Multi-source Data Fusion Model Research Articles

Droplet volume prediction methods in electrohydrodynamic jet printing based on multi-source data fusion

Electrohydrodynamic jet printing technology has garnered significant interest in additive manufacturing due to its advantages in higher resolution printing and greater viscosity material applicability. Nonetheless, the challenges associated with femtoliter-level droplets and the micrometer-scale printing space have rendered traditional optical observation methods for volume calculation impractical for integration into the printing systems. Obtaining the droplet volume accurately has become a formidable challenge in electrohydrodynamic printing. While existing research utilizes machine learning for the end-to-end prediction of process parameters to droplet volume, traditional prediction methods cannot achieve online prediction due to the complex spray states characteristic of electrohydrodynamic jet printing and pose integration difficulties within printing systems. This paper introduces a multi-source data fusion model suitable for electrohydrodynamic printing droplet volume prediction. The model employs the VGG network to extract features from Taylor cone images in the jetting state and synchronously fuse these with process parameter features. The fused features are then correlated with droplet volume labels through the MLP network for comprehensive model training. The performance of the proposed model has improved by 10% in prediction accuracy compared to single modal data. We integrated the prediction method into an electrohydrodynamic jet printing system and experimented with printing pixel-pit substrates. The results indicate that the prediction accuracy of the volume prediction system is over 92%. The printing efficiency has improved approximately 3 times compared to the traditional method, significantly enhancing overall performance.

A reduced-order configuration approach for the real-time calculation of three-dimensional flow behavior in a pipe network

The real-time computation of a three-dimensional pipe network flow is crucial for both pipe design and operational maintenance. This study devises a novel reduced-order configuration approach that combines the advantages of the acceleration characteristics of the reduced-order model and the structural applicability of the configuration model. First, a configuration model is established by categorizing sub-pipes extracted from a pipe network into sets based on the sub-pipes' type. Subsequently, reduced-order configurations are realized by a reduced-order model established for each type of configuration, enabling real-time computation of individual sub-pipes. Thus, the concatenation of sub-pipes allows the computation of an entire pipe network. A complex boundary–deep learning–reduced-order configuration model and a complex boundary–deep learning–reduced-order configuration–multi-source data–reduced-order configuration model integrated with a local multi-physical–discrete empirical interpolation method and a multi-source data fusion model are devised. These models were employed for the real-time computation and prediction of a three-dimensional velocity field for 300 snapshots composed of one to four sub-pipes extrapolated from a dataset of 294 pipe network snapshots composed of one to three sub-pipes. The maximum relative errors for snapshots from the dataset were similar to the limit precision of the proper orthogonal decomposition, with more precise accuracy than the relevant studies, indicating the excellent performance of our reduced-order configuration approach.

Research on knowledge graph-driven equipment fault diagnosis method for intelligent manufacturing

AbstractIn the process of rotating machinery fault diagnosis (RMFD), the lack of feature conditions leads to the problem of low accuracy of traditional rule-based reasoning methods FD. This paper proposed a knowledge graph (KG)-driven device FD method and applied it to RMFD. First, we proposed a multi-level KG construction method to get multi-source data based on each level and analyzed the levels that affected the system state. A single-level KG was constructed through data features, and a multilevel KG with a stereostructure was built using a multi-source data fusion model as data support for FD. Second, we proposed an approach based on multilevel KG and Bayes theory to detect the system state and located the source of faults by combining the KG reasoning based on relational paths, then used the relationships between the structures of rotating mechanical equipment for fault cause reasoning and used the KG as a knowledge base for a reason using machine learning. Finally, the proposed method was validated using a steelworks motor as an example and compared with other ways, such as rule-based FD. The results show that under the condition of missing input features, the accuracy of the proposed method reaches 91.1%, which is significantly higher than other methods and effectively solves the problem of low diagnostic accuracy.

A multi-source data fusion model for traffic flow prediction in smart cities

Abstract The emergence of problems such as increased urban traffic and transportation, traffic congestion, and road resource shortages prompted the city to prioritize the construction of intelligent transportation. Intelligent computing technology provides technical support for people’s smooth travel, as well as for the construction and development of the city. The article first delves into the basic theory of support vector machines, outlines a specific process for traffic prediction using these machines, and then suggests a method for preprocessing traffic data. This method primarily involves three steps: data collection, abnormal data elimination, and missing data recovery. Finally, it proposes a traffic flow prediction model that utilizes multi-source data from support vector machines. The experimental results demonstrate a higher degree of consistency between the prediction results and the actual results in the analysis of short-time traffic flow prediction on weekdays and weekends. Furthermore, the traffic flow prediction model based on Support Vector Machine, proposed in this paper, is capable of reliably predicting vehicular traffic flow in various severe weather environments, with a coefficient of parity below 0.018.

