Multi-source Heterogeneous Information Fusion Research Articles

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.

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.

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.

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

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.

A property perceived service quality evaluation method for public buildings based on multisource heterogeneous information fusion

The basic information of property perceived service quality (PPSQ) evaluation for public buildings is characterized by multisource heterogeneity, which cannot be achieved using the existing evaluation methods. Based on the classical linear programming technique for multidimensional analysis of preference (LINMAP), a multiobjective LINMAP model is constructed, and a new PPSQ evaluation method is proposed to solve the above problem. Constructing the multiobjective LINMAP model is the core of the new PPSQ evaluation method design. The proposed multiobjective LINMAP model includes the following: First, multisource heterogeneous evaluation information is collected by the generalized multi-index multi-scale (MIMS) method, and normalized by the TOPSIS method. Then, the LINMAP model is extended by the objective function, which is constructed with the minimum mean standard deviation (MMSD) between multiple information sources. Finally, the source weight and index weight are determined by the above model, the ranking order of each property service project is calculated, and the comprehensive value of the property service project is obtained. The example study verifies the feasibility and effectiveness of the multiobjective LINMAP model which is suitable for the new requirement of comprehensively and objectively evaluating PPSQ for public buildings.

T2V_TF: An adaptive timing encoding mechanism based Transformer with multi-source heterogeneous information fusion for portfolio management: A case of the Chinese A50 stocks

The Stock prediction has traditionally been an attractive and challenging topic for investors and researchers. Traditionally, people concern more about predicting stock prices, less effort has been made to recommend stocks for constructing a profitable portfolio. Moreover, in existing methods for stock prediction, most of them construct models based on one or two kinds of features like stock prices, news sentiment, or simple technical indicators, and disregard the importance of multi-source information fusion. In response to this concern, we propose a novel model T2V_TF based on deep learning by combining both Time2Vec and Transformer technologies. To introduce more diverse information into the proposed model, we further conduct an in-depth exploration of the extraction and fusion of multi-source heterogeneous information, which includes the trading data, time–frequency features, Alpha 101 and Alpha 191 technical indicators, and sentiment scores. Moreover, to increase the ranking ability of our model, T2V_TF takes the ranking loss as the loss function instead of the widely used regression loss. Finally, all the technological innovations of this paper are verified on the portfolio constructed based on the A50 stocks from the Chinese stock market. The experimental results demonstrate that our proposed T2V_TF can get better portfolio cumulative return, compared with other models including the multi-layer perceptron, the support vector machine, the gradient boosting decision trees, the long short-term memory model, and the attention-based long short-term memory model, and the Transformer.

A graph-based approach to multi-source heterogeneous information fusion in stock market

The stock market is an important part of the capital market, and the research on the price fluctuation of the stock market has always been a hot topic for scholars. As a dynamic and complex system, the stock market is affected by various factors. However, with the development of information technology, information presents multisource and heterogeneous characteristics, and the transmission speed and mode of information have changed greatly. The explanation and influence of multi-source and heterogeneous information on stock market price fluctuations need further study. In this paper, a graph fusion and embedding method for multi-source heterogeneous information of Chinese stock market is established. Relational dimension information is introduced to realize the effective fusion of multi-source heterogeneous data information. A multi-attention graph neural network based on nodes and semantics is constructed to mine the implied semantics of fusion graph data and capture the influence of multi-source heterogeneous information on stock market price fluctuations. Experiments show that the proposed multi-source heterogeneous information fusion methods is superior to tensor or vector fusion method, and the constructed multi-attention diagram neural network has a better ability to explain stock market price fluctuations.

Bearing fault diagnosis method based on multi-source heterogeneous information fusion

Bearing fault diagnosis is a critical component of the mechanical equipment monitoring system. In the complex and harsh environment in which bearings operate, the fault diagnosis approach of multi-source information fusion can extract fault features more stably and extensively than the traditional single-source fault diagnosis method. However, most existing multi-source fusion methods are in infancy, and there are a number of pressing issues to address, such as subjective elements having a significant impact, excessive data redundancy, fuzzy multi-source signal fusion strategy, and insufficient accuracy. As a result, a new multi-source fusion fault diagnosis method is proposed in this paper. First, the residual pyramid algorithm is utilized to fuse the acoustic and vibration signals of multiple spatial positions respectively and then form two fused acoustic and vibration signals. Second, two improved 2D-CNN are used to extract the fault features contained in the above two signals separately to form a multi-source fault feature set. Third, an AdaBoost algorithm with a dynamic deletion mechanism is designed to fuse multi-source fault feature sets and produce the fault diagnosis findings. Finally, six different experimental data sets are used to test the performance of the model. The results reveal that the model has better generalization, higher and more stable fault diagnostic accuracy, and stronger anti-interference capacity.

