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

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One-Dimensional Aerodynamic Design of Micro Radial Turbine Based on Genetic Algorithm 基于遗传算法的微型径向涡轮一维气动设计
2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00094
Chao Li, Zhiping Guo
{"title":"One-Dimensional Aerodynamic Design of Micro Radial Turbine Based on Genetic Algorithm","authors":"Chao Li, Zhiping Guo","doi":"10.1109/AIAM54119.2021.00094","DOIUrl":"https://doi.org/10.1109/AIAM54119.2021.00094","url":null,"abstract":"Micro radial inflow turbine, as an important component of micro gas turbine, directly determines its reliability. To improve design accuracy and efficiency of micro radial inflow turbine, a one-dimensional aerodynamic design method is investigated in this paper. The one-dimensional design software was developed based on the reference of the conventional radial inflow turbine aerodynamic design method, and the main aerodynamic design parameters were selected by combining genetic algorithm, the reliability of the design software was verified by comparing the calculation results with the commercial turbine one-dimensional design software. A three-dimensional model of the micro radial inflow turbine was developed and numerical simulation model were carried out using CFX software. The simulation results are compared with the parameters obtained from the one-dimensional aerodynamic design and the deviation between the two is less than 5%, indicating that the one-dimensional aerodynamic design method can provide reliable preliminary design and performance prediction for the micro radial inflow turbine.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"103 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":"127640902","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
Infrared Ship Target Segmentation Based on Active Contour Model 基于主动轮廓模型的红外舰船目标分割
2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00060
Ruiying He
{"title":"Infrared Ship Target Segmentation Based on Active Contour Model","authors":"Ruiying He","doi":"10.1109/AIAM54119.2021.00060","DOIUrl":"https://doi.org/10.1109/AIAM54119.2021.00060","url":null,"abstract":"Infrared ship image has low contrast, weak boundary and uneven gray-scale distribution, leading to difficult ship target segmentation. Chen-Vese model, a classic active contour model, does not rely on image boundary information, which can better segment images with weak or discontinuous edges. Moreover, it has certain noise resistance, but correct segmentation result is impossible for infrared images with uneven grayscales. In view of this, this paper first uses top-hat transform or bottom-hat transform to preprocess the infrared image, increase the image contrast, so that the gray value tends to be uniform in the target and the background. Then, the improved Chen-Vese model is used to test the ship target. Experimental results show that the new method can quickly and effectively detect infrared ship targets, which is superior to the Chen-Vese model in terms of curve evolution speed and noise resistance.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"4 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":"122557966","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 and Realization of Air Quality Grade Prediction Based on KNN 基于KNN的空气质量等级预测研究与实现
2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00068
Y. Gong, P. Zhang
{"title":"Research and Realization of Air Quality Grade Prediction Based on KNN","authors":"Y. Gong, P. Zhang","doi":"10.1109/AIAM54119.2021.00068","DOIUrl":"https://doi.org/10.1109/AIAM54119.2021.00068","url":null,"abstract":"Since entering modern society, people have paid more and more attention to air quality in order to better help predict the air quality level. This paper proposes an air quality grade prediction model based on the K-nearest neighbor algorithm. Firstly, the historical measurement data of air quality is crawled from the relevant weather website and saved to the local CSV file; then the data is read, and the scatter diagram is used to visually display the 6 characteristics that affect the air quality level evaluation; then the K nearest neighbor algorithm is selected, and the difference is adjusted. The parameter training model of, and then through the test set verification, the test accuracy rate is 95.10%. Finally, a set of new data is randomly given, and the prediction results are in line with the expected results, which can be extended to predict the air quality level.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"17 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":"117072165","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
Preparation of Gradient Porous TiAl Intermetallics with additive manufacturing technology 用增材制造技术制备梯度多孔TiAl金属间化合物
2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00115
Yucheng Shang, W. Hui, Junjie Li, Weilin Chen, Tianen Xie, Junsheng Yang
{"title":"Preparation of Gradient Porous TiAl Intermetallics with additive manufacturing technology","authors":"Yucheng Shang, W. Hui, Junjie Li, Weilin Chen, Tianen Xie, Junsheng Yang","doi":"10.1109/AIAM54119.2021.00115","DOIUrl":"https://doi.org/10.1109/AIAM54119.2021.00115","url":null,"abstract":"Gradient pore size porous material is a kind of porous material with asymmetric pore structure, the gradient character could ensure the smaller pore size on the basis of guarantee the larger filtration flux and other homogeneous structure. Therefore, it is of great significance to develop a novel porous material with gradient pore size. In this study, porous TiAl intermetallics support was prepared by solid partial diffusion activation reaction sintering, and then the gradient membrane was coated by additive manufacturing technology. The pore structures were characterized by pore size test. The results revealed that the matrix prepared with elemental powder of 47~74 µm has good match with of 23-25 µm coating material. The matching rule between the maximum pore size of the substrate and the particle size of the membrane was also investigated. This study has practical significance for the preparation of gradient porous materials by additive manufacturing.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"1 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":"122039312","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
Adjustable Load Capacity Forecasting Technology Based on Unsupervised Learning 基于无监督学习的可调负荷预测技术
2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00064
Yurui Yang, Jiantong Yue, Q. Yao, Qiuqiang Zhou, Jia Wu, Bailang Pan
