Y2002-63327-2017 0307678应用诺埃曼级数依据指数 X 射线投影的3维图像重构=3-D image reconstruction from exponential X-rayprojections using Neumann series[会,英]/Wagner,J.-M.& Noo.F.//The 2001 IEEE International Confe...Y2002-63327-2017 0307678应用诺埃曼级数依据指数 X 射线投影的3维图像重构=3-D image reconstruction from exponential X-rayprojections using Neumann series[会,英]/Wagner,J.-M.& Noo.F.//The 2001 IEEE International Confer-ence on Acoustics,Speech,and Signal Processing Vol.Ⅲ of Ⅵ.—2017～2020(HE)展开更多
The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process.The surface texture feature closely relates to the flotation working conditions and hence can be used ...The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process.The surface texture feature closely relates to the flotation working conditions and hence can be used as a visual indicator for the zinc fast roughing working condition. A novel working condition identification method based on the dual-tree complex wavelet transform(DTCWT) is proposed for process monitoring of zinc fast roughing.Three-level DTCWT is implemented to decompose the froth image into different directions and resolutions in advance, and then the energy parameter of each sub-image is extracted as the froth texture feature. Then, an improved random forest integrated classification(i RFIC) with 10-fold cross-validation model is introduced as the classifier to identify the roughing working condition, which effectively improves the shortcomings of the single model and overcomes the characteristic redundancy but achieves higher generalization performance. Extensive experiments have verified the effectiveness of the proposed method.展开更多
文摘Y2002-63327-2017 0307678应用诺埃曼级数依据指数 X 射线投影的3维图像重构=3-D image reconstruction from exponential X-rayprojections using Neumann series[会,英]/Wagner,J.-M.& Noo.F.//The 2001 IEEE International Confer-ence on Acoustics,Speech,and Signal Processing Vol.Ⅲ of Ⅵ.—2017～2020(HE)
文摘The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process.The surface texture feature closely relates to the flotation working conditions and hence can be used as a visual indicator for the zinc fast roughing working condition. A novel working condition identification method based on the dual-tree complex wavelet transform(DTCWT) is proposed for process monitoring of zinc fast roughing.Three-level DTCWT is implemented to decompose the froth image into different directions and resolutions in advance, and then the energy parameter of each sub-image is extracted as the froth texture feature. Then, an improved random forest integrated classification(i RFIC) with 10-fold cross-validation model is introduced as the classifier to identify the roughing working condition, which effectively improves the shortcomings of the single model and overcomes the characteristic redundancy but achieves higher generalization performance. Extensive experiments have verified the effectiveness of the proposed method.
