Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Huaqiao University, Xiamen, Fujian Province, China.
PLoS One. 2019 Jan 16;14(1):e0208706. doi: 10.1371/journal.pone.0208706. eCollection 2019.
Construction waste is a serious problem that should be addressed to protect environment and save resources, some of which have a high recovery value. To efficiently recover construction waste, an online classification system is developed using an industrial near-infrared hyperspectral camera. This system uses the industrial camera to capture a region of interest and a hyperspectral camera to obtain the spectral information about objects corresponding to the region of interest. The spectral information is then used to build classification models based on extreme learning machine and resemblance discriminant analysis. To further improve this system, an online particle swarm optimization extreme learning machine is developed. The results indicate that if a near-infrared hyperspectral camera is used in conjunction with an industrial camera, construction waste can be efficiently classified. Therefore, extreme learning machine and resemblance discriminant analysis can be used to classify construction waste. Particle swarm optimization can be used to further enhance the proposed system.
建筑废物是一个严重的问题,应该加以解决,以保护环境和节约资源,其中一些具有很高的回收价值。为了有效地回收建筑废物,开发了一种使用工业近红外高光谱相机的在线分类系统。该系统使用工业相机捕捉感兴趣区域,并使用高光谱相机获取与感兴趣区域相对应的物体的光谱信息。然后,使用光谱信息基于极限学习机和相似判别分析构建分类模型。为了进一步改进该系统,开发了一种在线粒子群优化极限学习机。结果表明,如果将近红外高光谱相机与工业相机结合使用,可以有效地对建筑废物进行分类。因此,可以使用极限学习机和相似判别分析对建筑废物进行分类。可以使用粒子群优化进一步增强所提出的系统。