Study of strip-filling for old room and pillar mining goaf
According to the problems of land resources destruction and environmental pollution caused by the collapse of old room and pillar mining goaf, a goaf security evaluation model was established and applied to the safety assessment of a colliery in Ordos city. The results indicated that the room and pillar mining goafs in this coal mine area would fail with time passing. To solve the problems, with analyzing the structural characteristics and activation mechanism of old room and pillar mining goaf, based on the theory of strip mining, a new method of strip-filling for controlling subsidence of old room and pillar mining goaf was put forward and used in designing of the strip-filling plan of the colliery, the filling effect prediction have been worked out. It demonstrates that the strip-filling method is economically feasible.
Research on surface movement parameters of strip-partial mining
The influencing factors of surface movement parameters were analyzed. And then the new formula of calculating surface movement parameters of strip-partial mining were set up based on observation station data and theoretical analysis. More influencing factors were taken into account in these formula than the existing methods. And also the variables in the formula are easily to make certain. It is convenient to calculate the surface movement parameters of strip-partial mining using these formula. The predicted mining subsidence values using the parameters that are calculated according to these formula nicely accord with the surveying results in-situ by comparison. So these new formula can be used to calculate surface movement parameters of strip-partial mining.
Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation
In this paper, we present a technique to estimate citrus fruit yield from the tree images. Manually counting the fruit for yield estimation for marketing and other managerial tasks is time consuming and requires human resources, which do not always come cheap. Different approaches have been used for the said purpose, yet separation of fruit from its background poses challenges, and renders the exercise inaccurate. In this paper, we use k-means segmentation for recognition of fruit, which segments the image accurately thus enabling more accurate yield estimation. We created a dataset containing 83 tree images with 4001 citrus fruits from three different fields. We are able to detect the on-tree fruits with an accuracy of 91.3%. In addition, we find a strong correlation between the manual and the automated fruit count by getting coefficients of determination R2 up to 0.99.