Multi-source Model Research Articles

Remaining useful life prediction across operating conditions based on deep subdomain adaptation network considering the weighted multi-source domain

As one of the core contents of transfer learning, domain adaptation (DA) has been widely used to predict of the remaining useful life (RUL) of rolling bearings across operating conditions. However, existing RUL prediction methods often adopt single-source DA and do not consider the sample's drift phenomenon and migration degree in the source domain. At the same time, the distribution of bearing samples in different subdomains (degradation stages) differs, forcing global adaptation to inevitably lead to negative migration. Therefore, a deep subdomain adaptation network considering weighted multi-source domain (DSAN-WM) is proposed to improve the prediction accuracy of RUL across operating conditions. Firstly, a migration metric is developed to measure the migration degree of samples in the source domain, and a high-quality multi-source domain sample set is constructed. Secondly, a fine-grained feature distribution alignment strategy for subdomains based on adaptive degradation stage identification is proposed to reduce the difference in sample distribution in the same subdomain. Thirdly, for each decision boundary obtained by the multi-source domain model, consistent alignment and degradation constraints based on physical knowledge are established to improve the consistency and prediction accuracy of each regressor output. Lastly, the effectiveness of the proposed method is verified using eight migration scenarios of two bearing data sets, and the superiority of the method is proven through a comparison with advanced deep learning and transfer learning approaches.

AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.

The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This paper introduces AG-MSTLN-EL, an attention-aided multi-source transfer learning ensemble learning model leveraging multi-source transfer learning (Visual Geometry Group ResNet and GoogLeNet), attention mechanisms, and ensemble learning to achieve robust and accurate brain tumor classification. Multi-source transfer learning allows knowledge extraction from diverse domains, enhancing generalization. The attention mechanism focuses on specific MRI regions, increasing interpretability and classification performance. Ensemble learning combines k-nearest neighbor, Softmax, and support vector machine classifiers, improving both accuracy and reliability. Evaluating the model's performance on a dataset with 3064 brain tumor MRI images, AG-MSTLN-EL outperforms state-of-the-art models in terms of all classification measures. The model's innovative combination of transfer learning, attention mechanism, and ensemble learning provides a reliable solution for brain tumor classification. Its superior performance and high interpretability make AG-MSTLN-EL a valuable tool for clinicians and researchers in medical image analysis.

BioKG-CMI: a multi-source feature fusion model based on biological knowledge graph for predicting circRNA-miRNA interactions

BioKG-CMI: a multi-source feature fusion model based on biological knowledge graph for predicting circRNA-miRNA interactions

Research on the Construction of a Digital Twin System for the Long-Term Service Monitoring of Port Terminals

Structural damage is a prevalent issue in long-term operations of harbor terminals. Addressing the lack of transparency in terminal infrastructure components, the limited integration of sensor monitoring data, and the insufficient support for feedback on service performance, we propose a novel digital twin system construction methodology tailored for the long-term monitoring of port terminals. This study elaborates on the organization and processing of foundational geospatial data, sensor monitoring information, and oceanic hydrometeorological data essential for constructing a digital twin of the terminal. By mapping relationships between physical and virtual spaces, we developed comprehensive dynamic and static models of terminal facilities. Employing a “particle model” approach, we visually represented oceanic and meteorological elements. Additionally, we developed a multi-source heterogeneous data fusion model to facilitate the rapid creation of data indexes for harbor elements under high concurrency conditions, effectively addressing performance issues related to scene-rendering visualization and real-time sensor data storage efficiency. Experimental validation demonstrates that this method enables the rapid construction of digital twin systems for port terminals and supports practical application in business scenarios. Data analysis and comparison confirm the feasibility of the proposed method, providing an effective approach for the long-term monitoring of port terminal operations.

