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In light of the rapid advancement in communication technology facilitated by the Internet of Things (IoT), the demand for effective data analysis and utilization has surged. This is particularly crucial as it involves the collection of data from various objects and devices in diverse ways. A robust methodology designed to improve data fusion while simultaneously upholding data confidentiality and optimizing data weight. This research work introduces a privacy-preserving lightweight cryptographic model (PP-LWC) tailored for IoT environments. It employs a novel data fusion process, integrating weight optimization and iterative data fusion, to enhance data integrity and confidentiality. The model utilizes differential privacy techniques, adding controlled noise to queries, ensuring data privacy without compromising on computational efficiency. By incorporating unique encryption and signature verification mechanisms within IoT clusters, it effectively safeguards against both internal and external threats, maintaining robust security in resource-constrained settings. This approach strikes a balance between preserving privacy and ensuring lightweight cryptographic operations, crucial for the vast and diverse landscape of IoT devices. PPLWC is evaluated considering the various parameters such as computation overhead for single signature generation, and computation overhead for single signature verification. Cost comparison for signature verification mechanism, Communication cost for sending a broadcast message. Comparison of communication cost for sending and broadcast messages. Comparative analysis with the existing model proves the model's efficiency by outperforming the existing technique. [ABSTRACT FROM AUTHOR]

The current methods for short-time precipitation nowcasting are based on radar echo extrapolation model, without fully considering the close influence of other meteorological factors on the evolution of precipitation generation and cancellation, thus limiting the accuracy of the forecasts. To address the above issues, this paper produced a short-time precipitation nowcasting dataset, and proposed the MFPNM(multiple factors precipitation nowcasting model). Based on data from the Fengyun4B satellite, the dataset toke quantitative precipitation estimation as the forecast object and contained four background meteorological factors. Taking the TransUNet as the backbone of the model, this model proposed the parallel dual encoder to extract the high-dimensional spatio-temporal features of the forecast object and the background meteorological data, respectively. Besides, it constructed the content coding module to encode the spatial features of the background data as the learnable positional embedding of the high-dimensional feature vectors of the forecast object. It used a Transformer module to construct the global relationship between the high-dimensional features of the sequence data for better sequence prediction. The metrics used in this paper included critical success index, false alarm rate, root-mean-square error, and structural similarity, etc. The MPFNM was evaluated on two datasets (the proposed dataset and an open-source dataset) and outperformed the baseline models, and it was analyzed for explainability through the SHAP technique. The experimental results and explainability analysis show that the model has better forecasting accuracy and reliability. [ABSTRACT FROM AUTHOR]

Korla pear is a characteristic fruit in Xinjiang. Among them, the crisp texture is one of its excellent quality parameters. However, the crispness differences of Korla pears can vary gradually in recent years, due to the origin, variety, and maturity. The current sensory evaluation by experts or trained panelists can be the most accurate to detect the crispness. However, the evaluation process has been limited to the time-consuming and labor-intensity. The accuracy of evaluation can gradually decrease over time. Alternatively, instrument detection can share fast and stable advantages over sensory evaluation. In this study, the instrument detection was performed on the Korla pear flesh crispness using mechanical-acoustic information fusion. 50 pears were selected to test the crispness every 7 days during the 35-day storage periods, resulting in a total of six crispness gradient samples: crisp, relatively crisp, slightly crisp, slightly mealy, relatively mealy, and mealy. Then, the mechanical-acoustic signals during the rupture of pear flesh were synchronously collected at 51 200 Hz sampling rate using a universal material testing machine combined with a microphone. Subsequently, the information on jaggedness analysis interval in mechanical-acoustic signals was fused at the data level. The correlation between mechanical-acoustic signals was utilized to align with the processing mode of the human brain's comprehensive perception of crispness. Later, mechanical-acoustic fusion signals were converted into the different images of the Gramian angular summation field (GASF), Gramian angular difference field (GADF), symmetric dot pattern (SDP), Markov transition field (MTF), and recurrence plot (RP). The deep features of different images were extracted by the ResNet50 network. 8, 8, 9, 10, and 10 principal components were obtained after PCA dimensionality reduction. Furthermore, Pearson’s correlation analysis was made to obtain the absolute mean correlation coefficients between principal components of different image features and sensory crispness scores. The results showed that the MTF image was the most suitable to quantitatively characterize the crispness scores of pear flesh with the highest absolute mean correlation coefficient. Finally, the principal components of MTF images were input into the KNN, ELM, RF, and SVR optimized by PSO. The ResNet50-SVR model achieved the best prediction accuracy and stability. The RP 2, RMSEP, and RPD values were 0.96, 0.24, and 4.88, respectively. Consequently, this finding can provide a strong foundation for instrument detection of the crispness of fruits and vegetables. [ABSTRACT FROM AUTHOR]

The increasing frequency and sophistication of cyber-attacks pose significant threats to organizational entities and critical national infrastructure, leading to substantial financial and operational consequences. Detecting such attacks early and accurately remains a complex endeavour, compounded by challenges in intrusion detection system (IDS) design, the exploitation of zero-day attacks, and issues of reliability and resiliency in physical systems. This research addresses these challenges through a two-fold approach: firstly, implementing input data fusion from diverse and heterogeneous sources, and secondly, fusing classifiers from multiple deep learning (DL)-based algorithms. The success of machine learning (ML) and DL models for IDS relies on meticulous data collection and classifier selection. The paper underscores the limitations of relying on single datasets and ML/DL algorithms, emphasizing potential biases and training restrictions. Rigorous experiments were conducted to identify optimal DL architectures, ensuring the creation of models that exhibit robust generalization on new traffic instances, leading to trusted and unbiased results. The study demonstrates the efficacy of the proposed models through comprehensive evaluations and metrics. Results indicate that the fusion of data and classifiers significantly improves model generalization. The paper also outlines key challenges and future trends in data fusion, emphasizing its role in enhancing IDS performance for securing critical infrastructure. [ABSTRACT FROM AUTHOR]

Under the background of globalization, cultural and creative tourism has gradually become a new trend of tourism, providing tourists with rich and diverse travel experience. This paper discusses the development status, characteristics and integration strategies of cultural and creative tourism under the new normal. Through the analysis of the existing literature, it is found that cultural creative tourism not only brings economic benefits to the region, but also promotes the protection and inheritance of local culture. At the same time, technological advances and data convergence provide new opportunities for the promotion and management of cultural and creative tourism. But it also brings challenges, such as how to ensure sustainable development and how to meet the growing demand of tourists. Finally, based on the method of multiple linear regression analysis, the paper analyzes the factors that affect the development of cultural creative tourism. [ABSTRACT FROM AUTHOR]

