Proceedings of the 4th International Conference on Advanced Information Science and System最新文献_第2页

Short-term air traffic flow forecasting based on model fusion 基于模型融合的短期空中交通流量预测

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574504

Jiawei Chen, Hongjie Liu, Kexian Gong, Zhongyong Wang, Wei Wang

{"title":"Short-term air traffic flow forecasting based on model fusion","authors":"Jiawei Chen, Hongjie Liu, Kexian Gong, Zhongyong Wang, Wei Wang","doi":"10.1145/3573834.3574504","DOIUrl":"https://doi.org/10.1145/3573834.3574504","url":null,"abstract":"Short-term air traffic flow prediction provides decision information for optimal air traffic flow control and management. To accurately predict the short-term air traffic flow, this study uses time series decomposition to determine that the air traffic flow has obvious segmentation characteristics, that is, different time periods are superimposed with different degrees of periodicity, trend and randomness, where periodicity is mixed with two kinds of short-term and long-term circulation patterns. Existing prediction methods cannot capture the complex features of the traffic flow data dynamics well. Herein, we develop a new multi component network (MCNet) composed of a deep learning component and an autoregressive component. For capturing the periodicity of traffic flow and extract the short- and long-term recurrent patterns of traffic flow data, we use the deep learning component, consisting of a convolutional neural network and a recurrent neural network with a self-attention mechanism. The autoregressive component is responsible for catching the trend of traffic flow, solving the problem that the deep learning component is insensitive to the scale of input and output. Experiments are conducted on air traffic data based on OpenSky statistics, and the results show that MCNet achieves optimal results compared to other models.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129016102","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

A method for task network partition with limited community number 社团数有限的任务网络划分方法

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574518

Liang Guo, Yunjun Lu, Qian Liu, Keren Zhu, Lv Zhao

引用次数: 0

Defect Classification Method of X-ray Images Based on Improved U-Net 基于改进U-Net的x射线图像缺陷分类方法

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574509

Haochen Qi, Huiyan Ji, Jiqiang Zhang, Liu Cheng, Xiangwei Kong

{"title":"Defect Classification Method of X-ray Images Based on Improved U-Net","authors":"Haochen Qi, Huiyan Ji, Jiqiang Zhang, Liu Cheng, Xiangwei Kong","doi":"10.1145/3573834.3574509","DOIUrl":"https://doi.org/10.1145/3573834.3574509","url":null,"abstract":"X-ray nondestructive testing means are widely used in the inspection process of internal defects of parts. In practical inspection, defects are generally determined and rated by manual inspection based on X-ray images, which is inefficient and cannot meet the requirements of high-volume automatic inspection. This paper proposes an automatic defect classification model based on an improved U-Net. First, a classifier is added behind the encoder of U-Net. The encoder and classifier are connected in series to form the main branch of the model to complete the classification task. Second, the decoder of U-Net is improved by adding an attention module. The encoder and the improved decoder form the auxiliary branch of the model, which completes the segmentation task. The model proposed in this paper has two advantages. First, for small defects in images, the segmentation-based auxiliary task enables the model to focus on these small targets during the learning process and learn features with more representational power. Second, the introduction of an attention mechanism in the decoder can suppress the interference information, retain the effective location information, focus on the learning of defective regions, adjust the different feature mapping weights, and improve the performance of the model. The method has been validated on the self-built dataset and the dataset collected from X-ray inspection industrial sites and compared with typical image classification methods. The results show that the proposed method in this paper has higher defect recognition accuracy than other classical deep network models and can effectively identify multiple types of defect features while providing assurance and reference for the quality and safety performance of the parts to be inspected.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123182292","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

A Safe Architecture of 5G Network Intelligence Based on Federated Learning and NWDAF 基于联邦学习和NWDAF的5G网络智能安全架构

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574529

Lu Yu, Lun Xin, M. Guo

引用次数: 0

Many-to-many perfect matching 多对多完美匹配

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574467

Musashi Takanezawa, Yoshifumi Manabe

引用次数: 0

Research on Radar Signal Recognition Technology Based on Residual Convolutional Neural Network 基于残差卷积神经网络的雷达信号识别技术研究

