2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)最新文献_第7页

Infrared and visible image fusion algorithms based on generative adversarial networks 基于生成对抗网络的红外与可见光图像融合算法

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00113

Wencheng Zhuang, Xiaona Tang, Binquan Zhang, Guangming Yuan

引用次数: 0

Emotional Dialogue Generation with Emotion Embedding 基于情感嵌入的情感对话生成

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00048

Yisheng Miao, Lin Zhang

引用次数: 0

How Crowd Anxiety Affects Emergency Evacuations Based on cellular automata 基于元胞自动机的人群焦虑如何影响紧急疏散

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00089

Kunqi Han, Wei Zhang, Yu-zhen Zhang

引用次数: 0

Machine Learning and Ensemble Learning for Transcriptome Data: Principles and Advances 转录组数据的机器学习和集成学习:原理和进展

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00137

Zijie Wang, Y. Jiang, Zhule Liu, Xinqiang Tang, Hongfu Li

{"title":"Machine Learning and Ensemble Learning for Transcriptome Data: Principles and Advances","authors":"Zijie Wang, Y. Jiang, Zhule Liu, Xinqiang Tang, Hongfu Li","doi":"10.1109/aemcse55572.2022.00137","DOIUrl":"https://doi.org/10.1109/aemcse55572.2022.00137","url":null,"abstract":"Nowadays, as the next-generation RNA-seq sequencing technology and machine learning algorithms continue to advance, an increasing number of machine learning methods are being used in plant transcriptome research. Because of its high robustness, good generalization performance and strong interpretability, the ensemble learning framework in machine learning outperforms classic linear statistical methods in the classification and prediction of plant attributes, gene importance evaluation, and molecular breeding. To begin, this article will focus on ensemble learning’s essential ideas and frontier models. Additionally, the advancement of RNA-seq technology and the establishment of databases for transcriptome research would be discussed. Furthermore, cutting-edge machine learning research in plant genome and transcriptome analysis will be given, together with the innovation points, benefits, and limitations of each machine learning model algorithm and transcriptome technology. The article establishes a framework for the integration of artificial intelligence and plant bioinformatics on an interdisciplinary and in-depth level.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116645641","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

Multi-Sensor Fusion Localization and Mapping of Indoor Mobile Robot 室内移动机器人多传感器融合定位与映射

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00008

Zhongwei Hua, Dongdong He

{"title":"Multi-Sensor Fusion Localization and Mapping of Indoor Mobile Robot","authors":"Zhongwei Hua, Dongdong He","doi":"10.1109/AEMCSE55572.2022.00008","DOIUrl":"https://doi.org/10.1109/AEMCSE55572.2022.00008","url":null,"abstract":"Due to the influence of overlapping objects, different materials, and uneven lighting in the indoor environment, the mobile robot equipped with only a single sensor cannot achieve accurate positioning and complete mapping. Aiming at the problem, this paper studies the multi-sensor fusion localization and mapping of indoor mobile robots. The research fuses multiple sensor data from 2D lidar, depth camera, IMU, and wheel encoder. Specifically, on the one hand, this paper uses the extended Kalman filter algorithm to fuse the wheel odometer calculated from the encoder data with the inertial sensing unit data, which reduces the drift error and improves the accuracy of the robot's own localization. On the other hand, the region proximity algorithm integrates richer visual information into the 2D laser data, which makes up for the spatial perception defect of single-line laser mapping and improves the spatial integrity of robot mapping. The simulation experiments in this paper verify that the proposed method can effectively improve the localization accuracy and mapping integrity of the indoor robot.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121726168","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

Parallel Convolutional Neural Network Based on Multi-Band Brain Networks for EEG Classification 基于多频带脑网络的并行卷积神经网络脑电分类

