Information Fusion Technology Research Articles

Multichannel concat-fusional convolutional neural networks

Because of the advantages of deep learning and information fusion technology, it has drawn much attention for researchers to combine them to achieve target recognition, positioning, and tracking. However, when the existing neural network process multichannel images (e.g., color images), multiple channels as a whole input into neural networks, which makes it hard for networks to fully learn information in R, G, and B channels of images. Therefore, it is not conducive to the final learning effect of the networks. To solve the problem, using different combinations of R, G, and B channels of color images for feature-level fusion, this paper proposes three fusion types as “R/G/B”, “R+G/G+B/B+R”, and “R+G+B/R+G+B/R+G+B” multichannel concat-fusional convolutional neural networks. Experimental results show that multichannel concat-fusional convolutional neural networks with fusional types of “R+G/G+B/B+R” and “R+G+B/R+G+B/R+G+B” achieve better performance than the corresponding non-fusional convolutional neural networks on different datasets. It shows that networks with fusion types of “R+G/G+B/B+R” and “R+G+B/R+G+B/R+G+B” can learn more fully information of R, G, and B channels of color images and improve the learning performance of networks.

Design and Optimization of a Decision Support System for Sports Training Based on Data Mining Technology

This paper presents an in-depth study and analysis of sports training decision support systems through data mining techniques, and designs a corresponding sports training decision support system for application in practical situations. It investigates the application of data mining techniques in solving the problem of optimal analysis of sports training indicators. The Apriori algorithm is used to find out the hidden correlations behind the data, while the correlation study applicable to college students’ physical fitness indicators is proposed and practiced, and the potential hidden relationships between the indicator data are found. The analysis results are further verified by the improved AP algorithm, FP algorithm, which provides important opinions for college physical education curriculum reform and youth physical culture development. Then, the Shi-Tomasi algorithm is used for feature point tracking and the support vector machine approach for human motion posture recognition. Considering that the collected action data contain a large amount of stationary action data, a threshold-based action segmentation algorithm is designed to extract useful action segments; after extracting the action segments, common action features are extracted from both statistical features, such as mean, variance, skewness, kurtosis, and physical features, such as acceleration and plantar pressure, to give a specific description of human daily actions. Thus, the fatigue of different postures is judged according to their number of occurrences and a preliminary assessment result is obtained. Then, the surface EMG data collected by the sensors are pre-processed to reduce noise, and the surface EMG signals are fused at the feature level based on the wavelet change algorithm, and the feature vectors are trained with BP neural networks to evaluate the fatigue of human muscle movements and obtain a preliminary evaluation result. Finally, by studying the application of information fusion technology at the decision level, the assessment results of the comprehensive human movement analysis are obtained using D-S evidence theory. In addition, a web platform is built on this basis to display and manage human exercise data and assessment results, and to facilitate the storage and query of historical exercise records.

Three-dimensional instantaneous orbit map for rotor-bearing system based on a novel multivariate complex variational mode decomposition algorithm

Full spectrum and holospectrum are homogeneous information fusion technology developed for the fault diagnosis of rotating machinery, which is extensively exploited in the analysis of the orbits of rotor-bearing systems. However, they are not adapted for non-stationary signals, nor can they be used for fusion analysis of vibrations of multiple bearing sections. By drawing inspiration from the multivariate variational mode decomposition (MVMD) and the complex-valued signal decomposition, we propose a method called multivariate complex variational mode decomposition (MCVMD). It can simultaneously extract the forward and backward components of multiple bearing sections and realize non-stationary complex signal decomposition of multiple bearing sections of the rotor. To achieve the visualization goal of condition monitoring, we propose the three-dimensional instantaneous orbit map (3D-IOM). It enables more features of shaft vibration of a rotor system to be displayed and offers a new way for the fusion analysis of vibration signals of multiple bearing sections of rotating machinery. Furthermore, making the most of the joint information, we also provide a high-resolution time-full spectrum (Time-FS) to display the forward and backward frequency components of multiple bearing sections. The effectiveness of the proposed method through both the simulated experiment and the real-life complex-valued signals is demonstrated in this paper.

E-Commerce Fraud Detection Model by Computer Artificial Intelligence Data Mining.