The stable hydrogen isotope in precipitation is an effective environmental tracer for climatic and hydrologic studies. However, accurate and high-precision precipitation hydrogen isoscapes are currently unavailable in China. In this study, a data fusion method based on Convolutional Neural Networks (CNN) is used to fuse the hydrogen isotopic composition (δ2Hp) of observations and isotope-equipped general circulation model (iGCM) simulations. A precipitation hydrogen isoscape with a temporal resolution of monthly and a spatial resolution of 50–60 km is established for East China for the 1969–2017 period. Prior to building the isoscape, the performance of three data fusion methods (DFMs) and two bias correction methods (BCMs) is compared. The results indicate that the CNN fusion method performs the best with a correlation coefficient larger than 0.90 and root mean square error smaller than 10.5‰when using observation as a benchmark. The fusion methods based on back propagation and long short-term memory neural network perform similarly, while slightly outperforming the bias correction methods. Thus, the CNN method is used to generate the hydrogen isoscape, and the temporal and spatial distribution characteristics of the hydrogen isotope in precipitation are analyzed based on this dataset. The generated isoscape shows similar spatial and temporal distribution characteristics to observations. In general, the distribution pattern of δ2Hp is consistent with the temperature effect in northern China, and consistent with the precipitation amount effect in southern China. The trend of the δ2Hp time series is consistent with that of observed precipitation and temperature. Overall, the generated isoscape effectively reproduces the observations, and has the characteristics of time continuity and relative spatial regularity, which can provide valuable data support for tracking atmospheric and hydrological processes. [ABSTRACT FROM AUTHOR]

Accurate quantification of snow cover facilitates the prediction of snowmelt runoff, the assessment of freshwater availability, and the analysis of Earth's energy balance. Existing fractional snow cover (FSC) data, however, often suffer from limitations such as spatial and temporal gaps, compromised accuracy, and coarse spatial resolution. These limitations significantly hinder the ability to monitor snow cover dynamics effectively. To address these formidable challenges, this study introduces a novel data fusion framework specifically designed to generate high-resolution (1 km) daily FSC estimation across vast regions like North America, regardless of weather conditions. It achieved this by effectively integrating the complementary spatiotemporal characteristics of both coarse- and fine-resolution FSC data through a multi-stage processing pipeline. This pipeline incorporates innovative strategies for bias correction, gap filling, and consideration of dynamic characteristics of snow cover, ultimately leading to high accuracy and high spatiotemporal completeness in the fused FSC data. The accuracy of the fused FSC data was thoroughly evaluated over the study period (September 2015 to May 2016), demonstrating excellent consistency with independent datasets, including Landsat-derived FSC (total 24 scenes; RMSE=6.8–18.9 %) and ground-based snow observations (14,350 stations). Notably, the fused data outperforms the widely used Interactive Multi-sensor Snow and Ice Mapping System (IMS) daily snow cover extent data in overall accuracy (0.92 vs. 0.91), F1_score (0.86 vs. 0.83), and Kappa coefficient (0.80 vs. 0.77). Furthermore, the fused FSC data exhibits superior performance in accurately capturing the intricate daily snow cover dynamics compared to IMS data, as confirmed by superior agreement with ground-based observations in four snow-cover phenology metrics. In conclusion, the proposed data fusion framework offers a significant advancement in snow cover monitoring by generating high-accuracy, spatiotemporally complete daily FSC maps that effectively capture the spatial and temporal variability of snow cover. These FSC datasets hold substantial value for climate projections, hydrological studies, and water management at both global and regional scales. [ABSTRACT FROM AUTHOR]

Street view imagery (SVI) is instrumental for sensing urban environments, benefitting numerous domains such as urban morphology, health, greenery, and accessibility. Billions of images worldwide have been made available by commercial services such as Google Street View and crowdsourcing services such as Mapillary and KartaView where anyone from anywhere can upload imagery while moving. However, while the data tend to be plentiful, have high coverage and quality, and are used to derive rich insights, they remain simple and limited in metadata as characteristics such as weather, quality, and lighting conditions remain unknown, making it difficult to evaluate the suitability of the images for specific analyses. We introduce Global Streetscapes — a dataset of 10 million crowdsourced and free-to-use SVIs sampled from 688 cities across 210 countries and territories, enriched with more than 300 camera, geographical, temporal, contextual, semantic, and perceptual attributes. The cities included are well balanced and diverse, and are home to about 10% of the world's population. Deep learning models are trained on a subset of manually labelled images for eight visual-contextual attributes pertaining to the usability of SVI — panoramic status, lighting condition, view direction, weather, platform, quality, presence of glare and reflections, achieving accuracy ranging from 68.3% to 99.9%, and used to automatically label the entire dataset. Thanks to its scale and pre-computed standard semantic information, the data can be readily used to benefit existing use cases and to unlock new applications, including multi-city comparative studies and longitudinal analyses, as affirmed by a couple of use cases in the paper. Moreover, the automated processes and open-source code facilitate the expansion and updates of the dataset and encourage users to create their own datasets. With the rich manual annotations, some of which are provided for the first time, and diverse conditions present in the images, the dataset also facilitates assessing the heterogeneous properties of crowdsourced SVIs and provides a benchmark for evaluating future computer vision models. We make the Global Streetscapes dataset and the code to reproduce and use it publicly available in https://github.com/ualsg/global-streetscapes. [Display omitted] • Largest labelled dataset, with 346 attributes that characterise street photos. • Baseline models and ground truth labels for benchmarking computer vision models. • Reproducible framework to sample and enrich SVIs from cities all around the world. • In-depth discussion of how the dataset could drive novel research questions. • Taking forward the work of Mapillary and KartaView, and their contributors. [ABSTRACT FROM AUTHOR]

Individual tree detection in urban areas using unmanned air vehicles (UAVs) RGB imagery poses challenges due to the diverse shapes and structures of urban trees and the complexity of urban forests. The digital surface model (DSM) provides elevation data, and the fusion of UAV RGB imagery with elevation data has emerged as a promising approach for tree detection. Here, we constructed a novel network structure based on the faster region-based convolutional neural network (Faster R-CNN) to detect camphor trees in urban environments using RGB-DSM data. First, an attention fusion module was proposed to effectively fuse the RGB and DSM features by leveraging their complementarity. Second, the bidirectional feature pyramid network (BiFPN) was introduced to enhance the model performance in detecting camphor tree crowns of varying sizes. The results showed that our approach could effectively detect urban camphor trees and achieved an AP of 85.7%. Notably, our approach yielded an AP of 81.3% in urban green spaces. The analysis indicated that our approach was feasible for detecting camphor trees in urban areas and demonstrated its potential to facilitate urban forestry research and applications. [ABSTRACT FROM AUTHOR]

