Laser-induced breakdown spectroscopy (LIBS) is a potential technology for online coal property analysis, but successful quantitative measurement of calorific value using LIBS suffers from relatively low accuracy caused by the matrix effect. To solve this problem, the support vector machine (SVM) and the partial least square (PLS) were combined to increase the measurement accuracy of calorific value in this study. The combination model utilized SVM to classify coal samples into two groups according to their volatile matter contents to reduce the matrix effect, and then applied PLS to establish calibration models for each sample group respectively. The proposed model was applied to the measurement of calorific values of 53 coal samples, showing that the proposed model could greatly increase accuracy of the measurement of calorific values. Compared with the traditional PLS method, the coefficient of determination (R 2 ) was improved from 0.93 to 0.97, the root-mean-square error of prediction was reduced from 1.68 MJ kg −1 to 1.08 MJ kg −1 , and the average relative error was decreased from 6.7% to 3.93%, showing an overall improvement.

The aim of this study was to reduce the influence of highly dynamic substrate network topology and large transmission delay caused by long communication distance between satellites and ground in Software Defined Satellite Networks (SDSNs), and a Partial Observation Markov Decision Process (POMDP)-based service function chain (SFC) deployment scheme of SDSN is proposed. Under this SDSN architecture, the network topology changes could be obtained through the SDN centralized control ability. Due to the topology changes, which may cause inevitable observation errors and transmission delays, the complete actual topology and network states cannot be obtained in real time. Thus, we put forward a POMDP model–based SFC deployment scheme, and an approximate iterative algorithm to solve the problem, aiming at optimizing the end-to-end network delay in the SDSN. The simulation results show that our model can optimize the delay of SFC deployment process, and improve the resource utilization and network throughput of the SDSN.

Unmanned helicopters (UH) can evade radar detection by flying at ultralow altitudes, so as to conduct raids on targets. Path planning is one of the key technologies to realize UH’s autonomous completion of raid missions. Since the probability of UH being detected by radar varies with height, how to accurately identify the radar coverage area to avoid crossing has become a difficult problem in UH path planning. Aiming at this problem, a heuristic deep Q-network (H-DQN) algorithm is proposed. First, as part of the comprehensive reward function, a heuristic reward function is designed. The function can generate dynamic rewards in real time according to the environmental information, so as to guide the UH to move closer to the target and at the same time promote the convergence of the algorithm. Second, in order to smooth the flight path, a smoothing reward function is proposed. This function can evaluate the pros and cons of UH’s actions, so as to prompt UH to choose a smoother path for flight. Finally, the heuristic reward function, the smooth reward function, the collision penalty, and the completion reward are weighted and summed to obtain the heuristic comprehensive reward function. Simulation experiments show that the H-DQN algorithm can help UH to effectively avoid the radar coverage area and successfully complete the raid mission.

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Information integration is a key way to solve the problem of the information isolated islands. In this paper, to deal with the problems of structured and semi-structured data integration in equipment domain, performance requests of the information integration framework in equipment domain are analyzed. An information integration framework at semantic level in equipment domain based on ontology is presented. The key techniques are studied. And the application example is also given. It is proved that the isolated information can be integrated effectively at semantic level through the presented information integration framework.