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A Power Data Anomaly Detection Model Based on Deep Learning with Adaptive Feature Fusion
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作者 Xiu Liu Liang Gu +3 位作者 Xin Gong Long An Xurui Gao Juying Wu 《Computers, Materials & Continua》 SCIE EI 2024年第6期4045-4061,共17页
With the popularisation of intelligent power,power devices have different shapes,numbers and specifications.This means that the power data has distributional variability,the model learning process cannot achieve suffi... With the popularisation of intelligent power,power devices have different shapes,numbers and specifications.This means that the power data has distributional variability,the model learning process cannot achieve sufficient extraction of data features,which seriously affects the accuracy and performance of anomaly detection.Therefore,this paper proposes a deep learning-based anomaly detection model for power data,which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction.Aiming at the distribution variability of power data,this paper developed a sliding window-based data adjustment method for this model,which solves the problem of high-dimensional feature noise and low-dimensional missing data.To address the problem of insufficient feature fusion,an adaptive feature fusion method based on feature dimension reduction and dictionary learning is proposed to improve the anomaly data detection accuracy of the model.In order to verify the effectiveness of the proposed method,we conducted effectiveness comparisons through elimination experiments.The experimental results show that compared with the traditional anomaly detection methods,the method proposed in this paper not only has an advantage in model accuracy,but also reduces the amount of parameter calculation of the model in the process of feature matching and improves the detection speed. 展开更多
关键词 data alignment dimension reduction feature fusion data anomaly detection deep learning
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Parameter Estimation of a Valve-Controlled Cylinder System Model Based on Bench Test and Operating Data Fusion
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作者 Deying Su Shaojie Wang +3 位作者 Haojing Lin Xiaosong Xia Yubing Xu Liang Hou 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第2期247-263,共17页
The accurate estimation of parameters is the premise for establishing a high-fidelity simulation model of a valve-controlled cylinder system.Bench test data are easily obtained,but it is challenging to emulate actual ... The accurate estimation of parameters is the premise for establishing a high-fidelity simulation model of a valve-controlled cylinder system.Bench test data are easily obtained,but it is challenging to emulate actual loads in the research on parameter estimation of valve-controlled cylinder system.Despite the actual load information contained in the operating data of the control valve,its acquisition remains challenging.This paper proposes a method that fuses bench test and operating data for parameter estimation to address the aforementioned problems.The proposed method is based on Bayesian theory,and its core is a pool fusion of prior information from bench test and operating data.Firstly,a system model is established,and the parameters in the model are analysed.Secondly,the bench and operating data of the system are collected.Then,the model parameters and weight coefficients are estimated using the data fusion method.Finally,the estimated effects of the data fusion method,Bayesian method,and particle swarm optimisation(PSO)algorithm on system model parameters are compared.The research shows that the weight coefficient represents the contribution of different prior information to the parameter estimation result.The effect of parameter estimation based on the data fusion method is better than that of the Bayesian method and the PSO algorithm.Increasing load complexity leads to a decrease in model accuracy,highlighting the crucial role of the data fusion method in parameter estimation studies. 展开更多
关键词 Valve-controlled cylinder system Parameter estimation The Bayesian theory data fusion method Weight coefficients
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Optimized air-ground data fusion method for mine slope modeling
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作者 LIU Dan HUANG Man +4 位作者 TAO Zhigang HONG Chenjie WU Yuewei FAN En YANG Fei 《Journal of Mountain Science》 SCIE CSCD 2024年第6期2130-2139,共10页
Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized charact... Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model. 展开更多
关键词 Air-ground data fusion method Mini batch K-Medoids algorithm Ebow rule Optimal cluster number 3D laser scanning UAV tilt photogrammetry
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Measuring moisture content of dead fine fuels based on the fusion of spectrum meteorological data
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作者 Bo Peng Jiawei Zhang +2 位作者 Jian Xing Jiuqing Liu Mingbao Li 《Journal of Forestry Research》 SCIE CAS CSCD 2023年第5期1333-1346,共14页
Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire risk.In order to achieve high-precision real-time measurement of DF... Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire risk.In order to achieve high-precision real-time measurement of DFFMC,this study established a long short-term memory(LSTM)network based on particle swarm optimization(PSO)algorithm as a measurement model.A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is proposed.The near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data set.The surface fine dead fuel of Mongolian oak(Quercus mongolica Fisch.ex Ledeb.),white birch(Betula platyphylla Suk.),larch(Larix gmelinii(Rupr.)Kuzen.),and Manchurian walnut(Juglans mandshurica Maxim.)in the maoershan experimental forest farm of the Northeast Forestry University were investigated.We used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy,meteorological,and spectral meteorological fusion methods.The results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1%for Mongolian oak,1.3%for white birch,1.4%for larch,and 1.8%for Manchurian walnut,and these values were lower than those of the near-infrared method and the meteorological method.The spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel. 展开更多
关键词 Near infrared spectroscopy Meteorological factors data fusion Long-term and short-term memory network Particle swarm optimization algorithm
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Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder
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作者 Xiaoxiong Feng Jianhua Liu 《Journal of Sensor Technology》 2023年第4期69-85,共17页
To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features e... To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion. 展开更多
关键词 Multi-Mode data fusion Coupling Convolutional Auto-Encoder Adaptive Optimization Deep Learning
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Enhancing Surface Soil Moisture Estimation through Integration of Artificial Neural Networks Machine Learning and Fusion of Meteorological, Sentinel-1A and Sentinel-2A Satellite Data
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作者 Jephter Ondieki Giovanni Laneve +1 位作者 Maria Marsella Collins Mito 《Advances in Remote Sensing》 2023年第4期99-122,共24页
For many environmental and agricultural applications, an accurate estimation of surface soil moisture is essential. This study sought to determine whether combining Sentinel-1A, Sentinel-2A, and meteorological data wi... For many environmental and agricultural applications, an accurate estimation of surface soil moisture is essential. This study sought to determine whether combining Sentinel-1A, Sentinel-2A, and meteorological data with artificial neural networks (ANN) could improve soil moisture estimation in various land cover types. To train and evaluate the model’s performance, we used field data (provided by La Tuscia University) on the study area collected during time periods between October 2022, and December 2022. Surface soil moisture was measured at 29 locations. The performance of the model was trained, validated, and tested using input features in a 60:10:30 ratio, using the feed-forward ANN model. It was found that the ANN model exhibited high precision in predicting soil moisture. The model achieved a coefficient of determination (R<sup>2</sup>) of 0.71 and correlation coefficient (R) of 0.84. Furthermore, the incorporation of Random Forest (RF) algorithms for soil moisture prediction resulted in an improved R<sup>2</sup> of 0.89. The unique combination of active microwave, meteorological data and multispectral data provides an opportunity to exploit the complementary nature of the datasets. Through preprocessing, fusion, and ANN modeling, this research contributes to advancing soil moisture estimation techniques and providing valuable insights for water resource management and agricultural planning in the study area. 展开更多
关键词 Soil Moisture Estimation Techniques fusion Active Microwave Multispectral data Agricultural Planning
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Multimodality Medical Image Fusion Based on Pixel Significance with Edge-Preserving Processing for Clinical Applications
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作者 Bhawna Goyal Ayush Dogra +4 位作者 Dawa Chyophel Lepcha Rajesh Singh Hemant Sharma Ahmed Alkhayyat Manob Jyoti Saikia 《Computers, Materials & Continua》 SCIE EI 2024年第3期4317-4342,共26页
Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by reta... Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases.However,recent image fusion techniques have encountered several challenges,including fusion artifacts,algorithm complexity,and high computing costs.To solve these problems,this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance.First,the method employs a cross-bilateral filter(CBF)that utilizes one image to determine the kernel and the other for filtering,and vice versa,by considering both geometric closeness and the gray-level similarities of neighboring pixels of the images without smoothing edges.The outputs of CBF are then subtracted from the original images to obtain detailed images.It further proposes to use edge-preserving processing that combines linear lowpass filtering with a non-linear technique that enables the selection of relevant regions in detailed images while maintaining structural properties.These regions are selected using morphologically processed linear filter residuals to identify the significant regions with high-amplitude edges and adequate size.The outputs of low-pass filtering are fused with meaningfully restored regions to reconstruct the original shape of the edges.In addition,weight computations are performed using these reconstructed images,and these weights are then fused with the original input images to produce a final fusion result by estimating the strength of horizontal and vertical details.Numerous standard quality evaluation metrics with complementary properties are used for comparison with existing,well-known algorithms objectively to validate the fusion results.Experimental results from the proposed research article exhibit superior performance compared to other competing techniques in the case of both qualitative and quantitative evaluation.In addition,the proposed method advocates less computational complexity and execution time while improving diagnostic computing accuracy.Nevertheless,due to the lower complexity of the fusion algorithm,the efficiency of fusion methods is high in practical applications.The results reveal that the proposed method exceeds the latest state-of-the-art methods in terms of providing detailed information,edge contour,and overall contrast. 展开更多
