Multi-source Data Fusion Method Research Articles

Stability Analysis of Tunnel Rock Mechanical Parameters Based on Multi-Source Sensor Data Fusion

In this paper, the sensor is applied to the collection of rock parameter data. Aiming at the classification and evaluation of stability (i.e. rock quality), an attribute recognition model for the classification and evaluation of surrounding rock quality of underground engineering is established. Using multi-source data fusion and orthogonal numerical simulation test methods, the effects of rock mechanics parameters on the horizontal convergence of the tunnel, the settlement of the vault and the plastic zone coefficient are studied. Six factors (elastic modulus, Poisson’s ratio, internal friction angle, tensile strength, cohesion and density) and three levels of orthogonal experimental solutions were selected. The method of defining similar weight by using similar number to determine the weight of evaluation index, so as to calculate the comprehensive attribute measure, and apply confidence criteria to identify the stability of rock samples. Through the analysis and evaluation of rock mass quality classification of underground engineering, the application of the model and the evaluation method of rock mass quality classification are explained. The test results match the orthogonal test results; Considering the stability of tunnel envelope, the horizontal convergence, vault settlement and plastic zone coefficient after excavation should be comprehensively considered.

The survey on multi-source data fusion in cyber-physical-social systems: Foundational infrastructure for industrial metaverses and industries 5.0

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer “Human-Centric” Safe, Secure, Sustainable, Sensitive, Service, and Smartness “6S” manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber–Physical–Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0.

A collapse risk assessment method for subway foundation pit based on cloud model and improved Dempster–Shafer evidence theory

Collapse is a major engineering hazard in open-cut foundation pit construction, and risk assessment is crucial for considerably reducing engineering hazards. This study aims to address the ambiguity problem of qualitative index quantification and the failure of high-conflict evidence fusion in risk assessment. Thus, a fast-converging and high-reliability multi-source data fusion method based on the cloud model (CM) and improved Dempster–Shafer evidence theory is proposed. The method can achieve an accurate assessment of subway pit collapse risks. First, the CM is introduced to quantify the qualitative metrics. Then, a new correction parameter is defined for improving the conflicts among evidence bodies based on conflict degree, discrepancy degree and uncertainty, while a fine-tuning term is added to reduce the subjective effect of global focal element assignment. Finally, the risk assessment result is obtained according to the maximum affiliation principle. The method is successfully applied to Luochongwei Station, where the difference between the maximum value and the second largest value of the basic probability assignment is 0.624, and the global uncertainty degree is 0.087. Both values satisfy the decision evaluation condition; however, values of other methods only satisfy one or neither condition. In addition, the proposed method requires only four cycles to reach the steady state by fusing data of the same index, which has faster convergence compared with that of other methods. The proposed method has good universality and effectiveness in subway pit collapse risk assessment.

Consumer evaluation mechanisms on e-commerce platforms: reputation management and analysis of influencing factors

Abstract This paper proposes a regression algorithm to statistically relate the relationship between the relationship variables and proposes a multiple linear regression model to conduct a linear correlation study between a dependent variable and multiple independent variables. The factors that affect the value of merchant reputation in the content of consumer evaluation are regressed and analyzed, and a model for evaluating merchant reputation is proposed based on the regression results. In the merchant reputation evaluation model, the Bayesian algorithm and multi-source data fusion method are used to calculate the merchant weight, the Euclidean distance is used to calculate the platform weight, and the judgment matrix is constructed to calculate the merchant e-commerce reputation score. In the model performance test, 10 Taobao merchants are randomly selected for reputation calculation. The highest merchant reputation scores of 4.582 and 4.584 can be obtained for merchants S9 and S10, and the index scores are more accurate. The original Taobao reputation scores of merchants S4, S8, and S9 are all 4.97. At the same time, the model in this paper calculates them to be 4.404, 4.304, and 4.582, respectively, which are more reflective of the real reputation level of merchants.

Intelligent Perception of Distribution Network State Based on Multi-source Data Fusion

This article proposes an intelligent state perception method based on multi-source data fusion to address the issue of the difference between the state perception range and the actual range caused by the abnormal operation status of active distribution networks. In this paper, by combining Ant colony optimization algorithms and fusion rules, a multi-source data fusion model of the operation situation is constructed. The operation situation indicators, such as line loss rate and light load rate, are determined. Using multi-source data fusion information entropy to train samples, the range of abnormal operating states of the power grid is determined. By fusing multi-source voltage data and using the multi-source data fusion method to distinguish voltage data, four operating state perception results of voltage are obtained. Finally, the perception data is modified using Analytic Hierarchy Process to ensure that the state perception results meet the compatibility and consistency requirements with the actual results. According to the experimental results, the proposed method can accurately perceive the fluctuation of abnormal data within 40~50 ranges, which is consistent with the actual value fluctuation range, so as to obtain accurate perception results under different operating conditions.

