A new limited memory method for unconstrained nonlinear least squares

This paper suggests a new limited memory trust region algorithm for large unconstrained black box least squares problems, called LMLS. Main features of LMLS are a new non-monotone technique, a new adaptive radius strategy, a new Broyden-like algorithm based on the previous good points, and a heuristic estimation for the Jacobian matrix in a subspace with random basis indices. Our numerical results show that LMLS is robust and efficient, especially in comparison with solvers using traditional limited memory and standard quasi-Newton approximations.

Classification of Handheld Laser Scanning Tree Point Cloud Based on Different KNN Algorithms and Random Forest Algorithm

Handheld mobile laser scanning (HMLS) can quickly acquire point cloud data, and has the potential to conduct forest inventory at the plot scale. Considering the problems associated with HMLS data such as large discreteness and difficulty in classification, different classification models were compared in order to realize efficient separation of stem, branch and leaf points from HMLS data. First, the HMLS point cloud was normalized and ground points were removed, then the neighboring points were identified according to three KNN algorithms and eight geometric features were constructed. On this basis, the random forest classifier was used to calculate feature importance and perform dataset training. Finally, the classification accuracy of different KNN algorithms-based models was evaluated. Results showed that the training sample classification accuracy based on the adaptive radius KNN algorithm was the highest (0.9659) among the three KNN algorithms, but its feature calculation time was also longer; The validation accuracy of two test sets was 0.9596 and 0.9201, respectively, which is acceptable, and the misclassification mainly occurred in the branch junction of the canopy. Therefore, the optimal classification model can effectively achieve the classification of stem, branch and leaf points from HMLS point cloud under the premise of comprehensive training.

Time Optimization for Radius Updates in Zone Dynamics of Zone Routing Protocol

Vehicular ad hoc networks are wireless network technologies that can be used to communicate without the need for fixed infrastructure. The use of zone routing protocol which is a hybrid routing protocol in a vehicular ad hoc network environment can reduce delay, packet flooding, and excess bandwidth usage on the network. However, traditional zone routing protocol is only configured for one fixed radius value, which makes it not adapt to existing network conditions. Zone dynamics with adaptive radius values in zone routing protocol are used so that zones formed by nodes are more optimal. In adapting the radius value to make the zone dynamics necessary, the optimal configuration of the radius update time is required. Simulations and tests that have been carried out with NS-2 show that the proper update time can improve zone routing protocol performance with a low end-to-end delay and routing overhead value, but has a high packet delivery ratio.

A METHOD OF URBAN ROAD NETWORK EXTRACTION BASED ON FLOATING CAR TRAJECTORY DATA

<p><strong>Abstract.</strong> With the gradual opening of floating car trajectory data, it is possible to extract road network information from it. Currently, most road network extraction algorithms use unified thresholds to ignore the density difference of trajectory data, and only consider the trajectory shape without considering the direction of the trajectory, which seriously affects the geometric precision and topological accuracy of their results. Therefore, an adaptive radius centroid drift clustering method is proposed in this paper, which can automatically adjust clustering parameters according to the track density and the road width, using trajectory direction to complete the topological connection of roads. The algorithm is verified by the floating car trajectory data of a day in Futian District, Shenzhen. The experimental results are qualitatively and quantitatively analyzed with ones of the other two methods. It indicates that the road network data extracted by this algorithm has a significant improvement in geometric precision and topological accuracy, and which is suitable for big data processing.</p>