2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)最新文献_第9页

Masked Face Recognition Using FaceNet 使用FaceNet进行蒙面人脸识别

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970601

Yuan Wu, Longfei Yang

引用次数: 0

Wind Power Interval Prediction Based on External Attention LSTM and Quality-Driven Loss Function 基于外部关注LSTM和质量驱动损失函数的风电区间预测

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970599

Hao Quan, Wei Zhang, Tao Zhou

引用次数: 0

Q-Learning Algorithm for Fourth Party Logistics Route Optimization Considering Tardiness Risk 考虑延误风险的第四方物流路线优化q -学习算法

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970625

Xin Liu, Guihua Bo

引用次数: 0

Sliding Mode Control of Rigid Flexible Hybrid Structure Manipulator with Compound Disturbance and Measurement Noise Using VGESO 具有复合扰动和测量噪声的刚柔混合结构机械臂滑模控制

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970652

Han Zheng, Liang Shan, Yangyang Cheng, Jianhu Yan, Ronghao Wang

引用次数: 0

Stochastic Greedy Two-neighborhood Search Algorithm For Hybrid Disassembly Line Balancing Problem Considering Human Posture 考虑人体姿态的混合拆解线平衡问题的随机贪心双邻域搜索算法

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970611

Peng Ji, Xu Wang, Xiwang Guo, Jiacun Wang, Shujin Qin, Ying Tang

引用次数: 0

3D Observer-based Connectivity Preserving Flocking Control of UAVs with Velocity-free Measurement and Disturbances 基于观测器的无航速测量和无干扰无人机保连通性群集控制

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970665

Fanjing Huang, Xingxiu Li, Panlong Wu, Zhouyu Zhang, Jimin Li, Ke Wang

引用次数: 0

Urban Rail Train Number Recognition System Based on Image Processing 基于图像处理的城市轨道交通车次识别系统

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970602

Weihua Shi, Z. Xing, Yu Duan, Kaihua Song, Yueguang Xiong, Jun Zhu

引用次数: 0

Design and Implementation of Autonomous Takeoff and Landing UAV System for USV Platform USV平台无人机自主起降系统的设计与实现

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970677

Beizhen Feng, Xiaofei Yang, Ronghao Wang, Xin Yan, Hong-wei She, Liang Shan

{"title":"Design and Implementation of Autonomous Takeoff and Landing UAV System for USV Platform","authors":"Beizhen Feng, Xiaofei Yang, Ronghao Wang, Xin Yan, Hong-wei She, Liang Shan","doi":"10.1109/ICCSI55536.2022.9970677","DOIUrl":"https://doi.org/10.1109/ICCSI55536.2022.9970677","url":null,"abstract":"In order to realize the autonomous takeoff and landing function of the unmanned aerial vehicle (UAV) on the unmanned surface vehicle (USV) platform to improve the operation capability of the UAV in wide waters. In this paper, a phased takeoff and landing method is proposed and a corresponding hardware system is designed. The system is mainly an UAV system and an USV platform system.The UAV system designed in this paper is divided into a communication control part and a sensor part. The communication control part contains flight control board, take-off and landing harpoon device and wireless digital transmission. The sensor part contains infrared thermal imaging module and non-differential global positioning system (GPS), etc.The equipped infrared thermal imaging sensor is used as a precise position data feedback when it is closer to the USV platform. It uses visual processing to accurately identify the location of the positive temperature coefficient PTC heat source in the center of the USV and feeds it back to the UAV. The non-differential GPS is used for position data feedback at longer distances. The landing harpoon device is used to secure the UAV to the landing net to prevent the small sway of the USV platform from causing the UAV to tip over.The USV system is equipped with a landing net, a PTC heat source, and a 4G communication module and a 2.4G communication module. Finally, the experimental verification of the autonomous takeoff and landing system in this paper was conducted.The designed system can realize the autonomous takeoff and landing function of the UAV on the USV platform. The landing can be completed when the USV platform is moving at 20cm/s and its attitude angle wobble is less than 10 degrees.","PeriodicalId":421514,"journal":{"name":"2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129383026","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

Research on Cartoon Face Generation Based on CycleGAN Assisted with Facial Landmarks 基于CycleGAN辅助人脸标记的卡通人脸生成研究

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970645

Keyi Ma, Xiaohong Wang

{"title":"Research on Cartoon Face Generation Based on CycleGAN Assisted with Facial Landmarks","authors":"Keyi Ma, Xiaohong Wang","doi":"10.1109/ICCSI55536.2022.9970645","DOIUrl":"https://doi.org/10.1109/ICCSI55536.2022.9970645","url":null,"abstract":"Turn real faces into cartoon faces is a topic of style transfer, and style transfer is a hot topic in the application of generative adversarial networks in image. CycleGAN is one of generative adversarial networks. It has obvious universal applicability, and has a good transformation effect on various types of style transfer. But to the facial style transfer, it only focuses on the transformation of the whole face, and it is not ideal for the transformation of the details of the facial features. How can this situation be improved? In this paper, we use facial landmarks to assist the transformation of facial features. In the beginning, we use stacked hourglass networks to detection and capture landmarks of real faces. And then, use them to assist cartoon faces generation. In view of the fact that the hourglass network has its own advantages in feature extraction, we use it to replace the generator structure of the original CycleGAN for transformation. And in order to avoid the Checkerboard Artifacts and ensure the quality of image generation, we use bilinear interpolation in the upsampling part of the generator to replace the deconvolution of the original generator and the nearest interpolation of the hourglass network. Experiments show that these practices have good results in optimizing conversion performance and improving image quality.","PeriodicalId":421514,"journal":{"name":"2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129959847","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 Improved Bayesian Loss Function for Crowd Counting 一种改进的贝叶斯损失函数用于人群计数

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2022-11-18 DOI: 10.1109/ICCSI55536.2022.9970647

Lixuan Zhang, Jun Yu Li, Shuheng Zhang, Ziwen Wang

{"title":"An Improved Bayesian Loss Function for Crowd Counting","authors":"Lixuan Zhang, Jun Yu Li, Shuheng Zhang, Ziwen Wang","doi":"10.1109/ICCSI55536.2022.9970647","DOIUrl":"https://doi.org/10.1109/ICCSI55536.2022.9970647","url":null,"abstract":"The annotations of all the crowd counting datasets so far are sparse binary matrices, so they cannot be used to supervise training directly. The mainstream crowd counting algorithm uses a Gaussian function to smooth each head label point, and then train their model by using it as “ground truth” density map. However, such “ground-truth” density maps are not perfect due to heavy occlusion, scale variation, background interference, etc. In this paper, we propose an improved BayesianLoss for the problems existing in the current crowd counting loss function. First of all, average distance to k-nearest neighbors is use to confirm the size of Gaussian likelihood estimation kernel for each labeled point to better distinguish the boundaries between crowds. Secondly, a new background likelihood estimation method is defined to better suppress the posterior probability of the edge background during training. In the evaluation of the mean absolute error metric, our method achieves state-of-the-art results on ShanghaiTech, UCF-CC-50and NWPU datasets. And on the largest dataset NWPU, our method outperforms the best loss-function-improving method DM-Count. At the same time, our loss function combined with other crowd counting models, such as MCNN, CAN, M-SFANet and TransCrowd, achieves better results than the original model.","PeriodicalId":421514,"journal":{"name":"2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132908433","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