2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)最新文献_第6页

Target Extraction from Remote Sensing Image Based on Scene Recognition and Target Segmentation 基于场景识别和目标分割的遥感图像目标提取

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754627

Xili Wang, Min Liang, Huimin Guo, Chenxiao Feng

{"title":"Target Extraction from Remote Sensing Image Based on Scene Recognition and Target Segmentation","authors":"Xili Wang, Min Liang, Huimin Guo, Chenxiao Feng","doi":"10.1109/CCIS53392.2021.9754627","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754627","url":null,"abstract":"Extracting dense and different sizes targets from large-scale remote sensing images is a challenging task. This paper proposes a remote sensing image target extraction method based on scene recognition and target segmentation. The method recognizes images having targets first and then extracts targets via segmentation, both implements using deep network models. Firstly, cropping large-scale remote sensing images into smaller images, and classifying scenarios by whether they contain targets or not. Next, a full-resolution neural network target segmentation model with multi-source input is constructed. In the segmentation model, feature resolution retaining, and feature fusion together with data exchange mechanism lead to better feature extraction for different sizes targets and overcome the problem of gradient vanishing. Experiments for building extraction on two remote sensing data sets show that the proposed method obtains better results than the comparable deep neural network models in accuracy, and does better in targets integrity and edges smoothness.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"410 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130755291","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 Pedestrian Re-identification Method Based on Visual Attention Mechanism 基于视觉注意机制的行人再识别方法研究

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754654

Zexin Jiang, Hang Ma, Wenbai Chen, Weizhao Chen, Tianxu Tong, Xibao Wu

{"title":"Research on Pedestrian Re-identification Method Based on Visual Attention Mechanism","authors":"Zexin Jiang, Hang Ma, Wenbai Chen, Weizhao Chen, Tianxu Tong, Xibao Wu","doi":"10.1109/CCIS53392.2021.9754654","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754654","url":null,"abstract":"Based on the method of attention mechanism, this paper researches on pedestrian re-identification. First, ResNet50 is used as the backbone network, and several model preprocessing methods are added as the baseline network. Then the channel attention mechanism module SENet is added to form the SE-ResNet50 network, and it can learn the importance of different dimensions of feature vectors, and focus attention on the corresponding dimensions. After the improvement, the model’s rank-1 on the Market-1501 data set increased by 1.5%, MAP increased by 2.0%, and the model’s rank-1 increased by 0.1% on the DukeMTMC-reID data set, MAP increased by 0.8%. In addition, this article also conducts a study on the importance of loss function, and the model obtains the best improvement effect when the ratio of ID loss to TriHard loss is 1 to 1.5. Rank-1 on Market-1501 increases by 0.2%, mAP increases by 0.2%, and on the DukeMTMC-reID data set, rank-1 increased by 1.1% and mAP increased by 2.1%. Finally, the actual video collection of campus scenes is carried out, and the trained model is applied to the pedestrian re-recognition in the actual scene. The results show that the model has an outstanding ability to recognize pedestrians in the context of a more complex environment.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133402232","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

Malicious Code Family Classification Method Based on Spatial Pyramid Pooling and Deep Residual Network 基于空间金字塔池化和深度残差网络的恶意代码族分类方法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754597

Jianyi Liu, Yansheng Qu, Jiaqi Li, Yunxiao Wang, Jing Zhang, H. Yin

引用次数: 3

A Lévy-Inspired Kinodynamic A* Algorithm for Quadrotor Fast Path Searching 四旋翼飞行器快速路径搜索的一种lsamv - inspired运动学A*算法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754685

Lin Zhao, Xinghui Zhang, Weiyan Ren

引用次数: 0

Agricultural super docking Traceability System of Fresh Agricultural Products 农业生鲜农产品超级对接溯源系统

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754684

Shuhui Pan, Min Zuo, Wenjing Yan, Qingchuan Zhang, Wei Wei

引用次数: 0

Research on 5G UAV Smart Grid Inspection Technology Based on Digital Twin 基于数字孪生的5G无人机智能电网巡检技术研究

