Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering最新文献_第4页

Non-singular Terminal Sliding Mode Control Algorithm for Buck Converter Based on Extended State Observer 基于扩展状态观测器的Buck变换器非奇异终端滑模控制算法

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573527

Daoyou Sun, Shilin Liu, Jingyi Li

引用次数: 0

Detection on chemical fiber silk detects by deep learning 基于深度学习的化纤丝检测

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573760

Lei Guo, Yang Wang, Z. Jin, Chao Chen, Jiangyi Chen, Peng Shen

引用次数: 0

Improved Inception Network for wild mammal Behavior Recognition 改进的Inception网络用于野生哺乳动物行为识别

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573683

Shichao Deng, Guizhong Tang, Lei Mei

{"title":"Improved Inception Network for wild mammal Behavior Recognition","authors":"Shichao Deng, Guizhong Tang, Lei Mei","doi":"10.1145/3573428.3573683","DOIUrl":"https://doi.org/10.1145/3573428.3573683","url":null,"abstract":"The wildlife resources are significantly important parts of the ecosystem, and protecting wildlife resources is vital to the environment on which people live. Therefore, the behavior analysis of wild animals has become an important initiative to protect wild animals. This paper proposes a convolutional neural network architecture based on spatial-temporal information for action recognition of wild mammal. Since pixel-based object segmentation methods cannot eliminate the influence of background, we use the contour-based method Deep Snake to detect the animal contours in images as spatial features. The skeleton-based animal action recognition model is used to extract the joint coordinates during consecutive frames, then the fluctuate of the joint coordinates is used to distinguish the diversity of different behaviors of wild mammal in temporal space, which helps to characterize the difference of joint point movement speed of different behaviors. In addition, we also compute leg joint angle for distinguishing the behaviors running and standing. Finally, the temporal features and spatial features are fused into the convolutional neural network for action recognition of wild mammal. The experiments analyze the effect of the joint point angle, contour features, joint coordinates as well as their fusion features for wild mammal behavior recognition. It is concluded that the fusion features of coordinate fluctuate of joint points during consecutive frames, contour features and knee joint angle can significantly improve the accuracy of wild mammal action recognition. The model can effectively recognize four representational behaviors of animals: running, sitting, walking, and standing. The average accuracy of the proposed scheme for recognizing behavior of wild mammal achieve 95.5%.","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128921297","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

Application research of photovoltaic power station operation and maintenance system based on Internet of Things technology 基于物联网技术的光伏电站运维系统应用研究

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573467

Yun Zheng, Rongrong Sun, Zhicheng Li, Qiunai Zhuang

引用次数: 1

Target temperature region detection of converter thermal infrared image based on improved YOLOv5s 基于改进YOLOv5s的转炉热红外图像目标温度区域检测

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573667

Yu Tong, Ailian Li

{"title":"Target temperature region detection of converter thermal infrared image based on improved YOLOv5s","authors":"Yu Tong, Ailian Li","doi":"10.1145/3573428.3573667","DOIUrl":"https://doi.org/10.1145/3573428.3573667","url":null,"abstract":"Aiming at the difficulty of real-time temperature detection in the converter smelting process, most of the production sites use sub-guns for only end point detection, In this paper, the YOLOv5s-XCB detection algorithm is used to automatically extract the target temperature area of the converter thermal infrared image. It lays the foundation for the next step to realize automatic temperature measurement combined with the temperature matrix of this area. Based on the YOLOv5s algorithm, the research adds a small target detection layer and a CBAM attention mechanism to solve the problem that small targets and weak target temperature regions are difficult to detect. The BiFPN structure is used in the Neck layer to fuse the original feature information extracted by the backbone network to enhance the detection accuracy. The results show that the average mean precision (mAP) of the improved algorithm is 95.8%, the FPS is 69.5, and the confidence of the detection frame is significantly improved, which solves the problem that the original YOLOv5s algorithm is difficult to detect small target temperature areas and weak target temperature areas.","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132854749","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

Deep Learning Approaches for Image Classification 图像分类的深度学习方法

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573691

Yan Yu

引用次数: 1

Prediction of flocculant dosage in water plant based on LSTM network 基于LSTM网络的水厂絮凝剂用量预测

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573489

Ying Hu, Jin Li

引用次数: 0

Task Offloading Based on GRU Model in IoT 物联网中基于GRU模型的任务分流

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573455

Xiao Zhang, Yuxiong He, Youhuai Wang, Xiaoming Chen, Shi Jin, Yeteng Liang

{"title":"Task Offloading Based on GRU Model in IoT","authors":"Xiao Zhang, Yuxiong He, Youhuai Wang, Xiaoming Chen, Shi Jin, Yeteng Liang","doi":"10.1145/3573428.3573455","DOIUrl":"https://doi.org/10.1145/3573428.3573455","url":null,"abstract":"With the rapid growth of the Internet of Things (IoTs), edge computing draws greater attention. Task offloading becomes the main part of edge computing which can affect performance. To reduce the tasks time delay and improve the utilization of the edge server, the task offloading problem can be modeled as a decision-making problem for minimizing the time latency and develop a GRU-based model to predict the computational task offloading. We choose a dataset from Google Cluster and offload the top 1000 tasks for comparison. Compering with existing offloading techniques such as total offloading (TOT), random offloading technique (ROT), and deep learning-based offloading technique (DOT), the GRU-based model can save 15.09% time than TOT, 13.46% time than ROT and 4.25% time than DOT while offloading 1000 tasks on an edge computing system in IoT. Experimental result showed that, compared with other techniques, our proposed GRU-based model is able to reduce the delay of tasks effectively, while increasing the number of tasks and enhancing the offloading performance on edge computing system.","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133637435","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

An Analysis of The Small Sample Datasets Based on Machine Learning 基于机器学习的小样本数据集分析

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573720

Shaoxuan Zhou

{"title":"An Analysis of The Small Sample Datasets Based on Machine Learning","authors":"Shaoxuan Zhou","doi":"10.1145/3573428.3573720","DOIUrl":"https://doi.org/10.1145/3573428.3573720","url":null,"abstract":"In machine learning, building the optimal model for small sample data has become a widespread issue in the data science community. Some methods have been proven to achieve high accuracy in training small sample datasets. However, the solution to more extreme minor sample problems still lacks further exploration. Therefore, this paper will explore the prediction accuracy of machine learning methods for small sample datasets. Collecting the forest fire dataset and pulsar dataset from Kaggle as examples, the prediction of various machine learning models (SVM, random forest, neural networks, regression) was carried out, respectively. It was found that the machine learning model failed to achieve high prediction accuracy in the imbalanced samples represented by the forest fire dataset. Because of the small number and the imbalanced distribution, the model cannot obtain an apparent discrimination degree for each feature. To summarize, the prediction of small sample datasets needs to adopt better methods in model building and obtain more cases in data collection. Otherwise, machine learning cannot provide much help to the actual situation.","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127400914","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 Connection-Based Point Cloud Segmentation Method Using Bipartite Graph 基于连接的二部图点云分割方法

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573577

Y. Li, Fei Chen

引用次数: 0