2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)最新文献_第7页

Imagery bionic modeling design of river dredging equipment vehicle 河道疏浚设备车辆图像仿生建模设计

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456584

Zhiguo Wei, Ji-Xiao Zhang, Fan-Zhuo Ding

{"title":"Imagery bionic modeling design of river dredging equipment vehicle","authors":"Zhiguo Wei, Ji-Xiao Zhang, Fan-Zhuo Ding","doi":"10.1109/AIID51893.2021.9456584","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456584","url":null,"abstract":"To vigorously improve the comprehensive performance of dredging equipment vehicles in China, and quickly satisfy the requirements of the sustainable development of modern urban ecology, the river dredging equipment vehicles was studied through the imagery bionic design. Starting from the theoretical basis of imagery bionics, the current situation of river dredging equipment vehicles was analyzed, and the design process in the program design process was refined. By extracting the modeling features of the bionic objects, the mapping relationship between the bionic biological features and the vehicle modeling elements was established, and the fuzzy comprehensive evaluation method was used to evaluate the design plan of the river dredging equipment vehicle, establish the evaluation model, and get the comprehensive evaluation value of design plan. Taking the river dredging equipment vehicle as the research object, through the image bionic design method, the overall shape of the vehicle was innovatively designed, and the vehicle design plan was evaluated with the fuzzy comprehensive evaluation method, so as to determine the design plan that meets the design purpose and provide new design ideas for related products in the future.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126937759","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

Data compression and decompression method and system for communication system 通信系统的数据压缩与解压缩方法及系统

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456475

XiuPing Ou, Qinyang Guan

{"title":"Data compression and decompression method and system for communication system","authors":"XiuPing Ou, Qinyang Guan","doi":"10.1109/AIID51893.2021.9456475","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456475","url":null,"abstract":"With the rapid development of mobile communication technology, the demand for signal transmission bandwidth is increasing. Technologies such as carrier aggregation will also increase the amount of data transmitted between the baseband processing unit and the remote radio unit. This puts forward higher requirements for optical fiber transmission capabilities. In the current communication system based on optical fiber transmission, the application of optical modules with a transmission rate of 10Gb/s has become popular. In 5G applications, 40G or 100G optical modules are even required to meet the ever-increasing application requirements. In order to reduce and control the consumption of optical fiber resources, it is necessary to develop a data compression method to reduce data transmission pressure and reduce costs. This paper proposes an IQ data compression and decompression algorithm based on FPGA hardware and according to the characteristics of programmable logic devices. As a result, the antenna system improves the efficiency of radio frequency power amplifiers, and reduces operation and maintenance costs.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122135766","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

Freight positioning technology of high speed railway carriage based on UWB 基于超宽带的高速铁路货运定位技术

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456549

Qinghua Li, F. Shen, Pan Diao

引用次数: 0

A Novel Multi-Task Self-Supervised Representation Learning Paradigm 一种新的多任务自监督表征学习范式

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456562

Yinggang Li, Junwei Hu, Jifeng Sun, Shuai Zhao, Qi Zhang, Yibin Lin

{"title":"A Novel Multi-Task Self-Supervised Representation Learning Paradigm","authors":"Yinggang Li, Junwei Hu, Jifeng Sun, Shuai Zhao, Qi Zhang, Yibin Lin","doi":"10.1109/AIID51893.2021.9456562","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456562","url":null,"abstract":"Self-supervised learning can be adopted to mine deep semantic information of visual data without a large number of human-annotated supervision by using a pretext task to pretrain a model. In this study, we proposed a novel self-supervised learning paradigm, namely multi-task self-supervised (MTSS) representation learning. Unlike existing self-supervised learning methods, which pretrain neural networks on the pretext task and then fine-tune the parameters of neural networks on the downstream task, in our scheme, downstream and pretext tasks are considered primary and auxiliary tasks, respectively, and are trained simultaneously. Our method involves maximizing the similarity of two augmented views of an image as an auxiliary task and using a multi-task network to train the primary task alongside the auxiliary task. We evaluated the proposed method on standard datasets and backbones through a rigorous experimental procedure. Experimental results revealed that proposed MTSS can achieve better performance and robustness than other self-supervised learning methods on multiple image classification data sets without using negative sample pairs and large batches. This simple yet effective method can inspire people to rethink self-supervised learning.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125937889","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

