Fusion Methodology Research Articles

Practical design and implementation of IoT-based occupancy monitoring systems for office buildings: A case study

This study introduces a scalable, cloud-based approach to occupancy monitoring designed to optimize HVAC operations in office buildings. It addresses the challenges of developing and implementing a multi-parameter IoT-based occupancy monitoring system by integrating various off-the-shelf sensors—CO2, infrared (IR), motion (PIR), and door status detection—into a cohesive system. Leveraging wireless LoRaWAN and novel cloud technologies, the system ensures easy installation, efficient maintenance, and robust data management. CO2-based occupancy detection models were trained using data from a reference office and validated in another office environment. Among the various models evaluated, the four best-performing ones—Decision Trees, Random Forest, LightGBM, and K-Nearest Neighbors—were selected for integration into a multi-parameter detection system. To further enhance system performance and identify optimal sensor combinations and configurations for cost-effective and accurate occupancy detection, a data fusion methodology was employed. This methodology, validated with ground-truth data from a test bed, tested the monitoring system in different office settings, ranging from single to quadruple-occupant rooms. Integration of additional parameters into the developed data fusion approach significantly improved system performance, achieving a True Positive Rate (TPR) of 95% compared to 81% with a simple baseline data fusion method. This approach also reduced false detections during unoccupied periods, as tested in multiple rooms within the studied building, thereby enhancing the system's reliability for integration into occupancy-aware HVAC control strategies.

Fusion regression methods with repeated functional data

Linear regression and classification methods with repeated functional data are considered. For each statistical unit in the sample, a real-valued parameter is observed over time under different conditions related by some neighborhood structure (spatial, group, etc.). Two regression methods based on fusion penalties are proposed to consider the dependence induced by this structure. These methods aim to obtain parsimonious coefficient regression functions, by determining if close conditions are associated with common regression coefficient functions. The first method is a generalization to functional data of the variable fusion methodology based on the 1-nearest neighbor. The second one relies on the group fusion lasso penalty which assumes some grouping structure of conditions and allows for homogeneity among the regression coefficient functions within groups. Numerical simulations and an application of electroencephalography data are presented.

A generic fusion framework integrating deep learning and Kalman filter for state of charge estimation of lithium-ion batteries: Analysis and comparison

Lithium-ion batteries (LIBs) are extensively utilized in power and energy storage systems. Accurately estimating state of charge (SOC) of LIBs using single methods is still challenging to fully address the problem associated with SOC. This paper proposes a novel generic fusion framework that integrates deep learning (DL) and Kalman filter (KF), characterized by an effective fusion of the results obtained by multiple-methods. Within the DL-based method, a spatial-temporal awareness model and an improved dung beetle optimizer are proposed to enhance the capture of spatial-temporal features and optimize hyperparameters. Additionally, three adaptive nonlinear KFs are employed for SOC estimation based on a simplified electrochemical model, with the adaptive cubature KF emerging as the most suitable following comparative analysis. Subsequently, the results from both approaches are integrated to derive the final result using various fusion methodologies. The effectiveness of the fusion framework is validated using experimental data, with kernel ridge regression emerging as the optimal fusion method. The best fusion results yield a mean absolute error of 0.2341% and a root mean square error of 0.3072%, demonstrating that proposed framework can effectively process and utilize multiple-source information, thereby enhancing accuracy and robustness compared to single methods and exhibiting adaptability to complex situations.

