Multi-source Information Fusion Research Articles

Research on the expression and creativity of traditional ethnic cultural elements in clothing design based on the fusion of multi-source and multi-objective statistical information

Abstract The use of multi-source, multi-objective statistical information fusion is utilized in this paper to identify garment design targets, and the correlation is judged based on garment data. Some parameters are pre-set to generate linear garment descriptions for multi-source, multi-objective information fusion and find the adaptability of ethnic elements under random coefficients using element information relationships. The clothing width vector of the radial basis function is calculated by clustering ethnic culture element data using centroid information. The results show that the innovation rate of ethnic elements of Miao batik reaches the highest 0.57, the average adaptation value in the garment design is increased by 4.92%, and the ethnic-cultural elements are embodied 280 times, which indicates that the proposed method can promote the traditional excellent culture.

Conjunctive and disjunctive combination rules in random permutation set theory: A layer-2 belief structure perspective

In uncertainty reasoning, conjunctive and disjunctive combination rules are the core tools for information updates. As an extension of evidential reasoning, random permutation set reasoning models uncertain information based on ordered focal sets. Existing combination rules in random permutation set theory, orthogonal sums, do not satisfy the commutativity of one of the original distributions and overly obey its order information. In this paper, the random permutation set theory is interpreted as an refined extension of Dempster–Shafer theory, and a layer-2 belief structure is proposed to describe the permutation event space. Compared with the traditional belief structure, the proposed structure can model both symbolic and numerical uncertainty. Based on the above, the conjunctive and disjunctive combination rules in Dempster–Shafer theory are extended to random permutation set theory. Through properties analysis and simulation demonstration, we demonstrate that the proposed methods can not only resolve the counter-intuitive results of orthogonal sums, but make full use of order information in distributions as well. In addition, we also extend the product space operations and discounting methods based on the proposed methods, and give a general framework of multi-source information fusion under the random permutation set theory.

Retracted: Construction of Holographic Immersion Chamber Based on Multisource Information Fusion and Interactive Virtual Reality Technology

Retracted: Construction of Holographic Immersion Chamber Based on Multisource Information Fusion and Interactive Virtual Reality Technology

Fast fault detection method for photovoltaic arrays with adaptive deep multiscale feature enhancement

Photovoltaic (PV) arrays have output characteristics such as randomness and intermittency, and faults can seriously affect the safe operation of the power system. In order to improve the comprehensive performance of the PV array fault diagnosis model, a new intelligent online fault monitoring method for PV arrays is proposed in this paper. (1) a three-dimensional channel feature map based on I, V, and P features is constructed because the I-V and P curves of the PV array have significantly different effects under different fault conditions. (2) The PV array fault diagnosis model based on a multi-source information fusion network (MIFNet) is proposed, and Channel Mixing Convolution (CMC) module, three-dimensional feature attention enhancement (TDFAE) module, and Channel normalized scaling (CNS) module are designed to improve the comprehensive performance of the model. (3) An adaptive nonlinear mutual sparrow search algorithm (ANMSSA) is proposed to optimize the hyperparameter configuration of the MIFNet network. The experimental results show that the average recognition accuracy, prediction accuracy, and sensitivity of the ANMSSA-MIFNet network proposed in this paper are 99.64%, 99.64%, and 99.71% respectively. When facing single-component faults and multi-component faults, the model has stronger diagnostic accuracy, robustness, anti-noise ability, and stability, and can efficiently diagnose different faults of PV arrays, providing the scientific basis and theoretical support for the operation of PV systems.

A planar dimension measurement optimization method with metric information compensation

Visual dimension measurement is commonly required in industrial detection. However, monocular vision measurements have a low accuracy with large field of view (FOV), while multi-vision measurement involves multi-source information fusion and incurs a large computation overhead. Hence, we propose a planar dimension measurement optimization method with metric information compensation (PMOM). This method constructs a linear representation model of pixel metric information using images at different heights along the camera’s optical axis; designs a multi-resolution multi-dimensional key point mapping mechanism (KPM) to realize the projection from a coarse resolution image point to a fine resolution image block and the secondary optimization of measurement results. By utilizing pixel-level and subpixel-level object extraction algorithms for both coarse resolution and fine resolution image blocks, the algorithm’s complexity and data processing requirements are notably reduced. The paper includes a theoretical proof of the method’s effectiveness, shown by analyzing the measurement errors of different resolution images. Comparative experiments on the measurement of spacing between planar patterns and workpiece machining accuracy validate the method meeting the requirements of industrial high-precision with a large FOV and low computational overhead.

