Multi-source Heterogeneous Data Research Articles

Federated Generative Model on Multi-Source Heterogeneous Data in IoT

The study of generative models is a promising branch of deep learning techniques, which has been successfully applied to different scenarios, such as Artificial Intelligence and the Internet of Things. While in most of the existing works, the generative models are realized as a centralized structure, raising the threats of security and privacy and the overburden of communication costs. Rare efforts have been committed to investigating distributed generative models, especially when the training data comes from multiple heterogeneous sources under realistic IoT settings. In this paper, to handle this challenging problem, we design a federated generative model framework that can learn a powerful generator for the hierarchical IoT systems. Particularly, our generative model framework can solve the problem of distributed data generation on multi-source heterogeneous data in two scenarios, i.e., feature related scenario and label related scenario. In addition, in our federated generative models, we develop a synchronous and an asynchronous updating methods to satisfy different application requirements. Extensive experiments on a simulated dataset and multiple real datasets are conducted to evaluate the data generation performance of our proposed generative models through comparison with the state-of-the-arts.

Understanding passenger travel choice behaviours under train delays in urban rail transits: a data-driven approach

The analysis of passenger travel choice behaviours under train delays has become a crucial topic in research on urban rail transit operation management. In this paper, we focus on analysing travel choices of affected regular passengers under train delays by utilizing the data collected through an automatic fare collection (AFC) system along with train delay log records. Along this line, we propose a data-driven four-stage framework for studying regular passengers’ responses under delays, consisting of data profiling, regular passenger screening and travel patterns extraction, affected regular passenger identification, and affected passenger behaviour prediction modelling. Using a real-world case of the Shenzhen Metro in China, we conduct extensive experiments for method validation and feature insights analysis. The proposed framework could provide a microscopic view of passenger travel behaviours under train delays for fine prediction and exhibit a possibility for multi-source heterogeneous data mining in passenger behaviour analysis and train delay-related tasks.

A Data-Driven Analysis Method for the Trajectory of Power Carbon Emission in the Urban Area.

"Industry 4.0" aims to build a highly versatile, individualized digital production model for goods and services. The carbon emission (CE) issue needs to be addressed by changing from centralized control to decentralized and enhanced control. Based on a solid CE monitoring, reporting, and verification system, it is necessary to study future power system CE dynamics simulation technology. In this article, a data-driven approach is proposed to analyzing the trajectory of urban electricity CEs based on empirical mode decomposition, which suggests combining macro-energy thinking and big data thinking by removing the barriers among power systems and related technological, economic, and environmental domains. Based on multisource heterogeneous mass data acquisition, effective secondary data can be extracted through the integration of statistical analysis, causal analysis, and behavior analysis, which can help construct a simulation environment supporting the dynamic interaction among mathematical models, multi-agents, and human participants.

Construction of a rural tourism information service management system for multi-source heterogeneous data processing.

This study offers an integrated service management system for rural tourist information based on a cloud platform to address the three main issues of high platform concurrency, difficulty storing and managing data, and trouble sharing data functions. Three levels-data, process, and architecture-are considered in the analysis and design of the platform. The Hadoop data storage system makes possible the collection, storage, administration, and exchange of data functions for large amounts of heterogeneous data from many different sources by utilising Netty data transmission technology, hybrid data storage technology, and the Web Foundation. The results demonstrate that the system's response time is low, and the CPU consumption time and the average utilisation rate meet the actual needs. They resolve issues with the current rural tourism platforms application, such as the difficulty of data collection, the low rate of reuse, the low rate of sharing, the lack of timely updates, and severe island phenomena.

Metabolite-disease interaction prediction based on logistic matrix factorization and local neighborhood constraints.

