Supporting the quality of service of unlicensed users in cognitive radio networks is very challenging, mainly due to dynamic resource availability because of the licensed users' activities. In this paper, we study the optimal admission control and channel allocation decisions in cognitive overlay networks in order to support delay sensitive communications of unlicensed users. We formulate it as a Markov decision process problem, and solve it by transforming the original formulation into a stochastic shortest path problem. We then propose a simple heuristic control policy, which includes a threshold-based admission control scheme and and a largest-delay-first channel allocation scheme, and prove the optimality of the largestdelay-first channel allocation scheme. We further propose an improved policy using the rollout algorithm. By comparing the performance of both proposed policies with the upper-bound of the maximum revenue, we show that our policies achieve closeto-optimal performance with low complexities. .hk), with main research focus on nonlinear optimization and game theoretical analysis of communication networks, especially on network economics, cognitive radio networks, and smart grid. He is the recipient of the IEEE Marconi Prize
The conventional method of detecting a gear fault is to demodulate the vibration signal collected from the gearbox based on the Hilbert transform; however, this requires human intervention and lacks sophistication. Empirical mode decomposition (EMD) is a significant timefrequency tool for adaptively decomposing vibration signals into a collection of intrinsic mode functions (IMFs); a fault feature can be extracted from one of IMFs to reveal the fault location and fault level of a gear or bearing in the mechanical drive system. In this paper, a multi-harmonic vibration model of a gearbox with fault modulation is presented, a conventional demodulation analysis using Hilbert transform is introduced, and the principle of EMD is illustrated. The Hilbert demodulation analysis and EMD are applied to processing field vibration signals collected from a wind turbine gearbox to detect a gear-pitting fault. The results show that EMD can extract the fault modulation information more adaptively and intelligently than Hilbert demodulation analysis can.
Selective laser melting (SLM) is a key technology for direct forming of metal parts in 3D printing technology. SLM technology can realize the direct forming of parts with complex shape, high dimensional accuracy and excellent mechanical properties. It is especially suitable for rapid manufacturing of personalized and customized structures of aerospace difficult-to-machine parts. SLM forming process involves complex physical and chemical behavior of materials. Forming mechanism is quite different from traditional casting processes. The process parameters are complex and difficult to control. In this paper, a set of SLM forming process parameters suitable for Ti6Al4V alloy has been explored through the development of a SLM forming equipment of ESO M290 in Germany. The innovation of this product application design is the SLM printing design and process of minimal size dual-antenna, and the process method and design idea are universal. SLM forming technology based on Ti6Al4V alloy will be more and more widely used in aerospace field.
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