Heterogeneous Information Fusion Research Articles

A Novel Temperature Rise Prediction Method of Multi-component Feed System for CNC Machine Tool Based on Multi-source Fusion of Heterogeneous Correlation Information

A Novel Temperature Rise Prediction Method of Multi-component Feed System for CNC Machine Tool Based on Multi-source Fusion of Heterogeneous Correlation Information

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.

Segmentation assisted Prostate Cancer Grading with Multitask Collaborative Learning

Medical image segmentation can provide doctors with more direct information on the location and size of organs or lesions, which can serve as an valuable auxiliary task for prostate cancer grading. Meanwhile, other types of diagnostic data besides images are also essential, such as patient age, Prostate-Specific Antigen (PSA), etc. Currently, there is a lack of in-depth research on how to effectively differentiate and select shared features and task-specific features in multitask learning, as well as how to balance and explore the potential correlations between different tasks. In this paper, we propose a novel Shared Feature Hybrid Gating Experts (SFHGE) architecture for collaborative main (lesion grading) and auxiliary (lesion segmentation) task learning, dynamically selecting shared and task-specific features. To efficiently utilize complementary features, we also introduce a Cross-Task Attention module (CrossTA) to capture cross-task integrated representation. Additionally, recognizing that non-image clinical information often provides crucial diagnostic insights, we further design a Heterogeneous Information Fusion Network (HIFN) to better integrate clinical data, thereby improving grading performance. Extensive experiments on the PI-CAI dataset demonstrate that our approach outperforms mainstream classification and segmentation models.

Self-Supervised Learning and Multisource Heterogeneous Information Fusion Based Quality Anomaly Detection for Heavy-Plate Shape

Plate shape quality anomaly detection suffers from the problems of less labeled and multisource heterogeneous (MSH) data due to the complex production process with multi-process and multi-equipment. To address these problems, this paper proposes a self-supervised learning framework based on MSH contrastive learning (MSH-CL). In the proposed framework, an encoder is designed to extract the MSH information, which contains an MSH feature extract network (MSH-FEN) and an MSH feature fusion network (MSH-FFN). In MSH-FEN, a two-pathway CNN network is employed to extract the MSH feature. Then, a cross-MSH (CMSH) data fusion strategy based on the attention mechanism is designed to characterize the relationship between the MSH data in the MSH-FFN. The learning process consists of two phases, namely the self-supervised feature learning phase based on the contrastive learning (CL) and the supervised fine-tuning phase. The former phase is responsible for training the encoder with massive unlabeled data. Then, a classifier followed by the encoder is built with less labeled data in the supervised fine-tuning phase, which is applied to plate shape quality anomaly detection. Finally, the experiments are carried out on the real-world data set collected from a heavy-plate production process. The experimental results demonstrate the effectiveness and superiority of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper focuses on the problem of shape quality anomaly detection for the heavy-plate production process, which is crucial to steel-making. However, the most quality-relevant data-driven modeling approaches suffer from the problem of requiring massive labeled data. In addition, the data generated in the practice heavy-plate production process have the characteristics of multisource heterogeneous (MSH). Motivated by the challenge of the less labeled and MSH data modeling, this paper proposed a self-supervised feature learning approach to learn a feature encoder to model the MSH data. Then, fine-tuning is used to obtain the model to detect the plate shape quality. In the feature encoder, a cross-MSH (CMSH) data fusion strategy is proposed. Finally, the effectiveness of the proposed approach is demonstrated by comparing it with other advanced algorithms. The proposed approach can be extended to other heavy-plate quality anomaly detection problems with less labeled and MSH data.

Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information.

The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky-Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.

MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.

