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3rd International Conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Hu, Xinyu Han, Wei Cao, Xinnan Zhou, Jinfei Jiang, Wangjie State Grid Nantong Power Supply Company Nantong Jiangsu226000 China

ISBN: (纸本)9798331519032

In the process of distribution network planning (DNP), massive heterogeneous data, such as power system operation data, enterprise operation management data, nonelectric energy data, etc., bring challenges to data processing because of their high dimension, large volume and strong coupling. Aiming at these problems, this paper proposes a DNP method combining multi-source heterogeneous data (MHD) processing technology and optimization algorithm. This method fuses multi-source data by Kalman filter (KF) to improve data accuracy, and extracts key features by principal component analysis (PCA) to provide a simplified data set for subsequent optimization. In the aspect of optimization algorithm, the multi-objective optimization algorithm NSGA-II is used to solve the DNP problem, taking into account the objectives of economy and reliability, and satisfying the physical constraints such as power flow balance, voltage limitation and line capacity. The experimental results show that this method is superior to the traditional planning method in terms of total investment cost, operation cost, average power outage time and power supply reliability, which effectively verifies its effectiveness and superiority. The research in this paper provides new ideas and methods for intelligent DNP, which is helpful to improve the safety, stability and economic operation level of power system. © 2024 IEEE.

关键词： distribution network planning system Kalman filtering multi-source heterogeneous data optimization algorithm principal component analysis

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multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

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Global Energy Interconnection 2024年第1期7卷 94-105页

作者： Pengtian Guo Kai Xiao Xiaohui Wang Daoxing Li China Electric Power Research Institute Co.Ltd. Beijing 100192P.R.China

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic *** the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application ***,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security *** address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal *** further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

关键词： Power Internet of Things Object model High concurrency access Zero trust mechanism multi-source heterogeneous data

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An intelligent prediction method of surface residual stresses based on multi-source heterogeneous data

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JOURNAL OF INTELLIGENT MANUFACTURING 2025年第1期36卷 441-457页

作者： Wang, Zehua Wang, Sibao Wang, Shilong Zhao, Zengya Tian, Zhifeng Chongqing Univ State Key Lab Mech Transmiss 174 Shazheng St Chongqing 400044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China China Acad Engn Phys Inst Machinery Mfg Technol Mianyang 621900 Peoples R China Acad Mil Sci Beijing 100091 Peoples R China

Surface residual stresses (Rs) have a significant impact on the performance of machined parts, including fatigue life and corrosion resistance. To enable online monitoring of Rs, many studies have focused on obtaining real-time Rs. However, direct measurement methods, including destructive and non-destructive techniques, will consume too much time or even damage the machined surface. Meanwhile, prediction methods rarely consider dynamic factors as identifying key features from dynamic data is challenging for humans. Therefore, this paper proposes an intelligent prediction method of Rs based on multi-source heterogeneous data, which contain cutting force, cutting temperature, power consumption, and cutting noise. Firstly, an Improved Convolutional Neural Network is established to identify features from the dynamic heterogeneous data. The mean training identification accuracy reaches 99.6%, which is significantly better than that (71%) obtained by the original convolutional neural network. Then, the Principal Component Analysis is built to automatically determine the key features, which benefit the subsequent Rs prediction. Finally, based on the key features, the Gaussian Process Regression is proposed to predict Rs in two directions. From the various experiments, the performance of the intelligent prediction method is validated, and the prediction accuracy rates for two directions reach 99.10% and 99.13%, respectively. Based on the proposed method, the real-time Rs can be predicted with the key features, which are automatically extracted from the multi-source heterogeneous data. This provides the basis for surface quality monitoring based on online data and greatly improves the level of intelligent manufacturing.

关键词： Surface residual stresses multi-source heterogeneous data Improved convolutional neural network (ICNN) Principal component analysis (PCA) Gaussian process regression (GPR)

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Research on Smart City Governance and Network Management Based on multi-source heterogeneous data Fusion Processing and Analysis

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2025年第3期32卷 1162-1171页

作者： Li, Hang Xiao, Xiao Big Data Dev Ctr Hainan Prov Haikou 570204 Peoples R China Wuhan Univ Sch Philosophy Wuhan 430072 Peoples R China

The digital transformation of smart city governance is an inevitable requirement for improving the urban governance system and enhancing the modernization of governance capacity in the context of the development of modern information technology. Firstly, the semantic heterogeneity and syntactic heterogeneity of multi-source heterogeneous data are solved through the unified ontology description of multi-sensor data, and the update algorithm of sensor ontology instance, attribute fusion and feature extraction algorithm are proposed. Secondly, the ontology mapping method is adopted. Aiming at the uncertainty characteristic of multi-source heterogeneous data fusion in smart city, a decision level fusion method is proposed. A transferable data model is used to fuse multiple identification frame information to generate smart city single network management situation assessment results. Finally, the operation of urban "one network management" requires the empowerment of data and technology, from the empirical judgment type to the data analysis type, from the passive disposition type to the active discovery type, to strengthen the coordination and cooperation between departments, from decentralized governance to the overall governance.

