2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)最新文献_第2页

An Improved Multi-Centroid Localization Algorithm for WiFi Signal Source Tracking 一种改进的WiFi信号源多质心定位算法

2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT) Pub Date : 2022-05-01 DOI: 10.1109/cniot55862.2022.00025

Wei Luo, Lizhi Zhang, Linbo Xu

引用次数: 2

Research on the Construction of Simulation Teaching Resource Library for Internet of Things in Transportation 交通物联网仿真教学资源库建设研究

2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT) Pub Date : 2022-05-01 DOI: 10.1109/cniot55862.2022.00024

Yuan Ruan, Qinghua Chen, Xiang-lin Pan

引用次数: 0

New Ideas and Methods of Coping Mechanism for Infectious Diseases Based on Big Data: A Critical Literature Review 基于大数据的传染病应对机制新思路与新方法——文献综述

2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT) Pub Date : 2022-05-01 DOI: 10.1109/cniot55862.2022.00011

Shuhong Chen, Zhiyi Zhuo

{"title":"New Ideas and Methods of Coping Mechanism for Infectious Diseases Based on Big Data: A Critical Literature Review","authors":"Shuhong Chen, Zhiyi Zhuo","doi":"10.1109/cniot55862.2022.00011","DOIUrl":"https://doi.org/10.1109/cniot55862.2022.00011","url":null,"abstract":"Based on big data analysis, we discuss how to formulate an optimal coping mechanism for infectious diseases, especially major and emerging infectious diseases. First, by combining big data analysis and statistical analysis model and deducing whether the emerging disease is contagious, the strength of the contagion effect and the possible consequences, this study will determine whether the corresponding coping strategies should be implemented for infectious diseases, especially major and emerging infectious diseases. Secondly, according to the inspection results and actual situation, the optimal coping strategy is formulated to minimize the loss of life and property security of the country and the society by using the optimization principle and the objective management in management science. Finally, the statistical analysis method and the six sigma principle are combined to develop a feedback mechanism to evaluate whether the formulated coping strategies can achieve the expected results in practice. Our research has improved the research framework of infectious diseases in theory and provided scientific reference and experience for the major and emerging infectious diseases in practice for the future.","PeriodicalId":251734,"journal":{"name":"2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127110829","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 Query Framework for Massive RDF Graph Data in Pay-As-You-Go Fashion 海量RDF图数据的现收现付查询框架

2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT) Pub Date : 2022-05-01 DOI: 10.1109/cniot55862.2022.00028

Xiaolong Liu, Ying Pan

引用次数: 0

A Residual Neural Network for Modulation Recognition of 24 kinds of Signals 基于残差神经网络的24种信号调制识别

2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT) Pub Date : 2022-05-01 DOI: 10.1109/cniot55862.2022.00032

Xinjie Tan, Zhidong Xie, Xinwang Yuan, Gang Yang, Yung-Su Han

{"title":"A Residual Neural Network for Modulation Recognition of 24 kinds of Signals","authors":"Xinjie Tan, Zhidong Xie, Xinwang Yuan, Gang Yang, Yung-Su Han","doi":"10.1109/cniot55862.2022.00032","DOIUrl":"https://doi.org/10.1109/cniot55862.2022.00032","url":null,"abstract":"With the development of wireless communication technology and the updates of communication equipment, the modulation of signal becomes more complex, and modulation recognition is becoming more and more difficult. Traditional signal modulation recognition methods rely on human experience, its feature extraction process is complex, and the empirical threshold is difficult to find. The recognition method combined with manual feature extraction and deep neural network can achieve better recognition accuracy, but it is still limited by the process of feature extraction. Compared with the above, automatic modulation recognition method based on deep learning is more efficient in complicated open environment. In this paper, a residual neural network for automatic modulation recognition was designed, and the experiment had achieved remarkable results. When SNR is 10dB, we got an accuracy of 95.3% faced to 24 kinds of signals, and when SNR is 12dB, we got an accuracy of 96.3%. Compared with existing models, this model reduces the network parameters, greatly shortens the training time, and lower the hardware requirements. This model shows a good result on the recognition of high-level modulation signal. When SNR is 10dB, the recognition accuracy of 128APSK, 128QAM and 256QAM is 97%, 88% and 88%.","PeriodicalId":251734,"journal":{"name":"2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115089814","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

