Data Fusion Method Research Articles

Probabilistic Seismic Hazard Assessment for the North China Plain Earthquake Belt: Sensitivity of Seismic Source Models and Ground Motion Prediction Equations

AbstractIn this study, a multi-source data fusion method was proposed for the development of a Hybrid seismic hazard model (HSHM) in China by using publicly available data of the 5th Seismic Ground Motion Parameter Zoning Map (NSGM) and historical seismic catalogues and integrating with modern ground motion prediction equations (GMPEs). This model incorporates the characteristics of smoothed seismicity and areal sources for regional seismic hazard assessment. The probabilistic seismic hazard for the North China Plain earthquake belt was investigated through sensitivity analysis related to the seismicity model and GMPEs. The analysis results indicate that the Hybrid model can produce a consistent result with the NSGM model in many cases. However, the NSGM model tends to overestimate hazard values in locations where no major events have occurred and underestimate hazard values in locations where major events have occurred. The Hybrid model can mitigate the degree of such biases. Compared to the modern GMPEs, the GMPE with epicentral distance measures significantly underestimate the seismic hazard under near-field and large-magnitude scenarios. In addition, a comparison of the uniform hazard spectra (UHS) obtained by the models, with China’s design spectrum, shows that the current design spectrum is more conservative than the calculated UHS.

Development of a Multi-Source Satellite Fusion Method for XCH4 Product Generation in Oil and Gas Production Areas

Methane (CH4) is the second-largest greenhouse gas contributing to global climate warming. As of 2022, methane emissions from the oil and gas industry amounted to 3.586 million tons, representing 13.24% of total methane emissions and ranking second among all methane emission sources. To effectively control methane emissions in oilfield regions, this study proposes a multi-source remote sensing data fusion method based on the concept of data fusion, targeting high-emission areas such as oil and gas fields. The aim is to construct an XCH4 remote sensing dataset that meets the requirements for high resolution, wide coverage, and high accuracy. Initially, XCH4 data products from the GOSAT satellite and the TROPOMI sensor are matched both spatially and temporally. Subsequently, variables such as longitude, latitude, aerosol optical depth, surface albedo, digital elevation model (DEM), and month are incorporated. Using a local random forest (LRF) model for fusion, the resulting product combines the high accuracy of GOSAT data with the wide coverage of TROPOMI data. On this basis, ΔXCH4 is derived using GF-5. Combined with the GFEI prior emission inventory, the high-precision fusion dataset output by the LRF model is redistributed grid by grid in oilfield areas, producing a 1 km resolution XCH4 grid product, thereby constructing a high-precision, high-resolution dataset for oilfield regions. Finally, the challenges that emerged from the study were discussed and summarized, and it was envisioned that, in the future, with the advancement of satellite technology and algorithms, it would be possible to obtain more accurate and high-resolution datasets of methane concentration and apply such datasets to a wide range of fields, with the expectation that significant contributions could be made to reducing methane emissions and combating climate change.

A multi-band spectral data fusion method for improving the accuracy of quantitative spectral analysis

The signal-to-noise ratio of the spectrum is a critical determinant of detection accuracy in compositional analysis utilizing spectroscopy. The spectral data acquired by the spectrometer, while intended to capture essential sample characteristics, is often interspersed with various noise interferences. This contamination can severely disrupt the integrity of measurement outcomes. Therefore, this paper proposes the "multi-band spectral data fusion" method. In order to verify the feasibility of this method, this paper takes blood detection based on dynamic spectroscopy as an example and develops two models for each of the various components in blood. The experimental results show that when compared to modeling the raw spectrum data of the samples directly, the prediction accuracy of the model constructed using the new spectra processed by the multi-band spectral data fusion method suggested in this paper is greater. The correlation coefficient of the hemoglobin prediction set has improved by 13.48 %, and the root mean square error has decreased by 21.00 %. The correlation coefficient of the blood glucose prediction set improved by 4.07 %, and the root mean square error decreased by 12.78 %. The result demonstrates that the proposed method effectively mitigates the impact of random errors without compromising the spectral information content. The approach is not limited to blood component analysis but has potential applications across diverse spectroscopic domains, providing new ideas and methods for improving the accuracy of quantitative spectroscopic analysis.

