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Although the data fusion of hyperspectral images (HSI) and light detection and ranging (LiDAR) has provided significant gains for land-cover classification, it also brings technical obstacles (i.e., it is difficult to capture discriminative local and global spatial-spectral from redundant data and build interactions between heterogeneous data). In this paper, a classification network named enhanced spectral attention and adaptive spatial learning guided network (ESASNet) is proposed for the joint use of HSI and LiDAR. Specifically, first, by combining a convolutional neural network (CNN) with the transformer, adaptive spatial learning (ASL) and enhanced spectral learning (ESL) are proposed to learn the spectral-spatial features from the HSI data and the elevation features from the LiDAR data in the local and global receptive field. Second, considering the characteristics of HSI with a continuous, narrowband spectrum, ESL is designed by adding enhanced local self-attention to enhance the mining of the spectral correlations across the adjacent spectrum. Finally, a feature fusion module is proposed to ensure an efficient information exchange between HSI and LiDAR during spectral features and spatial feature fusion. Experimental evaluations on the HSI-LiDAR dataset clearly illustrate that ESASNet performs better in feature extraction than the state-of-the-art methods. The code is available at https://github.com/AirsterMode/ESASNet. [ABSTRACT FROM AUTHOR]

The rapid and accurate estimation of leaf area index (LAI) through remote sensing holds significant importance for precise crop management. However, the direct construction of a vegetation index model based on multi-spectral data lacks robustness and spatiotemporal expansibility, making its direct application in practical production challenging. This study aimed to establish a simple and effective method for LAI estimation to address the issue of poor accuracy and stability that is encountered by vegetation index models under varying conditions. Based on seven years of field plot trials with different varieties and nitrogen fertilizer treatments, the Kalman filter (KF) fusion method was employed to integrate the estimated outcomes of multiple vegetation index models, and the fusion process was investigated by comparing and analyzing the relationship between fixed and dynamic variances alongside the fusion accuracy of optimal combinations during different growth stages. A novel multi-model integration fusion method, KF-DGDV (Kalman Filtering with Different Growth Periods and Different Vegetation Index Models), which combines the growth characteristics and uncertainty of LAI, was designed for the precise monitoring of LAI across various growth phases of rice. The results indicated that the KF-DGDV technique exhibits a superior accuracy in estimating LAI compared with statistical data fusion and the conventional vegetation index model method. Specifically, during the tillering to booting stage, a high R2 value of 0.76 was achieved, while at the heading to maturity stage, it reached 0.66. In contrast, within the framework of the traditional vegetation index model, the red-edge difference vegetation index (DVIREP) model demonstrated a superior performance, with an R2 value of 0.65, during tillering to booting stage, and 0.50 during the heading to maturity stage, respectively. The multi-model integration method (MME) yielded an R2 value of 0.67 for LAI estimation during the tillering to booting stage, and 0.53 during the heading to maturity stage. Consequently, KF-DGDV presented an effective and stable real-time quantitative estimation method for LAI in rice. [ABSTRACT FROM AUTHOR]

Human trajectories can be tracked by the internal processing of a camera as an edge device. This work aims to match peoples' trajectories obtained from cameras to sensor data such as acceleration and angular velocity, obtained from wearable devices. Since human trajectory and sensor data differ in modality, the matching method is not straightforward. Furthermore, complete trajectory information is unavailable; it is difficult to determine which fragments belong to whom. To solve this problem, we newly proposed the SyncScore model to find the similarity between a unit period trajectory and the corresponding sensor data. We also propose a Likelihood Fusion algorithm that systematically updates the similarity data and integrates it over time while keeping other trajectories in mind. We confirmed that the proposed method can match human trajectories and sensor data with an accuracy, a sensitivity, and an F1 of 0.725. Our models achieved decent results on the UEA dataset. [ABSTRACT FROM AUTHOR]

In order to avoid the loss of optimality of the optimal weighting factor in some cases and to further reduce the estimation error of an unbiased estimator, a multi-sensor adaptive weighted data fusion algorithm based on biased estimation is proposed. First, it is proven that an unbiased estimator can further optimize estimation error, and the reasons for the loss of optimality of the optimal weighting factor are analyzed. Second, the method of constructing a biased estimation value by using an unbiased estimation value and calculating the optimal weighting factor by using estimation error is proposed. Finally, the performance of least squares estimation data fusion, batch estimation data fusion, and biased estimation data fusion is compared through simulation tests, and test results show that biased estimation data fusion has a greater advantage in accuracy, stability, and noise resistance. [ABSTRACT FROM AUTHOR]

Existing methods for inverse synthetic aperture radar (ISAR) target recognition typically rely on a single high-resolution radar signal type, such as ISAR images or high-resolution range profiles (HRRPs). However, ISAR images and HRRP data offer representations of targets across different aspects, each containing valuable information crucial for radar target recognition. Moreover, the process of generating ISAR images inherently facilitates the acquisition of HRRP data, ensuring timely data collection. Therefore, to fully leverage the different information from both HRRP data and ISAR images and enhance ISAR ship target recognition performance, we propose a novel deep fusion network named the Separation-Decision Recognition network (SDRnet). First, our approach employs a convolutional neural network (CNN) to extract initial feature vectors from ISAR images and HRRP data. Subsequently, a feature separation module is employed to derive a more robust target representation. Finally, we introduce a weighted decision module to enhance overall predictive performance. We validate our method using simulated and measured data containing ten categories of ship targets. The experimental results confirm the effectiveness of our approach in improving ISAR ship target recognition. [ABSTRACT FROM AUTHOR]

Satellite precipitation products can help improve precipitation estimates where ground-based observations are lacking; however, their relative accuracy and applicability in data-scarce areas remain unclear. Here, we evaluated the accuracy of different satellite precipitation datasets for the Lancang River Basin, Western China, including the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals (GPM IMERG), and Fengyun 2G (FY-2G) datasets. The results showed that GPM IMERG and FY-2G are superior to TRMM 3B42RT for meeting local research needs. A subsequent bias correction on these two datasets significantly increased the correlation coefficient and probability of detection of the products and reduced error indices such as the root mean square error and mean absolute error. To further improve data quality, we proposed a novel correction–fusion method based on window sliding data correction and Bayesian data fusion. Specifically, the corrected FY-2G dataset was merged with GPM IMERG Early, Late, and Final Runs. The resulting FY-Early, FY-Late, and FY-Final fusion datasets showed high correlation coefficients, strong detection performances, and few observation errors, thereby effectively extending local precipitation data sources. The results of this study provide a scientific basis for the rational use of satellite precipitation products in data-scarce areas, as well as reliable data support for precipitation forecasting and water resource management in the Lancang River Basin. [ABSTRACT FROM AUTHOR]

