Multi-source Heterogeneous Data Research Articles

Deep Learning-Based Emergency Care Process Reengineering of Interventional Data for Patients with Emergency Time-Series Events of Myocardial Infarction.

This paper proposes a representation learning framework HE-LSTM model for heterogeneous temporal events, which can automatically adapt to the multiscale sampling frequency of multisource heterogeneous data. The proposed model also demonstrates its superiority over other typical approaches on real data sets. A controlled study is performed according to computerized randomization, with 38 patients in each of the two groups. The study group has a higher resuscitation success rate and patient satisfaction than the conventional group (P < 0.05), and the time from the first consultation to the completion of the first ECG, the time from the completion of the ECG to the activation of the path lab, and the time from the emergency admission to the balloon dilation were significantly shorter in the study group than in the conventional group (P < 0.05). The emergency care process reengineering intervention helps patients with acute myocardial infarction to be treated quickly and effectively, thus improving their resuscitation success rate and satisfaction rate, and is worthy to be caused in the clinic and widely applied.

A parallel deep learning algorithm with applications in process monitoring and fault prediction

Effective and timely fault detection and status monitoring of the industrial production process is essential to fully guarantee the operational safety. However, massive multi-source heterogeneous data analysis is facing many challenges. This paper proposes a process monitoring model combined with a parallel deep learning algorithm and Principal Component Analysis (PCA) method. Firstly, PCA is applied to realize fault diagnosis and extract fault characteristic variables, thus abnormal conditions can be revealed. In order to reduce the complexity of processing massive data, a parallel deep learning framework consisting of multi-models of convolutional neural network (CNN) and Long-Short Term Memory (LSTM) neural network is proposed to effectively predict the target variable status. Comprehensive experiments are taken under two real-system scenarios, and comparisons are made against four traditional neural network models to demonstrate its practicability and effectiveness. The generated results clearly show that the CNN-LSTM parallel model combined with PCA outperform other popular models due to its merged advantages of accurate time series prediction and effective fault feature extraction.

Research on Demand Forecasting of Engineering Positions Based on Fusion of Multisource and Heterogeneous Data

Aiming at the project demand forecasting problem based on multisource data fusion, a multisource heterogeneous data fusion model is established, and the unified quantitative representation method of heterogeneous data based on triangular fuzzy numbers is studied, and the ordered weighted average operator is used to integrate the preferences of decision-makers. A multisource heterogeneous data fusion algorithm that supports multiuser decision-making is designed. Based on the analysis of the internal and external environment of human resources in a company’s engineering positions, this paper qualitatively analyzes and selects the factors affecting the demand for talents in key positions in a company based on the characteristics of demand influencing factors and finds out the quantifiable and influential factors from the representative factors of talent demand for key positions in a company. Using historical data, statistical methods are used to process the eight related factors of a certain company, which confirms the factors that have a greater impact on the demand for talents in key positions of a company and influences the demand for talents in key positions in companies of the same type. The identification of factors provides a basic argument for a company. According to the results of statistical analysis and the characteristics of existing data, two variables of factory output and time are selected to be used in regression analysis forecasting model and gray system forecasting model of a certain company to predict the demand for key talents of a certain company. The company finally adopts combined forecasting. The method determines the predicted value of the talent demand for a certain company’s key positions. According to the results of demand forecasting and the current status of human resource management in a company, this article proposes a company’s key position talent management planning measures, in order to provide a reference for the management of a company’s key company position talents and ensure a company’s key company positions in the future talent demand reserve.