A novel data fusion method to leverage passively-collected mobility data in generating spatially-heterogeneous synthetic population

Conventional methods to synthesize population use household travel survey (HTS) data. They generate many infeasible attribute values due to sequentially generating sociodemographics and spatial attributes and encounter a low spatial heterogeneity issue due to a low sampling rate of the HTS data. Passively collected mobility (PCM) data (e.g., cellular traces) provides extensive spatial coverage but poses integration challenges with HTS data due to differences in spatial resolution and attributes. This study introduces a novel cluster-based data fusion method to address these limitations and simultaneously generate synthetic populations with accurate sociodemographics and home–work locations at high spatial heterogeneity. Spatial clustering is adopted to align the spatial resolution of HTS and PCM data, facilitating effective data integration. The data fusion process is reformulated into cluster-specific low-dimensional optimization subproblems to ensure computational tractability. Analytical properties are derived to retain essential distributional characteristics from both datasets in the fused distribution. The spatial clustering process is optimized to ensure such distributional consistencies while maintaining a balance between feasibility and heterogeneity of the synthetic population. The data fusion properties are validated using HTS and LTE/5G cellular signaling data from Seoul, South Korea. Validation against census data confirms the method’s efficacy in maintaining distributional consistency while increasing spatial heterogeneity, with 97% of the generated population being unobserved in the HTS data. This research advances methods to synthesize a population by leveraging the complementary strengths of HTS and PCM data, providing a robust framework for generating spatially diverse synthetic populations essential for urban planning.

A Method for Multimodal IVA Fusion Within a MISA Unified Model Reveals Markers of Age, Sex, Cognition, and Schizophrenia in Large Neuroimaging Studies.

With the increasing availability of large-scale multimodal neuroimaging datasets, it is necessary to develop data fusion methods which can extract cross-modal features. A general framework, multidataset independent subspace analysis (MISA), has been developed to encompass multiple blind source separation approaches and identify linked cross-modal sources in multiple datasets. In this work, we utilized the multimodal independent vector analysis (MMIVA) model in MISA to directly identify meaningful linked features across three neuroimaging modalities-structural magnetic resonance imaging (MRI), resting state functional MRI and diffusion MRI-in two large independent datasets, one comprising of control subjects and the other including patients with schizophrenia. Results show several linked subject profiles (sources) that capture age-associated decline, schizophrenia-related biomarkers, sex effects, and cognitive performance. For sources associated with age, both shared and modality-specific brain-age deltas were evaluated for association with non-imaging variables. In addition, each set of linked sources reveals a corresponding set of cross-modal spatial patterns that can be studied jointly. We demonstrate that the MMIVA fusion model can identify linked sources across multiple modalities, and that at least one set of linked, age-related sources replicates across two independent and separately analyzed datasets. The same set also presented age-adjusted group differences, with schizophrenia patients indicating lower multimodal source levels. Linked sets associated with sex and cognition are also reported for the UK Biobank dataset.

Enhancing Long-Term Robustness of Inter-Space Laser Links in Space Gravitational Wave Detection: An Adaptive Weight Optimization Method for Multi-Attitude Sensors Data Fusion

The stable and high-precision acquisition of attitude data is crucial for sustaining the long-term robustness of laser links to detect gravitational waves in space. We introduce an effective method that utilizes an adaptive weight optimization approach for the fusion of attitude data obtained from charge-coupled device (CCD) spot-positioning-based attitude measurements, differential power sensing (DPS), and differential wavefront sensing (DWS). This approach aims to obtain more robust and lower-noise-level attitude data. A system is designed based on the Michelson interferometer for link simulations; validation experiments are also conducted. The experimental results demonstrate that the fused data exhibit higher robustness. Even in the case of a single sensor failure, valid attitude data can still be obtained. Additionally, the fused data have lower noise levels, with root mean square errors of 9.5%, 37.4%, and 93.4% for the single CCD, DPS, and DWS noise errors, respectively.