Introduction: MicroRNAs (miRNAs) are a class of non-coding RNA molecules that play a crucial role in the regulation of diverse biological processes across various organisms. Despite not encoding proteins, miRNAs have been found to have significant implications in the onset and progression of complex human diseases. Methods: Conventional methods for miRNA functional enrichment analysis have certain limitations, and we proposed a novel method called MiRNA Set Enrichment Analysis based on Multi-source Heterogeneous Information Fusion (MHIF-MSEA). Three miRNA similarity networks (miRSN-DA, miRSN-GOA, and miRSN-PPI) were constructed in MHIF-MSEA. These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. These miRNA similarity networks were fused into a single similarity network with the averaging method. This fused network served as the input for the random walk with restart algorithm, which expanded the original miRNA list. Finally, MHIF-MSEA performed enrichment analysis on the expanded list. Results and Discussion: To determine the optimal network fusion approach, three case studies were introduced: colon cancer, breast cancer, and hepatocellular carcinoma. The experimental results revealed that the miRNA-miRNA association network constructed using miRSN-DA and miRSN-GOA exhibited superior performance as the input network. Furthermore, the MHIF-MSEA model performed enrichment analysis on differentially expressed miRNAs in breast cancer and hepatocellular carcinoma. The achieved p-values were 2.17e(-75) and 1.50e(-77), and the hit rates improved by 39.01% and 44.68% compared to traditional enrichment analysis methods, respectively. These results confirm that the MHIF-MSEA method enhances the identification of enriched miRNA sets by leveraging multiple sources of heterogeneous information, leading to improved insights into the functional implications of miRNAs in complex diseases.

Multi-source heterogeneous information fusion fault diagnosis method based on deep neural networks under limited datasets

Intelligent fault diagnosis of critical components of rotating machinery is essential for enhancing production efficiency and reducing maintenance costs. However, the scarce labeled samples and the single monitoring data hinder the engineering application and generalization of diagnostic models to some extent. To this end, a novel multi-source heterogeneous information fusion (MSHIF) network is proposed in this paper to identify the health status of rotating machinery more comprehensively and robustly under limited datasets. Specifically, the data enhanced deep belief network (DEDBN) and data enhanced one-dimension convolutional neural network (DE-1DCNN) are firstly designed by repeatedly appending raw data to the hierarchy of conventional deep belief network (DBN) and one-dimension convolutional neural network (1DCNN). DEDBN and DE-1DCNN improve the diagnostic performance of the model under limited datasets while effectively mitigating the loss of potentially valuable information during layer-by-layer feature extraction and compression of DBN and CNN. Then, the MSHIF is further constructed with the designed DEDBN and DE-1DCNN as structural branches. MSHIF aims to alleviate the limitations of scarce labeled samples and single monitoring data on diagnostic performance within a unified framework by mining the rich and complementary device status information in multi-source heterogeneous monitoring data. Extensive comparative experiments and detailed discussions are constructed on both publicly available datasets and rolling mill experimental dataset to verify the feasibility and effectiveness of MSHIF. The experimental results demonstrate that the proposed MSHIF outperforms other comparative methods in terms of diagnostic accuracy, stability, and robustness against noise, achieving 99.491%, 99.143%, and 99.037% average identification accuracy on three cases, respectively.

Optimization of Innovative Paths of Physical Education Teaching in Primary and Secondary Schools under Information Integration Technology

Abstract This article utilizes information fusion technology to provide a more diversified and hierarchical teaching process for physical education, making the content of physical education classroom richer and more vivid. The article firstly discusses the multi-source heterogeneous information fusion technology. It proposes a mass function that combines the output results of the cumulative layer of probabilistic neural network to construct the D-S evidence theory. The D-S evidence theory realizes the decision-level information fusion of the primary results of probabilistic neural networks. In the study, the TPACK knowledge framework was combined with the informationization platform to establish an intelligent classroom for physical education teaching applicable to primary and secondary schools, and a teaching experiment was designed for the selected research subjects. The experiment was conducted to analyze the data mainly from three dimensions: teachers’ and students’ behaviors in the smart classroom, teaching effectiveness and teaching evaluation. The results of data analysis showed that the average time of each learning node of students at the school was more than 50 seconds. At the same time, the coding coverage of teachers’ teaching behaviors was 41.85% lower than the mean value of coding coverage of students’ learning behaviors. In the experimental group, students’ jump rope scores improved by about 9.05% to 20.44% after one month of learning. These findings suggest that an intelligent classroom for physical education teaching relying on information fusion technology can effectively integrate multi-source data, thus providing strong support for improving the effectiveness of physical education teaching in primary and secondary schools.

