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In the era of big data, the field of enterprise risk is facing considerable challenges brought by massive multisource heterogeneous information sources. In view of the proliferation of multisource and heterogeneous enterprise risk information, insufficient knowledge fusion capabilities, and the low level of intelligence in risk management, this article explores the application process of enterprise knowledge service models for rapid responses to risk incidents from the perspective of semantic reasoning and data fusion and clarifies the elements of the knowledge service model in the field of risk management. Based on risk data, risk decision making as the standard, risk events as the driving force, and knowledge graph analysis methods as the power, the risk domain knowledge service process is decomposed into three stages: prewarning, in-event response, and postevent summary. These stages are combined with the empirical knowledge of risk event handling to construct a three-level knowledge service model of risk domain knowledge acquisition-organization-application. This model introduces the semantic reasoning and data fusion method to express, organize, and integrate the knowledge needs of different stages of risk events; provide enterprise managers with risk management knowledge service solutions; and provide new growth points for the innovation of interdisciplinary knowledge service theory. [ABSTRACT FROM AUTHOR]

Models that predict the future state of certain observations are commonly developed by utilizing heterogeneous data. Most traditional prediction models tend to ignore inconsistencies and imperfections in heterogeneous data, and they are also limited in their ability to consider spatial correlations among monitoring points, as well as predict for the entire study area simultaneously. As a solution, this paper proposes a deep learning method to fuse heterogeneous data collected from multiple monitoring points by exploiting graph convolutional networks (GCNs), thus perform prediction for certain observations. The effectiveness of this approach was evaluated by applying it to a prediction scenario for air quality. As the fundamental idea behind the proposed method, (1) the collected heterogeneous data is fused according to the coordinates of the monitoring points considering its spatial correlations, and (2) the prediction considers global information as opposed to local information. According to an air quality prediction scenario, (1) the fused data obtained by the RBF-based fusion method are reasonable and reliable; (2) the fusion significantly increases predictive model performance; and (3) the STGCN model enhanced by the fusion achieves the highest performance. This approach can be similarly applied in scenarios involving continuous heterogeneous data collected from scattered multiple monitoring points in the study area. [ABSTRACT FROM AUTHOR]

This work explores the importance of properly handling the big variety and amount of measurements data that nowadays can be more and more easily acquired. In fact, in the last decades a new challenge took hold: the academic and industrial focus, in the most different fields, extended from developing new technologies to acquire data and more accurate ways to process it, to also developing software environments to properly handle and process the big amount and large variety of available data. In surveying, when the size and the complexity of the site requires the acquisition of diverse data and the usage of different kind of devices, working in different coordinate systems and operated by multiple people, data process and interpretation is much more error prone. When surveying a complex construction site, early identification of deviations from the design during the construction phase is crucial and has as well a big impact on the project's economic budget. Building Information Modelling (BIM) is the accepted standard for managing information for an asset, and for the construction phase it provides working from a common visualisation of the as-planned model in a 3D environment. The as-built model is generated from a large variety of acquired data, especially when site complexity requires a high level of data integration. Data are usually acquired using GNSS receivers, Terrestrial Laser Scanners (TLS), Total Stations (TS), Digital Levels, and Aerial and Terrestrial photogrammetric devices. Specifically, over the last decade Unmanned Aerial Vehicles (UAV) have been proving their importance also in construction progress and quality monitoring, thanks to their relative easiness of use and the overview they can provide over the construction site. In this work, the value of merging and processing in a single software environment different data sources to reduce errors and get geodetically relevant data is analysed. Specifically, Leica Infinity, a specialised software able to bring various data sources together for working in a common reference frame, is investigated and its effectiveness and efficiency proved for monitoring a complex construction site. The advantages of an integrated data acquisition and process, and in particular the contribution of UAV data in construction site monitoring, both from an accuracy and completeness standpoint, are also assessed. [ABSTRACT FROM AUTHOR]

Many datasets in engineering applications are heterogeneous mixtures of noise-free data, noisy data with known noise variances, and noisy data with unknown noise variances. This article proposes a data fusion method called the multi-type data fusion (MTDF) model, which fully utilizes the information provided by each of these types of data. To capture the underlying trend implied in the multiple types of data, the method approximately interpolates the noise-free data, while regressing the noisy data. The prediction accuracy of the MTDF model is compared with those of various surrogate models (interpolation models, regression models, and multi-fidelity models) on both numerical and practical engineering problems. In the experiments, the proposed MTDF model demonstrates higher performance than the other benchmark models. The effects of noise level and sample size of the noise-free data on the model performance are investigated, along with the robustness of the MTDF model. The results demonstrate the satisfactory feasibility, practicality, and stability of the MTDF. [ABSTRACT FROM AUTHOR]

In urban environmental management and public health evaluation efforts, there is an urgent need for fine-grained urban air quality monitoring. However, the high price and sparse distribution of air quality monitoring equipment make it difficult to develop effective and comprehensive fine-scale monitoring at the city scale. This has also led to air quality estimation methods based on incomplete monitoring data, which lack the ability to detect urban air quality differences within a neighborhood. To address this problem, this study proposes a refined urban air quality estimation method that fuses multisource spatio-temporal data. Based on the fact that urban air quality is easily affected by social activities, this method integrates meteorological data with urban social activity data to form a comprehensive environmental data set. It uses the spatio-temporal feature extraction model to extract the multi-source spatio-temporal features of the comprehensive environmental data set. Finally, the improved cascade forest algorithm is used to fit the relationship between the multisource spatio-temporal features and the air quality index (AQI) to construct an air quality estimation model, and the model is used to estimate the hourly PM2.5 index in Beijing on a 1 km × 1 km grid. The results show that the estimation model has excellent performance, and its goodness-of-fit (R2) and root mean square error (RMSE) reach 0.961 and 17.47, respectively. This method effectively achieves the assessment of urban air quality differences within a neighborhood and provides a new strategy for preventing information fragmentation and improving the effectiveness of information representation in the data fusion process. [ABSTRACT FROM AUTHOR]

Data fusion describes the method of combining data from (at least) two initially independent data sources to allow for joint analysis of variables which are not jointly observed. The fundamental idea is to base inference on identifying assumptions, and on common variables which provide information that is jointly observed in all the data sources. A popular class of methods dealing with this particular missing-data problem in practice is based on covariate-based nearest neighbour matching, whereas more flexible semi- or even fully parametric approaches seem underrepresented in applied data fusion. In this article we compare two different approaches of nearest neighbour hot deck matching: One, Random Hot Deck, is a variant of the covariate-based matching methods which was proposed by Eurostat, and can be considered as a 'classical' statistical matching method, whereas the alternative approach is based on Predictive Mean Matching. We discuss results from a simulation study where we deviate from previous analyses of marginal distributions and consider joint distributions of fusion variables instead, and our findings suggest that Predictive Mean Matching tends to outperform Random Hot Deck. [ABSTRACT FROM AUTHOR]

Voltage sag is the most serious power quality problem in the three-phase symmetrical power system. The influence of multiple factors on the voltage sag level and low computational efficiency also pose challenges to the prediction of residual voltage amplitude of voltage sag. This paper proposes a voltage sag amplitude prediction method based on data fusion. First, the multi-dimensional factors that influence voltage sag residual voltage are analyzed. Second, these factors are used as input, and a model for predicting voltage sag residual voltage based on data fusion is constructed. Last, the model is trained and debugged to enable it to predict the voltage sag residual voltage. The accuracy and feasibility of the method are verified by using the actual power grid data from East China. [ABSTRACT FROM AUTHOR]