A modified extended Kalman filter based fusion of auto-correlation data for robot SLAM estimation

Abstract. With the popularization of robot technology, various technological developments related to robots have gradually entered a stage of rapid development. Especially path planning technology, which has been a very important and worthwhile part of robot motion and behavior since the concept of robots emerged. In this technology, various obstacles, environmental changes, and efficiency factors usually need to be considered. Whether in the past, present, or future, robot behavior pursues greater precision, reliability, adaptability to the environment, and even anthropomorphism. In order to achieve these goals, the idea of data fusion has emerged in the development of robot data processing. Therefore, the research direction is to use Kalman filtering for noise filtering and data correction to improve the accuracy of fused data. By using a feasible and strongly correlated data fusion method, combined with an improved extended Kalman filter, various data generated by robots can be fused in multiple dimensions and have correlations, reducing the negative impact of data differences and improving the quality and reliability of data fusion.

Estimation of Canopy Water Content by Integrating Hyperspectral and Thermal Imagery in Winter Wheat Fields

Rapid and accurate estimation of canopy water content (CWC) is important for agricultural water management and food security. Due to the complexity of dynamic changes in water transport during plant growth, estimation of CWC using a single sensor often leads to high uncertainty in the results. Multi-sensor data fusion is one of the solutions to this problem, but suitable spectral preprocessing methods and data fusion methods still need further research. The objectives of this study were to characterize the performance of two varieties at different growth stages under five water stress conditions and screen hyperspectral sensitive spectral bands by using continuous wavelet transform (CWT) and a successive projection algorithm (SPA). Ultimately, the CWC prediction model of winter wheat hyperspectral characteristic bands and thermal imaging information fusion was created using the GRA algorithm. The results showed that canopy temperature parameters and spectral parameters responded significantly to water deficits in winter wheat. Using the CWT-SPA method, a total of 285 hyperspectral feature bands with wavelet decomposition scales ranging from one to eight were selected. The sensitive bands were mainly distributed in the following ranges: 545–561, 746–1348, 1561–1810, and 2122–2430 nm. The GRA algorithm has good multi-source data model fusion capability, and its constructed prediction model based on hyperspectral and thermal image fusion has high accuracy on the canopy water content in winter wheat (R2 = 0.930, RMSE = 5.44%, nRMSE = 7.94%). Compared to the single-feature spectral model (R2 = 0.864, RMSE = 5.92%, nRMSE = 8.63%) and thermal image CWC prediction model (R2 = 0.813, RMSE = 7.22%, nRMSE = 10.49%), the model prediction accuracy based on the GRA algorithm is increased by 7.64% and 13.69%, respectively.

Solution for heterogeneous data fusion based on autonomous classification

Variance component estimation (VCE) is an essential component of heterogeneous data fusion, however, VCE has problems with stability and reliability in random models. To progress data fusion research, this paper proposes a new solution that establishes a data fusion model based on the theory of Msplit estimation. To determine the classification number of heterogeneous data accurately, a median function was applied for autonomous classification using Msplit estimation, according to the basic characteristics of random error distribution, a method of autonomous classification is established for heterogeneous data. Especially, the fusion method presented in this paper does not rely on prior information of observation data and can effectively overcome the limitations of current methods for data fusion based on the theory of VCE. An example of coordinate affine transformation was used to verify that the model proposed in this paper could achieve effective fusion of heterogeneous data.

Automatic Method for Detecting Deformation Cracks in Landslides Based on Multidimensional Information Fusion

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901.