Towards traffic supervision in 6G: a graph neural network-based encrypted malicious traffic detection method

As an important direction for the evolution of next-generation wireless communication technology, 6G will comprehensively promote the wave of economic and social digitization. Services carried by 6G network will rely heavily on the sharing and processing of massive amounts of data between entities, data security is therefore of great importance. Currently, most network applications utilize SSL/TLS protocols to ensure the confidentiality and security of network communications, while encryption mechanism also brings huge challenges to network security supervision. Though encrypted malicious traffic detection in traditional networks has become a research hotspot, existing technologies cannot be directly applied in 6G networks. In a 6G network with massive, instant and unlimited communications between heterogeneous terminals, network communication behavior patterns are much more diversified, which makes the boundary between normal traffic and malicious traffic more blurred in 6G networks than in traditional networks. Existing studies either analyze encrypted traffic in isolation or aggregation, while they all ignore the rich correlations among encrypted traffic. To this end, we propose an encrypted malicious traffic detection framework based on the graph neural network towards the network security problem of future 6G networks, ET-RSGAT. First, considering the characteristics of super high speed and super large connection of 6G network, we design a simple feature extraction method of encrypted traffic: extracting the TLS handshake raw bytes and TLS record length sequence for one single encrypted session. Second, in view of the correlations of large numbers of heterogeneous terminals and the coexistence of multi-source heterogeneous data communication in 6G networks, we analyze the correlations between encrypted sessions from 2 aspects, which are service correlations and communication behavior correlations. Then we construct an encrypted traffic graph, named ETG. On the basis of ETG, we introduce a graph attention network to utilize the correlations between encrypted sessions to enrich the feature representation of nodes. With rich representation, we build the detection model based on a multi-layer perceptron to identify threats. Considering that the simulation environment of 6G networks is immature, we deploy a variety of heterogeneous terminal nodes and run various network services to simulate the 6G communication scenario, and design related experiments for the interconnection of many heterogeneous terminals in 6G networks. The evaluation and experimental results show that our method can obtain satisfactory detection results in both traditional network and simulated environment datasets.

Typical Damage Prediction and Reliability Analysis of Superheater Tubes in Power Station Boilers Based on Multisource Data Analysis

The superheater and re-heater piping components in supercritical thermal power units are prone to creep and fatigue failure fracture after extensive use due to the high pressure and temperature environment. Therefore, safety assessment for superheaters and re-heaters in such an environment is critical. However, the actual service operation data is frequently insufficient, resulting in low accuracy of the safety assessment. Based on such problems, in order to address the issues of susceptibility of superheater and re-heater piping components to creep, inaccurate fatigue failure fracture, and creep–fatigue coupling rupture in a safety assessment, their remaining life prediction and reliability, as well as the lack of actual service operation data, multisource heterogeneous data generated from actual service of power plants combined with deep learning technology was used in this paper. As such, three real-time operating conditions’ data (temperature, pressure, and stress amplitude) during equipment operation are predicted by training a deep learning architecture long short-term memory (LSTM) neural network suitable for processing time-series data and a backpropagation through time (BPTT) algorithm is used to optimize the model and compared with the actual physical model. Damage assessment and life prediction of final superheater tubes of power station boilers are carried out. The Weibull distribution model is used to obtain the trend of cumulative failure risk change and assess and predict the safety condition of the overall system of pressurized components of power station boilers.

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods.

Multisource Heterogeneous Data Fusion Analysis of Regional Digital Construction Based on Machine Learning

In modern urban construction, digitalization has become a trend, but the single source of information of traditional algorithms can not meet people’s needs, so the data fusion technology needs to draw estimation and judgment from multisource data to increase the confidence of data, improve reliability, and reduce uncertainty. In order to understand the influencing factors of regional digitalization, this paper conducts multisource heterogeneous data fusion analysis based on regional digitalization of machine learning, using decision tree and artificial neural network algorithm, compares the management efficiency and satisfaction of school population under different algorithms, and understands the data fusion and construction under different algorithms. According to the results, decision-making tree and artificial neural network algorithms were more efficient than traditional methods in building regional digitization, and their magnitude was about 60% higher. More importantly, the machine learning-based methods in multisource heterogeneous data fusion have been better than traditional calculation methods both in computational efficiency and misleading rate with respect to false alarms and missed alarms. This shows that machine learning methods can play an important role in the analysis of multisource heterogeneous data fusion in regional digital construction.