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.

Conversion and fusion method of multi-source and different populations maintainability prior data

Maintainability is an important universal quality characteristic that reflects the convenience, speed and economy of weapon and equipment maintenance. Making full use of multi-source data to accurately verify the degree to which the developed equipment meets the maintainability requirements is an important basis for equipment identification and acceptance. To solve the low reliability of equipment maintainability verification results caused by inaccurate comprehensive prior distribution obtained by fusing multi-source and different populations' prior data, a method of data conversion and fusion is proposed. A data conversion model based on the mean value ratio of failure mode maintenance data is constructed. The conversion factor is defined according to objective data to convert the different populations' prior data to the same populations. Next, a comparison of the prior distribution fitting performance of Bayes bootstrap, bootstrap, and two improved sample-resampling methods to are used obtain the closest fitting distribution to the true distribution. By constructing a multi-source data fusion model based on improved KL divergence, a symmetrical KL divergence is constructed to describe the similarity between each prior distribution and the field distribution for the weighted fusion of multi-source prior distribution in addition to determining and testing the normal comprehensive prior distribution. The results show that the conversion and fusion method effectively converts the multi-source and different populations’ maintainability prior data and obtains an accurate, comprehensive prior distribution by fusion, laying the foundation for applying the Bayes test method to verify the quantitative index of equipment maintainability.

All-Weather and Superpixel Water Extraction Methods Based on Multisource Remote Sensing Data Fusion

The high spatial and temporal resolution of water body data offers valuable guidance for disaster monitoring and assessment. These data can be employed to quickly identify water bodies, especially small water bodies, and to accurately locate affected areas, which is significant for protecting people’s lives and property. However, the application of optical remote sensing is often limited by clouds and fog during actual floods. In this paper, water extraction methods of the multisource data fusion model (MDFM) and superpixel water extraction model (SWEM) are proposed, in which the MDFM fuses optical and synthetic aperture radar (SAR) images, and all-weather water extraction is achieved by using spectral information of optical images, texture information and the good penetration performance of SAR images. The SWEM further improves the accuracy of the water boundary with superpixel decomposition for extracted water boundaries using the fully constrained least squares (FCLS) method. The results show that the correlation coefficient (r) and area accuracy (Parea) of the MDFM and SWEM are improved by 2.22% and 9.20% (without clouds), respectively, and 3.61% and 18.99% (with clouds), respectively, compared with the MDFM, and 41.54% and 85.09% (without clouds), respectively, and 32.31% and 84.31% (with clouds), respectively, compared with the global surface water product of the European Commission Joint Research Centre’s Global Surface Water Explorer (JRC-GSWE). The MDFM and SWEM can extract water bodies with all weather and superpixel and improve the temporal and spatial resolution of water extraction, which has obvious advantages.

Using UAV Multispectral Remote Sensing with Appropriate Spatial Resolution and Machine Learning to Monitor Wheat Scab

This study took the wheat grown in the experimental area of Jiangsu Academy of Agricultural Sciences as the research object and used the unmanned aerial vehicle (UAV) to carry the Rededge-MX multispectral camera to obtain the wheat scab image with different spatial resolutions (1.44 cm, 2.11 cm, 3.47 cm, 4.96 cm, 6.34 cm, and 7.67 cm). The vegetation indexes (VIs) and texture features (TFs) extracted from the UAV multispectral image were screened for high correlation with the disease index (DI) to investigate the impact of spatial resolution on the accuracy of UAV multispectral wheat scab monitoring. Finally, the best spatial resolution for UAV multispectral monitoring of wheat scab was determined to be 3.47 cm, and then, based on the 3.47 cm best resolution image, VIs and TFs were used as input variables, and three algorithms of partial least squares regression (PLSR), support vector machine regression (SVR), and back propagation neural network (BPNN) was used to establish wheat scab, monitoring models. The findings demonstrated that the VIs and TFs fusion model was more appropriate for monitoring wheat scabs by UAV remote sensing and had better fitting and monitoring accuracy than the single data source monitoring model during the wheat filling period. The SVR algorithm has the best monitoring effect in the multi-source data fusion model (VIs and TFs). The training set was identified as 0.81, 4.27, and 1.88 for the coefficient of determination (R2), root mean square error (RMSE), and relative percent deviation (RPD). The verification set was identified as 0.83, 3.35, and 2.72 for R2, RMSE, and RPD. In conclusion, the results of this study provide a scheme for the field crop diseases in the UAV monitoring area, especially for the classification and variable application of wheat scabs by near-earth remote sensing monitoring.