Accurately identifying the spatial range of urban agglomerations holds significant practical importance for the precise allocation of various elements and coordinated development within urban agglomerations. However, current research predominantly focuses on the physical spaces of urban agglomerations, overlooking their sphere of influence. This study begins with the spatial interactions of population elements within urban agglomerations and fuses Weibo sign-in data with NTL data to identify the spatial range of urban agglomerations. It further compares and validates the results before and after the fusion of data. The results reveal that the accuracy of identifying the spatial range of urban agglomerations with the fusion of NTL data and Weibo sign-in data has improved by 7%, with a Kappa increase of 0.1766 compared to using NTL data alone, which indicates that fusing social media data can significantly enhance the accuracy of identifying the spatial range of urban agglomerations. This study proposes a novel approach for identifying the spatial range of urban agglomerations through the fusion of NTL data and social media data from a data fusion perspective. On one hand, it supplements the application of data fusion in the study of urban agglomeration spaces; on the other hand, it accurately identifies the spatial range of urban agglomerations, which holds great practical value for the sustainable development of urban agglomerations. [ABSTRACT FROM AUTHOR]

Approaching precipitation forecast refers to the prediction of precipitation within a short time scale, which is usually regarded as a spatiotemporal sequence prediction problem based on radar echo maps. However, due to its reliance on single-image prediction, it lacks good capture of sudden severe convective events and physical constraints, which may lead to prediction ambiguities and issues such as false alarms and missed alarms. Therefore, this study dynamically combines meteorological elements from surface observations with upper-air reanalysis data to establish complex nonlinear relationships among meteorological variables based on multisource data. We design a Residual Spatiotemporal Convolutional Network (ResSTConvNet) specifically for this purpose. In this model, data fusion is achieved through the channel attention mechanism, which assigns weights to different channels. Feature extraction is conducted through simultaneous three-dimensional and two-dimensional convolution operations using a pure convolutional structure, allowing the learning of spatiotemporal feature information. Finally, feature fitting is accomplished through residual connections, enhancing the model's predictive capability. Furthermore, we evaluate the performance of our model in 0–3 h forecasting. The results show that compared with baseline methods, this network exhibits significantly better performance in predicting heavy rainfall. Moreover, as the forecast lead time increases, the spatial features of the forecast results from our network are richer than those of other baseline models, leading to more accurate predictions of precipitation intensity and coverage area. [ABSTRACT FROM AUTHOR]

Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish patho-phenotypic features at the subcellular level for dilated cardiomyopathy (DCM). We employed a human induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model of a DCM mutation in the sarcomere protein troponin T (TnT), TnT-R141W, compared to isogenic healthy (WT) control iPSC-CMs. We established a multimodal data fusion (MDF)-based analysis to integrate source datasets for Ca2+ transients, force measurements, and contractility recordings. Data were acquired for three additional layer types, single cells, cell monolayers, and 3D spheroid iPSC-CM models. For data analysis, numerical conversion as well as fusion of data from Ca2+ transients, force measurements, and contractility recordings, a non-negative blind deconvolution (NNBD)-based method was applied. Using an XGBoost algorithm, we found a high prediction accuracy for fused single cell, monolayer, and 3D spheroid iPSC-CM models (≥92 ± 0.08 %), as well as for fused Ca2+ transient, beating force, and contractility models (>96 ± 0.04 %). Integrating MDF and XGBoost provides a highly effective analysis tool for prediction of patho-phenotypic features in complex human disease models such as DCM iPSC-CMs. [ABSTRACT FROM AUTHOR]

RGB-D salient object detection (SOD) has gained more and more research interest in recent years. Due to various imaging mechanisms of RGB and depth modalities, RGB-D images contain different information. Thus, how to effectively fuse multi-modality features and aggregate multi-scale features to generate accurate saliency prediction are still the problems. In this article, we present a Multi-Modality Information Refinement Fusion Network (MIRFNet) for RGB-D SOD to solve the problems. Specifically, a Feature-Enhancement and Cross-Refinement Module (FCM) is proposed to reduce redundant features and the gap between cross-modality data to achieve multi-modality feature fusion effectively. In FCM, the Feature-Enhancement step utilizes attention mechanisms to obtain enhanced features which contain less redundant information and more common salient information, and the Cross-Refinement step employs the enhanced features to reduce the gap between cross-modality features and achieve effective feature fusion. Then, we propose an Edge Guidance Module (EGM) to extract edge information from RGB features. Finally, to effectively aggregate multi-level features and achieve accurate saliency prediction, a Feature-Aggregation and Edge-Refinement Module (FEM) is designed, which introduces specific-modality information and edge information to conduct sufficient information interaction. In FEM, the Feature-Aggregation step aggregates multi-scale features with specific-modality information, and the Edge-Refinement step uses edge information to refine the aggregation features. Extensive experiments demonstrate that MIRFNet can achieve comparable performance against the other 12 SOTA methods on five datasets. [ABSTRACT FROM AUTHOR]

The devices deployed across diverse industrial scenarios have generated significant network traffic related to time. The system's irregular operation could result in substantial bad influence. Anomaly detection technologies utilized for identifying possible non-standard behaviours are paramount; furthermore, multivariate time series exhibit complex dependencies besides temporal correlation. However, most previous methods merely consider the temporal and variable correlation of time series data, neglecting the distance metrics among the sequences, leading to a deficiency in the model's anomaly detection ability. We propose a multivariate time series anomaly detection model based on the encoder–decoder architecture (CCER-ED). The model considers the similarity measure between temporal subsequences and designs a multi-scale feature embedding module for leveraging more interrelated properties. Moreover, the interrelations among sensors are explicitly learned using a manifold regularization graph structure. On this basis, an improved data fusion approach based on a multi-head self-attention mechanism is designed for capturing global feature representation, effectively integrating various aspects of information. Evaluations using the real-world datasets SWAT and WADI and performance analysis show that the proposed approach achieves improvement over the baselines in the recall and F1-score of anomaly detection performance at 9.3% and 8.5% (maximum), respectively, outperforming other existing methods. [ABSTRACT FROM AUTHOR]

The advancements of Internet of Things (IoT) technologies have enabled the implementation of smart and wearable sensors, which can be employed to provide older adults with affordable and accessible continuous biophysiological status monitoring. The quality of such monitoring data, however, is unsatisfactory due to excessive noise induced by various disturbances, such as motion artifacts. Existing methods take advantage of summary statistics, such as mean or median values, for denoising, without taking into account the biophysiological patterns embedded in data. In this research, a functional data analysis modeling method was proposed to enhance the data quality by learning individual subjects' diurnal heart rate (HR) patterns from historical data, which were further improved by fusing newly collected data. This proposed data-fusion approach was developed based on a Bayesian inference framework. Its effectiveness was demonstrated in an HR analysis from a prospective study involving older adults residing in assisted living or home settings. The results indicate that it is imperative to conduct personalized healthcare by estimating individualized HR patterns. Furthermore, the proposed calibration method provides a more accurate (smaller mean errors) and more precise (smaller error standard deviations) HR estimation than raw HR and conventional methods, such as the mean. [ABSTRACT FROM AUTHOR]

