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multi-source data integration and multi-scale modeling framework for progressive prediction of complex geological interfaces in tunneling

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Underground Space 2024年第2期15卷 1-25页

作者： Jingxiao Wang Peinan Li Xiaoying Zhuang Xiaojun Li Xi Jiang Jun Wu Department of Geotechnical Engineering College of Civil EngineeringTongji UniversityShanghai 200092China College of Environmental Science and Engineering Donghua UniversityShanghai 201620China Institute of Photonics Department of Mathematics and PhysicsLeibniz University HannoverHannover 30167Germany Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong Kong 100872China College of Civil Engineering Shanghai Normal UniversityShanghai 200233China

A reliable geological model plays a fundamental role in the efficiency and safety of mountain tunnel ***,regional models based on limited survey data represent macroscopic geological environments but not detailed internal geological characteristics,especially at tunnel portals with complex geological *** paper presents a comprehensive methodological framework for refined modeling of the tunnel surrounding rock and subsequent mechanics analysis,with a particular focus on natural space distortion of hard-soft rock interfaces at tunnel *** progressive prediction of geological structures is developed considering multi-source data derived from the tunnel survey and excavation *** improve the accuracy of the models,a novel modeling method is proposed to integrate multi-source and multi-scale data based on data extraction and potential field ***,a regional-scale model and an engineering-scale model are built,providing a clear insight into geological phenomena and supporting numerical *** addition,the proposed framework is applied to a case study,the Long-tou mountain tunnel project in Guangzhou,China,where the dominant rock type is *** results show that the data integration and modeling methods effectively improve model structure *** improved model’s calculation deviation is reduced by about 10%to 20%in the mechanical *** study contributes to revealing the complex geological environment with singular interfaces and promoting the safety and performance of mountain tunneling.

关键词： Mountain tunnel Geological modeling multi-source data Progressive prediction Tunnel portals

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multi-source data driven harmonic spectrum estimation of substation feeder current

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ENERGY REPORTS 2024年 11卷 3492-3500页

作者： Chen, Xiangwei Yang, Chaoyun Zhang, Yi Zhu, Longyang Liu, Bijie Zhang, Liangyu Lin, Nan State Grid Fuzhou Elect Power Supply Co Fuzhou 350000 Fujian Peoples R China Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Fujian Peoples R China

With the proliferation of non-linear loads and distributed power resources that connect through power electronic equipment, decentralized harmonic pollution in three-phase symmetric power system becomes increasingly serious. Despite this, monitoring the current harmonic of all substation feeders regardless of cost is unrealistic. To this end, a multi-source data driven method is proposed in this paper, utilizing data from the power quality monitoring system, dispatching system, and market business system, to estimate the harmonic spectrum of substation feeder currents and estimate the harmonic current components of each feeder with monitoring data of bus voltage harmonics and line harmonic currents. The objective function of the harmonic spectrum estimation model is constructed based on the analysis of current harmonic phasor and power relationship on the feeders. An improved particle swarm optimization (PSO) algorithm is proposed to solve the proposed estimation model thereby obtaining the current harmonic content of each feeder and the phase angle difference between the current harmonics of the feeders. The performance of the proposed model and algorithm is validated by numerical simulation with a test system and an actual system.

关键词： Harmonic spectrum estimation Harmonic monitoring Substation feeder Power quality multi-source data

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multi-source data fusion-driven urban building energy modeling

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SUSTAINABLE CITIES AND SOCIETY 2025年 123卷

作者： Choi, Sebin Yi, Dong Hyuk Kim, Deuk-Woo Yoon, Sungmin Sungkyunkwan Univ Dept Global Smart City BIST Lab Suwon 16419 South Korea Korea Testing Lab Syst & Energy Div Hwaseong Gyeonggi South Korea Korea Inst Civil Engn & Bldg Technol Dept Bldg Energy Res Goyang Gyeonggi South Korea Sungkyunkwan Univ Sch Civil Architectural Eng & Landscape Architectu Suwon 16419 South Korea

The energy efficiency of buildings is important for creating sustainable cities. In this context, urban building energy modeling (UBEM) is essential for comprehending and predicting building energy consumption and the patterns from a city-wide perspective. This study proposes a multi-source data-fusion-driven UBEM and its realworld applications in Seoul, South Korea. Unlike existing models that utilize energy data as input variables, models based solely on building features play a crucial role in various projects in which energy consumption data is unavailable, such as new building projects, retrofitting projects, and new city developments. In this modeling scenario, data fusion using multi-source data is crucial for developing highly accurate and robust models because of the limited availability of input features. In this study, the utilization of multi-source open data, along with simulated data such as shading factor, enables the fusion of these datasets to derive building features. These features are subsequently utilized to train the prediction model, leading to accurate and robust modeling outcomes. Thus, five case studies were conducted for 47,391 buildings, located in Seoul, South Korea, to demonstrate how and how much data fusion-driven features enhance the performance of UBEM. Discussions in each case provide insights into feature selection and effective utilization tailored to each modeling context. Finally, the developed model was tested in two additional regions, and it was observed that adding features to the model training improved the model performance in both regions. These findings emphasize the importance of data fusion for robust UBEM in the context of sustainable cities.

