The mining loss rate and dilution rate are the key indicators for the mining technology and management level of mining enterprises. Aiming at the practical problems such as the large workload but inaccurate data of th...The mining loss rate and dilution rate are the key indicators for the mining technology and management level of mining enterprises. Aiming at the practical problems such as the large workload but inaccurate data of the traditional loss and dilution calculation method, this thesis introduces the operating principle and process of calculating the loss rate and dilution rate at the mining fields by adopting geological models. As an example, authors establishes 3D models of orebody units in the exhausted area and mining fields in Yangshu Gold Mine in Liaoning Province, and conduct Boolean calculation among the models to obtain the calculation parameters of loss and dilution, and thereby calculate out the dilution rate and loss rate of the mining fields more quickly and accurately.展开更多
Active queue management(AQM)methods manage the queued packets at the router buffer,prevent buffer congestion,and stabilize the network performance.The bursty nature of the traffic passing by the network routers and th...Active queue management(AQM)methods manage the queued packets at the router buffer,prevent buffer congestion,and stabilize the network performance.The bursty nature of the traffic passing by the network routers and the slake behavior of the existing AQM methods leads to unnecessary packet dropping.This paper proposes a fully adaptive active queue management(AAQM)method to maintain stable network performance,avoid congestion and packet loss,and eliminate unnecessary packet dropping.The proposed AAQM method is based on load and queue length indicators and uses an adaptive mechanism to adjust the dropping probability based on the buffer status.The proposed AAQM method adapts to single and multiclass traffic models.Extensive simulation results over two types of traffic showed that the proposed method achieved the best results compared to the existing methods,including Random Early Detection(RED),BLUE,Effective RED(ERED),Fuzzy RED(FRED),Fuzzy Gentle RED(FGRED),and Fuzzy BLUE(FBLUE).The proposed and compared methods achieved similar results with low or moderate traffic load.However,under high traffic load,the proposed AAQM method achieved the best rate of zero loss,similar to BLUE,compared to 0.01 for RED,0.27 for ERED,0.04 for FRED,0.12 for FGRED,and 0.44 for FBLUE.For throughput,the proposed AAQM method achieved the highest rate of 0.54,surpassing the BLUE method’s throughput of 0.43.For delay,the proposed AAQM method achieved the second-best delay of 28.51,while the BLUE method achieved the best delay of 13.18;however,the BLUE results are insufficient because of the low throughput.Consequently,the proposed AAQM method outperformed the compared methods with its superior throughput and acceptable delay.展开更多
文摘The mining loss rate and dilution rate are the key indicators for the mining technology and management level of mining enterprises. Aiming at the practical problems such as the large workload but inaccurate data of the traditional loss and dilution calculation method, this thesis introduces the operating principle and process of calculating the loss rate and dilution rate at the mining fields by adopting geological models. As an example, authors establishes 3D models of orebody units in the exhausted area and mining fields in Yangshu Gold Mine in Liaoning Province, and conduct Boolean calculation among the models to obtain the calculation parameters of loss and dilution, and thereby calculate out the dilution rate and loss rate of the mining fields more quickly and accurately.
基金funded by Arab Open University Grant Number(AOURG2023–005).
文摘Active queue management(AQM)methods manage the queued packets at the router buffer,prevent buffer congestion,and stabilize the network performance.The bursty nature of the traffic passing by the network routers and the slake behavior of the existing AQM methods leads to unnecessary packet dropping.This paper proposes a fully adaptive active queue management(AAQM)method to maintain stable network performance,avoid congestion and packet loss,and eliminate unnecessary packet dropping.The proposed AAQM method is based on load and queue length indicators and uses an adaptive mechanism to adjust the dropping probability based on the buffer status.The proposed AAQM method adapts to single and multiclass traffic models.Extensive simulation results over two types of traffic showed that the proposed method achieved the best results compared to the existing methods,including Random Early Detection(RED),BLUE,Effective RED(ERED),Fuzzy RED(FRED),Fuzzy Gentle RED(FGRED),and Fuzzy BLUE(FBLUE).The proposed and compared methods achieved similar results with low or moderate traffic load.However,under high traffic load,the proposed AAQM method achieved the best rate of zero loss,similar to BLUE,compared to 0.01 for RED,0.27 for ERED,0.04 for FRED,0.12 for FGRED,and 0.44 for FBLUE.For throughput,the proposed AAQM method achieved the highest rate of 0.54,surpassing the BLUE method’s throughput of 0.43.For delay,the proposed AAQM method achieved the second-best delay of 28.51,while the BLUE method achieved the best delay of 13.18;however,the BLUE results are insufficient because of the low throughput.Consequently,the proposed AAQM method outperformed the compared methods with its superior throughput and acceptable delay.