Short-term hybrid prognostics of fuel cells: A comparative and improvement study

Accurately predicting the performance degradation trend of fuel cells helps take measures in advance and prolong the stack's service life, leading to a novel hybrid forecasting approach. The first grey model prediction method based on residual exponential smoothing optimization (ES-R-GM) can capture the voltage deterioration trend. We explore the integration of two different ES techniques, specifically the double ES (ES2) and cubic ES (ES3), to investigate their effect on enhancing the predictive accuracy of the GM model. The second method of the adaptive network fuzzy inference system (ANFIS) can characterize local nonlinear behavior. We utilize the simulated annealing (SA) algorithm to optimize ANFIS results under different fuzzy rule selection strategies. The outcomes of the two prediction methods mentioned above are combined to create a hybrid prediction using the data fusion method and the moving window technique. Various hybrid methods are evaluated under general conditions and further detailed optimization. The data collected from the experimental platform confirms the suggested hybrid framework. The results show that the hybrid ES3-R-GM + ANFIS-SC method outperformed the single models in final prediction accuracy and can effectively track both global trends and local changes. Simultaneously, it takes less time to calculate than the literature. Moreover, when applied to consistent public datasets, the hybrid approach maintains its robustness and accuracy compared with other hybrid prognostic methods.

Fog computing-enabled adaptive prognosis of cutting tool remaining life through multi-source data

Abstract Predicting cutting tool remaining life is important to sustainable machining. Accurate wear assessment improves efficiency, reduces waste, and lowers costs by minimizing tool failure. Traditional prognosis methods are often crippled by the inability to adapt to diverse working conditions across the machining process lifecycle. This paper introduces a fog computing-enabled adaptive prognosis framework utilizing multi-source data to address these challenges effectively. The key innovations include the following: (1) the proposed system integrates power and vibration data collected from LGMazak VTC-16A and IRON MAN QM200 machines. A standardized data fusion method combines multi-source data to enhance robustness and accuracy. (2) The transformer model is employed to improve prognosis accuracy of cutting tool remaining life; best accuracy of 98.24% and an average accuracy of 97.63% are achieved. (3) Finite element analysis is incorporated to validate the model’s predictions to validate reliability of deep learning model. (4) The fog computing optimization mechanism based on the bees algorithm, which shows fitness value of 0.92 and convergence within 15 iterations. The proposed method reduces total data volume in cloud by 54.12%, prediction time by 33.64%, and time complexity in the cloud layer by 4.62%. The effectiveness of fog computing in improving the operational efficiency and reliability of manufacturing systems is validated through the integration of advanced data analytics and deep learning techniques.

Theories and Methods for Indoor Positioning Systems: A Comparative Analysis, Challenges, and Prospective Measures.

In the era of the Internet of Things (IoT), the demand for accurate positioning services has become increasingly critical, as location-based services (LBSs) depend on users' location data to deliver contextual functionalities. While the Global Positioning System (GPS) is widely regarded as the standard for outdoor localization due to its reliability and comprehensive coverage, its effectiveness in indoor positioning systems (IPSs) is limited by the inherent complexity of indoor environments. This paper examines the various measurement techniques and technological solutions that address the unique challenges posed by indoor environments. We specifically focus on three key aspects: (i) a comparative analysis of the different wireless technologies proposed for IPSs based on various methodologies, (ii) the challenges of IPSs, and (iii) forward-looking strategies for future research. In particular, we provide an in-depth evaluation of current IPSs, assessing them through multidimensional matrices that capture diverse architectural and design considerations, as well as evaluation metrics established in the literature. We further examine the challenges that impede the widespread deployment of IPSs and highlight the potential risk that these systems may not be recognized with a single, universally accepted standard method, unlike GPS for outdoor localization, which serves as the golden standard for positioning. Moreover, we outline several promising approaches that could address the existing challenges of IPSs. These include the application of transfer learning, feature engineering, data fusion, multisensory technologies, hybrid techniques, and ensemble learning methods, all of which hold the potential to significantly enhance the accuracy and reliability of IPSs. By leveraging these advanced methodologies, we aim to improve the overall performance of IPSs, thus paving the way for more robust and dependable LBSs in indoor environments.