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the information collected from wireless sensor network nodes has a great similarity and some errors exist in the data results, this study proposes a data fusion algorithm based on Kalman filter for wireless sensor network, which improves the efficacy and accuracy of uploaded data by filtering invalid data and shrunk data packets. The algorithm uses the Kalman filter algorithm with high real-time performance to integrate the data in the wireless sensor network according to the time series. On the basis of time data fusion, according to the characteristics of spatial distribution, the data fusion of multi-sensor at the gateway layer is further carried out according to the weight. In view of the characteristics of real-time changes of different position errors, the gateway layer uses spatial data as the basis to dynamically adjust the weight of each node using an adaptive weighting algorithm. Simulation experiments show that the algorithm is easy to implement, can effectively remove redundant information, and improve data accuracy and reliability. Compared with the improved batch estimation and adaptive weighting method, the root mean square error is reduced by about 7.9% and the accuracy is improved by 2.% after using this method. [ABSTRACT FROM AUTHOR]

With the complex landform and climate in the Sichuan region, the need for practical and scientific research production by only utilising the rainfall data derived from ground stations or satellites has not been satisfied. To overcome this difficulty, rainfall data from 161 meteorological stations in 2016 are used in this study. According to the distribution of stations, 146 rainfall data from 161 meteorological stations in 2016 are used for inverse distance weighted interpolation, and then, linear regression, weighted regression, and Kalman filter fusion and optimal interpolation method data fusion are performed with TRMM 3B42 satellite rainfall data, respectively. Then, 15 meteorological stations evenly distributed in the study area are selected for the accuracy test. The results show that compared with the measurement at ground stations, linear regression shows the best merging effect on rainfall data derived from ground stations and satellite rainfall estimates across the daily scale: the correlation coefficient is the most significantly improved (0.2–0.7) and the reduction in root-mean-square error (RMSE) is the largest. The method is applicable for use in Sichuan Province when merging rainfall data. At the monthly scale, the rainfall data processed by using the Kalman filter present the highest accuracy (0.72–0.84). At this scale, the Kalman filter is more suitable. [ABSTRACT FROM AUTHOR]

Test case prioritization (TCP) aims at scheduling test case execution so that more important test cases are executed as early as possible. Many TCP techniques have been proposed, according to different concepts and principles, with dissimilarity‐based TCP (DTCP) prioritizing tests based on the concept of test case dissimilarity: DTCP chooses the next test case from a set of candidates such that the chosen test case is farther away from previously selected test cases than the other candidates. DTCP techniques typically only use one aspect/granularity of the information or features from test cases to support the prioritization process. In this article, we adopt the concept of data fusion to propose a new family of DTCP techniques, data‐fusion‐driven DTCP (DDTCP), which attempts to use different information granularities for prioritizing test cases by dissimilarity. We performed an empirical study involving 30 versions of five subject programs, investigating the testing effectiveness and efficiency by comparing DDTCP against DTCP techniques that use a dissimilarity granularity. The experimental results show that not only does DDTCP have better fault‐detection rates than single‐granularity DTCP techniques, but it also appears to only incur similar prioritization costs. The results also show that DDTCP remains robust over multiple system releases. [ABSTRACT FROM AUTHOR]

The quantification of uncertainty in intelligent material systems and structures requires methods to objectively compare complex models to measurements, where the majority of cases include multiple model outputs and quantities of interests given multiphysics coupling. This creates questions about constructing appropriate measures of uncertainty during fusion of data and comparisons between data and models. Novel materials with complex or poorly understood coupling can benefit from advanced statistical analysis to judge models in light of multiphysics data. Here, we apply the Maximum Entropy (ME) method to more complicated ferroelectric single crystals containing domain structures and soft electrostrictive membranes under both mechanical and electrical loading. Multiple quantities of interest are considered, which requires fusing heterogeneous information together when quantifying the uncertainty of lower fidelity models. We find that parameters, which were initially unidentifiable using a single quantity of interest, become identifiable using multiple quantities of interest. We also show that posterior densities may broaden or narrow when multiple data sets are fused together. This is likely due to conflict or agreement, respectively, between the different quantities of interest and the multiple model outputs. Such information is important to advance our predictions of intelligent materials and structures from multi-model inputs and heterogeneous data. [ABSTRACT FROM AUTHOR]

Data modeling based on the fusion of data from multiple sources can improve modeling accuracy compared to a single data source. A new modular information fusion model based on genetic neural networks is designed for the urban landscape design process. A digital elevation model is created using an ordered sequence of numbers based on preprocessed sensor images. A 3D orthophoto is then obtained to generate a 3D landscape using an artificial parallax-assisted mechanism. The scale and resources of the regional landscape are described by the three-dimensional geometric dimension after data processing, and a modular landscape model with a clear subject is constructed. Finally, a genetic algorithm based on real number coding optimizes the initial weights of the neural network and selects suitable learning factors to train the neural network to complete the data fusion task and error analysis. The maintenance situation is analyzed by introducing a multifactor landscape maintenance evaluation method. The simulation results show that the fusion process of the above model is stable and the energy consumption of information fusion is low, which can promote the efficient construction of the landscape and has important application value for improving the landscape design and maintenance management. [ABSTRACT FROM AUTHOR]

Ceramic culture as Chinese culture has a long history and is the Chinese people's spiritual home. The ceramic culture resource base contains a large number of images, videos, and other resources, so the collection of image category of data mining, as well as the finishing processing, is very critical. Thousands of years of ceramic data accumulation and impatient information demand have created a new point of contradiction for ceramic cultural resources. Therefore, in order to address this issue, we carried out a research study based on the concept of big data mining of ceramic cultural resource data, which is based on data fusion and feature extraction methods. We also considered semantic segmentation processing methods, which are used for data information management, scheduling, identification, collection, statistics, and aggregation of heterogeneous ceramic cultural big data. Further, a fine mining method for ceramic culture big data based on semantic segmentation is also proposed. As a result, the distributed storage of information and detection capability of ceramic cultural resource data are improved. The experimental results reveal that the proposed method performed better than the earlier approaches. [ABSTRACT FROM AUTHOR]

