Multi-source Information Fusion Method Research Articles

An effective multi-source information fusion method for electronic nose and hyperspectral to identify the spring tea quality at different harvesting periods

An effective multi-source information fusion method for electronic nose and hyperspectral to identify the spring tea quality at different harvesting periods

Research on multi-source information fusion tool wear monitoring based on MKW-GPR model

To enhance intelligent manufacturing capabilities and achieve the goal of online real-time high-precision monitoring of tool wear with small sample data, this paper proposes a multi-source information fusion method for turning tool wear monitoring based on the multi-kernel weighted Gaussian process regression (MKW-GPR) model. Firstly, the framework and process of multi-source information fusion for turning tool wear monitoring are established, and the experimental platform is constructed. Secondly, a full-life cutting experiment of the turning tool is conducted. During the experiment, tool wear image data and multi-sensor signal data, including cutting force and vibration signals, are collected. Then, the experimental data were processed to construct a dataset with multi-source information fusion, leading to the establishment of the MKW-GPR model. Subsequently, the experimental dataset was employed to validate the effectiveness of the turning tool wear monitoring model. The results demonstrate that, in recognizing the wear state, the MKW-GPR model exhibited superior recognition performance compared to the single kernel function Gaussian process regression model. Furthermore, in predicting the wear value, the MKW-GPR model is better than the long short-term memory (LSTM), random forest (RF), and scalable end-to-end tree boosting (XGBoost) models. During the small sample size a priori point data prediction performance test, the MKW-GPR model achieved the highest accuracy, with a maximum relative error of only 1.92% in predicting tool life during the severe wear stage. These findings substantiate the feasibility and effectiveness of the proposed turning tool wear monitoring method.

Multi-source information fusion based on evidence reasoning using double frames of discernment for estimating the number of remaining missiles

In air combat, multi-source information fusion is used to estimate the number of remaining missiles (NRM), which is a key premise for multi-aircraft cooperative decision-making. However, the existing multi-source information fusion methods are difficult to apply because prior probability and labeled data are hard to obtain accurately. This paper proposes a novel multi-source information fusion method using double frames of discernment (MIFDFD) for estimating the NRM, which can efficiently and dynamically fuse discrete uncertain information with time series to extract useful information. In the first frame of discernment, the basic belief assignment (BBA) function is constructed by modifying the soft maximum function to fuse the air combat situation information. The BBA of the second frame of discernment is constructed by fusing the believed degree of the launching missile source and historically estimated NRM by using a transferable belief model. The optimal value of the classification factor is determined by conducting parametric analyses in typical air combat scenarios. Performance experiments show that the MIFDFD method provides a more accurate estimate of the NRM and takes less time than the comparison method.

A novel multi-source information fusion method for emergency spatial resilience assessment based on Dempster-Shafer theory

In the midst of today's intricate and ever-changing natural and social landscape, this study is committed to proposing a novel multi-source information fusion method for assessing the spatial resilience of urban emergency evacuation and rescue. Its overarching aim is to uncover latent challenges and contribute to the enhancement of a city's capacity to respond to emergencies. To achieve this, we have devised a comprehensive assessment indicator system, capable of not only quantifying a city's spatial resilience but also offering indispensable guidance for urban emergency evacuation and rescue spatial planning. Furthermore, we have introduced an innovative evaluation methodology framework. This framework includes the establishment of the basic probability assignment (BPA) model, a pioneering method for generating BPA for observed values, and the novel application of hierarchical weighting and fusion techniques. By extending the Dempster-Shafer theory, we have not only enhanced the method's ability to express and integrate uncertain information but also significantly improved the precision of the evaluation. Ultimately, we conducted a quantitative assessment using Shenzhen, China, as a case study, identifying existing issues and proposing highly-targeted improvement strategies. These research findings not only provide robust support for the augmentation of urban emergency capabilities in Shenzhen but also offer pioneering insights for the quantitative assessment and advancement of emergency spatial resilience in cities across the globe.

