In this article, we investigate the triggering behaviors of periodic event-triggered multiagent systems (MASs) under multiplicative false data injection (FDI) attacks. An abstraction-based traffic model is established to characterize all possible triggering behaviors under arbitrary initial states, including the minimum interevent time (MIET) and the transition relations among IETs. We further answer the following two questions: 1) how FDI attacks affect the MIET and 2) how to select the sampling period for the anomalous MIET detection. As a potential application scenario, a behavior-based anomaly detection algorithm is developed based on the proposed traffic model to identify anomalous triggering behaviors caused by attacks. Simulations demonstrate the effectiveness and practical application of the proposed results.

The aboveground biomass (AGB) is an important index for assessing the growth of crop rubber trees and is directly related to the assessment of the health status and carbon storage of rubber plantations. Remote sensing imagery combined with deep learning (DL) is a powerful tool for estimating crop AGB. However, the current DL models are limited to using a convolution kernel with a fixed geometry structure and a single channel to extract the spatial and spectral features of multispectral images. Therefore, the accuracy of the existing AGB estimation models needs further improvement. In this study, we propose a two-branch deformable and atrous network (TDA-Net) for AGB estimation. We construct two branches of convolutional neural networks: a deformable convolutional module to extract the spatial features and an atrous convolutional module to extract the spectral features from satellite images. The AGB of rubber plantations is estimated using the proposed TDA-Net. The experimental results show that the estimation performance of the proposed TDA-Net is superior (${R}^{2}$= 0.802) to that of the currently used models. The results of this study can provide a basis for crop AGB estimation. To the best of our knowledge, this is the first time using submeter-resolution satellite images for the AGB estimation.

In this paper, we address the event-triggered consensus problem for double-integrator multi-agent systems with prescribed performance. First, we propose a unified event-triggered consensus framework that accommodates various performance specifications. Within this framework, we present a novel nonlinear control protocol that operates at intermittent time instants, utilizing relative positions and absolute velocities. These time instants are determined by a delicately designed edge-based event-triggering mechanism, while guaranteeing the prescribed performance specifications on relative positions. Furthermore, we extend the control protocol to encompass the guaranteed performance specifications for both relative positions and relative velocities. To eliminate the need for continuous monitoring of neighboring agents’ states, a unified self-triggered consensus framework with guaranteed performance is developed. With the proposed triggering mechanisms, no agent exhibits Zeno behavior. Finally, we validate the effectiveness of the proposed control protocols through an illustrative example.

This article addresses the performance guaranteed consensus tracking problem for high-dimensional double-integrator multiagent systems. By integrating a dynamic surface-based back-stepping method, we propose an edge-based prescribed performance control (PPC) scheme, independent of any topology-related information. Unlike most existing works on the 1-D error constraint, the transient tracking performance in our work is ensured by imposing a constraint on the high-dimensional error norm, thereby avoiding the decoupling of the states. Moreover, a composite predefined time-based performance function is developed to ensure the user-defined settling time and steady-state error accuracy, while relaxing the dependence on initial states. We further establish a unified error transformation framework for the norm-based PPC approach. Lastly, a numerical simulation and a formation application example verify the effectiveness of the proposed control scheme.

Quality of Experience (QoE) models need good generalization that necessitates sufficient amount of user-labeled datasets associated with measurements related to underlying QoE factors. However, obtaining QoE datasets is often costly, since they are preferably collected from many subjects with diverse background, and eventually dataset sizes and representations are limited. Models can be improved by sharing and merging those collected local datasets, however regulations such as GDPR make data sharing difficult, as those local user datasets might contain sensitive information about the subjects. A privacy-preserving machine learning approach such as Federated Learning (FL) is a potential candidate that enables sharing of QoE data models between collaborators without exposing ground truth, but only by means of sharing the securely aggregated form of extracted model parameters. While FL can enable a seamless QoE model management, if collaborators do not have the same level of data quality, more iterations of information sharing over a communication channel might be necessary for models to reach an acceptable accuracy. In this paper, we present an ensemble based Bayesian synthetic data generation method for FL, LOO (Leave-One-Out), which reduces the training time by 30% and the network footprint in the communication channel by 60%.

In this paper, we study the consensus problem for a class of linear multiagent systems, where the communication networks are directed. First, a dynamic event-triggering mechanism is introduced, including some existing static event-triggering mechanisms as its special cases. Second, based on the dynamic event-triggering mechanism, a distributed control protocol is developed, which ensures that all agents can reach consensus with an exponential convergence rate. Third, it is shown that, with the dynamic event-triggering mechanism, the minimum interevent time between any two consecutive triggering instants can be prolonged and no agent exhibits Zeno behavior. Finally, an algorithm is provided to avoid continuous communication when the dynamic event-triggering mechanism is implemented. The effectiveness of the results is confirmed through a numerical example.

