2022 14th International Conference on Advanced Computational Intelligence (ICACI)最新文献

Age of Information optimization with Heterogeneous UAVs based on Deep Reinforcement Learning 基于深度强化学习的异构无人机信息优化时代

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837720

Luan Shi, Xiao Zhang, Xin Xiang, Yu Zhou, Shilong Sun

{"title":"Age of Information optimization with Heterogeneous UAVs based on Deep Reinforcement Learning","authors":"Luan Shi, Xiao Zhang, Xin Xiang, Yu Zhou, Shilong Sun","doi":"10.1109/icaci55529.2022.9837720","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837720","url":null,"abstract":"Recent years have witnessed increasingly more Unmanned Aerial Vehicle (UAV) applications for data collection in the Internet of Things (IoT). Due to the limited energy and service capacity, it is very challenging for a single UAV to accomplish the data collection while guaranteeing the information freshness of IoT devices or sensor nodes (SNs). In practice, different types of UAVs may have different energy capabilities. In this paper, we propose a more practical heterogeneous UAV swarm path planning problem for optimizing the information freshness, in which the division and cooperation among UAVs with different energy capacities have been taken into consideration. The freshness, i.e., age of information (AoI) collected from each SN is characterized by the data uploading time and the time elapsed since the UAV leaves this SN. We successfully present a deep reinforcement learning algorithm based on attention mechanism by end-to-end training to optimize the average age under UAVs’ energy constraints. The simulation results show that our algorithm has fast convergence, high optimization capability and reliability, and can solve the heterogeneous UAV swarm cooperative AoI optimization problem effectively.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115673838","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

Neuro-adaptive Containment of Uncertain Complex Cyber Physical Networks with Directed Topology 具有有向拓扑的不确定复杂网络物理网络的神经自适应约束

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837620

Huanhuan Tian, Peijun Wang, Shuai Wang

引用次数: 0

Text modality enhanced based deep hashing for multi-label cross-modal retrieval 基于文本模态增强的深度哈希多标签跨模态检索

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837775

Huan Liu, Jiang Xiong, Nian Zhang, Jing Zhong

{"title":"Text modality enhanced based deep hashing for multi-label cross-modal retrieval","authors":"Huan Liu, Jiang Xiong, Nian Zhang, Jing Zhong","doi":"10.1109/icaci55529.2022.9837775","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837775","url":null,"abstract":"In the past few years, due to the strong feature learning capability of deep neural networks, deep cross-modal hashing (DCMHs) has made considerable progress. However, there exist two problems in most DCMHs methods: (1) most extisting DCMHs methods utilize single labels to calculate the semantic similarity of instances, which overlooks the fact that, in the field of cross-modal retrieval, most benchmark datasets as well as practical applications have multiple labels. Therefore, single labels based DCMHs methods cannot accurately calculate the semantic similarity of instances and may decrease the performance of the learned DCMHs models. (2) Most DCMHs models are built on the image-text modalities, nevertheless, as the initial feature space of the text modality is quite sparse, the learned hash projection function based on these sparse features for the text modality is too weak to map the original text into robust hash codes. To solve these two problems, in this paper, we propose a text modality enhanced based deep hashing for multi-label cross-modal retrieval (TMEDH) method. TMEDH firstly defines a multi-label based semantic similarity calculation formula to accurately compute the semantic similarity of cross-modal instances. Secondly, TMEDH introduces a text modality enhanced module to compensate the sparse features of the text modality by fuse the multi-label information into the features of the text. Extensive ablation experiments as well as comparative experiments on two cross-modal retrieval datasets demonstrate that our proposed TMEDH method achieves state-of-the-art performance.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115044071","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: 1

Improved Lightweight DeepLabv3+ Algorithm Based on Attention Mechanism 基于注意机制的改进轻量级DeepLabv3+算法

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837577

Lin Wu, J. Xiao, Zhe Zhang

引用次数: 1

An Improved Superpixel-based Fuzzy C-Means Method for Complex Picture Segmentation Tasks 一种改进的基于超像素的模糊c均值方法用于复杂图像分割

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837508

Keyi Chen, Hangjun Che, Man-Fai Leung, Yadi Wang

引用次数: 1

Design of ADRC Controller for Induction Motor Based on Improved fal Function 基于改进fal函数的感应电机自抗扰控制器设计

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837544

Jinzhan Xie, Wen Wei, Pengcheng Liao, Jiahao Liu

引用次数: 2

On Pinning Synchronization of an Array of Nonlinearly Coupled Dynamical Network With Time Delay 一类时滞非线性耦合动态网络阵列的钉住同步问题

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837612

Wudai Liao, Haoran Chen, Jinhuan Chen, YaoHua Yang, Jingyu Wang, Heng Jia

引用次数: 0

Fixed-time Projective Synchronization For Discontinuous Fuzzy Inertial Neural Networks Via Non-reduced Method 基于非约简方法的不连续模糊惯性神经网络定时投影同步

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837526

Yang Liu, Guodong Zhang

引用次数: 0

Pruner to Predictor: An Efficient Pruning Method for Neural Networks Compression 修剪器到预测器:一种有效的神经网络压缩修剪方法

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837622

Jiaxin Li, Xuan Rao, Songyi Xiao, Bo Zhao, Derong Liu

引用次数: 4

State Estimation for Memristive Neural Networks with Observer 具有观测器的记忆神经网络状态估计

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837560

Moxuan Guo, Song Zhu

引用次数: 0