2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)最新文献_第9页

Prediction Model of Demand for Shared Bikes based on Bayesian Theory 基于贝叶斯理论的共享单车需求预测模型

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00090

Hongyu Ma, Taiqin Peng, Y. Sun

引用次数: 0

Research on Key Technologies for Equipment of the Intelligent Support Platform 智能支撑平台装备关键技术研究

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00044

Huang Tong, Jin-Zhong Zhang, Zhong-Ting Su

引用次数: 0

Human Behavior Recognition Based on Deep Learning 基于深度学习的人类行为识别

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00027

Yin Chen

{"title":"Human Behavior Recognition Based on Deep Learning","authors":"Yin Chen","doi":"10.1109/AINIT54228.2021.00027","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00027","url":null,"abstract":"With the continuous advancement of technology, human behavior recognition, as an important scientific research in the field of computer vision, has important research in many fields such as intelligent surveillance, smart home, virtual reality. In the current complex environment, traditional manual methods have been difficult to meet the requirements of high recognition accuracy and applicability. The introduction of deep learning has brought new development directions for behavior recognition. This article mainly summarizes behavior recognition algorithms based on deep learning. Firstly, the research background and significance of behavior recognition are introduced, and then the traditional learning methods and deep learning methods of behavior recognition are discussed and analyzed respectively, and then the structure of algorithmic models and commonly used public data sets are introduced, and finally, the advantages and disadvantages of the various research directions of human behavior recognition methods based on deep learning are analyzed and some suggestions are given in the future research and expansion directions.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"624 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114087821","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

SAR image target recognition algorithm based on improved residual shrinkage network 基于改进残差收缩网络的SAR图像目标识别算法

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00026

Baodai Shi, Qin Zhang, Yuhuan Li, Miaomiao Wu

引用次数: 1

A Pet Detection System Based on YOLOv4 基于YOLOv4的宠物检测系统

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00074

Yu-Wei Yuan

引用次数: 1

Application of Wavelet Denoising and Time- Frequency Domain Feature Extraction on Data Processing of Modulated Signals 小波去噪与时频域特征提取在调制信号数据处理中的应用

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00123

Yujun Dai, Xizi Huang, Zhongrun Chen

{"title":"Application of Wavelet Denoising and Time- Frequency Domain Feature Extraction on Data Processing of Modulated Signals","authors":"Yujun Dai, Xizi Huang, Zhongrun Chen","doi":"10.1109/AINIT54228.2021.00123","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00123","url":null,"abstract":"Signal modulation is an essential part of the communication system. Researches on reducing the noise interference in the modulated signal and improving the signal-to- noise ratio can help recognize the signal. In this paper, it is proposed to apply the wavelet denoising and time-frequency domain feature extraction to the modulated signals. Combine the characteristics of the modulated signal and the theory of wavelet denoising and time-frequency domain feature extraction, remove the noise interference in the signal, extract the time-frequency domain feature, and input the processed data into the decision tree model for classification and recognition and evaluate the signal processing effect. In addition, the accuracy of decision trees before and after data processing under different signal-to- noise ratios is further studied. Experimental results show that wavelet denoising and time-frequency domain feature extraction obviously affect modulated signal processing and are generally applicable under different signal-to-noise ratios.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"813 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124599304","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: 4

Funny words detection via Contrastive Representations and Pre-trained Language Model 基于对比表征和预训练语言模型的搞笑词检测

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00078

Yiming Du, Zelin Tian

{"title":"Funny words detection via Contrastive Representations and Pre-trained Language Model","authors":"Yiming Du, Zelin Tian","doi":"10.1109/AINIT54228.2021.00078","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00078","url":null,"abstract":"Funniness detection of news headlines is a challenging task in computational linguistics. However, most existing works on funniness detection mainly tackle the scenario by simply judging whether a sentence is humorous, whose result is unstable due to factors such as sentence length. To solve this issue, in this paper, our idea is to fine-grained mine the detailed information of the words and the contextual relationship between different words in the sentence, which help to evaluate the correlation between keywords and the funniness of news headlines quantitatively. Specifically, we propose a funny words detection algorithm based on the contrastive representations learning and BERT model. To quantify the impact of different words on the degree of humor, we first subtract the funniness grades of the original news headlines and the funniness grades of the original news headlines with a single word replaced. Both funniness grades are predicted with a pre-trained model, which is supervised by a a threshold to limit the amount of data and ensure the validity of data. To ensure the accuracy of our prediction, we further introduce the contrastive learning to constrain the differences of news headlines before and after word replacement. Finally, according to the Root Mean Square Error (RMSE) matrix in our experiment, we develop a BERT model with mixed sequence embedding to generate a table about words and their corresponding funniness improvement about the news headlines.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127260799","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

DOA Estimation of Underwater Acoustic Signals Based on Deep Learning 基于深度学习的水声信号DOA估计

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00052

Pengfei Li, Yubo Tian

{"title":"DOA Estimation of Underwater Acoustic Signals Based on Deep Learning","authors":"Pengfei Li, Yubo Tian","doi":"10.1109/AINIT54228.2021.00052","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00052","url":null,"abstract":"Direction of arrival (DOA) estimation is an essential part of array signal processing and also one of the main tasks in the field of sonar arrays. The most commonly method among DOA estimation problems is to perform subspace decomposition of the covariance matrix. Since traditional neural networks can’t handle real and imaginary numbers at the same time, the subspace decomposition method is not suitable for neural networks. Inspired by the extensive application of ResNet in the field of computer vision, this paper proposes a method of using the covariance matrix as an image processing, which uses a dual-channel matrix image containing the imaginary covariance matrix and the real covariance matrix as the input of the ResNet to estimate the DOA of the underwater acoustic array. It provides a new perspective for DOA estimation to solve the acoustic field problem. The ResNet algorithm is compared with the KNN algorithm and traditional MUSIC algorithm in terms of estimation accuracy and time. Simulation experiments prove that the ResNet algorithm has greater accuracy and shorter prediction time in a low signal-to-noise ratio environment.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114828406","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: 3

Modeling and Optimization of Multi-Section Management Decision-making Information Process Based on Petri Net 基于Petri网的多段管理决策信息过程建模与优化

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00136

Qi Zhang, Biao Zhao, Xiang Li

引用次数: 0

Lung Cancer Diagnosis Based on Convolutional Neural Networks Ensemble Model 基于卷积神经网络集成模型的肺癌诊断

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00077

Lei Lyu

{"title":"Lung Cancer Diagnosis Based on Convolutional Neural Networks Ensemble Model","authors":"Lei Lyu","doi":"10.1109/AINIT54228.2021.00077","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00077","url":null,"abstract":"Lung cancer is a lethal disease that can be treated efficiently if diagnosed in an early stage. Screening is a technology involving using CT scan to diagnose whether the lung is attacked by malignant tumors. This study proposes a CNN-based framework to help classify if the CT scan detects a cancer or not. In the analysis, several individual CNN models, including AlexNet, VGG, DCNN and DenseNet, are applied to make predictions and their performances are compared. Subsequently, selected individual models are ensembled by voting and stacking strategy that synthesize their predicting results. According to the results, the best individual model is DenseNet with average pooling layers, which gains a 97.48% accuracy and a 0.99019 AUC score. In comparison, the best ensemble model turns out to be assembling predicting results of best three individual models by stacked generalization, which reaches a 99.37% accuracy and a 0.99984 AUC score. These results show that it is useful to apply ensemble algorithm to improving the performance above individual models in this lung cancer diagnosis framework. Moreover, the final ensemble structure is efficient and reliable on figuring out lung scan images with malignant tumors.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126859552","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