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2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)最新文献

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Photovoltaic Power Prediction Using Empirical Mode Decomposition Algorithm and Integrated Neural Network 基于经验模态分解算法和集成神经网络的光伏发电预测
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00015
Wenan Tan, Yiting Wang
{"title":"Photovoltaic Power Prediction Using Empirical Mode Decomposition Algorithm and Integrated Neural Network","authors":"Wenan Tan, Yiting Wang","doi":"10.1109/AIEA53260.2021.00015","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00015","url":null,"abstract":"Aiming at the low accuracy of photovoltaic power prediction, a photovoltaic power prediction algorithm based on empirical mode decomposition algorithm and integrated neural network is proposed. First, the empirical mode decomposition algorithm decomposes the historical photovoltaic power data into a series of stationary sequences, and then a prediction model is constructed using multiple backpropagation (BP) neural networks. Finally, these models’ predicted values are weighted and fused, forming the final photovoltaic power prediction algorithm. The experimental results show that the proposed model herein has higher prediction accuracy and fewer prediction errors than the single BP neural network model and the Autoregressive Integrated Moving Average (ARIMA) model.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114770012","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
Dual-Energy CT Image Super-resolution via Generative Adversarial Network 基于生成对抗网络的双能量CT图像超分辨率研究
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00079
Xinyi Zhong, Yizhong Wang, Ailong Cai, Ningning Liang, Lei Li, Bin Yan
{"title":"Dual-Energy CT Image Super-resolution via Generative Adversarial Network","authors":"Xinyi Zhong, Yizhong Wang, Ailong Cai, Ningning Liang, Lei Li, Bin Yan","doi":"10.1109/AIEA53260.2021.00079","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00079","url":null,"abstract":"Photon counting detector obtains CT images from multiple energy bins, and acquires X-ray intensity data of different energy bins through one X-ray exposure. However, the spatial resolution of the reconstructed image will decrease, and the image will be blurred due to the low photon count in the narrow energy box width, quantum noise and the response problem of detector cells. Deep learning is gradually applied to medical images to reduce noise or improve resolution, which has exhibited promising performance in image super-resolution (SR) by learning a nonlinear mapping function from low-resolution (LR) images to high-resolution (HR) images. Inspired by the cycle-GAN, we propose a novel network model which realize the mapping of HR images to LR images for Dual-Energy CT (DECT) reconstruction. Experimental results show that the reconstructed image has significant improvements in peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). Compared with the traditional super-resolution reconstruction method, this method has better experimental results.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123674844","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: 2
Research on the Methods of Panoramic Video Projection Mapping 全景视频投影映射方法研究
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00041
Jiangeng Li, K. Xu
{"title":"Research on the Methods of Panoramic Video Projection Mapping","authors":"Jiangeng Li, K. Xu","doi":"10.1109/AIEA53260.2021.00041","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00041","url":null,"abstract":"In recent years, panoramic video has gradually emerged in the multimedia industry. As an important part of the panoramic video processing process, the choice of projection mapping method will affect the efficiency of panoramic video coding and decoding, transmission mode and user experience. There have been a lot of researches on the projection method from the spherical surface to the plane, and its important purpose is to reduce the distortion of the spherical video in the projection and improve the performance of panoramic video encoding. At present, projection methods are mainly divided into uniform quality projection and non-uniform quality projection. This paper compares several common projection mapping methods, discusses their respective advantages and disadvantages, and makes related summary at last.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128474855","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 adaptive control of medium frequency DC resistance spot welding 中频直流电阻点焊自适应控制研究
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/aiea53260.2021.00014
Yuan Xiao-Jie, Wang Bin, Shi Xiao-yan, Li Yu-Xin