This article focuses on the macroeconomic early warning based on support vector machines under multi-sensor data fusion technology. The economic crisis has always been a topic of great concern to the entire world, and there has never been a lack of research and prevention of it in the development process. At present, the economic early warning system that is frequently mentioned includes statistical models and artificial intelligence models. Economic early warning is not just a topic for discussion on a national basis. In the personal field, a thorough enough understanding of economic early warning can also better control investment incomes such as stocks. Analyzing the direction of macroeconomic policies has also become an indispensable ability for investors. In order not to be affected by the economic crisis, early warning is a crucial link. Therefore, in this article, a multi-sensor data fusion macroeconomic early warning model based on support vectors is proposed. In addition, this article also discusses each subpart separately. First, this article mentions the multi-sensor data fusion model. It conducts reasonable collection and control of data information in multiple sensors and uses computers and smart devices to improve system performance and make data clearer. At the same time, this article shows the JDL model of data fusion. It can optimize signals and processes and targets and conduct situation assessment and effect assessment. Then, this article analyzes the support vector machine economic early warning system. It discusses the four parts of linear support vector machine, nonlinear support vector machine, SVC macroeconomic early warning principle, and support vector classification macroeconomic early warning system. By gradually analyzing the macroeconomic early warning process of support vector machines, this study uses the optimal hyperplane to optimize toward the direction of minimizing economic risks. Then, this article talked about the macroeconomic early warning method based on neural network. The difficult points of traditional early warning models can be discarded. It more easily handles the complex algorithms, qualitative indicators, and quantitative indicators of highly nonlinear models, and it demonstrates the macroeconomic early warning system that optimizes the BP neural network with genetic algorithms to solve the various shortcomings of the macroeconomic early warning process. Finally, this study conducts the multi-sensor data fusion macroeconomic early warning model experiment based on support vector machine. It is divided into three parts. In the first part, the model, design system, and application of 60 sets of sensors are compared with traditional weighted least-squares filtering, and it is concluded that the accuracy and trustworthiness prediction effect of this model is better. The second part uses the data of listed companies to conduct experiments, which verify the performance of the model with different data sets. It can be obtained that its prediction effect is better. The third part is to compare the performance with several traditional models, and it is concluded that the convergence effect is good and the error is small. Its average accuracy is 5.63% higher than the average accuracy of the highest precision warning model in the traditional model. This article discusses and concludes that this model has a good future in macroeconomic early warning. [ABSTRACT FROM AUTHOR]

As health issues continue to become more prevalent as the population grows, building a public health network is critical for enhancing the overall health quality of the community. This study offers an Internet of Things (IoT) based health care system that can be employed in the context of community medical care industrial areas. The main focus of this research is to develop a disease prediction strategy that could be applied to community health services using theoretical modelling. Using principal component analysis (PCA) and cluster analysis, an artificial bee colony (ABC) creates a nonlinear support vector machine (SVM) classifier pair. Feature-level fusion analysis was performed to detect probable abnormalities. The results of the experiments reveal that the SVM model offers significant benefits in disease prediction. In the SVM illness prediction model, the ABC algorithm has the best parameter optimization effect in terms of accuracy, time, and other factors. The suggested method outperformed the traditional SVM and BP neural network methods by 17.24 percent and 72.41 percent, respectively. It can lower the RMSE and improve assessment indicators like the precision recall rate and the F-measure, demonstrating the method's validity and accuracy. As a result, it is frequently used in community health management, geriatric community monitoring, and clinical medical therapy in an industrial environment. [ABSTRACT FROM AUTHOR]

This paper uses data analysis and action recognition algorithms to conduct in-depth research and analysis of professional sports competition judging and designs a professional sports competition-assisted judging system for use in actual judging. In this paper, a wearable motion capture system based on an inertial sensing unit is developed and designed for kayaking technical motion monitoring to achieve the acquisition, analysis, and quantitative evaluation of kayaker motion data. To limit the gyroscope and pose estimation error, a gradient descent method is used for multisensor data fusion to achieve athlete pose update, and a quaternion-driven human skeletal vector model is proposed to reconstruct the kayaker's paddling technical movements. By calculating the angular sequences of the left shoulder, right shoulder, left elbow, and right elbow joints of the athlete's upper limbs and comparing them with the optical motion capture system, the results show that the motion capture system developed in this paper is comparable to the optical motion capture system in terms of measurement accuracy. It ultimately affects the result of pose estimation. Therefore, high-resolution networks and low-resolution networks can continuously maintain high-resolution features of the image by allowing each representation layer to repeatedly accept the representation information of other networks. A step matrix model is constructed to encode the multiscale global temporal information of action sequences, and action classification is achieved by calculating the response of the step matrix of test samples to the step matrix of each category of actions. The algorithm achieves 78.96%, 91.84%, and 91.18% accuracy of action classification on the Northwestern-UCLA database, MSRC-12 database, and CAD-60 database, respectively. The designed visual motion tracking system was applied to record the motion data of the experimental subjects in the fine motion assessment task and construct the motion assessment database. The experimental results show that the average error between the prediction results of the proposed action assessment method and the manual scoring is 1.83, and the automated assessment of fine movements is effectively realized. [ABSTRACT FROM AUTHOR]

Intelligent computing provides efficient, real-time, and secure data analysis services for the Internet of Things (IoT). As the number of IoT devices increases, IoT generates massive, diverse, and multisourcing datasets that can be used to improve IoT services further. Models trained by intelligent computing from a single system or sensor are often not global, and sending all data directly to the computing platform wastes network bandwidth and may cause network congestion and even privacy leakage. To ensure IoT applications' quality of service and privacy, we propose a framework that integrates edge computing and blockchain to provide lightweight data fusion and secure data analysis for IoT. We propose a lightweight data fusion method that can reduce the amount of data at the node level and prevent network congestion and bandwidth waste. Furthermore, we propose a hierarchical fuzzy hashing method to check and locate anomalies of IoT machine learning models to ensure the validity of IoT intelligent computing and the security of sensitive data. Finally, we demonstrate the effectiveness of the method proposed in this paper through experiments. [ABSTRACT FROM AUTHOR]

This paper describes a 16-year dataset of air pollution concentrations and air quality indicators over France. Using a kriging method that combines background air quality measurements and modeling with the CHIMERE chemistry transport model, hourly concentrations of NO 2 , O 3 , PM 10 and PM 2.5 are produced with a spatial resolution of about 4 km. Regulatory indicators (annual average, SOMO35 (sum of ozone means over 35 ppb), AOT40 (accumulated ozone exposure over a threshold of 40 ppb), etc.) are also calculated from these hourly data. The NO 2 and O 3 datasets cover the period 2000–2015, as well as the annual PM 10 data. Hourly PM 10 concentrations are not available from 2000 to 2007 due to known artifacts in PM 10 measurements. PM 2.5 data are only available from 2009 onwards due to the limited number of measuring stations available before this date. The overall dataset was evaluated over all years by a cross-validation process against background stations (rural, sub-urban and urban) to take into account the data fusion between measurement and models in the method. The results are very good for PM 10 , PM 2.5 and O 3. They show an overestimation of NO 2 concentrations in rural areas, while NO 2 background values in urban areas are well represented. Maps of the main indicators are presented over several years, and trends are calculated. Finally, exposure and trends are calculated for the three main health-related indicators: annual averages of PM 2.5 , NO 2 and SOMO35. The DOI link for the dataset is 10.5281/zenodo.5043645 (Real et al., 2021). We hope that the publication of this open dataset will facilitate further studies on the impacts of air pollution. [ABSTRACT FROM AUTHOR]

