Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion.

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.

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.

In this paper, the sensor is applied to the collection of rock parameter data. Aiming at the classification and evaluation of stability (i.e. rock quality), an attribute recognition model for the classification and evaluation of surrounding rock quality of underground engineering is established. Using multi-source data fusion and orthogonal numerical simulation test methods, the effects of rock mechanics parameters on the horizontal convergence of the tunnel, the settlement of the vault and the plastic zone coefficient are studied. Six factors (elastic modulus, Poisson’s ratio, internal friction angle, tensile strength, cohesion and density) and three levels of orthogonal experimental solutions were selected. The method of defining similar weight by using similar number to determine the weight of evaluation index, so as to calculate the comprehensive attribute measure, and apply confidence criteria to identify the stability of rock samples. Through the analysis and evaluation of rock mass quality classification of underground engineering, the application of the model and the evaluation method of rock mass quality classification are explained. The test results match the orthogonal test results; Considering the stability of tunnel envelope, the horizontal convergence, vault settlement and plastic zone coefficient after excavation should be comprehensively considered.

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

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.

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.

To address the shortcomings of traditional single-sensor monitoring systems in environmental assessment, such as insufficient accuracy and poor fault tolerance, an indoor environmental monitoring system based on D-S evidence theory and multi-source data fusion was designed and implemented. This system is based on ZigBee short-range wireless communication technology, and the sensing layer integrates multiple sensors for temperature and humidity, PM2.5, CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, noise, light intensity, and combustible gases. Data storage and analysis are performed through a cloud platform. An improved algorithm is proposed to address the limitations of traditional D-S evidence theory methods and applied to the indoor environmental monitoring system for multi-source data fusion. By introducing fuzzy membership functions, adaptive weight calculation, and conflict resolution mechanisms, the accuracy, reliability, and robustness of multi-sensor data fusion in the indoor environmental monitoring system are significantly improved. Experimental results show that the improved fusion algorithm outperforms the traditional D-S evidence theory method in terms of accuracy, decision credibility, and conflict resolution capability. In practical operation, it can effectively identify various complex environmental anomalies, significantly improving the accuracy and reliability of the system's environmental state judgment, and providing an effective solution for intelligent indoor environmental monitoring.

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.

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.

Focusing on the problem of relational data fusion in the environment with “information isolated island”, this paper presents a multi-sources relational data fusion (MSF) framework. The framework consists of three components: schema matching, entity alignment, and entity fusion. Based on the Hungarian algorithm, we propose an alignment discovery mechanism for the attributes alignment among multi-sources relational data. By extracting the multi-dimensional features of attribute values, we efficiently realized schema matching of multi-sources relational data. To link the tuple pairs from multi-source data, we introduced the diversity sampling strategy and the entity feature extraction approach. These can effectively improve the performance of entity alignment. Finally, linked entities are fused to provide a unified view of data analysis. To verify the usefulness and efficiency of the proposed methods, we implemented a fusion system called Data Puzzle, which is verified with the real public multi-field data. Experimental results demonstrate that the proposed methods can fuse multi-source relational data efficiently with high recall and precision.

Functional urban land use recognition integrating multi-source geospatial data and cross-correlations

The study of the prediction of stock market volatility is of great significance to rationally control financial market risks and increase excessive investment returns and has received extensive attention from academic and commercial circles. However, as a dynamic and complex system, the stock market is affected by multiple factors and has a comprehensive capability to include complex financial data. Given that the explanatory variables of influencing factors are diverse, heterogeneous and complex, the existing intelligent algorithms have great limitations for the analysis and processing of multi-source heterogeneous data in the stock market. Therefore, this study adopts the edge weight and information transmission mechanism suitable for subgraph data to complete node screening, the gate recurrent unit (GRU) and long short-term memory (LSTM) to aggregate subgraph nodes. The compiled data contain the metapaths of three types of index data, and the introduction of the association relationship attention dimension effectively mines the implicit meanings of multi-source heterogeneous data. The metapath attention mechanism is combined with a graph neural network to complete the classification of multi-source heterogeneous graph data, by which the prediction of stock market volatility is realized. The results show that the above method is feasible for the fusion of heterogeneous stock market data and the mining of implicit semantic information of association relations. The accuracy of the proposed method for the prediction of stock market volatility in this study is 16.64% higher than that of the dimensional reduction index and 14.48% higher than that of other methods for the fusion and prediction of heterogeneous data using the same model.

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.

Spectrum sensing is a key technology within Cognitive Radio (CR) systems. Cooperative spectrum sensing using a distributed model provides improved detection for the primary user, which opens the CR system to a new security threat. This threat is the decrease of the cooperative sensing performance due to the spectrum sensing data falsification which is generated from malicious users. Our proposed scheme, based on robust statistics, utilizes only available past sensing nodes' received power data for estimating the distribution parameters of the primary signal presence and absence hypotheses. These estimated parameters are used to perform the Dempster-Shafer theory of evidence data fusion which causes the elimination of malicious users. Furthermore, in order to enhance performance, a node's reliability weight is supplemented along with the data fusion scheme. Simulation results indicate that our proposed scheme can provide a powerful capability in eliminating malicious users as well as a high gain of data fusion under various cases of channel condition.

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.