Multisource information fusion method for vegetable disease detection

Automated detection and identification of vegetable diseases can enhance vegetable quality and increase profits. Images of greenhouse-grown vegetable diseases often feature complex backgrounds, a diverse array of diseases, and subtle symptomatic differences. Previous studies have grappled with accurately pinpointing lesion positions and quantifying infection degrees, resulting in overall low recognition rates. To tackle the challenges posed by insufficient validation datasets and low detection and recognition rates, this study capitalizes on the geographical advantage of Shouguang, renowned as the “Vegetable Town,” to establish a self-built vegetable base for data collection and validation experiments. Concentrating on a broad spectrum of fruit and vegetable crops afflicted with various diseases, we conducted on-site collection of greenhouse disease images, compiled a large-scale dataset, and introduced the Space-Time Fusion Attention Network (STFAN). STFAN integrates multi-source information on vegetable disease occurrences, bolstering the model’s resilience. Additionally, we proposed the Multilayer Encoder-Decoder Feature Fusion Network (MEDFFN) to counteract feature disappearance in deep convolutional blocks, complemented by the Boundary Structure Loss function to guide the model in acquiring more detailed and accurate boundary information. By devising a detection and recognition model that extracts high-resolution feature representations from multiple sources, precise disease detection and identification were achieved. This study offers technical backing for the holistic prevention and control of vegetable diseases, thereby advancing smart agriculture. Results indicate that, on our self-built VDGE dataset, compared to YOLOv7-tiny, YOLOv8n, and YOLOv9, the proposed model (Multisource Information Fusion Method for Vegetable Disease Detection, MIFV) has improved mAP by 3.43%, 3.02%, and 2.15%, respectively, showcasing significant performance advantages. The MIFV model parameters stand at 39.07 M, with a computational complexity of 108.92 GFLOPS, highlighting outstanding real-time performance and detection accuracy compared to mainstream algorithms. This research suggests that the proposed MIFV model can swiftly and accurately detect and identify vegetable diseases in greenhouse environments at a reduced cost.

Health status perception of oil-immersed power transformers considering wind power uncertainty

The power transformer is the most important and critical component in the power grid of the electrical system. Its safe and stable operation is of great significance for the reliable transmission of renewable energy generation and the reliable power supply to end users. With the continuous development of new types of power systems, the load of the power system undergoes drastic changes, resulting in increased volatility and instability, which leads to issues such as overload, harmonics, and short circuits in transformers. Therefore, in order to accurately assess the operating status of transformers and promptly identify any existing conditions, a method is proposed in this paper. This method uses an optimal cloud entropy parameter calculation method and a variable weighting method to optimize the gray-cloud evidence model. Through a novel multi-source information fusion method, the results of various test items are fused to obtain the state awareness results. Meanwhile, different evaluation indicators are selected for different levels of renewable energy penetration and compared with other methods. The results are validated through examples, demonstrating that the method proposed in this paper can accurately reflect the operating status of transformers and has good scalability.

Abnormal Detection and Fault Diagnosis of Adjustment Hydraulic Servomotor Based on Genetic Algorithm to Optimize Support Vector Data Description with Negative Samples and One-Dimensional Convolutional Neural Network

Because of the difficulty in fault detection for and diagnosing the adjustment hydraulic servomotor, this paper uses feature extraction technology to extract the time domain and frequency domain features of the pressure signal of the adjustment hydraulic servomotor and splice the features of multiple pressure signals through the Multi-source Information Fusion (MSIF) method. The comprehensive expression of device status information is obtained. After that, this paper proposes a fault detection Algorithm GA-SVDD-neg, which uses Genetic Algorithm (GA) to optimize Support Vector Data Description with negative examples (SVDD-neg). Through joint optimization with the Mutual Information (MI) feature selection algorithm, the features that are most sensitive to the state deterioration of the adjustment hydraulic servomotor are selected. Experiments show that the MI algorithm has a better performance than other feature dimensionality reduction algorithms in the field of the abnormal detection of adjustment hydraulic servomotors, and the GA-SVDD-neg algorithm has a stronger robustness and generality than other anomaly detection algorithms. In addition, to make full use of the advantages of deep learning in automatic feature extraction and classification, this paper realizes the fault diagnosis of the adjustment hydraulic servomotor based on 1D Convolutional Neural Network (1DCNN). The experimental results show that this algorithm has the same superior performance as the traditional algorithm in feature extraction and can accurately diagnose the known faults of the adjustment hydraulic servomotor. This research is of great significance for the intelligent transformation of adjustment hydraulic servomotors and can also provide a reference for the fault warning and diagnosis of the Electro-Hydraulic (EH) system of the same type of steam turbine.

MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion.

