Stability Analysis of Tunnel Rock Mechanical Parameters Based on Multi-Source Sensor Data Fusion

This paper aims to design a method of multi-source data fusion in multi-target collaborative navigation. First, the respective features of GPS/INS/RS/MO data in the navigation process are clarified. Then a multi-source data fusion method is designed including GPS/INS data fusion with adaptive Kalman filter, RS/MO data fusion with ranging table matching of observation targets, and joint adjustment with fused GPS/INS and RS/MO data. Finally, a simulation experiment is carried out to verify the improvement in positioning efficiency and precision. The results show that collaborative navigation based on multi-source data fusion can increase the stability and accuracy of the navigation service.

To improve the effect of college English teaching, combines multi-source data (MSD) fusion technology to build a college English blended teaching system. Moreover, through the research on the functions of smarter classrooms and the analysis and exploration of blended teaching, this paper designs a college English blended teaching system based on MSD fusion. In addition, this paper analyzes the bandwidth-tunable optical routing module of the interference-coupled series double-ring involved in multi-data fusion. Finally, this paper studies the working principle and structural design of the module, and constructs a college English blended teaching system structure based on MSD fusion. From the evaluation results, it can be seen that the college English blended teaching system based on MSD fusion proposed in this paper can effectively improve the quality of English teaching.

With the development of science and technology, various kinds of data become more and more, it is increasingly important to obtain knowledge from data. The field of environmental monitoring is in the same situation. How to quickly and effectively dig out the useful information in large-scale multi-source data has become the biggest problem facing the environmental field. This paper focuses on multi-source data fusion methods, data Mining methods and existing cases, and proposed a multi-source data fusion framework for atmospheric environmental monitoring. The proposed framework effectively enhances the knowledge acquisition ability of environmental monitoring big data.

The application of mobile intelligent terminal in the environment is very complex, and its own computing capacity is also very limited, so it is vulnerable to malicious attacks. The security classification of mobile intelligent terminals can effectively ensure the security of their use. Therefore, a security classification method for mobile intelligent terminals based on multi-source data fusion is proposed. The Boolean value is used to count the multi-source data of the mobile intelligent terminal, and the word frequency method is used to calculate the weight of the multi-source data of the mobile intelligent terminal. The D-S evidence theory is used to complete the multi-source data fusion of the mobile intelligent terminal and implement the multi-source data fusion processing of the mobile intelligent terminal. On this basis, the security level permission value of mobile intelligent terminal is calculated to achieve the security level division of mobile intelligent terminal based on multi-source data fusion. The experimental results show that the accuracy of mobile intelligent terminal security classification is higher than 96% and the classification time is less than 3.8 ms after the application of the proposed method. Therefore, the security level of mobile intelligent terminals after the application of this method is high, and the security performance of mobile intelligent terminals is strong, which can effectively improve the accuracy of security classification and shorten the time of security classification.

This study proposes a joint identification algorithm based on multi-source data fusion and Graph Neural Networks (GNN) to simultaneously detect adolescent internet addiction and psychological disorders. With the rapid development of internet technology, adolescent internet addiction has become increasingly prominent, and there exists a complex bidirectional relationship between internet addiction and psychological abnormalities. Traditional single-source data modeling methods struggle to accurately capture these intricate psychological and behavioral patterns. To address this, the study integrates multi-source heterogeneous data, including online behavior logs, psychological assessment data, physiological signals, and social relationships, to construct a Heterogeneous Graph Neural Network (H-GNN) model. Through a multi-task learning mechanism, the model simultaneously predicts the risks of internet addiction and psychological disorders. Experimental results demonstrate that the model outperforms traditional machine learning and deep learning models in terms of accuracy and AUC, effectively capturing the complex interactions between adolescent behaviors, psychological states, and social relationships. Ablation studies further validate the critical role of psychological features in prediction, and the model's potential in real-world applications is highlighted.

Since 1991, the Research and Development (R&D) group at Lockheed Martin Canada (LM Canada) has been developing and demonstrating the application of Multi-Source Data Fusion (MSDF) techniques for target tracking and identification within the Naval Command and Control (C2) for the HALIFAX Class frigates. The current C2 as well as the sensor suite of the HALIFAX Class were designed in the early 80s and based around a proprietary hardware architecture. The sensor data is pre-processed and provided to the C2 in real time. Considering the late 70s and 80s design of the sensor interfaces, where a data fusion within the C2 was not a commonality, not all of the information beneficial for a data fusion system is provided to the C2. After a sequence of simulation and modelling efforts for an MSDF capability within the HALIFAX Class C2, this project is now at a point where real data captured from a ship trial on the HALIFAX Class is being injected into the MSDF. This is an ongoing activity and a number of iterations are foreseen before the MSDF becomes part of HALIFAX Class C2. This paper provides a summary of lessons learned in this exercise.

GeoAI in terrain analysis: Enabling multi-source deep learning and data fusion for natural feature detection

Big Data Mining of Website Recruitment Data Based on Multi-source Data Fusion and Machine Learning Methods

This article brings together the advances of multisource and multitemporal data fusion approaches with respect to the various research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references. More specifically, this work provides a bird's-eye view of many important contributions specifically dedicated to the topics of pansharpening and resolution enhancement, point cloud data fusion, hyperspectral and lidar data fusion, multitemporal data fusion, and big data and social media. In addition, the main challenges and possible future research in each area are outlined and discussed.

