Multi-source Information Fusion Method Research Articles

Research and application of dimming control system for highway tunnel lighting on demand

Aim at the widely used energy —saving lamp in highway tunnel, in accordance with the relevant requirements in JTG/T D70/2— 01—2014“ Design Details of Highway Tunnel Lighting”, traffic prediction theory and multi-source information fusion method, internet of things technology was applied to tunnel lighting control. Taking a highway tunnel as background, this parper carried out application study on the on-demand lighting technology of highway tunnel, and it built LED tunnel lighting system on demand and developed dimming control system for highway tunnel based on the Internet of things, and it achieved networked and intelligent management of lighting energy saving.

Leakage Detection of CFRDs Based on a Multi-source Information Fusion Method

Dam leakage is one of the most important failure causes of concrete face rockfill dams (CFRDs). Causes of dam leakage problems are various and hard to target due to the non-homogeneity of dam materials and the multi-dimensional deformation of CFRDs. This paper effectively performs multi-source information fusion of the safety monitoring, underwater sonar scanning, underwater high-definition cameras, and connection tests to propose a method for seepage detections of concrete face rockfill dams. The proposed method has been successfully applied in the seepage detection of a CFRD.

Prediction of coal and gas outbursts by a novel model based on multisource information fusion

As one type of geologic disaster, coal and gas outbursts seriously threaten safe production in coal mines, restricting the sustainable development of the mining industry. However, coal and gas outbursts are difficult to forecast due to their uncertainty and the limitation of sample size, which affect the accuracy of the traditional prediction methods to some extent. Therefore, this study developed a novel model based on multisource information fusion to realize the predictive progress of coal and gas outburst disasters. Through the application of Dempster-Shafer theory, a method of multisource information fusion, the proposed model combined the results of different forecasting approaches, including conventional techniques and an emerging method based on artificial intelligence. To enhance the performance of the established model, this study improved Dempster-Shafer theory and verified its effectiveness in dealing with highly conflicting information. We then applied this model to the No. 3 coal seam of the Xinjing coal mine, Shanxi Province, China. The fused prediction accurately reflected the situation of outburst hazards and showed good compensation for false prediction. An analysis of the results concluded that the model based on multisource information fusion increases the credibility of the forecast, which might provide technical support for safe coal mine production.

Multitask Multisource Deep Correlation Filter for Remote Sensing Data Fusion

With the amount of remote sensing data increasing at an extremely fast pace, machine learning-based technique has been shown to perform superiorly in many applications. However, most of the existing methods in the real-time application are based on single modal image data. Although a few approaches use the different source images to represent the object via a fusion scheme, it may not be appropriate for multimodality information processing. In addition, these methods hardly benefit from the end-to-end network training due to the limitations of implementation difficulty and computational cost. In this article, we propose a multitask multisource information fusion method in the deep learning and correlation filter frameworks, which is applied to the fields of tracking and remote sensing data processing. The contribution of individual layers from different source data inside the deep network model is considered as a task. The proposed method can employ interdependencies among different sources data and tasks to learn deep network parameters and filters jointly to improve the performance. Second, we present an effective object appearance selection scheme to adaptively capture the object appearance changes via an effective deep learning network, then integrating information from different modalities to achieve information fusion. Different source information can provide robust performance from different aspects with complementary properties. Third, we further extend the proposed approach to the field of remote sensing for semantic labeling. The layers’ sensitivity is utilized to verify the robustness of different classes. Extensively experiments on five benchmarks show that the proposed approach performs favorably against the state-of-the-arts.

The influence factors of the stability of tailings dam based on multi-source information fusion method

The BP neural network algorithm is used to construct a stable slope angle prediction model for open-pit mines, which can successfully predict the ultimate slope angle of the mine. At the same time, ...

Latent Leakage Fault Identification and Diagnosis Based on Multi-Source Information Fusion Method for Key Pneumatic Units in Chinese Standard Electric Multiple Units (EMU) Braking System

To identify and diagnose the latent leakage faults of key pneumatic units in the Chinese standard Electric Multiple Units (EMU) braking system, a multi-source information fusion method based on Kalman filtering, sequential probability ratio test (SPRT), and support vector machine (SVM) is proposed. The relay valve is taken as an example for research. Firstly, Kalman’s state estimation function is used to obtain the innovation sequence, and the innovation sequence is input into the SPRT model to help recognize latent leakage faults of the relay valve. Using this method, the problem of the incomplete training set of the traditional SPRT method due to the change of the braking level and the vehicle load is solved. Secondly, the eight time-domain parameters of the relay valve input and the output pressure signal are extracted as fault characteristics, and then input to the support vector machine to realize the internal and external leakage fault diagnosis of the relay valve, which provides a reference for maintenance. Finally, this method is verified by the fault simulation data by quickly identifying latent leakage faults and diagnosing the internal and external leakage at a fault recognition rate of 100% by SVM under small sample conditions.

