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2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)最新文献

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Point-Cloud-based Deep Learning Models for Finite Element Analysis 基于点云的有限元分析深度学习模型
2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2022-10-01 DOI: 10.1109/DCABES57229.2022.00049
Meduri Venkata Shivaditya, Francesca Bugiotti, F. Magoulès
{"title":"Point-Cloud-based Deep Learning Models for Finite Element Analysis","authors":"Meduri Venkata Shivaditya, Francesca Bugiotti, F. Magoulès","doi":"10.1109/DCABES57229.2022.00049","DOIUrl":"https://doi.org/10.1109/DCABES57229.2022.00049","url":null,"abstract":"In this paper, we explore point-cloud based deep learning models to analyze numerical simulations arising from finite element analysis. The objective is to classify automatically the results of the simulations without tedious human intervention. Two models are here presented: the Point-Net classification model and the Dynamic Graph Convolutional Neural Net model. Both trained point-cloud deep learning models performed well on experiments with finite element analysis arising from automotive industry. The proposed models show promise in automatizing the analysis process of finite element simulations. An accuracy of 79.17% and 94.5% is obtained for the Point-Net and the Dynamic Graph Convolutional Neural Net model respectively.","PeriodicalId":344365,"journal":{"name":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121852513","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
Diagnosis of power operation and maintenance records based on pre-training model and prompt learning 基于预训练模型和快速学习的电力运维记录诊断
2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2022-10-01 DOI: 10.1109/DCABES57229.2022.00029
Jun Jia, Hui Fu, Ziyang Zhang, Jinggang Yang
{"title":"Diagnosis of power operation and maintenance records based on pre-training model and prompt learning","authors":"Jun Jia, Hui Fu, Ziyang Zhang, Jinggang Yang","doi":"10.1109/DCABES57229.2022.00029","DOIUrl":"https://doi.org/10.1109/DCABES57229.2022.00029","url":null,"abstract":"The operation and maintenance records of power equipment contain abundant historical operation state information of equipment. However, due to the characteristics of multi ambiguity, difficult to segment ambiguity and multi noise, this paper proposes a two-stage model for the text diagnosis of power equipment. First, the large-scale pre-training model is trained based on the massive text, and then the pre-training language model is fine-tuned by the prompt technology for equipment diagnosis. The proposed solution is assessed through experiments and the numerical results demonstrate that the proposed solution can achieve about 20% improvement over the traditional method.","PeriodicalId":344365,"journal":{"name":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"2292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130297090","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
Radar emitter signal recognition based on coordinated attention 基于协调注意的雷达辐射源信号识别
2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2022-10-01 DOI: 10.1109/DCABES57229.2022.00051
Ding Jiajun, Yan Yunyang, L. Yian
{"title":"Radar emitter signal recognition based on coordinated attention","authors":"Ding Jiajun, Yan Yunyang, L. Yian","doi":"10.1109/DCABES57229.2022.00051","DOIUrl":"https://doi.org/10.1109/DCABES57229.2022.00051","url":null,"abstract":"Aiming at the problem that complex radar emitter signals are difficult to be recognized at low signal-to-noise ratio, a method based on improved coordinate attention network is proposed. Firstly, the radar signal is converted into a two-dimensional time-frequency image to reflect the signal feature information. Then the time-frequency image preprocessing and denoising by convolutional neural network. Finally, the coordinated attention network is used for feature extraction, and then the classification of radar emitter source signals are realized. Experiments results show that the proposed method can validly improve the accuracy of radar signal recognition under the condition of low SNR.","PeriodicalId":344365,"journal":{"name":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130243373","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
Intelligent judgment of rotating machinery based on multi-scale parallel network and attention mechanism 基于多尺度并行网络和注意机制的旋转机械智能判断
2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2022-10-01 DOI: 10.1109/DCABES57229.2022.00040
Zhixiang Fan, Pengjiang Qian