GNSS-INS-dynamic fusion with robustness to outliers based on external force state estimation

Multi-source information fusion state estimation algorithms are an important means for drones to perceive ego-state, and accurate and robust estimation of external forces is crucial for precise control of quadrotors. This paper proposes a method that integrates a dynamic model into a multi-rate extended Kalman filter (EKF) framework on manifold. By estimating the magnitude of the external force acting on vehicle, meanwhile, a dynamic constraint on velocity loop is established to reduce the discrepancy between the model-predicted motion and the actual motion. Moreover, the estimated external force is integrated into the zero velocity update criterion for zero speed judgment, effectively reducing false detections while improving the accuracy of zero speed state recognition. However, multi-source measurements significantly increase the probability of data signal errors. To address this issue, we use a robust estimation algorithm to improve EKF’s sensitivity to abnormal measurements, flexibly adjusting measurement weights while rejecting unreasonable measurements. Validation with open-source indoor and outdoor datasets shows that our algorithm improves pose estimation performance while maintaining accurate positioning accuracy compared to non-dynamic fusion under the same filtering parameters, particularly in global navigation satellite system short time denied. It provides accurate external force estimation, offering multi-source data support in areas such as human–machine interaction and carrying variable mass payloads.

A reliability assessment methodology of system-in-package based virtual qualification

System-in-Package (SiP) is a cutting-edge packaging technique that is flourishing in the semiconductor sector. Physical testing, which can be time-consuming and lead to design modification iterations delaying product release or delivery, has traditionally been used to assess the reliability of SiPs. Simulation methods that mimic physical tests can be used to shorten product development cycles, increase reliability, and lower development costs. This paper introduces a SiP reliability assessment methodology based on virtual qualification. A life prediction methodology of SiP based on physics-of-failure (PoF) is studied in conjunction with multi-source fusion life information of typical structures. Based on this, a comprehensive reliability assessment methodology of SiP is formed. The information input includes material types, structure size, and environmental load conditions. The finite element analysis (FEA) is used to analyze the typical structures. The life distribution of SiP components is obtained through the theory of the cumulative damage and competitive failure model. Through multi-source information fusion, the SiP comprehensive reliability assessment methodology is determined. Finally, a case study is done to determine the accuracy of the approach.

Research on Hazard Analysis and Control Strategy of Power System Dispatching Operation Based on Information Fusion Technology

Abstract In this paper, a decision model for grid fault diagnosis based on multi-source information fusion is established, and the probability of component fault is obtained by using the change of switching quantity characteristic information and electrical quantity characteristic information during grid fault, and the static fault degree obtained by switching quantity information and the voltage and current energy distortion degree and fault degree obtained by electrical quantity information is used as independent evidence bodies, which are fused by improved D-S evidence fusion technique, and the fused The results are decided by improved fuzzy C-mean decision model to finally determine the fault components. Then, the optimal control of the grid is studied, and the corresponding self-healing control model is established according to the different operating conditions of the system so as to propose a self-healing control strategy of the microgrid based on the improved particle swarm algorithm. After analysis and verification, the accuracy of the grid fault diagnosis method proposed in this paper reaches 0.9681, and the diagnosis results are consistent with the pre-defined fault elements compared with FCM. The system network loss decreases from 0.146 kW to 0.031 kW, and the maximum power supply capacity increases from 1.574 to 2.468 after using the improved particle swarm algorithm-based microgrid self-healing control strategy. Therefore, the method in this paper can improve the reliability of grid operation and resist the risk of accidents.

