The Structure Transfer Machine Theory and Applications

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. We propose a new representation learning method, termed Structure Transfer Machine (STM), which enables feature learning process to converge at the representation expectation in a probabilistic way. We theoretically show that such an expected value of the representation (mean) is achievable if the manifold structure can be transferred from the data space to the feature space. The resulting structure regularization term, named manifold loss, is incorporated into the loss function of the typical deep learning pipeline. The STM architecture is constructed to enforce the learned deep representation to satisfy the intrinsic manifold structure from the data, which results in robust features that suit various application scenarios, such as digit recognition, image classification and object tracking. Compared to state-of-the-art CNN architectures, we achieve the better results on several commonly used benchmarks\footnote{The source code is available. https://github.com/stmstmstm/stm }

Sensing Task Allocation for Heterogeneous Channels in Cooperative Spectrum Sensing

In the traditional centralized cooperative spectrum sensing, all secondary users sense the same channel. But, for a given channel, there exists detection performance diversity among all the users, due to the different signal-fading process. Involving the user with poor performance in cooperative sensing will not only deteriorate the detection correctness but also waste the sensing time. In the heterogeneous channels, the problem is even severe. A novel idea is to allocate the secondary users to sense different channels. We analyze the allocation problem before formulate it to be an optimization problem, which is a NP-hard problem. Then we propose the declined complexity algorithm in equal secondary user case and the two-hierarchy approach algorithm in unequal case. With the simulation, we verify the near optimality of the proposed algorithms and the advantage of the task allocation

Parametric design and optimization of engine disc

With the development of science and technology, traditional design methods can no longer meet the needs of modern design and production development. Parametric and optimal design has become one of the most popular application technologies in CAD. Parametric and optimal design was studied based on VB.net language for secondary development of AutoCAD and MATLAB language for secondary development of ANSYS Apdl for disc of aero-engine. The structural parameterization technology was used to realize the rapid forming, and genetic algorithm retains elite was used to optimal design. The optimal design was carried out by using the developed software in this paper, and the optimal results shown that the disc weight was reduced by 34.48 %, the reduction effect of weight was obvious