Analysis of the Key Factors Influencing Automation Transformation in Container Terminals Based on the Dempster–Shafer Evidence Interval Method

The steady growth of container volume and aggressive shipping alliances have intensified competition among terminals. With the rapid development and application of modern information and communication technology, automation is a great way to promote terminals’ sustainable development. The objectives of this study are to analyze the key factors influencing automation transformation and provide theoretical guidelines for implementing automation transformation. Based on the literature review, the main factors influencing the automation transformation of container terminals are characterized and analyzed. They are divided into four categories: technical feasibility, external promotion, self-constraints, and goals/demand. Thereafter, a novel multi-source data fusion method, the Dempster–Shafer evidence interval method (DSIM) based on Dempster–Shafer evidence theory and interval value, is proposed to fuse conflicting and uncertain opinions among experts. This method is effective for expert opinion elicitation. Using DSIM, the key factors’ relative importance and their relationship are examined based on the opinions of experts in China. The results show that technical feasibility is the most important factor category in the foundation of automation transformation. Finally, a generalized planning framework for constructing automation transformation schemes is established, providing references and suggestions for terminals’ decision-makers.

Improving the Accuracy of Urban Waterlogging Simulation: A Novel Computer Vision-Based Digital Elevation Model Refinement Approach for Roads and Densely Built-Up Areas

Urban waterlogging is a natural disaster that occurs in developed cities globally and has inevitably become severe due to urbanization, densification, and climate change. The digital elevation model (DEM) is an important component of urban waterlogging risk prediction. However, previous studies generally focused on optimizing hydrological models, and there is a potential improvement in DEM by fusing remote sensing data and hydrological data. To improve the DEM accuracy of urban roads and densely built-up areas, a multisource data fusion approach (MDF-UNet) was proposed. Firstly, Fuzhou city was taken as an example, and the satellite remote sensing images, drainage network, land use, and DEM data of the study area were collected. Secondly, the U-Net model was used to identify buildings using remote sensing images. Subsequently, a multisource data fusion (MDF) method was adopted to reconstruct DEM by fusing the buildings identification results, land use, and drainage network data. Then, a coupled one-dimensional (1D) conduit drainage and two-dimensional (2D) hydrodynamic model was constructed and validated. Finally, the simulation results of the MDF-UNet approach were compared with the raw DEM data, inverse distance weighting (IDW), and MDF. The results indicated that the proposed approach greatly improved the simulation accuracy of waterlogging points by 29%, 53%, and 12% compared with the raw DEM, IDW, and MDF. Moreover, the MDF-UNet method had the smallest median value error of 0.08 m in the inundation depth simulation. The proposed method demonstrates that the credibility of the waterlogging model and simulation accuracy in roads and densely built-up areas is significantly improved, providing a reliable basis for urban waterlogging prevention and management.

Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System

Due to the rapid development of the mobile Internet and the Internet of Things, the volume of generated data keeps growing. The topic of data quality has gained increasing attention recently. Numerous studies have explored various data quality (DQ) problems across several fields, with corresponding effective data-cleaning strategies being researched. This paper begins with a comprehensive and systematic review of studies related to DQ. On the one hand, we classify these DQ-related studies into six types: redundant data, missing data, noisy data, erroneous data, conflicting data, and sparse data. On the other hand, we discuss the corresponding data-cleaning strategies for each DQ type. Secondly, we examine DQ issues and potential solutions for a public bus transportation system, utilizing a real-world traffic big data platform. Finally, we provide two representative examples, noise filtering and filling missing values, to demonstrate the DQ improvement practice. The experimental results show that: (1) The GPS noise filtering solution we proposed surpasses the baseline and achieves an accuracy of 97%; (2) The multi-source data fusion method can achieve a 100% missing repair rate (MRR) for bus arrival and departure. The average relative error (ARE) of bus arrival and departure times at stations is less than 1%, and the correlation coefficient (R) is also close to 1. Our research can offer guidance and lessons for enhancing data governance and quality improvement in the bus transportation system.