关键词： Sustainable cities data fusion multi-source data Open data Urban building energy prediction Feature engineering

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multi-source data fusion for intelligent diagnosis based on generalized representation

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 267卷

作者： Peng, Weimin Chen, Aihong Chen, Jing Xu, Haitao Hangzhou Dianzi Univ Sch Comp Sci & Technol Key Lab Discrete Ind Internet Things Zhejiang Prov Hangzhou Peoples R China Hangzhou Dianzi Univ Sch Mech Engn Hangzhou Peoples R China IOT Technol Applicat Transportat Ind R&D Ctr Hangz Hangzhou Peoples R China

Motivated by addressing the problem of knowledge lack in a fusion process and overcoming the defect of insufficient data coverage, this paper proposes a new concept of generalized representation for multi-source medical data fusion to get more generalized fusion results from limited source data and precise medical diagnosis conclusions based on fusion results. The generalized representations of different feature types of data nodes are uniformly defined by their value and knowledge attributes which are reflected by their normalized representations and decision-making effect indexes. Based on the generalized representation of multi-source entity nodes, the classical and quantum inspired collaborative fusion methods based on linking topology are proposed, in which different entity subsets are formed consistently for autonomous fusion according to the horizontal correlative and vertical collaborative relationships between entity nodes. The proposed generalized representation based fusion method can produce reliable fusion results in experimental practice. Based on the reliable fusion results, the corresponding intelligent pneumonia diagnosis reaches a high accuracy of 96.02%.

关键词： Generalized representation multi-source data Collaborative fusion Intelligent diagnosis Decision-making effect

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multi-source data driven decision-support model for product ranking with consumer psychology behavior

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INFORMATION FUSION 2025年 118卷

作者： Wu, Peng Xie, Yutong Zhou, Ligang Deveci, Muhammet Martinez, Luis Anhui Univ Sch Business Hefei 230601 Peoples R China Anhui Univ Sch Math Sci Hefei 230601 Peoples R China Natl Def Univ Turkish Naval Acad Dept Ind Engn TR-34942 Tuzla Istanbul Turkiye Vilnius Gediminas Tech Univ Fac Fundamental Sci Dept Informat Syst LT-10223 Vilnius Lithuania Univ Jaen Dept Comp Sci Jaen 23071 Spain

In the context of information overload, leveraging online reviews for product ranking can enhance consumers' decision-making efficiency and facilitate informed purchasing decisions. However, previous studies have mainly focused on online review data from a single source and do not capture consumer psychology behavior for scientific and reasonable product ranking. Therefore, this study proposes a multi-source data driven decision support model for product ranking that considers consumer psychology behavior. In this model, after crawling and preprocessing multi-source data, text mining (including Latent Dirichlet Allocation (LDA) and sentiment analysis) is adopted to extract implicit evaluation criteria for the product and identify three sentiment polarities (e.g., positive, neutral, and negative), based on the sentiment analysis results they are represented as q-rung orthopair fuzzy numbers (q-ROFNs). Then, considering the interaction relationship between data, a q-rung orthopair fuzzy weighted geometric interaction averaging (q-ROFWGIA) aggregating function is defined to fuse multi-source data. In addition, explicit and implicit evaluation criteria of products are integrated to assess products, and sigmoid function is used to handle heterogeneous data. Furthermore, a regret theory-exponential TODIM (RT-ExpTODIM) ranking method is proposed. It can capture the loss avoidance and regret avoidance psychology of consumers during the product ranking process. Finally, a case study on ranking mobile phone through multi-source online reviews is provided to verify the validity of the proposed model.

关键词： Product ranking Online reviews multi-source data Q-rung orthopair fuzzy sets Consumer psychology

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multi-source data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

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Computer Modeling in Engineering & Sciences 2023年第7期136卷 861-881页

作者： Ze Xu Sanxing Cao State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing100024China

multi-source data plays an important role in the evolution of media *** fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media ***,it also faces serious problems in terms of protecting user and data *** privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption *** this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing *** deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential *** experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy *** significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.

