2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)最新文献_第8页

Accelerating Monte Carlo Transport in the Trade-off of Performance and Power 在性能和功率的权衡中加速蒙特卡罗传输

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456532

Siqing Fu, Tiejun Li, Jianmin Zhang

{"title":"Accelerating Monte Carlo Transport in the Trade-off of Performance and Power","authors":"Siqing Fu, Tiejun Li, Jianmin Zhang","doi":"10.1109/AIID51893.2021.9456532","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456532","url":null,"abstract":"Random simulation for particle transport theory is the main method for solving particle transport questions, which is widely used in medicine and computational physics. In this work, we present a multi-core reconfigurable architecture that aims to meet the performance per watt requirements of future Domain Specific Architectures (DSAs). The architecture proposed in this paper consists of heterogeneous lightweight cores, a reconfigurable cache structure, and High Bandwidth Memory. By targeting the different feature requirements of the Monte Carlo transport code at different stages, we design more necessary and efficient features for the lightweight calculating core, and continue to provide a trade-off of performance and energy consumption through reconfiguration. We designed and validated the accelerator architecture using gem5. Experiments show that compared with the traditional architecture composed of multiple out-of-order core, this architecture can obtain more than 3x in performance per watt. Some conclusions explored are not limited to the architecture proposed in this paper, but lay the foundation for further studies of large-scale transport accelerators.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132609232","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

Forecasting the impact of COVID-19 on GDP based on Adaboost 基于Adaboost的2019冠状病毒病对GDP影响预测

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456518

Ding Jiangying, Huaijin Shi, Yichen Zhang, Zhang Huiying

{"title":"Forecasting the impact of COVID-19 on GDP based on Adaboost","authors":"Ding Jiangying, Huaijin Shi, Yichen Zhang, Zhang Huiying","doi":"10.1109/AIID51893.2021.9456518","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456518","url":null,"abstract":"Covid-19 Pandemic has a unique impact on the economy as other infectious diseases. Epidemics affect people's daily consumption activities, for example, by causing them to shop less, travel less, consume less and invest less. The reduction of a large number of economic activities leads to the suppression of social demand and the reduction of consumption level, which further affects the GDP of various countries around the world. It is necessary to investigate and analyze the impact of the epidemic on GDP in order to control and analyze the economic situation under the impact of the epidemic. In this paper, we take the impact of COVID-19 on the GDP of each country as a regression problem, and propose to forecast GDP through feature engineering combined with Aaboost model. The model was tested on more than 50,000 data records from more than 200 countries provided by the Kaggle platform to prove the validity. The experiment shows that Adaboost has stronger robustness compared with other methods, such as random forest, SVR. Adaboost improves the MSE of random forest by 2.39 and SVR by 0.38.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127024861","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

Improved Model Compression Method Based on Information Entropy 基于信息熵的改进模型压缩方法

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456529

Chao Wu, Wen Dong, Duo-Xiu Hu, Chengziang Zhai

{"title":"Improved Model Compression Method Based on Information Entropy","authors":"Chao Wu, Wen Dong, Duo-Xiu Hu, Chengziang Zhai","doi":"10.1109/AIID51893.2021.9456529","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456529","url":null,"abstract":"The rapid development of deep learning has promoted more and more complex neural network models that require high computing power. Even though researchers have proposed various lightweight network models such as MobileNet, SqueezeNet and ShuffleNet, the amount of calculation is still huge. In order to further reduce the amount of model calculations, model compression is an effective means to reduce the amount of model parameters and calculations. Channel pruning is the most effective and direct means to accelerate model calculations and reduce model parameters. However, due to its radical approach, the effect of pruning is affected by the basis for determining the importance of the channel, and the accuracy cannot be guaranteed. Furthermore, pruning When the filter is smaller than the set threshold value is completely deleted, it is possible to discard important parameters. Therefore, this article intends to propose a channel pruning model compression method based on information entropy. The actual test results give convincing experimental results, which prove the effectiveness and practicability of the method.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123405441","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