Research on identification of flight cadets’ cognitive load based on multi-source physiological data and CGAN-DBN model

Modern aircraft cockpit system is highly information-intensive. Pilots often need to receive a large amount of information and make correct judgments and decisions in a short time. However, cognitive load can affect their ability to perceive, judge and make decisions accurately. Furthermore, the excessive cognitive load will induce incorrect operations and even lead to flight accidents. Accordingly, the research on cognitive load is crucial to reduce errors and even accidents caused by human factors. By using physiological acquisition systems such as eye movement, ECG, and respiration, multi-source physiological signals of flight cadets performing different flight tasks during the flight simulation experiment are obtained. Based on the characteristic indexes extracted from multi-source physiological data, the CGAN-DBN model is established by combining the conditional generative adversarial networks (CGAN) model with the deep belief network (DBN) model to identify the flight cadets’ cognitive load. The research results show that the flight cadets’ cognitive load identification based on the CGAN-DBN model established has high accuracy. And it can effectively identify the cognitive load of flight cadets. The research paper has important practical significance to reduce the flight accidents caused by the high cognitive load of pilots.

A global polycenter identification method with single‐source data: The integration of local multisource data recognition

AbstractWith the widespread application of multisource data, the identification of urban polycenters faces the challenge of increasing data costs. This study developed a cost‐effective model for identifying urban polycenters by employing a combination of the Random Forest algorithm and Local Moran's I index. Using point‐of‐interest data from Amap, our model was benchmarked against a multisource data model to verify its effectiveness and accuracy. The results indicate that the single‐source model possesses an accuracy comparable to that of the multisource model in determining the centrality and spatial distribution of urban centers, thus offering a substantial capability to reduce reliance on multisource data. The random forest method exhibits a significant accuracy advantage over traditional ordinary least squares regression methods. However, it also exhibited susceptibility to overfitting and variations in data sampling. This suggests that while the model is highly effective for large‐scale urban studies, it requires careful handling of data inputs. This model can be applied to actual urban planning and research, providing a useful instrument for investigating urban polycentric structures at different spatial scales. This will increase the usefulness of the model in real‐world scenarios and lower the expenses related to analyzing urban data.

Study of relativistic charged particle production using multi-source thermal model and emission feature of slowest target protons

Study of relativistic charged particle production using multi-source thermal model and emission feature of slowest target protons

Discussion on "LEAP: the latent exchangeability prior for borrowing information from historical data" by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim.

This discussion provides commentary on the paper by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim entitled "LEAP: the latent exchangeability prior for borrowing information from historical data". The authors propose a novel method to bridge the incorporation of supplemental information into a study while also identifying potentially exchangeable subgroups to better facilitate information sharing. In this discussion, we highlight the potential relationship with other Bayesian model averaging approaches, such as multisource exchangeability modeling, and provide a brief numeric case study to illustrate how the concepts behind latent exchangeability prior may also improve the performance of existing methods. The results provided by Alt et al. are exciting, and we believe that the method represents a meaningful approach to more efficient information sharing.

Propensity score weighted multi-source exchangeability models for incorporating external control data in randomized clinical trials.

Among clinical trialists, there has been a growing interest in using external data to improve decision-making and accelerate drug development in randomized clinical trials (RCTs). Here we propose a novel approach that combines the propensity score weighting (PW) and the multi-source exchangeability modelling (MEM) approaches to augment the control arm of a RCT in the rare disease setting. First, propensity score weighting is used to construct weighted external controls that have similar observed pre-treatment characteristics as the current trial population. Next, the MEM approach evaluates the similarity in outcome distributions between the weighted external controls and the concurrent control arm. The amount of external data we borrow is determined by the similarities in pretreatment characteristics and outcome distributions. The proposed approach can be applied to binary, continuous and count data. We evaluate the performance of the proposed PW-MEM method and several competing approaches based on simulation and re-sampling studies. Our results show that the PW-MEM approach improves the precision of treatment effect estimates while reducing the biases associated with borrowing data from external sources.

