Multi-source Information Fusion Research Articles

RETRACTED ARTICLE: Application of English education big data system based on multi-source information fusion and machine learning

For administrators in the field of education, the data generated in the actual education process is difficult to achieve effective management due to its large amount of information, complex structure and multiple types. However, in the actual construction of education informatization, it is impossible to explore the laws and development trends of education information only by manual analysis, so it is difficult to obtain effective concepts and relevant views. College education administrators cannot fully optimize the process of teaching diagnosis and reform through education informatization. Based on this background, this research introduces machine learning technology to achieve technical support for the integrated application of educational data. This paper focuses on the design and construction of an English teaching management data analysis system for college teaching, which combines machine learning technology with multi-source information technology. The system is mainly composed of hardware layer, data layer, service layer and application layer, which can assist teaching and realize learning career planning, faculty authority management, curriculum data modification, teaching resource allocation and scientific research review. The simulation results show that this big data system has high processing efficiency, and is superior to other algorithms in terms of mean value and FVDF extreme value, so it can effectively assist English teaching management. This paper completes the design of English education big data system by introducing machine learning and multi-source information fusion into the field of education.

Flame reconstruction of hydrogen fueled-scramjet combustor based on multi-source information fusion

Using various types of data to extract different features and reconstruct flame spontaneous image, the rapid acquisition of flame propagation data and automatic identification of the combustion state in a scramjet combustion chamber in confined space can be promoted. A reconstruction model based on multi-source information fusion is proposed that can effectively reconstruct a flame self-luminous image based on the combustor wall pressure and schlieren image data. A dense convolutional neural network is used to construct and reconstruct the model and train the network based on the experimental datasets. The ground pulse combustion wind tunnel test is conducted under the condition of hydrogen fuel self-ignition using the variation rules of different injection pressures. The evolution process of the combustor wall pressure, schlieren image, and flame self-luminescence image is obtained over time and a dataset is constructed. The reconstruction accuracy of the flame spontaneous luminescence image using the schlieren image and the fusion of the schlieren image and wall pressure under different injection pressure is comprehensively analyzed. Moreover, both qualitative and quantitative results show that the prediction accuracy of flame spontaneous illumination based on multi-source information fusion well matches the experimental result, and the flame boundary, flame head region, flame area, and burning intensity are all well reconstructed. The average peak signal to noise ratio reached 31.57 dB, average structural similarity was 0.939, average mean square error was 0.979, correlation coefficient was 0.0013, and intersection over union reached 0.876.

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.

Innovative reform strategy of college music education art practice based on information fusion technology

Abstract This paper is designed to improve the efficiency and quality of teaching and learning efforts and to meet the multidimensional information needs of music educators and learners. From the perspective of information fusion technology, we build a multi-source information fusion system based on the basic system architecture. The maximum likelihood, least squares, and D-S algorithms were used to calculate and construct the sampling evaluation index equations. A survey of the teaching methods used in music teaching in colleges and universities was conducted to analyze three strategies needed for college education reform: innovative teaching methods, strengthening teachers’ music competence and quality, and establishing feedback mechanisms for college music teaching evaluation. The combined survey results show that 72% of music teachers believe their teaching skills have been greatly enhanced, while students’ willingness to take music lessons after using information integration technology has all risen to over 80%.Therefore, in the process of music teaching in colleges and universities, the integration of information integration technology can not only improve students’ interest in learning but also improve teachers’ teaching ability, which provides a certain degree of reference for the reform of music teaching in colleges and universities.