Precipitation is a fundamental factor affecting many fields, including freshwater reservation, flood warning and prevention, agriculture, and hydropower planning. Observation-based precipitation data usually come from two primary sources, namely gauges and satellite images. The former provides high reliability but sparse coverage, while the latter offers fine-grained data but is still inaccurate. There have been several efforts to estimate precipitation, including mathematical and probabilistic models and machine learning-based techniques. All existing solutions consider the target problem as a satellite data calibration with gauge data as complementary information. However, this approach fails to provide accurate predictive results due to the sparsity of gauges and significant errors in satellite data. This paper presents a novel precipitation estimating method that highlights the importance of gauge data, the most trustworthy data source. To be more precise, we formulate the precipitation estimation issue as a spatial prediction task with the goal of predicting rainfall data for non-monitoring locations using gauge data at the monitored sites. To this end, we propose a data-driven approach that exploits the encoder–decoder architecture, graph neural network, and the multimodal data fusion strategy. Specifically, we design an encoder that leverages a graph neural network for capturing the spatial relationship among the gauges. Meanwhile, the decoder exploits the convolutional networks to learn the temporal correlation within the historical data. Finally, we integrate satellite images and meteorological information using a multimodal data fusion based on a multilayer perceptron to enhance prediction accuracy. The experimental results show that our proposed model increases the estimation accuracy from 24.3 to 65.2% compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

With the proliferation of digital devices in internet of things (IoT) environment featuring advanced visual capabilities, the task of Image Source Identification (ISI) has become increasingly vital for legal purposes, ensuring the verification of image authenticity and integrity, as well as identifying the device responsible for capturing the original scene. Over the past few decades, researchers have employed both traditional and machine‐learning methods to classify image sources. In the current landscape, data‐driven approaches leveraging deep learning models have emerged as powerful tools for achieving higher accuracy and precision in source prediction. The primary focus of this research is to address the complexities arising from diverse image sources and variable quality in IoT‐generated multimedia data. To achieve this, a Hybrid Data Fusion Approach is introduced, leveraging multiple sources of information to bolster the accuracy and robustness of ISI. This fusion methodology integrates diverse data streams from IoT devices, including metadata, sensor information, and contextual data, amalgamating them into a comprehensive data set for analysis. This study introduces an innovative approach to ISI through the implementation of a Twin Convolutional Neural Network Architecture (TCA) aimed at enhancing the efficacy of source classification. In TCA, the first CNN architecture, referred to as DnCNN, is employed to eliminate noise from the original data set, generating 256 × 256 patches for both training and testing. Subsequently, the second CNN architecture is employed to classify images based on features extracted from various convolutional layers using a 3 × 3 filter, thereby enhancing prediction efficiency. The proposed model demonstrates exceptional accuracy in effectively classifying image sources, showcasing its potential as a robust solution in the realm of ISI. [ABSTRACT FROM AUTHOR]

Intrusion detection in industrial control systems (ICS) is crucial for maintaining secu rity in modern industries. However, the rapid growth of data generated from various sources presents significant challenges, as complex and diverse attacks continue to threaten the integrity of these systems. Traditional intrusion detection systems face limitations in effectively detecting intrusions and suffer from processing delays. To address these issues, there is an urgent need for a real-time and efficient IDS. This study introduces a novel approach to real-time intrusion detection in ICS by leveraging Cloud Computing and Big Data techniques for data fusion. By fusing mul tiple streams of data, our approach enhances intrusion detection performance, reduces false alarm rates, and produces more consistent and accurate results. The contributions of this work are two-fold. Firstly, we propose a real-time IDS that overcomes the limitations of traditional systems through the efficient processing capabilities of Cloud Computing and Big Data techniques. Secondly, we employ data fusion to integrate diverse data sources, resulting in improved intrusion detection accuracy and efficiency. Our proposed IDS achieves higher accuracy rates and demonstrates superior efficiency in detecting intrusions compared to existing solutions. These findings underscore the potential of our approach in enhancing ICS security and mitigating risks posed by evolving attacks. [ABSTRACT FROM AUTHOR]

Regional ionospheric Total Electron Content (TEC) models are most significant to investigate the variability of ionosphere during all-weather conditions. Since the ground-based TEC measurements are not available in oceanic and polar regions, space-based observations are incorporated to increase the positional accuracy and reliability of Global Ionospheric TEC Maps (GIMs). Therefore, data fusion techniques are necessary to enhance the prediction capability of regional ionospheric models by considering multi-source data. In view of this, an attempt is made to investigate Equatorial Ionization Anomaly (EIA) structures in low-latitudinal Indian region using multi-instrument data. Data observations from background model CODEGIM, available International GNSS Service (IGS) stations in India, GNSS receivers at KLEF and Tripura stations, FORMOSAT/COSMIC radio occultation and SWARM during geomagnetic storm period August 26–28, 2021 are used for the study. Spherical Harmonics Function (SHF) of order 4 is employed along with Weighted Least Squares analysis (WLS), where separate weight constraint is computed for each data source. TEC maps of spatial resolution 5° x 5° and temporal resolution of 1 h are generated to illustrate the dynamic behavior of ionosphere. The standard deviation for 3 days is obtained as 0.42, 0.61 and 1.43 TECU respectively. TEC depletions and enhancements are observed on August 27 and 28, with mean values of 12.74 and 15.27 TECU respectively, demonstrating negative and positive storm effects. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of distinguishing the leading agents in the group. The problem is considered in the framework of classification problems, where the agents in the group select the items with respect to certain properties. The suggested method of distinguishing the leading agents utilizes the connectivity between the agents and the Rokhlin distance between the subgroups of the agents. The method is illustrated by numerical examples. The method can be useful in considering the division of labor in swarm dynamics and in the analysis of the data fusion in the tasks based on the wisdom of the crowd techniques. [ABSTRACT FROM AUTHOR]

Medical imaging techniques play a crucial role in diagnosing major diseases such as cardiovascular diseases, cancers, and neurological disorders. The BMC Methods Collection "Advances in medical imaging techniques" will showcase the latest advancements in this field, including state-of-the-art imaging modalities, novel biomedical applications, progress in molecular probes and radiopharmaceuticals, and innovative methodologies for image analysis, data fusion, and visualization. [ABSTRACT FROM AUTHOR]

Utilizing multi-modal data, as opposed to only hyperspectral image (HSI), enhances target identification accuracy in remote sensing. Transformers are applied to multi-modal data classification for their long-range dependency but often overlook intrinsic image structure by directly flattening image blocks into vectors. Moreover, as the encoder deepens, unprofitable information negatively impacts classification performance. Therefore, this paper proposes a learnable transformer with an adaptive gating mechanism (AGMLT). Firstly, a spectral–spatial adaptive gating mechanism (SSAGM) is designed to comprehensively extract the local information from images. It mainly contains point depthwise attention (PDWA) and asymmetric depthwise attention (ADWA). The former is for extracting spectral information of HSI, and the latter is for extracting spatial information of HSI and elevation information of LiDAR-derived rasterized digital surface models (LiDAR-DSM). By omitting linear layers, local continuity is maintained. Then, the layer Scale and learnable transition matrix are introduced to the original transformer encoder and self-attention to form the learnable transformer (L-Former). It improves data dynamics and prevents performance degradation as the encoder deepens. Subsequently, learnable cross-attention (LC-Attention) with the learnable transfer matrix is designed to augment the fusion of multi-modal data by enriching feature information. Finally, poly loss, known for its adaptability with multi-modal data, is employed in training the model. Experiments in the paper are conducted on four famous multi-modal datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and Houston2013 (HU). The results show that AGMLT achieves optimal performance over some existing models. [ABSTRACT FROM AUTHOR]