Several landslide susceptibility (LS) maps at various scales of analysis have been performed with specific zoning purposes and techniques. Supervised machine learning algorithms (ML) have become one of the most diffused techniques for landslide prediction, whose reliability is firmly based on the quality of input data. Site-specific landslide inventories are often more accurate and complete than national or worldwide databases. For these reasons, detailed landslide inventory and predisposing variables must be collected to derive reliable LS products. However, high-quality data are often rare, and risk managers must consider lower-resolution available products with no more than informative purposes. In this work, we compared different ML models to select the most accurate for large-scale LS assessment within the Municipality of Rome. The ExtraTreesClassifier outperformed the others reaching an average F1-score of 0.896. Thereafter, we addressed the reliability of open-source LS maps at different scales of analysis (global to regional) by means of statistical and spatial analysis. The obtained results shed light on the difference in hazard zoning depending on the scale and mapping unit. An approach for low-resolution LS data fusion was attempted, assessing the importance of the adopted criteria, which increased the ability to detect occurred landslides while maintaining precision. [ABSTRACT FROM AUTHOR]

Policymakers often require information on programs' long‐term impacts that is not available when decisions are made. For example, while rigorous evidence from the Oregon Health Insurance Experiment (OHIE) shows that having health insurance influences short‐term health and financial measures, the impact on long‐term outcomes, such as mortality, will not be known for many years following the program's implementation. We demonstrate how data fusion methods may be used address the problem of missing final outcomes and predict long‐run impacts of interventions before the requisite data are available. We implement this method by concatenating data on an intervention (such as the OHIE) with auxiliary long‐term data and then imputing missing long‐term outcomes using short‐term surrogate outcomes while approximating uncertainty with replication methods. We use simulations to examine the performance of the methodology and apply the method in a case study. Specifically, we fuse data on the OHIE with data from the National Longitudinal Mortality Study and estimate that being eligible to apply for subsidized health insurance will lead to a statistically significant improvement in long‐term mortality. [ABSTRACT FROM AUTHOR]

Accurate and timely monitoring of drought conditions in grape-producing regions is crucial for achieving healthy growth of grapes. Current soil moisture (SM) products are primarily available at coarse resolutions (e.g. several to tens of kilometres), constraining its applications at fine scales. Here, we trained a weighted stacking ensemble model including three tree-based models (categorical boosting, random forest, and gradient boosting decision tree), using seven forcing parameters related to spectral reflectance (SR), land surface temperature (LST), and evapotranspiration (ET), in conjunction with the digital elevation model (DEM) feature. The weighted stacking ensemble model exhibited an average R2 of 0.86 and an average RMSE of 0.021 m3/m3 in simulating SM in the vegetive stage and the mid-ripening stage of grape. Then we generated high spatiotemporal downscaled SM (HSM) data at a grape growing area at high spatiotemporal resolutions (30 m, 8-day) from 2009 to 2018. Our HSM dataset demonstrated strong spatial, seasonal and interannual dynamics that align with 500 m SM dataset derived from single MODIS data, and the HSM dataset shows more details in SM distribution. Additionally, the SM time series in the HSM is consistently correlated with drought events, offering intricate spatiotemporal information for drought monitoring. The application of downscaled SM results identified a concentration of drought events in the eastern foothills of the Helan Mountains, particularly severe drought conditions were observed in the Hongsipu production area. Drought occurrences in the Hongsipu production area ranged from 90% to 91% during May and June, decreasing to 73% and 41% in July and August, respectively. These findings significantly contribute to enhancing high spatiotemporal SM monitoring capabilities, offering valuable guidance for timely water management in grape-growing regions. [ABSTRACT FROM AUTHOR]

This paper is devoted to the application of object localization and identification with information combined from a radar system and a dedicated portable/mobile electronic device equipped with a global positioning system (GPS) receiver. This device is able to provide object's (staff member, and staff vehicle) rough location and identification. Such systems are required in very restrictive security areas like airports (e.g., open-air area and apron). Currently, the outdoor area of the airport is typically protected by a surveillance system operated by security guards. Surveillance systems are composed of different sensors, video and infrared cameras, and microwave radars. The sheer number of events generated via the system can lead to fatigue among staff, potentially resulting in the omission of critical events. To address this issue, we propose an electronic system equipped with a wireless module and a GPS module. This approach enables automatic identification of objects through the fusion of data from two independent systems (GPS and radar). The radar system is capable of precisely localizing and tracking objects, while the described system is able to identify registered objects. This paper contains a description of the subsystems of a portable/mobile electronic device. The fusion of information from the proposed system (rough location and identification) with the precise location obtained from short-range radar is intended to reduce the number of false alerts in the surveillance system. [ABSTRACT FROM AUTHOR]

With the increasing number of ongoing space missions for Earth Observation (EO), there is a need to enhance data products by combining observations from various remote sensing instruments. We introduce a new Transformer-based approach for data fusion, achieving up to a 10- to-30-fold increase in the spatial resolution of our hyperspectral data. We trained the network on a synthetic set of Sentinel-2 (S2) and Sentinel-3 (S3) images, simulated from the hyperspectral mission EnMAP (30 m resolution), leading to a fused product of 21 bands at a 30 m ground resolution. The performances were calculated by fusing original S2 (12 bands, 10, 20, and 60 m resolutions) and S3 (21 bands, 300 m resolution) images. To go beyond EnMap's ground resolution, the network was also trained using a generic set of non-EO images from the CAVE dataset. However, we found that training the network on contextually relevant data is crucial. The EO-trained network significantly outperformed the non-EO-trained one. Finally, we observed that the original network, trained at 30 m ground resolution, performed well when fed images at 10 m ground resolution, likely due to the flexibility of Transformer-based networks. [ABSTRACT FROM AUTHOR]

In recent years, deep learning techniques for pansharpening multiresolution images have gained increasing interest. Due to the lack of ground truth data, most deep learning solutions rely on synthetic reduced-resolution data for supervised training. This approach has limitations due to the statistical mismatch between real full-resolution and synthetic reduced-resolution data, which affects the models' generalization capacity. Consequently, there has been a shift towards unsupervised learning frameworks for pansharpening deep learning-based techniques. Unsupervised schemes require defining sophisticated loss functions with at least two components: one for spectral quality, ensuring consistency between the pansharpened image and the input multispectral component, and another for spatial quality, ensuring consistency between the output and the panchromatic input. Despite promising results, there has been limited investigation into the interaction and balance of these loss terms to ensure stability and accuracy. This work explores how unsupervised spatial and spectral consistency losses can be reliably combined preserving the outocome quality. By examining these interactions, we propose a general rule for balancing the two loss components to enhance the stability and performance of unsupervised pansharpening models. Experiments on three state-of-the-art algorithms using WorldView-3 images demonstrate that methods trained with the proposed framework achieve good performance in terms of visual quality and numerical indexes. [ABSTRACT FROM AUTHOR]

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity. [ABSTRACT FROM AUTHOR]

