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As the core link of intelligent manufacturing, the process planning of aviation parts still faces the challenges such as relying on manual experiences for process decision-making and lack of linkage between process design and manufacturing for process optimisation. Process knowledge could support scientific decision-making on process issues, while twin data, namely high-fidelity simulation data and feedback information of manufacturing site, could further verify the process plans and optimise process parameters, so as to continuously improve the quality of process plans. Consequently, this paper proposes a general framework for twin data and knowledge-driven intelligent process planning (TDKIPP) of aviation parts, and analyses four standard procedures that support the above-mentioned reference framework, namely mechanism-data fusion process digital twin model, dynamic process knowledge base, process decision-making and evaluation, machining quality prediction and process feedback optimisation. A thus constructed test bed of TDKIPP and its four application examples about the process planning of a micro turbojet engine integral impeller demonstrate the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In prognostics and health management, the system's degradation condition assessment and corresponding remaining useful life prediction are the most important tasks. Both of these processes are heavily dependent on information gathered by multiple sensors, which eventually causes data fusion-related complex problems. Typically, sensor information contains the speed, pressure, temperature, and similar other types of various system data. These systems' data obtained through sensors can be utilized as a part of the evidence in the evidence-based estimation method. In this work, an artificial intelligence-based novel framework for estimating the remaining useful life using data fusion has been presented. The Dempster–Shafer extended theory is adopted for sensor information modeling and data fusion. Besides, two different scenarios are introduced to determine the similarity between the studied system and the available evidence. As a case study, the turbofan dataset is demonstrated to assess the proposed method. Based on the results, our integrated proposed method performs very competitively compared with the existing methods based on standard scores and performance criteria. [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]

Integrative data analyses have recently been shown to be an effective tool for researchers interested in synthesizing datasets from multiple studies in order to draw statistical or substantive conclusions. The actual process of integrating the different datasets depends on the availability of some common measures or items reflecting the same studied constructs. However, exactly how many common items are needed to effectively integrate multiple datasets has to date not been determined. This study evaluated the effect of using different numbers of common items in integrative data analysis applications. The study used simulations based on realistic data integration settings in which the number of common item sets was varied. The results provided insight concerning the optimal numbers of common items sets to safeguard estimation precision. The practical implications of these findings in view of past research in the psychometric literature concerning the necessary number of common item sets are also discussed. [ABSTRACT FROM AUTHOR]

The present study evaluates the accuracy of SNAP-Sentinel-2 Prototype Processor (SL2P) derived Leaf Area Index (LAI) and proposes a new simple method to generate new datasets of LAI through data fusion. Rigorous optimization of the data fusion approaches (Kalman filter and Linear weighted) were performed for the generation of new LAI products over the complex hilly terrain of the Himalayan region. The results showed a good correlation (r = 0.79) and low error (RMSE = 1.63) between SNAP-derived (at 20 m) and ground-observed LAI. A lower correlation was obtained between the ground observed LAI data and the corresponding global LAI products for the Moderate Resolution Imaging Spectroradiometer (MODIS) (r = 0.1, RMSE = 1.19), Copernicus Global Land Service (CGLS) (r = 0.1, RMSE = 0.61) and the Visible Infrared Imaging Radiometer Suite (VIIRS) (r = 0.04, RMSE = 1.25). Notably, after implementing the data fusion, both SNAP-derived LAI and Global LAI products exhibited much-improved performance statistics with ground observed data sets. [ABSTRACT FROM AUTHOR]

To realise high quality, additively manufactured parts, real-time process monitoring and advanced predictive modelling tools are crucial for accelerating quality assurance in additive manufacturing. While previous research has demonstrated the effectiveness of physics- and model-based diagnosis and prognosis for additive manufacturing, very little research has been reported on real-time monitoring and predictive modelling of the surface roughness of additively manufactured parts. This paper presents a data fusion approach to predicting surface roughness in fused deposition modelling (FDM) processes. The predictive models are trained using random forests (RFs), support vector regression (SVR), ridge regression (RR), and least absolute shrinkage and selection operator (LASSO). A real-time monitoring system is developed to monitor the health condition of a FDM machine in real-time using multiple sensors. RFs, SVR, RR, and LASSO are demonstrated on the condition monitoring data collected from these sensors. To integrate the data sources, a feature-level data fusion method is introduced. Experimental results have shown that the predictive models trained by the machine learning algorithms are capable of predicting the surface roughness of additively manufacturing parts with very high accuracy. The prediction accuracy can be further improved using the data fusion method. [ABSTRACT FROM AUTHOR]

Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed. [ABSTRACT FROM AUTHOR]

Applications based on the fusion of aerial imagery and Light Detection and Ranging (LiDAR) data have attracted more and more interest in recent years. Registration is the crucial prerequisite of data integration because of the spatial shift between two data modalities. However, there are many challenges for registration due to the different mechanisms of LiDAR and camera, especially in complex urban scenes. To address the problem, this paper proposes an automatic and robust registration method by using the centres and corner points of building boundaries as tie points. Primary registration is achieved by using centers of building boundaries as control points. To increase the robustness and accuracy of the registration, corner points of building boundaries are utilized to estimate the transformation model repeatedly. Two datasets of optical imagery and LiDAR data with different scene coverage are used to verify the performance of the proposed method. Experimental results show that the proposed method can achieve the registration accuracy of less than 2 pixels for both cases based on an assessment of check lines. This result can basically meet the requirements of data fusion. [ABSTRACT FROM AUTHOR]