Application of multi-source data fusion on intelligent prediction of photovoltaic power

Accurate photovoltaic power prediction is crucial for reducing the uncertainty of photovoltaic systems in grid operation. Variations in cloud cover are one of the primary factors leading to fluctuations in photovoltaic power generation. However, clouds possess highly complex three-dimensional structures. Existing photovoltaic power prediction methods typically rely on two-dimensional cloud images, which are insufficient for fully capturing the impact of clouds on photovoltaic power generation. To address these challenges, this paper proposes a Multi-source data Photovoltaic power Prediction Model (MPPM) based on satellite cloud images and meteorological data. MPPM mainly includes SpatioTemporal feature Conditional Diffusion Model (STCDM), Attention Stacked LSTM network (ASLSTM) and Multidimensional Feature Fusion Module (MFFM). STCDM model utilizes a diffusion model to accurately forecast two-dimensional satellite cloud imagery. ASLSTM extracts the features of three-dimensional meteorological elements. MFFM module integrates the two-dimensional satellite cloud imagery features with the three-dimensional meteorological element features. The two-dimensional satellite cloud imagery reflects the visible aspect of cloud layers, while the three-dimensional meteorological data, which includes cloud water and ice content correlated with altitude, mainly captures the invisible aspect of cloud layers. These two sets of information complement each other, enabling a more comprehensive capture of the three-dimensional characteristics of clouds. Satellite cloud image prediction experiment and photovoltaic power prediction experiment are carried out on STCDM and MPPM model respectively. The experimental results demonstrate that the STCDM model achieved a Structural Similarity index (SSIM) of 0.909 for predicting satellite cloud imagery within 1 h and 0.789 within 24 h. Meanwhile, the MPPM model attained a pearson Correlation coefficient (CORR) of 0.945 for predicting PV power within 1 h and 0.856 within 24 h. These findings indicate that both the STCDM and MPPM models outperform other comparative algorithms in forecasting satellite cloud imagery and PV power.

Multi-source heterogeneous blockchain data quality assessment model for enterprise business activities.

Blockchain-based applications are becoming more and more widespread in business operations. In view of the shortcomings of existing enterprise blockchain evaluation methods, this paper proposes a multi-source heterogeneous blockchain data quality evaluation model for enterprise business activities, so as to achieve efficient evaluation of business activity information consistency, credibility and value. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method firstly proposes an entity information representation method based on the Representation learning for fusing entity category information (CEKGRL) model, which introduces the triad structure of related entities in blockchain, then associates them with enterprise business activity categories, and carries out similarity calculation through contextual information to achieve blockchain information consistency assessment. After that, a trustworthiness characterization method is proposed based on information sources, information comments, and information contents, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain.

Intelligent prediction of wave loads based on multi-source data-driven methods

ABSTRACT High-fidelity wave-load tests is pivotal in the design and development of modern ship structures. Nonetheless, the prohibitive cost of conducting such tests limits their applicability. A wave-load data fusion method was implemented using the model test data from a typical displacement-type ship to generalise the small sample test, and a multi-source data characterisation and model parameter optimisation method were established to achieve efficient wave-load (wave frequency and slamming superposition) prediction. Using multi-source data, a multi-fidelity wave-load prediction (MFWLP) model was constructed by combining numerical computation, regular wave tests (RWT), and irregular wave test (IWT) data based on transfer learning with a secondary correction wave load prediction under irregular wave conditions. The model was effectively employed in a ship wave-load engineering case. This study reveals that the adopted MFWLP model demonstrates adequate extrapolation and prediction capabilities, even with limited high-fidelity test samples. The discrepancy between the nonlinear wave load prediction and model test was found to be within 15%. Therefore, the proposed method can serve as a foundation for rapidly assessing ship wave loads across various sea conditions, especially under high sea states.

A data and physical model dual-driven based trajectory estimator for long-term navigation

Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation (DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory (Bi-LSTM) based quasi-static magnetic field (QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.

