Yong Wang

Yong Wang, Yanzhong Yao, Jiawei Guo et al.

For multi-scale problems, the conventional physics-informed neural networks (PINNs) face some challenges in obtaining available predictions. In this paper, based on PINNs, we propose a practical deep learning framework for multi-scale problems by reconstructing the loss function and associating it with special neural network architectures. New PINN methods derived from the improved PINN framework differ from the conventional PINN method mainly in two aspects. First, the new methods use a novel loss function by modifying the standard loss function through a (grouping) regularization strategy. The regularization strategy implements a different power operation on each loss term so that all loss terms composing the loss function are of approximately the same order of magnitude, which makes all loss terms be optimized synchronously during the optimization process. Second, for the multi-frequency or high-frequency problems, in addition to using the modified loss function, new methods upgrade the neural network architecture from the common fully-connected neural network to special network architectures such as the Fourier feature architecture, and the integrated architecture developed by us. The combination of the above two techniques leads to a significant improvement in the computational accuracy of multi-scale problems. Several challenging numerical examples demonstrate the effectiveness of the proposed methods. The proposed methods not only significantly outperform the conventional PINN method in terms of computational efficiency and computational accuracy, but also compare favorably with the state-of-the-art methods in the recent literature. The improved PINN framework facilitates better application of PINNs to multi-scale problems.

Wenjian Sun, Linying Pan, Jingyu Xu et al.

Autonomous driving technology can improve traffic safety and reduce traffic accidents. In addition, it improves traffic flow, reduces congestion, saves energy and increases travel efficiency. In the relatively mature automatic driving technology, the automatic driving function is divided into several modules: perception, decision-making, planning and control, and a reasonable division of labor can improve the stability of the system. Therefore, autonomous vehicles need to have the ability to predict the trajectory of surrounding vehicles in order to make reasonable decision planning and safety measures to improve driving safety. By using deep learning method, a safety-sensitive deep learning model based on short term memory (LSTM) network is proposed. This model can alleviate the shortcomings of current automatic driving trajectory planning, and the output trajectory not only ensures high accuracy but also improves safety. The cell state simulation algorithm simulates the trackability of the trajectory generated by this model. The research results show that compared with the traditional model-based method, the trajectory prediction method based on LSTM network has obvious advantages in predicting the trajectory in the long time domain. The intention recognition module considering interactive information has higher prediction and accuracy, and the algorithm results show that the trajectory is very smooth based on the premise of safe prediction and efficient lane change. And autonomous vehicles can efficiently and safely complete lane changes.

Yong Wang

We refine the relation of Web service orchestration, abstract process, Web service, and Web service choreography in Web service composition, under the situation of cross-organizational corporation. We also introduce the formal verification process of this relation through an example.

Yong Wang

Web Service Composition creates new composite Web Services from the collection of existing ones to be composed further and embodies the added values and potential usages of Web Services. Web Service Composition includes two aspects: Web Service orchestration denoting a workflow-like composition pattern and Web Service choreography which represents an aggregate composition pattern. There were only a few works which give orchestration and choreography a relationship. In this paper, we introduce an architecture of Web Service Composition runtime which establishes a natural relationship between orchestration and choreography through a deep analysis of the two ones. Then we use an actor-based approach to design a language called AB-WSCL to support such an architecture. To give AB-WSCL a firmly theoretic foundation, we establish the formal semantics of AB-WSCL based on concurrent rewriting theory for actors. Conclusions that well defined relationships exist among the components of AB-WSCL using a notation of Compositionality is drawn based on semantics analysis. Our works can be bases of a modeling language, simulation tools, verification tools of Web Service Composition at design time, and also a Web Service Composition runtime with correctness analysis support itself.