Improvement of prediction accuracy and early detection of the Alzheimer's disease is becoming increasingly important for managing its impact on lives of affected patients. Many machine learning approaches have been applied to support the diagnosis and prediction of this illness. In this paper we propose an approach for improving the Alzheimer's disease classification accuracy by using data fusion of several independent clinical datasets. Data fusion was performed twofold: 1) by enriching attributes of the base dataset with the attributes of the secondary dataset and 2) by enriching the examples set of the base dataset with the examples of the secondary dataset. In both cases the missing values (for newly added attributes and/or examples) were predicted by using linear regression for numeric and naive Bayes classifier for nominal attributes. We experimented on three data sources: on a dataset of Alzheimer's disease-impaired patients, on a dataset of patients with vascular dementia, and on a dataset of patients who have been affected by a stroke. We fused these datasets with different data fusion approaches and analysed the improvement in classification accuracy as well as the quality of the fused attributes. The experiments indicated that we obtained an increase of classification accuracy on the fused dataset compared with the accuracy obtained from individual dataset. [ABSTRACT FROM AUTHOR]

Information fusion is very effective and necessary to respond to a public epidemic outbreak such as COVID-19. Big data intelligent, as a product of information fusion, plays an important role in the prevention and control of COVID-19. The continuous mechanism of big data intelligent innovation (BDII) is fundamental to effectively prevent and control a public epidemic outbreak. In this study, the continuous mechanism of BDII was fused into a complex network, and a three-dimensional collaborative epidemic prevention model was constructed. Furthermore, adiabatic elimination principle was applied to explore the order parameter of the continuous mechanism. Finally, empirical analysis was conducted based on three-stage epidemic prevention strategies to reveal the effect of continuous epidemic prevention under different big data intelligent emergency management policy levels. The results of this study are as follows. Through the mutual influence and coupling of the subsystems, the continuous mechanism of BDII can be realized to manage a public epidemic outbreak emergency. The big data intelligent subsystem is integrated into the subsystems of public epidemic outbreak management and science and technology innovation. The big data intelligent emergency management policies play a positive role in the overall BDII for the continuous epidemic prevention of a public epidemic outbreak. The convention of BDII transformation is the continuous mechanism of BDII as the order parameter of a public epidemic outbreak. In the early stage of epidemic prevention, the convention is excessively pursued, while the neglect of BDII configuration is not conducive to the long-term collaborative governance of a public epidemic outbreak. The study provides practical guidelines for the formulation of fusion innovation policies, application of big data intelligent, and theoretical basis for the emergency management of a public epidemic outbreak in the medical field. [ABSTRACT FROM AUTHOR]

Although attention to data fusion has undergone rapid growth since the late 1980s, there are still relatively few applications in transportation management. Most research has based fusion weight estimation on the variance of each data source, assigning high weights to low variance data, implying that low variance means high accuracy. We propose a data fusion methodology where weights are assigned in a way data variance and sensor bias are minimized, but also consistency among data sources is maximized. The proposed methodology is flexible to work with multiple data sources, with different reliability and variability, and under different traffic conditions. The inclusion of consistency is shown to be statistically significant during special events and incidents and demonstrates its validity in successfully representing changes in traffic patterns by reasonably estimating their magnitude. Results from a case study that validate this method are shown. [ABSTRACT FROM AUTHOR]

Image fusion combines the images of different spectral, spatial, multi-date, as well as radiometric data to achieve a better quality image for improved classification results. Recently, Convolution Neural Network (CNN)-based classification algorithms are extensively used for remote sensing applications. Keeping this in view, present work proposes to use CNN-based fusion and classification of Sentinel 1 (VV and VH polarization) and Sentinel 2 datasets acquired over an agricultural area near Hisar (India). For image fusion, three CNN-based approaches are used to fuse Sentinel 2 (10 m) data with VV and VH bands of Sentinel 1 data. After fusion, classification was performed using 2D-CNN classifier to judge the performance of fused images in terms of classification accuracy. Results suggest that out of the three fusion approaches, only infrared image fusion (IVF) approach performed well with the considered dataset in terms of fusion indicators and classification accuracy. Keeping in view of its better performance, this study proposes a modified IVF approach by using different image pyramid methods. Comparison of results suggests an improved performance by modified IVF approach for the fusion of Sentinel 2 and Sentinel 1 data in comparison with the original IVF approach. [ABSTRACT FROM AUTHOR]

Assessment of rooftop rainwater harvesting (RRWH) quality and suitability requires detail and reliable information on roofs. Characterization of roof surface conditions affects the quality of harvested rainwater. Nevertheless, the implementation of the system requires improvement in terms of the roof detection techniques to ensure the roof of the building is selected appropriately. Thus, the classification techniques need to be optimized to detect roof materials and roof surface conditions (new or old) with high accuracy. This study aimed to produce high precision detailed roof materials and roof surface conditions map with using high-resolution remote sensing imagery, WorldView-3 (WV3) and light detection and ranging (LiDAR) data. Three different fusion methods; layer stacking (LS), Gram-Schmidt (GS) and principal components spectral sharpening (PCSS) were explored and their performances were compared to improve the spatial and spectral richness of the image. Subsequently, the roof materials and roof surface conditions classes which include old concrete, new concrete, old metal, new metal, old asbestos and new asbestos had been discriminated by employing support vector machine (SVM) and the rule-based technique known as a decision tree (DT). Generally, generated rule-sets present a higher overall accuracy with 87%, 72% and 66% for LS, GS and PCSS, respectively. For SVM classifier, the maximum accuracy recorded for LS, PCSS and GS were 70%, 63% and 43% respectively. Therefore, rule-based classification via LS fusion technique was utilized to identify suitable rooftops for the development of harvested rainwater system in the urban area. Findings indicate that the degradation status of a roof in heterogenous urban environments could be determined from satellite observation and the quality of roof-based harvested rainwater affected by roofing materials and roofing surface conditions can be analysed effectively. [ABSTRACT FROM AUTHOR]

