Semantic segmentation, aiming to assign semantic labels to each pixel, is broadly applied into many fields, such as video surveillance, medical image analysis, and autonomous driving. However, there are two challenges in semantic segmentation task: 1) the deficiency of rich contextual information; and 2) the lack of sufficient spatial information, all of which affect segmentation performance seriously. To solve these two challenges, the global feature capturing module (GFCM) and Conv Block are proposed in this paper to build a new model to improve segmentation performance. Specifically, GFCM, made of the global encoding module (GEM) and spatial attention module (SAM), is designed to extract adequate global contextual information and build global spatial dependencies. Composed of three convolution layers, Conv Block is proposed to preserve rich spatial information. Based on GFCM and Conv Block, a new model is designed, where a data-dependent upsampling operator (DUpsampling) is exploited to recover the pixel-wise prediction effectively. The extensive experiments have been made to prove the effectiveness of the design, and the new model achieves 73.69% mIoU on Cityscapes test set and 80.05% mIoU on PASCAL VOC 2012 test set without any post-processing.
The entity relation extraction in the form of triples from unstructured text is a key step for self-learning knowledge graph construction. Two main methods have been proposed to extract relation triples, namely, the pipeline method and the joint learning approach. However, these models do not deal with the overlapping relation problem well. To overcome this challenge, we present a relation-oriented model with global context information for joint entity relation extraction, namely, ROMGCJE, which is an encoder–decoder model. The encoder layer aims to build long-term dependencies among words and capture rich global context representation. Besides, the relation-aware attention mechanism is applied to make use of the relation information to guide the entity detection. The decoder part consists of a multi-relation classifier for the relation classification task, and an improved long short-term memory for the entity recognition task. Finally, the minimum risk training mechanism is introduced to jointly train the model to generate final relation triples. Comprehensive experiments conducted on two public datasets, NYT and WebNLG, show that our model can effectively extract overlapping relation triples and outperforms the current state-of-the-art methods.
Abstract. In mixed yards, the stacking status of the container is not in the order of the shipment, causing the efficiency of the process of loading and unloading decreased. Therefore, it is very important to come up with a method using the existing facilities to improve the efficiency and reduce the cost. In this paper(Produces the permission block, and copyright information Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).) We propose a method that sets a buffer in the yard to minimize the duration of the shifting process. To implement this method, the integer programming model is founded and the small scale accurate solution is used to implement the crane scheduling in the shifting process. The results showed that the model mentioned earlier can solve the problem of crane scheduling in the process of shifting with efficiency and provide strategic support for the management of yard.
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