This study aims to identify e-commerce fraud, solve the financial risks of e-commerce enterprises through big data mining (BDM), further explore more effective solutions through Information fusion technology (IFT), and create an e-commerce fraud detection model (FDM) based on IFT (namely, computer technology (CT), artificial intelligence (AI), and data mining (DM). Meanwhile, BDM technology, support vector machine (SVM), logistic regression model (LRM), and the proposed IFT-based FDM are comparatively employed to study e-commerce fraud risks deeply. Specifically, the LRM can effectively solve data classification problems. The proposed IFT-based FDM fuses different information sources. The experimental findings corroborate that the proposed Business-to-Business (B2B) e-commerce enterprises-oriented IFT-based FDM presents significantly higher fraud identification accuracy than SVM and LRM. Therefore, the IFT-based FDM is superior to SVM and LRM; it can process and calculate e-commerce enterprises' financial risk data from different sources and obtain higher accuracy. BDM technology provides an important research method for e-commerce fraud identification. The proposed e-commerce enterprise-oriented FDM based on IFT can correctly analyze enterprises' financial status and credit status, obtaining the probability of fraudulent behaviors. The results are of great significance to B2B e-commerce fraud identification and provide good technical support for promoting the healthy development of e-commerce.

Application of Spatial Digital Information Fusion Technology in Information Processing of National Traditional Sports

The rapid development of digital informatization has led to an increasing degree of reliance on informatization in various industries. Similarly, the development of national traditional sports is also inseparable from the support of information technology. In order to improve the informatization development of national traditional sports, this paper studies the fusion process of multisource vector image data and proposes an adjustment and merging algorithm based on topological relationship and shape correction for the mismatched points that constitute entities with the same name. The algorithm is based on topological relationship. The shape of the adjustment and merging algorithm is modified, and finally, a set of national traditional sports information processing system is constructed by combining the digital information fusion technology. Experiments have proved that the method of national traditional sports information processing based on digital information fusion technology proposed in this paper is effective and can play a good role in the digital development of national traditional sports.

Response time evaluation of mobile applications combining network protocol analysis and information fusion

The response time of a mobile application (app), especially a mobile stock trading app, is an important factor that affects customer satisfaction. However, it is considerably difficult to accurately evaluate the performance of mobile apps owing to numerous real-world settings such as operating systems, hardware, and test environments. This paper presents a novel method to evaluate the response time of mobile apps on different mobile phones through combining network protocol analysis and information fusion. To make the data collected from the mobile app more reliable and credible, we recruited some volunteers to collect data on their mobile phones. Then we used the network protocol analysis method to obtain the response time of the mobile app on a mobile phone. Next, we adopted information fusion technology using the rank-score characteristic function to evaluate the response time of mobile apps on different mobile phones. Experiments were conducted to evaluate our approach on three types of mobile apps. The results showed that the proposed method can effectively evaluate the response time of mobile apps with low cost.

Analysis of the Effect of Branched Chain Amino Acids on Muscle Health Information of Swimmers Based on Multisensor Fusion and Deep Learning.

Swimmers must fully mobilize the muscles of the whole body during exercise, and it is necessary to study the protection of swimmers from muscle damage. Now, muscle damage is increasing year by year, and more athletes are affected. Therefore, studying the causes of muscle injuries and exploring more effective treatments have become important research topics in the field of sports medicine. This study is mainly based on deep learning to analyze the protective effect of branched-chain amino acids on swimming athletes' muscle injury. Due to the complex and changeable environment and the interference of unknown factors, a single sensor cannot meet the needs of obtaining information. Therefore, people have developed the technology of multisensor information fusion to obtain enough information. Multisensor data fusion technology can synthesize the information of each sensor and then obtain more comprehensive and accurate decision-making information. This study is mainly based on multisensor fusion and deep learning to analyze the impact of branched chain amino acids on Swimmers' muscle health information. Finally, two experiments were designed in this article. The first experimental result showed that the pain level of the experimental group who took BCAA supplements was 19% lower than that of the control group that did not take the BCAA supplement within three days after exercise. The results of the second experiment show the following: after exercise, the creatine kinase activity value of the experimental group taking BCAA supplement was 4.38 ± 1.45, and the creatine kinase activity value of the control group taking placebo was 5.42 ± 2.12. It proves that BBCA can protect muscle damage by reducing the activity of creatine kinase.

Fault Diagnosis Method for Industrial Robots Based on DBN Joint Information Fusion Technology.

