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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Liu, Anjun Liao, Yin Xu, Xihao Chen, YanYan Zhong, Hongjun Wu, Jianming Kong, Bo Raikov, Alexander Guo, Meng Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Industrial Software Jinan China Zhejiang Yuansuan Technology Co. Ltd Jinan Supercomputing-Zhejiang Yuansuan Industrial Simulation System Joint Laboratory Hangzhou China Qilu University of Technology Shandong Academy of Sciences Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Key Laboratory of Computing Power Network and Information Security China Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Shandong Jinan China

ISBN: (纸本)9798350371024

code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1-2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry. © 2023 IEEE.

关键词： Supercomputers

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MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compression Framework

MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compressio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jiang, ZhaoYi Han, Dong Song, Cao Nan, FangZhe Yang, Bailin School of Computer Science and Technolgy Zhejiang Gongshang University China School of Statistics and Mathematics Zhejiang Gongshang University China Economic Forecasting and Policy Simulation Laboratory Zhejiang Gongshang University China Collaborative Innovation Center of Statistical Data Engineering Technology & Application Zhejiang Gongshang University China

ISBN: (纸本)9798350368741

The increasing data volume and the demand for real-time transmission highlight the necessity for efficient compression of dynamic point cloud data. Existing methods primarily focus on reducing inter-frame redundancy by calculating per-point motion information, overlooking the computational and storage costs involved. In this paper, we propose a novel motion proxy-based dynamic point cloud compression framework to enhance the efficiency and accuracy of motion information utilization. Specifically, we introduce a feature proxy module to adaptively locate proxy points, which represent the overall motion through the motion of proxy points. Additionally, a motion enhancement module is employed to refine motion details and prevent local information loss caused by dense motion trajectories. Extensive experiments demonstrate the superiority of our approach. Compared with baseline methods, it achieves an average BD-rate improvement of 12.07% (D1) and 11.90% (D2). © 2025 IEEE.

关键词： compression deep learning neural network point cloud

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The Fusion Model of Catalytic Combustion and Thermal Conductivity

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computers, Materials & Continua 2023年第1期74卷 1509-1521页

作者： Bin Lin Zhengyu Li Dong Wen Jianchao Liu Shan Yang Yong Zhou Chao Lu Qian Qiu Sichuan Normal University Chengdu610066China School of Computer Science Southwest Petroleum UniversityChengdu610500China Jackson State University Jackson39217MSUSA Key Laboratory on Aero-Engine Altitude Simulation Technology Sichuan Gas Turbine EstablishmentAECCMianyang621000China School of Power and Energy Northwestern Polytechnical UniversityXi’an710072China

The further development of catalytic elements has been plagued by activation and binary *** automatic shift model that has emerged in recent years helps components achieve full ***,the detection data still remains unstable in the shift area(7%∼13%).This paper proposes a Catalytic Combustion and Thermal Conductivity(CCTC)model for the specified range,which can be explained fromtwo aspects based on the existing *** the one hand,it uses iterative location search to process heterogeneous data,judges the prediction position of data points,and then givesweight *** the other hand,it corrects the abnormal points,determines the abnormal points in the horizontal direction,and gives the replacement value through the data of adjacent *** experimental results show that the CCTC model reduces the sum of variance from 17 of the automatic shift model to 13,and the comparison of experimental variance is reduced by 23%.In the full-scale real-time data,the experimental variance of CCTC model and automatic shift model is reduced by 18%.In conclusion,CCTC is a cross section stability framework for full-scale methane measurement,in which the specified heterogeneous combination and anomaly point correction methods improve the stability.

关键词： Catalytic combustion thermal conductivity activation problem binary problem data fusion

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Ontology-Based BERT Model for Automated Information Extraction from Geological Hazard Reports

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Journal of Earth Science 2023年第5期34卷 1390-1405页

作者： Kai Ma Miao Tian Yongjian Tan Qinjun Qiu Zhong Xie Rong Huang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Hubei Engineering Technology Research Center for Farmland Environment Monitoring China Three Gorges UniversityYichang 443002China School of Computer Science China University of GeosciencesWuhan 430074China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing 100045China College of Economics and Management China Three Gorges UniversityYichang 443002China

Geological knowledge can provide support for knowledge discovery, knowledge inference and mineralization predictions of geological big data. Entity identification and relationship extraction from geological data description text are the key links for constructing knowledge graphs. Given the lack of publicly annotated datasets in the geology domain, this paper illustrates the construction process of geological entity datasets, defines the types of entities and interconceptual relationships by using the geological entity concept system, and completes the construction of the geological corpus. To address the shortcomings of existing language models(such as Word2vec and Glove) that cannot solve polysemous words and have a poor ability to fuse contexts, we propose a geological named entity recognition and relationship extraction model jointly with Bidirectional Encoder Representation from Transformers(BERT) pretrained language model. To effectively represent the text features, we construct a BERT-bidirectional gated recurrent unit network(BiGRU)-conditional random field(CRF)-based architecture to extract the named entities and the BERT-BiGRU-Attention-based architecture to extract the entity relations. The results show that the F1-score of the BERT-BiGRU-CRF named entity recognition model is 0.91 and the F1-score of the BERT-BiGRU-Attention relationship extraction model is 0.84, which are significant performance improvements when compared to classic language models(e.g., word2vec and Embedding from Language Models(ELMo)).