KRAS is a pathogenic gene frequently implicated in non-small cell lung cancer (NSCLC). However, biopsy as a diagnostic method has practical limitations. Therefore, it is important to accurately determine the mutation status of the KRAS gene non-invasively by combining NSCLC CT images and genetic data for early diagnosis and subsequent targeted therapy of patients. This paper proposes a Semi-supervised Multimodal Multiscale Attention Model (S2MMAM). S2MMAM comprises a Supervised Multilevel Fusion Segmentation Network (SMF-SN) and a Semi-supervised Multimodal Fusion Classification Network (S2MF-CN). S2MMAM facilitates the execution of the classification task by transferring the useful information captured in SMF-SN to the S2MF-CN to improve the model prediction accuracy. In SMF-SN, we propose a Triple Attention-guided Feature Aggregation module for obtaining segmentation features that incorporate high-level semantic abstract features and low-level semantic detail features. Segmentation features provide pre-guidance and key information expansion for S2MF-CN. S2MF-CN shares the encoder and decoder parameters of SMF-SN, which enables S2MF-CN to obtain rich classification features. S2MF-CN uses the proposed Intra and Inter Mutual Guidance Attention Fusion (I2MGAF) module to first guide segmentation and classification feature fusion to extract hidden multi-scale contextual information. I2MGAF then guides the multidimensional fusion of genetic data and CT image data to compensate for the lack of information in single modality data. S2MMAM achieved 83.27% AUC and 81.67% accuracy in predicting KRAS gene mutation status in NSCLC. This method uses medical image CT and genetic data to effectively improve the accuracy of predicting KRAS gene mutation status in NSCLC. [ABSTRACT FROM AUTHOR]

Due to the complexity of the calculation process of the existing methods, the efficiency of data fusion of the power grid model is low. In order to improve the knowledge fusion effect of power grid model, this paper studied the knowledge fusion method of power grid model based on Seq2seq half pointer and half label method. The Text Rank algorithm is used to calculate the weight of semantic nodes of each grid model, and combined with the topological potential method, the semantic information of the grid model is extracted according to the final weight value, and the Seq2Seq semi-pointer semi-label model framework is constructed. The data of the scheduling automation system OMS and the production management system PMS are used as input. The extracted candidate mesh model semantics and the original mesh model semantics are encoded by Seq2Seq half-pointer half-label model. The semantic data of the power grid model is fused and sent to the Seq2Seq encoder. After the training is completed, the effective information is extracted from the power grid model through the Seq2Seq model to complete the knowledge fusion of the power grid model. Experimental results show that this method eliminates the redundant part of the basic attributes of each data source in the substation grid model after knowledge fusion, and the description of each basic attribute is more standardized, unified and perfect. Under different mesh model data dimensions, the support of the proposed method is all above 98%. The model trained by the proposed method tends to be stable after 120 iterations, and the precision, recall and F1 of the test set are 0.98, 0.93 and 0.91, respectively. At the same time, this method has high efficiency in the knowledge fusion processing of the power grid model, and its data processing speed is less than 160 s. The average integrity of the private data of the power grid model is 98.86%, indicating that the proposed method can better ensure the integrity of the data. Finally, compared with the application of other methods under different data amounts, the mean square error obtained by the proposed method is the smallest, indicating that the proposed method effectively improves the fusion accuracy. [ABSTRACT FROM AUTHOR]

Beijing Satellite 3 is a high-performance optical remote sensing satellite with a spatial resolution of 0.3–0.5 m. It can provide timely and independent ultra-high-resolution spatial big data and comprehensive spatial information application services. At present, there is no relevant research on the fusion method of BJ-3A satellite images. In many applications, high-resolution panchromatic images alone are insufficient. Therefore, it is necessary to fuse them with multispectral images that contain spectral color information. Currently, there is a lack of research on the fusion method of BJ-3A satellite images. This article explores six traditional pixel-level fusion methods (HPF, HCS, wavelet, modified-IHS, PC, and Brovey) for fusing the panchromatic image and multispectral image of the BJ-3A satellite. The fusion results were analyzed qualitatively from two aspects: spatial detail enhancement capability and spectral fidelity. Five indicators, namely mean, standard deviation, entropy, correlation coefficient, and average gradient, were used for quantitative analysis. Finally, the fusion results were comprehensively evaluated from three aspects: spectral curves of ground objects, absolute error figure, and object-oriented classification effects. The findings of the research suggest that the fusion method known as HPF is the optimum and appropriate technique for fusing panchromatic and multispectral images obtained from BJ-3A. These results can be utilized as a guide for the implementation of BJ-3A panchromatic and multispectral data fusion in real-world scenarios. [ABSTRACT FROM AUTHOR]

Bearing operation states will directly determine the performance of the equipment; thus, monitoring operation status and degradation indicators is the key to ensuring continuous and healthy operation of the equipment. However, most of the research uses single-source information data, which makes it difficult to model when dealing with multi-source information, complex data distribution, and noise. In this paper, a bearing performance degradation assessment method based on multi-source information is proposed to comprehensively utilize the data signals of different structures, spaces, types, and sources. First, the adversarial fusion convolutional autoencoder is constructed for obtaining the degradation index of the bearing, while the adversarial learning strategy is applied to achieve the effect of enhancing the robustness and sensitivity of the degradation indicators extracted by the network. Then the degradation index is input into the support vector data description to determine the fault anomalies of the degradation index adaptively and the fuzzy c-means algorithms to obtain the final rolling bearing performance degradation evaluation results. Through the verification results of two experiment datasets, it is found that the proposed model can achieve accurate evaluation and quantitative analysis of the performance degradation process of bearings. As a result, the entire network ensures the reconstruction accuracy of normal samples while simultaneously stretching the reconstruction error of abnormal samples to achieve accurate monitoring of degradation onset. [ABSTRACT FROM AUTHOR]

In the face of complex scenarios, the information insufficiency of classification tasks dominated by a single modality has led to a bottleneck in classification performance. The joint application of multimodal remote sensing data for surface observation tasks has garnered widespread attention. However, issues such as sample differences between modalities and the lack of correlation in physical features have limited the performance of classification tasks. Establishing effective interaction between multimodal data has become another significant challenge. To fully integrate heterogeneous information from multiple modalities and enhance classification performance, this paper proposes a dual-branch cross-Transformer feature fusion network aimed at joint land cover classification of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. The core idea is to leverage the potential of convolutional operators to represent spatial features, combined with the advantages of the Transformer architecture in learning remote dependencies. The framework employs an improved self-attention mechanism to aggregate features within each modality, highlighting the spectral information of HSI and the spatial (elevation) information of LiDAR. The feature fusion module based on cross-attention integrates deep features from two modalities, achieving complementary information through cross-modal attention. The classification task is performed using jointly obtained spectral and spatial features. Experiments were conducted on three multi-source remote sensing classification datasets, demonstrating the effectiveness of the proposed model compared to existing methods. [ABSTRACT FROM AUTHOR]

Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus presenting significant challenges in the integration of multi-omics data. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation module and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks; then, it applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we designed a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined-beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks. [ABSTRACT FROM AUTHOR]