This study aims to improve the accuracy of the detection of adulterated minced beef with chicken, duck, and pork. A series of tests were carried out to quantitatively detect the adulterants in beef using hyperspectral reflectance (R) and transmittance (T) spectral data fusion. Firstly, three types of adulterants (chicken, duck, and pork) were added to the beef with a mass percentage of 2% to 50% concentration interval of 2%, respectively. Then the reflectance spectral data was collected to combine with SVM (support vector machine, SVM), random forest (RF), and long short-term memory (LSTM). A classification model was established to classify the samples into chicken, duck, and pork adulterated and pure beef samples (4 groups). Then, a partial least squares model (PLSR) was built from the single reflectance and transmittance spectral data to quantify the concentrations of adulterants. The performance of the model was optimized by competitive adaptive reweighted sampling (CARS), irrelevant variable elimination (UVE), and primary and intermediate data fusion. The predictions were also conducted on adulterants such as chicken, duck, and pork, according to the intermediate-level data fusion. The results showed the greatest effectiveness values of improvement were 3.9%, 9.3%, and 4.5% over those in the optimal model with single spectral data, respectively. The models with the best prediction results for chicken and duck samples withadulterants were the de trending (DT) pre-processed UVE-PLSR, with corresponding coefficients of determination (r-squaredprediction, ) and root mean square error (RMSEP) of 0.984 5 and 1.8651、 0.986 0 and 1.7711, respectively, and the bestprediction model for adulterated pork samples was RAW-CARS-PLSR, with corresponding and RMSEP of 0.975 1 and 2.366 5. Hyperspectral imaging combined with data fusion can effectively detect the adulterants in beef with high accuracy and speed. The intermediate-level data fusion can maximize the performance of the improved model. [ABSTRACT FROM AUTHOR]

Monitoring the learning process during task solving through different channels will facilitate a better understanding of the learning process. This understanding, in turn, will provide teachers with information that will help them to offer individualised education. In the present study, monitoring was carried out during the execution of a task applied in a self-regulated virtual environment. The data were also analysed using data fusion techniques. The objectives were as follows: (1) to examine whether there were significant differences between students in cognitive load (biomarkers: fixations, saccades, pupil diameter, galvanic skin response—GSR), learning outcomes and perceived student satisfaction with respect to the type of degree (health sciences vs. engineering; and (2) to determine whether there were significant differences in cognitive load metrics, learning outcomes and perceived student satisfaction with respect to task presentation (visual and auditory vs. visual). We worked with a sample of 31 university students (21 health sciences and 10 biomedical engineering). No significant differences were found in the biomarkers (fixations, saccades, pupil diameter and GSR) or in the learning outcomes with respect to the type of degree. Differences were only detected in perceived anxiety regarding the use of virtual laboratories, being higher in biomedical engineering students. Significant differences were detected in the biomarkers of the duration of use of the virtual laboratory and in some learning outcomes related to the execution and presentation of projects with respect to the variable form of the visualisation of the laboratory (visual and auditory vs. visual). Also, in general, the use of tasks presented in self-regulated virtual spaces increased learning outcomes and perceived student satisfaction. Further studies will delve into the detection of different forms of information processing depending on the form of presentation of learning tasks. [ABSTRACT FROM AUTHOR]

The roadway space of coal mine is narrow, and the illumination is low and uneven. Dynamic mining is accompanied by dust and water mist. The accuracy and reliability of roadway data collected by vision and laser sensors are poor. Based on this, a digital modeling method of coal mine roadway based on millimeter-wave radar array is proposed. Firstly, aiming at the problem of complex environmental interference, combined with the characteristics of small amount of single frame data of millimeter-wave point cloud, a multi-layer filtering noise reduction processing and dynamic subgraph registration method of millimeter-wave point cloud is proposed to filter out interference points and realize single radar point cloud registration. Secondly, aiming at the problem that a single millimeter-wave radar cannot scan the complete roadway information at one time, combined with the characteristics of small elevation field of view of millimeter-wave radar, a millimeter-wave radar array acquisition system is built, and an improved iterative closest point (ICP) registration algorithm based on point cloud features is established to construct the roadway point cloud fusion model. Finally, aiming at the problem of uneven and sparse point cloud after array fusion, a Poisson surface reconstruction method based on point cloud density weighted interpolation is proposed to refine the roadway structure and realize the accurate reconstruction of digital roadway model. The experimental results show that the digital modeling method of millimeter-wave radar array can accurately obtain the information of roadway surrounding rock, the density of roadway point cloud is increased by 22.4%, and the average absolute error percentage of the width and height of the reconstructed roadway model is only 0.82% and 0.72%, which provides a new research method for the reconstruction of underground roadway and an important basis for the digital modeling method of coal mine roadway. [ABSTRACT FROM AUTHOR]

With the development of precision sensing instruments and data storage devices, the fusion of multi-sensor data in gearbox fault diagnosis has attracted much attention. However, existing methods have difficulty in capturing the local temporal dependencies of multi-sensor monitoring information, and the inescapable noise severely decreases the accuracy of multi-sensor information fusion diagnosis. To address these issues, this paper proposes a fault diagnosis method based on dynamic graph convolutional neural networks and hard threshold denoising. Firstly, considering that the relationships between monitoring data from different sensors change over time, a dynamic graph structure is adopted to model the temporal dependencies of multi-sensor data, and, further, a graph convolutional neural network is constructed to achieve the interaction and feature extraction of temporal information from multi-sensor data. Secondly, to avoid the influence of noise in practical engineering, a hard threshold denoising strategy is designed, and a learnable hard threshold denoising layer is embedded into the graph neural network. Experimental fault datasets from two typical gearbox fault test benches under environmental noise are used to verify the effectiveness of the proposed method in gearbox fault diagnosis. The experimental results show that the proposed DDGCN method achieves an average diagnostic accuracy of up to 99.7% under different levels of environmental noise, demonstrating good noise resistance. [ABSTRACT FROM AUTHOR]

In the United States, several land use and land cover (LULC) data sets are available based on satellite data, but these data sets often fail to accurately represent features on the ground. Alternatively, detailed mapping of heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences among scenes, landscape heterogeneity, and computational limitations. Various machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study were to evaluate bagging (Random Forest, RF), boosting (Gradient Boosting Machines [GBM] and extreme gradient boosting [XGB]), and stacking ensemble ML models. We used these techniques on a time series of Sentinel 2A data and NAIP orthoimagery to create a LULC map of a portion of Irion and Tom Green counties in Texas (USA). We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. We then compared accuracy based on random cross-validation without accounting for spatial autocorrelation and target-oriented cross-validation accounting for spatial structures of the training data set. Comparison of random and target-oriented cross-validation results showed that autocorrelation in the training data offered overestimation ranging from 2% to 3.5%. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base learners. We show that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use/land cover classification using geographic object-based image analysis. [ABSTRACT FROM AUTHOR]