Accurate grassland aboveground biomass (AGB) estimation is crucial for effective grassland utilization. However, most current satellites cannot provide data with high spatial and temporal resolutions simultaneously. Spatiotemporal fusion models can combine the resolution advantages of different remote sensing data and support high-precision vegetation monitoring. In order to obtain accurate grassland AGB maps with high resolution in the Three-River Headwaters Region (TRHR) of China, we developed an estimation method based on the synthetic 30 m growing season averaged normalized difference vegetation index (GS-NDVI), which was fused from 30 m Landsat 8 Operational Land Imager (OLI) and 250 m Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data. To choose the optimal fusion model, we investigated the performances of three spatiotemporal fusion models for NDVI fusion, the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the rule-based piecewise regression tree model (RPRTM). The three models all produced reasonable NDVI predictions, with the coefficient of determination (R2) ranging from 0.58 to 0.86. RPRTM had the highest efficiency and was more suitable for large-scale spatiotemporal data fusion. Compared with the models generated from 250 m MODIS GS-NDVI, the AGB estimation models based on 30 m synthetic GS-NDVI were more accurate, demonstrating the effectiveness of our methods. The resulting AGB map of 30 m resolution provides spatially detailed AGB information that will be useful for regional ecosystem studies and local land management decisions. [ABSTRACT FROM AUTHOR]

Inferences from different data sources can often be fused together, a process referred to as "fusion learning," to yield more powerful findings than those from individual data sources alone. Effective fusion learning approaches are in growing demand as increasing number of data sources have become easily available in this big data era. This article proposes a new fusion learning approach, called "iFusion," for drawing efficient individualized inference by fusing learnings from relevant data sources. Specifically, iFusion (i) summarizes inferences from individual data sources as individual confidence distributions (CDs); (ii) forms a clique of individuals that bear relevance to the target individual and then combines the CDs from those relevant individuals; and, finally, (iii) draws inference for the target individual from the combined CD. In essence, iFusion strategically "borrows strength" from relevant individuals to enhance the efficiency of the target individual inference while preserving its validity. This article focuses on the setting where each individual study has a number of observations but its inference can be further improved by incorporating additional information from similar studies that is referred to as its clique. Under the setting, iFusion is shown to achieve oracle property under suitable conditions. It is also shown to be flexible and robust in handling heterogeneity arising from diverse data sources. The development is ideally suited for goal-directed applications. Computationally, iFusion is parallel in nature and scales up easily for big data. An efficient scalable algorithm is provided for implementation. Simulation studies and a real application in financial forecasting are presented. In effect, this article covers methodology, theory, computation, and application for individualized inference by iFusion. [ABSTRACT FROM AUTHOR]

Current research investigations using remotely sensed images are offered with a plethora of sources to explore land cover/land use applicability. Some of the recent advances have shown the advantage of fusing different data sources in land-cover analysis. Though intuitively combined processing of multi-modal imagery should provide better classification of land cover, there are not many work towards this direction and a theoretical framework is not laid out properly. In this work, we are providing such a framework where scattering and spectral properties (from synthetic aperture radar and multi-spectral images, respectively) of ground materials are used to distinguish land-cover classes with higher precision. Different kinds of information that are represented by these two modes of imageries are semantically bridged to infer more distinguishable land-cover classes in an unsupervised framework. The proposed technique is implemented in two phases, i.e., (1) sampling of seed pixels from imageries, and (2) training of representative features and prediction of classes using random forest classifier. Experimental results also show the effectiveness of this fusion of multi-modal image characteristics in classifying the underlying land cover. [ABSTRACT FROM AUTHOR]

A previous study reported the fusion of spinous processes of thoracic vertebrae one and two in the ground sloth Glossotherium, proposing an injury or a congenital deformation as possible causes. In this paper we examined members of four families of Pleistocene sloths (Mylodontidae, Megatheriidae, Nothrotheriidae and Megalonychidae) to document the presence of this condition in different genera. We found that of nine genera analyzed, Glossotherium and Megalonyx were the only that present fusion of the spinous processes, which consistently occur between T1 and T2. When only identified T1 and T2 were considered, the incidence of the fusion was 87.5% in the case of Glossotherium and 40% in the case of Megalonyx. The high incidence of fusion in almost only one genus almost certainly would preclude the possibility of having been caused by an injury. We discuss the possibility that the observed fusions could be related to a pathological condition, or a mechanical response due to repetitive movements or particular habits. [ABSTRACT FROM AUTHOR]

Image fusion methods aim at fusing low resolution and high-resolution image to obtain a new image that provides new information for the specific application. The main goal of this article is multitemporal Sentinel-2 image fusion using single WorldView-4 satellite image for urban area monitoring. Fusing those images should provide Sentinel-2 image with similar radiometric band value as original Sentinel-2 image, but with a spatial resolution of WorldView-4. Ehlers, Brovey Transform, Modified Intensity-Hue-Saturation, High-Pass Filtering, Hyperspherical Colour Space and Wavelet resolution merge fusion techniques were used for spatial enhancement of Sentinel-2 images. Original and fused images were first compared using standard statistical parameters, mean, median and standard deviation. Image quality analysis was conducted with different objective image quality measures like root mean square error, peak signal to noise ratio, universal image quality index, structural similarity index, relative dimensionless global error, spatial correlation coefficient, relative average spectral error, spectral angle mapper, multi-scale structural similarity index. Using these quality measures helped in determining the spectral and spatial preservation of fused images. Hyperspherical colour space method was selected as the best method for image fusion of Sentinel-2 and WorldView-4 image-based on standard statistical parameters and quality measures. [ABSTRACT FROM AUTHOR]