Trajectory detection is a classic problem, which is often used to predict trajectories and optimize trajectory paths. In video games, trajectory algorithms are often used to implement the motion of three-dimensional objects such as bullets, shells, and arrows. However, archery games have poor simulation effect on the arrow trajectory. The motion of the arrow is not affected by the tension and the material of the bow. Arrow trajectory optimization is necessary to achieve a more realistic simulation. So, this paper uses Unity3D to simulate the trajectory of the arrow, and proposes an arrow trajectory algorithm that is influenced by the player's motion state, the material of the bow, initial velocity of the arrow, distance, gravity and air resistance.

Medical insurance management is the core content of social security management. In recent years, there are many events about medical insurance illegal behaviors cause a large number of medical insurance fund losses. How to effectively supervise the medical insurance fund to protect the legal rights of the insured person has become an urgent problem. Due to the low efficiency of audit work and the hidden illegal behavior, traditional medical insurance supervision systems often waste massive resources, but still cannot play an effective role. Based on analyzing the status of medical insurance supervision and the importance of transforming supervision mode, this paper mainly studies the design and implementation of an intelligent medical insurance supervision system.

3D model cutting is a classic problem in computer graphics. There are many cutting algorithms in the fields of engineering, medicine, architecture, education and games. Perforation is a special cutting problem. In many video games, there are such requirements that 3D game objects are punched at any positions and any angles at running time. This paper purposes a dynamic and efficient perforation algorithm which can apply to the 3D objects in video games.

Optics is an experiment-oriented lesson that plays an important role in middle school physics. Simulation technology based on game engine has attracted many educators and teachers to explore its application in pedagogy because it is attractive, interactive and low-cost. This paper designs an optical experiment simulation based on the Unity3D game engine, which implements three optical experiments: mirror reflection, water refraction and Young's double-slit interference. Furthermore, this paper constructs an optical experiment simulation software library, which can be reused to other optical simulation experiment system.

Electromagnetism is an experiment-oriented course which plays an important role in middle school physics. However, traditional electromagnetism experiments have many shortcomings. Simulation based on game engine has attracted many educators and teachers to explore its application in the educational field. This paper designs electromagnetism experiment simulation based on the Unity3D game engine, which implements three electromagnetism experiments: static charge, electrostatic induction and earth's magnetic field. Furthermore, this paper constructs a electromagnetism experiment simulation software library, which can be reused to other electromagnetism simulation experiment system.

Cloud simulation is an important research-field in computer graphics for a long time, which plays important roles in the weather forecast, flight simulation, big game scenes and so on. Large-scale cloud simulation, especially the realistic interactive cloud, is a difficult problem. This paper proposes a new method to simulate interactive large-scale cloud in games efficiently by combining 2D clouds and 3D volume clouds.

Chemical is an experiment-oriented lesson that plays an important role in middle school curriculum. However, traditional chemical experiment has many disadvantages. Simulation technology based on game platform has attracted many educators and teachers, which can overcome limitations of traditional chemical experiments [1]. This paper designs a chemistry experiment simulation based on Unity3D game engine, which simulates many chemical phenomena such as sedimentation, gas generation, combustion, discoloration, etc. Students can perform chemical experiments in this application via simple interaction and observe the experimental results. Furthermore, this paper constructs a chemical experimental simulation software library, which can be reused to other chemical experiment simulation applications.

The problem of fault diagnosis for static processes has been well studied over the last decades. However, fault diagnosis methods have rarely considered processes subject to unknown periodic disturbances. Using the well-established orthogonal function technique, this paper proposes a data-driven fault diagnosis method to deal with this challenge. The basic idea is to first design the orthogonal functions, and then identify the unknown weighting parameters. By removing the influence of the periodic disturbances, the residual signal can be obtained. Then, the fault detection problem is solved by monitoring the change of the residual signal. The performance and effectiveness of the proposed approach are demonstrated with a numerical case study and an experimental study based on a pilot scale, continuous stirred tank heater.

Ordinary Fluid Simulation focuses on the effects of the fluid surface, like seas, rivers and lakes, which is massive and unshakable. Few researches have devoted to simulate the phenomenon of fluid in containers, especially the behaviors of contained fluid being moved or tilted. This paper concentrates on dynamic simulation of fluid in containers. Based on Unity3D and Smoothed Particle Hydrodynamics (SPH), we proposes an interactive application, which demonstrates how to manipulate fluid in containers, such as shaking a cup of water, tilting and pouring water from one cup to another.