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00016

Jing Wang, Li Wang

{"title":"Parallel Convolutional Neural Network Based on Multi-Band Brain Networks for EEG Classification","authors":"Jing Wang, Li Wang","doi":"10.1109/aemcse55572.2022.00016","DOIUrl":"https://doi.org/10.1109/aemcse55572.2022.00016","url":null,"abstract":"To increase the classification accuracy of the mental tasks with speech imagery, a parallel convolutional neural network based on multi-band brain networks (MBBN-PCNN) is proposed. In this model, the hybrid experimental paradigm of motor imagery and speech imagery proposed in our previous studies is used. To acquire richer information in the frequency domain, the electroencephalography (EEG) signals are divided into 3 frequency bands, which are filtered with different frequency ranges for mu(8-12Hz), beta1(13-20Hz), and beta2(21- 30Hz) respectively. By calculating the correlation coefficient and phase- locked value (PLV) of each waveform to construct the brain network, the synchronization and correlation of EEG signals from different channels can be analyzed more effectively. Afterward, to realize the classification of different imagined EEG signals, the generated two-dimensional grayscale maps are fed into our parallel CNN model. The results show that the average classification accuracy of our proposed algorithm is 81.58% for 10 subjects. Compared with brain networks constructed with a single frequency band, multi-band brain networks have higher classification accuracy with the combination of multidimensional features.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125067815","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

Development of a Lightweight Software Engineering Laboratory Management System Based on Scrum 基于Scrum的轻量级软件工程实验室管理系统的开发

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00164

Jiujiu Yu, Jishan Zhang, Yun Chen, Ning Wu, Yingying Mei, Wenling Sun

引用次数: 3

SIR model considering influence of network rumor propagation 考虑网络谣言传播影响的SIR模型

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00063

Jiusheng Mu, Fengming Liu

引用次数: 0

Driving Behavior Evaluation Based on DBSCAN and Kmeans++ Clustering 基于DBSCAN和kmeans++聚类的驾驶行为评价

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00046

Ruifeng Wang, Wenyuan Zheng, Miaohua Huang, Guohang Li

{"title":"Driving Behavior Evaluation Based on DBSCAN and Kmeans++ Clustering","authors":"Ruifeng Wang, Wenyuan Zheng, Miaohua Huang, Guohang Li","doi":"10.1109/AEMCSE55572.2022.00046","DOIUrl":"https://doi.org/10.1109/AEMCSE55572.2022.00046","url":null,"abstract":"Public transportation plays an important role in residents’ daily travel, and it’s important to improve the comprehensive driving ability of drivers. This paper establishes a scientific and comprehensive evaluation system for bus drivers’ driving behavior. First, based on the big data of the operation of new energy buses, the kinematic segments are divided into consideration of the temperature changes in four seasons and traffic conditions, and the index features under the three dimensions of driving behavior security, energy consumption and passenger comfort are excavated, and then combined with DBCSAN and Kmeans++, the driving behavior of each dimension is clustered and classified, which improves the reliability of the clustering and the rationality of the classification. Finally, based on the weighted scoring method, the classification of the driving behavior is determined by combining the temperature of the four seasons and the proportion of different traffic conditions. The driver’s comprehensive driving behavior scoring model is established to realize the quantitative evaluation of the driver’s driving behavior. The model can reasonably evaluate the driver’s comprehensive driving ability and help the driver to improve their comprehensive driving skills.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"59 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120986529","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

Research on Defect Rotation Detection of Aircraft Flared Tube Based on Improved YOLOv4 基于改进YOLOv4的飞机扩口管缺陷旋转检测研究

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00019

Jian Zhang, Kexin Wang, GuanbangĀ Dai, Ping Zhang

{"title":"Research on Defect Rotation Detection of Aircraft Flared Tube Based on Improved YOLOv4","authors":"Jian Zhang, Kexin Wang, GuanbangĀ Dai, Ping Zhang","doi":"10.1109/AEMCSE55572.2022.00019","DOIUrl":"https://doi.org/10.1109/AEMCSE55572.2022.00019","url":null,"abstract":"Aircraft Flared Tube (AFT) is an important part of the aircraft system, and its surface defect detection has become a prerequisite for meeting the long-term normal operation of the aircraft. Although the existing deep learning defect detection methods have made great progress, there are still problems such as difficulty in detecting tiny defect, insufficient model generalization ability and harsh detection environment. Therefore, based on the analysis of the YOLOv4 model, we first utilize the Multistage Attention Module (MAM) to enhance the feature expression ability of the shallow network. Secondly, we exploit rotation detection to accommodate large aspect ratio defects on AFT surfaces. Finally, we convert the pytorch model into a tensorRT engine and deploy it on Agx xavier to achieve high efficiency, high-stability, and low-power inference. Experimental results show that the mean Average Precision (MAP) of our improved model reaches 97.87%, and the single image detection speed reaches 223.23ms, which further proves the good performance of our model on the AFT detection task.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"99 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120992750","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