The individual's social behavior is diverse in the big data environment, which makes the general trust evaluation model to take into account inaccuracy and causes some new problems for individual trust. Based on this, we present the trust situation risk model based on individual behavior oriented social big data. First, we use Multi data fusion to get the result of trust evaluation, then fuse the mass function of the individual trust associated with trust evaluation with the evaluation results by using the theory of D-S. Next, we get the probability of the individual appearing as distrust situation. Each related individual distrust situation is obtained by the fusion with the situation factor. According to each related individuals' trust situation, we can get the fusion of weight about each individual participating in the trust evaluation. On the basis of the above, the trust model evaluating the individual behavior is developed, the experiment shows the performance of the proposed model in this paper. [ABSTRACT FROM AUTHOR]

The first 10 m resolution global land-cover map, FROM-GLC10 was released in early March 2019. Due to its high spatial resolution and reliable global accuracy, we evaluated the suitability of FROM-GLC10 data for understanding agricultural ecosystems in Beijing using a comparable vector data set, Google Earth images and field survey data. The overall accuracy (OA) for FROM-GLC10 based on three data sets was 71.08%, 79.63% and 80.36%, respectively. Meanwhile, there were notable misclassifications between cropland, grassland and forest. The limited accuracy for these vegetation types might be attributed to the spatially mixed vegetation structures, seasonal variations of vegetation and the temporal inconsistence between Landsat and Sentinel data set. Due to its satisfactory OA, FROM-GLC10 data have the potential to be widely employed for evaluating the progress of large-scale ecological restoration projects. On the other hand, the data producers, who can pre-set and classify some customized land-cover types, consider time-series analysis and big data fusion methods and conduct large-scale verification, and data users, who can integrate different data sources, should work together to enhance the suitability and reliability of FROM-GLC10 data for understanding agricultural ecosystems in China. [ABSTRACT FROM AUTHOR]

According to the general law of likelihood, the strength of statistical evidence for a hypothesis as opposed to its alternative is the ratio of their likelihoods, each maximized over the parameter of interest. Consider the problem of assessing the weight of evidence for each of several hypotheses. Under a realistic model with a free parameter for each alternative hypothesis, this leads to weighing evidence without any shrinkage toward a presumption of the truth of each null hypothesis. That lack of shrinkage can lead to many false positives in settings with large numbers of hypotheses. A related problem is that point hypotheses cannot have more support than their alternatives. Both problems may be solved by fusing the realistic model with a model of a more restricted parameter space for use with the general law of likelihood. Applying the proposed framework of model fusion to data sets from genomics and education yields intuitively reasonable weights of evidence. [ABSTRACT FROM AUTHOR]

Modern data analytics was employed to understand and predict physics-based melt-pool formation by fabricating Ni alloy single tracks using powder bed fusion. An extensive database of melt-pool geometries was created, including processing parameters and material characteristics as input features. Correlation analysis provided insight for relationships between process parameters and melt-pools, and enabled the development of meaningful machine learning models via the use of highly correlated features. We successfully demonstrated that data analytics facilitates understanding of the inherent physics and reliable prediction of melt-pool geometries. This approach can serve as a basis for the melt-pool control and process optimization. [ABSTRACT FROM AUTHOR]

Data fusion from two sources of data could develop better output since the process may minimise any inherent disadvantages of the data. Cokriging data fusion requires a semivariogram fitting process, which is an important step for weight determination in the fusion process. The traditional method of cokriging fusion usually applies a specific model of semivariogram fitting based on the available options, such as circular or tetraspherical. This research aims to fuse height point data from two different sources using ordinary kriging based on LS-SVM regression, which is applied to the semivariogram fitting process. The data used are height points generated from stereo satellite imagery, GPS measurement, and topographic map points to generate DEMs. The research experiment begins by calculating the semivariogram model for all the data, and then the fitting process is performed by applying the same approach of functional approach for both sets of data. The following process is an ordinary cokriging interpolation, whose results are analysed and compared to the ordinary kriging interpolation. The experiment results prove that the ordinary cokriging fusion process could reduce interpolation error. The LS-SVM approach offers better precision in the semivariogram modelling by determining more precise weight calculation for cokriging fusion process. [ABSTRACT FROM AUTHOR]

Currently, many types of traffic data from different advanced data collection techniques are available. Plenty of effort has been spent to take full advantage of the heterogeneous data to enhance the prediction accuracy of the model in the advanced travel information system. The objective of this study is to build a deep feature fusion model to predict space–mean–speed using heterogeneous data. The temporal and spatial features are defined as the raw input which are extracted and trained by stacked autoencoders in the first step. Then, the extracted representative features from the data are fused. Finally, prediction models can be developed to capture the correlations. Therefore, the prediction model can consider both temporal–spatial correlation and correlation of heterogeneous data. Using the real-world data, some machine learning models including artificial neural network, support vector regression, regression tree and k-nearest neighbor are implemented and compared. The best result can be obtained when deep feature fusion model and support vector regression are jointly applied. Moreover, we compare the new proposed deep feature–level fusion method with the widely used data–level fusion method. The results indicate that proposed deep feature fusion model can achieve a better performance. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous development of synthetic aperture radar (SAR) techniques, radargrammetry technique has become an important method to obtain high-precision digital elevation model (DEM). Based on radargrammetry techniques, a new processing framework is proposed in this letter. In the new framework, in respect of radiometry, a one-to-many stereo image pairs structure and a multiple image matching algorithm based on this structure are introduced, which improves the robustness and quality of the results in the matching process. In terms of geometry, a fusion algorithm of stereo pairs with different intersection angle based on the terrain relief slope threshold is proposed, which improves the accuracy of the results. Subsequently, the experiment is conducted in the Hengduan Mountains in Southwest China and demonstrate the effectiveness of this framework compared with the classical radargrammetry technique. [ABSTRACT FROM AUTHOR]