Aiming at the problems of the traditional industrial robot fault diagnosis model, such as low accuracy, low efficiency, poor stability, and real-time performance in multi-fault state diagnosis, a fault diagnosis method based on DBN joint information fusion technology is proposed. By studying the information processing method and the deep learning theory, this paper takes the fault of the joint bearing of the industrial robot as the research object. It adopts the technique of combining the deep belief network (DBN) and wavelet energy entropy, and the fault diagnosis of industrial robot is studied. The wavelet transform is used to denoise, decompose, and reconstruct the vibration signal of the joint bearing of the industrial robot. The normalized eigenvector of the reconstructed energy entropy is established, and the normalized eigenvector is used as the input of the DBN. The improved D-S evidence theory is used to solve the problem of fusion of high conflict evidence to improve the fault model's recognition accuracy. Finally, the feasibility of the model is verified by collecting the fault sample data and creating the category sample label. The experiment shows that the fault diagnosis method designed can complete the fault diagnosis of industrial robot well, and the accuracy of the test set is 97.96%. Compared with the traditional fault diagnosis model, the method is improved obviously, and the stability of the model is good; the utility model has the advantages of short time and high diagnosis efficiency and is suitable for the diagnosis work under the condition of coexisting multiple faults. The reliability of this method in the fault diagnosis of the joint bearing of industrial robot is verified.

Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion.

In recent decades, although the research on gait recognition of lower limb exoskeleton robot has been widely developed, there are still limitations in rehabilitation training and clinical practice. The emergence of interactive information fusion technology provides a new research idea for the solution of this problem, and it is also the development trend in the future. In order to better explore the issue, this paper summarizes gait recognition based on interactive information fusion of lower limb exoskeleton robots. This review introduces the current research status, methods, and directions for information acquisition, interaction, fusion, and gait recognition of exoskeleton robots. The content involves the research progress of information acquisition methods, sensor placements, target groups, lower limb sports biomechanics, interactive information fusion, and gait recognition model. Finally, the current challenges, possible solutions, and promising prospects are analysed and discussed, which provides a useful reference resource for the study of interactive information fusion and gait recognition of rehabilitation exoskeleton robots.

Application of Neural Network Based on Multisource Information Fusion in Production Cost Prediction

Production cost forecasting is an important basis for cost accounting, cost decision-making, and cost planning. It is the scale necessary to reduce product costs and an important way to enhance enterprise competitiveness and improve system benefits. A neural network based on multisource information fusion is a manifestation of integrated internal knowledge. By learning to integrate multiple sources of information, it is easier to understand cognitive thinking and integrate the complex relationships of uncertain regions into regular signals. Fusion prediction does not need to understand the specific mechanism of the process but can fully approximate various nonlinear functional relationships determined by input and output with the continuous update of its internal weights. This paper mainly studies the application of neural network based on multisource information fusion in production cost prediction, analyzes the technology of multisource information fusion, and proposes a method of applying multisource information fusion theory to BP neural network and RBF network. Experiments have proved that through the comparison of the results of the BP neural network and the RBF network, for the six cost categories, compared with the BP neural network, the prediction results of the RBF network are closer to the true value, and they all show higher prediction capabilities. Among them, the error of the RBF network in predicting the total salary of the current month is 0.01004. The performance of the RBF network model is better than that of the BP neural network model.

Structure Strength Dynamic Strain Measurement Acquisition System Based on Data Fusion

Strain measurement technology plays an irreplaceable role in measuring the force change characteristics of mechanical components and monitoring important engineering structures. In structural health monitoring, dynamic stress and strain testing is the main way to determine its overall fatigue, and excessive stress will seriously affect the strength of the structure, causing the structure to deform or even break, which will affect the reliability of the experiment and the use of workpieces, so it is of great significance to carry out dynamic stress and strain tests on the structure. Resistance strain measurement technology is widely used in the civil engineering industry, but the principles of modern physics technology are unknown to many practitioners. Data fusion technology or information fusion technology is also called multisensor fusion technology. Using computer technology to automatically analyze and integrate the data processing process of the detection data from multiple sensors according to time sequence and certain criteria, it can complete the required decision-making and judgment.

