Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition最新文献_第6页

Industrial Internet Network Slice Prediction Algorithm Based on Multidimensional and Deep Neural Networks 基于多维深度神经网络的工业互联网网络切片预测算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573989

Jihong Zhao, Gao-Jing Peng

引用次数: 0

Small Object Detection Algorithm Based on RPANet and Positional Convolution Attention Mechanism 基于RPANet和位置卷积注意机制的小目标检测算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574031

Zongbing Tang, Dan Yang, Junsuo Qu

引用次数: 0

Fine-Grained Recognition with Incremental Classes 增量类的细粒度识别

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574078

Yangqiaoyu Zhou

引用次数: 0

Service Function Chain Deployment Algorithm Based on Double Deep Q Network 基于双深度Q网络的业务功能链部署算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573990

Guohui Zhu, Chaohang Zhao

引用次数: 0

Non-intrusive Automatic 3D Gaze Ground-truth System 非侵入式自动3D凝视地面真相系统

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574068

Feng Hu

引用次数: 0

Build an Agent-Based Model for COVID-19 Effect of Mitigation Policies 构建基于agent的COVID-19缓解政策效果模型

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574022

Jianhua Zeng, Ping Lu, Kai-Biao Lin

{"title":"Build an Agent-Based Model for COVID-19 Effect of Mitigation Policies","authors":"Jianhua Zeng, Ping Lu, Kai-Biao Lin","doi":"10.1145/3573942.3574022","DOIUrl":"https://doi.org/10.1145/3573942.3574022","url":null,"abstract":"Non-Drug Intervention (NDI) is one of the important means to prevent and control the outbreak of coronavirus disease 2019 (COVID-19), and the implementation of this series of measures plays a key role in the development of the epidemic. The purpose of this paper is to study the impact of different mitigation measures on the situation of the COVID 19, and effectively respond to the prevention and control situation in the \"post-epidemic era\". The present work is based on the Susceptible-Exposed-Infectious-Remove-Susceptible (SEIRS) Model, and adapted the agent-based model (ABM) to construct the epidemic prevention and control model framework to simulate the COVID-19 epidemic from three aspects: social distance, personal protection, and bed resources. The experiment results show that the above NDI are effective mitigation measures for epidemic prevention and control, and can play a positive role in the recurrence of COVID-19, but a single measure cannot prevent the recurrence of infection peaks and curb the spread of the epidemic; When social distance and personal protection rules are out of control, bed resources will become an important guarantee for epidemic prevention and control. Although the spread of the epidemic cannot be curbed, it can slow down the recurrence of the peak of the epidemic; When people abide by social distance and personal protection rules, the pressure on bed resources will be eased. At the same time, under the interaction of the three measures, not only the death toll can be reduced, but the spread of the epidemic can also be effectively curbed.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"51-52 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":"114474369","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

Traffic Road Detection Based on Dynamic Anchor Frame 基于动态锚框架的交通道路检测

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574111

Xingya Yan, Yujiao Ding, Yue Li

{"title":"Traffic Road Detection Based on Dynamic Anchor Frame","authors":"Xingya Yan, Yujiao Ding, Yue Li","doi":"10.1145/3573942.3574111","DOIUrl":"https://doi.org/10.1145/3573942.3574111","url":null,"abstract":"In recent years, deep convolution neural networks have made great progress in object detection tasks. Generally speaking, the bounding box and the type of bounding box play a very important role in object detection. However, it is not easy for convolution neural networks to directly generate disordered bounding boxes. A widely used solution is to adopt the idea of divide and conquer and introduce the concept of anchor box. At present, anchor frame mechanism has been widely used in top-level object detection framework, and has achieved good results on common datasets. The innovation of this paper is that a novel anchor frame generation method is proposed, which can generate error frames with various aspect ratios for object detection frames. Different from the previous method of generating the anchor box in a predefined way, the anchor box in this method is dynamically generated by the anchor box generator. The feature is that the anchor box generator is not fixed, but learns from anchor boxes defined by fixed rules, which means that the anchor box generator can be adapted to a variety of scenarios. In this paper, the dynamic anchor frame method is used to detect the traffic road. In addition, the weights of the anchor box generator are predicted by a small network whose inputs are predefined anchor boxes. Compared with the traditional anchor frame generation methods, the proposed anchor frame generator has the following innovations: (1) it adaptive adjusts the size and aspect ratio of the anchor frame to improve the quality of the anchor frame. (2) The adaptive IOU country value is used to balance the number of positive samples of the size target. Finally, good efficiency and results are obtained.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"18 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":"114786325","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

Based on Coupled Associative Feedback Control of Quantum Satellite Communication Performance Tuning Strategy 基于耦合关联反馈控制的量子卫星通信性能调谐策略