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 Sequence and Network Embedding Method for Bus Arrival Time Prediction Using GPS Trajectory Data Only

Recently, achieving high accuracy in bus arrival time prediction has become a hot research topic. Extensive research has been conducted around this topic, among which fusion of multi-source heterogeneous information is the mainstream. However, using only GPS trajectory data to predict the arrival time of buses is meaningful for small cities since they do not have easy access to external sources of data. Therefore, we propose a novel deep-learning model based on sequence and network(i.e., graph) embedding (SGE-net) which adopts only GPS trajectories as input and achieves accurate bus arrival time prediction without external data. Specifically, the sequence embedding part, composed of the Echo State Network (ESN), the Auto-Encoder (AE), and the k-means cluster method, is constructed to extract the complex and potential sequence patterns from a large number of trajectory sequences, and cluster different patterns of trajectories. Then, the network embedding part with the node2vec algorithm is proposed to capture the spatial correlation among bus stops on different lines under the condition of limited data. Finally, a fusion prediction part based on the specifically designed parallel Gated Recurrent Unit (GRU) networks is introduced to combine sequence and network embedding vectors with hidden outputs of the GRUs and then get the prediction. Both real-world offline and online experiments are conducted based on GPS data of bus lines in QiTaiHe city and ShiJiaZhuang city. The results clearly show that the SGE-net outperforms the baseline models, and validate the effectiveness and robustness of the SGE-net.

Key Technologies of Marine Environment Safety

An increasing number of marine disasters and maritime accidents occurred frequently in coastal area of China which lead to giant loss of life and property in recent years. In response to those disasters and accidents, China had put enormous effort into ocean observation, disaster prediction and forecasting, risk assessment and informationization, which lead to the preliminary improvement on safety capability of marine environment. In this paper, China’s marine environment safety situation are analyzed for concluding the insufficient research on Multi-source Information Aggregation, Assistant Decision-Making, Emergency Dispatching and relevant informationization. Hence plenty of key technologies as Multi-source Information Aggregation and Fusion, Dynamic Risk Assessment, Scenario Deduction, Command Coordination and Information Platform Construction Technology are also analyzed for feasible implementation mean and possible development direction..

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.

Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian Network

The landing phase of an airdrop process is prone to accidents, and thus, it is important to assess the landing reliability for an airdrop system. However, full field tests to assess the reliability are unacceptable due to their cost and the time required. As such, it is necessary to estimate the reliability in the design stage. To address this problem, a method based on vine-Bayesian Network (vine-BN) is proposed to assess the landing reliability by fusing multisource information. First, the network structure is determined by the relationship between data of simulation or ground tests and failure modes. Then, nodes are defined as random variables on [0, 1] based on the definition of the performance metric. Finally, the dependence between nodes is quantified by expert opinions. To illustrate the effectiveness of the method, a particular ground test or simulation is chosen to establish a network for a typical heavy cargo airdrop system (HCADS). Forward and backward propagation is carried out on the network. The forward analysis predicts the landing reliability in the design stage through multisource information fusion. Beta distribution is applied to fit the fusion result, so Bayesian inference is made to perform field test times decision-making. The backward analysis works to identify the key performance metrics related to landing reliability. The results and analysis manifest that vine-BN is feasible for fusing multisource information. Through the network, the reliability of the current design can be predicted effectively, and the field test times can be remarkably reduced. This method plays a crucial role in airdrop system design and reducing test time and labor.

Acoustic Characterization for The Feeding Activities of Haliotis discus Hannai

In order to analyze the sound production mechanism and the acoustic characteristics of Haliotis discus hannai during feeding, this paper proposes a multi-source information fusion approach combining passive acoustics with videos. In the experiments, abalones with a shell length of 60 ± 2.7 mm were divided into two groups: Group A was fed with fresh macro algae Gracilaria lemaneiformis as food once each day; Group B was placed on a small amount of sand as impurities at the bottom of the tank. As control groups, Group C did not have abalone or food and Group D did not have abalones but food was added. The eating acoustic signals of abalone were mainly concentrated in the frequency range between 9.49 kHz and 44.36 kHz, wherein the peak frequency is 37.86 ± 2.55 kHz, with the maximum energy −66.43 ± 5.17 dBm/Hz. Each pulse sequence is with a duration of 119.12 ± 70.51 ms and consists of several sub-pulses. Nearly 70% of the pulse sequences consist of 1~2 sub-pulses and the duration of the pulse containing one sub-pulse is 42.62 ± 19.72 ms. The eating rate was kept at 0.61 ± 0.04 times/min at the beginning and was decreased significantly to 0.48 ± 0.08 times/min after 60 min. Note that the characteristic analysis of abalone acoustic signals during feeding are first reported in this manuscript to the best of our knowledge, and this paper also demonstrates that the sound of abalone is produced by scraping and grinding food with radula. Because the eating rate decreases with the reduction in the abalone’s level of hunger, the results may be used as an acoustic indicator of feeding strategy for the abalone aquaculture industry.