Research progress on electronic health records multimodal data fusion based on deep learning

Currently, the development of deep learning-based multimodal learning is advancing rapidly, and is widely used in the field of artificial intelligence-generated content, such as image-text conversion and image-text generation. Electronic health records are digital information such as numbers, charts, and texts generated by medical staff using information systems in the process of medical activities. The multimodal fusion method of electronic health records based on deep learning can assist medical staff in the medical field to comprehensively analyze a large number of medical multimodal data generated in the process of diagnosis and treatment, thereby achieving accurate diagnosis and timely intervention for patients. In this article, we firstly introduce the methods and development trends of deep learning-based multimodal data fusion. Secondly, we summarize and compare the fusion of structured electronic medical records with other medical data such as images and texts, focusing on the clinical application types, sample sizes, and the fusion methods involved in the research. Through the analysis and summary of the literature, the deep learning methods for fusion of different medical modal data are as follows: first, selecting the appropriate pre-trained model according to the data modality for feature representation and post-fusion, and secondly, fusing based on the attention mechanism. Lastly, the difficulties encountered in multimodal medical data fusion and its developmental directions, including modeling methods, evaluation and application of models, are discussed. Through this review article, we expect to provide reference information for the establishment of models that can comprehensively utilize various modal medical data.

Research progress of breast pathology image diagnosis based on deep learning

Breast cancer is a malignancy caused by the abnormal proliferation of breast epithelial cells, predominantly affecting female patients, and it is commonly diagnosed using histopathological images. Currently, deep learning techniques have made significant breakthroughs in medical image processing, outperforming traditional detection methods in breast cancer pathology classification tasks. This paper first reviewed the advances in applying deep learning to breast pathology images, focusing on three key areas: multi-scale feature extraction, cellular feature analysis, and classification. Next, it summarized the advantages of multimodal data fusion methods for breast pathology images. Finally, the study discussed the challenges and future prospects of deep learning in breast cancer pathology image diagnosis, providing important guidance for advancing the use of deep learning in breast diagnosis.

AI-Based solutions for current challenges in regenerative medicine

The emergence of Artificial Intelligence (AI) and its usage in regenerative medicine represents a significant opportunity that holds the promise of tackling critical challenges and improving therapeutic outcomes. This article examines the ways in which AI, including machine learning and data fusion techniques, can contribute to regenerative medicine, particularly in gene therapy, stem cell therapy, and tissue engineering. In gene therapy, AI tools can boost the accuracy and safety of treatments by analyzing extensive genomic datasets to target and modify genetic material in a precise manner. In cell therapy, AI improves the characterization and optimization of cell products like mesenchymal stem cells (MSCs) by predicting their function and potency. Additionally, AI enhances advanced microscopy techniques, enabling accurate, non-invasive and quantitative analyses of live cell cultures. AI enhances tissue engineering by optimizing biomaterial and scaffold designs, predicting interactions with tissues, and streamlining development. This leads to faster and more cost-effective innovations by decreasing trial and error. The convergence of AI and regenerative medicine holds great transformative potential, promising effective treatments and innovative therapeutic strategies. This review highlights the importance of interdisciplinary collaboration and the continued integration of AI-based technologies, such as data fusion methods, to overcome current challenges and advance regenerative medicine.

