2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)最新文献_第8页

Research on EEMD-MCKD Method of Bearing Vibration Feature Extraction 轴承振动特征提取的eemd - mcd方法研究

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00117

Mingshuai Liu, Yuanjun Dai, Kuniv Shi

引用次数: 0

Fish Behavior Detection Using Computer Vision: A Review 基于计算机视觉的鱼类行为检测研究进展

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00082

Qingxiao Wang, Wei Long, Yawen Wang, Linhua Jiang, Lingxi Hu

引用次数: 1

Finite Element Simulation Analysis of Temperature Field of MEMS Gas Sensor Based on ANSYS Software 基于ANSYS软件的MEMS气体传感器温度场有限元仿真分析

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00099

Xiaojuan Li, Yidong Yuan, Yabing Li, Jinwen Liu, Siqi Li, Yu Liu, Yan Cheng

引用次数: 1

Hierarchical Recommendation Algorithm Incorporated with Book Descriptions 结合图书描述的分层推荐算法

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00077

Ming Xie

引用次数: 0

A Large Scale Extended Algorithm for 2D Halton Points with Low-Discrepancy Sequences 二维低差序列Halton点的大规模扩展算法

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00043

Wenxing Chen, Shuyang Dai, B. Zheng

引用次数: 0

Image Recognition of Human Faces based on BP Neural Network and Particle Swarm Optimization 基于BP神经网络和粒子群优化的人脸图像识别

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00092

J. Feng, T. Gong

引用次数: 0

Deep Learning-Based Comparative Modeling of Carbon Emissions Projections 基于深度学习的碳排放预测比较模型

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00057

Dacheng Hou, Haoyu Zhang, Lili Li, Xiaojun Wang, Yifan Lin, Huandi Du

{"title":"Deep Learning-Based Comparative Modeling of Carbon Emissions Projections","authors":"Dacheng Hou, Haoyu Zhang, Lili Li, Xiaojun Wang, Yifan Lin, Huandi Du","doi":"10.1109/AIAM54119.2021.00057","DOIUrl":"https://doi.org/10.1109/AIAM54119.2021.00057","url":null,"abstract":"Scientific statistics of carbon emission data and reasonable prediction of its development trend can help stabilize carbon emission to ensure that China can achieve the carbon peak by 2030. In this paper, we use Pearson correlation analysis to select the main factors that optimally affect carbon emissions and predict them through the back propagation neural network model optimized by whale algorithm, the BP neural network model optimized by genetic algorithm and the back propagation neural network model, so as to provide an effective prediction method for scientists to study the growth rate of carbon emissions production in China The back propagation neural network model is used to predict the carbon peak and the time of carbon neutralization in China by the optimal model. The Pearson correlation coefficient screening was performed with 85 groups of factors affecting carbon emissions collected in China from 1998–2019 under carbon emissions and (energy, agriculture, industry, integrated, etc.) resource environment, and eight optimal groups of data were used for prediction. The data were compared between R Square, MSE, RMSE and MAPE data by back propagation neural network, optimization using whale algorithm and optimization using genetic algorithm, and the optimal model was used to predict carbon emissions for three years from 2020 to 2069. The results of this study show that the four groups of data, R Square, MSE, RMSE and MAPE, predicted by the BP neural network model optimized based on the whale algorithm are the best among the three models, and the data from 2016 to 2069 can be accurately predicted by the whale algorithm optimized BP network to determine the time of reaching carbon peak and carbon neutrality in China. Compared with other prediction models, the BP neural network model optimized by the whale algorithm can effectively predict carbon emissions and provide an optimal method for carbon emissions prediction.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129743005","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

Real-Time Detection and Early Warning of Driving Fatigue Based on Basic Scale Entropy 基于基本尺度熵的驾驶疲劳实时检测与预警

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00044

Fuwang Wang, Xiaogang Kang, Bin Lu

{"title":"Real-Time Detection and Early Warning of Driving Fatigue Based on Basic Scale Entropy","authors":"Fuwang Wang, Xiaogang Kang, Bin Lu","doi":"10.1109/AIAM54119.2021.00044","DOIUrl":"https://doi.org/10.1109/AIAM54119.2021.00044","url":null,"abstract":"Driving fatigue is the main cause of traffic accidents, and traffic accidents often cause serious personal injury and huge property losses. Therefore, it is of great significance for traffic safety to accurately and quickly detect the mental fatigue of the driver and perform fatigue warning. In this paper, preprocess the collected the electroencephalogram (EEG) signals to remove interference signals. The Butterworth band-pass filter is used to extract the EEG signals of α and β rhythms, and then the basic scale entropy of αand β rhythms is used as driving fatigue features. Using these features to analyze the driving fatigue state of the subjects in different driving stages, according to the change law of driving fatigue features and combined with the fatigue scale SOFI-25 (swedish occupational fatigue inventory-25), driving fatigue is divided into 3 levels (awake state, mild fatigue state and severe fatigue state). When the fatigue reaches a mild fatigue state or a severe fatigue state, a fatigue warning is given to the driver, and a piece of music that the driver is interested in is played. The results show that using the basic scale entropy of α and βrhythms as driving fatigue characteristics can effectively detect driver fatigue. The basic scale entropy is an entropy measurement algorithm with fast calculation, strong anti-interference and certain suppression of noise, which can realize real-time and more accurate detection of driving fatigue. In addition, when the fatigue reaches the state of mild fatigue or severe fatigue, this study provides fatigue warning to the driver, and plays a piece of music that the driver is interested in to relieve fatigue, which has practical value in actual driving and can effectively improve driving safety.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"44 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132476826","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

Evolution of Intelligent Feeding System for Aquaculture: A Review 水产养殖智能饲养系统的发展综述

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00085

Gaoxiang Wang, Lin Hu, Wei Long, Linhua Jiang

引用次数: 1

Experimental study on wear mechanism of wire with pre-stranded anti-vibration hammer 预绞抗振锤钢丝磨损机理试验研究

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00021

Wenbin Li, Y. Feng, Y. Wang, Si-Liang Zeng, Jinkui Li, HUI-RU Zhao

{"title":"Experimental study on wear mechanism of wire with pre-stranded anti-vibration hammer","authors":"Wenbin Li, Y. Feng, Y. Wang, Si-Liang Zeng, Jinkui Li, HUI-RU Zhao","doi":"10.1109/AIAM54119.2021.00021","DOIUrl":"https://doi.org/10.1109/AIAM54119.2021.00021","url":null,"abstract":"The anti-vibration hammer is widely used to eliminate the breeze vibration of overhead transmission lines' grounding grid. According to the type of wire clamp, the anti-vibration hammer can be divided into bolt and pre-stranded types. The traditional bolt-type vibration hammer is easy to run on the wire due to the loosening of the fastening bolt. Recently, the pre-stranded anti-vibration hammer has gained recognition by construction and transportation inspection units due to convenient installation, anti-running and maintenance-free. Due to the anti-running characteristics, the wear on wire is difficult to be detected only after opening the wire clamp on the tower. If the early wire wear of the pre-stranded hammer is not found in time, the wear will be accelerated in the later stage, leading to breakage. In this paper, the influence of pre-stranded wire number, diameter, pitch and other parameters on the occurrence of wear phenomenon is analyzed by testing typical wire and pre-stranded hammer. The experimental comparison with bolt type vibration hammer is carried out, and the mechanism of wire wear by pre-stranded vibration hammer is studied.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127029564","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