Multisource information fusion based parameterization study of lithium-ion battery electrolyte leakage

Electrolyte leakage can cause serious accidents and is a major hazard for battery thermal runaway due to the flammability of electrolyte. Currently, the method for diagnosing electrolyte leakage depends greatly on sensors, but its reliability remains questionable. The paper characterizes the electrolyte leakage based on multiple signals and proposes a multi-parameter fusion diagnostic framework for different conditions. Firstly, the electrolyte leakage behavior of commercial lithium-ion batteries is imitated on two aging conditions. Secondly, the evolution characteristics of the cyclic capacity of leaky and normal cells are compared. The features of leaky batteries are then extracted based on measurable electrical, thermal, and gas signals during aging period, and the diagnostic sensitivity and reliability of the characteristic parameters are analyzed. According to the analysis, the sudden capacity drop of leaky batteries is found to occur at the constant voltage charging stage, and the triggering mechanism of the sudden capacity drop is revealed. Furthermore, the parametric characterization for electrolyte leakage detection is elaborated. Finally, the electro-thermal-gas multi-parameter fusion framework for electrolyte leakage diagnosis is proposed based on the priority and combination relationships of each parameter. As the result, the proposed parametric characterization is proved to be of great significance for electrolyte leakage diagnosis.

Evaluate the reliability of information sources using the non-parametric plausibility ReliefF algorithm for multi-source information fusion

How to evaluate the reliability of the information sources is a significant and open issue in the information fusion of Dempster-Shafer evidence theory (DST). We propose a new method, called the non-parametric plausibility ReliefF (NPReliefF) algorithm, to measure the reliability of information sources based on the criterion that sources providing more evidence aligned with “greater similarity within classes than between classes” are more reliable. The proposed method significantly improves the effect of normal BPAs and weakens the impact of abnormal BPAs on the fusion, even when abnormal BPAs are in the majority, thereby maximizing the correctness of the fusion results. Several numerical examples and an application of pattern recognition show the effectiveness and flexibility of the proposed methodology.

Belief structure-based Pythagorean fuzzy entropy and its application in multi-source information fusion

Non-standard fuzzy sets play a significant role in uncertainty modeling. In addition to membership and non-membership degree, how to handle the hesitant degree is the key issue in the uncertain information process. In this paper, we model the Pythagorean fuzzy set (PFS) under the belief structure and measure its uncertainty based on fractal-based belief (FB) entropy. A novel fuzzy entropy for PFS called belief structure-based Pythagorean fuzzy (BSPF) entropy is proposed, whose effectiveness and advantages are proven based on mathematical analysis and numerical examples. A comparative analysis between BSPF entropy and other methods shows that BSPF entropy can obtain more reasonable results. Besides, a BSPF entropy-based multi-criteria decision-making (MCDM) method and a classification method are designed to solve practical problems. The experimental results demonstrate the effectiveness of these two proposed methods in solving real-world problems of decision-making and classification.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings, the detection methods using acceleration sensors suffer from signal aliasing, while magnetic detection methods are susceptible to interference from ferromagnetic materials, thereby posing challenges in accurately determining the number of layers. To address this issue, this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion, utilizing both the temporal convolutional network (TCN) and the long short-term memory (LSTM) recurrent network. By leveraging the strengths of these two network structures, the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process, establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate. A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates, capturing the multi-source physical field signals and their patterns during the penetration process. The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting, respectively. The model's predictive performance is evaluated under various operating conditions, including different ratios of added noise to random sample positions, penetration speeds, and spacing between target plates. The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms, 0.12 ms, and 0.16 ms, respectively, confirming the robustness of the proposed model. Moreover, the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Fractal belief Jensen–Shannon divergence-based multi-source information fusion for pattern classification

Multi-source information fusion is an effective method to handle pattern classification problems. Dempster–Shafer evidence theory (DSET) plays an important role in handling uncertainty problems in multi-source information fusion. However, highly conflicting evidence in DSET may cause counter-intuitive fusion results. Belief divergence theory is one of the solutions to conflict management, which is also beneficial for the improvement of accuracies of pattern classification. In this paper, a novel belief divergence measurement method, fractal belief Jensen–Shannon (FBJS) divergence is proposed to better measure the discrepancy between Basic probability assignments (BPAs) and address the problem of highly conflicting evidence in DSET. The proposed FBJS divergence is the first belief divergence that incorporates the belief divergence theory and the concept of fractal. In addition, it has the properties of non-negativeness and symmetry. Then, based on FBJS divergence, a novel multi-source information fusion algorithm is proposed. Ultimately, the proposed algorithm is effectively applied to solve a pattern classification problem with a higher classification accuracy.

