With the continuous development of intelligent mine construction, researchers are applying knowledge graph technology to further realize the intellectualization of coal mine. However, in the research field of coal mine safety equipment, there are little organization and construction of relevant knowledge, resulting in fragmentation, redundancy and limited localization of knowledge in this field. Based on this, this paper constructs knowledge graph of coal mine safety equipment to expand knowledge construction in this field. Firstly, self-built CMSE-EntityRel dataset. Secondly, entity relationship joint extraction is carried out based on CasREL-Mine framework. Finally, we use the graph database Neo4j to store and visually display entity and relational knowledge triples. At this stage, we construct the knowledge graph of coal mine safety equipment field. The method adopted in this paper can effectively realize the knowledge construction and organization in the field of coal mine safety equipment, and provides data support for intelligent management level of coal mine safety production.
KEYWORDS: Video, Video compression, Detection and tracking algorithms, Databases, Image segmentation, Video processing, Lithium, Feature extraction, Color difference, Machine learning
Shot boundary detection is the main task in the preprocessing stage of content-based video operations. There are unforeseen illumination change and motion effects in a video due to the complexity of video content, which may lead to the detection of wrong shot boundaries. This paper proposes a novel shot boundary detection method to solve this problem. The method mainly includes two parts: abrupt and gradual transition detection. In the first stage, CIEDE2000 color-difference and adaptive threshold are used to find the possible abrupt transition frames. Then BRISK feature is utilized to extract real abrupt transition frames. In the next stage, the brightness change of video frames is utilized to detect the frame group that may be gradual. Then CIEDE2000 color-difference along with cumulative frame algorithm is used to detect actual gradual transition frames. The experiment is evaluated on the TRECVid2001 and ClipShots datasets. Experimental results show that the method proposed in this paper can improve the precision of shot segmentation.
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