2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)最新文献_第5页

Detection of Rice Seed Hole Seeding Amount Based on Deep Learning 基于深度学习的水稻播种孔播量检测

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082466

Xiangwu Deng, Song Liang

{"title":"Detection of Rice Seed Hole Seeding Amount Based on Deep Learning","authors":"Xiangwu Deng, Song Liang","doi":"10.1109/ITNEC56291.2023.10082466","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082466","url":null,"abstract":"Cultivating rice seedlings by using seedling tray sowing is an important part of large-scale industrialized rice planting. Rice seedling raising is to sow after germination. During the seed cultivation process, it will produce different characteristics and thus affect the seedling development or sowing effect. In order to improve the accuracy and efficiency of rice seed target detection of rice pot floppy disk hole seeding amount, this paper proposes a method of rice pot floppy disk hole seeding amount detection based on depth learning. The production data set should use the pictures taken from the seeded floppy disk on the top to train the network on the GPU computing platform by using the data set through the YOLOv5 neural network model. The analysis of the training results and the verification results show that the trained neural network model is a very suitable network model for the detection of the amount of rice seeds planted in holes in terms of real-time and other comprehensiveness. This method can automatically learn and extract the characteristics of rice seeds in the picture of the bowl floppy disk, and realize the real-time automatic detection of 0-7 and more rice seeds planted in holes in the bowl floppy disk, which is conducive to migration to embedded platforms.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129427114","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 Study of Customer Satisfaction in Milk Tea Stores Based on Online Comment Clustering 基于在线评论聚类的奶茶店顾客满意度研究

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082667

Rong Huang, Shuai Mao, Yin Chen

引用次数: 0

Carbon Sequestration-oriented Forest Management Plans 以碳封存为导向的森林管理计划

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082340

Jie Wu, Junjie Shi, Deyu Song, Shengyao Zheng, Shuangshuang Chen

{"title":"Carbon Sequestration-oriented Forest Management Plans","authors":"Jie Wu, Junjie Shi, Deyu Song, Shengyao Zheng, Shuangshuang Chen","doi":"10.1109/ITNEC56291.2023.10082340","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082340","url":null,"abstract":"Recently, climate change can pose a significant threat to the lives of plants and animals. And forests play a vital role in climate change mitigation efforts. From a general point of view, we built a dynamic carbon sequestration model with the help of the assessment provided by IPCC and a BCEF-based set of differential equations that were optimized. Then we build a decision model for the optimal forest exploitation direction by analyzing detailed forest data specifically. First, we used the biomass expansion factor to determine the amount of carbon sequestered per unit area of trees for the first model. The data were also queried to determine the carbon sequestration of below-ground and shrub trees. Then, we focused on the footprint of trees of different species ages to build a dynamic differential model of forest area to predict future forest carbon sequestration. Secondly, for the second model, we first collected data extensively. And then we determined the weights for some specific indicators by hierarchical analysis, followed by forwarding and dimensionless processing of the data, and then scored by the Topsis method to synthesize each indicator into the corresponding target layer, which can be clustered and analyzed to into three groups: unsuitable, more suitable, and suitable. We determine the transition point by calculating the critical value of each group according to the grouping.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127025006","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

Identification of SSH Honeypots Using Machine Learning Techniques Based on Multi-Fingerprinting 基于多指纹识别的机器学习SSH蜜罐识别

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082467

Yong-Jian Zhang, Wen-Jie Liu, Ke-Nan Guo, Yanyan Kang

{"title":"Identification of SSH Honeypots Using Machine Learning Techniques Based on Multi-Fingerprinting","authors":"Yong-Jian Zhang, Wen-Jie Liu, Ke-Nan Guo, Yanyan Kang","doi":"10.1109/ITNEC56291.2023.10082467","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082467","url":null,"abstract":"Honeypots-a new active defense technique-can accomplish the goal of identifying security vulnerabilities and extracting attack features by constructing controlled vulnerability traps and deceiving attackers into launching intrusion assaults. Attackers typically use honeypot identification techniques to go around honeypots in order to conceal their attack strategies. In this paper, we proposes a new method for detecting and classifying SSH honeypots based on multi-fingerprinting. Target samples are first classified into suspected honeypots and normal hosts using the Random Forest algorithm, and then suspected honeypots are classified using multi-fingerprint features. This five-element detection model can increase the accuracy of honeypot classification while also cutting down on wasted time. Finally, through experimental measurements and comparative analysis with the other method for identifying honeypot, the method in this paper significantly improves the accuracy of identifying SSH honeypot types. It is also more efficient in classifying and detecting large-scale target IPs for honeypots, and there are a lot of real SSH honeypot IPs that can be found by searching the Internet, which can then be further analyzed to obtain their geographical distribution and survival rate characteristics.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"10 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129977872","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

