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1,103 results on '"Li, Chengjun"'

1. Rethinking Closed-loop Planning Framework for Imitation-based Model Integrating Prediction and Planning

Author

Guo, Jiayu, Feng, Mingyue, Zhu, Pengfei, Li, Chengjun, and Pu, Jian

Subjects
Computer Science - Robotics
Abstract

In recent years, the integration of prediction and planning through neural networks has received substantial attention. Despite extensive studies on it, there is a noticeable gap in understanding the operation of such models within a closed-loop planning setting. To bridge this gap, we propose a novel closed-loop planning framework compatible with neural networks engaged in joint prediction and planning. The framework contains two running modes, namely planning and safety monitoring, wherein the neural network performs Motion Prediction and Planning (MPP) and Conditional Motion Prediction (CMP) correspondingly without altering architecture. We evaluate the efficacy of our framework using the nuPlan dataset and its simulator, conducting closed-loop experiments across diverse scenarios. The results demonstrate that the proposed framework ensures the feasibility and local stability of the planning process while maintaining safety with CMP safety monitoring. Compared to other learning-based methods, our approach achieves substantial improvement., Comment: 7 pages,5 figures

Published
2024

33. Research and Design of Grid Safety Inspection Path Planning and Work Order Assignment Based on Dijkstra and Optimal Scheduling

Author

Liu Lei, Zhou Xiangfeng, Li Chengjun, Gao Xiaolan, Luo Shuxian, and Xue Guoquan

Subjects
monte carlo simulation, improved dijkstra algorithm, grid inspection, risk assessment, unmanned aerial vehicle (uav), 97u10, Mathematics, QA1-939
Abstract

The power grid is the core of power energy transmission and distribution in modern society and is affected by many factors, such as the number and variety of its equipment and the intricate structure of the grid, which leads to a more severe situation that the safe operation of the power grid will face. This paper gathers data on the operational status of grid equipment, historical maintenance records, customer complaints, and more. It then employs Monte Carlo simulation sampling to select and analyze the risk state of the grid system, as well as to determine the overall risk probability of the calculation system and the level of consequence indicators. An improved Dijkstra algorithm is applied to plan the optimal path for UAV grid inspection. Based on the inspection and risk assessment results, work orders are automatically generated and assigned to the corresponding maintenance personnel. Simulation results show that the proposed method obtains two optimal inspection schemes, TSP and EETSP. The TSP scheme obtains the shortest path when the inspection data volume is 50 Mbits, and the EETSP scheme obtains the shortest moving time when the inspection data volume is 200 Mbits. The UAV-cooperative transmission line inspection path planning method proposed in this paper can improve grid inspection efficiency and effectively solve the optimal path planning problem in large inspection areas.

Published
2024
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34. A study on the effect of different machine learning algorithms on soccer footwork recognition under trajectory tracking theory

Author

Li Chengjun, Yang Hao, and Wang Jingyan

Subjects
k-nearest neighbor algorithm, support vector machine, convolutional neural network, soccer step recognition, inertial sensor, 68m01, Mathematics, QA1-939
Abstract

This paper aims to investigate the effectiveness and influencing factors of different machine learning algorithms on soccer footwork recognition. In this paper, we use inertial sensors to obtain the basic data of soccer players’ movements, then convert them into initial data of footwork using pose representation and pose-solving filtering. The value of K mainly influences the classification accuracy of KNN, and the highest accuracy of 67.23% is achieved when K is 5. The classification accuracy of SVM is related to the choice of the distance function. The accuracy of CNN is mainly affected by the size of the convolutional kernel and the convolutional step size, and the highest accuracy is 73.82%. The machine learning-based soccer step recognition can improve the recognition accuracy of traditional physical methods and provide scientific sports guidance for soccer players’ training.

Published
2024
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35. Differential Evolution with Individuals Redistribution for Real Parameter Single Objective Optimization

Author

Li, Chengjun and Li, Yang

Subjects
Computer Science - Artificial Intelligence
Abstract

Differential Evolution (DE) is quite powerful for real parameter single objective optimization. However, the ability of extending or changing search area when falling into a local optimum is still required to be developed in DE for accommodating extremely complicated fitness landscapes with a huge number of local optima. We propose a new flow of DE, termed DE with individuals redistribution, in which a process of individuals redistribution will be called when progress on fitness is low for generations. In such a process, mutation and crossover are standardized, while trial vectors are all kept in selection. Once diversity exceeds a predetermined threshold, our opposition replacement is executed, then algorithm behavior returns to original mode. In our experiments based on two benchmark test suites, we apply individuals redistribution in ten DE algorithms. Versions of the ten DE algorithms based on individuals redistribution are compared with not only original version but also version based on complete restart, where individuals redistribution and complete restart are based on the same entry criterion. Experimental results indicate that, for most of the DE algorithms, version based on individuals redistribution performs better than both original version and version based on complete restart.

Published
2020

50. Efficient Spatial Nearest Neighbor Queries Based on Multi-layer Voronoi Diagrams

Author

Li, Yang, Liu, Gang, Gao, Junbin, He, Zhenwen, Bai, Mingyuan, and Li, Chengjun

Subjects
Computer Science - Databases
Abstract

Nearest neighbor (NN) problem is an important scientific problem. The NN query, to find the closest one to a given query point among a set of points, is widely used in applications such as density estimation, pattern classification, information retrieval and spatial analysis. A direct generalization of the NN query is the k nearest neighbors (kNN) query, where the k closest point are required to be found. Since NN and kNN problems were raised, many algorithms have been proposed to solve them. It has been indicated in literature that the only method to solve these problems exactly with sublinear time complexity, is to filter out the unnecessary spatial computation by using the pre-processing structure, commonly referred to as the spatial index. The recently proposed spatial indices available for NN search, are almost constructed through spatial partition. These indices are tree-like, and the tree-like hierarchical structure can usually significantly improve the efficiency of NN search. However, when the data are distributed extremely unevenly, it is difficult to satisfy both the balance of the tree and the non-overlap of the subspace corresponding to the nodes. Thus the acceleration performance of the tree-like indices is severely jeopardized. In this paper, we propose a non-tree spatial index which consists of multiple layers of Voronoi diagrams (MVD). This index can entirely avoid the dilemma tree-like structures face, and solve the NN problems stably with logarithmic time complexity. Furthermore, it is convenient to achieve kNN search by extending NN search on MVD. In the experiments, we evaluate the efficiency of this indexing for both NN search and kNN search by comparing with VoR-tree, R-tree and kd-tree. The experiments indicate that compared to NN search and kNN search with the other three indices, these two search methods have significantly higher efficiency with MVD.

Published
2019

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