Sensor placement for optimal aerodynamic data fusion

Aircraft design is recently evolving towards a digital twin representation that involves many heterogeneous data sources. The aerodynamic development of aircraft usually incorporates data from computational fluid dynamics simulations, wind tunnel testing, and flight tests. All of these data sources have their advantages and disadvantages, which can optimally be combined using data fusion methods. However, the quality of the data fusion result strongly depends on the experimental design, i.e. the placement of discrete sensors. Therefore, an optimized sensor placement is essential for data fusion applications, as the number of sensors is limited. This work presents a sensor placement strategy for the widely used Gappy proper orthogonal decomposition data fusion methodology. The sensor placement relies on a Bayesian formulation of the data fusion, allowing accurate error estimates. Based on the Bayesian posterior, a utility function characterizes the quality of the fused result by quantifying the expected information gain for the proper orthogonal decomposition coefficients. As the optimization of the sensor locations involves a complex combinatorial problem, we introduce an efficient genetic algorithm for this task. The method is demonstrated on a two-dimensional airfoil and the NASA Common Research Model with synthetic measurement errors. For both test cases, an optimal sensor placement results in smaller reconstruction errors than a conventional layout. The Bayesian approach leads, in most cases, to more accurate reconstructions and is more versatile than other well-established sensor placement methods. The proposed genetic algorithm finds better optima with significantly fewer function evaluations than the widely used greedy algorithms.

The Potential of Hybrid Data Integration Approaches Based UAV-SfM Photogrammetry in Identifying New Archaeological Features in The Archaeological UR Site

Structure-from-Motion (SfM) Photogrammetry-based unmanned aerial vehicles (UAV) are considered one of the most effective non-destructive techniques to detect features in archaeology. However, relying on only standalone-based photogrammetric approaches is not always valid in revealing new archaeological features and monuments. This study aims to verify the efficiency of the integration-based methodologies of SfM photogrammetry products to improve the detection of new archaeological features in the non-excavated regions of the archaeological site of Ur, south of Iraq. The methodology proposes an automatic workflow to extract 3D topographic models such as Digital Terrain Model (DTM) by Agisoft Metashape (MS) software and orthomosaic images as standard products for UAV-photogrammetry. These have delivered the following Structure-from-Motion and Multi-View Stereo (SfM-MVS) algorithms using an optimized flight planning, data capturing, and data processing pipeline. Stand-alone raster images derived from photogrammetry, such as (slope images, hill shade rasters, etc.), have limitations in archaeology compared to hybrid raster images in detecting covered and unrevealed features. The methodology focuses on fusing multiple rasters extracted from DTM using the QGIS software package analysis tools to detect features that may never be detected from single standalone image rasters. This is because combining multiple rasters of different parameters may highlight new potential features such as paths, temples, etc. The results findings revealed that the fusion methodology of raster images obtained from UAV-SfM Photogrammetry through its standard products and using high precision settings could successfully help to identify many new archaeological features in the undiscovered site in Ur, such as walls and houses, which may date back to the late periods of Ur. The results concluded that this enhances the role of photogrammetry and remote sensing techniques as non-destructive excavation tools to provide a rich database for archaeologists for further analysis and interpretation of undiscovered archaeological sites without destruction. Many new archaeological features were also revealed, and a general digital map of the site was prepared. It also prompts a review of the settlement theories in previous studies in the ancient city of UR and the documentation of excavated archaeological ruins.

Enhancing heart disease prediction with reinforcement learning and data augmentation

The study presents a novel method to improve the prediction accuracy of cardiac disease by combining data augmentation techniques with reinforcement learning. The complex nature of cardiac data frequently presents challenges for traditional machine learning models, which results in subpar performance. In response, our fusion methodology improves predictive capabilities by augmenting data and utilizing reinforcement learning's skill at sequential decision-making. Our method predicts cardiac disease with an astounding 94 % accuracy rate, which is an outstanding result. This significant improvement outperforms existing techniques and shows a deeper comprehension of intricate data relationships. The amalgamation of reinforcement learning and data augmentation not only yields superior predictive accuracy but also bears noteworthy consequences for patient care and accurate cardiac diagnosis. Through the efficient combination of these approaches, our method provides a powerful response to the difficulties presented by complicated cardiac data. The potential to transform illness prediction and prevention techniques and ultimately improve patient outcomes is demonstrated by this integration's success.

Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning

Machine learning (ML) models for predicting gas permeability through polymers have traditionally relied on experimental data. While these models exhibit robustness within familiar chemical domains, reliability wanes when applied to new spaces. To address this challenge, we present a multi-tiered multi-task learning framework empowered with advanced machine-crafted polymer fingerprinting algorithms and data fusion techniques. This framework combines scarce “high-fidelity” experimental data with abundant diverse “low-fidelity” simulation or synthetic data, resulting in predictive models that display a high level of generalizability across novel chemical spaces. Additionally, this multi-task scheme capitalizes on known physics and interrelated properties, such as gas diffusivity and solubility, both of which are closely tied to permeability. By amalgamating high throughput generated simulation data with available experimental data for gas permeability, diffusivity, and solubility for various gases, we construct multi-task deep learning models. These models can simultaneously predict all three properties for all gases under consideration, with markedly enhanced predictive accuracy, particularly compared to traditional models reliant solely on experimental data for a singular property. This strategy underscores the potential of coupling high-throughput classical simulations with data fusion methodologies to yield state-of-the-art property predictors, especially when experimental data for targeted properties is scarce.

Real-time vision-inertial landing navigation for fixed-wing aircraft with CFC-CKF

Vision-inertial navigation offers a promising solution for aircraft to estimate ego-motion accurately in environments devoid of Global Navigation Satellite System (GNSS). However, existing approaches have limited adaptability for fixed-wing aircraft with high maneuverability and insufficient visual features, problems of low accuracy and subpar real-time arise. This paper introduces a novel vision-inertial heterogeneous data fusion methodology, aiming to enhance the navigation accuracy and computational efficiency of fixed-wing aircraft landing navigation. The visual front-end of the system extracts multi-scale infrared runway features and computes geo-reference runway image as observation. The infrared runway features are recognized efficiently and robustly by a lightweight end-to-end neural network from blurry infrared images, and the geo-reference runway is generated through projection of the runway’s prior geographical information and prior pose. The fusion back-end of the navigation system is the Covariance Feedback Control based Cubature Kalman Filter (CFC-CKF) framework, which tightly integrates visual observations and inertial measurements for zero-drift pose estimation and curbs the effect of inaccurate kinematic noise statistics. Finally, real flight experiments demonstrate that the algorithm can estimate the pose at a frequency of 100 Hz and fulfill the navigation accuracy requirements for high-speed landing of fixed-wing aircraft.

Interfacial characterization and bonding mechanism of W/ODS-316 L steel multi-material structure fabricated by laser powder bed fusion

Interfacial characterization and bonding mechanism of W/ODS-316 L steel multi-material structure fabricated by laser powder bed fusion

Interactive Mesh Sculpting with Arbitrary Topologies in Head-Mounted VR Environments

Shape modeling is a dynamic area in computer graphics with significant applications in computer-aided design, animation, architecture, and entertainment. Virtual sculpting, a key paradigm in free-form modeling, has traditionally been performed on desktop computers where users manipulate meshes with controllers and view the models on two-dimensional displays. However, the advent of Extended Reality (XR) technology has ushered in immersive interactive experiences, expanding the possibilities for virtual sculpting across various environments. A real-time virtual sculpting system implemented in a Virtual Reality (VR) setting is introduced in this paper, utilizing quasi-uniform meshes as the foundational structure. In our innovative sculpting system, we design an integrated framework encompassing a surface selection algorithm, mesh optimization technique, mesh deformation strategy, and topology fusion methodology, which are all tailored to meet the needs of the sculpting process. The universal, user-friendly sculpting tools designed to support free-form topology are offered in this system, ensuring that the meshes remain watertight, manifold, and free from self-intersections throughout the sculpting process. The models produced are versatile and suitable for use in diverse fields such as gaming, art, and education. Experimental results confirm the system’s real-time performance and universality, highlighting its user-centric design.