Multi-source Sensor Data Fusion and Deep Reinforcement Learning Anomaly Detection Method for Power UAV Inspection

The uncertainties in quality evaluations of rock mass are embedded in the underlying multi-source data composed by a variety of testing methods and some specialized sensors. To mitigate this issue, a proper method of data-driven computing for quality evaluation of rock mass based on the theory of multi-source data fusion is required. As the theory of multi-source data fusion, Dempster–Shafer (D-S) evidence theory is applied to the quality evaluation of rock mass. As the correlation between different rock mass indices is too large to be ignored, belief reinforcement and Murphy’s average belief theory are introduced to process the multi-source data of rock mass. The proposed method is designed based on RMR14, one of the most widely used quality-evaluating methods for rock mass in the world. To validate the proposed method, the data of rock mass is generated randomly to realize the data fusion based on the proposed method and the conventional D-S theory. The fusion results based on these two methods are compared. The result of the comparison shows the proposed method amplifies the distance between the possibilities at different ratings from 0.0666 to 0.5882, which makes the exact decision more accurate than the other. A case study is carried out in Daxiagu tunnel in China to prove the practical value of the proposed method. The result shows the rock mass rating of the studied section of the tunnel is in level III with the maximum possibility of 0.9838, which agrees with the geological survey report.

In order to solve the problem that the target is easily lost in the process of multisource target data fusion tracking, a multisource target data fusion tracking method based on data mining is proposed. Multisource target data fusion tracking belongs to location level fusion. Firstly, a hybrid heterogeneous network fusion model is established, and then, data features are extracted, and a fusion source big data acquisition algorithm is designed based on compressed sensing to complete data preprocessing to reduce the amount of data acquisition. Based on data mining association multisource fusion target, get the relationship between each measurement and target, and build multisource target data fusion tracking model to ensure the stable state of fusion results. It shows that the proposed method can save the tracking time and improve the tracking accuracy compared with the methods based on NNDA and PDA, which is more conducive to the real-time tracking of multisource targets.

In order to improve the accuracy of distribution equipment fault prediction, a distribution equipment fault prediction method based on multi-source data fusion and hybrid neural network is proposed. Firstly, after preprocessing the multi-source data such as equipment fault and meteorological information in the distribution network, the outliers of the data are removed by K-means method, and the feature vector of the data is obtained by wavelet packet decomposition; Secondly, the features of the preprocessed data are extracted by convolutional neural network (CNN), and the extracted features are processed by long short term memory (LSTM), and then the fault is predicted; Finally, the actual distribution network equipment operation data in Henan Province are analyzed. The results show that compared with the compared fault prediction methods, the proposed method can obtain more reliability parameters according to the equipment characteristics, significantly improve the recall rate, accuracy and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> value, and can predict the distribution network equipment faults more accurately.

Understanding the spatial dynamics of China’s marine economic geography is essential for sustainable coastal development and marine spatial governance. This study examines the spatial distribution patterns and influencing factors of spatial differentiation in China’s marine economy from 2013 to 2023, utilizing AI techniques to facilitate multi-source data fusion and employing a Random Forest analytical method. The research was integrated with AI-based web-scraping, automated data-cleaning procedures, multi-source data preprocessing, Min–Max normalization, and Random Forest regression to accomplish multi-source data fusion and factor-importance analysis. Kernel density estimation, global Moran’s I, Getis-Ord Gi* statistics, and buffer zone analysis were employed to characterize spatial heterogeneity across coastal, island, and maritime economic zones, while Spearman’s correlation was used to quantify the relationships of influencing factors. Results indicate that China’s marine economy exhibits a pronounced “south–hot–north–cold and east–strong–west–weak” spatial gradient, with high-value clusters concentrated in the Bohai Rim, Yangtze River Delta, and Guangdong–Hong Kong–Macao Greater Bay Area. The coastal zone economy accounts for over 65% of the national marine GDP and acts as the dominant driver of spatial agglomeration. Policy implications suggest strengthening cross-regional industrial cooperation and optimizing spatial planning to enhance marine economic resilience and sustainability.

With the rapid increase of wind power centralized grid-connection scale, the security and stability of power grid are greatly affected. In order to simulate the dynamic characteristics of wind farm accurately, a large-scale wind power online equivalent method based on multi-source data fusion is proposed. The operating state of the state estimation modeling network is identified by the fusion of multi-source real-time data such as SCADA, PMU and security and stability control system. When the error of branch power flow or node voltage at the boundary is large, the active power regulation range of wind power generators is determined by comprehensively considering the actual measurement collected by the wind farm centralized control system and the prediction information of the wind power prediction system. The active power of each wind farm is determined by quadratic programming model. The wind power generators are grouped according to the topology in the wind farm, then each group is subdivided according to static characteristic and operating state in turn. The dynamic models and static parameters of equivalent wind power generators and equivalent transformers are calculated. The proposed method is proved to be fast and effective through the analysis of a practical power grid example.