Based on the current research on the wine grape variety recognition task, it has been found that traditional deep learning models relying only on a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is a high degree of similarity between varieties. In order to effectively distinguish these similar varieties, this study proposes a multisource information fusion method, which is centered on the SynthDiscrim algorithm, aiming to achieve a more comprehensive and accurate wine grape variety recognition. First, this study optimizes and improves the YOLOV7 model and proposes a novel target detection and recognition model called WineYOLO-RAFusion, which significantly improves the fruit localization precision and recognition compared with YOLOV5, YOLOX, and YOLOV7, which are traditional deep learning models. Secondly, building upon the WineYOLO-RAFusion model, this study incorporated the method of multisource information fusion into the model, ultimately forming the MultiFuseYOLO model. Experiments demonstrated that MultiFuseYOLO significantly outperformed other commonly used models in terms of precision, recall, and F1 score, reaching 0.854, 0.815, and 0.833, respectively. Moreover, the method improved the precision of the hard to distinguish Chardonnay and Sauvignon Blanc varieties, which increased the precision from 0.512 to 0.813 for Chardonnay and from 0.533 to 0.775 for Sauvignon Blanc. In conclusion, the MultiFuseYOLO model offers a reliable and comprehensive solution to the task of wine grape variety identification, especially in terms of distinguishing visually similar varieties and realizing high-precision identifications.

Leakage diagnosis of natural gas pipeline based on multi-source heterogeneous information fusion

Due to long-term service, natural gas pipelines are prone to corrosion, resulting in pipeline leakage failure and environmental pollution. However, it is challenging to provide an accurate leakage diagnosis for existing single-sensor detection techniques. In this paper, we propose a multi-source heterogeneous information fusion method for the complementary fusion of laser optical sensing and weak magnetic technologies. Firstly, the laser and weak magnetic signals are converted into two-dimensional images using continuous wavelet transform (CWT) and then fused in data-level. Secondly, deep reinforcement learning (DRL) combines the perception ability of deep learning and the decision-making ability of reinforcement learning. Consequently, the deep Q-network (DQN) method is proposed as a novel method for leakage diagnosis of natural gas pipelines. Then, an improved capsule network based on dense block is designed for feature enhancement. Finally, experimental results verify the effectiveness of the proposed method in recognizing the formed leakage and potential leakage. Moreover, the results demonstrate that the proposed method outperforms single-sensor-based and state-of-the-art methods in terms of diagnostic accuracy and cross-domain transfer tasks. This will provide a theoretical basis for pipeline leakage failure prevention and maintenance decision-making.

A novel quantum Dempster's rule of combination for pattern classification

Dempster's rule of combination (DRC) is widely used for uncertainty reasoning in intelligent information system, which is generalized to complex domain recently. However, as the increase of identification framework elements, the computational complexity of Dempster's rule of combination increases exponentially. To address this issue, we propose a novel quantum Dempster's rule of combination (QDRC) by means of Toffoli gate. The QDRC combination process is completely implemented using quantum circuits. To validate the efficiency of the proposed QDRC, we conduct simulation experiments on IBM and IonQ quantum cloud platforms. Besides, we implement the QDRC algorithm to the real quantum computer of IonQ Harmony. The experiment results show that the result of QDRC is getting more and more accurate as the number of measurements increases. In addition, we propose a quantum multisource information fusion method on the basis of QDRC. Finally, we demonstrate its effectiveness to solve pattern classification problem under several diverse real UCI datasets.

A New Divergence Based on the Belief Bhattacharyya Coefficient with an Application in Risk Evaluation of Aircraft Turbine Rotor Blades

Belief divergence is a significant measure to quantify the discrepancy between evidence, which is beneficial for conflict information management in Dempster–Shafer evidence theory. In this article, three new concepts are given, namely, the belief Bhattacharyya coefficient, adjustment function, and enhancement factor. And based on them, a novel enhanced belief divergence, called EBD, is proposed, which can assess the correlation of subsets and fully reflect the uncertainty of multielement sets. The important properties of the EBD have been studied. In particular, a new EBD-based multisource information fusion method is designed to handle evidence conflict, where the weight of evidence is decided by the EBD between evidence and the information volume of each evidence. Compared with other methods, the proposed method in the applications of target recognition and iris classification can produce more rational and telling outcomes when dealing with conflict information. Finally, an application in risk priority evaluation of the failure modes of rotor blades of an aircraft turbine is provided to validate that the proposed method has the extensive applicability.