Intelligent control system for industrial robots based on multi-source data fusion

Abstract Industrialization has advanced quickly, bringing intelligent production and manufacturing into people’s daily lives, but it has also created a number of issues with the ability of intelligent control systems for industrial robots. As a result, a study has been conducted on the use of multi-source data fusion methods in the mechanical industry. First, the research analyzes and discusses the existing research at home and abroad. Then, a robot intelligent control system based on multi-source fusion method is proposed, which combines multi-source data fusion with principal component analysis to better fuse data of multiple control periods; In the process, the experimental results are dynamically evaluated, and the performance of the proposed method is compared with other fusion methods. The results of the study showed that the confidence values and recognition correctness of the intelligent control system under the proposed method were superior compared to the Yu, Murphy, and Deng methods. Applying the method to the comparison of real-time and historical data values, it is found that the predicted data under the proposed method fits better with the actual data values, and the fit can be as high as 0.9945. The dynamic evaluation analysis of single and multi-factor in the simulation stage demonstrates that the control ability in the training samples of 0–100 is often better than the actual results, and the best evaluation results may be obtained at the sample size of 50 per batch. The aforementioned findings demonstrated that the multi-data fusion method that was suggested had a high degree of viability and accuracy for the intelligent control system of industrial robots and could offer a fresh line of enquiry for the advancement and development of the mechanical industrialization field.

A belief logarithmic similarity measure based on Dempster-Shafer theory and its application in multi-source data fusion

Dempster-Shafer theory (DST) has attracted widespread attention in many domains owing to its powerful advantages in managing uncertain and imprecise information. Nevertheless, counterintuitive results may be generated once Dempster’s rule faces highly conflicting pieces of evidence. In order to handle this flaw, a new belief logarithmic similarity measure ( BLSM ) based on DST is proposed in this paper. Moreover, we further present an enhanced belief logarithmic similarity measure ( EBLSM ) to consider the internal discrepancy of subsets. In parallel, we prove that EBLSM satisfies several desirable properties, like bounded, symmetry and non-degeneracy. Finally, a new multi-source data fusion method based on EBLSM is well devised. Through its best performance in two application cases, specifically those pertaining to fault diagnosis and target recognition respectively, the rationality and effectiveness of the proposed method is sufficiently displayed.

Artificial Intelligent Power Forecasting for Wind Farm Based on Multi-Source Data Fusion

Wind power forecasting is a typical high-dimensional and multi-step time series prediction problem. Data-driven prediction methods using machine learning show advantages over traditional physical or statistical methods, especially for wind farms with complex meteorological conditions. Thus, effective use of different data sources and data types will help improve power forecasting accuracy. In this paper, a multi-source data fusion method is proposed, which integrates the static information of the wind turbine with observational and forecasting meteorological information together to further improve the power forecasting accuracy. Firstly, the characteristics of each time step are re-characterized by using the self-attention mechanism to integrate the global information of multi-source data, and the Res-CNN network is used to fuse multi-source data to improve the prediction ability of input variables. Secondly, static variable encoding and feature selection are carried out, and the time-varying variables are combined with static variables for collaborative feature selection, so as to effectively eliminate redundant information. A forecasting model based on the Encoder–Decoder framework is constructed with LSTM as the basic unit, and the Add&Norm mechanism is introduced to further enhance the input variable information. In addition, the self-attention mechanism is used to integrate the global time information of the decoded results, and the Time Distributed mechanism is used to carry out multi-step prediction. Our training and testing data are obtained from an operating wind farm in northwestern China. Results show that the proposed method outperforms a classic AI forecasting method such as that using the Seq2Seq+attention model in terms of prediction accuracy, thus providing an effective solution for multi-step forecasting of wind power in wind farms.

A Multi-Source Data Fusion Method for Assessing the Tunnel Collapse Risk Based on the Improved Dempster–Shafer Theory

Collapse is the main engineering disaster in tunnel construction when using the drilling and blasting method, and risk assessment is one of the important means to significantly reduce engineering disasters. Aiming at the problems of random decision-making and misjudgment of single indices in traditional risk assessment, a multi-source data fusion method with high accuracy based on improved Dempster–Shafer evidence theory (D-S model) is proposed in this study, which can realize the accurate assessment of tunnel collapse risk value. The evidence conflict coefficient K is used as the identification index, and the credibility and importance are introduced. The weight coefficient is determined according to whether the conflicting evidence is divided into two situations. The advanced geological forecast data, on-site inspection data and instrument monitoring data are trained by Cloud Model (CM), Gradient Boosting Decision Tree (GBDT) and Support Vector Classification (SVC), respectively, to obtain the initial BPA value. Combined with the weight coefficient, the identified conflict evidence is adjusted, and then the evidence from different sources is fused to obtain the overall collapse risk value. Finally, the accuracy is selected to verify the proposed method. The proposed method has been successfully applied to Wenbishan Tunnel. The results show that the evaluation accuracy of the proposed multi-source information fusion method can reach 88%, which is 16% higher than that of the traditional D-S model and more than 20% higher than that of the single-source information method. The high-precision multi-source data fusion method proposed in this paper has good universality and effectiveness in tunnel collapse risk assessment.