Landsat has provided the longest fine resolution data archive of Earth's environment since 1972; however, one of the challenges in using Landsat data for various applications is its frequent large data gaps and heavy cloud contaminations. One pressing research topic is to generate the regular time series by integrating coarse-resolution satellite data through data fusion techniques. This study presents a novel spatiotemporal fusion (STF) method based on a depthwise separable convolutional neural network (DSC), namely, STFDSC, to generate Landsat-surface reflectance time series at 8-day intervals by fusing Landsat 30 m with high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m surface reflectance data. The STFDSC method consists of three main stages: feature extraction, feature fusion and prediction. Features were first extracted from Landsat and MODIS surface reflectance changes, and the extracted multilevel features were then stacked and fused. Both low-level and middle-level features that were generally ignored in convolutional neural network (CNN)-based fusion models were included in STFDSC to avoid key information loss and thus ensure high prediction accuracy. The prediction stage generated a Landsat residual image and is combined with original Landsat data to obtain predictions of Landsat imagery at the target date. The performance of STFDSC was evaluated in the Greater Khingan Mountains (GKM) in Northeast China and the Ziwuling (ZWL) forest region in Northwest China. A comparison of STFDSC with four published fusion methods, including two classic fusion methods (FSDAF, ESTARFM) and two machine learning methods (EDCSTFN and STFNET), was also carried out. The results showed that STFDSC made stable and more accurate predictions of Landsat surface reflectance than other methods in both the GKM and ZWL regions. The root-mean-square-errors (RMSEs) of TM bands 2, 3, 4, and 7 were 0.0046, 0.0038, 0.0143, and 0.0055 in GKM, respectively, and 0.0246, 0.0176, 0.0280, and 0.0141 in ZWL, respectively; it can be potentially used for generating the global surface reflectance and other high-level land products. [ABSTRACT FROM AUTHOR]

Aiming at the problem of multi-sensor data fusion, an adaptive weighted data fusion algorithm based on batch estimation is proposed in this paper. The algorithm uses time series and space series to calculate the variance of the collected data in batches, and uses the data consistency detection to eliminate noise points, and then obtain the adaptive factor. Then, the data is fused by adaptive weighting method to obtain the predicted value. In this paper, simulation experiments are carried out to simulate the data of the Internet of Things. The results show that the adaptive weighted multi-sensor data fusion technology of batch estimation can improve the accuracy of sensor measurement and the reliability of the system when processing data. The weighted average method reduces the root mean square error by 10% and improves the accuracy by 2.3% compared with the traditional adaptive method. [ABSTRACT FROM AUTHOR]

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Surface soil moisture (SSM) has a considerable impact on land‐atmosphere exchanges of water and energy fluxes. However, due to the inherent deficiencies of remotely sensed data (e.g., cloud contamination in thermal remote sensing and coarse resolutions for microwave remote sensing), none of the current algorithms alone can provide daily and seamless field‐scale (30 m) SSM information. To fill the gap, we proposed a novel SSM fusion framework to Generate high Resolution, Accurate, Seamless data using Point‐Surface fusion (GRASPS) based on remotely sensed, reanalysis, and in‐situ data sets. First, 30 m seamless continuous SSM correlated variables (land surface temperature, NDVI, and albedo) were downscaled by enhanced spatial and temporal adaptive reflectance fusion model. Then, downscaled auxiliary variables and other background variables were input into a deep learning model to produce 30 m daily and seamless SSM fields. To further improve the SSM estimation accuracy, a pixel classification‐based bias correction method was developed. The GRASPS method was validated over an in situ soil moisture sensor network (HOAL network) in Austria. The average Pearson correlation coefficient, root mean square error (RMSE), unbiased RMSE (ubRMSE), bias, and mean absolute error (MAE) over all validation sites achieved 0.78, 0.048, 0.033, −0.001, and 0.041 m3m−3 ${\mathrm{m}}^{3}{\mathrm{m}}^{-3}$, respectively. After bias correction, RMSE, ubRMSE, bias, and MAE decreased by 13%, 7%, 22%, and 18%, respectively. The proposed method maximizes the potential of data fusion and deep learning in generating field‐scale seamless SSM, which is promising for fine‐scale studies and applications in agricultural, hydrological, and environmental domains. Key Points: The proposed framework applied point‐surface data fusion through deep learning30 m seamless surface soil moisture (SSM) with high accuracy was generatedDownscaled SSM showed good agreement with in situ SSM observations [ABSTRACT FROM AUTHOR]

Remote sensing satellites allow users to acquire detailed information about the Earth's surface on a temporal basis. Widen time-series analysis at a large geographical scale involves a huge amount (in Terabytes) of satellite data downloading and processing operations. Such processes need good computational power, large storage, and sophisticated tools. Maintaining such infrastructure can cost heavily to the research/commercial enterprises. To overcome such issues, Amazon Web Service (AWS) offers a sophisticated cloud computing environment. We developed an in-house automated satellite data downloading and processing (ADDPro) pipeline on the AWS platform. The ADDPro pipeline employed Sentinel-2 satellite data to offer current and relative vegetation health information of the agriculture region on a temporal basis at the pan-India scale. Image compositing and multi-sensor data fusion technique have been incorporated into the ADDPro pipeline to produce cloud-free raster (GeoTIFF) outputs. ADDPro pipeline also facilitates lossless raster data compression, which reduces AWS data transfer costs between regions. Data compression also aids in reducing raster publishing time on GeoServer. Operationally, AWS allows users to download only the bands required to generate a certain index (e.g. NDVI) rather than the entire Sentinel-2 data package. The entire ADDPro pipeline is extremely cost-effective, efficient, and scalable. [ABSTRACT FROM AUTHOR]

How to effectively realize the perception and fusion of data in the enterprise management society is the core problem that must be solved by enterprise management. On the basis of defining the resource system of enterprise management society, a semantic-oriented metadata model is proposed, combined with the classification of user needs, to build a data fusion framework for enterprise management society based on multisource data. The study designs a multilayer convolutional neural network model to process the data and proposes a recommendation path for the implementation of user data services for the enterprise management society based on the data operation center of the enterprise management society. Finally, it proposes suggestions for the development of the data fusion of the enterprise management society by improving the data fusion standard of the enterprise management society from multiple sources, actively formulating the data opening policy, and exploring the personal data collection and storage protection scheme. Experiments show that the scheme designed in this study is 5% more accurate than the state-of-the-art scheme. [ABSTRACT FROM AUTHOR]

With the development of information collection technology, the data that people need to deal with are also increasing, which brings problems such as isomerization of data types, poor data quality, and fast data generation speed. At present, as an important method of data fusion technology, data fusion method based on deep learning has become an effective way of data fusion under the background of big data, which has important research significance. There is a problem with heterogeneous data types between time series data and text data, and it is difficult to fuse them effectively by traditional data fusion methods. In order to make full use of the information contained in text data and improve the accuracy of time series prediction, this paper proposes a data fusion model based on FC-SAE. In this model, GloVe and CNN are used to extract the features of text data, FC neural network is used to extract the potential features of time series data, and then, the SEA model is used to fuse the data, which fully discovers the relationship between data and greatly improves the prediction accuracy. [ABSTRACT FROM AUTHOR]

The volatility of the stock market is related to the vital interests of stockholders and is essential for maintaining a stable financial environment. Through the analysis of data changes, excellent professional traders can extract information about the direction of stock changes, whether it is worth investing, and long-term or short-term trading. This article aims to study the forecasting methods of stock market volatility, by integrating multiparty data, in-depth analysis of the direction of data changes, predicting the price changes of the stock market, and better guiding stockholders' investment. This paper proposes a multisource data fusion method to analyze the stock market price changes and find the best risk prediction method. The experimental results in this paper show that multisource data fusion can better help the stock market predict stock changes and reduce financial investment risks by 20%. Comparing the obtained prediction results with the real data, the MSE predicted by the ARIMA model is calculated to be 2.35. It provides a new idea for effectively analyzing nonstationary time series data with complex trend fusion characteristics by rationally screening feature signals and trend signals and modeling probability distribution. [ABSTRACT FROM AUTHOR]