Interactive Art Design Based on Intelligent Sensors and Information Fusion Technology

In the era of rapid development of information technology, the application of smart sensors is becoming more and more extensive. All measurement and control equipment need to obtain raw data through sensors, and machines can also obtain various information through sensors. And this information, especially from the perspective of reliability, accuracy, and intelligent interaction, requires higher interactivity in people’s lives. In order to solve the problem that the existing interactive design is difficult and not intelligent enough, and the information fusion is not uniform enough, the results of the design have various deviations. This article intends to design through the use of smart sensors and information fusion technology to make an improvement to its interactive art design.

Dynamic Invariant-Specific Representation Fusion Network for Multimodal Sentiment Analysis.

Multimodal sentiment analysis (MSA) aims to infer emotions from linguistic, auditory, and visual sequences. Multimodal information representation method and fusion technology are keys to MSA. However, the problem of difficulty in fully obtaining heterogeneous data interactions in MSA usually exists. To solve these problems, a new framework, namely, dynamic invariant-specific representation fusion network (DISRFN), is put forward in this study. Firstly, in order to effectively utilize redundant information, the joint domain separation representations of all modes are obtained through the improved joint domain separation network. Then, the hierarchical graph fusion net (HGFN) is used for dynamically fusing each representation to obtain the interaction of multimodal data for guidance in the sentiment analysis. Moreover, comparative experiments are performed on popular MSA data sets MOSI and MOSEI, and the research on fusion strategy, loss function ablation, and similarity loss function analysis experiments is designed. The experimental results verify the effectiveness of the DISRFN framework and loss function.

SM-PageRank Algorithm-Based User Interest Model for Mobile Smart Tourism Platform

Smart tourism, also known as smart tourism, actively captures tourism activities, tourists, tourism economy, tourism resources, and other information through mobile Internet and mobile terminal Internet of things devices and emerging technologies such as cloud computing and Internet of things. In order to release the intelligent tourism information in time, let the masses know the information in time, and adjust the work and tourism plan in time, this paper proposes SM-PageRank algorithm and secondary ranking based on user interest model, in order to study the accuracy of tourism information retrieval. The methods used in this paper include the principle of three weighted information fusion algorithms, LBS technology, and the design of intelligent tourism system. The function of information fusion algorithm is to find the global optimal solution for travel routing. LBS technology collects real-time tourism information through some entity sensors. Through information retrieval experiment and fusion technology solution experiment, the results show that the SM-PageRank algorithm and the secondary sorting based on user interest model proposed in this paper improve the average accuracy by 20.1% compared with the traditional algorithm and 2.6% compared with Google search. The Internet of things fusion algorithm gives a line planning set with standard deviation of 0.4 for the set of travel days with standard deviation of 1.92.

Monitoring and Analysis of Physical Exercise Effects Based on Multisensor Information Fusion

In this paper, multiple sensors are used to track human physiological parameters during physical exercise, and data information fusion technology is used to extract useful information for monitoring and analyzing the effects of physical exercise. This paper explores the interaction and developmental dynamics of multisensor information fusion technology and physical exercise data monitoring based on the interrelationship and interpenetration between the two. The design ideas and principles that should be followed for the software designed in this study are discussed from the perspective of the portable design of measurement instruments and the perspective of multisensor information fusion, and then, the overall architecture and each functional module are studied to propose a scientific and reasonable design model. The general methodological model to be followed for the development of this resource is designed, and the basic development process of the model is explained and discussed, especially the requirement analysis and structural design, and how to build the development environment are explained in detail; secondly, based on the course unit development process in this model, we clarify the limitations of the system through meticulous analysis of the measurement results, which provides a solid foundation for the next step of system optimization. Finally, with a focus on future development, we elaborate on the potential possible role and development trend of multisensor information fusion in the future period. In this paper, we propose to apply the multisensor data fusion algorithm to the monitoring, analysis, and evaluation of the effect of physical exercise, by collecting multiple human physiological parameters during physical exercise through multiple sensors and performing data fusion processing on the collected physiological parameters to finally evaluate the effect of physical exercise.