A unified deep learning framework for urban functional zone extraction based on multi-source heterogeneous data

Remote sensing imagery (RSI) and point of interest (POI) are two complementary data for urban functional zone (UFZ) extraction. However, current methods only use single data or just simply fuse the features extracted from these two data, which may not fully exploit their complementary strength. To solve this problem, in this paper, we propose a unified deep learning framework containing two modules to jointly use RSI and POIs for UFZ extraction. In the first module, the complementary feature learning and fusing module, two coupled convolutional neural networks (CNNs) are used to learn the visual feature from RSI and social feature from POIs, respectively. Specifically, to apply CNN on discrete POIs, we convert them into a hierarchical distance heatmap, and employ CNN with the attention mechanism to extract the co-occurrence relationship of POIs in a specific UFZ for social feature representation. Afterward, we ensemble these two coupled CNNs by a feature attention based fusion strategy, to fuse the complementary features learned from RSI and POIs with adaptively learned weights in an end-to-end manner. In the second module, the UFZ spatial relationship modeling module, different from previous methods that only consider features in single UFZ, we design a spatial relation learning network, which can aggregate both local and long-range between-UFZ spatial relationship for UFZ classification. Experiments on three urban regions demonstrate that the proposed framework can take full advantage of visual, social, and spatial features learned from both RSI and POIs, and thus achieve more satisfactory result than the methods using single data or simple fusion strategy. Furthermore, we analyze the impacts of several factors on UFZ extraction, including the contributions of different category of POIs on UFZ extraction improvement, the synergy mechanism of RSI and POIs for the classification of UFZs, and the influence of using different mapping unit for UFZ mapping. The insights distilled from this study can potentially help researchers use heterogeneous data for UFZ extraction. The source codes are available at: https://github.com/GeoX-Lab/UnifiedDL-UFZ-extraction

Geomatic-Based Flood Loss Assessment and Its Application in an Eastern City of China

Flash-flood disasters pose a serious threat to lives and property. To meet the increasing demand for refined and rapid assessment on flood loss, this study exploits geomatic technology to integrate multi-source heterogeneous data and put forward the comprehensive risk index (CRI) calculation with the fuzzy comprehensive evaluation (FCE). Based on mathematical correlations between CRIs and actual losses of flood disasters in Weifang City, the direct economic loss rate (DELR) model and the agricultural economic loss rate (AELR) model were developed. The case study shows that the CRI system can accurately reflect the risk level of a flash-flood disaster. Both models are capable of simulating disaster impacts. The results are generally consistent with actual impacts. The quantified economic losses generated from simulation are close to actual losses. The spatial resolution is up to 100 × 100 m. This study provides a loss assessment method with high temporal and spatial resolution, which can quickly assess the loss of rainstorm and flood disasters. The method proposed in this paper, coupled with a case study, provides a reliable reference to loss assessment on flash floods caused disasters and will be helpful to the existing literature.

A blockchain-based cooperative modeling method for digital twin ontology model of the mechanical product

With the rapid increase of multi-source heterogeneous dynamic data of mechanical products, the digital twin technology is considered to be an important method to realize the deep integration of product data and intelligent manufacturing. As a digital archive of the physical entity in entire life cycle, the mechanical product digital twin model is cross-phased and multi-domain. Therefore, safe and stable cooperative modeling has become a basic technical problem that needs to be solved urgently. In this paper, we proposed a blockchain-based collaborative modeling method for the digital twin ontology model of mechanical products. First, an authorization network was constructed among stakeholders. Then modeling processes of the digital twin were mapped to ontology operations and formatted through extensible markup language. Finally, consensuses were obtained based on practical byzantine fault tolerance. And a material modification process of a helicopter damper bearing was taken as an example to verify. The proposed method enables all participants to accurately obtain the latest state of the digital twin model, and has the advantages of tamper-proof, traceability, and decentralization.

