Collaborative Modeling Method Research Articles

Design and implementation of a collaborative modeling architecture based on MDE (Multi-DDS Edge)

Abstract Virtual space is an integral part of the Metaverse. Typical virtual spaces are modeled step by step using artificial means. With the development needs of Metaverse modeling and simulation technology and the need for simulation fidelity and content richness in virtual space construction, efficient and correct modeling methods are required. It has become a bottleneck restricting the efficiency of virtual space modeling. This paper designs a multi-person collaborative virtual space modeling method, which improves the modeling efficiency, communication security, and interaction stability of the virtual space collaborative modeling method. Compared with existing collaborative modeling methods, this paper proposes a virtual space collaborative modeling method based on multi-edge DDS technology. By constructing MDE (multi-DDS edges) and MDE on user migration, collaborative construction in multiple virtual spaces is completed. Model data communication extends collaborative modeling technology from the virtual space creation stage to the virtual space simulation and deduction stage, and designs and verifies collaborative content, collaborative architecture, and collaborative communication. Experiments show that the architecture synergy efficiency is 145.6%, the architecture synergy efficiency is 212.5%, and the architecture synergy efficiency is 105.4%. As the number of people in collaborative modeling increases, the operational complexity and demonstration take a logarithmic form, meeting the needs of the future development of the metaverse.

Deep Federated Learning-Based Privacy-Preserving Wind Power Forecasting

Given the growing installed capacity, wind energy will exert a profound impact on the flexibility of modern energy systems. Wind power forecasting is a practical solution for dealing with the attributed variations and uncertainties, balancing supply and demand, and improving the reliability of the system. To achieve more accurate and generalizable forecast models, comprehensive data sets, supplied by multiple wind farms owing to their spatio-temporal dependencies, are required. In addition, data aggregation/collaboration across many wind farms scattered around a country is difficult, if not impossible, due to complex administrative processes, industry competition, and data privacy and security concerns. This article offers federated learning-based wind energy forecasting as a novel decentralized collaborative modeling method capable of training a single model on data from many wind farms without jeopardizing the privacy or security of data. To this end, rather than sending private data across sites, local model parameters are securely transmitted. A comparison between the proposed private distributed model and non-private centralized and fully private localized models indicates the high performance of the proposed federated learning-based wind power forecasting with 87.96% accuracy. Enjoying the smoothing effect, the higher generalizability of the proposed model with 83.63% accuracy is also substantiated in comparison to localized and centralized approaches while the privacy of the underlying data is preserved.

A Web-Based Collaborative Method for SysML Modeling

Abstract Model-based systems engineering (MBSE) with SysML has become one of the best solutions to the system design of complex systems, which usually requires the collaboration of different disciplines. Web-based collaborative modeling method looks like a prioritized option because of its advantages such as “light” client, convenient maintenance, and high model reliability. In this study, we propose a web-based collaborative SysML modeling method for MBSE. First, the architectures of asynchronous and synchronous collaboration are given. Then, some key technologies for both collaborative modes are discussed. Finally, a case study is given to demonstrate the effectiveness of the proposed method.

A blockchain-based cooperative modeling method for digital twin ontology model of the mechanical product

With the rapid increase of multi-source heterogeneous dynamic data of mechanical products, the digital twin technology is considered to be an important method to realize the deep integration of product data and intelligent manufacturing. As a digital archive of the physical entity in entire life cycle, the mechanical product digital twin model is cross-phased and multi-domain. Therefore, safe and stable cooperative modeling has become a basic technical problem that needs to be solved urgently. In this paper, we proposed a blockchain-based collaborative modeling method for the digital twin ontology model of mechanical products. First, an authorization network was constructed among stakeholders. Then modeling processes of the digital twin were mapped to ontology operations and formatted through extensible markup language. Finally, consensuses were obtained based on practical byzantine fault tolerance. And a material modification process of a helicopter damper bearing was taken as an example to verify. The proposed method enables all participants to accurately obtain the latest state of the digital twin model, and has the advantages of tamper-proof, traceability, and decentralization.

Modeling and Simulation Using Artificial Neural Network-Embedded Cellular Automata

For the accurate prediction of a complex system, determining how to model well it is essential. A classical simulation modeling method that abstracts causality between inputs and outputs utilizing knowledge such as physical laws or operating rules is widely used. However, it may cause a problem in reliability of the model’s validity if data acquisition of the actual system is difficult. Machine learning, on the other hand, is a method to represent a correlation between one set of data and another. The model can be built using the big data of the target system. It has a limitation in that it is impossible to predict accurately using the learned model if the parameters or the operating rules are changed after the model is learned. In this paper, we propose a collaborative modeling method using big data-based machine learning and simulation modeling. Specifically, a hypothetical model can be constructed through a cellular automata model (simulation modeling), and parameters and functions necessary for a hypothetical model can be simulated by learning and applying an artificial neural network model (machine learning). This paper shows that the proposed method can be applied to the traffic model to predict traffic congestion in an unsteady state.

Multi-view collaborative modeling method for complex system

It is hard to model a complex system by simply combining its partial characteristics. This paper presents a multi-view collaborative modeling method for complex system. Multi users, multi subjects, multi granularities and multi models in complex system modeling are unified to multi views. The principles and schemes for the decomposition of complex systems are introduced. According to the principle of separation of concerns, a complex system can be decomposed to a variety of model views. Collaborative modeling means that the views can be associated and integrated to form complete system model through their semantic. According to the form of representation, the models are generalizes to three kinds, i.e., view models, semantic models and storage models, which provides a unified framework for conversion and mapping between the models. Under the guidance of the method, a tool for the modeling of warship C2 system is developed. Applications show that the tool can support collaborative modeling and design for C2 system outstandingly.

Robotic Design Choice Overview Using Co-Simulation and Design Space Exploration

Rapid robotic system development has created a demand for multi-disciplinary methods and tools to explore and compare design alternatives. In this paper, we present a collaborative modeling technique that combines discrete-event models of controller software with continuous-time models of physical robot components. The proposed co-modeling method utilizes the Vienna development method (VDM) and MATLAB for discrete-event modeling and 20-sim for continuous-time modeling. The model-based development of a mobile robot mink feeding system is used to illustrate the collaborative modeling method. Simulations are used to evaluate the robot model output response in relation to operational demands. An example of a load-carrying challenge in relation to the feeding robot is presented, and a design space is defined with candidate solutions in both the mechanical and software domains. Simulation results are analyzed using design space exploration (DSE), which evaluates candidate solutions in relation to preselected optimization criteria. The result of the analysis provides developers with an overview of the impacts of each candidate solution in the chosen design space. Based on this overview of solution impacts, the developers can select viable candidates for deployment and testing with the actual robot.

The Study on Dynamic Modeling of Complex Mechanism Based on Multi Software Collaboration

At present, the accurate dynamic modeling and simulation of complex movement mechanism using a single software is a difficult problem. The paper proposes the "collaborative simulation run" method of multi-domain model based on the interfaces of multi simulation software, and it can achieve the design and analysis of complex mechanism system. Selecting the 3D modeling software Pro/E, the dynamics analysis software ADAMS and finite element analysis software ANSYS as a collaborative modeling analysis platform, the transmission relationship between software data interfaces is studied, and the rigid flexible coupling dynamic modeling process of complex mechanism through the Pro/E, ANSYS and ADAMS is explored, which prepares accurate model for dynamic simulation of complex mechanism. The study results show that the multi-software collaborative modeling method is suitable for complex mechanical dynamics modeling.