Well-estimated air pollutant concentration fields are critically important to compensate for observations that are only sparsely available, especially over non-urban areas. Previous data fusion methods generally used statistical models to relate observations of target variables to proxy data and supporting variables at known stations. In this study, we developed a new data fusion paradigm by designing a deep-learning model framework and workflow to learn multivariable spatial correlations from chemical transport model (CTM) simulations, before using it to estimate PM 2.5 reanalysis fields from station observations. The model was composed of two modules as an explainable PointConv operation to pre-process isolated observations and a regression grid-to-grid network to build correlations among multiple variables. The model was trained with only CTM simulations and supporting geographical covariates. The trained model was evaluated in two aspects of (1) reproducing raw PM 2.5 CTM simulations and (2) generating reanalysis and fused PM 2.5 fields. First, the model was able to reproduce the CTM simulations well on a full domain from sampled CTM data items at sparse locations with an average R2=0.94 and RMSE = 4.85 µgm-3. Second, the fused PM 2.5 fields estimated from observations achieved a good performance with R2=0.77 (RMSE = 14.29 µgm-3) and R2=0.84 (RMSE = 12.96 µgm-3) respectively evaluated at the stringent city level and station level. The generated reanalysis PM 2.5 fields have complete spatial coverage within the modeling domain. One significant benefit of the fusion framework is that the model training does not rely on observations, which can be used to predict PM 2.5 fields in newly set up observation networks such as those using portable sensors. Meanwhile, in the prediction procedure, only station observations are used along with supporting covariates. The fusion model has high computing efficiency (< 1 s d -1) due to acceleration using a graphical processing unit (GPU). As an alternative to generate chemical reanalysis fields, the method can be readily implemented in near-real time and be universally applied for other simulated variables with measurements available. [ABSTRACT FROM AUTHOR]

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The present research develops a framework to refine the classification of an individual's activities and recognize wellness associated with their routine. The framework improves the accuracy of the classification of routine activities of a person, the activation time data of sensors fixed on objects linked with the routine activities of the person, and the aptness of an incessant activity pattern with the routine activities. The existing techniques need continuous monitoring and are non-adaptive to a person's persistent habitual variations or individualities. The research involves applying Internet of Medical Things (IoMT)-based sensor information fusion to the novel multimodel data analytics to develop Activities of Daily Living (ADL) pattern, behavioral pattern generation and anomaly recognition. The novel multimodel data analytics approach is named AiCareLiving. AicareLiving is an IoMT and artificial intelligence (AI) enabled approach. The research work describes activity data using an individual's activities within a specified area before evaluating the activity data to detect the existence of an anomaly by identifying the deviation of the activity data from the activity profile, which indicates the anticipated behavior and activity of the person. This wellness information would be shared to the caregivers, related healthcare professionals, care providers and municipalities through the secured healthcare information exchange protocol and IoMT. AiCareLiving framework aims to least false positive in terms of anomaly detection and forecasting; the high precision is close to the confidence level of 95%. [Display omitted] • IoMT seamlessly integrates multimodal sensors, revolutionizing healthcare through real-time ADL monitoring. • Multimodal ADL data fusion provides comprehensive insights into elderly living patterns and events. • AI algorithms enable predictive analytics, for event generation, attribution score and anomaly detection. • Digital enhancements reduce hospital readmissions by enabling continuous, remote elderly care management. • AI and IoMT algorithms predicted wellness metrics with a 95% confidence level. [ABSTRACT FROM AUTHOR]

Dynamic stall often leads to unsteady load and performance degradation in horizontal axis wind turbines. Therefore, accurate modeling of dynamic stall is crucial. However, due to the large variations of the blade aerodynamic profiles and the complexity of dynamic stall flow, numerical simulation and wind tunnel experiment are costly. On the other hand, widely used semi-empirical models have limited accuracy. Hence, this paper proposes a data-knowledge fusion method that incorporates physical knowledge into a neural network to improve its accuracy and generalization ability. Firstly, the force components of the Leishman–Beddoes model are incorporated into the network. An efficient dynamic stall model for the S809 airfoil is thus established with a small amount of high-precision experimental data. It achieves extrapolation predictions of reduced frequency and angle of attack with only 1/5 of the samples in the database to train. Moreover, to make full use of the accumulated existing airfoil data to assist in modeling other airfoils, the obtained S809 model is fused in the network to predict the aerodynamics of S810 and S814. The average relative error of the prediction cases is nearly 10%. Comprehensively, this paper provides a new paradigm for assessing the dynamic stall of the wind turbine blade. [Display omitted] • Develop a dynamic stall modeling neural network by integrating knowledge. • Create an efficient model that incorporates the force components of the L-B model. • Utilize accumulated aerodynamic data to model the other cross-sections of the blade. • The data fusion strategy reveals the importance of interpretable basis functions. [ABSTRACT FROM AUTHOR]

Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics. This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing—encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers. Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis. [Display omitted] • MCR-ALS was applied for resonance integration, data compression and exploratory data analysis. • A new guideline is proposed for performing the joint analysis of different metabolomic datasets (serum, urine, metadata). • Two data fusion strategies are proposed which use MCR-ALS in a consecutive way. • Both methodologies were able to successfully merge and process the multiblock datasets. [ABSTRACT FROM AUTHOR]

• The relationship between repeatability error and data confidence is established. • The spatial error prediction model based on confidence weighting is derived. • The simulation process based on statistics is designed. • A spatial error measurement scheme based on data fusion is proposed. • The accuracy and reliability are verified on a horizontal machining center prototype. A spatial error prediction method for precision machine tools considering the influence of repeatability errors is proposed in this paper. Firstly, the reciprocal of the repeatability error is used to evaluate the measurement data confidence to distinguish the difference of useful information contained in the measurement data of different positions. Secondly, the proposed confidence index is used to weight the measured values of PDGEs at different positions, so that the data containing more useful information can play a full role in the error prediction, and the predicted error is substituted into the error model to predict the spatial error. Then, the computer simulation based on statistical analysis is designed, the PDGEs and spatial error prediction accuracy of the weighted algorithm are verified by simulation. Finally, a multi-instrument fusion spatial error measurement scheme is proposed, and the accuracy and reliability of the model are verified on a horizontal machining center prototype. [ABSTRACT FROM AUTHOR]