This study demonstrates a new approach to process hyperspectral images where both the contextual spatial information as well as the spectral information are used to predict sample properties. The deep contextual spatial information is extracted using the deep feature extraction from pretrained resnet‐18 deep learning architecture, while the spectral information was readily available as the average pixel values. To fuse the information in a complementary way, a multiblock modeling approach called sequential orthogonalized partial least squares was used. The sequential model guarantees that the information learned is complementary from spatial and spectral domains. The potential of the approach is demonstrated to predict several physical and chemical properties in pork bellies. The fusion of spatial and spectral information allowed better prediction of physical properties of pork samples. The spatial feature alone was able to predict both the chemical and physical properties. Deep features can be extracted for any type of images and can be fused with spectral data to enhance data modeling. [ABSTRACT FROM AUTHOR]

Current research on deep learning-based intelligent landslide detection modeling has focused primarily on improving and innovating model structures. However, the impact of terrain factors and data fusion methods on the prediction accuracy of models remains underexplored. To clarify the contribution of terrain information to landslide detection modeling, 1022 landslide samples compiled from Planet remote sensing images and DEM data in the Sichuan–Tibet area. We investigate the impact of digital elevation models (DEMs), remote sensing image fusion, and feature fusion techniques on the landslide prediction accuracy of models. First, we analyze the role of DEM data in landslide modeling using models such as Fast_SCNN, the SegFormer, and the Swin Transformer. Next, we use a dual-branch network for feature fusion to assess different data fusion methods. We then conduct both quantitative and qualitative analyses of the modeling uncertainty, including examining the validation set accuracy, test set confusion matrices, prediction probability distributions, segmentation results, and Grad-CAM results. The findings indicate the following: (1) model predictions become more reliable when fusing DEM data with remote sensing images, enhancing the robustness of intelligent landslide detection modeling; (2) the results obtained through dual-branch network data feature fusion lead to slightly greater accuracy than those from data channel fusion; and (3) under consistent data conditions, deep convolutional neural network models and attention mechanism models show comparable capabilities in predicting landslides. These research outcomes provide valuable references and insights for deep learning-based intelligent landslide detection. [ABSTRACT FROM AUTHOR]

Objectives: The response law of ancient (old) landslides in the reservoir area is an important research topic. Previous research primarily analyzed real-time surface displacement and reservoir water level data. However, professional monitoring conditions are often lacking on most reservoir bank slopes. This complicates tracking the landslide's historical deformation. Satellite and airborne remote sensing platforms enable multi-scale, long-term monitoring of landslide deformation and damage. Methods: This study employs multi-source three-dimensional observation technologies including aerial, space-based, and terrestrial platforms to monitor the deformation and evolution of the Pubugou Hydropower Station's Hongyanzi landslide over approximately 10 years. It utilizes unmanned aerial vehicle photography (optical imaging) and light detection and ranging (LiDAR) for detailed topographic mapping and deformation analysis from 2009 to 2020. Additionally, time-series interferometric synthetic aperture radar (InSAR) technology is used to track long-term surface deformations from October 2014 to July 2020. Field investigations have identified typical deformation and failure characteristics of the landslide, incorporating geological conditions and external factors such as rainfall and reservoir water levels to analyze causal mechanisms and dynamic trends. Results: The irregularly semicircular Hongyanzi landslide spans 20-50 m in thickness, encompasses approximately 15.53 million m³ in volume, and slides at an approximate bearing of 340°. Composed of quaternary pebbled stones, silty sand, and clay, the landslide's bed slopes between 20° and 25°. Its lithology includes Emeishan Formation basalt and Yangxin Formation dolomite. Existing since 2006 or earlier, the landslide features elements like walls and steps. LiDAR imagery from 2009 clearly delineates its boundaries, though it shows no new signs of deformation or failure. Following reservoir impoundment, the reactivated landslide develops new, widening cracks along its rear edge. Post-reactivation, the landslide predominantly undergos uniform deformation, with more significant movement at the trailing edge than the leading edge, without marked acceleration. Heavy rainfall is the most significant control factor, imparting stepwise deformation characteristics to the landslide. Conclusions: A comprehensive analysis of multi-source data reveals that phenomena like the Hongyanzi landslide exhibit typical long-term, gradual, and seasonal movements. Long-term InSAR effectively captures these characteristics. Multi-stage optical remote sensing and surface point cloud data from LiDAR, after vegetation removal, enable more intuitive comparisons of macroscopic deformation across different landslide areas. Integrating this with geological assessments and field investigations allows for detailed engineering analyses to ascertain the causes, patterns, and future trends of landslide activity. [ABSTRACT FROM AUTHOR]

Introduction: Longitudinal metabolomics data from a meal challenge test contains both fasting and dynamic signals, that may be related to metabolic health and diseases. Recent work has explored the multiway structure of time-resolved metabolomics data by arranging it as a three-way array with modes: subjects, metabolites, and time. The analysis of such dynamic data (where the fasting data is subtracted from postprandial states) reveals dynamic markers of various phenotypes, and differences between fasting and dynamic states. However, there is still limited success in terms of extracting static and dynamic biomarkers for the same subject stratifications. Objectives: Through joint analysis of fasting and dynamic metabolomics data, our goal is to capture static and dynamic biomarkers of a phenotype for the same subject stratifications providing a complete picture, that will be more effective for precision health. Methods: We jointly analyze fasting and dynamic metabolomics data collected during a meal challenge test from the COPSAC 2000 cohort using coupled matrix and tensor factorizations (CMTF), where the dynamic data (subjects by metabolites by time) is coupled with the fasting data (subjects by metabolites) in the subjects mode. Results: The proposed data fusion approach extracts shared subject stratifications in terms of BMI (body mass index) from fasting and dynamic signals as well as the static and dynamic metabolic biomarker patterns corresponding to those stratifications. Specifically, we observe a subject stratification showing the positive association with all fasting VLDLs and higher BMI. For the same subject stratification, a subset of dynamic VLDLs (mainly the smaller sizes) correlates negatively with higher BMI. Higher correlations of the subject quantifications with the phenotype of interest are observed using such a data fusion approach compared to individual analyses of the fasting and postprandial state. Conclusion: The CMTF-based approach provides a complete picture of static and dynamic biomarkers for the same subject stratifications—when markers are present in both fasting and dynamic states. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) geological modeling from limited and scattered information is essential for engineering geological investigation and design. Previous studies have encountered limitations when using a single modeling approach in complex tasks involving diverse geological structures, due to difficulties in accommodating the heterogeneity of geological structures and data imbalances. In response to this situation, this work presented an integrated geological modeling framework enabling the fusion of multi-source data, the integration of data and knowledge, and the combination of multiple modeling methods. Initially, multi-source data were merged into a unified format and integrated with knowledge extracted from geological texts to create a geological knowledge graph and a geospatial database for modeling. The complexity of the geological setting was then quantified by constructing a joint influence function, which informed the division of the modeling area into several subregions with geological significance. According to the geological characteristics and data conditions, the appropriate method for each subregion was automatically matched for independent modeling and finally integrated into a complete 3D geological model. The results indicated that the proposed integrated framework provided a flexible solution for complex modeling tasks, simplifying the process by addressing simpler subtasks while retaining the ability to capture structural information in geological domains with diverse characteristics. Moreover, the integration of geological data and knowledge promoted the structured representation and utilization of geological knowledge, promising to provide richer information for model construction and validation. This is crucial for the developed model to be able to effectively support engineering geological exploration. [ABSTRACT FROM AUTHOR]

