2022 IEEE International Conference on Smart Internet of Things (SmartIoT)最新文献_第3页

Pooling Pyramid Vision Transformer for Unsupervised Monocular Depth Estimation 无监督单目深度估计的池化金字塔视觉变压器

2022 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2022-08-01 DOI: 10.1109/SmartIoT55134.2022.00025

Qingyu Zhang, Chunyan Wei, Qingxia Li, Xiaosen Tian, Chuanpeng Li

{"title":"Pooling Pyramid Vision Transformer for Unsupervised Monocular Depth Estimation","authors":"Qingyu Zhang, Chunyan Wei, Qingxia Li, Xiaosen Tian, Chuanpeng Li","doi":"10.1109/SmartIoT55134.2022.00025","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00025","url":null,"abstract":"Compared with other sensors, high-quality depth estimation based on monocular camera has strong competitiveness and widespread application in intelligent transportation, etc. Although the barrier of training has been greatly lowered by unsupervised learning, most related works are still based on convolutional neural networks (CNNs) that suffer from unbridgeable gaps in the full-stage global information and high-resolution features while extracting multi-scale features. To break this predicament, we attempt to introduce vision transformer. However, the vision transformer with large sequence length due to image embedding brings great challenges to the computational cost. Thus, this work proposes a new pure transformer backbone named pooling pyramid vision transformer (PPViT), simultaneously shrinking out multi-scale features and reducing sequence length used for attention operation. Then, we provide two backbone settings including PPViT10 and PPViT18 whose number of parameters are close to the common ResNet18 and ResNet50, respectively. The experiments on KITTI dataset demonstrate that our work show a great potentiality of improving the capability of model and produce superior results to the previous CNN-based works. Equally important, we have lower latency than the related transformer-based work.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131409307","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

Self-Train: Self-Supervised On-Device Training for Post-Deployment Adaptation 自我训练:自我监督的设备上培训部署后适应

2022 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2022-08-01 DOI: 10.1109/SmartIoT55134.2022.00034

Jinhao Liu, Xiaofan Yu, T. Simunic

引用次数: 0

A Subspace Fusion of Hyper-parameter Optimization Method Based on Mean Regression 一种基于均值回归的子空间融合超参数优化方法

2022 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2022-08-01 DOI: 10.1109/SmartIoT55134.2022.00035

Jianlong Zhang, Tianhong Wang, Bin Wang, Chen Chen

{"title":"A Subspace Fusion of Hyper-parameter Optimization Method Based on Mean Regression","authors":"Jianlong Zhang, Tianhong Wang, Bin Wang, Chen Chen","doi":"10.1109/SmartIoT55134.2022.00035","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00035","url":null,"abstract":"For object detection methods based on neural networks in computer vision, hyper-parameter is a crucial factor in the detection performance. Traditional hyperparameter optimization methods share the following shortcomings. (1) Search performance depends heavily on historical data and computational resources. (2) The open-loop structure of models may lead to unstable search results. We take missed detection targets as feedback to establish an iterative search model and propose a subspace-fusion optimization method based on mean regression. Firstly, the Successive Halving algorithm is deployed to determine the initial seeds, then detection subspaces and missed detection subspaces are generated according to the object detection results, and anchor-vector-based mean regressions are performed in the two subspaces respectively. Finally, we obtain the optimal parameters by a linear fusion of the two regression results. An early termination strategy is embedded into the search process to stop the invalid searches. Experiments show that within limited resource, this paper achieves significant improvement in search efficiency and detection performance compared with the classical methods.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114092009","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

Dynamic Resource Scheduling and Frequency Scaling in NOMA-Based Multi-access Edge Computing System 基于noma的多址边缘计算系统的动态资源调度和频率缩放

2022 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2022-08-01 DOI: 10.1109/SmartIoT55134.2022.00040

Li Cui, Xin Chen, Zhuo Ma

引用次数: 0

A SAR Remote Sensing Image Change Detection Method Based on DR-UNet-CRF Model 基于DR-UNet-CRF模型的SAR遥感图像变化检测方法

2022 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2022-08-01 DOI: 10.1109/SmartIoT55134.2022.00037

Jianlong Zhang, Yifan Liu, Bin Wang, Chen Chen

{"title":"A SAR Remote Sensing Image Change Detection Method Based on DR-UNet-CRF Model","authors":"Jianlong Zhang, Yifan Liu, Bin Wang, Chen Chen","doi":"10.1109/SmartIoT55134.2022.00037","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00037","url":null,"abstract":"A Synthetic aperture radar (SAR) image change detection method based on DR-UNet-CRF iterative structure is proposed by introducing a regional dynamic convolutional network to address the problems of semantic information fading phenomenon and indeterminacy of change boundaries due to differential image computation in remote sensing image change detection. Firstly, a DR-UNet segmentation network based on the dynamic region-aware convolution (DRConv) kernel is conceived to supply a univalent potential function for the conditional random field, and a guide-mask generation method guided mask generation method with feature pyramid network (FPN) based structure is presented to guide an improved dynamic convolutional UNet to obtain accurate remote sensing change regions by learning fine spatial region delineation. Secondly, the pair-wise potential function based on image grayscale features and spatial features is designed to model the inter-pixel relationship. Finally, we use a fully connected conditional random field (CRF) model to iteratively optimize for change regions to achieve semantic compensation, thus defining the boundaries of remote sensing images more precisely. By comparing with the mainstream change detection methods, it can be considered that method in this paper has better detection performance.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133082121","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

Profit-driven UAV Green Wireless Charging for WSN 利润驱动的无人机WSN绿色无线充电

2022 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2022-08-01 DOI: 10.1109/SmartIoT55134.2022.00010