• The time transfer link is a necessary support for Universal Time Coordinated. In order to improve the accuracy of time transfer link, it is imperative to study appropriate data fusion methods. • Before fusing data, time scale matching must typically be finished and interpolated in the time domain due to the varying sample intervals of various time transfer link types. The method showed in this article uses wavelet analysis methods to decompose raw data into many approximation signals at different sampling rates as needed, is very ideal for processing data of time transfer links at different sampling rates. • The fusion method has the capacity to specifically suppress the link's phase modulation and frequency modulation white noise, thereby increasing the accuracy of the links. The stability of the links has also been somewhat enhanced at the same time. • The fusion approach also works well to deal with jumps and data loss brought on by unusual system conditions. To raise the precision and stability of time transfer links, a wavelet-based multi-resolution data fusion technique is offered. This approach fixes the "Day-Boundary discontinuities" in global positioning system (GPS) precise point positioning (PPP) link and lessens the diurnal in Two-way satellite time and frequency transfer (TWSTFT) link by integrating the model-based dynamic system analysis method with the statistically based multi-scale signal transformation method. It can also handle data from GPS PPP links and TWSTFT links at different sampling rates. More specifically, after applying wavelet transformation to the raw data and Kalman filtering estimation on various scales, this method uses the Mallat fast reconstruction algorithm of wavelet transform to achieve the fused results. The National Time Service Center (NTSC) and the Physikalisch-Technische Bundesanstalt (PTB) handle time transfer results using this method. Experiments show that this strategy greatly improves the link's short-term stability and can manage atypical interruptions while also boosting the link's robustness and reliability, which has a wide range of possible applications. [ABSTRACT FROM AUTHOR]

Accurate prediction of global horizontal irradiance (GHI) is crucial for anticipating the volatility of solar power output. Under clear-sky conditions, aerosols play a significant role in influencing GHI, with different types of aerosols exhibiting distinct radiation effects. In this study, we employed the Planck mean aerosol optical depth (AOD) —Informer model to forecast future GHI, utilizing historical data of AOD, meteorological parameters, and GHI as input variables. Two data-fusion schemes were proposed, and the calculation results indicate that Scheme 1, organized in the order of Xianghe—Beijing—Shangdianzi (XBS) based on the day as a unit, demonstrates superior prediction performance. Additionally, the site information was blurred, allowing for the flexible selection of input and output lengths. The calculation results remained stable and reliable, suggesting that the inclusion of fuzzy site information does not degrade the prediction effect. Building upon this, we categorized aerosols into three types based on their time span and data volume. GHI predictions for different aerosol types revealed that the mean square error (0.145) for marine aerosols were significantly lower than those for the other types. The GHI predictions for continental aerosols were closest to the true values. [ABSTRACT FROM AUTHOR]

• A novel probabilistic model to study process dynamics, data variabilities, and diverse source data fusion. • The model tested on two cases, an enzymatic reaction system and a real-world scenario of pandemic risk forecasting. • The enzymatic reaction system case depicts effectiveness of the proposed model in a closed system with limited data. • The pandemic risk forecasting case illustrates the scope of the proposed model to open system. • Both cases demonstrate data fusion from different sources. This work presents a hierarchical Bayesian network-based model for handling dynamic nature, data variabilities, and information fusion in a dynamical process system. The overall dynamics has been captured by physics-based models while uncertainty and local variabilities in the model parameters have been accounted for by using a hierarchical Bayesian network formalism. The resulting semi-mechanistic model has been investigated in two cases, a simulated case of an enzymatic reaction system and a real-world scenario of pandemic risk forecasting. The first case study on the enzymatic reaction system shows effectiveness of the proposed semi-mechanistic framework in a closed system with limited data from different sources. The second case illustrates scope of the proposed semi-mechanistic framework to open systems by investigating transmission of disease under evolving conditions of the advent of multiple waves of a pandemic outbreak. The findings have been presented in terms of the most probable values and credible intervals that account for data variabilities in different scenarios. The model is benchmarked and validated for the fatality forecast of the Coronavirus Disease of 2019 by the observed data and benchmark models reported by the Center for Disease Prevention and Control of the United States [ https://covid.cdc.gov/covid-data-tracker/#datatracker-home ]. [ABSTRACT FROM AUTHOR]

[Display omitted] • To improve the accuracy of the model and reduce the computational effort, three algorithms including UVE, CARS and PCA are used to extract the spectral features from terahertz absorption spectra and refractive index spectra. • Because of the large data volume of terahertz time-domain spectra, temporal features, including maximum value, minimum values, variance, are calculated to subsequent data processing. • To further enhance classification accuracy of classifiers, the data fusion method of feature-level fusion is employed. • The results show that the performance of data fusion model is best than that of the model using single-type spectra. The model with the highest prediction accuracy is the KNN model, with an accuracy of 97.37%, when the refractive index spectra and time-domain spectra are fused as inputs. Rubber, an essential industrial product, plays a crucial role in industry and human life. However, some manufacturers of rubber use inferior, recycled or low-cost rubber materials to replace high-quality rubber products. Therefore, in order to ensure industrial production, protect natural environment and ensure personal safety, it is of great economic and social significance to enable rapid and non-destructive qualitative identification of rubber products. In this paper, eight different types of black rubber, including Nitrile Butadiene Rubber (NBR), Choloroprene Rubber (CR) and Ethylen Propylen Diene Monomer (EPDM), were selected as research objects. Terahertz Time-Domain Spectroscopy (THz-TDS) technology was utilized to get the absorption spectra, refractive index spectra and time-domain spectra of these samples for exploring the differences between different types of rubber in terahertz spectra. To reduce the data volume, three band selection algorithms were employed to extract features from the absorption spectra and refractive index spectra, and temporal features of time-domain spectra were extracted for subsequent data processing. In the selection of classification model algorithms, K-Nearest Neighbor (KNN), Random Forest (RF) and Supervised Kohonen Neural Networks (S-Kohonen) were employed to recognize these spectral data. To fully exploit the latent information within these data and considering the possibility of the loss of partial information in single-type signal, a feature-level data fusion method was applied to different optical parameter signals to obtain more information about terahertz spectra during the process of data modeling. The results of the research indicated that the model with the highest prediction accuracy was the KNN model, with an accuracy of 97.37%, when refractive index spectra and time-domain spectra were fused as inputs, the F1-score reached 99.64. The performance of data fusion was noticeably better than that of a single-type signal. This study validates the feasibility of using terahertz time-domain spectroscopy for qualitative identification of different types of rubber. [ABSTRACT FROM AUTHOR]

Experienced firefighters can fuse the flame image, smoke pattern, and varying temperature, sound, and odour in complex and fast-changing fire scenes to foresee flashover and explosion. This study mimics firefighters and proposes a novel transformer algorithm for the fusion of fire images and temperature sensor data to forecast the backdraft explosion in a building fire. The model of backdraft forecast is demonstrated with full-scale fire tests. After training 2674 fire scenarios with various fire intensities and images from various view angles, the Fusion-Transformer model can forecast the risk of backdraft with an overall accuracy of 84%. Moreover, the occurrence time and explosion scale of backdraft can be predicted with the Mean Absolute Error (MAE) of 1.6 s and 0.14 m, respectively. Compared with the single modal model, the fusion of fire images and temperature sensor data improves the accuracy of backdraft forecast by over 50%. This work demonstrates the use of a transformer algorithm in forecasting fire evolution and critical events. It also bridges the gap between data fusion methods and fire forecast, which inspires future universal AI-driven smart firefighting practices. [Display omitted] • Full-scale fire experiments are conducted to establish a real backdraft fire database. • Visual and temperature sensor data are integrated in a data fusion model to forecast backdraft. • The AI model via fusing visual and temperature data predicts backdraft with accuracy of 84%. • The multimodal model demonstrates a prototype of future smart firefighting application. [ABSTRACT FROM AUTHOR]