Credibility Assessment Method of Sensor Data Based on Multi-Source Heterogeneous Information Fusion.

The credibility of sensor data is essential for security monitoring. High-credibility data are the precondition for utilizing data and data analysis, but the existing data credibility evaluation methods rarely consider the spatio-temporal relationship between data sources, which usually leads to low accuracy and low flexibility. In order to solve this problem, a new credibility evaluation method is proposed in this article, which includes two factors: the spatio-temporal relationship between data sources and the temporal correlation between time series data. First, the spatio-temporal relationship was used to obtain the credibility of data sources. Then, the combined credibility of data was calculated based on the autoregressive integrated moving average (ARIMA) model and back propagation (BP) neural network. Finally, the comprehensive data reliability for evaluating data quality can be acquired based on the credibility of data sources and combined data credibility. The experimental results show the effectiveness of the proposed method.

Research on ontology-based situation understanding and decision-making approach for MASS

Aiming at the problems of miscellaneous navigation safety information and the difficulty of multi-source heterogeneous information fusion, a novel ontology-based collision avoidance decision making method is proposed in this paper. first of all, the structured ontology model of navigation situation is built on the basis of analysing the hierarchy and interactivity of various scene elements in driving scene and constructing the semantic model of entity class and binary attribute of scene elements, implement effective modelling of navigation scenarios. Secondly, the knowledge base of collision avoidance decision and online reasoning system are constructed based on the semantic expression of typical scene elements to realize the efficient utilization of navigation prior knowledge. Finally, the simulation experiment is carried out for the typical ship encounter scenarios in open water. The results show that the ontology method can effectively improve the cognition ability of Maritime Autonomous Surface Ships (MASS), and meet the requirements for real-time, safety and rationality of collision avoidance decision.

Attention enhanced long short-term memory network with multi-source heterogeneous information fusion: An application to BGI Genomics

The recent availability of enormous amounts of both data and computing power has created new opportunities for predictive modeling. This paper compiles an analytical framework based on multiple sources of data including daily trading data, online news, derivative technical indicators, and time–frequency features decomposed from closing prices. We also provide a real-life demonstration of how to combine and capitalize on all available information to predict the stock price of BGI Genomics. Moreover, we apply a long short-term memory (LSTM) network equipped with an attention mechanism to identify long-term temporal dependencies and adaptively highlight key features. We further examine the learning capabilities of the network for specific tasks, including forecasting the next day’s price direction and closing price and developing trading strategies, comparing its statistical accuracy and trading performance with those of methods based on logistic regression, support vector machine, gradient boosting decision trees, and the original LSTM model. The experimental results for BGI Genomics demonstrate that the attention enhanced LSTM model remarkably improves prediction performance through multi-source heterogeneous information fusion, highlighting the significance of online news and time–frequency features, as well as exemplifying and validating our proposed framework.

Rotor unbalance fault diagnosis using DBN based on multi-source heterogeneous information fusion

In the age of Internet of Things and Industrial 4.0, new advanced methods need to be proposed to analyse massive multi-source heterogeneous data from rotating machinery since traditional data analysis methods are difficult to mine features effectively and provide accurate fault results automatically. This paper proposes a rotor unbalance fault diagnosis method using deep belief network (DBN) to learn the representative features automatically and accurately identify fault states. Multi-source heterogeneous information composed with vibration signal and shaft orbit plots generated by raw displacement signals can fully exploit multi-sensor information in fault diagnosis. And multi-DBN model was introduced to deal with multi-source heterogeneous information fusion problem containing all fault information which could adaptively learn useful features through multiple nonlinear transformations compared with traditional approaches depending on time-consuming and labour-intensive manual feature extraction. The results indicate that the accuracy of classifying rotor unbalance fault states is up to 100% under proper parameters of DBN which significantly improves the effect of fault recognition and validates effectiveness using the proposed method.

A semantics-based approach to multi-source heterogeneous information fusion in the internet of things

Multi-source heterogeneous information fusion in the Internet of Things (IoT) is a type of information processing that attempts to provide a comprehensive, timely and accurate perception and feedback relative to physical things. Traditional multi-sensor data fusion can deal with the same type of data effectively. However, as new characteristics emerge in the IoT, interoperable service-oriented technologies are required to share real-world data among heterogeneous devices to integrate and fuse the multi-source heterogeneous IoT data. To address these issues, an architecture that can provide guidance for the development of IoT information fusion is required. We compare features of IoT data and information with an existing wireless sensor network and the Internet, which, to the best of our knowledge, is the first comparison of this kind. Then, we design a framework for multi-source heterogeneous information fusion in the IoT and use an experimental simulation platform to build an environmental monitoring system to assess the framework.

Improved GSO optimized ESN soft-sensor model of flotation process based on multisource heterogeneous information fusion.

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process.