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573986

Yeliang Gong, Min Nie, Guang Yang

{"title":"Based on Coupled Associative Feedback Control of Quantum Satellite Communication Performance Tuning Strategy","authors":"Yeliang Gong, Min Nie, Guang Yang","doi":"10.1145/3573942.3573986","DOIUrl":"https://doi.org/10.1145/3573942.3573986","url":null,"abstract":"Quantum satellite communication has the natural advantages of strong survival reliability and wide coverage, and is currently a research hotspot in the field of communication at home and abroad. The successful launch of the \"Mozi\" scientific experimental satellite has laid the foundation for the construction of the quantum space-earth integrated communication network. In order to further improve the communication performance of the quantum satellite-ground link, a tuning strategy based on quantum coupled associative feedback control (QCAFC) is proposed in this paper. QCAFC estimates the state information of atoms by measuring the photons leaked in the optical cavity, and adjusts the controller to change the spin state of the atoms in the optical cavity. The evolution of the system is studied, the influence of the QCAFC system on the performance parameters of the satellite-ground quantum communication is analyzed, and the simulation is verified. The simulation results show that in the amplitude damped channel, the QCAFC system can significantly improve the channel capacity and coherence. When transmitting in the plasma environment, when the particle radius is 5, when the transmission distance between the satellite and the earth increases from 50km to 200km, the bit error rate of the system without QCAFC is increased from 7.34×10-3 to 21.93×10-3, and the bit error rate of the system with QCAFC is increased from 4.81×10-3 to 14.72×10-3. Simulation results show that the use of QCAFC system has a significant improvement in the performance of quantum satellite communication.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"161 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":"114863085","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

Ultra-short-term wind forecast of the wind farm based on VMD-BiGRU 基于VMD-BiGRU的风电场超短期风力预报

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574009

Lei Li, Yao Liu, Wenjin Zhang, Xiangyu Li, Jiantao Chang

{"title":"Ultra-short-term wind forecast of the wind farm based on VMD-BiGRU","authors":"Lei Li, Yao Liu, Wenjin Zhang, Xiangyu Li, Jiantao Chang","doi":"10.1145/3573942.3574009","DOIUrl":"https://doi.org/10.1145/3573942.3574009","url":null,"abstract":"The ultra-short-term forecast of wind conditions is mainly concentrated in the forecast range of a few minutes and has an important guiding role in wind power system dispatching, wind turbine control, and wind power load tracking. Due to the characteristics of sudden change, non-stationarity, and volatility of short-term wind direction and wind speed, these random and volatile properties bring great difficulties to the prediction of ultra-short-term wind conditions. The current research only predicts a single wind speed or wind direction and does not predict both at the same time, which also brings certain limitations to the dispatching of wind power systems. Given the above characteristics of wind speed and wind direction, the decomposition method can be used to divide it into multi-scale components, thereby reducing the complexity of the original signal, increasing the stability of the signal, and improving the accuracy of prediction. Therefore, this paper uses the VMD decomposition method to decompose the original wind direction and wind speed data constructs multi-scale prediction features, and explores the laws of each component. The bi-directional GRU model has a strong ability to capture the sequence fluctuation law, and the decomposed modal components are input into the bi-directional GRU model to predict the wind speed. Through a large number of experiments and the comparison of different methods, it is shown that the VMD-BiGRU-based model has high prediction accuracy, small error, and higher efficiency in wind direction and wind speed prediction.","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":"126107307","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

Lightweight Improved Based on YOLOv4 Object Detection Algorithm 基于YOLOv4的轻量化改进目标检测算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574027

Rui Chen, Zhenzhong Li, Yuzhao Zhang, Yuehang Li

{"title":"Lightweight Improved Based on YOLOv4 Object Detection Algorithm","authors":"Rui Chen, Zhenzhong Li, Yuzhao Zhang, Yuehang Li","doi":"10.1145/3573942.3574027","DOIUrl":"https://doi.org/10.1145/3573942.3574027","url":null,"abstract":"To address the problem that the existing object detection network models are large in size and complex in operation and cannot satisfy both detection speed and accuracy under the limited resources and small size platform. Based on YOLOv4 as the benchmark network, a lightweight object detection model LW-YOLO is proposed. Firstly, the backbone feature extraction network is replaced with MobileNetv1, while the number of feature fusion network parameters is significantly reduced by the depth separable convolutional module. Then the BN layer coefficients are used as scaling factors for the importance of the convolutional channels, the scaling factors are sparse using polarization regularization, the errors before and after pruning are reconstructed using least squares and channel weighting methods. The appropriate pruning thresholds are obtained by minimizing the reconstructed errors, the channels with small scaling factor values are eliminated to achieve the lightweight. The experimental results on the VOC (Visual Object Classes) dataset show that the detection accuracy of LW-YOLO is 87.00%, and the FPS(Frames Per Second ) reaches 48.89, which is better than the original YOLOv4 algorithm. It also significantly reduces the number of parameters, computation, and model size, which is more suitable for application in resource-poor embedded mobile devices.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"48 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":"125711588","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