Developing a cloud evidence method for dynamic early warning of tunnel construction safety risk in undersea environment

Traditional methods have limitations in achieving precise predictions of risk occurrence at an exact future time and have difficulties transforming between qualitative and quantitative indicators and handling multi-source heterogeneous risk data. This study quantifies and analyzes the multi-source construction safety risks classified into the categories of man, machine, material, method and environment (4M1E), and presents a cloud evidence method that integrates wavelet de-noising algorithm, cloud model, and Dempster-Shafer (D-S) evidence theory. A real-time risk prediction and warning is provided using this method after the fusion of multi-source uncertain information and the transformation between qualitative and quantitative indicators, enabling the timely detection of potential risks for project managers. This method analyzing “uncertainty” with “certainty” is verified by an undersea tunnel construction project. The result shows that this method is effective in early warning risks two days before their actual occurrence, providing reference significance for risk early warning of the tunnel construction project.

A Lightweight and Multisource Information Fusion Method for Real-Time Monitoring of Lump Coal on Mining Conveyor Belts

Since the underground transportation of coal mainly relies on the mine conveyor belt to complete, the mine conveyor belt with large pieces of coal will affect transportation safety. Therefore, to address the problem of real-time monitoring of lump coal, the method Ghost-ECA-Bi FPN (GEB) YOLOv5 for lump coal in the process of mining conveyor belt transportation is proposed based on a lightweight neural network and multisource information fusion. First, the image preprocessing is performed by adaptive histogram equalization, which reduces the influence of coal dust, dust, and uneven lighting on target monitoring. Second, the redundancy of the convolution process is exploited, and a lightweight neural network GhostNet is introduced to optimize the feature extraction process. In addition, combined with the efficient channel attention mechanism, the 1D convolution enables local cross-channel information interaction, which can solve the problem of imbalance between model complexity and performance. Finally, the feature information of the three stages is fused using a weighted bidirectional feature pyramid network to enhance the generalization ability of the model. The experimental results show that the improved GEB YOLOv5 algorithm has obvious advantages. In terms of model structure, the number of network layers reduces by 36.97%, and the number of model structure parameters and floating-point operations reduce by 64.53% and 69.14%, respectively. Moreover, the model volume reduces from 92.7 M to 33.0 M. Regarding the monitoring performance, the precision and recall rates improve by 1.19% and 1.11%, respectively. Furthermore, the real-time performance improves from 68.34 FPS to 110.70 FPS. It can be seen that the problem of the model performance against the model complexity is effectively solved in this experiment and the real-time monitoring of lump coal is realized.

TaBE: Decoupling spatial and spectral processing with Taylor’s unfolding method in the beamspace domain for multi-channel speech enhancement

In recent years, significant advancements have been made in neural beamforming, leveraging spectral and spatial cues to enhance their performance in multi-channel speech enhancement. When the frame-wise processing mechanism is required, there exists a trade-off between performance and algorithmic delay for existing all-neural beamformers. However, from the perspective of multi-source information fusion, the network is often encapsulated into a black box to entangle and fuse the spatial and spectral features into a non-linear feature space, which hinders our understanding of how they work collaboratively for target speech extraction. In this regard, this paper proposes to decouple the spatial and spectral domain processing inspired by Taylor’s approximation theory. Specifically, we reformulate the time-variant beamforming defined in the spatial domain into the adaptive weighting and mixing of different beam components in the beamspace domain. This reformulation enables us to model the recovery of target speech as a weighted sum operation in the beamspace domain, where each beam component is associated with an introduced unknown term for residual interference cancellation. By virtue of Taylor’s series expansion, the recovery process can be decomposed into the superimposition of the 0th-order non-derivative and high-order derivative terms, where the former acts as spatial filtering in the spatial domain, and the latter serves as a residual interference canceller in the spectral domain. We conduct extensive experiments on the spatialized LibriSpeech and L3DAS Challenge datasets. Experimental results show that, compared with existing advanced approaches, the proposed method not only achieves competitive performance in terms of multiple objective metrics but also provides feasible guidance in multi-channel speech enhancement pipeline design.