Determining the geographical origins of goji berries using the Twin-Tower model for Multi-Feature

Gogi berry products, known for their medicinal value, are exclusively available in the Ningxia region of China. However, the nutritional components, active ingredients, and economic value of these berries vary considerably among different cultivation zones in Ningxia. Accurate geographical traceability is essential not only for fostering the sustainable growth of the goji berry industry but also to ensure consumer safety, safeguard regional brands, and prevent fraudulent labelling practices that can erode market confidence and disrupt economic stability. Consequently, identification of the precise geographical origin of goji berries in Ningxia has emerged as a focal point in the progression of the industry. In this study, the Twin-Tower Model (TTM) was employed in conjunction with hyperspectral spectral and image data to establish a classification model for determining the proximate geographical origins of goji berries in Ningxia, including the regions of Zhongning, Guyuan, Tongxin, and Huinong. The results show that the graph-based training method built on TTM improves the accuracy by 3.7% compared to traditional data fusion methods. Notably, it attained a flawless 100% recognition rate for the highest quality Zhongning goji berries. These findings highlight that the implementation of the TTM method for multitask learning signifies a novel direction for transforming the conventional data fusion paradigm. Furthermore, it provides an essential technological means for the robust development of the goji berry industry.

A NOVEL APPROACH OF HUMAN EMOTIONAL ESTIMATION SYSTEM VIA MULTISENSORY DATASETS FUSION METHODS

Human emotional understanding has become one of the most rapidly emerging fields developed over the years since the emotional state is created and influenced by several neither easily measured and nor predicted factors. The proposed novel approach is based on the mixture of the processed acceleration data of several designed activities and processed heart rate data under several emotional states, recorded by the application of`smart band devices. Since the processed acceleration and heart rate data are running on different dimensionalities and recorded by different types of sensors, some machine learning-based methods applied to fusing these two kinds of data. The results of the data fusion methods are defined as the new fused datasets for Multi-Layer Perceptron and Support Vector Machines classifiers based on a selected criterion. At last, the accuracy results of the selected classifiers running under the defined machine-learning-based fusion dataset compared to each other to find out the effectiveness of the proposed fusion method.

Data fusion method for aircraft surveillance in flight zone based on Trans-Attention

Aiming at the problem of low monitoring accuracy and position jump of single monitoring source of aircraft in the flight zone, a method of aircraft monitoring data fusion based on Transformer and attention mechanism is proposed. Firstly, the encoder structure of Transformer is used to extract features of each monitoring source data respectively, and then weight values are assigned to different monitoring sources through the attention mechanism. Finally, regression calculation is performed through the fully connected network to obtain the final fusion result. The monitoring data of the surface surveillance radar and the broadcast automatic dependent surveillance system are selected as the fusion source, and the multi-point positioning data is used as the true label. The experimental results show that this method effectively reduces the monitoring error of a single monitoring source, and the fusion effect is better than the long short-term memory network, recurrent neural network and extended Kalman filter fusion method based on the attention mechanism, and the mean absolute error is improved by 2.20%、14.32%and respectively 33.94%.

Updating assembly parameters for spacecraft assembly process state changes: based on data fusion method

Thermal protection systems (TPSs) are important components of reusable spacecraft, and their assembly quality has a crucial impact on flight safety. Owing to the complex assembly process and variable states of spacecraft thermal protection systems, assembly parameters may vary under different assembly states. Therefore, to obtain assembly parameters accurately and efficiently under different assembly states, in this study, 3D point cloud data and fiber optic sensor data were fused to develop an assembly parameter update method for assembly process state changes. Firstly, based on the measured data of thermal protection components and load-bearing structure, the gap, flush and matching parameters solution model are proposed. Secondly, to address the deformation problem of the load-bearing structure caused by changes in assembly status, a fusion method based on laser scanning and sensor detection was devised to achieve deformation prediction of the assembly structure during the assembly process. Thirdly, based on the assembly parameter solution model and point cloud prediction model, a constraint-based assembly parameter optimisation model was established, and an improved quantum particle swarm optimisation (LQPSO) algorithm was employed to achieve assembly parameter updates oriented toward changes in assembly status. Finally, an experimental system for array-based thermal protection structure simulation was established to validate the proposed method. The results show that the proposed parameter update method can achieve ideal results for different assembly state simulation components.