Novel Adaptive DNN Partitioning Method Based on Image-Stream Pipeline Inference between the Edge and Cloud 基于边缘和云之间图像流管道推理的自适应DNN划分新方法

2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT) Pub Date : 2022-05-01 DOI: 10.1109/cniot55862.2022.00021

Chenchen Ji, Yanjun Wu, Pengpeng Hou, Yang Tai, Jiageng Yu

{"title":"Novel Adaptive DNN Partitioning Method Based on Image-Stream Pipeline Inference between the Edge and Cloud","authors":"Chenchen Ji, Yanjun Wu, Pengpeng Hou, Yang Tai, Jiageng Yu","doi":"10.1109/cniot55862.2022.00021","DOIUrl":"https://doi.org/10.1109/cniot55862.2022.00021","url":null,"abstract":"The cloud-only and edge-computing approaches have recently been proposed to satisfy the requirements of complex neural networks. However, the cloud-only approach generates a latency challenge because of the high data volumes that must be sent to a centralized location in the cloud. Less-powerful edge computing resources require a compression model for computation reduction, which degrades the model trading accuracy. To address this challenge, deep neural network (DNN) partitioning has become a recent trend, with DNN models being sliced into head and tail portions executed at the mobile edge devices and cloud server, respectively. We propose Edgepipe, a novel partitioning method based on pipeline inference with an image stream to automatically partition DNN computation between the edge device and cloud server, thereby reducing the global latency and enhancing the system-wide real-time performance. This method adapts to various DNN architectures, hardware platforms, and networks. Here, when evaluated on a suite of five DNN applications, Edgepipe achieves average latency speedups of 1.241× and 1.154× over the cloud-only approach and the state-of-the-art approach known as “Neurosurgeon”, respectively.","PeriodicalId":251734,"journal":{"name":"2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116624910","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 Small Target Detection Algorithm of Catenary Based on DA-YOLOv4 基于DA-YOLOv4的接触网小目标检测算法研究

2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT) Pub Date : 2022-05-01 DOI: 10.1109/cniot55862.2022.00019

Bo Li, Wei-dong Jin, Junxiao Ren

{"title":"Research on Small Target Detection Algorithm of Catenary Based on DA-YOLOv4","authors":"Bo Li, Wei-dong Jin, Junxiao Ren","doi":"10.1109/cniot55862.2022.00019","DOIUrl":"https://doi.org/10.1109/cniot55862.2022.00019","url":null,"abstract":"In recent years, computer vision has been greatly developed in the detection of catenary equipment. With its high efficiency and accuracy, it meets the needs of safety detection of catenary equipment in the safe operation of trains. In the catenary monitoring image, some equipment targets are small, which makes it difficult to identify. To solve this problem, this paper proposes an improved small target detection algorithm -DA-YOLOv4. In this method, Dual Attention Network for Scene Segmentation ( DANet ) is integrated into YOLOv4 model. Position Attention Module ( PAM ) and Channel Attention Module ( CAM ) are applied to enhance the attention of feature extraction network to small targets from two aspects of spatial location and feature channel. The context information is fully utilized to solve the problems of difficult feature extraction and low recognition rate of small targets. Experiments show that the DA-YOLOv4 algorithm can effectively improve the detection effect of small targets in the catenary, and the average detection accuracy on the catenary data set is 77.6 %, which is 4.7 % higher than that of the YOLOv4 network.","PeriodicalId":251734,"journal":{"name":"2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121918004","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 Constrained Interaction Network for Aspect-level Sentiment Classification Task 面向方面级情感分类任务的约束交互网络