Increasing evidence indicates that metabolites are closely related to human diseases. Identifying disease-related metabolites is especially important for the diagnosis and treatment of disease. Previous works have mainly focused on the global topological information of metabolite and disease similarity networks. However, the local tiny structure of metabolites and diseases may have been ignored, leading to insufficiency and inaccuracy in the latent metabolite-disease interaction mining. To solve the aforementioned problem, we propose a novel metabolite-disease interaction prediction method with logical matrix factorization and local nearest neighbor constraints (LMFLNC). First, the algorithm constructs metabolite-metabolite and disease-disease similarity networks by integrating multi-source heterogeneous microbiome data. Then, the local spectral matrices based on these two networks are established and used as the input of the model, together with the known metabolite-disease interaction network. Finally, the probability of metabolite-disease interaction is calculated according to the learned latent representations of metabolites and diseases. Extensive experiments on the metabolite-disease interaction data were conducted. The results show that the proposed LMFLNC method outperformed the second-best algorithm by 5.28 and 5.61% in the AUPR and F1, respectively. The LMFLNC method also exhibited several potential metabolite-disease interactions, such as "Cortisol" (HMDB0000063), relating to "21-Hydroxylase deficiency," and "3-Hydroxybutyric acid" (HMDB0000011) and "Acetoacetic acid" (HMDB0000060), both relating to "3-Hydroxy-3-methylglutaryl-CoA lyase deficiency." The proposed LMFLNC method can well preserve the geometrical structure of original data and can thus effectively predict the underlying associations between metabolites and diseases. The experimental results show its effectiveness in metabolite-disease interaction prediction.

A novel LASSO-ATT-LSTM model of stock price prediction based on multi-source heterogeneous data

Stock price volatility is influenced by many factors, including unstructured data that is not easy to quantify, such as investor sentiment. Therefore, given the difficulty of quantifying investor sentiment and the complexity of stock price, the paper proposes a novel LASSO-ATT-LSTM intelligent stock price prediction system based on multi-source data. Firstly, establish a sentiment dictionary in the financial field, conduct sentiment analysis on news information and comments according to the dictionary, calculate sentiment scores, and then obtain daily investor sentiment. Secondly, the LASSO (Least absolute shrinkage and selection operator) is used to reduce the dimension of basic trading indicators, valuation indicators, and technical indicators. The processed indicators and investor sentiment are used as the input of the prediction model. Finally, the LSTM (Long short-term memory) model that introduces the attention mechanism is used for intelligent prediction. The results show that the prediction of the proposed model is close to the real stock price, MAPE, RMSE, MAE and R2 are 0.0118, 0.0685, 0.0515 and 0.8460, respectively. Compared with the existing models, LASSO-ATT-LSTM has higher accuracy and is an effective method for stock price prediction.

A compatible carbon efficiency information service framework based on the industrial internet identification

Green and low-carbon is a new development model that seeks balance between environmental sustainability and high economic growth. If explainable and available carbon emission data can be accurately obtained, it will help policy regulators and enterprise managers to more accurately implement this development strategy. A lot of research has been carried out, but it is still a difficult problem that how to accommodate and adapt the complex carbon emission data computing models and factor libraries developed by different regions, different industries and different enterprises. Meanwhile, with the rapid development of the Industrial Internet, it has not only been used for the supply chain optimization and intelligent scheduling of the manufacturing industry, but also been used by more and more industries as an important way of digital transformation. Especially in China, the Industrial Internet identification and resolution system is becoming an important digital infrastructure to uniquely identify objects and share data. Hence, a compatible carbon efficiency information service framework based on the Industrial Internet Identification is proposed in this paper to address the problem of computing and querying multi-source heterogeneous carbon emission data. We have defined a multi cooperation carbon emission data interaction model consisting of three roles and three basic operations. Further, the implementation of the framework includes carbon emission data identification, modeling, calculation, query and sharing. The practice results show that its capability and effectiveness in improving the responsiveness, accuracy, and credibility of compatible carbon efficiency data query and sharing services.

