Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition最新文献_第5页

A Concept Drift Detection Approach Based on Jensen-Shannon Divergence for Network Traffic Classification 基于Jensen-Shannon散度的网络流量分类概念漂移检测方法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573979

Wujun Yang, Rui Su, Yuanzheng Cheng, Juan Guo

引用次数: 0

Channel Estimation Algorithm of OFDM-RoF System in 5G Mobile Front-end Network Based on Artificial Neural Network 基于人工神经网络的5G移动前端网络OFDM-RoF系统信道估计算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574000

Yun Zhang, Siyuan Liang, Chunting Wang, Feng Zhao

引用次数: 0

A Method with Universal Transformer for Multimodal Sentiment Analysis 基于通用变压器的多模态情感分析方法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573968

Hao Ai, Ying Liu, Jie Fang, Sheikh Faisal Rashid

{"title":"A Method with Universal Transformer for Multimodal Sentiment Analysis","authors":"Hao Ai, Ying Liu, Jie Fang, Sheikh Faisal Rashid","doi":"10.1145/3573942.3573968","DOIUrl":"https://doi.org/10.1145/3573942.3573968","url":null,"abstract":"Multimodal sentiment analysis refers to the use of computers to analyze and identify the emotions that people want to express through the extracted multimodal sentiment features, and it plays a significant role in human-computer interaction and financial market prediction. Most existing approaches to multimodal sentiment analysis use contextual information for modeling, and while this modeling approach can effectively capture the contextual connections within modalities, the correlations between modalities are often overlooked, and the correlations between modalities are also critical to the final recognition results in multimodal sentiment analysis. Therefore, this paper proposes a multimodal sentiment analysis approach based on the universal transformer, a framework that uses the universal transformer to model the connections between multiple modalities while employing effective feature extraction methods to capture the contextual connections of individual modalities. We evaluated our proposed method on two benchmark datasets for multimodal sentiment analysis, CMU-MOSI and CMU-MOSEI, and the results outperformed other methods of the same type.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121151314","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

Research on Image Description Generation Method Based on G-AoANet 基于G-AoANet的图像描述生成方法研究

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574072

Pi Qiao, Ruixue Shen, Yuan Li

引用次数: 0

Image Encryption Algorithm Based on Latin Squares and Adaptive Z-Diffusion 基于拉丁平方和自适应z扩散的图像加密算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574062

Yangguang Lou, Shu-cui Xie, Jianzhong Zhang

引用次数: 0

An Age-Based Data Collection and Path Planning Algorithm in UAV-Assisted Wireless Sensor Networks 无人机辅助无线传感器网络中基于年龄的数据采集与路径规划算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573981

Chi Sun, De Wei

{"title":"An Age-Based Data Collection and Path Planning Algorithm in UAV-Assisted Wireless Sensor Networks","authors":"Chi Sun, De Wei","doi":"10.1145/3573942.3573981","DOIUrl":"https://doi.org/10.1145/3573942.3573981","url":null,"abstract":"In view of the importance of Age of Information (AoI) in delay sensitive applications of Wireless Sensor Networks (WSNs), an improved gray wolf algorithm (POPAGA) based on the combination of particle swarm optimization possibility fuzzy C-mean clustering is proposed. POPAGA is optimized from the clustering stage and the path planning stage. In the clustering stage, the particle swarm optimization algorithm is first used to optimize the possibility fuzzy hybrid clustering algorithm, which not only overcomes the problem that the fuzzy C-means is sensitive to the initial clustering center, but also avoids the poor initialization effect of the possibility fuzzy c-means clustering, so as to determine the Hovering Collection Data points (HCD) and their associated Sensor Nodes (SNs). In the path planning stage, based on the hover collection data points obtained in the previous stage, the improved gray wolf optimization algorithm (GWO) is used to find the optimal path to minimize the maximum AoI and the average AoI. The simulation results show that POPAGA can obtain the global minimum AoI optimal value, whether compared with the traditional genetic algorithm (GA) and simulated annealing algorithm (SA) for solving TSP problem, or compared with the genetic algorithm (GA) and greedy algorithm based on AoI.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128407001","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

Radar micro moving gesture recognition method based on multi-scale fusion deep network 基于多尺度融合深度网络的雷达微动手势识别方法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574076

Zhiqiang Bao, Tiantian Liu

{"title":"Radar micro moving gesture recognition method based on multi-scale fusion deep network","authors":"Zhiqiang Bao, Tiantian Liu","doi":"10.1145/3573942.3574076","DOIUrl":"https://doi.org/10.1145/3573942.3574076","url":null,"abstract":"In order to solve the problem that the micro moving gesture features are not obvious and difficult to be identified, a micro moving gesture recognition method based on multi-scale fusion deep network for millimeter wave radar is proposed in this paper. The method is mainly composed of 2D convolution module, multi-scale fusion module and attention mechanism module. The multi-scale fusion module is composed of three residual blocks of different scales, which can obtain receptive fields of different sizes and obtain multi-scale features. Meanwhile, residual blocks of different scales are fused to increase the diversity of the network and better extract the deep features of the data. The Squeeze-and-congestion (SE) attention mechanism module is added to suppress the channel characteristics with little information. This improves the network identification accuracy and reduces the number of parameters and computation. The experimental results show that this method is simple to implement, doesn't need to do complex data preprocessing. The convergence speed of the network is fast, which can realize the effective recognition of the micro moving gesture.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134167521","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

Research on Multi-hop Transmission in Quantum Wireless Communication Networks Based on Improved Ant Colony Algorithm 基于改进蚁群算法的量子无线通信网络多跳传输研究

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573985

Xinyuan Mao, Min Nie, Guang Yang

引用次数: 0

Optimization Algorithm of Spotted Hyena Based on Chaotic Reverse Learning Strategy 基于混沌反向学习策略的斑点鬣狗优化算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574018

Xu He, Hengzhi Lu, Zixing Ling

引用次数: 0

Research on Recognition Model of Crop Diseases and Insect Pests Based on Convolutional Neural Network 基于卷积神经网络的农作物病虫害识别模型研究

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574087

Pi Qiao, Zilu Wang

{"title":"Research on Recognition Model of Crop Diseases and Insect Pests Based on Convolutional Neural Network","authors":"Pi Qiao, Zilu Wang","doi":"10.1145/3573942.3574087","DOIUrl":"https://doi.org/10.1145/3573942.3574087","url":null,"abstract":"Most of the traditional detection methods for crop diseases and insect pests are manually operated in the field according to the experience and technology of the staff, which have the disadvantages of long time and low efficiency. With the development of deep learning technology, the application of complex deep neural network algorithm models in the field of crop diseases and insect pests can effectively solve the above problems, however, the current research on the identification method of crop diseases and insect pests only focuses on the identification and analysis of single crop diseases and insect pests, and does not analyze and improve the analysis and improvement of various crops. Therefore, this paper proposes a recognition model of crop pests and diseases based on convolutional neural network. First, on the bilinear network model, the ResNet50 network is used as the feature extractor, that is, the backbone network of the network, instead of the original VGG-D and VGG-M backbone networks. Secondly, a connect module is added to design the bilinear network model and the extractor to do mutual outer product with the previous features of different levels, so that it is connected with the outer product of the feature vector. Finally, the loss function is used to conduct experiments on the AI Challenger 2018 crop pest and disease dataset. The experimental results show that the average recognition rate of the improved B-CNN-ResNet50-connect network model reaches 89.62%.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133422484","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