Accurate measurement of stem diameter is essential to forest inventory. As a millimeter-level measuring tool, terrestrial laser scanning (TLS) has not yet reached millimeter-level accuracy in stem diameter measurements. The objective of this study is to develop an accurate method for deriving the stem diameter from TLS data. The methodology of stem diameter measurement by diameter tape was adopted. The stem cross-section at a given height along the stem was determined. Stem points for stem diameter retrieval were extracted according to the stem cross-section. Convex hull points of the extracted stem points were calculated in a projection plane. Then, a closed smooth curve was interpolated onto the convex hull points to simulate the path of the diameter tape, and stem diameter was calculated based on the length of the simulated path. The stems of different tree species with different properties were selected to verify the presented method. Compared with the field-measured diameter, the RMSE of the method was 0.0909 cm, which satisfies the accuracy requirement for forest inventory. This study provided a method for determining the stem cross-section and an efficient and precise curve fitting method for deriving stem diameter from TLS data. The importance of the stem cross-section and convex hull points in stem diameter retrieval was demonstrated.
Network virtualization (NV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of 5G communication. Resource scheduling in virtual network mapping is an important problem faced by NV technology. To enhance the performance of the network, optimal resource scheduling schemes should be determined. In general, maximum index of frequency slots used, energy consumption, and the ratio of used frequency slots are three very important indicators for the network. Based on the previous research studies, we first take these three indicators into the objective of the mathematical model and define a novel multiobjective optimization model. Then, the three objectives are integrated into one objective to be minimized by using the sum weighted strategy. Finally, an efficient algorithm, which integrates the advantages of artificial raindrop algorithm (ARA), particle swarm optimization, and differential evolution, is proposed and denoted as IARA. Moreover, simulation experiments have been conducted in several experimental scenes with other compared algorithms. The experimental results show that the proposed algorithm IARA can obtain better scheduling schemes than the compared algorithms.
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