The Polarized Light Field Enables Underwater Unmanned Vehicle Bionic Autonomous Navigation and Automatic Control

In response to the critical need for autonomous navigation capabilities of underwater vehicles independent of satellites, this paper studies a novel navigation and control method based on underwater polarization patterns. We propose an underwater course angle measurement algorithm and develop underwater polarization detection equipment. By establishing the automatic control model of an ROV (Remote Operated Vehicle) with polarization information, we develop a strapdown navigation method combining polarization and inertial information. We verify the feasibility of angle measurement based on polarization in the water tank. The measurement accuracy of polarization azimuth is less than 0.69°. Next, we conduct ROV navigation at different water depths in a real underwater environment. At a depth of 5 m, the MSE (Mean Square Error) and SD (Standard Deviation) of angle error are 16.57° and 4.07°, respectively. Underwater navigation accuracy of traveling 100 m is better than 5 m within a depth of 5 m. Key technologies such as underwater polarization detection, multi-source information fusion, and the ROV automatic control model with polarization have been broken through. This method can effectively improve ROV underwater work efficiency and accuracy.

A Tight Coupling Algorithm for Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) Adaptive Integrated Navigation Based on Variational Bayesian

Multi-source nonlinear noise exists in the process of multi-source navigation information fusion in long-endurance positioning systems in complex environments. In such engineering applications, the classical Kalman filter (KF) and the extended Kalman filter (EKF) have the phenomena of noise instability and parameter drift, which lead to the divergence of filtering results and reductions in accuracy over long periods of time. Aiming at the above problems, this paper proposes a fusion algorithm of the variational Bayesian (VB) and the cubature Kalman filter (CKF). Firstly, the system is modeled through nonlinear filtering, and the CKF error equation is established by taking the position difference and velocity difference between SINS and GPS as observation variables. Then, to address the problem of poor self-adaptation of the CKF algorithm, the variational Bayesian adaptive estimation method is introduced into the CKF algorithm, and a measurement noise variance estimation model is introduced to the process of time and measurement updates of the CKF algorithm to finally obtain the adaptive VB–CKF algorithm. The simulation results from the experimental platform show that the proposed fusion algorithm improves the combined SINS/GPS system by about 30% in terms of attitude angle accuracy and reduces speed and position estimation errors (RMSE) by about 45%. At the same time, comprehensive experiments on multiple types of sites show that compared with the CKF algorithm, the VB–CKF algorithm improves the positioning accuracy by 10% when the GPS signal is stable and improves the accuracy by about 38% when the GPS measurement noise changes dramatically in complex terrain, which effectively suppresses the accuracy divergence of the CKF algorithm and has high value for engineering applications.

Research on multi-dimensional disaster information perception and fusion service technology in underground and shelter space

Abstract. The disaster information of human casualties and infrastructure damage in underground and sheltered spaces usually demands to be acquired timely during natural disasters such as earthquakes. Therefore, to address the last link problem in the field of disaster relief, the paper investigates the service technology of multi-dimensional disaster information perception and fusion in underground and sheltered space. In this paper, the classification and characteristics of disaster information in underground space are summarized. The key technologies for the extraction and identification of geo-environmental information, the detection methods and sensors used for the data of real-time environmental parameters, and the means of multi-source disaster information fusion under a unified spatio-temporal framework are discussed respectively in the underground space. Finally, a method of dynamic visualization of underground space disaster information is proposed to visualize the underground space scene and environmental parameters. It is achieved the visualization of 2-3D linkage and the simulation of environmental parameters.

Attention-aware temporal–spatial graph neural network with multi-sensor information fusion for fault diagnosis

Intelligent fault diagnosis has attracted intensive efforts in machine predictive maintenance. However, the structural information from multi-sensor signals has not been fully investigated. In this study, a novel temporal–spatial graph neural network with an attention-aware module (A-TSGNN) is proposed to accomplish multi-source information fusion. First, the graph structure naturally organizes the diverse sensors. The graph convolution model realizes the feature representation in the spatial dimension. Then, time-dependent learning is applied in the temporal dimension, and a temporal–spatial learning framework is built. An additional attention module is designed to learn the flexible weights and model the importance of individual sensors and their correlations. Experiments on a wind turbine dataset achieves an accuracy of 0.9669 and an F1-score of 0.9649. For the gearbox dataset, the values are 0.9927 and 0.9920, respectively. The overall macro-average area under the curve metrics reach a perfect score of 1.00 for both datasets, indicating exceptional performance. The adaptive attention mechanism is also discussed to verify the superiority of the A-TSGNN. Furthermore, comparisons with the single-sensor scheme and other fusion models demonstrate the stable performance of the proposed method. The A-TSGNN provides a potential model for comprehensively utilizing multi-sensor data, showing a promising prospect.