An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update

Accurate prediction of wind turbine power is important for the safe operation of wind farms. However, most of the previous online transfer learning methods are partially updated and time-consuming. Here we propose a novel system-wide update online transfer learning model to overcome these shortcomings. To improve the multi-source data fusion accuracy, a new time trend quantification method is applied to expand the data source, a convolutional neural network multi-source data fusion method is proposed to reduce the dimension of data, and a Hilbert spatial feature construction method is used to construct spatial information of data. To achieve system-wide update and rapid prediction, we have deleted the weight unit of traditional method and added two data buffers. The results show that: (1) the proposed multi-source data processing method has the smallest mapping errors, which mean absolute error for all wind turbines is less than 32.1; (2) the proposed online transfer learning model has the highest prediction accuracy, which is higher than 92.5%.

Dissolution profile prediction of sinomenine hydrochloride sustained release tablets, part II: a multisource data fusion approach

Multi-source data fusion methods were utilized to developed dissolution prediction models of sinomenine hydrochloride sustained-release tablets.

A multi-source heterogeneous spatial big data fusion method based on multiple similarity and voting decision

A multi-source heterogeneous spatial big data fusion method based on multiple similarity and voting decision

A Multi-Source Data Fusion Method to Improve the Accuracy of Precipitation Products: A Machine Learning Algorithm

In recent decades, several products have been proposed for estimating precipitation amounts. However, due to the complexity of climatic conditions, topography, etc., providing more accurate and stable precipitation products is of great importance. Therefore, the purpose of this study was to develop a multi-source data fusion method to improve the accuracy of precipitation products. In this study, data from 14 existing precipitation products, a digital elevation model (DEM), land surface temperature (LST) and soil water index (SWI) and precipitation data recorded at 256 gauge stations in Saudi Arabia were used. In the first step, the accuracy of existing precipitation products was assessed. In the second step, the importance degree of various independent variables, such as precipitation interpolation maps obtained from gauge stations, elevation, LST and SWI in improving the accuracy of precipitation modelling, was evaluated. Finally, to produce a precipitation product with higher accuracy, information obtained from independent variables were combined using a machine learning algorithm. Random forest regression with 150 trees was used as a machine learning algorithm. The highest and lowest degree of importance in the production of precipitation maps based on the proposed method was for existing precipitation products and surface characteristics, respectively. The importance degree of surface properties including SWI, DEM and LST were 65%, 22% and 13%, respectively. The products of IMERGFinal (9.7), TRMM3B43 (10.6), PRECL (11.5), GSMaP-Gauge (12.5), and CHIRPS (13.0 mm/mo) had the lowest RMSE values. The KGE values of these products in precipitation estimation were 0.56, 0.48, 0.52, 0.44 and 0.37, respectively. The RMSE and KGE values of the proposed precipitation product were 6.6 mm/mo and 0.75, respectively, which indicated the higher accuracy of this product compared to existing precipitation products. The results of this study showed that the fusion of information obtained from different existing precipitation products improved the accuracy of precipitation estimation.

Estimation and Dynamic Analysis of Soil Salinity Based on UAV and Sentinel-2A Multispectral Imagery in the Coastal Area, China

An efficient, convenient, and accurate method for monitoring the distribution characteristics of soil salinity is required to effectively control the damage of saline soil to the land environment and maintain a virtuous cycle of the ecological environment. There are still problems with single-monitoring data that cannot meet the requirements of different regional scales and accuracy, including inconsistent band reflectance between multi-source sensor data. This article proposes a monitoring method based on the multi-source data fusion of unmanned aerial vehicle (UAV) multispectral remote sensing, Sentinel-2A satellite remote sensing, and ground-measured salinity data. The research area and two experimental fields were located in the Yellow River Delta (YRD). The results show that the back-propagation neural network model (BPNN) in the comprehensive estimation model is the best prediction model for soil salinity (modeling accuracy R2 reaches 0.769, verification accuracy R2 reaches 0.774). There is a strong correlation between the satellite and UAV imagery, while the Sentinel-2A imagery after reflectivity correction has a superior estimation effect. In addition, the results of dynamic analysis show that the area of non-saline soil and mild-saline soil decreased, while the area of moderately and heavily saline soils and solonchak increased. Additionally, the average area share of different classes of saline soils distributed over the land use types varied in order, from unused land > grassland > forest land > arable land, where the area share of severe-saline soil distributed on unused land changed the most (89.142%). In this study, the results of estimation are close to the true values, which supports the feasibility of the multi-source data fusion method of UAV remote sensing satellite ground measurements. It not only achieves the estimation of soil salinity and monitoring of change patterns at different scales, but also achieve high accuracy of soil salinity prediction in ascending scale regions. It provides a theoretical scientific basis for the remediation of soil salinization, land use, and environmental protection policies in coastal areas.