Research on dynamic positioning of navigation system using K nearest neighbor algorithm and bayesian fusion based on visible light communication

This study presents a novel Bayesian-K Nearest Neighbor (B-KNN) fusion algorithm aimed at mitigating dynamic positioning errors in navigation systems that utilize visible light communication. The proposed fusion methodology leverages a fingerprint database constructed from the received signal strength data acquired by a mobile receiver. Through the selection of the nearest navigation path points for clustering and the utilization of a Bayesian algorithm with Gaussian fitting, the posterior probability of each cluster is computed to facilitate the prediction of the location of navigation path points via fingerprint matching. The results indicate that 89.8% of simulations exhibit a dynamic positioning error below 1 m within a 20 m×5 m×4 m model, achieving a success rate of 89.8%. Comparative analysis reveals that the B-KNN fusion algorithm enhances dynamic positioning accuracy by 68.3% while reducing computational overhead by approximately 48.3% in contrast to conventional KNN algorithms. Experimental assessments demonstrate that in scenarios involving ambient light interference within a 2 m×1 m×1.5 m model, the majority (95%) of dynamic positioning errors exceed 9 cm, with a significant portion surpassing 10 cm. Conversely, under conditions devoid of ambient light interference, only 4% of errors exceed 9 cm, with 14% falling below 5 cm, indicative of a marked 39.81% improvement in accuracy.

Optimization of Distributed Energy Resources Operation in Green Buildings Environment.

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant's comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant's comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA-GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler.

Calibrating Car-Following Models Using SUMO-in-the-Loop and Vehicle Trajectories From Roadside Radar

This paper presents an innovative calibration method for car-following (CF) models in the Simulation of Urban MObility (SUMO) using real-world trajectory data from a 1.5 km signalized urban corridor, captured by roadside radars. By applying a sophisticated track-level association and fusion methodology, the study extends trajectory analysis beyond individual radar fields of view. The enhanced data is then utilized to refine the Krauss, IDM, and W99 CF models within SUMO, addressing the literature gap by integrating SUMO into the calibration loop, thereby accommodating the simulator's integration scheme and any model adaptations. The research identifies that default SUMO models tend to exhibit shorter time headways compared to real-world data, with calibration effectively reducing this discrepancy. Moreover, the W99 model, despite its unrealistic acceleration profiles when calibrated without considering acceleration, most accurately captures the higher-end energy consumption distribution. Conversely, the IDM model, with its default parameters, provides the closest approximation to observed acceleration behaviors, highlighting the nuanced performance of CF models in traffic simulation and their implications for energy consumption estimation. Detailed results of optimized parameters for each CF model are provided in appendix in addition to distribution information that may be useful for other modelers to use directly or other datasets to be compared in the future (including expansion of the work to include vehicle classification).

Slow feature-based feature fusion methodology for machinery similarity-based prognostics

Similarity-based prediction methods utilize degradation trend analysis based on degradation indicators (DIs). These methods are gaining prominence in industrial predictive maintenance because they effectively address prognostics for machines with unknown failure mechanisms. However, current studies often neglect the discrepancies in degradation trends when constructing DIs from multi-sensor data and lack automatic normalization of operating regimes during feature fusion. In this study, a feature fusion methodology based on a signal-to-noise ratio metric that leverages slow feature analysis (SFA) is proposed. This customized metric utilizes SFA to quantify degradation trend discrepancies of constructed DIs, while automatically filtering out the effects of multiple operating regimes during feature fusion. The effectiveness and superiority of the proposed method are demonstrated using publicly available aero-engine and rolling bearing datasets.

Estimation of Rice Leaf Area Index Utilizing a Kalman Filter Fusion Methodology Based on Multi-Spectral Data Obtained from Unmanned Aerial Vehicles (UAVs)

Estimation of Rice Leaf Area Index Utilizing a Kalman Filter Fusion Methodology Based on Multi-Spectral Data Obtained from Unmanned Aerial Vehicles (UAVs)

Optimizing Object Classification in Robotic Perception Environments Exploring Late Fusion Strategies