Surface topography of cylindrical precision grinding based on multi-source information fusion

In this study, spindle precision bearings and rotary vector (RV) reducer thin-walled bearings were taken as research objects. Based on the analysis of the metamorphic layer of bearings, the multidimensional research on the surface topography of parts by multisource information fusion (grinding force, grinding temperature, grinding strain, grinding vibration, and grinding acoustic emission (AE)) was put forward. The research shows that there are “dark layers” in the outer ring and inner ring of spindle precision bearings, and “white layers” and “dark layers” in the outer ring and inner ring of RV reducer thin-walled bearings. Feed speed has the greatest influence on grinding force, wheel speed, and grinding depth have the greatest influence on grinding temperature, wheel speed has the greatest influence on grinding strain, feed speed, and wheel speed have the greatest influence on grinding vibration, and workpiece speed has the greatest influence on grinding AE. When the grinding force is minimum ( x-axis: 1.3 N, y-axis: 0.9 N); the grinding temperature is highest (110.8°C), the grinding strain is maximum (46.6%), the grinding vibration is maximum (vibration range: 400 m/s2–400 m/s2) and the grinding AE is maximum (variation range: 2 V–1 V), the surface topography is most prominent (Ra: 0.97 μm). Grinding wheel speed and grinding depth have the greatest influence on surface topography, followed by feed speed, while workpiece speed has less influence.

Intelligent Parking Control Method Based on Multi-Source Sensory Information Fusion and End-to-End Deep Learning

To address the challenges of inefficient intelligent parking performance and reduced efficiency in complex environments, this study presents an end-to-end intelligent parking control model based on a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) architecture incorporating multi-source sensory information fusion to improve decision-making and adaptability. The model can produce real-time intelligent parking control decisions by extracting spatiotemporal features, including comprehensive 360-degree panoramic images and ultrasonic sensor distance measurements. To enhance the coverage of real-world environments in the dataset, a data collection platform was developed, leveraging the PreScan simulation platform in conjunction with actual parking environments. Consequently, a comprehensive parking environment dataset comprising various types was constructed. A deep learning model was devised to ameliorate horizontal and vertical control in intelligent parking systems, integrating Convolutional Neural Networks and Long Short-Term Memory in a parallel configuration. By meticulously accounting for parking environment characteristics, sliding window parameters were optimized, and transfer learning was employed for secondary model training to fortify the prediction accuracy. To ascertain the system’s robustness, simulation tests were performed. The ultimate results from the actual environment experiment revealed that the end-to-end intelligent parking model substantially surpassed the existing approaches, bolstering parking efficiency and effectiveness in complex contexts.

Three-Dimensional Point Cloud Data Pre-Processing for the Multi-Source Information Fusion in Aircraft Assembly

Wing-body assembly is a key part of aircraft manufacturing, and during the process of wing assembly, the 3D point cloud data of the components are an important basis for attitude adjustment. The large amount of measured point cloud data and the obvious noise affect the quality and efficiency of the final assembly. To address this problem, research on the pre-processing method of the component point cloud data is carried out. Firstly, a feature-enhanced point cloud resampling method is proposed to preserve key features such as part contours in the resampling process. Then, a multi-scale point cloud data noise filtering method is proposed, which can effectively filter out the outliers. The experimental results show that the proposed method improves the speed and accuracy of the subsequent point cloud analysis effectively and is successfully applied to the assembly process of a large passenger aircraft, laying the foundation for high-quality assembly.