Research on a methodology for intelligent seismic performance evaluation and optimization design of buildings based on IFC and ontology

Seismic loss prediction and performance assessment for buildings have already been hot spots for resilience after earthquakes with the emergence of the concepts of digital twins. However, because a large amount of multisource heterogeneous data and a real-time accurate prediction model are required to implement the process, the popularization and implementation of these technologies are hampered. Based on performance-based seismic design (PBSD) theory, this study introduces BIM and ontology and other emerging technologies to carry out methodology research on intelligent earthquake damage prediction and seismic performance evaluation for individual buildings. First, based on FEMA P-58 (Seismic Performance Assessment of Buildings), this paper uses BIM technology to integrate and transmit component-level engineering information. Ontology method is used to express the assessment content and logic, and a multidimensional semantic ontology model based on IFC is proposed to form the linked data foundation to organize, store, associate and interact the multisource heterogeneous data required for assessment in a unified way to realize the semi-automated seismic performance assessment for individual RC frame building. On this basis, the seismic optimization design under the guidance of the “investment - benefit” criterion is regarded as the problem of seeking the balance between the initial construction cost and the expectation of seismic loss. The multi-objective genetic algorithm (NSGA-II) is used to realize the optimization iteration at the component level. According to the results of the case study, the new method in this paper significantly improves the quality of seismic performance assessment and seismic design optimization.

Circular Economy Model for Elderly Tourism Operation Based on Multi-source Heterogeneous Data Integration

ABSTRACT The emerging senior tourism industry has attracted increasing attention in China due to the continuous development of social, economic and technological factors. However, the senior tourism market in China still faces significant shortages, and further market development is needed. To address this issue, a senior tourism business model based on the circular economy, artificial intelligence (AI) technology and multi-source heterogeneous data integration has been proposed in this study. The model uses AI technology to integrate and analyze multi-source heterogeneous data, providing insights into the senior tourism market and facilitating precise marketing. Additionally, the model combines senior tourism products with sustainable development by adopting a circular economy model, achieving both economic and environmental benefits. The application of this model is expected to enhance the competitiveness and sustainable development of the Chinese senior tourism market, providing new ideas and references for the development of the industry. The integration of multi-source heterogeneous data is a significant feature of this model, which facilitates a more in-depth understanding of the senior tourism market. By analyzing various data sources, such as social media, search engines, and online reviews, the model can better identify senior tourists’ preferences and behaviors, thereby improving the precision of marketing strategies. Furthermore, by integrating environmental data, such as carbon footprint and energy consumption, the model can assess the sustainability of senior tourism products, contributing to the development of eco-friendly senior tourism.

A novel coal-rock cutting state identification model based on the Internet of Things

The sudden change in the interface between coal-rock mass can lead to the increased abrasion of picks and the failure rate of mining machinery. The safe and efficient coal-rock identification technology is the key to realize the intelligent control of coal mining machinery. To realize the real-time perception and accurate recognition of coal-rock cutting state information, a novel coal-rock cutting state identification model based on the Internet of Things (IoT) is explored. Specifically, by using the virtual prototype technology, multi-source heterogeneous data from acquisition, processing and identification of coal-rock cutting state information are fused and analyzed. First, the paper analyzes the physical and mechanical characteristics of coal-rock mass, and takes the cutting drum of bolter miner as the research object to theoretically analyze its load, which provides a foundation for the research on coal-rock cutting state identification. Second, a rigid-flexible coupling virtual prototype model of the cutting drum and coal-rock models under different cutting conditions are established. The simulation process is implemented by employing the discrete element method (DEM) to ensure the real-time transmission of motion information and state characteristic signals. Ultimately, the dynamic information of drum load during the coal-rock cutting process is obtained. Finally, LVQ (Learning Vector Quantization) and PSO-BP (Particle Swarm Optimization-Back Propagation) neural networks are created, and the variation coefficient, waveform factor, and peak factor of load curves of cutting drums in the coal-rock mass with different firmness coefficients are input into the neural networks as feature vectors for state recognition. The experimental results show that LVQ and PSO-BP neural networks can be used for coal-rock cutting state identification, and PSO-BP network has faster convergence speed and higher recognition efficiency, which provides a new scheme for coal-rock cutting state identification to improve the safety and efficiency of coal mining machinery.