Robotic perception systems often include approaches that can extract valuable features or information from the studied dataset. These methods often involve the application of deep learning approaches, such as convolutional neural networks (CNNs), for processing of images, as well as the incorporation of 3D data. The notion of image categorization is well delineated via the use of networks that include convolutional networks. However, some network topologies exhibit a substantial scope and need significant amounts of time and memory resources. On the other hand, the neural networks FlowNet3D and PointFlowNet have the capability to accurately predict scene flow. Specifically, these networks are capable of estimating the three-dimensional movements of point clouds (PCs) within a dynamic environment. When using PCs in robotic applications, it is crucial to examine the robustness of accurately recognizing the points that belong to the object. This article examines the use of robotic perception systems inside autonomous vehicles and the inherent difficulties linked to the analysis and processing of information obtained from diverse sensors. The researchers put out a late fusion methodology that integrates the results of many classifiers in order to enhance the accuracy of categorization. Additionally, the authors propose a weighted fusion technique that incorporates the proximity to objects as a significant factor. The findings indicate that the fusion methods described in this study exhibit superior performance compared to both single modality classification and classic fusion strategies.

Physics-Informed Approximation of Internal Thermal History for Surface Deformation Predictions in Wire Arc Directed Energy Deposition

Abstract This work presents a physics-informed fusion methodology for deformation detection using multi-sensor thermal data. A challenge with additive manufacturing (AM) is that abnormalities commonly occur due to rapid changes in the thermal gradient. Different non-destructive in-situ thermal sensors capture parts of the thermal history but are limited by the visible temperature spectrum and sensor field of view of the fabrication process. Various sensors mitigate problems with the loss of thermal history information; however, it brings forth challenges with integrating different data streams and the need to interpolate the internal thermal history. This study develops a thermal data-informed heat flux methodology that fills the gap in fusing numerical temperature approximation with data-driven knowledge of the surface of additive manufactured components. First, this study fuses infrared (IR) thermal data complexities during the AM process with the Goldak double ellipsoidal heat flux to model the energy input into the component. Second, a thermal physics-informed model input (PIMI) is created with thermal data-informed heat flux to capture internal thermal history. Lastly, a regression convolutional neural network (CNN) captures the relationship between the three-dimensional thermal gradient and the resulting surface deformation. The rapid thermal gradient formation and identification of deformation is a key step toward using thermal history data and machine learning to improve quality control in AM. The proposed surface deformation detection model achieved an mean squared error of 1.14 mm and an R2 of 0.89 in the case study when fabricating thin-walled structures.

HyperMRI: hyperspectral and magnetic resonance fusion methodology for neurosurgery applications

PurposeMagnetic resonance imaging (MRI) is a common technique in image-guided neurosurgery (IGN). Recent research explores the integration of methods like ultrasound and tomography, among others, with hyperspectral (HS) imaging gaining attention due to its non-invasive real-time tissue classification capabilities. The main challenge is the registration process, often requiring manual intervention. This work introduces an automatic, markerless method for aligning HS images with MRI.MethodsThis work presents a multimodal system that combines RGB-Depth (RGBD) and HS cameras. The RGBD camera captures the patient’s facial geometry, which is used for registration with the preoperative MR through ICP. Once MR-depth registration is complete, the integration of HS data is achieved using a calibrated homography transformation. The incorporation of external tracking with a novel calibration method allows camera mobility from the registration position to the craniotomy area. This methodology streamlines the fusion of RGBD, HS and MR images within the craniotomy area.ResultsUsing the described system and an anthropomorphic phantom head, the system has been characterised by registering the patient’s face in 25 positions and 5 positions resulted in a fiducial registration error of 1.88 ± 0.19 mm and a target registration error of 4.07 ± 1.28 mm, respectively.ConclusionsThis work proposes a new methodology to automatically register MR and HS information with a sufficient accuracy. It can support the neurosurgeons to guide the diagnosis using multimodal data over an augmented reality representation. However, in its preliminary prototype stage, this system exhibits significant promise, driven by its cost-effectiveness and user-friendly design.