{"title":"Research on adaptive control of medium frequency DC resistance spot welding","authors":"Yuan Xiao-Jie, Wang Bin, Shi Xiao-yan, Li Yu-Xin","doi":"10.1109/aiea53260.2021.00014","DOIUrl":"https://doi.org/10.1109/aiea53260.2021.00014","url":null,"abstract":"The electrode wear and spot area change in the continuous welding process of medium frequency DC resistance spot welding lead to unstable welding current, spatter and nugget diameter reduction. A fuzzy PI adaptive control scheme is studied and designed. In each control cycle, the dynamic resistance change rate and the instantaneous maximum welding power are used as the inputs of the fuzzy controller. Through the current closed-loop feedback control, the output of the fuzzy PI controller is used to control the switch on and off of the full bridge inverter, so as to maintain the constant welding current. Simulation and experimental results show that the scheme has the advantages of small ripple, small overshoot, high control precision, fast response speed and better welding quality than the traditional PID control.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121408141","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 Survey on Few-Shot Learning in Natural Language Processing 自然语言处理中短时学习研究综述
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/aiea53260.2021.00069
Meng Yang
{"title":"A Survey on Few-Shot Learning in Natural Language Processing","authors":"Meng Yang","doi":"10.1109/aiea53260.2021.00069","DOIUrl":"https://doi.org/10.1109/aiea53260.2021.00069","url":null,"abstract":"The annotated dataset is the foundation for Supervised Natural Language Processing. However, the cost of obtaining dataset is high. In recent years, the Few-Shot Learning has gradually attracted the attention of researchers. From the definition, in this paper, we conclude the difference in Few-Shot Learning between Natural Language Processing and Computer Vision. On that basis, the current Few-Shot Learning on Natural Language Processing is summarized, including Transfer Learning, Meta Learning and Knowledge Distillation. Furthermore, we conclude the solutions to Few-Shot Learning in Natural Language Processing, such as the method based on Distant Supervision, Meta Learning and Knowledge Distillation. Finally, we present the challenges facing Few-Shot Learning in Natural Language Processing.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128113940","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: 24
The present and future of electric vehicles : Market analysis and forecast of different types of electric vehicles 电动汽车的现状与未来:不同类型电动汽车的市场分析与预测
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00042
Yi Jing, Zeheng Zhang, Huinan Shi, Jie Wang, Runjun Xu, Mingrun Li, G. Zhang
{"title":"The present and future of electric vehicles : Market analysis and forecast of different types of electric vehicles","authors":"Yi Jing, Zeheng Zhang, Huinan Shi, Jie Wang, Runjun Xu, Mingrun Li, G. Zhang","doi":"10.1109/AIEA53260.2021.00042","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00042","url":null,"abstract":"With the increasingly serious energy crisis and environmental problems, people's demand for new energy is more urgent, so new energy products emerge in an endless stream. New energy vehicles are highly praised by most countries for their energy saving and environmental protection, and there are many kinds of new energy vehicles.This paper first introduces several common new energy vehicles in the market. Then, this paper obtains the output data of three kinds of new energy vehicles, and makes a correlation analysis by using gray correlation, and concludes that the correlation between the total output of plug-in hybrid vehicles and vehicles is strong, which is the representative of today's new energy vehicles. This paper also uses time series to predict the electric vehicles in the next two years, and obtains a reasonable prediction model. Moreover, this paper creatively puts forward the impact of technological innovation on new energy vehicles, and uses a reasonable model to predict the changes of the output and total output of fuel cell vehicles after technological innovation. Finally, this paper tests the time series and further proves the rationality of the prediction.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131272552","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
Design of a two-phase stepper motor drive controller for 6000m deep-sea electric Pan&Tilt 6000m深海电动转台两相步进电机驱动控制器的设计
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/aiea53260.2021.00039
Xu Zhang, Hao Pan
{"title":"Design of a two-phase stepper motor drive controller for 6000m deep-sea electric Pan&Tilt","authors":"Xu Zhang, Hao Pan","doi":"10.1109/aiea53260.2021.00039","DOIUrl":"https://doi.org/10.1109/aiea53260.2021.00039","url":null,"abstract":"In this paper, a two-phase stepper motor drive controller which can be used in 6000m deep-sea electric Pan&Tilt is designed. The two-phase stepper motor drive controller adopts the architecture of MCU combined with Universal high voltage driver of two-phase bipolar stepper motor, interacts with external control unit through RS-485 communication bus, and uses Hall magnetic angle sensor to perform the position closed loop control of the stepper motor, thereby achieving high precision positioning of the deep-sea electric Pan&Tilt under water. The hydrostatic pressure test shows that the two-phase stepper motor drive controller has withstood the test of 75Mpa, and has the feasibility of application on the 6000m deep-sea electric Pan&Tilt.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133273929","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