Heterogeneous information fusion recognition method based on belief rule structure

Heterogeneous information fusion recognition method based on belief rule structure

GCN-Based Heterogeneous Complex Feature Learning to Enhance Predictability for LncRNA-Disease Associations.

Using computational models to predict potential lncRNA-disease associations (LDAs) has emerged as an effective supplement to bioexperiments for exploring the pathogenesis of diseases. However, current computational models still face limitations in their ability to learn the complex features of bionetworks. In this study, HGCNLDA, a model which combines graph convolutional network (GCN)-based aggregation, heterogeneous information fusion, and a bilinear-decoder to infer LDAs was proposed. Recognizing the need to extract essential features during data processing, our HGCNLDA explored four key steps for uncovering interaction patterns within the bionetwork: (1) a novel type of tripartite heterogeneous network, known as the lncRNA-disease-miRNA network (LDMN), was constructed using computed similarities and known associations. (2) Homogeneous and heterogeneous features of nodes were extracted from domains within the LDMN by a GCN-based encoder. (3) Feature fusions, including bipolymerization operations and attention mechanism, were employed to capture a more accurate and comprehensive representation of nodes. (4) Bilinear-decoder was used to rebuild the edge type (or rating type) for a specific node pair, resulting in the predicted association score. Through a 5-fold cross-validation on two data sets, namely, data set1 and data set2, our HGCNLDA consistently demonstrated superior performance compared to five related models. It almost achieved the highest AUROC and AUPR values on both data sets, especially on data set2 where the results obtained were more challenging and objective. Case studies involving three real cancer scenarios further validated the practicality of HGCNLDA in identifying potential LDAs in real-world contexts. The source code and data for this study are available at https://github.com/zywait/HGCNLDA.

Heterogeneous information fusion based graph collaborative filtering recommendation

Nowadays, with the application of 5G, graph-based recommendation algorithms have become a research hotspot. Graph neural networks encode the graph structure information in the node representation through an iterative neighbor aggregation method, which can effectively alleviate the problem of data sparsity. In addition, more and more information graph can be used in collaborative filtering recommendation, such as user social information graph, user or item attributed information graph, etc. In this paper, we propose a novel heterogeneous information fusion based graph collaborative filtering method, which models graph data from different heterogeneous graph, and combines them together to enhance presentation learning. Through information propagation and aggregation, our model can learn the latent embeddings effectively and enhance the performance of recommendation. Experimental results on different datasets validate the outperformance of the proposed framework.

A target spatial location method for fuze detonation point based on deep learning and sensor fusion

The spatial location of fuze detonation point is crucial for evaluating the working condition and improving the performance of fuze. Considering the observation safety, the non-contact long-distance accurate measurement technology is essential. In this paper, we propose a method that takes the sensor data of optics, spatial attitude and GPS as input and outputs the spatial position of the fuze detonation point. The proposed method consists of two steps. First, an object detection algorithm with post-processing algorithm is proposed to obtain rich information of the target. The algorithm achieves high-accuracy detection by introducing powerful backbone, attention mechanism, group convolution, and improved multi-scale feature fusion. Second, a Variational Auto-Encoder (VAE) algorithm model improved by dense connection structure and multiple source heterogeneous sensor information fusion structure is proposed as the position regression algorithm. It receives the status information of the observer camera and the output of the object detection algorithm, and then outputs the three-dimensional coordinates of the explosion point. Finally, method validation and performance analysis are realized through virtual scene simulation. Experiment results show the superiority of the proposed object detection algorithm over other typical algorithms on explosion flare detection, with its Average Precision (AP) of 0.889. The positioning error of the spatial location method is 0.896 m, while that of the binocular stereo vision method is 2.863 m. Therefore, the proposed target spatial location method is proved to be effective and accurate.