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574510

Hao Wu, Chong Zhang, Lily. Shui, Yamiao Zhang, Mengda Lei, Xianlong Li

{"title":"Research on Radar Signal Recognition Technology Based on Residual Convolutional Neural Network","authors":"Hao Wu, Chong Zhang, Lily. Shui, Yamiao Zhang, Mengda Lei, Xianlong Li","doi":"10.1145/3573834.3574510","DOIUrl":"https://doi.org/10.1145/3573834.3574510","url":null,"abstract":"The research on recognition of radar intra-pulse modulated signals is an important development direction of radar countermeasure technology. In order to recognize the radar intra-pulse modulated signals effectively, many sgnal recognition techniques have been developed. In which, the one that based on residual convolutional neural network is one of the most promising techniques. In this paper, radar signal recognition techniques based on residual convolutional neural network are researched. The influence of model depth and residual block on time-frequency image recognition is verified. Firstly, using the feature extraction and recognition method of radar signal time-frequency image from Choi Williams, the problem of signal recognition is transformed into image recognition. Time-frequency images of 8 kinds of common radar signals are converted to grayscale images by the Choi Williams time-frequency transformation. Secondly, these grayscale images are recognized with six kinds of signal recognition algorithm models (AlexNet, DarkNet-19, GoogLeNet, VGG-16, ResNet-18, MobileNet-v2) and the recognition effect is compared. Thirdly, the residual block of the above six models are modified by increasing or decreasing residual and Inverted residual block, and the experimental results are compared. The results show that the shallow model AlexNet has the best accuracy and speed in recognizing time-frequency images, and the shallow network with an inverted residual block will improve the recognition speed with the cost of reducing the recognition accuracy slightly.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131812480","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 the Information Security Investment Strategies Considering Budget Constraints and the Attacker's Preferences 考虑预算约束和攻击者偏好的信息安全投资策略研究

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574573

Chongxia Pan

引用次数: 0

Research of Network Hotspot Events Joint Extracting Based on BERT-CNN-CRF Model for Internet Public Opinion 基于BERT-CNN-CRF模型的网络舆情热点事件联合提取研究

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574540

Yang Liu

{"title":"Research of Network Hotspot Events Joint Extracting Based on BERT-CNN-CRF Model for Internet Public Opinion","authors":"Yang Liu","doi":"10.1145/3573834.3574540","DOIUrl":"https://doi.org/10.1145/3573834.3574540","url":null,"abstract":"Public emergencies affect the vital interests of a large number of citizens and are extremely concerned because of their complexity and strong influence. Early warning of public opinion is an important part of reducing secondary harm of public emergencies and ensuring social stability. There are problems of insufficient ability to capture the semantics of trigger words and the identification ambiguity of entity boundaries in event elements in the current study of internet hot events extraction. In this paper, we explore the joint extraction of events using sequence annotation, and construct a joint extracting method based on BERT-CNN-CRF model to obtain the input text fusion full-text semantic information vector by using BERT, and extract local contextual semantic features by convolutional neural network (CNN), and obtain joint extraction results by combining conditional random fields (CRF). After crawling several large public social media platforms with over ten thousand of data and comparing three baseline approaches in the latest event extraction methods, our proposed joint BERT-CNN-CRF extraction model has higher accuracy and better efficiency.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122074263","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

Mine drainage optimization scheduling based on genetic algorithm 基于遗传算法的矿井排水优化调度

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574480

Zihang Zhang, Lei Bo, Yang Liu, Zecheng Li

{"title":"Mine drainage optimization scheduling based on genetic algorithm","authors":"Zihang Zhang, Lei Bo, Yang Liu, Zecheng Li","doi":"10.1145/3573834.3574480","DOIUrl":"https://doi.org/10.1145/3573834.3574480","url":null,"abstract":"The mine water produced in the process of coal mining is an important water resource in the mining area. If it is not treated and discharged directly, it will not only cause the waste of water resources but also lead to serious environmental pollution. Aiming at the problems of complex operation, low reuse efficiency, high cost, insufficient data collection and analysis in the current mine water reuse system, this paper proposes a mine water treatment system based on Internet of Things architecture, and uses genetic algorithm to process and optimize mine water data. In order to improve the reuse efficiency of mine water, this paper establishes a reuse model aiming at the minimum reuse time and cost, and uses genetic algorithm to calculate a reasonable scheduling scheme. This paper takes the water quality and quantity of Nalinhe No.2 mine as the research objective and analyzes and compares the reuse situation under different dispatching schemes. The research results show that the mine water reuse cost is reduced by 6.13% under the optimization of genetic algorithm, and the mine water reuse efficiency is increased by 2.99%, which verifies the effectiveness of the optimization scheme.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121049149","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

Bitcoin Price Prediction Using Autoregressive Integrated Moving Average (ARIMA) Model 基于自回归综合移动平均(ARIMA)模型的比特币价格预测

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574559

Chunyu Wen, Tianer Li, Zhiyang Qiu

引用次数: 0