Multiple cylinders detection from large-scale and complex point clouds is a historical but challenging problem, considering the efficiency and accuracy. We propose a novel framework, named slicing-tracking-detection (STD), that detects multiple cylinders accurately and simultaneously from point clouds of large-scale and complex process plants. In this framework, the 3D cylinder detection problem is reformulated as a cylinder ingredients tracking task based on multi-object tracking (MOT). First, we generate slices from the input point cloud, and render them to slice sequence. Then, the cycle of a cylinder is modeled with a Markov Decision Process (MDP), where the ingredient is tracked with a template and the miss tracking is associated with ingredient proposals through reinforcement learning. Finally, by applying MDP for each cylinder, multiple cylinders can be detected simultaneously and accurately. Extensive experiments show that the proposed STD framework can significantly outperform the state-of-the-art approaches in efficiency, accuracy, and robustness. The source code is available at http://zhiyongsu.github.io.

物理层调制类无线隐信道将秘密信息转化为人工噪声传输,以避免被检测发现.在多输入多输出通信场景下,由于传输介质的开放性,检测方可以利用各天线信号存在相关性而发现隐信道.针对这个不足,提出一种基于预编码的多输入多输出无线隐信道.假设发送方与检测方均能获得两者之间多输入多输出信道状态信息,发送方可以利用该信道状态信息对生成的人工噪声进行预编码处理,以去除检测方接收到的多路信号之间的相关性.接收方可通过公开信道传输的该信道状态信息生成预编码矩阵,进而提取出秘密信息.仿真结果表明,所提出的多输入多输出无线隐信道相较于现有方法,去除了检测方接收到的多路信号之间的相关性,有效地提高了抗检测性,同时可靠性有了 一定的提高.

3D process plant models(PPMs) in the process industry normally consists of thousands of components. And, there are many similar local structures in the PPM. Due to the complex process flow, the topology relationship among components is very complicated. Therefore, designing a new PPM is quite time consuming. In order to shorten the design cycle, content based model retrieval for PPMs is an imperative requirement. In this paper, we propose a partial matching framework for PPMs based on graph matching aiming at improving design efficiency and realizing design reuse. The random walk algorithm is employed to distinguish similar local structures. Specifically, each PPM is represented by an undirected labeled graph. The local topological feature of each component is extracted based on the random walk algorithm. For partial matching, a subgraph isomorphism algorithm is introduced. The matching process is accelerated by using the local topological feature to generate an optimized initial state and alleviate the computation of feasible rules. Experimental results show the feasibility and effectiveness of our matching framework.