A SINS/GNSS/VDM Integrated Navigation Fault-Tolerant Mechanism Based on Adaptive Information Sharing Factor

Autonomous driving vehicle is a kind of unmanned vehicle on the ground. Navigation and positioning technology is becoming an indispensable part of practical applications, especially in intelligent transportation systems and military fields. In this study, multi-source information fusion technology plays a necessary role in enhancing the quality and reliability of navigation and positioning. In the actual complex environment, the accuracy requirements of vehicle navigation and positioning in the case of nonlinear systems and non-Gaussian distribution are fully considered. In this paper, a new adaptive sharing factor integrated navigation information fusion technology scheme is designed. From a geometric point of view, the VDM is studied, and a new VDM observation information that fully considers the turning process of the vehicle is established. It combines SINS, GNSS and VDM multi-source sensors, a VDM and GNSS measurement model based on non-integrity constraints is developed. The performance of the local filter is adaptively reflected by the F-norm adaptive information sharing factor algorithm established based on the variance. At the same time, the development process of a federated filter fault diagnosis algorithm based on Unscented Kalman Filter (UKF) is reported, which has fault diagnosis and information recovery functions. The independent characteristics of the sub-filters are considered, and a fault-tolerant mechanism for state chi-square detection is established. Both GNSS and VDM are used as observation sources to improve the accuracy of low-cost SINS. This strategy is implemented on the basis of Carsim professional vehicle dynamics software. The evaluation test indicated that the algorithm has high navigation accuracy, high fault tolerance and guaranteed system reliability, which is verified in this paper.

A Novel Information Fusion Method of RFID Strain Sensor Based on Microstrip Notch Circuit

According to the current development situation, the application of chipless RFID sensors in structural health monitoring has the disadvantages of difficult detection, poor effect, and single design function, limiting the further development of this technology. Therefore, a new type of RFID strain sensor that realizes the compact structure and separated function of miniaturized chipless RFID coded tags is proposed. The integrated disc monopole antennas make wireless measurement possible. Through single-parameter strain simulation analysis, six characteristic parameters with good linearity are determined. The time sequences data enhancement algorithm and the background noise data enhancement algorithm are used to expand the training data set. Then the BP neural network is utilized for data fusion, and the training error finally converges to 0.0005. A comparison experiment between wired and wireless is designed, and the wireless experiment is optimized through the wired experimentation. The results of wireless measurement experiments show that combined with multi-parameter information fusion technology, the average error between the proposed sensor and the actual strain is 6.04%, and the minimum error is 0.25%. The multi-parameter fusion monitoring method of the strain sensor corrects the error of single-parameter measurement, and its accuracy and robustness are improved.

Study and Application on Big Data Information Fusion System Based on IoT

In our study, we first illustrated information fusion technology and Internet of Things (IoT), and then we built farmland IoT information collection platform on the basis of ZigBee technology and agricultural sensors to collect climate data including air pressure, temperature, soil water content, light intensity, and relative humidity. Finally, prediction model was used to evaluate crop growth condition. Results show that temperature increases with time and reaches the maximum at 13:00 PM. But relative humidity decreases with time and reaches the maximum at 3:30 AM. Light intensity presents a straight trend with time and reaches the maximum at 13:30 PM. CO2 concentration presents a fluctuation trend with time and reaches high point at 7:00 AM. Prediction model presented a high accuracy outcome with 99% accuracy in training data and 100% in testing set. Therefore, we can conclude that big data fusion technology on the basis of IoT has a good future in many fields excepting agriculture crop, which is also an irreversible trend.

The Strategy of Information Fusion Technology to Improve Micro-course Design and Online Education in the Context of Smart Education

The Strategy of Information Fusion Technology to Improve Micro-course Design and Online Education in the Context of Smart Education

Enterprise Performance Management following Big Data Analysis Technology under Multisource Information Fusion

With the development of the Internet, big data collection, analysis, and processing are flourishing. The study aims to explore the performance management of power enterprises based on multisource information fusion and big data. First, the application of big data to enterprise management is analyzed. Second, the multisource information fusion method is introduced, and the multisource information fusion model is implemented. Finally, the fuzzy language algorithm is used to evaluate the performance management of power enterprises. The results show that the proposed multisource information fusion algorithm has high efficiency in evaluating enterprise performance management. The evaluation result is closer to the actual value than other algorithms, and the maximum acceleration ratio can reach 7, indicating that the algorithm is suitable for processing big data. The performance evaluation shows that enterprises pay most attention to the quality of their products; the weight reached 0.414; and the index weight difference is large. This study promotes the reform of the performance management mode and improves the management efficiency of enterprises through the proposed enterprise performance management strategy. It provides a great reference for the application of big data and information fusion technology.