Geometric Error-Based Multi-Source Error Identification and Compensation Strategy for Five-Axis Side Milling

Based on a multi-source error model, this paper discusses the principle of error element identification and uses the mirror bias method to compensate the geometric errors of a process system. Firstly, a nine-line measurement method to determine the geometric error of the linear feed axes of machine tools is introduced, and the geometric error identification model based on the “nine-line method” is established. Then, using a ballbar mounted in the axial, tangential, and radial directions of the machine, the geometric error elements of the rotation axis are identified by three simple measurements in each direction. Subsequently, for the more common flat vise clamping workpiece in actual production, the workpiece position error is identified by using the traditional process of dimensional chain, and the workpiece attitude error is identified by fitting the angle between the positioning plane and the horizontal plane by the least squares method. Finally, based on the tool position points and tool axis vectors obtained from the multi-source error model, the error compensation value is solved using inverse machine tool kinematics to offset the machining error by mirroring the error value of the same size, and based on the “S-shaped specimen” to compensate the processing experiments, after compensation, the processing error is reduced by 30~45%, verifying the effectiveness of the compensation method.

A Multi-Source Stream Analysis Model for Adult Education Sustainability Study

Adult education is instruction intended for students who are elder and have recently gone into employment. People have the opportunity to increase their expertise and develop new abilities by learning as adult students. Investigation into adult education has been ongoing for a while, and there have been efforts made all around the globe to increase its application. The study's objectives were to investigate the features of the courses provided by education centers (EC), determine if they promote equality, and determine any organizational obstacles to adult education that were connected to these courses. Through the training courses they provide, ECs attempt to satisfy the educational requirements of people and industries in a variety of subjects. As a result, this research suggested a Multi-Source Stream Analysis (MSSA) Model for Adult Education Sustainability Study. The adult education sessions were the source of the information for this investigation. The adult education provided by ECs was evaluated by several criteria, including technical knowledge, communication skill, art and music, customs management, and self confidence. The research demonstrates that adult education enhances educational achievement and other professional abilities and makes adults more capable of realizing their full capability.

On cost-efficient parallel iterative solvers for 3D frequency-domain seismic multisource viscoelastic anisotropic wave modeling

Solving large sparse linear systems in 3D frequency-domain seismic wave modeling, especially in viscoelastic anisotropic media, poses significant challenges due to the increasing number of discrete moduli and nonzero elements in the linear system matrix. The computational load surpasses that of acoustic or viscoacoustic media, making it even more challenging when dealing with multisource problems. Popular scientific tools for solving a linear system, such as multifrontal massively parallel sparse direct solver (MUMPS), structured matrix package (STRUMPACK), and portable extensible toolkit for scientific computation (PETSc), can be used, but their applicability to our specific problem has not been comprehensively evaluated. Our study aims to tackle the challenges in solving large, sparse, complex-valued symmetric linear systems with multiple right-hand side vectors for 3D frequency-domain seismic wave modeling. We have adopted the preconditioned conjugate gradient iterative algorithms as the foundation for our research, introducing two highly cost-effective parallel iterative solvers: the parallel symmetric successive overrelaxation conjugate gradient (P-SSORCG) and the parallel incomplete Cholesky conjugate gradient (P-ICCG). These novel solvers are subjected to a comprehensive comparative analysis against well-established scientific tools, such as MUMPS, STRUMPACK, and PETSc, in the context of 3D frequency-domain seismic wave modeling. We show their promising performances in a practical 3D SEG/EAGE overthrust model and demonstrate that the grouped P-SSORCG offers an efficient alternative to parallel direct solvers, particularly in situations wherein computational resources are limited.

Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings

The effective prediction of building energy consumption can be used to optimize building operating modes and reduce the overall energy consumption and carbon emission of the building. To predict the energy consumption of the same type of building in the same area, it is often necessary to train separate prediction models for different buildings, which is usually costly computationally in order to get better prediction results. Therefore, this study will combine deep neural networks and multi-source domain generalization in an encoder-decoder architecture to train a model that can be directly applied to predict the energy consumption of multiple buildings in same type. In order to validate the predictive effectiveness of the model, it will be tested on real office building energy consumption dataset and different comparative experiments will be designed on the source and target domains. The results show that the constructed multi-source domain generalization model is able to accurately predict the energy consumption trend of different buildings 1 h in advance. It also has some energy consumption prediction ability for the unknow training set of buildings.