关键词： ontology BERT model name entity recognition relation extraction knowledge graph

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Stable Periodic Frequent Itemset Mining on Uncertain Datasets 4

Stable Periodic Frequent Itemset Mining on Uncertain Dataset...

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4th IEEE International Conference on computer and Communication Engineering technology, CCET 2021

作者： He, Ruimeng Chen, Jinchao Du, Chenglie Duan, Yuxin Northwestern Polytechnical University Department of Computer Science Xi'an China Science and Technology on System Simulation Laboratory Beijing Simulation Center Beijing China

ISBN: (纸本)9781665438902

In recent years, data mining has attracted great attention from the information industry. The main reason is that there is a large amount of data, which can be widely used, and there is an urgent need to convert these data into useful information and knowledge. Frequent itemset mining as an important research basis in data mining research has also been greatly developed. However, most of these frequent itemset mining algorithms only consider the frequency of itemsets and ignore the periodicity of itemsets, which makes the results incomplete. In order to solve the above problem, this paper presents a a stable periodic frequent itemset mining (SPFIM) algorithm on uncertain dataset, by considering both the frequency and periodicity of itemsets. The experimental evaluation on real datasets shows that the SPFIM algorithm is efficient and can find patterns that are not found by traditional algorithms. © 2021 IEEE.

关键词： Data mining

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Neural Machine Translation by Fusing Key Information of Text

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computers, Materials & Continua 2023年第2期74卷 2803-2815页

作者： Shijie Hu Xiaoyu Li Jiayu Bai Hang Lei Weizhong Qian Sunqiang Hu Cong Zhang Akpatsa Samuel Kofi Qian Qiu Yong Zhou Shan Yang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Science and Technology on Altitude Simulation Laboratory Sichuan Gas Turbine Establishment Aero Engine Corporation of ChinaMianyang621000China School of Power and Energy Northwestern Polytechnical UniversityXi’an710072China School of Computer Science Southwest Petroleum UniversityChengdu610500China Department of Chemistry Physics and Atmospheric SciencesJackson State UniversityJacksonMS 39217USA

When the Transformer proposed by Google in 2017,it was first used for machine translation tasks and achieved the state of the art at that *** the current neural machine translation model can generate high quality translation results,there are still mistranslations and omissions in the translation of key information of long *** the other hand,the most important part in traditional translation tasks is the translation of key *** the translation results,as long as the key information is translated accurately and completely,even if other parts of the results are translated incorrect,the final translation results’quality can still be *** order to solve the problem of mistranslation and missed translation effectively,and improve the accuracy and completeness of long sentence translation in machine translation,this paper proposes a key information fused neural machine translation model based on *** model proposed in this paper extracts the keywords of the source language text separately as the input of the *** the same encoding as the source language text,it is fused with the output of the source language text encoded by the encoder,then the key information is processed and input into the *** incorporating keyword information from the source language sentence,the model’s performance in the task of translating long sentences is very *** order to verify the effectiveness of the method of fusion of key information proposed in this paper,a series of experiments were carried out on the verification *** experimental results show that the Bilingual Evaluation Understudy(BLEU)score of the model proposed in this paper on theWorkshop on Machine Translation(WMT)2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test *** experimental results show the advantages of the model proposed in this paper.

关键词： Key information transformer fusion neural machine translation

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Research and Implementation of Model Engineering Environment Integration Based on OpenMBEE 21st

Research and Implementation of Model Engineering Environment...

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21st Asia simulation Conference, AsiaSim 2022

作者： Xue, Junjie Zhou, Junhua Shi, Guoqiang Feng, Chaoqun Xu, Lin Liu, Penghua Quan, Hongyan Engineering Research Center Beijing Simulation Center Beijing100854 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing100854 China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing100854 China Yunnan Chensheng Tendering Consulting Co. Ltd. Kuming650102 China School of Computer Science and Technology East China Normal University Shanghai200062 China

