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Key characteristics (KCs) play a significant role in product lifecycle management (PLM) and in collaborative and global product development. Over the last decade, KCs methodologies and tools have been studied and practiced in several domains of the product lifecycle, and many world-class companies have introduced KCs considerations into their product development practices. However, there has been no systematic survey of KCs techniques, methodologies, and practices in this respect. This paper aims to give a comprehensive survey of KCs methodologies, and practices from the perspective of enterprise integration and PLM. The paper firstly presents a holistic framework of KCs methodologies and practices through the product lifecycle, and summarizes the fundamentals of KCs including their definition and classification, KC flowdown, and the identification and selection of KCs. A review of the KCs methods and practices in the product lifecycle is then presented, particularly in engineering design, manufacturing planning, production and testing as well as information and knowledge management respectively. Finally, the problems and challenges for future research on KCs techniques are discussed.

In mobile augmented/virtual reality (AR/VR), real-time 6-Degree of Freedom (DoF) motion tracking is essential for the registration between virtual scenes and the real world. However, due to the limited computational capacity of mobile terminals today, the latency between consecutive arriving poses would damage the user experience in mobile AR/VR. Thus, a visual-inertial based real-time motion tracking for mobile AR/VR is proposed in this paper. By means of high frequency and passive outputs from the inertial sensor, the real-time performance of arriving poses for mobile AR/VR is achieved. In addition, to alleviate the jitter phenomenon during the visual-inertial fusion, an adaptive filter framework is established to cope with different motion situations automatically, enabling the real-time 6-DoF motion tracking by balancing the jitter and latency. Besides, the robustness of the traditional visual-only based motion tracking is enhanced, giving rise to a better mobile AR/VR performance when motion blur is encountered. Finally, experiments are carried out to demonstrate the proposed method, and the results show that this work is capable of providing a smooth and robust 6-DoF motion tracking for mobile AR/VR in real-time.

Nowadays, real-time scheduling is one of the key issues in cyber-physical system. In real production, dispatching rules are frequently used to react to disruptions. However, the man-made rules have strong problem relevance, and the quality of results depends on the problem itself. The motivation of this paper is to generate effective scheduling policies (SPs) through off-line learning and to implement the evolved SPs online for fast application. Thus, the dynamic scheduling effectiveness can be achieved, and it will save the cost of expertise and facilitate large-scale applications. Three types of hyper-heuristic methods were proposed in this paper for coevolution of the machine assignment rules and job sequencing rules to solve the multi-objective dynamic flexible job shop scheduling problem, including the multi-objective cooperative coevolution genetic programming with two sub-populations, the multi-objective genetic programming with two sub-trees, and the multi-objective genetic expression programming with two chromosomes. Both the training and testing results demonstrate that the CCGP-NSGAII method is more competitive than other evolutionary approaches. To investigate the generalization performance of the evolved SPs, the nondominated SPs were applied to both the training and testing scenarios to compare with the 320 types of man-made SPs. The results reveal that the evolved SPs can discover more useful heuristics and behave more competitive than the man-made SPs in more complex scheduling scenarios. It also demonstrates that the evolved SPs have a strong generalization performance to be reused in new unobserved scheduling scenarios. INDEX TERMS Scheduling, flexible job shop, hyper-heuristic, multi-objective, genetic programming. NOMENCLATURE MO-DFJSP multi-objective dynamic flexible job shop scheduling problem MAR machine assignment rule JSR job sequencing rule SP scheduling policy GEP genetic expression programming CCGP cooperative coevolution genetic programming with two sub-populations TTGP genetic programming with single population that a GP individual contains two sub-trees NSGAII nondominated sorting genetic algorithm II SPEA2 strength Pareto evolutionary algorithm 2