Multi-Source Data Processing and Fusion Method for Power Distribution Internet of Things Based on Edge Intelligence

With the rapid advancement of the Energy Internet strategy, the number of sensors within the Power Distribution Internet of Things (PD-IoT) has increased dramatically. In this study, an edge intelligence-based PD-IoT multi-source data processing and fusion method is proposed to solve the problems of confusing storage and insufficient fusion computing performance of multi-source heterogeneous distribution data. First, a PD-IoT multi-source data processing and fusion architecture based on edge smart terminals is designed. Second, the multi-source sensor data in the distribution network is unified in dimension and magnitude. By introducing the Box–Cox transform to improve the data offset problem in the Z-score normalization process, a multi-source heterogeneous data processing method for distribution networks based on the Box–Cox transform Z-score is proposed. Then, the conflicting phenomena of DS inference methods in data source fusion are optimally handled based on the PCA algorithm. A multi-source data fusion model based on DS inference with conflict optimization is constructed to ensure the effective fusion of distribution data sources from different domains. Finally, the effectiveness of the proposed method is verified by an experimental analysis of an IEEE39 node system in a regional distribution network in China.

A Multi-Source Data Fusion Decision-Making Method for Disease and Pest Detection of Grape Foliage Based on ShuffleNet V2

Disease and pest detection of grape foliage is essential for grape yield and quality. RGB image (RGBI), multispectral image (MSI), and thermal infrared image (TIRI) are widely used in the health detection of plants. In this study, we collected three types of grape foliage images with six common classes (anthracnose, downy mildew, leafhopper, mites, viral disease, and healthy) in the field. ShuffleNet V2 was used to build up detection models. According to the accuracy of RGBI, MSI, TIRI, and multi-source data concatenation (MDC) models, and a multi-source data fusion (MDF) decision-making method was proposed for improving the detection performance for grape foliage, aiming to enhance the decision-making for RGBI of grape foliage by fusing the MSI and TIRI. The results showed that 40% of the incorrect detection outputs were rectified using the MDF decision-making method. The overall accuracy of MDF model was 96.05%, which had improvements of 2.64%, 13.65%, and 27.79%, compared with the RGBI, MSI, and TIRI models using label smoothing, respectively. In addition, the MDF model was based on the lightweight network with 3.785 M total parameters and 0.362 G multiply-accumulate operations, which could be highly portable and easy to be applied.

Data Fusion for Multi-Source Sensors Using GA-PSO-BP Neural Network

The development of real-time road condition systems will better monitor road network operation status. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) floating vehicles and 2) remote traffic microwave sensors (RTMS). The former consists of the use of mobile probe vehicles as mobile sensors, and the latter consists of a set of fixed point detectors installed in the roads. First, the structure of a three-layer BP neural network is designed to achieve the fusion of the floating car data (FCD) and the fixed detector data (FDD) efficiently. Second, in order to improve the accuracy of traffic speed estimation, a multi-source data fusion model that combines information from floating vehicles and microwave sensors, and that, by using GA-PSO-BP neural network is proposed. The proposed model has combined GA and PSO ingeniously. The hybrid model can not only overcome the difficulties of the traditional fusion model of its estimation inaccuracy, but also compensate the insufficiency of the traditional BP algorithm. Finally, this system has been tested and implemented on actual roads, and the simulation results show the accuracy of data has reached 98%.

Adaptive Neuro-Fuzzy Fusion of Multi-Sensor Data for Monitoring a Pilot's Workload Condition.

To realize an early warning of unbalanced workload in the aircraft cockpit, it is required to monitor the pilot’s real-time workload condition. For the purpose of building the mapping relationship from physiological and flight data to workload, a multi-source data fusion model is proposed based on a fuzzy neural network, mainly structured using a principal components extraction layer, fuzzification layer, fuzzy rules matching layer, and normalization layer. Aiming at the high coupling characteristic variables contributing to workload, principal component analysis reconstructs the feature data by reducing its dimension. Considering the uncertainty for a single variable to reflect overall workload, a fuzzy membership function and fuzzy control rules are defined to abstract the inference process. An error feedforward algorithm based on gradient descent is utilized for parameter learning. Convergence speed and accuracy can be adjusted by controlling the gradient descent rate and error tolerance threshold. Combined with takeoff and initial climbing tasks of a Boeing 737–800 aircraft, crucial performance indicators—including pitch angle, heading, and airspeed—as well as physiological indicators—including electrocardiogram (ECG), respiration, and eye movements—were featured. The mapping relationship between multi-source data and the comprehensive workload level synthesized using the NASA task load index was established. Experimental results revealed that the predicted workload corresponding to different flight phases and difficulty levels showed clear distinctions, thereby proving the validity of data fusion.