关键词： fusion processing multi-source heterogeneous data one network management smart city transitive data model

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Automatic generation of system model diagrams driven by multi-source heterogeneous data

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JOURNAL OF ENGINEERING DESIGN 2024年第11期35卷 1442-1486页

作者： Zhang, Qiang Liu, Jihong Li, Lin Chen, Xu Wang, Ruiwen Beihang Univ Sch Mech Engn & Automat Beijing Peoples R China

The demand for functionality, performance, safety, and reliability in complex product domains is continuously increasing, leading to growing product complexity. Traditional document-based systems engineering approaches face numerous challenges in terms of communication efficiency, traceability, and maintenance when dealing with complex product system design. Consequently, Model-Based Systems Engineering (MBSE) has been widely adopted. To improve the efficiency of system modelling, an effective method is needed to automate the process by reusing existing design resources. This study proposes a knowledge graph-based approach for the automatic generation of system model diagrams, consisting of three main ***, a system model graph is constructed using an existing system model repository. Then, the required modelling elements are extracted from design documents, and reusable model elements are obtained based on the system model graph. Lastly, a mapping relationship is established between system model elements extracted from multi-source heterogeneous data and SysML metamodel, enabling the automatic generation of system model diagrams based on SysML views. This approach effectively leverages the information from the existing system model repository and achieves automated mapping conversion from multi-source heterogeneous data to the SysML system model. A communication satellite case study is presented to demonstrate the capability of this method.

关键词： Model-based systems engineering knowledge graph system model reuse automatically generating system model diagrams multi-source heterogeneous data

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Evidential reasoning and lightweight multi-source heterogeneous data fusion-driven fire danger level dynamic assessment technique

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2024年 185卷 350-366页

作者： Sun, Bin Guo, Tong Southeast Univ China Pakistan Belt & Rd Joint Lab Smart Disaster Nanjing 210096 Peoples R China Southeast Univ Sch Civil Engn Nanjing 210096 Peoples R China

Dynamic fire assessment under many uncertainties make danger level difficult to be evaluated reasonably. This investigation aims to establish a fire danger level dynamic assessment technique, which can be utilized to achieve automatically rules generation of fire assessment and multiple attributes -based fire danger level determination. In the technique, image segmentation with particle swarm optimization is used to generate the rules of fire assessment related to image type data automatically. Evidential reasoning with particle swarm optimization is used to generate the rules of fire assessment related to numeric type data automatically. Then, final fire danger level can be evaluated together with the help of the multi -source heterogeneous data. The main novelty of this investigation is that the developed multiple attributes -based assessment technique is not limited to the specific fire scenario, which is without resort to professional knowledges for determining the rules of fire assessment and training based on mass data in advance. In addition, the results of the case study of a tunnel fire support the ability of the technique, which can be used to achieve the reasonable fire danger level dynamic assessment with only the monitored data at the current time condition.

关键词： Fire danger level dynamic assessment Evidential reasoning Image segmentation Particle swarm optimization multi-source heterogeneous data Tunnel fire

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Ecological restoration for mega-infrastructure projects: a study based on multi-source heterogeneous data

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2024年第9期31卷 3653-3678页

作者： Song, Ruizhen Gao, Xin Nan, Haonan Zeng, Saixing Tam, Vivian W. Y. Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai Peoples R China Western Sydney Univ Sch Comp Engn & Math Penrith Australia

PurposeThis research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological ***/methodology/approachBased on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are *** research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies *** implicationsManagers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological ***/valueUnlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration p

关键词： Railway engineering Ecological restoration Complexity degradation multi-source heterogeneous data Machine learning

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A model fusion method based on multi-source heterogeneous data for stock trading signal prediction

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SOFT COMPUTING 2023年第10期27卷 6587-6611页

作者： Chen, Xi Hirota, Kaoru Dai, Yaping Jia, Zhiyang Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Fujian Normal Univ Coll Phys & Energy Fuzhou 350117 Peoples R China

