2022 IEEE International Conference on Unmanned Systems (ICUS)最新文献

The Design and Implementation of Control Mode Switching Equipment for a Type of In-flight Simulator 某型飞行模拟器控制模式切换装置的设计与实现

2022 IEEE International Conference on Unmanned Systems (ICUS) Pub Date : 2022-10-28 DOI: 10.1109/ICUS55513.2022.9986842

Hanxiang Gao, Boyang Ren, Quanbo Ge

引用次数: 0

An Adversarial Defense Algorithm Based on Triplet Network and Voting Decision 基于三重网络和投票决策的对抗防御算法

2022 IEEE International Conference on Unmanned Systems (ICUS) Pub Date : 2022-10-28 DOI: 10.1109/ICUS55513.2022.9986996

Haonan Zheng, Jiajia Zhao, Wei Tan

{"title":"An Adversarial Defense Algorithm Based on Triplet Network and Voting Decision","authors":"Haonan Zheng, Jiajia Zhao, Wei Tan","doi":"10.1109/ICUS55513.2022.9986996","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9986996","url":null,"abstract":"In the field of artificial intelligence, neural network is one of the key technologies used for image classification and recognition. However, recent work has demonstrated that deep neural networks are easily attacked by adversarial examples to make misjudgments. Adversarial examples are almost indistin-guishable from normal examples and yet cannot be classified correctly by neural networks. The existence of adversarial examples is a major obstacle to the practical application and deployment of neural networks, so the research on adversarial defense algorithms is an important topic in the field of AI security. This paper proposes an adversarial example defense algorithm based on a triplet network and voting decision mechanism. Firstly, two neural networks with different structures are trained based on normal dataset. Secondly, the first network is fine-tuned using the adversarial examples generated by these two networks, resulting in a third neural network. Then, these three neural networks are used as sub-networks in parallel to construct a triplet network. Through adversarial training and differences in structures, the transferability of adversarial examples among the three sub-networks is weakened. Finally, the final classification result is obtained by majority voting, based on the parallel output results of the three sub-networks. Through the complementarity between these three sub-networks, the defense against adversarial examples is realized. The experimental results demonstrate the effectiveness of this algorithm.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116926114","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

Event-triggered Distributed Cooperative Guidance for Multiple Aircraft 多机事件触发分布式协同制导

2022 IEEE International Conference on Unmanned Systems (ICUS) Pub Date : 2022-10-28 DOI: 10.1109/ICUS55513.2022.9986602

Q. Tan, Xiaoyang Xie, Tao Yu, Jian Gao

引用次数: 0

Rocket Self-learning Control based on Lightweight Neural Network Architecture Search 基于轻量级神经网络结构搜索的火箭自学习控制

2022 IEEE International Conference on Unmanned Systems (ICUS) Pub Date : 2022-10-28 DOI: 10.1109/ICUS55513.2022.9986957

Zhaolei Wang, Kunfeng Lu, Chunmei Yu, Na Yao, Ludi Wang, Jikang Zhao

{"title":"Rocket Self-learning Control based on Lightweight Neural Network Architecture Search","authors":"Zhaolei Wang, Kunfeng Lu, Chunmei Yu, Na Yao, Ludi Wang, Jikang Zhao","doi":"10.1109/ICUS55513.2022.9986957","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9986957","url":null,"abstract":"Aiming at the problem that the traditional control law design process is complex and relies heavily on accurate mathematical models, this paper uses the Deep Deterministic Policy Gradient (DDPG) reinforcement learning to realize the self-learning of continuous motion control law. However, since the performance of the DDPG algorithm depends heavily on the hyper-parameters, there is no clear design basis for the Actor-Critic framework neural network architecture. Considering that the reinforcement learning requires a large amount of computation, the repetitive manual trial and error of hyper-parameters greatly reduces the design efficiency of the algorithm and increases labor costs. On the basis of converting the network architecture design problem into a graph topology generation problem, an automatic search and optimization framework for deep reinforcement learning neural network structure is given in this paper, where the graph topology generation algorithm based on LSTM recurrent neural network, the weight sharing-based lightweight training and evaluation mechanism of deep reinforcement network parameter, and the policy gradient-based learning algorithm of graph topology generator parameter are innovatively combined. Thus, the neural network hyper-parameters in the DDPG algorithm are automatically optimized, and the control law is obtained by self-learning training. Finally, taking rocket vertical recovery control as an ex-ample, the effectiveness of the proposed method is verified.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121053481","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

Stability Augmentation Control of Tilting Dual-Rotor UAV with Balance Tail 倾斜平衡尾翼双旋翼无人机增稳控制

2022 IEEE International Conference on Unmanned Systems (ICUS) Pub Date : 2022-10-28 DOI: 10.1109/ICUS55513.2022.9986615

Yurui Xu, Liang Gao, Benshan Liu, Junming Zhang, Yanhe Zhu, Jie Zhao

引用次数: 0

Multi-UAVs Trajectory Planning Method with Coordinated Attack Angle-Time Constraints 具有协调攻角-时间约束的多无人机轨迹规划方法

2022 IEEE International Conference on Unmanned Systems (ICUS) Pub Date : 2022-10-28 DOI: 10.1109/ICUS55513.2022.9987057

Jie Xu, Weinan Wu, Yiming Sun

引用次数: 0

Long-term Tracker with Adaptive Occlusion and Recovery Judgment 具有自适应遮挡和恢复判断的长期跟踪器

2022 IEEE International Conference on Unmanned Systems (ICUS) Pub Date : 2022-10-28 DOI: 10.1109/ICUS55513.2022.9986584

Ying Mi, Chan Liu, Chaohui Wang, Xiangyang Yue, Xiaohan Zhao, Lu Chen

引用次数: 0

Health Assessment of Unmanned Aerial Vehicle Formation Systems under False Data Injection Attack 虚假数据注入攻击下无人机编队系统健康评估

2022 IEEE International Conference on Unmanned Systems (ICUS) Pub Date : 2022-10-28 DOI: 10.1109/ICUS55513.2022.9986994

Shuo Pan, Zhiyu Xi

引用次数: 0

Local Path Planning for Unmanned Surface Vehicles based on Hybrid A* and B-spline 基于A*和b样条混合的无人水面车辆局部路径规划

2022 IEEE International Conference on Unmanned Systems (ICUS) Pub Date : 2022-10-28 DOI: 10.1109/ICUS55513.2022.9986811

Liang Zhao, Ruyin Mao, Yong Bai

引用次数: 1

Efficient Reinforcement Learning for Autonomous Ship Collision Avoidance under Learning Experience Reuse 学习经验重用下船舶自主避碰的高效强化学习

2022 IEEE International Conference on Unmanned Systems (ICUS) Pub Date : 2022-10-28 DOI: 10.1109/ICUS55513.2022.9986793

Chengbo Wang, Xinyu Zhang, Hongbo Gao, Huiping Su, Kangjie Zheng, Weisong Wang

引用次数: 1