Cloud service selection method based on overall similarity in cloud manufacturing environment 云制造环境下基于整体相似度的云服务选择方法

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456452

Wenli Liao, Le Wei

引用次数: 1

SAR Image Target Recognition Algorithm Based On Convolutional Neural Network 基于卷积神经网络的SAR图像目标识别算法

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456459

Bodi Feng, Hai-tao Yang, Changgong Zhang, Jingyu Wang, Gaoyuan Li, Yuge Gao

引用次数: 4

Local Matrix Factorization with Social Network Embedding 基于社会网络嵌入的局部矩阵分解

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456514

Jinmao Xu, Shuaiheng Peng, Daofu Gong, Fenlin Liu

引用次数: 0

Near-field BP 3D imaging paraflap suppression method 近场BP三维成像副翼抑制方法

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456502

Haohao Jiang, Qu Wei, Pengda Wang, Yaoyao Dong, Tianhao Gao, Shuo Zhang

{"title":"Near-field BP 3D imaging paraflap suppression method","authors":"Haohao Jiang, Qu Wei, Pengda Wang, Yaoyao Dong, Tianhao Gao, Shuo Zhang","doi":"10.1109/AIID51893.2021.9456502","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456502","url":null,"abstract":"With the increasing number of terrorist attacks, radar imaging systems are not only limited to imaging, but also expanded to the field of detection of some hidden dangerous targets. At present, most of the domestic security screening systems that have been put into commercial application are two-dimensional imaging mode. Compared to three-dimensional imaging, two-dimensional imaging is a lot of limitations, such as spatial blur and shadow effect. Therefore, in order to solve the limitations brought by two-dimensional imaging, near-field radar imaging technology gradually to three-dimensional imaging development, the technology can be applied to a variety of scenarios such as security checks, through-wall detection. In this paper, on the basis of BP algorithm, the formula of 3D BP imaging algorithm is derived, and the effect of different window functions on the paraflap is analyzed. Through simulation experiments, near-field BP 3D imaging is realized to verify the performance of this 3D imaging algorithm, and the effects of different window functions on the paraflaps and resolutions in three dimensions are compared and analyzed.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123712824","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

Identity alignment algorithm across social networks based on attention mechanism 基于注意力机制的社交网络身份对齐算法

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456551

Xinlan Wang, Xiaodong Cai, Qingsong Zhou, Tao Hong

{"title":"Identity alignment algorithm across social networks based on attention mechanism","authors":"Xinlan Wang, Xiaodong Cai, Qingsong Zhou, Tao Hong","doi":"10.1109/AIID51893.2021.9456551","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456551","url":null,"abstract":"In recent years, more and more business scenarios need accurate cross social network user identity alignment. Existing methods directly splice user attributes and network structure features in the case of sparse network structure, which affects the accuracy of alignment. At the same time, the semantic expression deviation of the same user attribute in different networks further increases the challenge of user identity alignment. This paper proposes a cross social network user identity alignment algorithm based on attention mechanism. Firstly, to learn distinguishing semantic features, a feature extraction method of user attribute text based on attention mechanism is designed. It uses Highway network to dynamically balance the word and character embedding of attribute text, extracts different granular fusion semantic information through convolution neural network with three convolution kernels and uses attention mechanism to give higher weight to key semantics to learn distinguishing semantic features. Secondly, to strengthen the attribute information and weaken the influence of network structure sparsity, a new fusion loss method is proposed. It uses Cosine and cross-entropy loss for attribute feature and fusion feature of attribute and network structure respectively and carries out weighted fusion calculation. The experimental results show that the accuracy of this method can reach 94.95% and the F1 score can reach 92.52% on the Aminer-LinkedIn social network matched data set, which is better than other algorithms.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130208355","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

Prediction of GDP in Time Series Data Based on Neural Network Model 基于神经网络模型的时间序列GDP预测

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456509

Jingui Wu, Y. He

引用次数: 4

CRU-Net: A Deep Learning Network for Semantic Segmentation of Pathological Tissue Slices 一种用于病理组织切片语义分割的深度学习网络

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456469

Yang Li

引用次数: 1