In the prediction of turning points (TPs) of time series, the improved model of integrating piecewise linear representation and weighted support vector machine (IPLR-WSVM) has achieved good performance. However, due to the single data source and the limitation of algorithm, IPLR-WSVM has encountered challenges in profitability. In this paper, a model fusion method based on multi-source heterogeneous data and different learning algorithms is proposed for the prediction of TPs (MF-MSHD). multi-source heterogeneous data include weighted unstructured and structured information with different granularities. RF, WSVM, BPNN, GBDT, and LSTM are selected to be the learning algorithms. The differences among meta-models are constructed by different inputs and algorithms as much as possible, and a model fusion rule is designed to determine the final TPs. Moreover, the TPs are generated based on the characteristics of individual stock. For sentiment analysis, a more accurate sentiment dictionary of stock market comments is established. Specifically, the fine-grained data is introduced to jointly determine the accurate trading moment. The prediction level of the proposal improves the accuracy and profitability, and also outperforms the composite indexes. Experimental results show that the profit rate of randomly selected stocks in MF-MSHD reaches 0.5172, while the highest value is 0.2841 in single meta-model and 0.0992 in buy and hold strategy, respectively. The other indicators including the accuracy are also modified. Compared with the increases of 0.1648, 0.4051, and 0.3397 in Shanghai Composite Index, Shenzhen Composite Index, and CSI 300 Index, MF-MSHD shows higher profitability in stock trading signal prediction.

关键词： Stock trading signal prediction Model fusion multi-source heterogeneous data Sentiment analysis

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Ontology construction and mapping of multi-source heterogeneous data based on hybrid neural network and autoencoder

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NEURAL COMPUTING & APPLICATIONS 2023年第36期35卷 25131-25141页

作者： Zhao, Wenbin Fu, Zijian Fan, Tongrang Wang, Jiaqi Shijiazhuang Tiedao Univ Sch Informat Sci & Technol Shijiazhuang Hebei Peoples R China

In big data era, multi-source heterogeneous data become the biggest obstacle to data sharing due to its high dimension and inconsistent structure. Using text classification to solve the ontology construction and mapping problem of multi-source heterogeneous data can not only reduce manual operation, but also improve the accuracy and efficiency. This paper proposes an ontology construction and mapping scheme based on hybrid neural network and autoencoder. Firstly, the proposed text classification method uses the multi-core convolutional neural network to capture local features and uses the improved Bidirectional Long Short-Term Memory network to compensate for the shortcomings of the convolutional neural network that cannot obtain context-related information. Secondly, a similarity matching method is used for ontology mapping, which integrate autoencoder to improve anti-interference ability. We have carried out several sets of experiments to test the validity of the proposed ontology construction and mapping scheme.

关键词： Ontology construction Ontology mapping Hybrid neural network Autoencoder multi-source heterogeneous data

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MHDF: multi-source heterogeneous data Progressive Fusion for Fake News Detection 28th

MHDF: Multi-source Heterogeneous Data Progressive Fusion for...

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28th Pacific-Asia Conference on Knowledge Discovery and data Mining (PAKDD)

作者： Yu, Yongxin Ji, Ke Gao, Yuan Chen, Zhenxiang Ma, Kun Wu, Jun Univ Jinan Sch Informat Sci & Engn Jinan Peoples R China Univ Beijing Jiaotong Sch Comp & Informat Technol Beijing Peoples R China

ISBN: (纸本)9789819722648;9789819722624

Social media platforms are inundated with an extensive volume of unverified information, most of which originates from heterogeneous data from a variety of diverse sources, spreading rapidly and widely, thereby posing a significant threat to both individuals and society. An existing challenge in multimodal fake news detection is its limitation to acquiring textual and visual data exclusively from a single source, which leads to a high level of subjectivity in news reporting, incomplete data coverage, and difficulties in adapting to the various forms and sources of fake news. In this paper, we propose a fake news detection model (MHDF) for multi-source heterogeneous data progressive fusion. Our approach begins with gathering, filtering, and cleaning data from multiple sources to create a reliable multi-source multimodal dataset, which involved obtaining reports from diverse perspectives on each event. Subsequently, progressive fusion is achieved by combining features from diverse sources. This is achieved by inputting the features obtained from the textual feature extractor and visual feature extractor into the news textual and visual feature fusion module. We also integrated sentiment features from the text into the model, allowing for multi-level feature extraction. Experimental results and analysis indicate that our approach outperforms other methods.

关键词： Fake News detection multi-source heterogeneous data Feature fusion

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