Real-time Estimation of Urban Rail Transit Passenger Flow Status Based on Multi-source Data

The AFC data uploaded in real time for urban rail transit is incomplete and delayed. In order to improve the dynamic management and control level of rail transit, this paper describes the real-time state estimation process of passenger flow and its key issues, and analyses the relationship between real-time uploaded AFC data and mobile phone signal data, establishing a multi-source data fusion model based on gradient descent method; On this basis, combined with the multi-source data after fusion as the basis of real-time estimation, a realtime passenger flow estimation model based on Kalman filter is established. Practice shows that the fusion of multi-source data improves the accuracy of estimating basic data, the error of the real-time passenger flow estimation model based on Kalman filter is less than 10%, and the accuracy of the estimation model is good. The needs of the urban rail transit operation management department to grasp the real-time passenger flow distribution status of the network are met.

Location‐dependent sensing data collection and processing mechanism in vehicular network

AbstractWith the promotion of the Internet of Things, in which a growing number of objects in everyday life are able to communicate with each other, crowdsensing has attracted public attention. Among different devices, vehicles with various sensors can perform the traffic data collection tasks released by the traffic center, which is helpful for monitoring road conditions. Thus, we use crowdsensing between vehicles to improve the precision of traffic state estimation. We also propose a location‐dependent sensing task assignment mechanism for traffic data collection and a multisource data fusion model for traffic data processing in a vehicular network. First, we establish a mathematical model for the sensing task assignment considering the vehicle's time budget constraint, location, and collection task requirements. We then propose a task assignment algorithm consisting of determining the order for vehicle allocation and scheduling the optimal collection path for each vehicle, which aims to achieve maximum platform utility. Furthermore, we put forward a fusion model, including spatial fusion and temporal fusion, to better process the collected data. We use a power average operator for the spatial fusion and a temporal correlation–based data compression algorithm for the temporal fusion. The obtained simulation results validate the accuracy and correctness of our approach.

Research on Analysis of Multi-source Data Fusion Model in Smart Classroom

Smart Classroom, as a new learning environment, is a new direction in the development of education informatization and has been a hot research in the field of educational technology in recent years. Its wisdom goal is based on the large amounts of data that produced by learners study in the learning process. And for the Smart Classroom, the fusion analysis of the data in the learning process can provide some advice for learners and teachers. At present, there is little study on multi‐source data fusion analysis. In view of this, this paper shows the research status of data fusion, and analyzes the Smart Classroom from the dimension of data sources and data fusion, then builds a process model of multi‐source data fusion analysis, finally applies it into practice from the dimension of data fusion.

Establish the multi-source data fusion model of the shape of blast furnace burden surface based on co-universal kriging estimation method

This paper presents a multi–source data fusion model method which could improve the blast furnace (BF) burden surface model accuracy. First, the three sections of straight line are used to describe the cross section of BF burden surface, and apply the motion law of the furnace burden to constrain the specific parameters of the three sections of straight line. Secondly, a multi–source data fusion method based on co–universal kriging estimation method is proposed. The temperature and height data are combined to build the unbiased estimation for the burden surface shape. Finally, an example of surface shape model using our proposed method in a 2500 m³ BF of a steel plant is discussed. The application shows that, contrasted with the traditional model, the model accuracy has arisen by 8%, and the resolution of surface shape has arisen by 0.32. The novel method can provide necessary guidance for energy saving and emission reduction in operation of the BF.

Semantic intrusion detection with multisensor data fusion using complex event processing

Complex Event Processing (CEP) is an emerging technology for processing and identifying patterns of interest from multiple streams of events in real/near real time. Sensor network-based security and surveillance is a topic of recent research where events generated from distributed sensors at an unpredictable rate need to be analysed for possible threats and respond in a timely manner. Traditional software architectures like client/server architecture where the interactions are pull-based (DBMS) do not target the efficient processing of streams of events in real time. CEP which is a push-based system can process streaming data to identify the intrusion patterns in near real time and respond to the threats. An Intrusion Detection System (IDS) based on single sensor may fail to give accurate identification of intrusion. Hence there is a need for multisensor based IDS. A multisensor-based IDS enables identification of the intrusion patterns semantically by correlating the events and context information provided by multiple sensors. JDL multisource data fusion model is a well-known research model first established by the Joint Directorate Laboratories. This paper proposes JDL fusion framework-based CEP for semantic intrusion detection. The events generated from heterogeneous sensors are collected, aggregated using logical and spatiotemporal relations to form complex events which model the intrusion patterns. The proposed system is implemented and the results show that the proposed system out performs the pull-based solutions in terms of detection accuracy and detection time.