The fusion of RGB and thermal data for dense prediction tasks has been demonstrated to be an effective and robust approach in autonomous driving. Nevertheless, the challenge lies in fusing features from different modalities, as a simple fusion strategy may lead to redundant or conflicting semantic information. In this paper, we delve into the hierarchical connections between multi-modal features and propose a novel fusion paradigm termed DHFNet, which decouples multi-modal features into similar global long-distance and discrepant local detail features for hierarchical feature fusion. At each fusion stage, a Lightweight Global Self-attention (LGSA) module is designed to decouple the global long-distance features with cheap computational complexity cost, and a Cross Modal Long-distance Feature Fusion (CMLFF) module is designed to eliminate redundant features by facilitating information interaction between different modalities. During the process of decoupling local detail features, a Cross Modal Deformable Convolution (CMDC) module is proposed to dynamically extract effective local features and capture misaligned features between different modalities. Finally, the fused global long-distance and local detail features are recoupled to achieve efficient hierarchical fusion. The results on RGB-T semantic segmentation and object detection tasks demonstrate the effectiveness of proposed method. The code will be available at: https://github.com/donggaomu/DHFNet. [ABSTRACT FROM AUTHOR]

Chaotic time series data is extensively applied in financial stocks, climate monitoring, and sea clutter, in which data fusion from various sources and multi-sensor information make accurate predictions of chaotic time series challenging under complex nonlinear conditions. Previous works focus on designing different model frameworks to capture the temporal dependence and extract richer nonlinear features to improve the accuracy of univariate chaotic time series prediction, which ignores the spatial dependence of multivariable. However, in this paper, we argue that spatial correlation among multiple variables is essential to improve the prediction accuracy of chaotic time series. To fill the gap, we innovatively propose a D ynamic A daptive G raph C onvolutional T ransformer with a B road L earning S ystem (DAGCT-BLS), a GCN and Transformer-based model utilizing multivariate spatial dependence for multi-dimensional chaotic time series forecasting. In DAGCT-BLS, the multivariate chaotic time series are reconstructed into the phase space, and the reconstructed data are rapidly feature-extracted using a cascade network BLS with frozen weights to maximize the retention of chaotic properties and nonlinear relationships. Then, the Dynamic Adaptive Graph Convolutional Network (DAGCN) is proposed to capture the spatial correlation among the multiple variables. Finally, improved multi-head attention of the Transformer Encoder is used to capture the temporal dependence of the phase point sequence. Experiments of our proposed model on three datasets (Lorenz, Rossler, and Sea clutter) show that DAGCT-BLS can achieve the best prediction performance and have strong interpretability, and multivariate-based joint modeling of chaotic time series helps to improve the prediction performance. • A novel DAGCT-BLS model is proposed to sovel multi-dimensional chaotic time series forecasting problem, unreported previously. • The multivariate chaotic time series are reconstructed into the phase space, which are rapidly feature-extracted using a cascade network BLS with frozen weights. • Improved multi-head attention is proposed to capture the temporal dependence of the phase point sequence. • DAGCN is proposed to model multivariate spatial relationships dynamically. [ABSTRACT FROM AUTHOR]

6-DoF grasp estimation methods based on point clouds have long been a challenge in robotics due to the limitations of single data input, which hinder the robot's perception of real-world scenarios, thus reducing its robustness. In this work, we propose a 6-DoF grasp pose estimation method based on RGB-D data, which leverages ResNet to extract color image features, utilizes the PointNet++ network to extract geometric information features, and employs an external attention mechanism to fuse both features. Our method is an end-to-end design, and we validate its performance through benchmark tests on a large-scale dataset and evaluations in a simulated robot environment. Our method outperforms previous state-of-the-art methods on public datasets, achieving 47.75mAP and 40.08mAP for seen and unseen objects, respectively. We also test our grasp pose estimation method on multiple objects in a simulated robot environment, demonstrating that our approach exhibits higher grasp accuracy and robustness than previous methods. [ABSTRACT FROM AUTHOR]

The angular vibration sensors must be calibrated regularly to ensure the precise measurement of dynamic angle motion in various engineering applications such as attitude estimation, inertial navigation, and machine automation. Currently, the high-accuracy vibration calibration has been developed in the medium and high-frequency range. However, it is difficult to accomplish such calibration over a wide low-frequency band because the current angular exciter cannot generate the high-dynamic and low-distortion excitation acceleration for the sensors. Aiming at this problem, a complementary filter-based data fusion control method is proposed and successfully applied to an exciter for achieving high-precision calibration in a wide low-frequency range. The results demonstrate that the proposed method has the ability to generate high-dynamic and low-distortion excitation accelerations in the range of 0.01 Hz-150 Hz. The maximum total harmonic distortion of these excitations measured by the commonly used laser interferometry and circular grating ruler method is 0.80 % and 0.42 %, respectively. In addition, the corresponding maximum relative standard deviation is 0.039 % and 0.005 %. The experiment results show that the proposed method can well meet the high-accuracy calibration requirements. [Display omitted] • A broad-band and low-distortion excitation generation method was proposed. • Several amplitude-frequency response and vibration excitation measurements were accomplished. • The working frequency band, THD and RStd comparison experiments were achieved. [ABSTRACT FROM AUTHOR]

The current nondestructive inspection paradigm of mono-modal testing and signal-over-threshold call criteria is insufficient for characterization of microtexture regions in titanium alloys, which exceeds the capabilities of any single, practical sensing technique that could be deployed to a manufacturing environment for quality control. In this work, we develop a data fusion-based solution to microtexture region characterization. The material problem and potential inspection methods are reviewed, and registered datasets are presented. Next, we develop a data fusion algorithm that combines covariance-generalized matching component analysis and a conditionally Gaussian hypermodel to successfully estimate the boundaries and average crystallographic orientations of microtexture regions from scanning electron microscopy electron backscatter diffraction and eddy current testing data. • Ti MTR cannot be characterized in a practical environment for quality control. • Inspection techniques like SAM and ECT are sensitive to key MTR parameters. • A conditionally Gaussian hypermodel enables MTR characterization from fused SEM/ECT. [ABSTRACT FROM AUTHOR]

• An experimental variational mode decomposition (VMD)-based data-fusion displacement measurement framework is proposed. • Vision, acceleration, and fusion methods are compared. • A four-story RC structure on a 25-ton table is detailed. Dynamic displacement is a crucial parameter in structural health monitoring (SHM) for assessing the safety, dependability, and suitability of structures under various types of excitations. Computer vision-based methods for dynamic displacement estimation have attracted much interest owing to their cost-effectiveness and convenience. However, these methods are limited by their low sampling rates and high data sensitivity. To compensate for these limitations, methods for combining data obtained from other sensors have been proposed. In this study, an experimental data-fusion framework for displacement estimation based on variational mode decomposition (VMD) was developed to leverage the advantages of vision- and acceleration-based measurements. The measurements were decomposed into ensembles of modes and recomposed to reconstruct the displacement with a higher accuracy and over a wider frequency range. An optimal mode recomposition method was proposed to achieve optimal mode combinations. Furthermore, this study introduced an improved vision-based displacement measurement method and a VMD-based indirect acceleration measurement method. The proposed framework was validated through four-story RC structure tests, which demonstrated that the method could enhance the accuracy of displacement estimation and extend the feasible frequency range compared with single-source displacement measurements. The method provides a promising solution for more effective health monitoring of modern structures subjected to a wide variety of dynamic loads. [ABSTRACT FROM AUTHOR]