ISBN: (纸本)9789811991974

Aiming at the problem of integrating open tools in a user development environment in complex product design, an integration strategy of model engineering environment based on OpenMBEE is studied. It is based on the technologies of user identity unified authentication and user interaction data collaboration. Specifically, in the solution of unified identity authentication on cross-platform, the extended controller method is provided based on the message queue information flow mechanism. Then, the separation of business logic, data information, and interactive view of user unified authentication are realized, on the basis, that the function of unified identity authentication is realized by using the Software Development Kit (SDK) of OpenMBEE. Besides, in the study of user interaction data collaboration technology, three-layer collaboration mechanisms are proposed, they are the presentation layer collaboration, the business layer collaboration, and the persistence layer collaboration. When the jQuery programming framework is integrated into the bootstrap front-end framework, combing with the OpenMBEE back-end server interface, the collaboration of user interaction data is realized. Through experiments and project practice analysis, the user unified authentication strategy and user interactive collaboration strategy are tested respectively for proving the effectiveness and feasibility of the proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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BCTM: A Topic Modeling Method Based on External Information 13th

BCTM: A Topic Modeling Method Based on External Information

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Proceedings of the 13th EAI International Conference on Broadband Communications, Networks, and Systems, BROADNETS 2022

作者： Liu, Gang Wan, Taiying Yu, Jinfeng Zhan, Kai Wang, Wei College of Computer Science and Technology Harbin Engineering University Harbin150001 China PwC Enterprise Digital PricewaterhouseCoopers SydneyNSW2070 Australia National Engineering Laboratory of E-Government Modeling Simulation Harbin Engineering University Harbin150001 China

ISBN: (纸本)9783031404665

Topic models are often used as intermediate algorithms for text mining and semantic analysis in natural language processing, and have a wide range of functions. However, most of the existing improvements to the topic model use word embedding to improve the accuracy of text modeling, but ignore the external information in the text. This paper proposes a topic model BCTM (Bi-Concept Topic Model) using the word feature information and concept information. Based on the BTM topic model, BCTM introduces word feature information through word vector technology and concept information based on ConceptNet to optimize topic modeling. The construction method of Bi-Concept pair is proposed. Based on ConceptNet semantic network, and the content of text is enriched with concept information. A more accurate topic distribution is obtained through the improved topic model, at the same time, due to the rich feature information, the model is also superior to the baseline model in short text modeling. The experiments prove that the bilingual topic model proposed in this paper has a good performance in modeling accuracy. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Embeddings

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A study on the prediction model of dam seepage volume based on isolated forest-multiple stepwise linear regression 4

A study on the prediction model of dam seepage volume based ...

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4th International Conference on computer Engineering and Application, ICCEA 2023

作者： Chen, Guanyu Zhang, Suli Zhang, Chenlin Qian, XiaoChun Changchun University of Technology School of Computer Science & Engineering Jilin Provincial Key Laboratory of Changbai Historical Culture and Vr Reconstruction Technology Jilin Prov. Changbai Mt. Ecol. Culture and Virtual Reality Simulation Engineering Research Center Changchun China Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin Provincial Key Laboratory of Changbai Historical Culture and Vr Reconstruction Technology Jilin Prov. Changbai Mt. Ecol. Culture and Virtual Reality Simulation Engineering Research Center Changchun China

ISBN: (纸本)9798350347548

In the study of hydraulic seepage prediction, it is often necessary to target multiple feature dimensions. In this paper, a variety of traditional machine learning algorithms based on stepwise regression filtering features are used to train a dam seepage prediction model based on isolated forest processing of data outliers. The results show that the algorithm can be applied to predict the seepage data of a reservoir more accurately, and further suggest improvements to the dam's seepage control function and observation facilities. The experimental results show that the correlation coefficient of the linear regression model is significantly improved when the training sample ratio is 0.8 after the isolated forest treatment of outliers and stepwise regression screening features. © 2023 IEEE.

关键词： Forecasting

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Engineered common environmental effects on multitransistor systems

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Physical Review B 2023年第7期107卷 075440-075440页

作者： Uthpala N. Ekanayake Sarath D. Gunapala Malin Premaratne Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA

In this paper, we analyze the impact of physically large baths used in terminals of thermal multitransistor systems formed using two-level systems (TLSs). In particular, we simulate the effects of a two-transistor system introduced as a thermal Darlington pair (DP). The size and proximity of the baths can cause multiple interactions with the transistor terminals represented by the TLS, not just the TLS directly connected to them. Such interactions can ultimately suppress the heat flows or impair the transistor action. However, we demonstrate that the DP model can achieve more than a 50% increase in heat flows. Using the engineered interactions leading to the correlated TLS-thermal bath interactions, we establish an incoherent (no quantum coherence in the density matrix at the steady state) yet correlated (joint excitation of two TLSs due to bath interaction) heat transfer model to a two-transistor arrangement in a substrate. This model helps us to interpret the environmental effects on the device by treating the common environment as separate thermal baths and all the transitions in the system as independent. We also show that this model can be mapped to contain dark-states. These dark-states can provide an external channel for transistor switching. We use this knowledge to design thermal counterparts of electronic AND and OR gates, and to study their switching time and operation, paving the way to realizing thermal logic gates.

关键词： Open quantum systems Quantum gates Thermal properties Nanotechnology Transistors

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