{"title":"Intelligent judgment of rotating machinery based on multi-scale parallel network and attention mechanism","authors":"Zhixiang Fan, Pengjiang Qian","doi":"10.1109/DCABES57229.2022.00040","DOIUrl":"https://doi.org/10.1109/DCABES57229.2022.00040","url":null,"abstract":"The primary problem solved in rotating machinery fault diagnosis is how to effectively extract fault features from the vibration signals with noise. To extract fault features accurately, this study proposes a multi-scale parallel convolutional neural network fault recognition algorithm, which can carry out feature fusion. The above method combines empirical feature extraction (e.g., fast Fourier transform) to enrich feature information, which can effectively implement deep learning. The effectiveness and reliability of the method are verified through example studies on JNU, SEU and PU rolling bearing experimental data sets. The algorithm has the higher classification capability and diagnostic accuracy compared with four common deep learning algorithms.","PeriodicalId":344365,"journal":{"name":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121662809","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
Contour extraction method and implementation of active contour model algorithm based on N-order Bezier curve for cardiac medical images 基于n阶Bezier曲线的心脏医学图像轮廓提取方法及主动轮廓模型算法的实现
2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2022-10-01 DOI: 10.1109/DCABES57229.2022.00026
Du Hailong
{"title":"Contour extraction method and implementation of active contour model algorithm based on N-order Bezier curve for cardiac medical images","authors":"Du Hailong","doi":"10.1109/DCABES57229.2022.00026","DOIUrl":"https://doi.org/10.1109/DCABES57229.2022.00026","url":null,"abstract":"Optimize the active contour model. In the process of optimization, the n-order Bezier model curve is used to smooth the evolution curve of the active contour model to make it more smooth. The optimized curve is applied to the contour extraction and analysis of medical image, so as to promote the processing of medical image. The n-order Bezier curve algorithm is used to optimize the active contour model algorithm. The active contour model is a discrete point curve, and the active contour is smooth and seamless. The active contour obtained by the optimized algorithm is smooth and seamless, which is more suitable for image processing operations such as medical image and contour extraction of medical image. The algorithm of using n-order Bezier curve to optimize the active contour model should be applied to the image analysis of medical images.","PeriodicalId":344365,"journal":{"name":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122756188","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-Scale Multi-Stage Single Image Super-Resolution Reconstruction Algorithm Based on Transformer 基于变压器的多尺度多阶段单图像超分辨率重建算法
2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2022-10-01 DOI: 10.1109/DCABES57229.2022.00044
Wei Wang, Yinfang Zhu, D. Ding, Jing Li, Yuxiang Luo
{"title":"Multi-Scale Multi-Stage Single Image Super-Resolution Reconstruction Algorithm Based on Transformer","authors":"Wei Wang, Yinfang Zhu, D. Ding, Jing Li, Yuxiang Luo","doi":"10.1109/DCABES57229.2022.00044","DOIUrl":"https://doi.org/10.1109/DCABES57229.2022.00044","url":null,"abstract":"In this paper, creatively combining Transformer with image super-resolution reconstruction, we proposes a multi-scale multi-stage single image super-resolution reconstruction algorithm based on Transformer (MSTN). The algorithm uses Transformer as a feature sharing module, thus it realizes network parameter sharing, dynamically focuses on the correlation between feature information of adjacent stages, and then extracts the high-frequency texture information embedded in the current stage features from the feature information learned in the previous stage, which achieves a coarse-to-fine enhancement of image reconstruction. Experiments show that our method can not only per-form better image super-resolution reconstruction compared with other advanced methods, but also reduce the network parameters to a great extent.","PeriodicalId":344365,"journal":{"name":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"316 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126940376","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 of Data-driven Method for Automatic Machine Learning in Economic Research 数据驱动的自动机器学习方法在经济研究中的应用
2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2022-10-01 DOI: 10.1109/DCABES57229.2022.00019
Wen Wang, Wenbo Xu, Xiang Yao, Huajun Wang
