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With the explosive growth of temporal data, how to query and manage temporal data has become an important research issue. Resource description framework (RDF), as the standard data and knowledge description language of the semantic web, has been widely used to represent various domain data. Aiming at the representation and querying of temporal data, this paper proposes a temporal data representation model based on RDF and its corresponding querying method. First, a representation model called RDFt is proposed, which can represent temporal data with both the time information and the update count information, and the syntax and semantics of the RDFt model are given. Then, we propose a query language called SPARQL[t] for RDFt, and we give the query syntax and operations of SPARQL[t] in detail. In addition, a querying transformation algorithm from SPARQL[t] to SPARQL is proposed, in order to achieve compatibility with the existing RDF query engines. Finally, we implemented a prototype system that can support RDFt temporal data representation and querying, and the case studies and experimental results verify the feasibility of the proposed approach.

Sparse learning is an efficient technique for feature selection and avoiding overfitting in machine learning research areas. Considering sparse learning for real-world problems with online learning demands in neural networks, an online sparse supervised learning of extreme learning machine (ELM) algorithm is proposed based on alternative direction method of multipliers (ADMM), termed OAL1-ELM. In OAL1-ELM, an ℓ1-regularization penalty is added in loss function for generating a sparse solution to enhance the generalization ability. This convex combinatorial loss function is solved by using ADMM in a distributed way. Furthermore, an improved ADMM is used to reduce computational complexity and to achieve online learning. The proposed algorithm can learn data one-by-one or batch-by-batch. The convergence analysis for the fixed point of the solution is given to show the efficiency and optimality of the proposed method. The experimental results show that the proposed method can obtain a sparse solution and have strong generalization performance in a wide range of regression tasks, multiclass classification tasks, and a real-world industrial project.

This paper deals with the speed synchronization controller design for networked integrated motor-transmission (IMT) powertrains via controller area network (CAN). It is well known that, in current implementations, CAN has been widely used in the control system design of automotive powertrains. However, on the other hand, the application of CAN would not only lead to network-induced delays but also bring about protocol constrains, e.g., data package capability and utilization ratio limitation, which would deteriorate the system and make the controller design a challenging problem. This paper is to provide a co-design methodology that can cope with all these problems and ensure satisfactory control effect for the speed synchronization control of IMT powertrain systems. First, a networked IMT powertrain system using CAN as underlying network is presented and the dynamic model for the speed synchronization control is derived. Second, the network-induced delay model is introduced and improved considering data packet capability and utilization ratio limitation. The control-orient discrete-time model is also derived based on the improved delay model. Third, a co-design methodology using sliding mode controller and offline priority scheduling based on Lyapunov stability criterion is proposed. The results of simulations and tests show the effectiveness of the proposed co-design methodology.