2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT) Pub Date : 2022-05-01 DOI: 10.1109/cniot55862.2022.00036

Rongcheng Duan, Yao Qin, Haokun He, Chang Cai

{"title":"The Constrained Interaction Network for Aspect-level Sentiment Classification Task","authors":"Rongcheng Duan, Yao Qin, Haokun He, Chang Cai","doi":"10.1109/cniot55862.2022.00036","DOIUrl":"https://doi.org/10.1109/cniot55862.2022.00036","url":null,"abstract":"The purpose of aspect-level sentiment classification is to predict the sentiment polarity of specific aspect words in a sentence. Recently many works exploit LSTM models based on the attention mechanism. However, the prior work only attends to using the aspect terms to capture the aspect-specific sentiment information in the text. It may cause the mismatch of sentiment when the aspect words are extracted incorrectly. To solve this problem, we propose a simple but effective framework called the Constrained Interaction Network(CIN), which consists of the context-aspect level interaction layer(CAI-Layer), the long and short-term memory network layer(LSTM-Layer), and Constraint Attention layer(CA-Layer). CIN can extract the sentiment features of specific aspects with the assistance of LSTM-Layer and CAI-Layer, which greatly share the attention layer. The experiment conducted on three widely used data sets in SemEval 2014 and Twitter shows that the constrained attention mechanism is always better than other existing attention mechanisms, which also confirms that the CA- Layer can indeed help LSTM to extract the specified aspect-level sentiment characteristics.","PeriodicalId":251734,"journal":{"name":"2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129245449","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

GTGR-Net: Graph Attentional-Temporal Network for Surface-Electromyography-Based Gesture Recognition GTGR-Net:基于表面肌电图的手势识别图注意-时间网络

2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT) Pub Date : 2022-05-01 DOI: 10.1109/cniot55862.2022.00039

Xiaoxu Jia, Hongbo Wang, Jingjing Luo, Zhiping Lai, Xueze Zhang, Weiqi Zhang, Xiuhong Tang

引用次数: 0

Further Enhancement of KNN Algorithm Based on Clustering Applied to IT Support Ticket Routing 基于聚类的KNN算法在IT支持票务路由中的进一步改进

2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT) Pub Date : 2022-05-01 DOI: 10.1109/cniot55862.2022.00040

Clarissa Faye G. Gamboa, Matthew B. Concepcion, Antolin J. Alipio, Dan Michael A. Cortez, Andrew G. Bitancor, M. S. Santos, F. A. L. Atienza, M. A. S. Mercado

{"title":"Further Enhancement of KNN Algorithm Based on Clustering Applied to IT Support Ticket Routing","authors":"Clarissa Faye G. Gamboa, Matthew B. Concepcion, Antolin J. Alipio, Dan Michael A. Cortez, Andrew G. Bitancor, M. S. Santos, F. A. L. Atienza, M. A. S. Mercado","doi":"10.1109/cniot55862.2022.00040","DOIUrl":"https://doi.org/10.1109/cniot55862.2022.00040","url":null,"abstract":"Companies receive millions of tickets from their clients. Unfortunately, manual ticket routing takes time and relies heavily on human resources. To help automate the ticket routing, text classification can assist as it is the process of categorizing a document into a predetermined class based on its content. One algorithm is the K-Nearest Neighbors (KNN) which is a popular supervised technique but ranks average to lowest compared to other classification models. An improved KNN algorithm utilized clustering and improved the accuracy of the classifier. This paper proposed a further enhancement of this algorithm by adding preprocessing techniques, changing the distance formula, and computing for the k-value rather than choosing one. Two datasets of IT support tickets were used to train and test the algorithms. Results showed that this further enhanced algorithm significantly performed better than the initial algorithm with the highest accuracy score of 97.83% in one dataset while the initial algorithm performed best with an accuracy score of 86.34% using a k-value of 4 in another dataset.","PeriodicalId":251734,"journal":{"name":"2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133972324","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