Accurate forecasting of regional solar photovoltaic power (SPVP) generation is essential for efficient energy management and planning. Existing approaches have shown the effectiveness of decomposing the time series to model the stochastic variability in SPVP data. However, these approaches have limitations in extracting and exploiting both spatial and temporal information from complex and high-dimensional data from multiple sources with intricate relationships, which can impact the accuracy of predictions. In this paper, we propose a novel approach called multilevel data fusion and neural basis expansion analysis (MF-NBEA) for forecasting aggregated regional-level SPVP generation. MF-NBEA integrates exogenous data at multiple levels, uses supervised and unsupervised encoders to provide compact data representation, and enhances model learning from complex data by incorporating spatial information. It also includes a sequence analyser module based on a neural network decomposition mechanism to learn the variability in data and incorporates a residuals learner module to improve overall predictions. We evaluate MF-NBEA using two real-world datasets and find that it outperforms state-of-the-art deep learning methods in terms of forecast accuracy. Furthermore, MF-NBEA facilitates information fusion and knowledge extraction to provide interpretable predictions regarding trend, seasonality, and residual components. The insights gained from our approach inform decision-making for energy management and planning, and can lead to more efficient and sustainable resource utilisation. • MF-NBEA for multivariate solar power time series forecasting. • A 3D autoencoder encodes spatial and temporal information from heterogeneous sources. • Interpretable forecasting in terms of trend, seasonality, and residuals. [ABSTRACT FROM AUTHOR]

Providing accurate and reliable railway regional environmental data is a key consideration in operation control and dynamic dispatching of high-speed train. However, there are problems of low reliability and high uncertainty in the single data processing of high-speed train operating area environment. Therefore, this paper proposes a novel multisource sensor data fusion method based on a three-level information fusion framework. Firstly, the feature of the same kind of sensor data is extracted by the Kalman Filter (KF) algorithm as the input of back propagation neural network (BPNN). Then input the sample site into the BPNN for training and recognition, the feature fusion of heterogeneous sensor data is carried out, the decision output of BPNN is obtained, the output results are normalized, and its output is used as the basic probability assignment of Dempster–Shafer (D-S) evidence theory and synthesis rules. Finally, the decision fusion of multisource data is realized by D-S evidence theory. The simulation results show that compared with the traditional single fusion algorithm, the algorithm improves the accuracy of the prediction of high-speed train operation environment and reduces the MAPE from 13.82% to 7.455%, and the RMSE from 0.77 to 0.69, and meanwhile, increases the R2 from 0.87 to 0.97. [ABSTRACT FROM AUTHOR]

In order to further enhance the ecological environment construction of smart cities and promote its deep integration with advanced technologies, such as the new generation of artificial intelligence and big data, this study constructs a data fusion framework for the ecological environment of smart cities driven by multisource data and constructs an ecological environment evaluation index system of smart cities in Guangzhou from 2010 to 2018. The ecological environment status of smart cities in Guangzhou is analyzed by principal component analysis, and finally the, correlation influence degree of each principal component content on the ecological environment index of smart cities is analyzed. The results show that environmental excellence ( K 1 ), environmental restoration response ( K 2 ), and environmental pollution pressure ( K 3 ) are the main components of the ecological environment in Guangzhou smart city. The environmental excellence and environmental restoration response level are relatively high, and the environmental pressure system is relatively low, in which the greenland coverage rate increases from 41.3% to 44.0%, and the urban sewage treatment rate and the decontamination rate of urban refuse increase year by year, from 73.1% to 94.6% and from 72.1% to 99.9%, respectively. The results of principal component correlation analysis show that there are interactive influences among environmental excellence ( K 1 ), environmental restoration response (K2), and environmental pollution pressure ( K 3 ). The ecological environment index of smart cities increases with the improvement of environmental excellence and environmental restoration response capacity, but gradually decreases with the increase of environmental pollution pressure. Generally speaking, improving environmental excellence and environmental restoration response will be the key to improve the ecological environment construction capacity of smart cities in the future. [ABSTRACT FROM AUTHOR]

The technology and functions of data fusion and image teaching are constantly improving with the development of society and are gradually produced in life in order to adapt to the progress of science and technology. Data fusion technology refers to the information processing technology that uses computer to automatically analyze and synthesize several observation information obtained in time series under certain criteria to complete the required decision-making and evaluation tasks. In this paper, the traditional teaching mode on students' learning and other aspects of help is not very obvious, and video teaching method can not only increase students' interest in learning, but also help their learning is very huge. Teaching video is to make the knowledge, skills, and other content that teachers want to impart to students into video form to assist modern multimedia teaching. In this paper, two classes with the same number and similar average test scores were selected as the experimental objects. The test results are evaluated by f-s-o algorithm, f-s-o matrix, variance proportion method, and teaching evaluation, so as to get more accurate results. In the experiment, two classes with 30 students were selected, and different teaching methods were adopted for the two classes. One class adopted the traditional teaching mode, and the other class used the video teaching method. During the 4-month experiment, there was no significant difference in test scores and comprehension between the two classes in the first month. The ratio of the two classes was 5 : 3 in the second month, 10 : 5 in the third month, and 20 : 7 in the fourth month. Through the comparative evaluation of the test results of four months, the experimental results show that the video teaching method can not only mobilize the enthusiasm of students but also plays a very important role in promoting students' performance. [ABSTRACT FROM AUTHOR]

Product differentiation is one of the highlights of the success of today's increasingly competitive marketing. This article aims to study the construction of differentiated marketing strategies for high-tech products. This paper proposes to investigate and study customers, high-tech industry workers and leaders, etc., through the analysis of massive data, introduce the concept of data fusion and the weighting algorithm for data fusion, introduce DS evidence theory to judge the accuracy of the data on this basis, make the data more real and clean up abnormal data, perform relevant data processing on the determined data, and calculate and analyze the data differences of relevant groups. It also proposes to refer to the characteristics of high-tech products, understand the characteristics and implementation directions of differentiated marketing, draw differences between different groups, summarize customer characteristics and marketing directions, so as to trace the needs of customer groups, find customer pain points and difficulties, pinpoint product positioning, and achieve the goal of differentiated marketing. The experimental results of this paper show that big data fusion analysis can find customers in a targeted manner and polish the highlights of high-tech products. Compared with previous results, the performance is improved by more than 30%. Consumer satisfaction increased by more than 20%. [ABSTRACT FROM AUTHOR]

In order to explore the problem of human energy consumption in sports, a method based on MEMS sensor is proposed. Firstly, the data of the whole system is analyzed, including acceleration signal preprocessing, data fusion between accelerometer and gyroscope using the Kalman filter method, and feature extraction. Secondly, each module and the whole system are tested, respectively. Finally, the accuracy experiment is compared with other human motion energy consumption measurement devices to verify the feasibility and superiority of the system. The experimental results show that when measuring human motion energy consumption, the average accuracy of a bracelet 1 is 87%, the average accuracy of a bracelet 2 is 88%, the average accuracy of a bracelet 3 is 96%, and the average accuracy of the system is 94%.The system has relatively high accuracy in measuring human motion energy consumption, and its algorithm is more accurate. It is proved that MEMS sensor can effectively detect human energy consumption in sports. [ABSTRACT FROM AUTHOR]