Complex pipe layout plays an important role in ship building. Pipes prefabricating and assembling often depends on testing and rebuilding in outfitting. This leads to more workload, high cost and longer manufacturing cycles. In this paper, a new virtual assembly system called Integrated Virtual Assembly Environment (IVAE) is introduced, in which the ship outfitting can be simulated and process planning can be completed. With the help of assembly constraints management, collision detection and response, assembly sequence and paths planning, users can assemble the parts interactively in a virtual outfitting environment, just as in an actual cabin. As well as for virtual ship outfitting, IVAE can also be used in general product development. As a typical case, the pipe layout in the engine room of a 5000 M³ hydraulic dredge from Hudong-Zhonghua Shipbuilding (Group) Co., Ltd is given to test virtual ship outfitting in IVAE.

Recently, crowdsensing receives extensive attentions to solve complex sensing tasks. However, these existing works mainly focus on the incentive issues of traditional bidding model. In practice, solutions of the incentive issues of the posted-pricing model, are more important for some realistic mobile crowdsensing (MCS) applications. However, only few works focus on the homogeneous MCS applications (each user can perform only a single task). To this end, in this article we propose a posted-pricing incentive mechanism for heterogeneous MCS applications (different users have different limits of the number of assignments). Furthermore, considering more general MCS applications (satisfying $K$ -submodular function: the $K$ -set functions that are submodular in every type $k\in [K]$ ), we define the weighted marginal utilities and explore two posted-pricing incentive mechanisms for heterogeneous and $K$ -submodular MCS applications, respectively. Moreover, both of the two mechanisms are based not only on heterogeneous or $K$ -submodular sensing tasks, but also on heterogeneous sensing types. Finally, performance analysis shows that our mechanisms satisfy the desired properties, such as the truthfulness of acceptance decision, the individual rationality,budget feasibility,computational efficiency, and the regret minimization. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our proposed mechanism.