Construction and application of logistics scheduling model based on heterogeneous graph neural network

The core of logistics is scheduling and monitoring. After the modern interprise logistics development concept change, the development prospect of enterprise logistics is more optimistic. Major enterprises have begun to use intelligent logistics scheduling platforms. In order to solve the problem that heterogeneous information fusion is complex in the temporal heterogeneous graphs, this paper proposes to dynamically store and update node representation through an augmented memory matrix in a memory network. At the same time, the model also designs a novel read-write module for the memory matrix, which can effectively capture the timing information in the long interaction sequence and has high flexibility. The model has significantly improved in tasks such as node classification, timing recommendation and visualization. This paper studies the logistics supply chain of modern enterprises and establishes the mathematical model of vehicle scheduling. This paper takes the non-full load scheduling model as the critical research object. Based on the research of logistics supply chain, the vehicle scheduling model is established. The intelligent heuristic algorithm is applied to solve it, and the effective vehicle distribution scheme and driving route are formed. The simulation results show that the approximate Pareto optimal solution obtained by our designed model and algorithm has good robustness. NSGAIIROELSDR can get a better solution in small-scale scheduling. However, in large-scale numerical experiments, the final solution obtained by MOEA/DROELSDR is obviously better than that of NSGAIIROELSDR, and the running time of MOEA/DROELSDR is also shorter. Therefore, we conclude that MOEA/DROELSDR is more suitable for large-scale scheduling, and NSGAIIROELSDR is more suitable for more minor scheduling.

HealthNet: A Health Progression Network via Heterogeneous Medical Information Fusion.

Numerous electronic health records (EHRs) offer valuable opportunities for understanding patients' health status at different stages, namely health progression. Extracting the health progression patterns allows researchers to perform accurate predictive analysis of patient outcomes. However, most existing works on this task suffer from the following two limitations: 1) the diverse dependencies among heterogeneous medical entities are overlooked, which leads to the one-sided modeling of patients' status and 2) the extraction granularity of patient's health progression patterns is coarse, limiting the model's ability to accurately infer the patient's future status. To address these challenges, a pretrained Health progression network via heterogeneous medical information fusion, HealthNet, is proposed in this article. Specifically, a global heterogeneous graph in HealthNet is built to integrate heterogeneous medical entities and the dependencies among them. In addition, the proposed health progression network is designed to model hierarchical medical event sequences. By this method, the fine-grained health progression patterns of patients' health can be captured. The experimental results on real disease datasets demonstrate that HealthNet outperforms the state-of-the-art models for both diagnosis prediction task and mortality prediction task.

A Social Recommendation Model Based on Basic Spatial Mapping and Bilateral Generative Adversarial Networks.

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. Therefore, how to effectively fuse interaction information and social information becomes a hot research topic in social recommendation, and how to mine and exploit the heterogeneous information in the interaction and social space becomes the key to improving recommendation performance. In this paper, we propose a social recommendation model based on basic spatial mapping and bilateral generative adversarial networks (MBSGAN). First, we propose to map the base space to the interaction and social space, respectively, in order to overcome the issue of heterogeneous information fusion in two spaces. Then, we construct bilateral generative adversarial networks in both interaction space and social space. Specifically, two generators are used to select candidate samples that are most similar to user feature vectors, and two discriminators are adopted to distinguish candidate samples from high-quality positive and negative examples obtained from popularity sampling, so as to learn complex information in the two spaces. Finally, the effectiveness of the proposed MBSGAN model is verified by comparing it with both eight social recommendation models and six models based on generative adversarial networks on four public datasets, Douban, FilmTrust, Ciao, and Epinions.