The influence of the internal structure of loaded composite coal-rock on the variation characteristics of electromagnetic radiation (EMR) signal

In this study, authors analysed the distribution law of the loaded coal-rock internal current field, and then derived a theoretical model of the loaded composite coal-rock with fracture. Then, the influence of fracture state in coal-rock on the time-frequency characteristics of the EMR signal was studied, and the relationship between the EMR characteristics and the coal-rock states was summarized and verified by uniaxial experiments. The results showed that: the superposition of multiple micro-current sources in the loaded coal-rock leads to changes in the EMR, and the properties of the internal fracture of the coal-rock affect the distribution of the internal current field, which causes the external EMR intensity to exhibit a characteristic of “slow increase - slow increase - steady increase - slow increase - rapid decrease” with time. In addition, the proportion of internal charged fractures is highly correlated with the degree of damage, and there is a quadratic or linear fit between the internal charged fractures and the EMR intensity at each stress stage, with a fit above 93%. Meanwhile, the frequency-domain features contain more information than the time-domain's, and the more severe the damage to the coal-rock, the more complex the EMR spectrum measured from the outside will be. When the main frequency is around 5 kHz, the deformation of the coal-rock is mainly elastic, but when the main frequency is around 20 kHz, the damage is mainly plastic. On this basis, loaded coal-rock could be divided into six loading stages from I to VI according to EMR characteristics, and the EMR intensity, main frequency bandwidth, energy distribution characteristics and spectrum composition of each stage are different, which are related to the coal-rock's internal fracture state. Moreover, the experimental results are consistent with the theoretical and simulation results. These results can help improve the coupling theory between coal-rock internal structure and EMR, and have theoretical and reference value for the study of coal and rock dynamic disaster prevention methods based on multi-source information fusion.

Multi-Source Information Fusion Technology and Its Application in Smart Distribution Power System

Compared to traditional measurement devices, the micro-synchrophasor measurement unit (D-PMU or μPMU) in the distribution power system has great differences in data acquisition frequency, data format, data dimension, time-stamped information, etc. Hence, it is imperative to research the integration mechanism of heterogeneous data from multiple sources. Based on the analysis of the current technology of multi-source information fusion, this paper proposes a novel approach, which considers two aspects: the interoperability of multi-source data and the real-time processing of large-scale streaming data. To solve the problem of data interoperability, we have modified the model of D-PMU data and established a unified information model. Meanwhile, an advanced distributed processing technology has been deployed to solve the problem of real-time processing of streaming data. Based on this approach, a smart distribution power system wide-area measurement and control station can be established, and the correctness and practicality of the proposed method are verified by an on-field project.

GEJS: A Generalized Evidential Divergence Measure for Multisource Information Fusion

Multisource information fusion (MSIF) technologies play an important role in various fields and practical applications. As a useful methodology to represent and handle uncertain information, the Dempster–Shafer evidence theory has been broadly used in many fields of MSIF. In evidence theory, however, Dempster’s combination rule (DCR) may result in counterintuitive results when fusing highly conflicting evidence. To address this issue, in this article, a novel MSIF method based on a newly defined generalized evidential divergence measure among multiple sources of evidence is proposed for decision making. Specifically, we first design a new generalized evidential Jensen–Shannon (GEJS) divergence to measure the conflict and discrepancy among multiple sources of evidence. The proposed GEJS divergence has three main characteristics, which are beneficial for evidential divergence measurement: 1) it measures the divergence among multiple sources of evidence, not just two, and thus provides a more generalized framework; 2) it considers different weights of multiple sources of evidence to measure divergence among them, which is desirable for application requirements; and 3) it considers the cardinality of propositions of multiple sources of evidence to measure divergence by considering features of evidential subsets. Given these advantages of GEJS, an appropriate weight for each source of evidence can be obtained. Next, according to the corresponding weights, we amend the bodies of evidence to generate a weighted average evidence. DCR is then used to fuse the weighted average evidence, and the final result is used to support decision making. Finally, we present a case study of fault diagnosis and a sensitivity analysis to demonstrate that the proposed MSIF is effective and robust for addressing conflicting situations compared to related works. Additionally, an application of classification validates the practicability of the proposed MSIF with a good decision level.