Completing traffic data is a basic requirement for intelligent transportation systems. However, completing spatiotemporal traffic data poses a significant challenge, especially for high-dimensional data with complex missing mechanisms. Various completion methods targeting different missing mechanisms have showcased the superiority of tensor learning by effectively characterizing intricate spatiotemporal correlations. In this study, a novel tensor completion framework, known as the multi-source tensor completion method for data fusion, is proposed. This framework incorporates passenger transfer relationships between buses and subways into subway data completion, enhancing the data completion accuracy. Moreover, by combining bus transfer passenger flow data with other data dimensions, such as the different road sections, time intervals, and days, an innovative 4D low-rank tensor completion data framework was obtained. In addition, to boost the completion accuracy, a truncated l 2 , p norm optimization model was derived. This model enhances the non-convex performance of the objective function throughout the tensor completion process. The results highlight the superiority of the proposed completion method, leveraging fused subway/bus data over other completion methods that rely solely on subway data. [ABSTRACT FROM AUTHOR]

Many engineering problems involve optimizing a high-dimensional expensive black-box (HEB) design space. To solve such problems efficiently, a knowledge transfer (KT) assisted efficient global optimization (EGO) algorithm is proposed, called the KT-EGO, which extends the EGO algorithm for solving problems over higher dimensions (i.e.d>20). Specifically, the original design space is divided into several low-dimensional subset design spaces. More importantly, in order to extract information from the subset design spaces to accelerate the progress of full optimization, a surrogate-based data fusion strategy is proposed in the KT-EGO. And further, a searching strategy with an adaptive variable range is devised to enhance the exploitation of promising areas. To show the effectiveness of the proposed algorithm, it is compared against state-of-the-art algorithms over 12 benchmark functions and a 28-dimensional engineering optimization for the design of a compressor blade, which fully validates the effectiveness of the KT-EGO for solving HEB problems. [ABSTRACT FROM AUTHOR]

Accurate prediction of Electro-Discharge Machining (EDM) results is crucial for industrial applications, aiming to achieve high-performance and cost-efficient machining. However, both the current physical model and the standard Artificial Neural Network (ANN) model exhibit inherent limitations, failing to fully meet the accurate requirements for predicting EDM machining results. In addition, Micro-EDM Drilling can lead to the distortion of the macroscopic shape of machining pits under different input conditions, rendering the use of only the volume of machining pits as the evaluation index insufficient to express the complete morphological information. In this study, we propose a novel hybrid prediction model that combines the strengths of both physical and data-driven models to simultaneously predict Material Removal Rate (MRR) and shape parameters. Our experiment demonstrates that the hybrid model achieves a maximum prediction error of 4.92% for MRR and 5.28% for shape parameters, showcasing excellent prediction accuracy and stability compared to the physical model and the standard ANN model. [ABSTRACT FROM AUTHOR]

Information from multiple data sources is increasingly available. However, some data sources may produce biased estimates due to biased sampling, data corruption or model misspecification. Thus there is a need for robust data combination methods that can be used with biased sources. In this paper, a robust data fusion-extraction method is proposed. Unlike existing methods, the proposed method can be applied in the important case where researchers have no knowledge of which data sources are unbiased. The proposed estimator is easy to compute and employs only summary statistics; hence it can be applied in many different fields, such as meta-analysis, Mendelian randomization and distributed systems. The proposed estimator is consistent, even if many data sources are biased, and is asymptotically equivalent to the oracle estimator that uses only unbiased data. Asymptotic normality of the proposed estimator is also established. In contrast to existing meta-analysis methods, the theoretical properties are guaranteed for our estimator, even if the number of data sources and the dimension of the parameter diverge as the sample size increases. Furthermore, the proposed method provides consistent selection for unbiased data sources with probability approaching 1. Simulation studies demonstrate the efficiency and robustness of the proposed method empirically. The method is applied to a meta-analysis dataset to evaluate surgical treatment for moderate periodontal disease and to a Mendelian randomization dataset to study the risk factors for head and neck cancer. [ABSTRACT FROM AUTHOR]

We aim to make inferences about a smooth, finite-dimensional parameter by fusing together data from multiple sources. Previous works have studied the estimation of a variety of parameters in similar data fusion settings, including estimation of the average treatment effect and average reward under a policy, with the majority of them merging one historical data source with covariates, actions and rewards, and one data source of the same covariates. In this article, we consider the general case where one or more data sources align with each part of the distribution of the target population, such as the conditional distribution of the reward given actions and covariates. We describe potential gains in efficiency that can arise from fusing these data sources together in a single analysis, which we characterize by a reduction in the semiparametric efficiency bound. We also provide a general means of constructing estimators that achieve these bounds. Numerical simulations demonstrate marked improvements in efficiency from using the proposed estimators rather than their natural alternatives. Finally, we illustrate the magnitude of efficiency gains that can be realized in vaccine immunogenicity studies by fusing data from two HIV vaccine trials. [ABSTRACT FROM AUTHOR]

The number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of these ships, it is important to monitor the capacity that can be stored in the batteries, and classification societies typically require that this can be verified by independent tests—annual capacity tests. However, this paper discusses data‐driven alternatives based on operational sensor data collected from the batteries. There are different strategies for such data‐driven state of health (SOH) estimation. Some approaches require full operational history of the batteries in order to predict SOH, and this may be impractical due to several reasons. Thus, methods that are able to give reliable estimation of SOH based on only snapshots of the data streams are more attractive from a practical point of view. In this paper, data‐driven snapshot methods are explored and applied to degradation data from battery cells cycled in different laboratory tests. Hence, data from different sources are fused together with the aim of achieving better predictions. The paper presents the battery data show how relevant features can be extracted from snapshots of the data and presents data‐driven models for SOH estimation. It is discussed how such methods could be utilized in a data‐driven classification regime for maritime battery systems. Results are encouraging, and yields reasonable degradation estimates for nearly 40% of the tested cells, although the fusion of data from different laboratory tests did not improve results significantly. Results are greatly improved if data from the actual cell is included in the training data, and indicates that better results can be achieved if more representative training data is available. [ABSTRACT FROM AUTHOR]

The roughness of the part surface is one of the most crucial standards for evaluating machining quality due to its relationship with service performance. For a preferable comprehension of the evolution of surface roughness, this study proposes a novel surface roughness prediction model on the basis of the unity of fuse d signal features and deep learning architecture. The force and vibration signals produced in the milling of P20 die steel are collected, and time and frequency domain feature from the acquired signals are extracted by variational modal decomposition. The GA-MI algorithm is taken to select the signal features that are relevant to the surface roughness of the workpiece. The optimal feature subset is analyzed and used as the input of the prediction model. DBN is adopted to estimate the surface roughness and the model parameters are optimized by ISSA. (Reviewer 1, Q1) The separate force, vibration and fusion signal information are brought into the DBN model and the ISSA-DBN model for the prediction of surface roughness, and the results show that the accuracy of the roughness prediction is as follows, respectively DBN: 78.1%, 68.8% and 84.4%, and ISSA-DBN: 93.8%, 87.5% and 100%. [ABSTRACT FROM AUTHOR]