Adaptive Heterogeneous Support Tensor Machine: An Extended STM for Object Recognition Using an Arbitrary Combination of Multisource Heterogeneous Remote Sensing Data

Multiple observation data from multisensor satellites lead to data with complex heterogeneous tensor structures, and the available object data are observed by the arbitrary combination of multisource satellites, which brings significant challenges to object recognition using typical deep learning methods, classical support vector machines (SVMs), and support tensor machines (STMs). To process multisource data represented as heterogeneous tensors effectively, the heterogeneous STM (HSTM) is proposed by integrating rank- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> decomposition of multitensors with nu-STM to directly separate samples in heterogeneous tensor space. Furthermore, combining the shared projecting tensors used to mine related information for intercombinations and the auxiliary projecting tensors exploited to capture the individual information for intracombination, the HSTM is extended to Adaptive HSTM (AHSTM) to address the arbitrary combination of multisource data by only training once. To accelerate the training of AHSTM, the AHSTM-oriented decomposition method is proposed to use small sequential analytic optimizations instead of the original large numerical optimization. Using multiangle optical and synthetic aperture radar (SAR) satellite images, experimental results demonstrate that the proposed AHSTM obtains higher accuracy than typical SVM and STM methods for each combination of multisource data and reduces the time consumption by over 75% for the training set with different sizes compared with the typical interior-point method and the active-set method.

Multi-objective Decision Making Model for Stock Price Prediction Using Multi-source Heterogeneous Data Fusion

Stock exchanges are developed as an essential component of economies, as they can promote financial and capital gain. The stock market is network of economic connections where share is bought and sold. Stock Market Prediction (SMP) is quite useful to investors. An effective forecast of stock prices is offer shareholders with suitable help in making appropriate decisions regarding if sell or purchase shares. The employ of Machine Learning (ML) and Sentiment Analysis (SA) on data in microblogging sites are developed as a famous approach to SMP. However, the heterogenous data fusion in stock market field is a big challenge. This paper introduces an effective Cat Swarm Optimization with Machine Learning Enabled Microblogging Sentiment Analysis for Stock Price Prediction technique. The presented model investigates the social media sentiments to foresee SPP. Firstly, the proposed model executes data pre-processing and Glove word embedding approach. Next, the weighted extreme learning machine approach was utilized for the classification of sentiments for SPP. Lastly, the CSO system was exploited for optimal adjustment of the parameters related to the WELM model. The experimental validation of the proposed approach was executed using microblogging data. The results show that the proposed method outperforms the previous studies.

HTGCE: An Graph-Based and Classifier-Oriented Dimension Reduction Method for Multisource Heterogeneous Feature Tensors

Considering the multiple observations from satellites with different types of sensors, the available object data are regarded as different combinations of multisource heterogeneous data. To achieve accurate object recognition, the dimension reduction (DR) technology is important to capture the low-dimensional discriminative representation containing complementary information from multisource data, while off-the-shelf DR methods can only handle input represented as vector or homogeneous tensors and fail to deal with multisource data represented as heterogeneous tensors. In addition, the existing DR methods are classifier-independent, making it difficult to ensure effective recognition under a specific classifier. To solve these problems, the DR method for Heterogeneous Tensors based on Graph-based and Classifier-oriented Embedding (HTGCE) method is proposed to learn the low-dimensional representation of multisource data using different combinations of samples. First, self-learning adjacency matrices are constructed to capture the local structure of different combinations of multisource data autonomously. Then, unlike the classifier-independent discriminant term used in existing DR methods, a classifier-oriented discriminant term is constructed to enhance the specific classifier-based recognition results. Furthermore, the reconstruction error minimization term is created to enable DR results to inherit the main information of the original data. Moreover, an adaptive weight factor is built to balance the importance of different sources for object recognition. Finally, an alternative optimization strategy is presented to solve the optimization problem of HTGCE. Using the multiresolution multiangle optical dataset and the paired optical and SAR dataset, the experimental results demonstrate that the HTGCE outperforms typical vector- and tensor-based DR methods in terms of recognition accuracy.