Joint-individual fusion structure with fusion attention module for multi-modal skin cancer classification

Many convolutional neural network (CNN) based approaches for skin cancer classification primarily rely on dermatological images, yielding commendable results in classification accuracy. However, leveraging patient metadata, a crucial source of clinical information for dermatologists, can further enhance accuracy. Current methodologies predominantly employ basic joint fusion structures (FS) and fusion modules (FMs) for multi-modal classification, leaving room for advancement in enhancing accuracy through exploration of more sophisticated FS and FM architectures. Thus, this paper introduces a novel fusion method that integrates dermatological images (dermoscopy images or clinical images) with patient metadata for skin cancer classification, focusing on enhancing FS and FM components. Initially, we propose a joint-individual fusion (JIF) structure that simultaneously learns shared features across multi-modality data while preserving specific characteristics. Subsequently, we introduce a multi-modal fusion attention (MMFA) module designed to amplify the most relevant image and metadata features through a combination of self and mutual attention mechanisms, thereby bolstering the decision-making pipeline. Our study compares the efficacy of the proposed JIF-MMFA method with other state-of-the-art fusion techniques across three distinct public datasets. Results demonstrate that the JIF-MMFA method consistently enhances classification outcomes across various CNN backbones, outperforming alternative fusion methodologies on all three datasets. These findings underscore the effectiveness and robustness of our proposed approach in skin cancer classification.

Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

Background and Objective:Artificial Intelligence (AI) has shown significant advancements across several industries, including healthcare, using better fusion methodologies, improved data accessibility, and heightened processing capabilities. Although deep learning algorithms excel in challenging scenarios, their widespread adoption presents a challenge due to their need for more transparency. The advent of Explainable AI (XAI) has addressed this limitation by offering AI-driven systems with attributes such as transparency, interpretability, and reliability. The need for transparency in critical sectors like healthcare has prompted an increased academic focus on investigating and understanding these frameworks. The present paper thoroughly examines the latest research and advancements in Explainable Artificial Intelligence (XAI), specifically focusing on its integration into the Internet of Medical Things (IoMT) in healthcare settings. The last section tackles outstanding issues concerning XAI and IoMT and outlines prospective directions for further research. Methods:A thorough investigation was carried out across many scholarly databases and online digital libraries, such as IEEE Xplore, ACM Digital Library, Wiley Interscience, Taylor and Francis, ScienceDirect, Scopus, Springer, Google Scholar, Citeseer Library, Semantic Scholar, and other relevant sources. An analysis was conducted on articles published from March 2004 to February 2024, specifically focusing on AI models that explain different healthcare issues. The search query included “Explainable AI” in conjunction with “Open Black Box, Healthcare, Transparent Models, Interpretable Deep Learning, Machine Learning, Medical Information System, Accountability, Smart Healthcare, and Responsible AI”, indicating the field’s dynamic nature. The authors also examined various techniques and datasets to elucidate healthcare difficulties, including incorporating the Internet of Medical Things (IoMT). Results:The evaluation included more than 105 published models that used clinical data input to diagnose diverse disorders, focusing on incorporating Internet of Medical Things (IoMT) components. In addition to illness diagnosis, these models also elucidate the decisions made. The models were classified according to the input data, used methodologies, and integration of the Internet of Medical Things (IoMT). The categorization included machine learning and deep learning methodologies, particularly on explainability models connected to the Internet of Medical Things (IoMT). These models were classified as model-agnostic, model-specific, global, local, ante-hoc, and post-hoc. Conclusion:This extensive study examines machine learning, and deep learning models developed based on clinical data and associated with the Internet of Medical Things (IoMT) for illness detection. These models improve efficiency and accuracy and provide essential assistance to medical personnel. Explaining each choice made by individuals contributes to the acceleration of sickness detection, resulting in decreased medical expenses for patients and healthcare systems. The integration of Explainable Artificial Intelligence (XAI) and the Internet of Medical Things (IoMT) highlights a fundamental change in healthcare towards more transparent and linked systems, leading to significant breakthroughs in medical decision-making.