Field Verification of Recursive Structure Beamforming 递归结构波束形成的现场验证

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456564

Pengcheng Ge, Jun Yang, Tingting Feng, Shuo Zhang, Dong Li, Yu Du

引用次数: 0

A Novel CTR Prediction Model Based On DeepFM For Taobao Data 基于深度调频的淘宝数据点击率预测模型

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456556

LinShu Li, Jianbo Hong, Sitao Min, Yunfan Xue

引用次数: 6

Image Semantic Segmentation Based on Dilated Convolution and Multi-Layer Feature Fusion 基于扩展卷积和多层特征融合的图像语义分割

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456560

J. Liu, Zhongliang Wu, Yang Hong, Guoyun Zhong, Meifeng Liu

引用次数: 0

Study on the Influence of Driving Simulator S3D Visual Display on Distance Judgment Accuracy and User Experience in Following Task 驾驶模拟器S3D视觉显示对跟随任务距离判断精度和用户体验的影响研究

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456512

Zhang Zhiqiang, Yang Zhibo, Zhang Jiarui, Shi Juan, Zhang Li

{"title":"Study on the Influence of Driving Simulator S3D Visual Display on Distance Judgment Accuracy and User Experience in Following Task","authors":"Zhang Zhiqiang, Yang Zhibo, Zhang Jiarui, Shi Juan, Zhang Li","doi":"10.1109/AIID51893.2021.9456512","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456512","url":null,"abstract":"In order to study the influence of S3D visual display in driving simulator on the accuracy of distance judgment and the user experience under the vehicle following task, in this paper, ten people were called to carry out the following experiment based on the real vehicle test, and then the reference following distance can be collected. The driving simulator experiment was carried out by adopting the 2×4 within-subjects design experiment, and the difference ΔD between the following distance of the driving simulator and the real vehicle was taken as the analysis index to characterize the following simulation fidelity in the simulator. The results show that there is a significant difference in the following distance between S3D and 2D display modes. Among them, the following distance of S3D mode is closer to the actual following distance, which has an accuracy advantage of 2.5 m (95% CI: 1.11-3.85 m) compared with 2D mode. Generally speaking, S3D plays a positive role in improving distance perception and judgment in driving simulator, and the results have not witnessed any discomfort feeling caused by S3D.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115480457","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

X-ray Classification of Tuberculosis Based on Convolutional Networks 基于卷积网络的肺结核x线分类

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456476

K. Cao, Jingyi Zhang, Mengge Huang, Tao Deng

{"title":"X-ray Classification of Tuberculosis Based on Convolutional Networks","authors":"K. Cao, Jingyi Zhang, Mengge Huang, Tao Deng","doi":"10.1109/AIID51893.2021.9456476","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456476","url":null,"abstract":"Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis, which can invade many organs, and pulmonary tuberculosis is the most common infection. It is the key to treat tuberculosis to detect and diagnose the disease in the early stage. The existing computer-aided detection system has made preliminary progress in the diagnosis of pulmonary tuberculosis based on chest X-ray, but there is still a lack of further research on the classification of image signs of tuberculosis. In recent years, with the in-depth research and development in the field of deep learning, convolutional networks have emerged. Convolutional networks have achieved the best current results in image recognition, image classification, image segmentation, and other fields. Therefore, this paper applies the convolutional network to tuberculosis CT images and uses different convolutional network models to study the classification of tuberculosis CT images. Experiments show that the DenseNet121 model has higher performance than VGGNet16, VGGNet19, and ResNet152 models. As a result of classification, the accuracy rate is over 90%.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115689353","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

Research on the Algorithm of Art Style Transfer of Xin'an Painting School 新安画派艺术风格迁移算法研究

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456474

Decheng Wang, Yan Chen

引用次数: 0