Multi-Objective Spatial Suitability Evaluation and Conflict Optimization Considering Productivity, Sustainability, and Livability in Southwestern Mountainous Areas of China

Space is the fundamental carrier for production, living, and ecological activities, and optimizing the spatial pattern is of vital importance to promote regional sustainable development. To achieve this goal, the core issues are to identify the risks of resource and environmental constraints of development and to realize the rational distribution of human living space. Based on the integration of multisource heterogeneous data, taking Yunnan Province, a typical mountainous area in China, as an example, this research proposes a multi-object suitability evaluation method based on 50 × 50 m grid data at the provincial scale. We build a spatial conflict analysis model to identify production–living–ecological space (PLES) and propose governance suggestions for different functional areas. The results show that (1) areas suitable for ecology make up the greatest proportion of Yunnan Province, but areas with living and ecological functions show obvious spatial complementarity; (2) areas suitable for production are restricted by steep slope, geological hazards and fragmented pattern; (3) areas suitable for living is rare, and they are mainly concentrated in the plains of central Yunnan; and (4) twenty-seven percent of area has potential spatial conflicts, among which 4.38% of the area is all suitable for production–living–ecological. The production–living advantage areas are concentrated in the central Yunnan UA (Urban agglomeration), which has a high spatial overlap. These results are expected to provide valuable insights to support comprehensive multifunctional spatial utilization and sustainable development in mountainous areas.

Risk Chain and Key Hazard Management for Urban Rail Transit System Operation Based on Big Data Mining

With the promotion of the national transportation power strategy, super large operation networks have become an inevitable trend, and operational safety and risk management and control have become unavoidable problems. Existing safety management methods lack support from actual operational and production data, resulting in a lack of guidance of fault cause modes and risk chains. Large space is available to improve the breadth, depth, and accuracy of hazard source control. By mining multisource heterogeneous operation big data generated from subway operation, this study researches operation risk chain and refined management and control of key hidden dangers. First, it builds a data pool based on the operation status of several cities and then links them into a data lake to form an integrated data warehouse to find coupled and interactive rail transit operation risk chains. Second, it reveals and analyzes the risk correlation mechanisms behind the data and refines the key hazards in the risk chain. Finally, under the guidance of the risk chain, it deeply studies the technologies for refined control and governance of key hidden dangers. The results can truly transform rail transit operation safety from passive response to active defense, improving the special emergency rail transit operation plans, improving the current situation of low utilization of rail transit operation data, but high operation failure rate, and providing a basis for evidence-based formulation and revision of relevant industry standards and specifications.

Research on multi-label classification method of transformer based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm

With the improvement of the intelligent level of power grid and the enhancement of the integrated characteristics of power grid, the degree of discretization of massive data of power equipment gradually increases, which brings great challenges to the safe and stable operation of power grid. How to process and analyze data effectively has become an important research content. Transformer is an important electrical equipment, therefore it is of great significance to monitor the operation status of transformer, to construct transformer operation characteristic label system based on multi-source heterogeneous data, and to realize multi-label classification function. In this paper, a transformer multi-label classification method of transformer based on DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is proposed, which can accurately identify outliers as Noise without input of the number of clustering to be divided, realize the key feature mining of transformer state, and to realize to provide flexible information association and historical data for dispatch and control operators.

Leveraging Multisource Heterogeneous Data for Financial Risk Prediction: A Novel Hybrid-Strategy-Based Self-Adaptive Method

Emerging phenomena of ubiquitous multisource data offer promising avenues for making breakthroughs in financial risk prediction. While most existing methods for financial risk prediction are based on a single information source, which may not adequately capture various complex factors that jointly influence financial risks, we propose a hybrid-strategy-based self-adaptive method to effectively leverage heterogeneous soft information drawn from a variety of sources. The method uses a proposed new feature- sparsity learning method to adaptively integrate multisource heterogeneous soft features with hard features and a proposed improved evidential reasoning rule to adaptively aggregate base classifier predictions, thereby alleviating both the declarative bias and the procedural bias of the learning process. Evaluation in two cases at the individual level (concerning borrowers at a P2P lending platform) and the company level (concerning listed companies in the Chinese stock market) showed that, compared with relying solely on hard features, effectively incorporating multisource heterogeneous soft features using our proposed method enabled earlier prediction of financial risks with desirable performance.