Multi-source monitoring data filtering assisted deformation analysis model updating of ultra-high rockfill dam

The deformation monitoring of ultra-high rockfill dams is now characterized by the applications of various monitoring instruments and the comparably dense measurement point deployment. The deformation analysis of rockfill dam can be greatly improved based on the multi-source monitoring data using the model updating technique. However, the model updating of rockfill dam faces challenges such as imbalanced spatial distribution of data, uneven data quality, and information redundancy, which leads to low performance and limits its application. Thus, this study proposes an innovative scheme for deformation analysis model updating, involving multi-source monitoring data filtering and model parameter identification. This scheme reduces the information redundancy, and ensures the finite element analysis (FEA) model of rockfill dam better reflects the actual deformation. The application in the 295 m Lianghekou rockfill dam demonstrates its notably enhancement of FEA accuracy, further improving the safety assessment.

A novel multi-source TWD model based on multi-granularity ball for multiple decision makers

Three-way incomplete multi-attribute decision-making model (TWIMADM) for multiple decision makers is an important topic in data science, which is an effective tool for decision making. However, calculation results of neighborhood blocks are still affected by neighborhood radius. In addition, most of the existing models without considering the information from the evaluations of multiple decision makers separately is difficult to obtain a comprehensive assessment. To address these problems, this paper builds a novel multi-source TWD model based on multi-granularity ball. First, the well-established granular ball is extended to multi-granularity, by which a weighted conditional probability is generated to divide the concerned region into three parts: positive, negative and boundary region. The calculation of the multi-granularity ball is independence of the neighborhood radius parameter. Subsequently, the joint entropy is developed by using the defined multi-granularity to obtain the optimal decision. Furthermore, the relative utility function is utilized to capture the preferences of decision makers, the classification and ranking of each object are determined by calculating the threshold value and expected utility value. Evaluation information from all decision makers are combined and fused. Finally, a novel TWD model is developed to address the challenges of incomplete multi-attribute decision-making problems involving multiple decision makers. The comparative case analyses and experimental evaluations have demonstrated the stability, validity, and feasibility of our model.

Water use characteristics and the mechanism of water uptake in two typical sand-fixing species in Mu Us sandy land

Understanding water absorption mechanisms of sand-fixing plants is important for the rational establishment of plant community structures, thereby providing a scientific basis for desertification control and the efficient utilization of water resources in sandy areas. Based on the hydrogen and oxygen isotopic compositions of precipi-tation, soil water, xylem water, and groundwater, coupled with soil water-heat dynamics, annual water consumption characteristics of vegetation, using the multi-source linear mixing model (IsoSource), we analyzed the differences in water sources between Salix psammophila and Artemisia ordosica, during winter and the growing season. We further examined the effects of groundwater depth (2 m and 10 m), soil freezing-thawing, and drought on their water utilization to elucidate water absorption mechanisms of those species. The results showed that: 1) During soil freezing-thawing period (January to March), S. psammophila mainly utilized soil water in 60-120 cm depths below the frozen layer (69.1%). In the green-up season (April and May), soil water from the 0-60 cm layers could satisfy the water demand of S. psammophila (30.9%-87.6%). During the dry period of the growing season (June), it predominantly utilized soil water at the depth of 120-160 cm (27.4%-40.8%). Over the rainy season (July and September), soil water in 0-60 cm depths provided 59.8%-67.9% of the total water required. A. ordosica, with shallow roots, could not utilize soil water after complete freezing of root zone but could overwinter by storing water in rhizomes during autumn. During the growing season, it primarily relied on 0-40 cm soil layer (23.4%-86.8%). During the dry period, it mainly utilized soil water from 40-80 cm and 80-160 cm soil layers, with utilization rates of 14.6%-74.4% and 21.8%-78.2%, respectively. 2) With decreasing groundwater depth, vegetation shifted its water absorption depth upward, with water source of S. psammophila transitioning from 120-160 cm to 60-160 cm layers, while A. ordosica shifted water absorption depth from 80-160 cm to 0-40 cm. S. psammophila's utilization of soil water is influenced by transpiration, adopting an "on-demand" approach to achieve a balance between water supply and energy conservation, whereas A. ordosica tends to utilize shallow soil water, exhibiting a higher depen-dence on water sources from a single soil layer.