Key Technology Research of Autonomous Attack Tactical Missile

The autonomous attack tactical missile is not only an important development direction of the army’s precision strike equipment in the future, but also the only way to achieve spanning development of the army’s anti-tank missiles. With the advancement of technological progress, the technical breakthroughs made in the fields of multi-source information fusion, autonomous identification, autonomous guidance and controllable damage will be directly applied to the autonomous attack tactical missile to realize autonomous attack. They will lead the army’s equipment to develop from informatization to autonomy and intelligence. Through the combat capacity design for autonomous attack tactical missiles, we put forward the three-ring design method, constructed the combat capacity support elements, sort out the main technical characteristics of autonomous attack level by level, summarized the key technologies from “reconnaissance-strike-evaluation”, the all-element kill chain of autonomous attack, and proposed solutions that may represent the future development direction based on the current research status at home and abroad.

ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understanding

Spoken language understanding (SLU) plays a crucial role in the performance of dialogue systems. It usually includes slot filling and intent detection (SFID) tasks aiming at semantic parsing of utterances. At present, researchers focus mainly on English SLU tasks, while such investigations on Chinese utterances are not sufficient. In this paper, we first propose a reading comprehension based multi-source information fusion network, called ReCoMIF for Chinese SFID tasks by transforming the SLU task into a multi-turn question answering procedure comprising multiple choice for intent detection and span extraction for slot filling. Moreover, we present a TCM_CLS module with a concise architecture composed of TextCNN, MaxPooling, and feed forward network plus the [CLS] representation. Such three TCM_CLS modules are stacked in the proposed ReCoMIF network that can serve as sufficient integration of multi-source information originating from contexts, reading comprehension based queries, and hidden representations concerning intent and slot semantics. Finally, experimental results and ablation studies on three Chinese SLU datasets show that our proposed model can effectively fuse intent and slot information achieved by\\ state-of-the-art performance compared with other baseline methods.

Higher order belief divergence with its application in pattern classification

Multi-source information fusion is a sophisticated estimation process that generates a unified profile to assess complex situations. Dempster–Shafer evidence theory (DSET) is a practical theory in handling uncertain information in multi-source information fusion. However, highly-conflicting evidence may cause the combination rule of Dempster to provide counterintuitive results. Thus, how to effectively reconcile highly-conflicting evidence in DSET is still an open issue. To address this problem, in this paper, a novel belief divergence, higher order belief Jensen-Shannon divergence is proposed to measure the discrepancy between BPAs in DSET. The proposed higher order belief Jensen-Shannon divergence is the first method to dynamically measure the discrepancy between BPAs over the time evolution, i.e., to measure the discrepancy between BPAs with different time scale in the future. Besides, the proposed higher order belief Jensen-Shannon divergence has benefits from the perspective of measurement. It satisfies the properties of nonnegativeness and nondegeneracy, symmetry, and the triangle inequality of root form. Based on the proposed higher order belief Jensen-Shannon divergence, a novel multi-source information fusion algorithm is proposed. Eventually, the proposed algorithm is applied to a pattern classification experiment with real-world datasets.

Spatial Multi-Source Information Fusion Localization Algorithm in Non-Line-of-Sight Environments

In this paper, a high-accuracy localization problem under a complex non-line-of-sight (NLOS) condition is addressed by a new method that utilizes multiple NLOS paths to improve localization accuracy, as opposed to the traditional method of suppressing them. The spatial multi-path array fusion localization model is presented and analyzed, followed by an angle-of-arrival (AOA) and time-of-arrival (TOA) algorithm based on spatial multi-information fusion that seeks to improve localization accuracy. Multi-path of spatial signals, measurement of the multi-element antenna, and geographic environment information are integrated into the proposed method for localization optimization. Simulation experiments were carried out, and the results revealed that the proposed algorithm is capable of making full use of spatial multi-location information for localization, thus improving the accuracy of localization in a the NLOS environment effectively and increasing the locatable probability of complex environment localization applications.