In the big data era, robust solutions are obliged to be proposed to integrate and represent data from different formats and with different contents to assist the decision-making. Current cartographic and geographic information systems have limited capabilities for solving these problems. This paper describes an automatic and comprehensive system that conducts data fusion from all potentially related sources. In this system, a new Semantic Location Model (SemLM) is established to present the semantic concepts and location feature and demonstrate how locations are interrelated. In the SemLM, various types of location descriptors in different application scenarios can be analyzed and understood. Additionally, considering the challenges involved in data-intensive computation and visualization, this paper implements a Place-based Pan-Information System (P2S) as an innovative 4D system that dynamically associates and visualizes place-based information, using public security as the case study. [ABSTRACT FROM AUTHOR]

This study introduces a fusion mechanism by incorporating a risk metric with two-level data envelopment analysis (two-level DEA) for describing a corporate's operation status and then constructs a hybrid model that combines rough set theory with artificial fish swarm algorithm (RSAFSA) and fuzzy support vector machine (FSVM) in order to forecast corporate operating performance. The introduced mechanism, supported by real-life cases, can assist both public and private market participants who must allocate their economic resources to suitable places as well as maximize their personnel wealth under anticipated risk exposure. [ABSTRACT FROM AUTHOR]

This paper develops a new quantum inspired feature fusion method based on relative von Neumann entropy. The motivation is to more effectively reduce data redundancy and further improve the completeness and conciseness of the existing feature data. Regarding this, we quantise the source dataset into the collection of basic quantum states and constructed a weighted linking network to calculate the relative von Neumann entropies between feature samples. Thus, the detection and fusion of the duplicate feature samples in a subset is turned to the computation of the average relative von Neumann entropy and the measurement probabilities of the quantised feature samples. In parallel, the classical feature fusion method based on relative Shannon entropy is also proposed following similar idea. The experimental results show that the proposed feature fusion methods perform better in their completeness, conciseness, and stability. [ABSTRACT FROM AUTHOR]

This article proposes a spectral–spatial method for classification of hyperspectral images (HSIs) by modifying traditional random walker (RW). The proposed method consists of suggesting two main modifications. First, to construct a spatial edge weighting function, low-frequency edge weighting function is proposed. In this function, the detail weights are removed. Second, to enhance the classification accuracy, a fusion of spectral and spatial Laplacian matrix in RW is suggested. This fusion can improve the classification performances compared to traditional RW using only spatial Laplacian matrix. In comparison with some of the state-of-the-art RW and spectral–spatial classifier methods, the experimental results of the proposed method (spectral–spatial RW) show that the proposed method significantly increases the classification accuracy of HSI. [ABSTRACT FROM AUTHOR]

This study proposes a synthetic aperture radar (SAR) target recognition method via sparse representation of multi-view images with correlation analysis. The multi-view SAR images are first clustered into several view sets and in each set the included SAR images share high correlations. For the view set with only one SAR image, the sparse representation-based classification (SRC) is used for classification. The joint sparse representation (JSR) is employed to classify the view sets with more than one images in order to exploit their correlations. The decisions from different view sets are then fused based on the Bayesian theory. Therefore, both the independency and inner correlations in the multi-view SAR images can be better exploited to improve the target recognition performance. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) data set. The results show the superiority of the proposed approach over some other methods. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) and X-ray fluorescence spectra were recorded for 15 different samples of marmora, from the Mediterranean Basin and of different colours. After appropriate pretreatment (SNV transform + second derivative), the results were subjected to principal component analysis (PCA) treatment with a view to differentiating them. The observed differences among the samples were chemically interpreted by highlighting the NIR wavelengths and minerals, respectively, contributing the most to the PCA models. Moreover, a mid-level data fusion protocol allowed integrating the information from the different techniques and, in particular, to correctly identify (based on the distance in the score space) three test samples of known type. Moreover, it should be stressed that positive results on the differentiation and identification of marmora were obtained using two completely non-invasive, non-destructive and relatively inexpensive techniques, which can also be used in situ. [ABSTRACT FROM AUTHOR]

Land cover and its change are one of the important factors of global environmental change. The new GaoFen-2 (GF-2) satellite provides abundant spectral features and texture information, and airborne light detection and ranging (lidar) provides accurate three-dimensional coordinates at a finer scale. Fusing these data has the potential to improve land-cover classification. In the article, we selected the Random Forest (RF) as a classifier. The spectral bands of GF-2, normalized difference vegetation index (NDVI), normalized digital surface model derived from lidar data, and their grey-level co-occurrence matrix (GLCM) textures including mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation were generated to create seven scenarios with different combination of RF input variables. We estimated the classification performance on GF-2 satellite data, compared and assessed the individual and combined contributions of GF-2 and lidar data with regard to classification accuracy using fusion data, and identified the optimum combination of input variables that best balances classification performance with the computational challenges for land-cover classification. The results showed that GF-2 multispectral data alone or the fusion of GF-2 data and other sources can provide relatively good classification mapping accuracy in complex urban environments. The classification accuracy of fused data from the GF-2 satellite and lidar data exceeded those of GF-2 or lidar data alone. Moreover, GLCM textures significantly improved the classification performance whether using GF-2 satellite data or lidar height data. A fusion of GF-2 multispectral data, NDVI, lidar data, and their texture features (102 RF variables) achieved the best classification accuracy in seven scenarios. Using the optimum set of variables (55 variables) for RF yielded the most satisfactory classification result, achieving a total accuracy and kappa coefficient of 94.51% and 0.93, respectively. The producer's accuracy and user's accuracy exceeded 90% for almost all classes. This study aims to provide a reference for the efficient improvement of land-cover classification and offer support for extending the applications of classification algorithms and data sources. [ABSTRACT FROM AUTHOR]