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754652

Ming Xu

{"title":"Research on 5G UAV Smart Grid Inspection Technology Based on Digital Twin","authors":"Ming Xu","doi":"10.1109/CCIS53392.2021.9754652","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754652","url":null,"abstract":"At present, 5G, UAV, Beidou Positioning, artificial intelligence and digital twin technology are accelerating the deep integration with industrial departments such as transmission grid. This paper studies the intelligent application of 5G UAV based on digital twin in transmission grid, especially the key technologies of transmission line inspection, digital twin technology and 5G UAV technology, Through the research, it is found that the intelligent inspection of transmission line using UAV for data acquisition and supervision, 5G as transmission channel and digital twin as dynamic simulation tool has the characteristics of immersive monitoring operation, intelligent fault diagnosis, automatic problem tracking and remote centralized control command, which can improve the inspection efficiency of transmission line, dynamic monitoring around substation. The results of fault detection, emergency treatment and self-healing are remarkable. The application of digital twin technology to simulate unmanned unit inspection of transmission lines, substations and key components such as insulators, as well as inspection track and early warning tracking, are more realistic, intelligent, effective and safe, which can improve the overall economic efficiency of power grid transmission supply, operation and maintenance, At the same time, it also improves the intelligent level of power grid and provides a reference for the in-depth research of the technical system.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114208137","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

An Automatic Text Region Selection Method on Optical Inspection for Airborne Instrument 机载仪器光学检测文本区域自动选择方法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754630

Siyuan Wu, Jieyi Liu, Hongliang Luo, Zhao Nie, Hao Li, Jie Wu

引用次数: 2

Single-task Temporal Face Synthesis Model Based on Migration Confrontation 基于迁移对抗的单任务时间人脸合成模型

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754660

Linlin Tang, Ruipei Sun, Shiyu Qin, Xing Huang, Yijie Fan, Minghua Hou

引用次数: 0

Knowledge Graph Completion via Bidirectional Translation and Interaction 通过双向翻译和交互完成知识图谱

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754641

Qianjin Zhang, Dan Jiang, Xiongfei Wang, Ronggui Wang

{"title":"Knowledge Graph Completion via Bidirectional Translation and Interaction","authors":"Qianjin Zhang, Dan Jiang, Xiongfei Wang, Ronggui Wang","doi":"10.1109/CCIS53392.2021.9754641","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754641","url":null,"abstract":"Knowledge graphs have achieved great success for many artificial intelligence related downstream tasks. However, they are still far from being incomplete. In order to automatically predict missing facts, researchers proposed many knowledge graph completion methods. To address the issues that translation-based methods in handling the complex relations and symmetric relations of knowledge graph completion task, a new knowledge graph completion method via bidirectional translation and interaction called BI-TransE is proposed in this paper. First, BI-TransE embeds the entities and relations of triples into low dimensional vectors. Then, BI-TransE models the complex relations, such as 1-to-N, N-to-1, N-to-N, through the interaction between entities and relations, and models symmetric and inverse relations leveraging bidirectional translation score function. We evaluate our BI-TransE for knowledge graph link prediction task. Experimental results show that, BI-TransE can work well on complex relations and symmetric and inverse relation connectivity patterns, and achieves new state-of-the-art performance on four large-scale popular datasets.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129103113","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

WNN Short-Term Traffic Flow Prediction Based on Improved Mind Evolutionary Algorithm and Error Compensation 基于改进思维进化算法和误差补偿的小波神经网络短期交通流预测

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754602

Yumeng Zhou, Yuchao Lv, Xi Jiang, Xijun Zhu

{"title":"WNN Short-Term Traffic Flow Prediction Based on Improved Mind Evolutionary Algorithm and Error Compensation","authors":"Yumeng Zhou, Yuchao Lv, Xi Jiang, Xijun Zhu","doi":"10.1109/CCIS53392.2021.9754602","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754602","url":null,"abstract":"In the research and design of intelligent traffic system, urban road traffic control and guidance is an important research topic, and short-term traffic flow prediction is also an important research content of urban road traffic control and guidance. The combined prediction model is the research trend of short-term traffic flow prediction model in recent years. Nowadays, there is a prediction model of mind evolutionary algorithm to optimize wavelet neural network (MEA-WNN). The convergence and alienation of mind evolutionary algorithm are too random, and the bulletin board information is not supplemented. This paper introduces the particle movement update position method after convergence, which is similar to particle swarm optimization(PSO). Thus, WNN prediction model based on improved mind evolution algorithm (IMEA-WNN) is constructed. In order to improve the accuracy of the prediction model, the error compensation method is introduced to construct the combined prediction model (IMEA-EC-WNN). In this paper, the simulation results of IMEA-EC-WNN model are compared with other prediction models. The prediction effect of IMEA-EC-WNN model is better, and it has practical application value.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124612221","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