Prior to further processing, completed questionnaires must be screened for the presence of careless respondents. Different people will respond to surveys in different ways. Some take the easy path and fill out the survey carelessly. The proportion of careless respondents determines the survey's quality. As a result, identifying careless respondents is critical for the quality of obtained results. This study aims to explore the characteristics of careless respondents in survey data and evaluate the predictive power and interpretability of different types of data and indices of careless responding. The research question focuses on understanding the behavior of careless respondents and determining the effectiveness of various data sources in predicting their responses. Data from a three-month web-based survey on participants' personality traits such as honesty-humility, emotionality, extraversion, agreeableness, conscientiousness and openness to experience was used in this study. Data for this study was taken from Schroeders et al.. The gradient boosting machine-based prediction model uses data from the answers, time spent for answering, demographic information on the respondents as well as some indices of careless responding from all three types of data. Prediction models were evaluated with tenfold cross-validation repeated a hundred times. Prediction models were compared based on balanced accuracy. Models' explanations were provided with Shapley values. Compared with existing work, data fusion from multiple types of information had no noticeable effect on the performance of the gradient boosting machine model. Variables such as "I would never take a bribe, even if it was a lot", average longstring, and total intra-individual response variability were found to be useful in distinguishing careless respondents. However, variables like "I would be tempted to use counterfeit money if I could get away with it" and intra-individual response variability of the first section of a survey showed limited effectiveness. Additionally, this study indicated that, whereas the psychometric synonym score has an immediate effect and is designed with the goal of identifying careless respondents when combined with other variables, it is not necessarily the optimal choice for fitting a gradient boosting machine model. [ABSTRACT FROM AUTHOR]

The relevance of data fusion in handling big data in blockchain-enabled healthcare systems is of utmost importance in today's data-driven healthcare landscape. As healthcare systems continue to generate vast amounts of data from various sources, the need to effectively manage and analyze this data becomes crucial for informed decision-making, improved patient outcomes, and efficient healthcare operations. The gaps for time complexity and inaccuracy of feature fusion in the existing algorithms have been identified by surveying the existing literature and this article is proposing a multi-attribute feature fusion algorithm blockchain communications based on ant colony neural networks (ACNN) to overcome the problems in the state-of-the-arts. This article applies the feature decomposition method for communication between blockchain-based healthcare transactions, and optimizes the information based on the data characteristics. The proposed algorithm identifies the properties and attributes of the data by making use of rough set theory. A genetic algorithm is also used to improve the ACNN which enhances the search ability by minimizing the space complexity of the solution space. The multi-attribute fusion mechanism extracts the information from the blockchain transactions and filters out the characteristics of data using the proposed method, and achieves better accuracy. The empirical results show that the fusion error of the blockchain communications based on multi-attribute feature fusion algorithm is relatively small and stable. The outcomes of the proposed fusion mechanism are promising and it has been found that the proposed mechanism produces accurate results with minimal errors. The average energy consumption rate during the transition of data is below 2% which reflects the viability of the proposed fusion mechanism for blockchain-based healthcare transactions. [ABSTRACT FROM AUTHOR]

In recent years, machine learning methods based on epileptic signals have shown good results with brain-computer interfaces (BCIs). With the continuous expansion of their applications, the demand for labeled epileptic signals is increasing. For a large number of data-driven models, such signals are not suitable, as they extend the calibration cycle. Therefore, a new domain-adaptive TSK fuzzy system model based on multisource data fusion (DA-TSK) is proposed. The purpose of DA-TSK is to maintain high classification performance when the amount of labeled data is insufficient. The DA-TSK model not only has a strong learning ability to learn characteristic information from EEG data but is also interpretable, which aids in the understanding of the analytic process of the model for medical purposes. In particular, this model can make full use of a small amount of labeled EEG data in the source domain and target domain through domain adaptation. Therefore, the DA-TSK model can reduce data dependence to a certain extent and improve the generalization performance of the target classifier. Experiments are performed to evaluate the effectiveness of the DA-TSK model on public EEG datasets based on epileptic signals. The DA-TSK model can obtain satisfactory accuracy when the labeled data are insufficient in the target domain. [ABSTRACT FROM AUTHOR]

The traditional leveling, total station, and global navigation satellite system (GNSS) and the new differential interferometric synthetic aperture radar (DInSAR) and terrestrial laser scanning (TLS) systems have their own advantages and limitations in the deformation monitoring of mining areas. It is difficult to obtain accurate deformation information only using single-source measurement data. In this study, we propose an LOS deformation correction method for DInSAR in mining areas by fusing ground data without control points. Based on free space data, small deformations at the edges of mining influence areas accurately obtained using DInSAR. By combining leveling/GNSS and TLS methods, it was possible to obtain large deformations in central areas without the need for control points located outside the mining influence range. For overcoming the non-uniform coordinates of the "space–ground" data and the limited overlap of the effective measurement ranges, the subsidence prediction model was employed to assist in its fusion. In addition, in LOS deformation correction, we retained the non-full cycle phase of DInSAR and replaced the full cycle phase with the one from the data fusion. Engineering experiments have shown that the correction results preserve the differences in the LOS deformations at the edge areas of the mine influence range, and they recover the lost LOS deformations at the center areas. Using the difference in the LOS deformation before and after correction as the verification indicator, the maximum absolute value of the errors after correction was 143 mm, which was approximately 6.4% of the maximum LOS deformation. In addition, there were still two errors that were large (−112 mm and −89 mm, respectively), and the absolute values of errors were not more than 75 mm. For all errors, the mean absolute value was 36 mm. Compared with 399 mm before correction, the error was reduced by 91%. This study provides technical support and theoretical reference for deformation monitoring and control in mining areas. [ABSTRACT FROM AUTHOR]

In order to expand the application of remote sensing prospecting technology in gold prospecting, this paper takes the Zhuanshanzi area in Inner Mongolia as an example, and selects Landsat 8 OLI remote sensing data. On the basis of image data preprocessing, the regional tectonic information is enhanced by the fusion of B7B5B3 band. The principal component analysis is used to extract the alteration anomaly information through threshold segmentation. Based on the geological characteristics and remote sensing interpretation information of the region and Zhuanshanzi Gold Deposit, the prospecting indicator of gold deposits in Zhuanshanzi area is established, and 3 prospect areas for prospecting prediction are delineated. The field verification shows that there is gold mineralization in the prospect area. It is believed that the linear and ring structure and remote sensing anomaly information extraction based on OLI remote sensing images can be used as auxiliary means for gold prospecting prediction. [ABSTRACT FROM AUTHOR]

Single molecule localization microscopy offers resolution nearly down to the molecular level with specific molecular labelling, and is thereby a promising tool for structural biology. In practice, however, the actual value to this field is limited primarily by incomplete fluorescent labelling of the structure. This missing information can be completed by merging information from many structurally identical particles in a particle fusion approach similar to cryo-EM single-particle analysis. In this paper, we present a data analysis of particle fusion results of fluorescently labelled Nup96 nucleoporins in the Nuclear Pore Complex to show that Nup96 occurs in a spatial arrangement of two rings of 8 units with two Nup96 copies per unit giving a total of 32 Nup96 copies per pore. We use Artificial Intelligence assisted modeling in Alphafold to extend the existing cryo-EM model of Nup96 to accurately pinpoint the positions of the fluorescent labels and show the accuracy of the match between fluorescent and cryo-EM data to be better than 3 nm in-plane and 5 nm out-of-plane. [ABSTRACT FROM AUTHOR]

