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Coal mine safety production forewarning based on improved BP neural network 被引量:39
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作者 Wang Ying Lu Cuijie Zuo Cuiping 《International Journal of Mining Science and Technology》 SCIE EI CSCD 2015年第2期319-324,共6页
Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method... Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production. 展开更多
关键词 Improved PSO algorithm BP neural network Coal mine safety production Early warning
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Integration system research and development for three-dimensional laser scanning information visualization in goaf 被引量:2
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作者 罗周全 黄俊杰 +2 位作者 罗贞焱 汪伟 秦亚光 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2016年第7期1985-1994,共10页
An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, clo... An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable. 展开更多
关键词 GOAF laser scanning visualization integration system 1 Introduction The goaf formed through underground mining of mineral resources is one of the main disaster sources threatening mine safety production [1 2]. Effective implementation of goaf detection and accurate acquisition of its spatial characteristics including the three-dimensional morphology the spatial position as well as the actual boundary and volume are important basis to analyze predict and control disasters caused by goaf. In recent years three-dimensional laser scanning technology has been effectively applied in goaf detection [3 4]. Large quantities of point cloud data that are acquired for goaf by means of the three-dimensional laser scanning system are processed relying on relevant engineering software to generate a three-dimensional model for goaf. Then a general modeling analysis and processing instrument are introduced to perform subsequent three-dimensional analysis and calculation [5 6]. Moreover related development is also carried out in fields such as three-dimensional detection and visualization of hazardous goaf detection and analysis of unstable failures in goaf extraction boundary acquisition in stope visualized computation of damage index aided design for pillar recovery and three-dimensional detection
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