BACKGROUND: Astragalus is a widely used traditional Chinese medicine material that is easily confused due to its quality, price and other factors derived from different origins. This article describes a novel method for the rapid tracing and detection of Astragalus via the joint application of an electronic tongue (ET) and an electronic eye (EE) combined with a lightweight convoluted neural network (CNN)–transformer model. First, ET and EE systems were employed to measure the taste fingerprints and appearance images, respectively, of different Astragalus samples. Three spectral transform methods – the Markov transition field, short‐time Fourier transform and recurrence plot – were utilized to convert the ET signals into 2D spectrograms. Then, the obtained ET spectrograms were fused with the EE image to obtain multimodal information. A lightweight hybrid model, termed GETNet, was designed to achieve pattern recognition for the Astragalus fusion information. The proposed model employed an improved transformer module and an improved Ghost bottleneck as its backbone network, complementarily utilizing the benefits of CNN and transformer architectures for local and global feature representation. Furthermore, the Ghost bottleneck was further optimized using a channel attention technique, which boosted the model's feature extraction effectiveness. RESULTS: The experiments indicate that the proposed data fusion strategy based on ET and EE devices has better recognition accuracy than that attained with independent sensing devices. CONCLUSION: The proposed method achieved high precision (99.1%) and recall (99.1%) values, providing a novel approach for rapidly identifying the origin of Astragalus, and it holds great promise for applications involving other types of Chinese herbal medicines. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Reinforced concrete corrosion inspection methods based on deep learning have been widely used in the engineering field to monitor the service status of reinforced concrete. However, in engineering practice, it is difficult to obtain a large amount of reinforced concrete corrosion data of different types, which greatly hinders the improvement of the accuracy of neural network models in predicting corrosion conditions. The classic generative adversarial network (GAN) model gives poor model quality for datasets with small amounts of data and high concentration. This paper proposes an improved generative adversarial network approach to optimise reinforced concrete corrosion data. Firstly, a quantum particle swarm optimisation (QPSO) algorithm is used to improve the generative adversarial network. Then, existing corrosion characteristic data is used to train the improved generative adversarial network until the ideal equilibrium state is reached. Next, the feature data generated by the generator are fused with the original data and the fused data are input into several common machine learning models for training. Experimental results show that compared with other conventional results obtained by directly inputting corrosion data into a neural network model for training, the improved method makes full use of multi-source signal data and achieves better classification performance. [ABSTRACT FROM AUTHOR]

Quantile regression is increasingly encountered in modern big data applications due to its robustness and flexibility. We consider the scenario of learning the conditional quantiles of a specific target population when the available data may go beyond the target and be supplemented from other sources that possibly share similarities with the target. A crucial question is how to properly distinguish and use useful information from other sources to improve the quantile estimation and inference at the target. We develop transfer learning methods for high-dimensional quantile regression by detecting informative sources whose models are similar to the target and using them to improve the target model. We show that under reasonable conditions, the detection of the informative sources based on sample splitting is consistent. Compared to the naive estimator with only the target data, the transfer learning estimator achieves a much lower error rate as a function of the sample sizes, the signal-to-noise ratios, and the similarity measures among the target and the source models. Extensive simulation studies demonstrate the superiority of our proposed approach. We apply our methods to tackle the problem of detecting hard-landing risk for flight safety and show the benefits and insights gained from transfer learning of three different types of airplanes: Boeing 737, Airbus A320, and Airbus A380. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSMs) are of wide utilization in various industrial applications for their precise control capabilities. Hereby, these PMSMs are consistently subjected to operational faults that might lead to heavy consequences effecting overall safety. Responsively, health state monitoring techniques for early failure detection are of paramount necessity to ensure optimum performance and longevity of such applications. A qualification-based methodology is presented in the current study in response to fault diagnosis of three-phased PMSMs using vibration–current fusion of data analytics. Stator faults were induced as inter-turn short circuits by the use of bypassing resistances where experimental datasets from a manufactured test rig were acquired which are current and vibration time-domain signals then progressed into statistical features. Different operating cases were diagnosed and classified based on AdaBoost, a currently-growing machine learning model. Utilizing vibration statistical features alone has improved fault detection to 83.0%, while the vibration–current data fusion has achieved an accuracy of 90.7% which was the highest of them all. The precision, F1 score, and recall values were all of 0.907 that sill validated the accuracy results of the data fusion methodology. This study highlights the potential of the data fusion analysis in early fault diagnosis of which enables proactive maintenance strategies, and enhances reliability of PMSMs in various applications of industrial machinery in addition to renewable energy systems.. [ABSTRACT FROM AUTHOR]

Lithium compounds such as lithium hydride (LiH) and lithium hydroxide (LiOH) have a wide range of industrial applications, but are highly reactive in environments with H2O and CO2. These reactions lead to the ingrowth of secondary lithium compounds, which can alter the homogeneity and affect the application of particular lithium chemicals. This study performed an exploratory analysis of different lithium compounds using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Machine learning models are trained on the recorded spectral data to discriminate emission features that differ between LiH, LiOH, and Li2CO3 to perform high-fidelity classification. Support vector machine classifiers yield perfect prediction accuracy between the three compounds with optimal training time. Multivariate methods are then used to produce regression models quantifying the ingrowth of LiOH in LiH. Performing a mid-level data fusion of selected LIBS and Raman features with partial least-squares regression produces the superlative model with a root mean square error of 2.5 wt % and a detection limit of 6.3 wt %. [ABSTRACT FROM AUTHOR]

In wireless sensor network (WSN) monitoring systems, redundant data from sluggish environmental changes and overlapping sensing ranges can increase the volume of data sent by nodes, degrade the efficiency of information collection, and lead to the death of sensor nodes. To reduce the energy consumption of sensor nodes and prolong the life of WSNs, this study proposes a dual layer intracluster data fusion scheme based on ring buffer. To reduce redundant data and temporary anomalous data while guaranteeing the temporal coherence of data, the source nodes employ a binarized similarity function and sliding quartile detection based on the ring buffer. Based on the improved support degree function of weighted Pearson distance, the cluster head node performs a weighted fusion on the data received from the source nodes. Experimental results reveal that the scheme proposed in this study has clear advantages in three aspects: the number of remaining nodes, residual energy, and the number of packets transmitted. The data fusion of the proposed scheme is confined to the data fusion of the same attribute environment parameters. [ABSTRACT FROM AUTHOR]