Performance Analysis of Sonar Target Detection Based on Vector Hydrophone Array 基于矢量水听器阵列的声纳目标检测性能分析

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10081955

Zhe Chen, Shi-bai Sun, Z. Liu, Haitao Li, Yichuan Wang

引用次数: 0

Multi-domain Network Intrusion Detection Based on Attention-based Bidirectional LSTM 基于注意力的双向LSTM的多域网络入侵检测

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10081953

Xiaoning Wang

{"title":"Multi-domain Network Intrusion Detection Based on Attention-based Bidirectional LSTM","authors":"Xiaoning Wang","doi":"10.1109/ITNEC56291.2023.10081953","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10081953","url":null,"abstract":"Different types of network traffic can be treated as data originated from different domains with the same objectives of problem solving. Most previous work utilizing multi-domain machine learning simply assumes that data in different domains have the same distribution, which can neither address the domain offset problem effectively, nor achieve excellent performance in every domain. This study proposes an attention-based bidirectional LSTM (Bi-LSTM) model to detect different types of coordinated network attacks (i.e., malware detection, VPN encapsulation recognition, and Trojan horse classification). First, the HTTP traffic is modeled as a series of natural language sequence, and each request follows strict structural standards and language logic. Second, the model is designed in the frame of multi-domain machine learning technologies to rec-ognize anomalies of network attacks from different domains. Experiments on real HTTP traffic data sets demonstrate that the model proposed in this study has good performance on detection of abnormal network traffic and generalization ability and can effectively detect different network attacks at the same time.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130579860","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 energy control strategy of four-wheel drive hybrid electric vehicle based on dynamic programming 基于动态规划的四轮驱动混合动力汽车能量控制策略研究

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10081950

Wen Wang, Fufan Qu, Wenbo Li, Peng Wang, Jiawen Cai

引用次数: 0

A Review of Heterogeneous Wireless Network Selection Algorithms 异构无线网络选择算法综述

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082485

Danni Xiao, Haitao Lin, Bin Wang

引用次数: 0

Error Analysis of an Airborne Dispenser Based on Monte-Carlo Method 基于蒙特卡罗方法的机载分配器误差分析

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082619

Chunyan Tian, Zhishu Feng, Tian Zhao, Jixin Che, X. Ge

引用次数: 0

Intelligent Simulation of Water Environmental Pollutant Flux in River Basins 流域水环境污染物通量的智能模拟

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082452

Yanjun Wang, Zhanjun Jin, Junjie Ma, Yu Li, Pengchao Run, Houxin Cui

{"title":"Intelligent Simulation of Water Environmental Pollutant Flux in River Basins","authors":"Yanjun Wang, Zhanjun Jin, Junjie Ma, Yu Li, Pengchao Run, Houxin Cui","doi":"10.1109/ITNEC56291.2023.10082452","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082452","url":null,"abstract":"In order to realize the simulation of water environmental pollutant flux of different cross sections in rivers basins with storm water management model (SWMM), an intelligent pollutant flux simulation method based on the LSTM-PSO hybrid algorithm is proposed. This method is focused on solving two problems, the data discretization of online water quality monitoring and the complicated calibration of model parameters. Taking typical urban rivers in the eastern region of China as the research object, the experimental analysis is carried out. After the basic SWMM model construction of the study area, long short-term memory (LSTM) neural networks are used to fit the discrete data of online water quality monitoring, and particle swarm optimization (PSO) is used to optimize the selection of model parameters. The pollutant flux in the river basin is simulated, and the simulated pollutant flux of the key cross section is compared with the measured pollutant flux. The experimental results show that the LSTM-PSO algorithm obtains higher accuracy than the conventional approach in the pollutant flux simulation and its efficiency is verified.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130480795","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