Image pansharpening methods are usually grouped into two main classes: the spectral methods and the spatial methods. For the first class, the multispectral image undergoes a spectral transformation and then one of the resultant components is totally substituted with the panchromatic image, hence leading to a considerable color distortion compared with the second class. In the literature, this issue is addressed by integrating the wavelet transform to the spectral methods in order to transfer only the spatial details of the panchromatic image. Furthermore, the spatial information quantity transferred during the fusion is usually defined by the resolution ratio between the multispectral and panchromatic images, and this is, however, not necessarily the optimal quantity providing the best images. Therefore, a simple iterative Principal Component Analysis (PCA) based method is proposed in this letter, to continuously transfer the spatial information from the panchromatic to the multispectral image until the best fused image is obtained. The spatial distortion Ds of the Quality with No Reference (QNR) index is used as a stopping criterion. The experiments applied on the Worldview-3 images show that the suggested method presents the best visual and numerical results comparatively to the PCA and the Additive Wavelet Principal Component (AWPC) methods. [ABSTRACT FROM AUTHOR]

Monitoring long-term forest dynamics is essential for assessing human-induced land-cover changes, and related studies are often based on the multi-decadal Landsat archive. However, in areas such as the Tropical Andes, scarce data and the resulting poor signal-to-noise ratio in time series data render the implementation of automated time-series analysis algorithms difficult. The aim of this research was to investigate a novel approach that combines image compositing, multi-sensor data fusion, and postclassification change detection that is applicable in data-scarce regions of the Tropical Andes, exemplified for a case study in Ecuador. We derived biennial deforestation and reforestation patterns for the period from 1992 to 2014, achieving accuracies of 82 ± 3% for deforestation and 71 ± 3% for reforestation mapping. Our research demonstrated that an adapted methodology allowed us to derive the forest dynamics from the Landsat time series, despite the abundant regional data gaps in the archive, namely across the Tropical Andes. This study, therefore, presented a novel methodology in support of monitoring long-term forest dynamics in areas with limited historical data availability. [ABSTRACT FROM AUTHOR]

Many applications require remotely sensed brightness temperature (BT) data acquired with high temporal and spatial resolutions. In this regard, a viable strategy to overtake the physical limitations of space-borne sensors to achieve these data relies on fusing low temporal but high spatial resolution (HSR) data with high temporal but low spatial resolution data. The most promising methods rely on the fusion of spatially interpolated high temporal resolution data with temporally interpolated HSR data. However, the unavoidable presence of cloud masses in the acquired image sequences is often neglected, compromising the functionality and/or the effectiveness of the most of these fusion algorithms. To overcome this problem, a framework combining techniques of temporal smoothing and spatial enhancement is proposed to estimate surface BTs with high spatial and high temporal resolutions even when cloud masses corrupt the scene. Numerical results using real thermal data acquired by the SEVIRI sensor show the ability of the proposed approach to reach better performance than techniques based on either only interpolation or only spatial sharpening, even dealing with missing data due to the presence of cloud masses. [ABSTRACT FROM AUTHOR]

Stir-heating is a common method to process Gardenia jasminoides Ellis. The processing of the G. jasminoides Ellis which do not meet the regulated requirements can result in the unqualified products, finally decreasing the efficacy of the drug. To guarantee the quality of the stir-baked G. jasminoides Ellis, a rapid and efficient method to discriminate the qualified stir-baked G. jasminoides Ellis from the unqualified was developed based on the middle level data fusion. 61 batches of qualified and unqualified stir-baked G. jasminoides Ellis were collected and analyzed using colorimeter and near-infrared spectroscopy, separately. L*, A*, and B* were apparent features that quantitatively described the characteristics of samples. The latent variables of partial least squares discriminant analysis model were extracted as the features of near-infrared spectroscopy. The extracted features from colorimeter and near-infrared spectroscopy were fused and a discriminant model was built using partial least squares discriminant analysis method. Accuracy, sensitivity, and specificity were used to evaluate the ability of classification. As a result, the accuracy, sensitivity, and specificity of the optimal partial least squares discriminant analysis model based on fused data were all equal to 1. It demonstrated that the developed method can be capable of discriminating the qualified stir-baked G. jasminoides Ellis from the unqualified. Compared with classification model results using color parameters or near infrared data alone, this data fusion strategy greatly improved the accuracy of classification. [ABSTRACT FROM AUTHOR]

In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis. [ABSTRACT FROM AUTHOR]

In this study, the combination of surface reflectance products from Terra- Moderate Resolution Imaging Spectroradiometer and Landsat-Enhanced Thematic Mapper Plus sensors are explored through the Flexible Spatiotemporal DAta Fusion (FSDAF) algorithm within the framework of forest fire studies over tropical savannah environments. Thus, 60 fusion-derived images were generated from four spectral bands [red, near-infrared, shortwave infrared (SWIR1 and SWIR2)] and six spectral indices [normalized difference vegetation index, normalized difference moisture index, global environment monitoring index, soil-adjusted vegetation index, normalized burn ratio (NBR), and differenced normalized burn ratio (dNBR)] over two selected study sites. For all fusion processes performed, the actual Landsat images for the corresponding dates are available, which supports validation of the blended images. Additionally, integration of blended spectral indices in the immediate post-fire evaluation and the generation of fire severity were analysed. The blended bands presented correlation and Structure Similarity Index Measure (SSIM) values that were consistently higher than 0.819 and root mean square error values of less than 0.027, which confirms good accuracy levels obtained from the model. Similar correlation and SSIM accuracy levels were observed in the blended indices assessment for both study sites, which enables its values to be well-integrated for an analysis of the immediately post-fire date. However, the fire severity mapping from fused images needs to be carefully implemented since the dNBR index is generally less accurate than other blended indices. FSDAF fusion proved to be a useful alternative to retrieving multispectral information from savannah environments affected by fires. [ABSTRACT FROM AUTHOR]