Junlong Chen, Xilong Liu

{"title":"Profit-driven UAV Green Wireless Charging for WSN","authors":"Junlong Chen, Xilong Liu","doi":"10.1109/SmartIoT55134.2022.00010","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00010","url":null,"abstract":"Wireless Sensor Network (WSN) has been widely applied in Internet of Things (IoT). WSN brings great convenience to people's daily lives. In reality, the limited battery's capacity always constrains normal function time of a wireless sensor. Replacing the batteries one by one for the wireless sensors deployed in large-scale network or in dangerous places is quite infeasible. The recent research results reveal that unmanned aerial vehicle (UAV) equipped with a point-to-point far-field wireless charging unit can efficiently facilitate remote powering for WSN. The advantage of adopting charging UAV is that the distance between the wireless energy emitter and the receiver can be shortened; thus, enhancing the wireless charging efficiency. However, a UAV's energy supply usually does not allow the long-term charging mission, hence, the cost and profit of UAV wireless charging should be considered in UAV provisioned charging service. In this work, we first build a UAV wireless charging pricing model to calculate the profit of the charging service, and then, we propose the Profit-driven UAV Charging (PUC) algorithm to maximize the UAV charging profit. Through extensive simulations, we have validated that the performance of our proposed algorithm outperforms the conventional Nearest-Job-Next with Preemption (NJNP) algorithm.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122892866","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

Steel Delivery Order Recognition Based on Deep Learning and Posterior Error Correction Technology 基于深度学习和后验误差校正技术的钢材交货单识别

2022 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2022-08-01 DOI: 10.1109/SmartIoT55134.2022.00036

Ming Li, Weigang Wang, Kedong Wang, Xueliang Leng, Chuan-qin Zhang, Zhongwen Guo

{"title":"Steel Delivery Order Recognition Based on Deep Learning and Posterior Error Correction Technology","authors":"Ming Li, Weigang Wang, Kedong Wang, Xueliang Leng, Chuan-qin Zhang, Zhongwen Guo","doi":"10.1109/SmartIoT55134.2022.00036","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00036","url":null,"abstract":"The main voucher for the circulation of bulk goods is the delivery order. The traditional warehousing enterprise receives the goods through manual input, which is inefficient and error-prone. The recognition rate of the traditional algorithm model is low and cannot be applied on a large scale. According to the characteristics of steel delivery order, through the integration of algorithm technology, this paper proposes an algorithm model based on image correction, text location, text recognition, and post verification, which solves the problem of the low recognition rate of the traditional algorithm. The character recognition rate of the recognition model is more than 95%. Finally, a visual manual correction function is developed to ensure 100% accuracy of output text data. Based on the intelligent identification technology of delivery orders, the traditional goods circulation mode is transformed from the series process of offline circulation based on traditional paper documents to the parallel process with an information-sharing cloud platform as the carrier. We build an intelligent information management system of delivery order of bulk goods is constructed. The business practice shows that the system can quickly and accurately extract the text information of delivery documents and effectively improve the efficiency of goods warehousing and circulation.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131891737","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

The Scheme and System Architecture of Product Quality Inspection based on Software-Defined Edge Intelligent Controller (SD-EIC) in Industrial Internet of Things 工业物联网中基于软件定义边缘智能控制器(SD-EIC)的产品质量检测方案及系统架构

2022 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2022-08-01 DOI: 10.1109/SmartIoT55134.2022.00019

Pengfei Hu, Chunming He, Yiming Zhu

{"title":"The Scheme and System Architecture of Product Quality Inspection based on Software-Defined Edge Intelligent Controller (SD-EIC) in Industrial Internet of Things","authors":"Pengfei Hu, Chunming He, Yiming Zhu","doi":"10.1109/SmartIoT55134.2022.00019","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00019","url":null,"abstract":"The Industrial Internet of Things (IIoT) enables the improvement of the productivity and intelligent level of factory. The procedure of product quality inspection has generally adopted machine intelligence algorithms instead of manual operation to improve efficiency. In this paper, we propose a product quality inspection system scheme based on software-defined edge intelligent controller (SD-EIC). By adopting the software definition and resource virtualization technologies, the hardware platform of SD-EIC is designed to support the real-time control tasks and non-real-time edge computing tasks at the same time. To this end, we propose the scheme and architecture of product quality inspection system based on SD-EIC. Multiple virtual controllers and virtual edge computing nodes are constructed on a set of SD-EIC hardware platform to realize the integrated deployment of the real-time control for terminal devices and the AI model reasoning of product defect recognition algorithm based on machine vision respectively. In addition, the management and control scheme of product quality inspection system based on industrial information model is proposed. By constructing the semantic based digital twin information model of terminal device, the flexible adjustment and parameter configuration of terminal device are realized to meet the demands of flexible production and manufacturing. The proposed product quality inspection system solution can effectively improve the utilization of hardware resources and the efficiency of product quality inspection, and reduce the overall deployment cost of the system. It can flexibly adapt to a variety of different industrial scenarios.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121344198","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: 2

Long-Term Traffic Speed Prediction Based on Geometric Algebra ConvLSTM and Graph Attention 基于几何代数卷积stm和图注意的长期交通速度预测

2022 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2022-08-01 DOI: 10.1109/SmartIoT55134.2022.00026

Chenglin Miao, Wen Su, Yanqing Fu, Xihao Chen, D. Zang

引用次数: 2

Intelligent system for data protection in higher education institutions: A systematic review 高等教育机构数据保护智能系统综述

2022 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2022-08-01 DOI: 10.1109/SmartIoT55134.2022.00024

Victor Gonzalo Rodriguez-Ahuanari, Miguel Angel Vega-Ramirez, Hugo Eladio Chumpitaz-Caycho, Ericka Nelly Espinoza-Gamboa, Franklin Cordova-Buiza

引用次数: 2