A Practical Exploration of the Integration of Civics into Student Education and Management in Colleges and Universities Empowered by Information Integration Technology in the Context of Three-Whole Parenting

Abstract Internet information technology has brought challenges to the integration of college and university Civics in student education and management work, and at the same time, it also provides an opportunity for the integration of information technology. In this paper, on the basis of two kinds of nodes of basic Petri nets, the library and change, we constructed the workflow of identification Petri nets oriented to the education and management of students in the Civic Government of colleges and universities. The workflow engine is designed as the center of gravity of the reference model, consisting of six basic elements of student management. In the information fusion workflow, a multi-source information fusion method based on CNN and LSTM is proposed to further integrate Civics into student education management. The response time of the student management action flow is shorter than the retrieval time based on the original mechanism, and the average response time is reduced by 57.4 ms when the workflow reaches 5000. The fusion of Civic Politics and education management in the context of three-whole-parenting optimizes the management work method.

Multisource information fusion considering the weight of focal element’s beliefs: a Gaussian kernel similarity approach

Similarity has been extensively utilized to measure the degree of conflicts between evidences in multisource information fusion. The existent works, however, assumed that the contribution of each focal element’s belief to the similarity measure is the same, and the influence of the weights of focal element’s belief is not considered, which is unreasonable. This article proposes a new Gaussian kernel similarity approach to measure the similarity between evidences. The proposed Gaussian kernel similarity coefficient can effectively take account of the weights of focal element’s beliefs. In addition, it possesses some preferable properties, such as, bounded, consistent, and symmetrical. A multisource information fusion method based on the Gaussian kernel similarity coefficient is, therefore, investigated. The developed method mainly contains three steps: (1) the Gaussian kernel similarity coefficient, as a connection, is leveraged to calculate the weight of evidences based on the weight of focal element’s beliefs; (2) the initial evidences are, thereby, modified based on the weight of evidence via the weight-average method; and (3) the final multisource information fusion can be achieved by the Dempster’s combination rule using the modified evidences. An illustrative example with single-element subset and an application with multi-element subset are presented, and it is verified that the proposed method is effective in dealing with conflicting evidences.

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.

An improved multi-source information fusion method for IMU compensation of missile

AbstractThe conventional SINS/CNS integrated navigation system equipped on ballistic missiles is typically equipped with attitude measurement, which can only estimate the gyro drift and has no effect on accelerometer bias. To address the issue, an improved multi-source information fusion method containing a new nonlinear framework called SINS/RKCNS with the indirect horizon reference and kinematic constraint is proposed, and a MAP-based modified iterated CKF is involved to increase positioning accuracy and system robustness. Furthermore, to reduce the influence of correlated noise, state augmentation is employed in the iterative process. Eventually, experiments are conducted, and the results confirm the effectiveness of the proposed approach.

Identification of Robust Antibiotic Subgroups by Integrating Multi-Species Drug-Drug Interactions.

Previous studies have shown that antibiotics can be divided into groups, and drug-drug interactions (DDI) depend on their groups. However, these studies focused on a specific bacteria strain (i.e., Escherichia coli BW25113). Existing datasets often contain noise. Noisy labeled data may have a bad effect on the clustering results. To address this problem, we developed a multi-source information fusion method for integrating DDI information from multiple bacterial strains. Specifically, we calculated drug similarities based on the DDI network of each bacterial strain and then fused these drug similarity matrices to obtain a new fused similarity matrix. The fused similarity matrix was combined with the T-distributed stochastic neighbor embedding algorithm, and hierarchical clustering algorithm can effectively identify antibiotic subgroups. These antibiotic subgroups are strongly correlated with known antibiotic classifications, and group-group interactions are almost monochromatic. In summary, our method provides a promising framework for understanding the mechanism of action of antibiotics and exploring multi-species group-group interactions.