关键词： Homomorphic encryption blockchain technology multi-source data data privacy protection privacy data processing

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multi-source data-Driven Local-Global Dynamic multi-Graph Convolutional Network for Bike-Sharing Demands Prediction

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ALGORITHMS 2024年第9期17卷 384-384页

作者： Chen, Juan Huang, Rui Shanghai Univ SHU UTS SILC Business Sch Shanghai 201899 Peoples R China Shanghai Univ Smart City Res Inst Shanghai 201899 Peoples R China

The prediction of bike-sharing demand plays a pivotal role in the optimization of intelligent transportation systems, particularly amidst the COVID-19 pandemic, which has significantly altered travel behaviors and demand dynamics. In this study, we examine various spatiotemporal influencing factors associated with bike-sharing and propose the Local-Global Dynamic multi-Graph Convolutional Network (LGDMGCN) model, driven by multi-source data, for multi-step prediction of station-level bike-sharing demand. In the temporal dimension, we dynamically model temporal dependencies by incorporating multiple sources of time semantic features such as confirmed COVID-19 cases, weather conditions, and holidays. Additionally, we integrate a time attention mechanism to better capture variations over time. In the spatial dimension, we consider factors related to the addition or removal of stations and utilize spatial semantic features, such as urban points of interest and station locations, to construct dynamic multi-graphs. The model utilizes a local-global structure to capture spatial dependencies among individual bike-sharing stations and all stations collectively. Experimental results, obtained through comparisons with baseline models on the same dataset and conducting ablation studies, demonstrate the feasibility and effectiveness of the proposed model in predicting bike-sharing demand.

关键词： bike-sharing demand prediction graph convolutional network multi-source data

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multi-source data Preprocessing Method Research Based on Python 5

Multi-Source Data Preprocessing Method Research Based on Pyt...

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5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS)

作者： Liu, Miao Ma, Hongli Zhang, Yongzhen Yue, Feng MNR Shaanx Basic Geog Informat Ctr Xian Peoples R China Shaanxi Surveying & Mapping Instrument Measuremen Xian Peoples R China

ISBN: (纸本)9798350365719;9798350365726

Surveying and mapping project operation, data analysis is a key link, when faced with complex data storage, different specifications and organization forms of multi-source data, the conventional method of data processing steps, time consuming, inefficient and error-prone. At this point, how to efficiently process these multi-source data, improve production efficiency and quality of results is the focus of research. Based on the actual production projects, from the Angle of the operation, using the Python programming technology to procedural data preprocessing steps. The results show that the man-machine cooperative method based on program processing and manual inspection is faster and more efficient in multi-source data preprocessing, and can be operated by many people at the same time, which greatly shortens the duration of data preprocessing.

关键词： multi-source data preprocessing Python

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A Deep Neural Collaborative Filtering Based Service Recommendation Method with multi-source data for Smart Cloud-Edge Collaboration Applications

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Tsinghua Science and Technology 2024年第3期29卷 897-910页

作者： Wenmin Lin Min Zhu Xinyi Zhou Ruowei Zhang Xiaoran Zhao Shigen Shen Lu Sun Alibaba Business School Hangzhou Normal UniversityHangzhou 311121China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and TechnologyShouguang 262700China School of Computer Science Qufu Normal UniversityRizhao 276827China School of Information Engineering Huzhou UniversityHuzhou 313000China

Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user ***,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation *** view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation *** specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service *** multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation ***,we evaluate the effectiveness of the NCF-MS method on three public *** experimental results show that our proposed method achieves better performance than existing methods.

关键词： deep neural collaborative filtering multi-source data cloud-edge collaboration application stackeddenoising auto encoder multiple layer perceptron

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A Study on Residential Community-Level Housing Vacancy Rate Based on multi-source data: A Case Study of Longquanyi District in Chengdu City

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APPLIED SCIENCES-BASEL 2025年第6期15卷 3357-3357页

作者： Zou, Yuchi Zhu, Junjie Chen, Defen Liang, Dan Wei, Wen Cheng, Wuxue Sichuan Normal Univ Fac Geog Resources Sci Chengdu 610101 Peoples R China Sichuan Normal Univ Key Lab Land Resources Evaluat & Monitoring Southw Chengdu 610066 Peoples R China

As a pillar industry of China's economy, the real estate sector has been challenged by the increasing prevalence of housing vacancies, which negatively impacts market stability. Traditional vacancy rate estimation methods, relying on labor-intensive surveys and lacking official statistical support, are limited in accuracy and scalability. To address these challenges, this study proposes a novel framework for assessing residential community-level housing vacancy rates through the integration of multi-source data. Its core is based on night-time lighting data, supplemented by other multi-source big data, for housing vacancy rate (HVR) estimation and practical validation. In the case study of Longquanyi District in Chengdu City, the main conclusions are as follows: (1) with low data resolution, the model estimates a root mean square error (RMSE) of 0.14, which is highly accurate;(2) the average housing vacancy rate (HVR) of houses in Longquanyi District's residential community is 46%;(3) the HVR rises progressively with the increase in the distance from the city center;(4) the correlation between the HVR of Longquanyi District and the house prices of the area is not obvious;(5) the correlation between the HVR of Longquanyi District and the time of completion of the communities in the region is not obvious, but the newly built communities have extremely high HVR. Compared to the existing literature, this study innovatively leverages multi-source big data to provide a scalable and accurate solution for HVR estimation. The framework enhances understanding of urban real estate dynamics and supports sustainable city development.

关键词： residential vacancy rate multi-source data night light data real estate

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