The operating state of axle box bearings is crucial to the safety of high-speed trains, and the vibration acceleration signal is a commonly used bearing-health-state monitoring signal. In order to extract hidden characteristic frequency information from the vibration acceleration signal of axle box bearings for fault diagnosis, a method for extracting the fault characteristic frequency based on principal component analysis (PCA) fusion and the optimal bandpass filtered denoising signal analytic energy operator (AEO) demodulation spectrum is proposed in this paper. PCA is used to measure the dimension reduction and fusion of three-direction vibration acceleration, reducing the interference of irrelevant noise components. A new type of multi-channel bandpass filter bank is constructed to obtain filtering signals in different frequency intervals. A new, improved average kurtosis index is used to select the optimal filtering signals for different channel filters in a bandpass filter bank. A dimensionless characteristic index characteristic frequency energy concentration coefficient (CFECC) is proposed for the first time to describe the energy prominence ability of characteristic frequency in the spectrum and can be used to determine the bearing fault type. The effectiveness and applicability of the proposed method are verified using the simulation signals and experimental signals of four fault bearing test cases. The results demonstrate the effectiveness of the proposed method for fault diagnosis and its advantages over other methods. [ABSTRACT FROM AUTHOR]

Due to the scarcity of modeling samples and the low prediction accuracy of the matte grade prediction model in the copper melting process, a new prediction method is proposed. This method is based on enhanced generative adversarial networks (EGANs) and random forests (RFs). Firstly, the maximum relevance minimum redundancy (MRMR) algorithm is utilized to screen the key influencing factors of matte grade and remove redundant information. Secondly, the GAN data augmentation model containing different activation functions is constructed. And, the generated data fusion criterion based on the root mean squared error (RMSE) and the coefficient of determination (R2) is designed, which can tap into the global character distributions of the copper melting data to improve the quality of the generated data. Finally, a matte grade prediction model based on RF is constructed, and the industrial data collected from the copper smelting process are used to verify the effectiveness of the model. The experimental results show that the proposed method can obtain high-quality generated data, and the prediction accuracy is better than other models. The R2 is improved by at least 2.68%, and other indicators such as RMSE, mean absolute error (MAE), and mean absolute percentage error (MAPE) are significantly improved. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional semantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal features are not only salient but also complementary to sentiment words directly. Experimental results show that the authors' method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets. [ABSTRACT FROM AUTHOR]

Detection of kiwifruit bruising time is one of the essential indicators for reducing postharvest losses and assessing internal quality. To investigate if the bruised time of kiwifruit could be non-destructively detected, hyperspectral imaging (HSI) technology was used to acquire hyperspectral images of kiwifruit from two different varieties at various bruising time. 70 kiwifruit samples from each of the two varieties were included in the study, with a total of 490 (7 × 70) hyperspectral images collected for each variety across seven bruising time. The spectral feature of the bruised areas of kiwifruit were extracted using the uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA) methods, combined with the successive projection algorithm (SPA), respectively. Besides, the textural feature of the bruised kiwifruit were extracted using the gray-level co-occurrence matrix (GLCM). Finally, Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and one-dimensional convolutional neural network (1D-CNN) were built to identify the bruising time of kiwifruit using the spectral feature, textural feature, and fused data (spectra and texture), respectively. The results suggests that the 1D-CNN model built by fused data was the most effective in identifying kiwifruit bruising time, with identification accuracies of 94.55% for 'Hayward', 97.95% for 'Wanhong', and 95.23% for the mixed kiwifruits. The studies suggests that the HSI technique combined with machine learning could effectively identify the bruising time of kiwifruit and provide a reference for kiwifruit quality grading detection. [ABSTRACT FROM AUTHOR]

The analysis and research of composite structure specimens depend on test methods. However, due to the high cost, complex test conditions, time-consuming, and other problems, it is difficult to carry out a large number of tests. A large amount of data is often required for parametric analysis and structural optimization of composite structure specimens. Therefore, to solve the problem of insufficient data samples in the analysis and research of specimens. In this paper, a finite element model updating method based on Bayesian theory and a Gaussian process data fusion method is proposed, that is, the amount of data is expanded by the proposed model updating method, and then the experimental and numerical simulation data are fused based on the Gaussian process data fusion method. Finally, the effectiveness of the proposed model updating method and data fusion method is verified by a numerical example of π type beam-column joints. The results show that the method has high generalization ability and prediction accuracy in the case of small samples through the fusion of numerical simulation and experimental data. [ABSTRACT FROM AUTHOR]

A profile of geotechnical properties is often needed for geotechnical design and analysis. However, site-specific data might be characterized as MUSIC-X (i.e., Multivariate, Uncertain and Unique, Sparse, Incomplete, and potentially Corrupted with "X" denoting the spatial/temporal variability), posing a significant challenge in accurately interpreting geotechnical property profiles. Different sources, or types, of data are commonly available from a specific site investigation program, and they are usually cross-correlated, and thus can provide complementary information. This leads to an important question in geotechnical site investigation: how to integrate multiple sources of sparse data for enhancing the profiling of different geotechnical properties. To address this issue, this study proposes a novel method, called fusion Bayesian compressive sampling (Fusion-BCS), for integrating sparse and non-co-located geotechnical data. In the proposed method, the auto- and cross-correlation structures of different sources of data are exploited in a data-driven manner through a joint sparse representation. Then, profiles of different geotechnical properties are jointly reconstructed from all measurements under a framework of compressive sampling/sensing. The proposed method is illustrated using simulated and real geotechnical data. The results indicate that the accuracy of the interpreted geotechnical property profiles may be significantly improved by integrating multiple sources of site investigation data. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Affective communication, encompassing verbal and non-verbal cues, is crucial for understanding human interactions. This study introduces a novel framework for enhancing emotional understanding by fusing speech emotion recognition (SER) and sentiment analysis (SA). We leverage diverse features and both classical and deep learning models, including Gaussian naive Bayes (GNB), support vector machines (SVMs), random forests (RFs), multilayer perceptron (MLP), and a 1D convolutional neural network (1D-CNN), to accurately discern and categorize emotions in speech. We further extract text sentiment from speech-to-text conversion, analyzing it using pre-trained models like bidirectional encoder representations from transformers (BERT), generative pre-trained transformer 2 (GPT-2), and logistic regression (LR). To improve individual model performance for both SER and SA, we employ an extended dynamic Bayesian mixture model (DBMM) ensemble classifier. Our most significant contribution is the development of a novel two-layered DBMM (2L-DBMM) for multimodal fusion. This model effectively integrates speech emotion and text sentiment, enabling the classification of more nuanced, second-level emotional states. Evaluating our framework on the EmoUERJ (Portuguese) and ESD (English) datasets, the extended DBMM achieves accuracy rates of 96% and 98% for SER, 85% and 95% for SA, and 96% and 98% for combined emotion classification using the 2L-DBMM, respectively. Our findings demonstrate the superior performance of the extended DBMM for individual modalities compared to individual classifiers and the 2L-DBMM for merging different modalities, highlighting the value of ensemble methods and multimodal fusion in affective communication analysis. The results underscore the potential of our approach in enhancing emotional understanding with broad applications in fields like mental health assessment, human–robot interaction, and cross-cultural communication. [ABSTRACT FROM AUTHOR]

