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Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC). Specifically, we design a component to predict potential relations, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity. Extensive experiments show that PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples.

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Process plant Computer-Aided Design (CAD) models can be roughly divided into 3D models and 2D engineering drawings. Matching calculation of these CAD models not only contributes to their coherency verification, but also benefits for the development of model retrieval. However, in process plant area, 3D models and 2D engineering drawings are different in both graphical representations and content structures, which leads to the inapplicability of current shape-feature based 2D & 3D matching approaches. To resolve this problem, a topological structure based algorithm is proposed. Exploiting component as the basic unit, we firstly transform 2D engineering drawings and 3D models into attribute graphs. Then, by introducing related researches of graph similarity, we calculate attribute graph similarity to measure the matching degree between their corresponding CAD models. The proposed algorithm is translation, rotation and similarity transformation consistent. Experimental results demonstrate its effectiveness and feasibility.

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While machining width is an important factor of the machining time of freeform surface finishing operations, in reality the kinematic capability of the machine tool is usually the bottleneck of achieving higher feed speed and optimal machining time. The purpose of this paper is to conveniently (and approximately) determine the optimal cut direction considering the speed kinematic capability of the machine tool, without having to compute the actual tool path. We propose a mathematical instrument, called Machine Kinematic Metric (MKM), to easily evaluate infinitesimal machining time on a freeform surface based on machine kinematic consideration. It's a tensor field similar to the metric tensor in differential geometry. MKM is integrated over the part surface to approximate the cut-direction-dependent total machining time, and used to determine the optimal cut direction that minimizes the machining time. To validate the accuracy of the prediction using MKM, we apply the method and compute the machining time at every direction with one degree apart and derive the optimal cut-direction. The computation is performed on two examples: a simple freeform surface and a complex die face model. We then use a commercial CNC emulator software from Huazhong CNC to precisely simulate the machining time in distributed cut directions (five degree apart) for the two models. We find that the optimal cut direction determined from CNC simulation is consistent with the prediction from the proposed method. It validates that the proposed method is a convenient and economical tool to approximately determine the optimal cut direction based on machine speed kinematic capability.