A Garbage Detection and Classification Method Based on Visual Scene Understanding in the Home Environment

Garbage classification is a social issue related to people’s livelihood and sustainable development, so letting service robots autonomously perform intelligent garbage classification has important research significance. Aiming at the problems of complex systems with data source and cloud service center data transmission delay and untimely response, at the same time, in order to realize the perception, storage, and analysis of massive multisource heterogeneous data, a garbage detection and classification method based on visual scene understanding is proposed. This method uses knowledge graphs to store and model items in the scene in the form of images, videos, texts, and other multimodal forms. The ESA attention mechanism is added to the backbone network part of the YOLOv5 network, aiming to improve the feature extraction ability of the network, combining with the built multimodal knowledge graph to form the YOLOv5-Attention-KG model, and deploying it to the service robot to perform real-time perception on the items in the scene. Finally, collaborative training is carried out on the cloud server side and deployed to the edge device side to reason and analyze the data in real time. The test results show that, compared with the original YOLOv5 model, the detection and classification accuracy of the proposed model is higher, and the real-time performance can also meet the actual use requirements. The model proposed in this paper can realize the intelligent decision-making of garbage classification for big data in the scene in a complex system and has certain conditions for promotion and landing.

3D City Online Visualization and Cluster Architecture for Digital City

As an important carrier of human production, life, and social development, the emergence of cities symbolizes the maturity and civilization of mankind. For more than 40 years of reform and opening up, our country’s economic development has become increasingly prosperous, and urbanization is booming. At present, our country is in a decisive period for building a well-off society in an all-round way, with rapid progress in socio-economic growth and urbanization. Based on this, this article is oriented towards urban visualization modeling work and proposes a cluster modeling method that is compatible with the combination of urban geological structure and three-dimensional urban space, so that urban space modeling work not only pays attention to the rationality of above-ground planning and construction but also fully considers underground geology the stability and safety of the structure. This paper uses the 3D city online visualization modeling technology to efficiently and reasonably complete the 3D urban geological modeling under the fusion of multiple geological data and combines the organic combination of multisource heterogeneous model data to convert the geological model data into a 3D geographic information model; the universal standard format analyzes the rapid construction of large-scale complex geological structure models and the integrated expression of multisource heterogeneous model data. Experiments have proved that from the cluster capacity of 5,000 to 100,000, no matter how much the modeling time is different, whether it is to search the entire territory or part of the scope, the search time of the 3D city visualization model is less than 20 ms, and the 3D city visualization model map of the city can be well established. This shows that the three-dimensional city visualization model highlights the impact of the urban geological environment on urban construction and development and visually and vividly displays region geological structure and other information in a three-dimensional way, providing corresponding information for urban geological stability assessment and geological disaster rescue reference and help.

Big Data Cleaning Model of Multi-Source Heterogeneous Power Grid Based On Machine Learning Classification Algorithm

Aiming at the low cleaning rate of the traditional multi-source heterogeneous power grid big data cleaning model, a multi-source heterogeneous power grid big data cleaning model based on machine learning classification algorithm is designed. By capturing high-quality multi-source heterogeneous power grid big data, weight labeling of data source importance measurement, data attributes and tuples, and constructing Tan network based on the idea of machine learning classification algorithm, the data probability value is finally used to complete the classification and cleaning of inaccurate data. Experiments show that the model based on machine learning classification algorithm can effectively improve the imprecise data cleaning rate compared with the traditional model to solve multi-source heterogeneous imprecise data cleaning.