AE-CNN-Based Multisource Data Fusion for Gait Motion Step Length Estimation

Step length estimation is essential for people to overcome different types of environmental obstacles in daily life. Aiming at the problem of low estimation accuracy based solely on surface electromyography (sEMG), in this article, a multisource (MS) data fusion method based on an auto-encoder-convolutional neural network (AE-CNN) is proposed to estimate the motion parameters of human lower limbs, which improve the accuracy of step length estimation. Specifically, the time-domain feature data (TDFD) of sEMG and insole pressure data from the lower limbs are first collected simultaneously to form the MS. Then, by applying auto-encoder (AE) as the fusion units, fused data are obtained and dimensionality reduction is realized. Finally, by integrating AE and convolutional neural network (CNN), the step length can be well estimated by utilizing the MS. For experimental validation, the performances of different data fusion units and regression units in step length estimation are compared. The results show that the proposed method can achieve good estimation. The normalized root-mean-square error (NRMSE) and Pearson correlation coefficient (PCC) of the proposed method reach 0.0479 ± 0.0263 and 0.8273 ± 0.1082, respectively. The NRMSE of the best trial is 0.0217 and the PCC is 0.9774. Furthermore, experiments at different walking speeds are carried out. The results show that the combination of AE and CNN has good performance with the increase in walking speed. In addition, a dimensionality reduction experiment based on AE is also carried out, and the optimal dimension is obtained.

Construction and Application of Piano to Intelligent Teaching System Based on Multi-Source Data Fusion

With the rapid development of modern information technology represented by the Internet, cloud computing and big data, education and teaching have gradually realized the deep integration of the Internet, which has changed people’s way of life, study and work to a certain extent. Intelligence [Formula: see text] education provides a new information-based teaching method for the development of education. From the perspective of improving the accuracy of data fusion results, this paper proposes a new multi-source data fusion method based on a set pair analysis connection degree for the situation that multiple sensors with unknown prior knowledge detect the same target feature parameters multiple times. By using the advantages of the set pair analysis feature function, the degree of opposition, the identity and difference between the measurement data are mined to adjust the degree of connection between the data. According to the existing signal-to-noise ratio weighting method in the fusion process, the weight of the measurement data is reasonably allocated to realize the weighted fusion of multi-source data, and the effectiveness and reliability of the algorithm are verified through simulation experiments. Through the summary of the questions, it can be seen that the learners are very satisfied with the mobile piano learning mode based on the intelligent teaching system, and they believe that this learning mode is conducive to the learner’s mastery of the basic knowledge of piano, and effectively improves the learners’ learning ability. It also improves the individual’s self-directed learning ability and academic performance. Through the research, it is concluded that the students’ piano mobile learning mode based on the intelligent teaching system is more conducive to the learner’s mastery of knowledge, and improves the learning interest and academic performance.

A fault diagnosis method with multi-source data fusion based on hierarchical attention for AUV

Multi-source data fusion is an important method to improve the performance of Autonomous Underwater Vehicle (AUV) fault diagnosis. However, most of the current fault diagnosis methods are based on a single data source or treat multi-source data as single. Firstly, we demonstrate the necessity of multi-source data fusion and propose a universal data hierarchy. Then, a hierarchical attention based multi-source data fusion method is proposed for fault diagnosis (HAMFD). The method consists of an encoder–decoder network, a fusion network stacked with encoders and attention mechanisms, and a fault recognition method based on attention distribution. The fusion network uses the encoder and hierarchical attention to extract the deep features, and fuse the features hierarchically. We use the multi-layer attention distribution to explain the fault evaluation and realize fault recognition. A random mask fusion strategy is designed for redundancy and a feature orthogonalization method is proposed for the strong coupling among multiple data sources. The proposed method is validated on the monitoring data of Qianlong-2 AUV obtained during the sea trial in the South China Sea. The fault detection rate is more than 98%, the recognition rate is about 100% for strong faults, and more than 90% for other faults.