{"title":"Application of Data-driven Method for Automatic Machine Learning in Economic Research","authors":"Wen Wang, Wenbo Xu, Xiang Yao, Huajun Wang","doi":"10.1109/DCABES57229.2022.00019","DOIUrl":"https://doi.org/10.1109/DCABES57229.2022.00019","url":null,"abstract":"At present, the role of machine learning in data analysis is becoming increasingly important, and the digital economy has become the major economic form in the world, as well as the core driving force for China's economic development. Machine learning plays an increasingly significant role in economic research based on big data. To reduce the difficulty of using machine learning and improve the efficiency of machine learning, this paper systematically studies the application of automated machine learning (Au-toML) in economic research, focusing on the principles and characteristics of data-driven automated machine learning. Through the experimental comparison of specific automated machine learning methods on the classification of data sets, the optimal applicable method is found. Data-driven automated machine learning can be effectively applied in economic data mining, economic indicator analysis, and policy evaluation.","PeriodicalId":344365,"journal":{"name":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131551467","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
Multilayer Perceptron-based Surrogate Models for Finite Element Analysis 基于多层感知器的有限元分析代理模型
2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2022-10-01 DOI: 10.1109/DCABES57229.2022.00045
Lawson Oliveira Lima, Julien Rosenberger, E. Antier, F. Magoulès
{"title":"Multilayer Perceptron-based Surrogate Models for Finite Element Analysis","authors":"Lawson Oliveira Lima, Julien Rosenberger, E. Antier, F. Magoulès","doi":"10.1109/DCABES57229.2022.00045","DOIUrl":"https://doi.org/10.1109/DCABES57229.2022.00045","url":null,"abstract":"Many Partial Differential Equations (PDEs) do not have analytical solution, and can only be solved by numerical methods. In this context, Physics-Informed Neural Networks (PINN) have become important in the last decades, since it uses a neural network and physical conditions to approximate any functions. This paper focuses on hypertuning of a PINN, used to solve a PDE. The behavior of the approximated solution when we change the learning rate or the activation function (sigmoid, hyperbolic tangent, GELU, ReLU and ELU) is here analyzed. A comparative study is done to determine the best characteristics in the problem, as well as to find a learning rate that allows fast and satisfactory learning. GELU and hyperbolic tangent activation functions exhibit better performance than other activation functions. A suitable choice of the learning rate results in higher accuracy and faster convergence.","PeriodicalId":344365,"journal":{"name":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123501605","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
Fault Diagnosis of Power Grid Based on Convolutional Neural Network 基于卷积神经网络的电网故障诊断
2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2022-10-01 DOI: 10.1109/DCABES57229.2022.00033
Liping Qu, J Zhang, Tailu Gao
{"title":"Fault Diagnosis of Power Grid Based on Convolutional Neural Network","authors":"Liping Qu, J Zhang, Tailu Gao","doi":"10.1109/DCABES57229.2022.00033","DOIUrl":"https://doi.org/10.1109/DCABES57229.2022.00033","url":null,"abstract":"Due to the operation of regional networking, the scale of the power grid is becoming larger and larger, and a fault in the power grid needs to be located in the fault area timely and accurately. The models and structures of BP neural network and convolution neural network are analyzed. The training and test samples are constructed for a power grid model, and the BP neural network and convolution neural network are used for simulation verification respectively. The simulation results show that the convolutional neural network based grid fault diagnosis method has higher accuracy and fault tolerance.","PeriodicalId":344365,"journal":{"name":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125927284","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 Automatic Exhaust Control System of Central Heating System Based on S7-200 SMART PLC 基于S7-200 SMART PLC的集中供热自动排风控制系统设计
2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2022-10-01 DOI: 10.1109/DCABES57229.2022.00027
Q. Feng, Tian-le Sun, Zhang Zhuo, Gang Lv
{"title":"Design of Automatic Exhaust Control System of Central Heating System Based on S7-200 SMART PLC","authors":"Q. Feng, Tian-le Sun, Zhang Zhuo, Gang Lv","doi":"10.1109/DCABES57229.2022.00027","DOIUrl":"https://doi.org/10.1109/DCABES57229.2022.00027","url":null,"abstract":"In the central heating system, air accumulation in the heating pipe caused by hydraulic and thermal imbalance and other faults. In this paper, Siemens S7-200 smart PLC is adopted as the core to design the automatic exhaust device, including water, vacuum, drainage automatic control system. The air accumulated will be discharged timely and effective with touch screen interface, PLC control program and monitoring system combined to implement the control mode. Through the using automatic exhaust device and equipped with automatic control system to discharge dissolved gas in water, improving the stability of heat network equipment operation and achieving ideal heating effect.","PeriodicalId":344365,"journal":{"name":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117105312","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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