{"title":"Adjustable Load Capacity Forecasting Technology Based on Unsupervised Learning","authors":"Yurui Yang, Jiantong Yue, Q. Yao, Qiuqiang Zhou, Jia Wu, Bailang Pan","doi":"10.1109/AIAM54119.2021.00064","DOIUrl":"https://doi.org/10.1109/AIAM54119.2021.00064","url":null,"abstract":"In the existing power grid demand management, there is a lack of perfect demand side resource load response analysis and load schedulable capacity analysis. Aiming at the problem of load adjustable capacity in the power grid, an adjustable load adjustment capacity evaluation model is established to convert the load into load characteristic parameters, and the fuzzy c-means clustering algorithm based on peak density is used to process the load characteristic parameters to accurately identify the adjustable load; Aiming at several influencing factors of adjustable load, the adjustable capacity of adjustable load is explored by using multi-core function, and the capacity is evaluated by different indexes.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"39 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":"122040090","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
Unmanned Aerial Vehicle Cluster Operations under the Background of Intelligentization 智能化背景下的无人机集群作战
2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00110
Yawei Cai, Haifeng Guo, Kai Zhou, Liang Xu
{"title":"Unmanned Aerial Vehicle Cluster Operations under the Background of Intelligentization","authors":"Yawei Cai, Haifeng Guo, Kai Zhou, Liang Xu","doi":"10.1109/AIAM54119.2021.00110","DOIUrl":"https://doi.org/10.1109/AIAM54119.2021.00110","url":null,"abstract":"In this paper, the prominent contradiction between unmanned aerial vehicle (UAV) operation and single UAV operation is analyzed. By studying the related items and operational capabilities of UAV cluster operation in the US military, the trends of UAV single-machine intelligence, multi-machine intelligence and task autonomy intelligence are pointed out, and the typical patterns of UAV cluster operation under the background of intelligentization are studied.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"362 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":"114770116","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
[Copyright notice] (版权)
2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/aiam54119.2021.00003
{"title":"[Copyright notice]","authors":"","doi":"10.1109/aiam54119.2021.00003","DOIUrl":"https://doi.org/10.1109/aiam54119.2021.00003","url":null,"abstract":"","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"3 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":"134556134","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 Intelligent Proofreading System Based on Artificial Intelligence 基于人工智能的智能校对系统设计
2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00042
H. Zhao
{"title":"Design of Intelligent Proofreading System Based on Artificial Intelligence","authors":"H. Zhao","doi":"10.1109/AIAM54119.2021.00042","DOIUrl":"https://doi.org/10.1109/AIAM54119.2021.00042","url":null,"abstract":"With rapidly developing of computer technologies, English translation based on artificial intelligence has gradually become one of the research directions. Limited to the factors of algorithms as well as matching degree, some problems still bottleneck English translation. To develop an intelligent system for English translation, the author proposed a computer intelligent proofreading model and further developed it. The system can directly integrate additional annotations with input text at the word level, including language markers or automatically generated word classes. Experiments on English translation show that this proofreading model has good performance in automatic scoring and more grammatical coherence. Finally, the author discussed the technological design of this proofreading system from the aspects of hardware and software.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"63 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":"130092093","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 Scene Text Detection Algorithm with Multiple Feature Fusion Based on Multiple Kernel Support Vector Machine 基于多核支持向量机的多特征融合场景文本检测算法
2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00102
Aidong Fang, S. Xie, Lin Cui, Zhiwei Zhang, Zhuang Sheng
{"title":"A Scene Text Detection Algorithm with Multiple Feature Fusion Based on Multiple Kernel Support Vector Machine","authors":"Aidong Fang, S. Xie, Lin Cui, Zhiwei Zhang, Zhuang Sheng","doi":"10.1109/AIAM54119.2021.00102","DOIUrl":"https://doi.org/10.1109/AIAM54119.2021.00102","url":null,"abstract":"Since the text information in the image includes rich semantic meaning, the detection of scene text has very important meaning. The traditional use of single or certain feature methods to detect text is not ideal in complex scenes, and the use of deep learning methods requires a lot of calculations and a large number of training samples. In the case of a small sample field, the detection effect is not satisfactory. Satisfactory. This paper proposes a method based on the fusion of multiple features such as character geometric features and high-level features, and uses multi-core SVM to detect the detection area. By using multiple feature fusion, the text area can be detected more effectively in complex scenes. Multi-core functions can avoid the limitations of single-core functions, thereby improving its performance. Experimental results show that this method can effectively detect scene text.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"230 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133523214","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
Active Learning Approach for Spam Filtering 垃圾邮件过滤的主动学习方法
2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) Pub Date : 2021-10-01 DOI: 10.1109/AIAM54119.2021.00080
Tianhao Zang
{"title":"Active Learning Approach for Spam Filtering","authors":"Tianhao Zang","doi":"10.1109/AIAM54119.2021.00080","DOIUrl":"https://doi.org/10.1109/AIAM54119.2021.00080","url":null,"abstract":"It is general to relate an optimization problem with bound constraints and a linear equality constraint with training a support vector machine (SVM). In this paper, the Support Vector Machine (SVM) model trained by the Active Learning algorithm is proposed for spam filtering. This experiment proceeds the data preprocessing, training of the SVM model, application of Active Learning (AL), evaluation of performances, and comparisons of results between random sampling and AL. The graphics clearly exhibit the result that the Active Learning method could use only a small size of the dataset to achieve efficient and accurate spam filtering work. This experiment assists in enhancing the efficiency of working and processing emails.","PeriodicalId":227320,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)","volume":"15 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":"121254095","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
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