Few-Shot Fault Diagnosis Based on Heterogeneous Information Fusion and Meta Learning

Few-Shot Fault Diagnosis Based on Heterogeneous Information Fusion and Meta Learning

Influential Node Detection and Ranking With Fusion of Heterogeneous Social Media Information

Identification of influential nodes has emerged as one of the major challenges, especially after their use in the rapid propagation of information, epidemics, and so on in social media. Most of the previous works in this field deal with the homogeneous interactions that are not pertinent in the determination of the accurate context of the nodes due to their noisy and sparse nature. Hence, heterogeneous interactions need to be explored for the identification of influential nodes in the network. To consider heterogeneous interactions available within the network, a multilayer network (ML) has been designed in this work. Each layer of the network represents a particular type of interaction, e.g., upload, comment, retweet, reply, and mention. A heterogeneous degree ranking (HDR)-based influential nodes&#x2019; detection and ranking are proposed for the designed ML. Furthermore, multiple-criteria decision-making (MCDM) methods, such as the analytic hierarchy process (AHP), the technique for order of preference by similarity to ideal solution (TOPSIS), fuzzy AHP, fuzzy TOPSIS, and the analytic network process (ANP), are explored for the proposed ML for identification and ranking of the influential nodes. The susceptible&#x2013;infected&#x2013;recovered (SIR) model is used to evaluate the proposed work. In addition to this, statistical analysis is performed using the Pearson correlation, Kendall&#x2019;s correlation, Spearman&#x2019;s correlation, and the Friedman test on the ranks generated by different methods, which shows that the results generated by different proposed methods are consistent. Furthermore, the performance of the proposed method is compared with state-of-the-art approaches.

Thermal deformation prediction for spindle system of machining center based on multi-source heterogeneous information fusion

Thermal deformation prediction for spindle system of machining center based on multi-source heterogeneous information fusion

Combination of deep learning with representation learning in X-ray prohibited item detection

During X-ray inspection detection, a detector converts the collected X-rays from objects into electrical signals, which are then transmitted to a computer for image processing and analysis. From the aspect of digital image processing, detection tasks mainly focus on data processing and transformation to identify valuable features, which make the algorithms more effective. The consistent requirement for speed and accuracy in X-ray prohibited item detection is still not fully satisfied, especially in pictures obtained under special imaging conditions. For noisy X-ray images with heavy occlusion, a direct and suitable approach of representation learning is the optimal solution. According to our study, we realized that heterogeneous information fusion from different extraction approaches can be applied effectively to overcome this issue. We proposed two innovative algorithms to extract effective features of X-ray objects to significantly improve the efficiency of X-ray prohibited item detection. The brief model we proposed fuses the representations learned from the noisy X-ray images and outperforms the best model (DOAM-O) so far on OPIXray. Furthermore, the attention module we designed to select information on deep learning and representation strengthens the model; considering this, the model utilizes lesser time for both training and inference, which makes it easier to be trained on a lightweight computing device.

TLSEA: a tool for lncRNA set enrichment analysis based on multi-source heterogeneous information fusion.

Long non-coding RNAs (lncRNAs) play an important regulatory role in gene transcription and post-transcriptional modification, and lncRNA regulatory dysfunction leads to a variety of complex human diseases. Hence, it might be beneficial to detect the underlying biological pathways and functional categories of genes that encode lncRNA. This can be carried out by using gene set enrichment analysis, which is a pervasive bioinformatic technique that has been widely used. However, accurately performing gene set enrichment analysis of lncRNAs remains a challenge. Most conventional enrichment analysis methods have not exhaustively included the rich association information among genes, which usually affects the regulatory functions of genes. Here, we developed a novel tool for lncRNA set enrichment analysis (TLSEA) to improve the accuracy of the gene functional enrichment analysis, which extracted the low-dimensional vectors of lncRNAs in two functional annotation networks with the graph representation learning method. A novel lncRNA-lncRNA association network was constructed by merging lncRNA-related heterogeneous information obtained from multiple sources with the different lncRNA-related similarity networks. In addition, the random walk with restart method was adopted to effectively expand the lncRNAs submitted by users according to the lncRNA-lncRNA association network of TLSEA. In addition, a case study of breast cancer was performed, which demonstrated that TLSEA could detect breast cancer more accurately than conventional tools. The TLSEA can be accessed freely at http://www.lirmed.com:5003/tlsea.