639 research outputs found
Experimental study on mechanical performance of partially precast steel reinforced concrete beams
[EN] In order to exploit the potentials in mechanical and constructional performance of steel reinforced concrete structures and prefabricated structures, three innovative kinds of partially precast steel reinforced concrete beams, which are abbreviated here as PPSRC, HPSRC and PPCSRC beam, are presented in this paper. The PPSRC beam is composed of two parts, which are the precast outer shell with high-performance concrete and the cast-in-place inner part with common-strength concrete. Meanwhile, on the basis of PPSRC beam, the PPCSRC beam applies castellated steel shape and the HPSRC beam keeps the beam core hollow. With the aim to investigate the mechanical behavior, failure mode and bearing capacity of the PPSRC, PPCSRC and HPSRC beams, a static loading experiment with twenty four specimens was carried out. The effects of aspect ratio, construction method, section shape, concrete flange and strength of concrete were critically examined. Test results indicate that the HPSRC, PPCSRC and PPSRC beams both exhibit similar mechanical performance and bonding performance. The flexural capacity and shear capacity are seldom affected by the construction method and section shape, and increase with the increasing of the cast-in-place concrete strength. The shear strength of the specimens is significantly affected by the concrete flange and aspect ratio.Yang, Y.; Xue, Y.; Yu, Y.; Liu, R. (2018). Experimental study on mechanical performance of partially precast steel reinforced concrete beams. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 107-114. https://doi.org/10.4995/ASCCS2018.2018.6942OCS10711
Integrated Sensing, Computation, and Communication: System Framework and Performance Optimization
Integrated sensing, computation, and communication (ISCC) has been recently
considered as a promising technique for beyond 5G systems. In ISCC systems, the
competition for communication and computation resources between sensing tasks
for ambient intelligence and computation tasks from mobile devices becomes an
increasingly challenging issue. To address it, we first propose an efficient
sensing framework with a novel action detection module. It can reduce the
overhead of computation resource by detecting whether the sensing target is
static. Subsequently, we analyze the sensing performance of the proposed
framework and theoretically prove its effectiveness with the help of the
sampling theorem. Then, we formulate a sensing accuracy maximization problem
while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve
it, we propose an optimal resource allocation strategy, in which the minimal
resource is allocated to computation tasks, and the rest is devoted to sensing
tasks. Besides, a threshold selection policy is derived. Compared with the
conventional schemes, the results further demonstrate the necessity of the
proposed sensing framework. Finally, a real-world test of action recognition
tasks based on USRP B210 is conducted to verify the sensing performance
analysis, and extensive experiments demonstrate the performance improvement of
our proposal by comparing it with some benchmark schemes
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