The roughness of the part surface is one of the most crucial standards for evaluating machining quality due to its relationship with service performance. For a preferable comprehension of the evolution of surface roughness, this study proposes a novel surface roughness prediction model on the basis of the unity of fuse d signal features and deep learning architecture. The force and vibration signals produced in the milling of P20 die steel are collected, and time and frequency domain feature from the acquired signals are extracted by variational modal decomposition. The GA-MI algorithm is taken to select the signal features that are relevant to the surface roughness of the workpiece. The optimal feature subset is analyzed and used as the input of the prediction model. DBN is adopted to estimate the surface roughness and the model parameters are optimized by ISSA. (Reviewer 1, Q1) The separate force, vibration and fusion signal information are brought into the DBN model and the ISSA-DBN model for the prediction of surface roughness, and the results show that the accuracy of the roughness prediction is as follows, respectively DBN: 78.1%, 68.8% and 84.4%, and ISSA-DBN: 93.8%, 87.5% and 100%. [ABSTRACT FROM AUTHOR]

Early diagnosis of Alzheimer's disease (AD) plays a key role in preventing and responding to this neurodegenerative disease. It has shown that, compared with a single imaging modality‐based classification of AD, synergy exploration among multimodal neuroimages is beneficial for the pathological identification. However, effectively exploiting multimodal information is still a big challenge due to the lack of efficient fusion methods. Herein, a multimodal fusion network based on attention mechanism is proposed, in which magnetic resonance imaging (MRI) and positron emission computed tomography (PET) images are converted into feature vectors with the same dimension, while the demographic information and clinical data are preprocessed and converted into feature vectors through embedding. This attention model can focus on important feature points, fuse the multimodal information more effectively, and thus provide accurate diagnosis and prediction for different pathological stages. The results show that the model achieves an accuracy of 84.1% for triple classification tasks in normal cognition (NC) versus mild cognitive impairment (MCI) versus AD and 93.9% prediction accuracy in stable MCI (sMCI) versus progressive MCI (pMCI). In contrast to the existing multimodal diagnosis methods, our model yields a state‐of‐the‐art accuracy of AD diagnosis, which is powerful and promising in clinical practice. [ABSTRACT FROM AUTHOR]

The three-dimensional detection in point cloud data for pavement cracks has drawn the attention of many researchers recently. In the field of pavement surface point cloud detection, the key tasks include the identification of pavement cracks and the extraction of the location and size information of pavement cracks. Based on the point cloud data of pavement surface, we developed two methods to directly extract and detect cracks, respectively. The first method is based on the improved sliding window algorithm by combining the random sample consensus (RANSAC) technique to directly extract the crack information from point clouds. The second method is developed based on YOLOv5 to process the two-dimensional images transformed from point cloud data for automatic pavement crack detection. We also attempted to fuse the point cloud images with greyscale images as input for the YOLOv5. Analysis results show that the improved sliding window algorithm efficiently extracts pavement cracks with less noise, and the YOLOv5-based method obtains a good detection of pavement cracks. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'. [ABSTRACT FROM AUTHOR]

In a world in continuous evolution and in which human needs grow exponentially according to the increasing world population, the advent of new technologies plays a fundamental role in all fields of industry, especially in agriculture. Optimizing times, automating machines, and guaranteeing product quality are key objectives in the field of Agriculture 4.0, which integrates various innovative technologies to meet the needs of producers and consumers while guaranteeing respect for the environment and the planet's resources. In this context, our research aims to propose an integrated system using data coming from an innovative experimental atmospheric and forecasting simulator (capable of predicting some characteristic climate variables subsequently validated with local sensors), combined with indices deriving from Remote Sensing and UAV images (treated with the data fusion method), that can give fundamental information related to Agriculture 4.0 with particular reference to the subsequent phases of system automation. These data, in fact, can be collected in an open-source GIS capable of displaying areas that need irrigation and fertilization and, moreover, establishing the path of an automated drone for the monitoring of the crops and the route of a self-driving tractor for the irrigation of the areas of interest. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most harmful natural hazards in Tehran metropolitan. Particles with a diameter of less than 2.5 micrometers (PM2.5) as one of the most harmful pollutants have endangered the health of people living in Tehran. One of the PM2.5 estimation techniques is the use of Aerosol Optical Depth (AOD) products derived from satellite observations. Various AOD products are retrieved with different algorithms that do not have the same accuracy and spatial resolution. Due to the differences in many assumptions and approximations adopted in the AOD retrieval process, the generated AOD products involve uncertainties. This issue reduces the accuracy of PM2.5 concentration estimation. The purpose of this study was to investigate the possibility of fusing AOD products obtained from MODIS sensor observations (retrieved by Deep Blue and Dark Target algorithms) to estimate PM2.5 more accurately. First, the performance of different machine learning algorithms in estimating PM2.5 from AOD data was evaluated. As a result, the XGBoost algorithm with the highest performance was selected as the base model for PM2.5 estimation. Then, the AOD products were fused using a weighted averaging based on the retrieval quality of the primary products. Finally, the fused AOD product along with meteorological data were employed to estimate PM2.5 using XGboost. The results demonstrated that the accuracy of PM2.5 estimation from the fused AOD product is better than when the AOD products are used individually (R2= 0.77, MAE = 7.00 μg/m³, RMSE = 9.59 μg/m³). Thus, the retrieval quality of AOD products will lead to more accurate estimation of PM2.5 in the end. [ABSTRACT FROM AUTHOR]

Symbolic time series analysis (STSA) plays an important role in the investigation of continuously evolving dynamical systems, where the capability to interpret the joint effects of multiple sensor signals is essential for adequate representation of the embedded knowledge. This technical brief develops and validates, by simulation, an STSA-based algorithm to make timely decisions on dynamical systems for information fusion and pattern classification from ensembles of multisensor time series data. In this context, one of the most commonly used methods has been neural networks (NN) in their various configurations; however, these NN-based methods may require large-volume data and prolonged computational time for training. An alternative feasible method is the STSA-based probabilistic finite state automata (PFSA), which has been shown in recent literature to require significantly less training data and to be much faster than NN for training and, to some extent, for testing. This technical brief reports a modification of the current PFSA methods to accommodate (possibly heterogeneous and not necessarily tightly synchronized) multisensor data fusion and (supervised learning-based) pattern classification in real-time. Efficacy of the proposed method is demonstrated by fusion of time series of position and velocity sensor data, generated from a simulation model of the forced Duffing equation. [ABSTRACT FROM AUTHOR]