Remote testing and trial of new energy grid-connected power generation for complex data

For the data monitoring, diagnostics and evaluation of new energy grid-connected power generation, multi-source complex data such as real-time operation data and characteristic test data are synchronized into the new energy grid-connected power generation system. And multi-source information fusion technology and online data evaluation method are used to analyze and process the data. On this basis, the establishment of a new energy grid-connected power generation remote test diagnostics system is designed. The real-time curve, test curve and fusion data curve’s simulation waveform and the parameters of simulation credibility were analyzed. The experiment proves the feasibility and rationality of multi-source information fusion and online evaluation method. The system runs stably. And the test data is accurate. It provides the system platform for comprehensive grasp of the grid-connected operation characteristic and the grid-connected test diagnostics information.

A Maximum Entropy Multisource Information Fusion Method to Evaluate the MTBF of Low-Voltage Switchgear

When analyzing the reliability of low-voltage switchgear by Bayesian method, the maximum entropy multisource information fusion method was proposed to obtain the prior information of low-voltage switchgear and then evaluate the reliability. The historical data of low-voltage switchgear was collected and organized from a manufacturer. According to the expert experience and the data, the creditability analysis and the compatibility test were presented by the Smirnov test method. Based on the high creditability and compatibility, the result of the maximum entropy multisource information fusion method is the determination of prior information. Therefore, the distribution type of the prior information was confirmed by using the maximum entropy method, and the parameter of the prior information was received by bootstrap method with MATLAB. Then the posterior distribution was obtained to evaluate the MTBF of low-voltage switchgear. Finally, the historical data of years from 2007 to 2010 was taken as prior information to illustrate the maximum entropy multisource information fusion method and to get the MTBF of low-voltage switchgear. The evaluation result reduces the experimental period and test cost, which is an improvement for the reliability evaluation and management of low-voltage switchgear and also an improvement for other systems with simple sample data. Compared with traditional Bayesian networks, the proposed method can fuse experts experience and historical data and has advantages for the use of prior information effectively.

Multi-source and Multi-feature Image Information Fusion Based on Compressive Sensing

Image fusion is a comprehensive information processing technique and its purpose is to enhance the reliability of the image via the processing of the redundant data among multiple images, improve the image definition and information content through fusion of the complementary information of multiple images so as to obtain the information of the objective or the scene in a more accurate, reliable and comprehensive manner. This paper uses the sparse representation method of compressive sensing theory, proposes a multi-source and multi-feature image information fusion method based on compressive sensing in accordance with the features of image fusion, performs sparsification processing on the source image with K-SVD algorithm and OMP algorithm to transfer from spatial domain to frequency domain and decomposes into low-frequency part and high-frequency park. Then it fuses with different fusion rules and the experimental results prove that the method of this paper is better than the traditional methods and it can obtain better fusion effects.

A Multi-source Information Fusion Method for Error AIS Targets Identification

Automatic Identification System (AIS) is ship navigation equipment. Due to the fact that data-link guarantee mechanism has not been fully resolved, AIS sometimes broadcasts error messages and confused the marine administrators and sailors, so it’s important to automatically identify the error messages. In this paper, we proposed an error AIS messages identification framework, which based on prior probability, expert assessment, fuzzy membership degree and Dempster- Shafer’s (DS) evidence combining rule. In the framework, ship speed, course and longitude-latitude position are core indexes to evaluate the confidence coefficient of an AIS message. Field experiment is carried out at Wuhan section of the Yangtze River, by field observation, the vessels’ real behavior and AIS messages are compared to verify the validity of the algorithm. The experiment results show that for normally sailing ships, the algorithm is able to judge the AIS messages accurately. For ships whose sailing condition changes sharply, such as the touring maritime patrol ship, the algorithm’s recognition accuracy is higher than 84.21%.

Multi-source irrigation information fusion method based on fuzzy rough set and D-S evidence theory

Concerning the problem that uncertainty information is difficult to be merged during the decision-making process of multi-source irrigation information,a decision fusion method based on fuzzy rough set and Dempster-Shafer(D-S) evidence theory was proposed.Using the fuzzy rough set theory,the basic probability distribution function was established,the interdependence between irrigation factors and irrigation decision was calculated,and the identification framework of irrigation decision on the multiple fusion irrigation factors was built.Using the improved D-S evidence theory,the multi-source irrigation information was fused at the decision-making level,the expression and synthesis problems of uncertain information were solved.The information of winter wheat such as soil moisture,photosynthetic rate and stomatal conductance in north China was fused in irrigation decision by the application of the methods mentioned above.The results show that the uncertainty of the irrigation decision decreases from 38.0% before fusion to 9.84%.The method can effectively improve the accuracy of irrigation decision and reduce the uncertainty of the irrigation decision.

Research Based on Information Fusion of Safety Risks Prediction and Control Management in Construction

Safety risks management of safety potential information and hazard sources has been taken in Construction process. It is the most important measure to solve the current situation that safety accidents is frequent in Chinese Construction projects. The main reasons of frequent accidents were find out and the D-S evidence theory of multi-source information fusion method was used to reduce the accident rate in Construction safety in this paper. Through analyzing and predicting the engineering data and information in human, machine, environment and management four aspects in Construction, prediction model was built in Construction safety risks management, the possibility of dangerous and harmful level in the construction project can be known, preventive measures for specific situations were taken and promptly the safety state of the Construction will be ensured.