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

Research on Prediction Model of Flight Departure Runway 飞机离场跑道预测模型研究

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082471

Huang Qisong, Wu Zixuan, Yin Wen, Chen Guoqiang, Zou Yun

引用次数: 0

Comparative Analysis of 420kN Cylindrical and Conical Head Suspension Porcelain Insulators 420kN圆柱形和锥形头悬浮瓷绝缘子的对比分析

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082606

Haotian Wu, Jianghua Zhang, Jie Huang, Yongqiang Pan, Tangyin Qiu, Jungang Yin, Senlin Wang

引用次数: 0

Research on Target Detection Algorithm for Complex Scenes 复杂场景下目标检测算法研究

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082670

Changyu Yang, H. Fan, Hongjin Zhu

引用次数: 0

Research on Multi-objective Course Scheduling Method in Colleges based on Epidemic Prevention and Control 基于疫情防控的高校多目标课程排课方法研究

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082461

Peng Wang, Jie Huang

引用次数: 0

Establishment of the Evaluation Model Based on Dynamic Fuzzy Theory 基于动态模糊理论的评价模型建立

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082411

Lusheng Shi, Huizhe Zhu

引用次数: 0

Research on Classification of Wild Mushroom Based on Feature Fusion and Attention Mechanism 基于特征融合和注意机制的野生蘑菇分类研究

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082401

Xiangyang Li, Lei Guo, Jundong Yang, Fuhua Ge, Pengfei Yu

{"title":"Research on Classification of Wild Mushroom Based on Feature Fusion and Attention Mechanism","authors":"Xiangyang Li, Lei Guo, Jundong Yang, Fuhua Ge, Pengfei Yu","doi":"10.1109/ITNEC56291.2023.10082401","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082401","url":null,"abstract":"The occurrence of poisoning by edible wild mushroom is frequent, identifying the species of wild mushroom can effectively prevent such incidents. However, due to the wide variety of wild mushrooms and small feature differences between classes, as well as the complex image background in the actual scene, the fine-grained classification accuracy is low. To solve this problem, this paper proposes an improved ResNext50 network, by combining low-level features with high-level features through short connections, the network can learn more useful details from low-level and high-level semantic features. At the same time, in order to reduce the number of parameters of the model, the 3×3 convolution layer of the short connection part adopts depthwise convolution. Transfer learning is used to reduce the training time of the model. Using attention mechanism to reduce the learning of complex background. The experimental results show that compared with the unimproved ResNext50, the number of parameters in our improved ResNext50 is only increased by 2.7M, and the accuracy is 97.94%, which is increased by 1.53%. The comparison results show that compared with DenseNet121, MobilNet-v3, Vgg19 and GoogleNet, the accuracy of the four network models is improved by 7.17%, 3.16%, 2.92% and 2.12%, respectively.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"67 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":"122279445","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

Study on monitoring and self-healing method of AC tampering fault 交流篡改故障监测与自愈方法研究

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082523

Daobo Li, Jinglin Yang, Qian Wang, Shihe Zhang, Tieshan Pan

引用次数: 0

Neuronal Morphology Classification based on Improved Residual Network 基于改进残差网络的神经元形态分类

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082045

Yan Wei, Fuyun He, Youwei Qian, Fangyu Feng

{"title":"Neuronal Morphology Classification based on Improved Residual Network","authors":"Yan Wei, Fuyun He, Youwei Qian, Fangyu Feng","doi":"10.1109/ITNEC56291.2023.10082045","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082045","url":null,"abstract":"To address the problem that traditional convolutional neural networks have difficulty in fully extracting important neuronal morphological features when classifying neuron types, resulting in low accuracy of neuron classification. We propose a neuron morphology classification method based on the optimization of the ResNet18 network model. First, ResNet18 is used as the base network and initialized using the weights trained by ImageNet. Second, at the back-end of the base network, we use a feature reconstruction module to suppress edge feature loss and the drawbacks associated with the padding strategy. Finally, experiments are conducted on the NeuroMorpho-rat dataset to verify the effectiveness of the proposed method. The experimental results showed that the accuracy of 2 classification on Img_raw, Img_resample and Img_XYalign datasets reached 94.32%, 85.37% and 86.74%, respectively, and the accuracy of 12 classification reached 94.15%, 85.47% and 85.81%, respectively, which effectively improved the neuron compared to the original residual network the accuracy of morphological classification was effectively improved compared to the original residual network.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"15 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":"115256622","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 Multi-Feature Base Multi-Adaptation Lidar Odometry 基于多特征的多自适应激光雷达里程计

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082662

Gao Junjie, Lv Fu, Liu Jun, Chen Jie

{"title":"A Multi-Feature Base Multi-Adaptation Lidar Odometry","authors":"Gao Junjie, Lv Fu, Liu Jun, Chen Jie","doi":"10.1109/ITNEC56291.2023.10082662","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082662","url":null,"abstract":"To address the problems that the current feature-based lidar odometry algorithms have difficulties in achieving high accuracy based on sparse features and adapting to different kinds of lidar at the same time, a multi-adaptation lidar odometry algorithm based on multi-features (MFMA-LOAM) is proposed. In addition to the traditional edge points and plane points, intensity points and ground points are extracted as supplementary features. Among them, the intensity point features that meet the requirements are extracted based on lidar intensity reflection information. The ground feature extraction method is improved by using the estimated normal vector to achieve simultaneous adaptation of multiple types of lidar. Further, in order to ensure acceptable speed and improve the alignment accuracy at the same time, a two-stage alignment method is applied to complete the positional calculation, in which feature weights are added to improve the alignment accuracy and categorical nearest neighbor search is used to speed up the alignment. Finally, experimental validation is carried out using the KITTI dataset and the self-collected dataset respectively; the experimental results show that the MFMA-LOAM algorithm with satisfactory real-time performance is more accurate than related classical feature-based algorithms and can be adapted to both solid-state lidar and mechanical lidar at the same time.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"22 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":"115310944","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

Simulation Analysis of Stability Operation of Hunan Power Grid Supported by Energy Storage 储能支持下湖南电网稳定运行仿真分析

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082501

Luping Wang, Yuzheng Xie, Kuang Qin, Haifeng Yu, Hanyang Dai

引用次数: 0