Image-domain material decomposition for single-energy CT images using cascaded network 基于级联网络的单能量CT图像域材料分解
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00064
Yizhong Wang, Zhiwei Feng, Xiao-Kun Yu, Lei Li, Bin Yan
{"title":"Image-domain material decomposition for single-energy CT images using cascaded network","authors":"Yizhong Wang, Zhiwei Feng, Xiao-Kun Yu, Lei Li, Bin Yan","doi":"10.1109/AIEA53260.2021.00064","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00064","url":null,"abstract":"Dual-energy computer tomography (DECT) has high application prospects in distinguishing and quantifying materials. However, DECT requires higher hardware cost and higher radiation dose than single-energy CT imaging. In this paper, we have developed a cascaded network method to realize DECT imaging through SECT images, then get the basis material decomposition images. Specifically, we design the mapping convolutional neural network and the material decomposition U-Net to realize the mapping of low- to high-energy images and material decomposition, respectively. To verify the feasibility of the proposed method, we extracted 1442 cranial cavity slice images of 5 patients for experiments. The qualitative and quantitative results show that the proposed method can achieve high-quality DECT imaging of single energy data and high-precision basis material decomposition.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125876269","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: 3
Classification of Bearing Fault Based on Multi-class Recurrent Neural Network 基于多类递归神经网络的轴承故障分类
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00032
Li Shi-bo, Yang Yang, Li Fan, Li Gang
{"title":"Classification of Bearing Fault Based on Multi-class Recurrent Neural Network","authors":"Li Shi-bo, Yang Yang, Li Fan, Li Gang","doi":"10.1109/AIEA53260.2021.00032","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00032","url":null,"abstract":"Aiming at the strong correlation between the current state of the bearing and the state information of the previous moment, this paper proposes to use the multi-class recurrent neural network (RNN) to classify and predict the bearing fault. The bearing vibration and current information are collected from three directions of the shaft. The sliding window is used to reduce the noise of the signal, so as to solve the problem of low signal-to-noise ratio of bearing fault signal in actual production. The wavelet packet decomposition and reconstruction technology is used to extract the features, and the bearing signal is converted from the time domain to the frequency domain to construct the features, and the bearing information entropy and wavelet energy are extracted. Three recurrent neural networks (RNNs) are combined to build multi-class RNNs, and the classification and prediction of bearing fault categories were carried out. After training and testing, the model was evaluated by using confusion matrix, and the results verified the effectiveness of the model.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127029812","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
A loop closure detection method based on semantic segmentation and convolutional neural network 一种基于语义分割和卷积神经网络的闭环检测方法
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00063
Hongyang Li, Chengjun Tian, Lequan Wang, Hongfu Lv
{"title":"A loop closure detection method based on semantic segmentation and convolutional neural network","authors":"Hongyang Li, Chengjun Tian, Lequan Wang, Hongfu Lv","doi":"10.1109/AIEA53260.2021.00063","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00063","url":null,"abstract":"As artificial intelligence flourishes and many related technologies continue to develop, Visual Simultaneous Localization and Mapping, as the “vision” of robots, can utilize a large amount of environmental information. In addition, semantic segmentation can distinguish the background in the image from the moving objects. In recent years, convolutional neural networks have been successfully applied to image processing, and subsequently entered the field of V-SLAM research. Related researchers have tried to directly use convolutional neural networks to extract image features for loop closure detection, but the effect has not surpassed traditional methods. In order to make full use of image information, this paper proposes a loop closure detection and SLAM method based on semantic segmentation and convolutional neural network.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134380890","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: 2
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