Cloud-Edge Computing Technology-Based Internet of Things System for Smart Classroom Environment

In view of the main challenges faced by the realization of smart classroom environment in classroom teaching, such as inconvenient use of equipment, inability to comprehensively obtain multi-source heterogeneous data in real time, and lack of effective support for smart teaching, an IoT system archi-tecture based on cloud-edge computing technology is proposed. The system architecture consists of four parts: sensor layer, edge computing layer, core network layer and platform application layer, which are respectively used to obtain multi-source heterogeneous data, configure edge nodes on demand, transfer information and provide various educational services. A question-naire survey was adopted to investigate the teachers and students in the smart classrooms of Central China Normal University where the proposed system was implemented. The results showed that the overall satisfaction of students and teachers is high. Among them, 100.00% of teachers and 97.14% of students agreed that the system can effectively collect educational data and give students feed-back; 96.00% of teachers and 91.43% of students believed that it can pro-mote classroom teaching and bring pleasant experience to teachers and stu-dents. In brief, the proposed solution is completely practicable and can pro-vide a reference for the research, construction and application of smart classrooms.

Carbon mitigation potential afforded by rooftop photovoltaic in China

Rooftop photovoltaics (RPVs) are crucial in achieving energy transition and climate goals, especially in cities with high building density and substantial energy consumption. Estimating RPV carbon mitigation potential at the city level of an entire large country is challenging given difficulties in assessing rooftop area. Here, using multi-source heterogeneous geospatial data and machine learning regression, we identify a total of 65,962 km2 rooftop area in 2020 for 354 Chinese cities, which represents 4 billion tons of carbon mitigation under ideal assumptions. Considering urban land expansion and power mix transformation, the potential remains at 3-4 billion tons in 2030, when China plans to reach its carbon peak. However, most cities have exploited less than 1% of their potential. We provide analysis of geographical endowment to better support future practice. Our study provides critical insights for targeted RPV development in China and can serve as a foundation for similar work in other countries.

Surface roughness prediction using multi-source heterogeneous data and Bayesian quantile regression in milling process

To quantify the influence of cutting parameters on surface roughness and improve the prediction accuracy, a novel method is proposed for surface roughness prediction in milling process. A quantile regression (QR) model is constructed using multi-source heterogeneous (MSH) data comprising monitoring signals and cutting parameters within a Bayesian framework. The model parameters are then estimated using Gibbs sampling, and the predicted roughness and confidence interval (CI) can be obtained using the proposed Bayesian quantile model (BQR). Experimental results show that the proposed method has a prediction error of 15.05 %, which is 13.11 %, 0.01 %, and 1.7 % lower than least squares regression (LSR), back propagation network (BPN), and Bayesian linear regression (BLR), respectively. Through the comparison between predictions and experimental measurements, the proposed method has no great advantages over BPN in terms of prediction error, and is even 2.39 % higher than the prediction error of Support Vector Machine (SVM). However, the advantage of the proposed method compared with the above methods is that it can provide the analysis results of the influence of cutting parameters on surface roughness, which has practical significance in providing guidance for operators to reduce surface roughness in the milling process.

The Design of Vehicle Profile Based on Multivehicle Collaboration for Autonomous Vehicles in Roundabouts

The collaborative strategy of vehicle-road-environment based on intelligent and connected vehicles (ICVs) to assist in driving vehicles safely and relieve traffic congestion has become an effective solution. This paper proposed a strategy for vehicle lane change and roundabouts traffic based on vehicle profile (VP) in combination with the driving characteristics of roundabouts. Initially, in order to solve the confusion problem of multisource heterogeneous data of ICVs in roundabouts, this paper defines VP to describe and characterize the multidimensional data of ICVs, so the data in ICVs can be further applied. Furthermore, the weights of relevant parameters in the VP are updated based on the random forest algorithm. In addition, the payoff function is designed for the lane change decision at the exit of roundabouts based on the VP and dynamic weights. Finally, the performance of the proposed algorithm is compared with other algorithms through the SUMO platform and three scenarios are used in the simulation verification, including traffic congestion, normal, and sparse. The experimental results verify the optimization effect of vehicle profile on roundabout traffic strategy and also show that this algorithm can effectively improve the efficiency of vehicle traffic in roundabouts. In particular, the efficiency and comfort of vehicles in roundabouts are effectively improved in normal traffic scenarios.

Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models.

MuKGB-CRS: Guarantee privacy and authenticity of cross-domain recommendation via multi-feature knowledge graph integrated blockchain

In distributed cross-domain recommendation systems, current privacy protection techniques are ineffective at verifying data correctness and existing authenticity techniques have limitations in privacy protection. This poses a conflict between security and credibility. This paper designs a trusted cross-domain recommendation model based on multi-feature knowledge graph (MuKG) and blockchain to boost recommendation accuracy, data credibility and security. To the best of our knowledge, MuKG is proposed, for the first time, to realize a unified representation of multi-source heterogeneous data in a secure manner. Under this model, federated learning integrated with blockchain is used to implement a co-trust mechanism for distributed learning while guaranteeing the credibility of data through blockchain traceability. Our model can guarantee both data security and authenticity with no need of generalization for privacy protection and coordination of centralized servers for distributed controls. Experiments are performed on the two classic datasets, MovieLens and Amazon, with different sparsity. The results have shown that MuKG improves the recommendation accuracy by 1.5 % and diversity by 18 % while ensuring data security and credibility in sparse data. We also verify that different data characteristics have different influence on the recommendation results.

A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems

Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate derivation and reasoning of knowledge graphs play an important role in guiding power equipment operation and decision-making. Due to the complex and weak relations from multi-source heterogeneous data, the use of KGs has become popular in research to represent potential information in power knowledge reasoning. In this review, we first summarize the key technologies of knowledge graph representation and learning. Then, based on the complexity and real-time changes of power system operation and maintenance, we present multiple data processing, knowledge representation learning, and the graph construction process. In three typical power operation and fault decision application scenarios, we investigate current algorithms in power KG acquisition, representation embedding, and knowledge completion to illustrate accurate and exhaustive recommendations. Thus, using KGs to provide reference solutions and decision guidance has a significant role in improving the efficiency of power system operations. Finally, we summarize the achievements and difficulties of current research and give an outlook for future, promising roles of KG in power systems.

Main Factors on Effect of Precise Measurement and Precise Tamping Based on BP Neural Network

With the continuous development of precise measurement and precise tamping (PMPT) technology on Chinese railway conventional speed lines, the efficiency of machinery tamping operation and the quality of the track have been effectively improved. A variety of PMPT modes have been tried in the field operation, however there are some differences in the operation effect. The quality of the tamping operation is affected by multiple factors. In order to identify the key factors affecting the operation quality and to further improve the tamping operation effect, this paper establishes both the database of PMPT operation modes and the selection index system for evaluating the operation effect. Based on mega multi-source heterogeneous data and track geometry inspection data, this paper adopts the Back Propagation Neural Network (BPNN) prognosis model to quantify and sort the main factors affecting the effect of PMPT. The research results show that the initial quality of the track before tamping, whether the stabilizing operation or the tamping modes have great influence weights. It can scientifically guide the field operation to control the key factors and put forward some practical suggestions for promoting the field application of PMPT and the optimization of operation modes on the conventional speed lines.

Wind load identification of lattice towers using multi-source heterogeneous monitoring data

Wind load is one of the governing design loads for lattice tower structures. However, such information is difficult to be measured directly, and the traditional indirect identification methods highly depend on the accuracy of finite element model and are limited to fluctuating wind loads. To solve this problem, a novel wind load identification method from limited multi-source heterogeneous monitoring data is proposed. First, the complex model of a lattice tower is simplified into a reduced-order model based on the similarity principle of dynamic characteristics. And then, limited strain and acceleration data are fused to reconstruct wind-induced responses and modal parameters based on Kalman filter algorithm and mode-superposition method. Finally, the total wind loads, including both mean and fluctuating components, are obtained from identified modal parameters and modal responses. Numerical simulation of a lattice tower verified that the proposed method can reconstruct wind load and base reaction forces with high accuracy, and the anti-noise analysis shows that the proposed method has strong noise robustness. Moreover, the field test of a transmission tower during Typhoon Mangkhut was further carried out to validate the proposed method.

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

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