Herbal products produced from multiple plants have special characteristics in the clinical practice of traditional Chinese medicine. These traits provide the opportunity for fraudulent merchants to mix other herbal products similar in appearance into authentic herbal medicine. Shihu is a tonic herbal medicine from the Dendrobium plants with complex botanical origins. In this context, 11 Dendrobium plants including 109 individuals from China were collected for authentication work. Nine species have been described as herbal medicines in the literature while D. hookerianum and D. xichouense are not reported to have medicinal benefits. A key feature of this study was that multiple recognition approaches, based on near-infrared and ultraviolet-visible spectra as well as their combination, were compared to investigate their classification performance. Intuitively, score plots using principal component analysis and hierarchical cluster diagrams were used to evaluate the genetic relationships among these species. Compared with support vector machine discrimination analysis and k-nearest neighbor models, the partial least square discrimination analysis model combined with low-level data fusion provided excellent performance for authentication and was the most robust model with 100% accuracy rates for the training and prediction sets. The results indicated that near-infrared and ultraviolet-visible spectra and their fusion dataset combined with supervised recognition analysis are effective and therefore recommended for the authentication of genuine and sham of herbal Shihu species. [ABSTRACT FROM AUTHOR]

Recently, distribution element trees (DETs) were introduced as an accurate and computationally efficient method for density estimation. In this work, we demonstrate that the DET formulation promotes an easy and inexpensive way to generate random samples similar to a smooth bootstrap. These samples can be generated unconditionally, but also, without further complications, conditionally using available information about certain probability-space components. This article is accompanied by the R codes that were used to produce all simulation results. Supplementary material for this article is available online. [ABSTRACT FROM AUTHOR]

Many analyses require linking records from two databases comprising overlapping sets of individuals. In the absence of unique identifiers, the linkage procedure often involves matching on a set of categorical variables, such as demographics, common to both files. Typically, however, the resulting matches are inexact: some cross-classifications of the matching variables do not generate unique links across files. Further, the variables used for matching can be subject to reporting errors, which introduce additional uncertainty in analyses. We present a Bayesian file matching methodology designed to estimate regression models and match records simultaneously when categorical variables used for matching are subject to errors. The method relies on a hierarchical model that includes (1) the regression of interest involving variables from the two files given a vector indicating the links, (2) a model for the linking vector given the true values of the variables used for matching, (3) a model for reported values of the variables used for matching given their true values, and (4) a model for the true values of the variables used for matching. We describe algorithms for sampling from the posterior distribution of the model. We illustrate the methodology using artificial data and data from education records in the state of North Carolina. [ABSTRACT FROM AUTHOR]

Generally, in an advanced manufacturing system hundreds of sensors are deployed to measure key process variables in real time. Thus it is desirable to develop methodologies to use real-time sensor data for on-line system condition monitoring and anomaly detection. However, there are several challenges in developing an effective process monitoring system: (i) data streams generated by multiple sensors are high-dimensional profiles; (ii) sensor signals are affected by noise due to system-inherent variations; (iii) signals of different sensors have cluster-wise features; and (iv) an anomaly may cause only sparse changes of sensor signals. To address these challenges, this article presents a real-time multiple profiles sensor-based process monitoring system, which includes the following modules: (i) preprocessing sensor signals to remove inherent variations and conduct profile alignments, (ii) using multichannel functional principal component analysis (MFPCA)-based methods to extract sensor features by considering cluster-wise between-sensor correlations, and (iii) constructing a monitoring scheme with the top-R strategy based on the extracted features, which has scalable detection power for different fault patterns. Finally, we implement and demonstrate the proposed framework using data from a real manufacturing system. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of change detection through a Wireless Sensor Network (WSN) whose nodes report only binary decisions (on the presence/absence of a certain event to be monitored), due to bandwidth/energy constraints. The resulting problem can be modelled as testing the equality of samples drawn from independent Bernoulli probability mass functions, when the bit probabilities under both hypotheses are not known. Both One-Sided (OS) and Two-Sided (TS) tests are considered, with reference to: (i) identical bit probability (a homogeneous scenario), (ii) different per-sensor bit probabilities (a non-homogeneous scenario) and (iii) regions with identical bit probability (a block-homogeneous scenario) for the observed samples. The goal is to provide a systematic framework collecting a plethora of viable detectors (designed via theoretically founded criteria) which can be used for each instance of the problem. Finally, verification of the derived detectors in two relevant WSN-related problems is provided to show the appeal of the proposed framework. [ABSTRACT FROM AUTHOR]

This focused study aimed to generate a fully polarimetric synthetic aperture radar (SAR) data set of 1 m resolution based on the spotlight and stripmap COSMO-SkyMed (CSK) satellite data fusion. The results show the feasibility of overcoming the limitation of the nominal 3 m resolution generated by a series of multi-temporal stripmap SAR data observed in all the polarisations. The CSK satellite system does not allow the observation of cross-polar data in the spotlight acquisition mode because only co-polar data are available. In this work, a fully polarimetric scattering matrix is estimated by using two spotlights in the horizontal horizontal (HH) and vertical vertical (VV) polarisations and two stripmaps in the horizontal vertical (HV) and vertical horizontal (VH) polarisations. The stripmaps were resampled and super-resolved by using the amplitude and phase estimation of sinusoids (APES) filter to achieve the spotlight resolution. The results show that the proposed strip-spot approach has immediate operative applications. [ABSTRACT FROM AUTHOR]

