Coal is expected to be an important energy resource for some developing countries in the coming decades; thus, the rapid classification and qualification of coal quality has an important impact on the improvement in industrial production and the reduction in pollution emissions. The traditional methods for the proximate analysis of coal are time consuming and labor intensive, whose results will lag in the combustion condition of coal-fired boilers. However, laser-induced breakdown spectroscopy (LIBS) assisted with machine learning can meet the requirements of rapid detection and multi-element analysis of coal quality. In this work, 100 coal samples from 11 origins were divided into training, test, and prediction sets, and some clustering models, classification models, and regression models were established for the performance analysis in different application scenarios. Among them, clustering models can cluster coal samples into several clusterings only by coal spectra; classification models can classify coal with labels into different categories; and the regression model can give quantitative prediction results for proximate analysis indicators. Cross-validation was used to evaluate the model performance, which helped to select the optimal parameters for each model. The results showed that K-means clustering could effectively divide coal samples into four clusters that were similar within the class but different between classes; naive Bayesian classification can distinguish coal samples into different origins according to the probability distribution function, and its prediction accuracy could reach 0.967; and partial least squares regression can reduce the influence of multivariate collinearity on prediction, whose root mean square error of prediction for ash, volatile matter, and fixed carbon are 1.012%, 0.878%, and 1.409%, respectively. In this work, the built model provided a reference for the selection of machine learning methods for LIBS when applied to classification and qualification.
Because all the nodes are multi-cast, the minimum spanning tree is the best, therefore, it is expected by the niche tree dynamic greed multicast routing algorithms produce more of the performance with a reasonable level. As for the greedy algorithm and the text of the characters tree dynamic greedy algorithm are made detailed emulation, Simulations result indicates that the results of the algorithm in multicast node are with greater density of the advantages and it shows that the invalid in other cases were acceptable, and has complicated the low quality. Keywords-Computer networks , Multicast routing algorithm,dynamic multi astrouting algorithmMany applications are required to support multicast dynamic multicast, and multicast of members in the group often change and they can accede to or away from that correspondence members who have a dynamic [1]. Support for such applications require so many members of the network broadcast to the left or change the existing multicast tree and multicast routing is dynamic multicast routing. In extreme cases, you can change in each group with static heuristic to operate multicast tree, but it is time to run the price that is too much, multicast tree changed a lot, the review on the causes are the groups lost [2][3][4][5]. Therefore, an ideal approach should be able to make time to join or left after the incident multicasts trees change the small tree, the air cost to the minimum and on updating of time of the complex degrees are lower. The dynamic routing is multicast communication problem because of the point-to-point communications, the only one is left, communication is broken down, and if there are new members, it is not a point-to-point communication. Only with multicast, members join or leave such as the TV, and online games. Of course, the dynamic multicast can also extend the concept, for example, the link which obstructs or partially due to certain multicast users of the communications group, the user's process is equivalent to a new member of the accession process [6]. Dynamic multicast routing algorithm is a problem than the static routing problem will be more difficult, this is because the dynamic routing optimization is for communication, predicting what members will join or leave the communications group. In addition, the dynamic routing optimization is in communication, adjustment for routing to other members it doesn't affect or influence the extent to the minimum. Therefore, it is often in the routing performance with a group or dynamic between the middle of the network. The dynamic multicast that is routing algorithm appropriate research should be less a lot.
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