A novel approach based on fault tree analysis and Bayesian network for multi-state reliability analysis of complex equipment systems

Due to the complex structure of multi-state complex systems and the lack of data, information, and knowledge, the uncertainty of the logical relationship between the failure states of systems and components and the uncertainty of related failure data become the key issues in the reliability analysis of multi-state complex systems. In this paper, combined with multi-state fault tree (MSFT), a multi-state reliability assessment framework for complex systems considering uncertainty based on multi-source information fusion and multi-state Bayesian network (MSBN) is proposed. The multi-source information fusion method combines historical data and experts’ opinions to solve the uncertainty problem of multi-state failure data in complex equipment systems effectively. Based on the multi-source information fusion method, the calculation method of multi-state prior probability and the construction method of conditional probability are given. By constructing the conditional probability table (CPT), the uncertain logic relationship between the multi-state nodes is effectively expressed, which effectively improves the efficiency of CPT acquisition for MSBN and reduces the workload of experts scoring. Finally, a mud circulating system is taken as an example to prove the proposed method, and the specific calculation process, evaluation results, and some discussions are given. The results show that the proposed method is an effective multi-state reliability analysis method for complex uncertain multi-state systems.

A Multi-Source Data Fusion Method for Assessing the Tunnel Collapse Risk Based on the Improved Dempster–Shafer Theory

Collapse is the main engineering disaster in tunnel construction when using the drilling and blasting method, and risk assessment is one of the important means to significantly reduce engineering disasters. Aiming at the problems of random decision-making and misjudgment of single indices in traditional risk assessment, a multi-source data fusion method with high accuracy based on improved Dempster–Shafer evidence theory (D-S model) is proposed in this study, which can realize the accurate assessment of tunnel collapse risk value. The evidence conflict coefficient K is used as the identification index, and the credibility and importance are introduced. The weight coefficient is determined according to whether the conflicting evidence is divided into two situations. The advanced geological forecast data, on-site inspection data and instrument monitoring data are trained by Cloud Model (CM), Gradient Boosting Decision Tree (GBDT) and Support Vector Classification (SVC), respectively, to obtain the initial BPA value. Combined with the weight coefficient, the identified conflict evidence is adjusted, and then the evidence from different sources is fused to obtain the overall collapse risk value. Finally, the accuracy is selected to verify the proposed method. The proposed method has been successfully applied to Wenbishan Tunnel. The results show that the evaluation accuracy of the proposed multi-source information fusion method can reach 88%, which is 16% higher than that of the traditional D-S model and more than 20% higher than that of the single-source information method. The high-precision multi-source data fusion method proposed in this paper has good universality and effectiveness in tunnel collapse risk assessment.

Multi-source monitoring information fusion method for dam health diagnosis based on Wasserstein distance

The fusion of multi-source monitoring information has become the main trend in the field of dam health diagnosis because of the increasing amount of monitoring data that can be obtained from different sensors. However, the Dempster–Shafer (D–S) evidence theory, an important method in multi-source information fusion, may produce counter-intuitive results when fusing conflicting pieces of evidence. To some extent, existing distance measures can deal with highly conflicting evidence, however, the fusion of completely conflicting evidence (indicated by the conflict coefficient K, K = 1) is typically ignored. This study mainly focuses on the fusion of pieces of completely conflicting evidence considering K = 1. The Wasserstein distance in the field of deep learning is introduced to the D–S evidence theory. Based on the foregoing, the belief Wasserstein–1 distance (BWD) method combined with basic probability assignment is proposed to measure evidence distance. The application of the BWD method in dam health diagnosis is presented to demonstrate the validity and effectiveness of this method in multi-source fusion with completely conflicting evidence.

MSIF: Multi-source information fusion based on information sets

Multi-source information fusion is a sophisticated estimating technique that enables users to analyze more precisely complex situations by successfully merging key evidence in the vast, varied, and occasionally contradictory data obtained from various sources. Restricted by the data collection technology and incomplete data of information sources, it may lead to large uncertainty in the fusion process and affect the quality of fusion. Reducing uncertainty in the fusion process is one of the most important challenges for information fusion. In view of this, a multi-source information fusion method based on information sets (MSIF) is proposed in this paper. The information set is a new method for the representation of granularized information source values using the entropy framework in the possibilistic domain. First, four types of common membership functions are used to construct the possibilistic domain as the information gain function (or agent). Then, Shannon agent entropy and Shannon inverse agent entropy are defined, and their summation is used to evaluate the total uncertainty of the attribute values and agents. Finally, an MSIF algorithm is designed by infimum-measure approach. The experimental results show that the performance of Gaussian kernel function is good, which provides an effective method for fusing multi-source numerical data.