The paper discusses the tracking of a manoeuvring target using fuzzy information fusion filters (FIFFs), based on bearing only measurements received from multiple sensors. While a number of tracking filters including the Kalman filters have been proposed and demonstrated successfully for tracking, the information filter has been reported to be capable of estimating the state of a target even without an initial estimate. However, the estimate is often seen to diverge after a short period. A FIFF has been shown to track small manoeuvres in target following a constant velocity model but is not effective when the target switches to a coordinated turn model. In this paper, the idea of using the FIFF for tracking manoeuvring target is proposed and demonstrated. The filter uses only the bearing measurements in tracking the target and also for detecting the manoeuvre. Using an adaptive turn rate model, the turning rate of the moving target is estimated from the range rate. Here, the range rate is also computed directly from bearing measurements. The detection of the onset of manoeuvre is on the basis of consistent change in the turning rate of the target. A number of different scenarios involving manoeuvring targets are given to demonstrate the efficacy of this approach. [ABSTRACT FROM AUTHOR]

This letter proposes a robust decision fusion strategy for synthetic aperture radar (SAR) automatic target recognition (ATR). The reliabilities of individual decisions and the consistency between different decisions are quantitatively evaluated. Then, only those decisions discriminative enough for target recognition are selected for the final decision fusion. Both the efficiency and robustness of the decision fusion methods can be enhanced using the proposed strategy. Moreover, as a general strategy, the proposed strategy can be applied to different types of decision fusions, e.g., the multi-feature decision fusion, multi-classifier decision fusion and multi-view decision fusion. Experiments are conducted on the moving and stationary target acquisition recognition (MSTAR) dataset under the standard operating condition and several typical extended operating conditions (EOCs) to validate the effectiveness and robustness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Operating units, in practice, often suffer from multiple modes of failure, and each failure mode has a distinct influence on the service life cycle path of a unit. The rapid development of sensor and communication technologies has enabled multiple sensors to simultaneously monitor and track the health status of a unit in real time. However, one challenging question that remains to be resolved is how to leverage data from multiple sensors for better degradation modeling and prognostic analysis, especially when there are multiple failure modes. Currently, many of the existing approaches in prognostics either (a) fail to capture the dependency between sensors and instead focus on analyzing each sensor independently or (b) fail to incorporate the failure-mode diagnosis for better degradation modeling and prognostics during condition monitoring. To address the limitations in the existing literature, we propose a data-level fusion methodology to construct a composite failure-mode index, named FM-INDEX, via the fusion of multiple sensor data. Our goal is to utilize the FM-INDEX to better characterize the failure mode of an operating unit in real time, thus leading to better degradation modeling and prognostic analysis. A case study that involves the degradation data set of an aircraft gas turbine engine with two potential failure modes is conducted to numerically evaluate the performance of our proposed method compared to other techniques in the related literature. [ABSTRACT FROM AUTHOR]

Warranty data contain valuable information on product field reliability and customer behaviors. Most previous studies on analysis of warranty data implicitly assume that all failures within the warranty period are reported and recorded. However, the failed-but-not-reported (FBNR) phenomenon is quite common for a product whose price is not very high. Ignorance of the FBNR phenomenon leads to an overestimate of product reliability based on field return data or an overestimate of warranty cost based on lab data or tracking data. Being an indicator of customer satisfaction, the FBNR proportion provides valuable managerial insights. In this study, statistical inference for the FBNR phenomenon as well as field lifetime distribution is described. We first propose a flexible FBNR function to model the time-dependent FBNR behavior. Then, a framework for data analysis is developed. In the framework, both semiparametric and parametric approaches are used to jointly analyze warranty claim data and supplementary tracking data from a follow-up of selected customers. The FBNR problem in the tracking data is minimal and thus the data can be used to effectively decouple the FBNR information from the warranty claim data. The proposed methods are illustrated with an example. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

The Fay-Herriot area-level model for correlated response data is augmented with a between-groups-of-domains effect. Correlated-response parameters of small-area estimates no longer need the assumption of spatial contiguity. A simulation shows that area-level correlated-response observations increase the efficiency of the estimates, but do not reduce the biases. [ABSTRACT FROM AUTHOR]

In this study, the individual and data fusion of Fourier transform infrared (FT-IR) spectroscopy and inductively coupled plasma atomic emission spectrometry (ICP-AES) were used for the discrimination of five species of Boletaceae mushrooms with the aid of support vector machine (SVM). First, the original FT-IR spectra of 230 samples with different species were preprocessed and optimized by second derivative (2D), Savitzky-Golay filter (15:1) and standardized normal variate. Second, the datasets of FT-IR spectra and ICP-AES were integrated, and the low-level data fusion strategy was used to classify different species mushrooms. Third, the latent variables of elements concentration and FT-IR spectra were extracted by partial least square discriminant analysis and two datasets were fused into a new matrix. Finally, the classification models were established by SVM. Compared with single spectroscopic technique, the mid-level data fusion strategy can provide better result. Especially, the accuracy of correct classification of samples in calibration and test sets were 100.00% and 98.68%, respectively. The results demonstrated that the mid-level data fusion of FT-IR and ICP-AES can provide higher synergic effect for the discrimination of different species Boletaceae mushrooms, which could be benefited for the further authentication and quality control of edible mushrooms. [ABSTRACT FROM AUTHOR]