Featured Application: In the last two decades, there has been an upward trend in obtaining redundant strap-down inertial navigators of reduced size and weight, which can be used on small vehicles (miniaturized satellites, miniaturized space robots, space rovers, MAVs, UAVs) or on vehicles that require onboard equipment with such properties (satellites, launch vehicles, missiles, aircraft, robots used in various industrial applications). A special application of the here-proposed methodology is the estimation of small satellites' attitude-based gyro measurements, providing, at the same time, a high degree of redundancy of the inertial detection unit. The significant technological changes related to the manufacturing of the miniaturized sensors produced a higher impact at the level of the detection units equipping the strap-down inertial navigation systems (INSs). Together with miniaturization, many more advantages are brought by these technologies, related to low costs, low necessary energy, high robustness and high potential for adapting the design solutions. However, reducing the dimensions and weight of the sensors is reflected by a decrease in their performance in terms of sensitivity, noise and the possibility of controlling sensitive elements. On the other hand, there is a permanent increase in the need to have in-space applications of miniaturized systems with a high degree of redundancy and to equip miniaturized satellites, miniaturized space robots or space rovers. The paper proposes a new methodology to increase the quality of the signals received from the miniaturized inertial measurement units (IMUs), but also to increase the degree of redundancy, by using low-cost sensors arranged in redundant linear configurations. The presentation is focused on the development of an attitude system based on strap-down inertial techniques which uses a redundant IMU equipped with three linear clusters of miniaturized gyros. For each of the three clusters, a data fusion mechanism based on the maximal ratio combining method is applied. This fusion mechanism reduces the noise power and bias of the signal delivered to the navigation processor. Shown are the theory, software modeling and experimentation results for the attitude algorithm, for the data fusion method, and for the integrated system. [ABSTRACT FROM AUTHOR]

A central paradigm for building predictive models from time series data is to convert the data into a feature vector representation and then apply standard inductive learners. Typically, the conversion is done by manually defining features, which is an extremely time-consuming and error-prone process. This has motivated the development of algorithms that automatically construct features from time series. However, these systems are typically designed for univariate time series data. In contrast, many real-world applications require analyzing time series consisting of data collected by multiple sensors. In this context, it is often useful to derive new series by fusing the collected data both within a sensor and across multiple different sensors. Unfortunately, this poses additional challenges for automated construction as exponentially more operations are possible than in the univariate case. This paper proposes an automated feature construction system called TSFuse, which supports fusion and explores the search space in a computationally efficient way. We perform an empirical evaluation on real-world time series classification datasets and show that our system is able to find a better feature representation compared to existing feature construction systems for univariate time series data. [ABSTRACT FROM AUTHOR]

This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area. Specifically, it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road network. Due to its good adaptability and flexibility for spatiotemporal data, the Gaussian process (GP) regression was employed to provide short-term forecasts using data collected by loop detectors (sensors) and supplemented by telematics data. The GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the sensors. Consequently, the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training points. Policymakers in the transportation sector can find the results of this work helpful for making informed decisions relating to current and future transportation conditions in the area. [ABSTRACT FROM AUTHOR]

Remote sensing observations represent the primary means in the production of geologic maps of planetary surfaces. However, they do not provide the same level of detail as Earth's geologic maps, which rely also on field observations and laboratory analyses. Color‐derived basemaps can help to bridge this gap by highlighting peculiar surface and compositional properties. Here, we analyzed the spectral properties of the lunar Tsiolkovskiy crater through the definition of spectral units summarizing the information enclosed by a set of selected spectral parameters. We then performed a compositional analysis of the newly derived spectral units that helped us in discriminating the presence and relative abundance of the main mineralogical phases on the Moon. As a final step, we produced a geo‐stratigraphic map of the Tsiolkovskiy crater integrating in a single mapping product both morphologic, stratigraphic and compositional information. The basaltic infilling of the crater is distinguished by three spectral units associated with distinct effusive events presenting a different composition. On the central peak, plagioclase and olivine suggest the presence of Mg‐suite rocks from the lower crust. The continuous ejecta deposits are mostly characterized by impact melts and shocked materials rich in glass or agglutinates related to more mature terrains from which occasionally appear fresher anorthositic and gabbroic outcrops exposed by the inward sliding of the crater walls. Overall, the geo‐stratigraphic map allows inferring compositional variations associated with the different morpho‐stratigraphic units, which clarify and elaborate on the compositional heterogeneities within the lunar crust and the Tsiolkovskiy crater, and its geologic evolutionary history. Plain Language Summary: The main data used to produce geologic maps of planetary surfaces come from orbiting missions. However, geologic maps of Earth provide much more information, relying also on observations made on the field and analyses made in the laboratory. Color images derived from the combination and processing of spectral information can help to make planetary maps more comprehensive, similarly to the Earth's ones, by drawing attention to surface and compositional aspects. In this work, we performed a spectral and compositional study of the Tsiolkovskiy crater on the Moon which enabled us to distinguish the presence and relative quantity of the most common minerals constituting the lunar rocks. We also produced a geo‐stratigraphic map coupling the information about the surface textures and shapes, relative time of deposition, and composition. On the basaltic floor, we discriminated the presence of three different spectral characteristics correlated with a sequence of flooding events showing distinct properties and a central peak exhibiting rocks emerged from the lowest strata of the lunar crust. The continuous ejecta blanket, instead, is characterized by mature materials interspersed by fresher exposures of subsurface materials. To conclude, the new mapping product allows an in‐depth interpretation of the geologic evolution of the Tsiolkovskiy crater. Key Points: Production of a 10‐unit Spectral Units map conveying the spectral and compositional properties within the Tsiolkovskiy craterIntegration of geologic and spectral units to produce a more comprehensive mapping product, namely a geo‐stratigraphic mapElaboration on the geological and compositional evolution of our study area [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to reduce network energy consumption and prolong the network lifetime in wireless sensor networks, a data fusion algorithm named CFLDF is proposed. Firstly, upon completion of the arrangement of network nodes, network clustering is achieved using fuzzy c-means optimized by the improved butterfly optimization algorithm, and a data fusion model is established on the clustering structure. Then, reliable data is sent to the cluster head by the nodes with the assistance of a fuzzy logic controller, and data fusion is performed by the cluster head using a fuzzy logic algorithm. Finally, cluster heads transmit the fused data to the base station. Finally, the fused data is transmitted to the base station by the cluster heads. Simulation experiments are conducted to evaluate the CFLDF algorithm against the LEACH, LEACH-C, and SEECP algorithms. The results demonstrate that network energy consumption is effectively reduced and the network lifetime is extended by the CFLDF algorithm. [ABSTRACT FROM AUTHOR]