In order to maximize the overall economic gain from a metal mine operation, selection of cutoff grades must consider two important aspects: the time value of money and the spatial variation of the grade distribution i...In order to maximize the overall economic gain from a metal mine operation, selection of cutoff grades must consider two important aspects: the time value of money and the spatial variation of the grade distribution in the deposit. That is, cutoff grade selection must be dynamic with respect to both time and space. A newly developed method that fulfills these requirements is presented. In this method, the deposit or a portion of it under study is divided into "decision units" based on the mining method and sample data. The statistical grade distribution and the grade-tonnage relationship of each decision unit are then computed based on the samples falling in the unit. Each decision unit with its grade-tonnage relationship is considered as a stage in a dynamic programming scheme and the problem is solved by applying a forward dynamic programming based algorithm with an objective function of maximizing the overall net present value (NPV). A software package is developed for the method and applied to an underground copper mine in Africa.展开更多
Feature selection is always an important issue in the visual SLAM （simultaneous location and mapping） literature. Considering that the location estimation can be improved by tracking features with larger value of vi...Feature selection is always an important issue in the visual SLAM （simultaneous location and mapping） literature. Considering that the location estimation can be improved by tracking features with larger value of visible time, a new feature selection method based on motion estimation is proposed. First, a k-step iteration algorithm is presented for visible time estimation using an affme motion model; then a delayed feature detection method is introduced for efficiently detecting features with the maximum visible time. As a means of validation for the proposed method, both simulation and real data experiments are carded out. Results show that the proposed method can improve both the estimation performance and the computational performance compared with the existing random feature selection method.展开更多
基金Project(50974041) supported by the National Natural Science Foundation of China Project(20090450112) supported by the Postdoctoral Foundation of ChinaProject(20093910) supported by the Natural Science Foundation of Liaoning Province, China
文摘In order to maximize the overall economic gain from a metal mine operation, selection of cutoff grades must consider two important aspects: the time value of money and the spatial variation of the grade distribution in the deposit. That is, cutoff grade selection must be dynamic with respect to both time and space. A newly developed method that fulfills these requirements is presented. In this method, the deposit or a portion of it under study is divided into "decision units" based on the mining method and sample data. The statistical grade distribution and the grade-tonnage relationship of each decision unit are then computed based on the samples falling in the unit. Each decision unit with its grade-tonnage relationship is considered as a stage in a dynamic programming scheme and the problem is solved by applying a forward dynamic programming based algorithm with an objective function of maximizing the overall net present value (NPV). A software package is developed for the method and applied to an underground copper mine in Africa.
文摘Feature selection is always an important issue in the visual SLAM （simultaneous location and mapping） literature. Considering that the location estimation can be improved by tracking features with larger value of visible time, a new feature selection method based on motion estimation is proposed. First, a k-step iteration algorithm is presented for visible time estimation using an affme motion model; then a delayed feature detection method is introduced for efficiently detecting features with the maximum visible time. As a means of validation for the proposed method, both simulation and real data experiments are carded out. Results show that the proposed method can improve both the estimation performance and the computational performance compared with the existing random feature selection method.