Machinima is which use real-time graphics rendering platform game engine to create animated videos or films. Traditional methods of machinima filming are stand-alone methods that using artificial intelligence scripting to implement the plot. However, massive script writing work of artificial intelligence will cost a lot of manpower and resources. This paper proposes a method based on network game and Kinect. Compared to traditional machinima filming in stand-alone environment, this method based on network game framework, uses Kinect to control character's motions and added functions to control actor's position, implements a more complete machinima system.

Machinima is which use real-time graphics rendering platform game engine to create animated videos or films. Traditional methods of machinima filming are stand-alone methods that using artificial intelligence scripting to implement the plot. However, massive script writing work of artificial intelligence will cost a IoT of manpower and resources. This paper proposes a method based on network game and Kinect. Compared to traditional machinima filming in stand-alone environment, this method based on network game framework, uses Kinect to control character's motions and added functions to control actor's position, implements a more complete machinima system.

Serious game as a new educational technology product, with the concepts of education and game widening, gradually has a lot of new highlights. The update of the hardware device and applications, increasingly interesting contents and interactions make serious game carrying more diverse functions and playing more important parts. At the same time, more possibilities also bring more challenges and risks. This article will take the serious game app Beaker as an example to analyze the current situation of serious game development, integration with the field of education, interactive innovation in the serious games, and upcoming problems with available development strategies.

Along With the progress of computer games, a considerable variety of game has appeared. There is an increasing demand of digital entertainment. It is common for game players to pursuit a better game interaction experience and visual experience. Game art designers pay more attention to achieve realistic effects. In these years, particle effects are widely used in game development. In this article, through the analysis of the basic principle and application method of Unity3D particle system, we design and implement common game scene effects and characters skills effects, mainly simulation and realize the effect of rain fall and design the Special “lightning” effects of characters skills.

In this paper, we consider the cooperative output regulation problem of heterogeneous linear multi-agent systems (MASs) by event-triggered control. We first develop an event-triggering mechanism for leader-following consensus of homogeneous MASs. Then by proposing an internal reference model for each agent, a novel distributed event-triggered control scheme is developed to solve the cooperative output regulation problem of heterogeneous MASs. Furthermore, a novel self-triggered control scheme is also proposed, such that continuous monitoring of measurement errors can be avoided. The feasibility of both proposed control schemes is studied by excluding Zeno behavior for each agent. An example is finally provided to demonstrate the effectiveness of the control schemes.

This paper addresses the output consensus problem of heterogeneous linear multi-agent systems. We first propose a novel distributed event-triggered control scheme. It is shown that, with the proposed control scheme, the output consensus problem can be solved if two matrix equations are satisfied. Then, we further propose a novel self-triggered control scheme, with which continuous monitoring is avoided. By introducing a fixed timer into both event- and self-triggered control schemes, Zeno behavior can be ruled out for each agent. The effectiveness of the event- and self-triggered control schemes is illustrated by an example.

Because of the development of the technology, people's material and cultural conditions is developing faster and faster, life conditions is getting better and better living conditions. However, the improvement of inherent virtue of people has failed to match the growing material and cultural criteria. Many parents are busy in work, leading to many children cannot obtain a culture etiquette and upbringing at an early age, children cannot treat people how to do things correctly, this is more harm than good for children's emotional intelligence development and future life. But the growing material conditions has created a possibility for the popularity of computer games, children can come into contact with computers and other modern electronic products when they are very small, which also getting children premature contact with, even hooked on computer games. So game developers can combine computer games with children etiquette education, let the children play games, but also by the influence of culture and etiquette. This game is arranged two scenes which are sitting room scene and bus scene, the player can choose a scene casually. After entering the game scenes, there will be a plot unfolds, players need to react differently and politely according to different scenarios before clearance. Meanwhile, the game also leave interface for future game developers, the child's answer can be speech recognized in the future.

This paper studies the consensus problem of multi-agent systems with general linear dynamics. We propose a novel event-triggered control scheme with some desirable features, namely, distributed, asynchronous, and independent. It is shown that consensus of the controlled multi-agent system can be reached asymptotically. The feasibility of the event-triggered strategy is further verified by the exclusion of both singular triggering and Zeno behavior. Moreover, a self-triggered algorithm is developed, where the next triggering time instant for each agent is determined based on its local information at the previous triggering time instant. Continuous monitoring of measurement errors is thus avoided. The effectiveness of the proposed control schemes is demonstrated by two examples.