobi-Bellman equations developed herein. Nash equilibrium solution for the graphical game is given in terms of the solution to the underlying coupled Hamilton-Jacobi-Bellman equations. An online model-free policy iteration algorithm is developed to learn the Nash solution for the dynamic graphical game. This algorithm does not require any knowledge of the agents＇ dynamics. A proof of convergence for this multi-agent learning algorithm is given under mild assumption about the inter-connectivity properties of the graph. A gradient descent technique with critic network structures is used to implement the policy iteration algorithm to solve the graphical game online in real-time.

Graphic Methods in Topological Dynamics

This paper considers the graph sequence graph （f^n ）={x×f^n ： x ∈X} of the iterates f^n of a continuous map f ： X →X on a compact metric space （X, d）. The main aim is to introduce serval limit sets by using different recurrent time sets, and discuss the relation between dynamics of the underlying compact system （X, f） and the structure of these limit sets, from a topological or fractal viewpoint.

Development of a Simulation Package with Expert System for FMS in Windows Environment

This paper introduces the method of developing a simulation package with expert systemfor a flexible manufacturing system(FMS)in Windows environment and presents some sim-ulation results for real examples.

Co-regulated Protein Functional Modules with Varying Activities in Dynamic PPI Networks

Current methods for the detection of differential gene expression focus on finding individual genes that may be responsible for certain diseases or external irritants. However, for common genetic diseases, multiple genes and their interactions should be understood and treated together during the exploration of disease causes and possible drug design. The present study focuses on analyzing the dynamic patterns of co-regulated modules during biological progression and determining those having remarkably varying activities, using the yeast cell cycle as a case study. We first constructed dynamic active protein-protein interaction networks by modeling the activity of proteins and assembling the dynamic co-regulation protein network at each time point. The dynamic active modules were detected using a method based on the Bayesian graphical model and then the modules with the most varied dispersion of clustering coefficients, which could be responsible for the dynamic mechanism of the cell cycle, were identified. Comparison of results from our functional module detection with the state-of-art functional module detection methods and validation of the ranking of activities of functional modules using GO annotations demonstrate the efficacy of our method for narrowing the scope of possible essential responding modules that could provide multiple targets for biologists to further experimentally validate.