2021 International Conference on Computer Engineering and Application (ICCEA)最新文献_第9页

UCAV maneuvering trajectory prediction based on PSO-CNN 基于PSO-CNN的无人飞行器机动轨迹预测

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00018

Xie Lei, Ding Dali, Zhang Hongpeng, Wang Jianpu, Zhang Zhuoran

引用次数: 3

A Hierarchical Autonomous Driving Framework Combining Reinforcement Learning and Imitation Learning 结合强化学习和模仿学习的分层自动驾驶框架

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00084

Zeyu Li

{"title":"A Hierarchical Autonomous Driving Framework Combining Reinforcement Learning and Imitation Learning","authors":"Zeyu Li","doi":"10.1109/ICCEA53728.2021.00084","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00084","url":null,"abstract":"Autonomous driving technology aims to make driving decisions based on information about the vehicle’s environment. Navigation-based autonomous driving in urban scenarios has more complex scenarios than in relatively simple scenarios such as highways and parking lots, and is a task that still needs to be explored over time. Imitation learning models based on supervised learning methods are limited by the amount of expert data collected. Models based on reinforcement learning methods are able to interact with the environment, but are data inefficient and require a lot of exploration to learn effective policy. We propose a method that combines imitation learning with reinforcement learning enabling agent to achieve a higher success rate in urban autonomous driving navigation scenarios. To solve the problem of inefficient reinforcement learning data, our method decomposes the action space into low-level action space and high-level actin space, where low-level action space is multiple pre-trained imitation learning action space is a combination of several pre-trained imitation learning action spaces based on different control signals (i.e., follow, straight, turn right, turn left). High-level action space includes different control signals, the agent executes a specific imitation learning policy by selecting control signals from the high-level action space through a DQN-based reinforcement learning approach. Moreover, we propose a new reward for high level action selection. Experiments on the CARLA driving benchmark demonstrate that our approach outperforms both imitation learning methods and reinforcement learning methods on a variety of navigation-based driving tasks.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116247302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Botnet Detection Based on Flow Summary and Graph Sampling with Machine Learning 基于流量汇总和图采样的机器学习僵尸网络检测

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00068

Chun Long, Xisheng Xiao, Wei Wan, Jing Zhao, Jinxia Wei, Guanyao Du

引用次数: 1

The Practice of Computer Graphic Illustration Art in Commercial Design 计算机图形插画艺术在商业设计中的实践

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00087

Jing Liu

引用次数: 1

A wo-dimensional research framework for analysing dark side of AI 一个分析人工智能阴暗面的二维研究框架

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00064

Lifan Zeng, Junjie Wu

引用次数: 0

Research on Urban Audio Classification Based on Residual Neural Network 基于残差神经网络的城市音频分类研究

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00047

Duling Xv, Li Yang

引用次数: 0

Key Node Detection in Financial Complex Network 金融复杂网络中的关键节点检测

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00077

Chenglong Wang, Le Kang, Zhihong Zhang, Zhaohui Zhang, Xiaofeng Wang

{"title":"Key Node Detection in Financial Complex Network","authors":"Chenglong Wang, Le Kang, Zhihong Zhang, Zhaohui Zhang, Xiaofeng Wang","doi":"10.1109/ICCEA53728.2021.00077","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00077","url":null,"abstract":"With the development of the financial sector, the growing complexity of financial transaction network, effectively identify a trading network of key trader has a important significance. Trading network abstraction for complex networks, traders abstraction for nodes, trandings between traders abstraction for the edges. The method of degree centrality, clustering coefficient, betweenness centrality, closeness centrality and the like is not sufficient to evaluate the importance of the node. Therefore, we propose a novel algorithm to evaluate the importance of nodes in undirected and unweighted network. We take the degree centrality and clustering coefficient of the nodes as the evaluation indicators, and combine the importance contribution of the nearest and the next nearest nodes. Through normalization and averaging, the benchmark ranking of node importance is obtained, which comprehensively considers the global and local features of the nodes. We used the real trading network data from Zhengzhou Commodity Exchange (ZCE) to conduct three comparative experiments and analyses. The experiment results show that our method has achieved better results, and can effectively identify key trading traders in ZCE.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127221657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of Monitoring System for Height Limiting Device Based on Acceleration Sensor 基于加速度传感器的限高装置监控系统设计

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00066

Tianqi Li, Shifeng Yang, Zhidong Guo, Zhe Sheng

引用次数: 1

Topological structure optimization algorithm of military communication network based on genetic algorithm 基于遗传算法的军用通信网络拓扑结构优化算法

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00010

Zhiang Xu, Zhiqiang Fan

{"title":"Topological structure optimization algorithm of military communication network based on genetic algorithm","authors":"Zhiang Xu, Zhiqiang Fan","doi":"10.1109/ICCEA53728.2021.00010","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00010","url":null,"abstract":"With the development of information technology and the transformation of war concepts, the traditional combat method centered on weapons and equipment platforms has gradually transformed into network-centric information operations, among which military communication networks are the basis of military command and control in information warfare. Firstly, this article analyzes the basic characteristics and formation mechanism of the military communication network model, and then analyzes the performance indicators of the communication network model from the perspective of information flow integrity, information timeliness, and network anti-destructive ability. Secondly, this article obtains the optimization goal of the military communication network. And starting from the network topology, an innovative genetic algorithm is designed to adapt to the network model. Finally, this paper compares the optimization effects of genetic algorithm and other heuristic algorithms through a series of simulation experiments. The experiment proves that the improved genetic algorithm performs best in the optimization effect. This method provides theoretical guidance for the optimization of the topological structure of military communication networks.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133554298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UUV multi-task route planning under obstacles constrait 障碍物约束下UUV多任务航路规划

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00096

Cao Xin, Zhou Yuan, Chen Yi, Mai Weiqiang

引用次数: 0