This paper presents an efficient method to detect human pose with monocular color imagery using a parallel architecture based on deep neural network. The network presented in this approach consists of two sequentially connected stages of 13 parallel CNN ensembles, where each ensemble is trained to detect one specific kind of linkage of the human skeleton structure. After detecting all skeleton linkages, a voting score-based post-processing algorithm assembles the individual linkages to form a complete human structure. This algorithm exploits human structural heuristics while assembling skeleton links and searches only for adjacent link pairs around the expected common joint area. The use of structural heuristics in the presented approach heavily simplifies the post-processing computations. Furthermore, the parallel architecture of the presented network enables mutually independent computing nodes to be efficiently deployed on parallel computing devices such as GPUs for computationally efficient training. The proposed network has been trained and tested on the COCO 2017 person-keypoints dataset and delivers pose estimation performance matching state-of-art networks. The parallel ensembles architecture improves its adaptability in applications aimed at identifying only specific body parts while saving computational resources.

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This paper focuses on the effects of the off-design operation of CAES on the dynamic characteristics of the triple-gear-rotor system. A finite element model of the system is set up with unbalanced excitations, torque load excitations, and backlash which lead to variations of tooth contact status. An experiment is carried out to verify the accuracy of the mathematical model. The results show that when the system is subjected to large-scale torque load lifting at a high rotating speed, it has two stages of relatively strong periodicity when the torque load is light, and of chaotic when the torque load is heavy, with the transition between the two states being relatively quick and violent. The analysis of the three-dimensional acceleration spectrum and the meshing force shows that the variation in the meshing state and the fluctuation of the meshing force is the basic reasons for the variation in the system response with the torque load. In addition, the three rotors in the triple-gear-rotor system studied show a strong similarity in the meshing states and meshing force fluctuations, which result in the similarity in the dynamic responses of the three rotors.