It is known that packet collisions in wireless networks will deteriorate system performance, hence substantial efforts have been made to avoid collision in multi-user access designs. Also, there have been many studies...It is known that packet collisions in wireless networks will deteriorate system performance, hence substantial efforts have been made to avoid collision in multi-user access designs. Also, there have been many studies on throughput analysis of CSMA wireless networks. However, for a typical CSMA network in which not all nodes can sense each other, it is still not well investigated how link throughputs are affected by collisions. We note that in practical 802.11-like networks, the time is divided into mini-timeslots and packet collisions are in fact unavoidable. Thus, it is desirable to move forward to explore how collisions in such a network will affect system performance. Based on the collision-free ideal CSMA network(ICN) model, this paper attempts to analyze link throughputs when taking the backoff collisions into account and examine the effect of collisions on link throughputs. Specifically, we propose an Extended Ideal CSMA Network(EICN) model to characterize the collision effects as well as the interactions and dependency among links in the network. Based on EICN, we could directly compute link throughputs and collision probabilities. Simulations show that the EICN model is of high accuracy. Under various network topologies and protocol parameter settings, the computation error of link throughputs using EICN is kept to 4% or below. Interestingly, we find that unlike expected, the effect of collisions on link throughputs in a modest CSMA wireless network is not significant, which enriches our understanding on practical CSMA wireless networks such as Wi-Fi.展开更多
In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integr...In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.展开更多
基金partially supported by the National Natural Science Foundation of China under Grant 61571178,Grant 61771315 and Grant 61501160
文摘It is known that packet collisions in wireless networks will deteriorate system performance, hence substantial efforts have been made to avoid collision in multi-user access designs. Also, there have been many studies on throughput analysis of CSMA wireless networks. However, for a typical CSMA network in which not all nodes can sense each other, it is still not well investigated how link throughputs are affected by collisions. We note that in practical 802.11-like networks, the time is divided into mini-timeslots and packet collisions are in fact unavoidable. Thus, it is desirable to move forward to explore how collisions in such a network will affect system performance. Based on the collision-free ideal CSMA network(ICN) model, this paper attempts to analyze link throughputs when taking the backoff collisions into account and examine the effect of collisions on link throughputs. Specifically, we propose an Extended Ideal CSMA Network(EICN) model to characterize the collision effects as well as the interactions and dependency among links in the network. Based on EICN, we could directly compute link throughputs and collision probabilities. Simulations show that the EICN model is of high accuracy. Under various network topologies and protocol parameter settings, the computation error of link throughputs using EICN is kept to 4% or below. Interestingly, we find that unlike expected, the effect of collisions on link throughputs in a modest CSMA wireless network is not significant, which enriches our understanding on practical CSMA wireless networks such as Wi-Fi.
基金the management of Sierra Rutile Company for providing the drillhole dataset used in this studythe Japanese Ministry of Education Science and Technology (MEXT) Scholarship for academic funding
文摘In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.