Prior to further processing, completed questionnaires must be screened for the presence of careless respondents. Different people will respond to surveys in different ways. Some take the easy path and fill out the survey carelessly. The proportion of careless respondents determines the survey's quality. As a result, identifying careless respondents is critical for the quality of obtained results. This study aims to explore the characteristics of careless respondents in survey data and evaluate the predictive power and interpretability of different types of data and indices of careless responding. The research question focuses on understanding the behavior of careless respondents and determining the effectiveness of various data sources in predicting their responses. Data from a three-month web-based survey on participants' personality traits such as honesty-humility, emotionality, extraversion, agreeableness, conscientiousness and openness to experience was used in this study. Data for this study was taken from Schroeders et al.. The gradient boosting machine-based prediction model uses data from the answers, time spent for answering, demographic information on the respondents as well as some indices of careless responding from all three types of data. Prediction models were evaluated with tenfold cross-validation repeated a hundred times. Prediction models were compared based on balanced accuracy. Models' explanations were provided with Shapley values. Compared with existing work, data fusion from multiple types of information had no noticeable effect on the performance of the gradient boosting machine model. Variables such as "I would never take a bribe, even if it was a lot", average longstring, and total intra-individual response variability were found to be useful in distinguishing careless respondents. However, variables like "I would be tempted to use counterfeit money if I could get away with it" and intra-individual response variability of the first section of a survey showed limited effectiveness. Additionally, this study indicated that, whereas the psychometric synonym score has an immediate effect and is designed with the goal of identifying careless respondents when combined with other variables, it is not necessarily the optimal choice for fitting a gradient boosting machine model. [ABSTRACT FROM AUTHOR]

In order to predict the thermal deformation of CNC machine tool spindle system more accurately, a method based on multi-source heterogeneous information fusion is proposed. Aiming at the problem that it is difficult to obtain the global information of thermal deformation with a single type of information source, the effective prediction of thermal deformation of spindle system in machining center is realized under the fusion of temperature and vibration signals. First, the combined denoising method of empirical mode decomposition (EMD) and wavelet threshold is used to preprocess the vibration data. Then, the time-domain, frequency-domain, and time-frequency features of vibration signals are extracted, and the feature dimension is reduced based on correlation analysis and kernel principal component analysis (KPCA). After dimensionality reduction, the vibration data and temperature information are fused in the eigenmatrix. The nonlinear prediction of thermal deformation is studied by support vector regression for grid search parameter optimization (GS-SVR) and support vector regression for particle swarm optimization (PSO-SVR) methods. In order to realize the information acquisition of multi-channel temperature, vibration, and verify the effectiveness of the prediction model in the case of information fusion, a specific machining center is taken as the research object and experiments are performed based on multi-channel and heterogeneous signal acquisition. Finally, the prediction results based on different information sources are compared and analyzed. The results show that the thermal deformation of the machine tool obtained by the multi-source heterogeneous information fusion method is consistent with the actual test results. As compared with the predicted performance using only temperature information, the mean square error (MSE) decreased by 0.1663 µm. Therefore, the temperature - vibration information fusion model has higher accuracy in terms of predicting the thermal deformation of the spindle system model. [ABSTRACT FROM AUTHOR]

In real world industrial applications, the working environment of a bearing varies with time, and some unexpected vibration noises from other equipment are inevitable. In order to improve the anti-noise performance of neural networks, a new prediction model and a multi-channel sample generation method are proposed to address the above problem. First, we proposed a multi-channel sample representation method based on the envelope time–frequency spectrum of a different channel and subsequent three-dimensional filtering to extract the fault features of samples. Second, we proposed a multi-channel data fusion neural network (MCFNN) for bearing fault discrimination, where the dropout technique is used in the training process based on a dataset with a wide rotation speed and various loads. In a noise-free environment, our experimental results demonstrated that the proposed method can reach a higher fault classification of 99.00%. In a noisy environment, the experimental results show that for the signal-to-noise ratio (SNR) of 0 dB, the fault classification averaged 11.80% higher than other methods and 32.89% higher under a SNR of −4 dB. [ABSTRACT FROM AUTHOR]

In arid and semi-arid areas, soil moisture (SM) plays a crucial role in land-atmosphere interactions, hydrological processes, and ecosystem sustainability. SM data at large scales are critical for related climatic, hydrological, and ecohydrological research. Data fusion based on satellite products and model simulations is an important way to obtain SM data at large scales; however, little has been reported on the comparison of the data fusion methods in different categories. Here, we compared the performance of two widely used data fusion methods, the Ensemble Kalman Filter (EnKF) and the Back-Propagation Artificial Neural Network (BPANN), in the degraded grassland site (DGS) and the alpine grassland site (AGS). The SM data from the Community Land Model 5.0 (CLM5.0) and the Soil Moisture Active and Passive (SMAP) were fused and validated against the observations of the Cosmic-Ray Neutron Sensor (CRNS) to avoid the impacts of scale-mismatch. Results show that compared with the original data sets at both sites, the RMSE of the fused data by BPANN (FD-BPANN) and EnKF (FD-EnKF) had improved by more than 50% and 31%, respectively. Overall, the FD-BPANN performs better than the FD-EnKF because the BPANN method assigned higher weights to input data with better performance and the EnKF method is affected by the strong variabilities of both the fused CLM5.0 and SMAP data and the CRNS data. However, in terms of the percentile range, the FD-BPANN showed the worst performance, with overestimations in the low SM range of 25th percentile (

In wearable sensing, accurate estimation of physiological parameters is paramount, although these signals can be corrupted by noise. The fusion of data from multiple sensor sources has the potential to enhance accuracy, even in the presence of disruptive noise. This paper aims to introduce and compare various existing state-of-the-art and novel data fusion techniques to improve the reliability of heart rate estimation. The comparisons were implemented using the MIT-BIH Arrhythmia database with additive noise signals taken from MIT Noise Stress Test Database. When it comes to the challenging low signal-to-noise ratio (SNR) regions, the Kalman fusion and the α -trim mean filtering approach exhibits the best performance. The Kalman fusion approach dominates when both channels are corrupted, while the α -trim mean filtering elimination algorithm takes the lead when at least one channel is clean. To make the most of these strengths, we have developed an innovative algorithm that can switch between the two fusion methods based on a signal quality indicator (SQI) that serves as a surrogate SNR. This algorithm outperforms the baseline 2-channel RR-interval averaging approach by ≃ 54 % and ≃ 21 % at SNRs of 20 dB and − 20 dB respectively. Moreover, it outperforms other cutting-edge heart rate estimation methods. • Investigation of fusion methods for heart rate estimation from ECG signals. • Signal quality aware fusion algorithm selection. • Fusion methods analyzed at varying input signal-to-noise ratios. • Signal quality index proposed as a surrogate for input signal-to-noise ratio. • Fusion selection method proposed for varying input signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

This study presents a novel multimodal heterogeneous perception cross-fusion framework for intelligent vehicles that combines data from millimeter-wave radar and camera to enhance target tracking accuracy and handle system uncertainties. The framework employs a multimodal interaction strategy to predict target motion more accurately and an improved joint probability data association method to match measurement data with targets. An adaptive root-mean-square cubature Kalman filter is used to estimate the statistical characteristics of noise under complex traffic scenarios with varying process and measurement noise. Experiments conducted on a real vehicle platform demonstrate that the proposed framework improves reliability and robustness in challenging environments. It overcomes the challenges of insufficient data fusion utilization, frequent leakage, and misjudgment of dangerous obstructions around vehicles, and inaccurate prediction of collision risks. The proposed framework has the potential to advance the state of the art in target tracking and perception for intelligent vehicles. [ABSTRACT FROM AUTHOR]