关键词 Image fusion fractal data analysis BIOMEDICAL DISEASES research multiresolution analysis numerical analysis
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Fusion SST from Infrared and Microwave Measurement of FY-3D Meteorological Satellite
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作者 张淼 徐娜 陈林 《Journal of Tropical Meteorology》 SCIE 2024年第1期89-96,共8页
Sea surface temperature(SST)is one of the important parameters of global ocean and climate research,which can be retrieved by satellite infrared and passive microwave remote sensing instruments.While satellite infrare... Sea surface temperature(SST)is one of the important parameters of global ocean and climate research,which can be retrieved by satellite infrared and passive microwave remote sensing instruments.While satellite infrared SST offers high spatial resolution,it is limited by cloud cover.On the other hand,passive microwave SST provides all-weather observation but suffers from poor spatial resolution and susceptibility to environmental factors such as rainfall,coastal effects,and high wind speeds.To achieve high-precision,comprehensive,and high-resolution SST data,it is essential to fuse infrared and microwave SST measurements.In this study,data from the Fengyun-3D(FY-3D)medium resolution spectral imager II(MERSI-II)SST and microwave imager(MWRI)SST were fused.Firstly,the accuracy of both MERSIII SST and MWRI SST was verified,and the latter was bilinearly interpolated to match the 5km resolution grid of MERSI SST.After pretreatment and quality control of MERSI SST and MWRI SST,a Piece-Wise Regression method was employed to correct biases in MWRI SST.Subsequently,SST data were selected based on spatial resolution and accuracy within a 3-day window of the analysis date.Finally,an optimal interpolation method was applied to fuse the FY-3D MERSI-II SST and MWRI SST.The results demonstrated a significant improvement in spatial coverage compared to MERSI-II SST and MWRI SST.Furthermore,the fusion SST retained true spatial distribution details and exhibited an accuracy of–0.12±0.74℃compared to OSTIA SST.This study has improved the accuracy of FY satellite fusion SST products in China. 展开更多
关键词 SST data fusion FY3 INFRARED MICROWAVE
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Bearing fault diagnosis based on a multiple-constraint modal-invariant graph convolutional fusion network
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作者 Zhongmei Wang Pengxuan Nie +3 位作者 Jianhua Liu Jing He Haibo Wu Pengfei Guo 《High-Speed Railway》 2024年第2期92-100,共9页
Multisensor data fusionmethod can improve the accuracy of bearing fault diagnosis,in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between... Multisensor data fusionmethod can improve the accuracy of bearing fault diagnosis,in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between different modal data in most existing multisensor data fusion methods for bearing fault diagnosis,a bearing fault diagnosis method based on a Multiple-Constraint Modal-Invariant Graph Convolutional Fusion Network(MCMI-GCFN)is proposed in this paper.Firstly,a Convolutional Autoencoder(CAE)and Squeeze-and-Excitation Block(SE block)are used to extract features of raw current and vibration signals.Secondly,the model introduces source domain classifiers and domain discriminators to capture modal invariance between different modal data based on domain adversarial training,making use of the redundancy and complementarity between multimodal data.Then,the spatial aggregation property of Graph Convolutional Neural Networks(GCN)is utilized to capture the dependency relationship between current and vibration modes with similar time step features for accurately fusing contextual semantic information.Finally,the validation is conducted on the public bearing damage current and vibration dataset from Paderborn University.The experimental results showed that the delivered fusion method achieved a bearing fault diagnosis accuracy of 99.6%,which was about 9%–11.4%better than that with nonfusion methods. 展开更多
关键词 Bearing fault diagnosis data fusion Domain adversarial training GCN
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Synergy Decision for Radar and IRST Data Fusion 被引量:5
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作者 窦丽华 杨国胜 +1 位作者 陈杰 侯朝桢 《Journal of Beijing Institute of Technology》 EI CAS 2002年第3期229-233,共5页
A new synergy decision method for radar and infrared search and track (IRST) data fusion is proposed, to solve such problems as how to decrease opportunities for radar suffering from being locked on by adverse electr... A new synergy decision method for radar and infrared search and track (IRST) data fusion is proposed, to solve such problems as how to decrease opportunities for radar suffering from being locked on by adverse electronic support measures (ESM), how to retrieve range information of the target during radar off, and how to detect the maneuver of the target. Firstly, polynomials used to predict target motion states are constructed. Secondly, a set of discriminants for detecting target maneuver are established by comparing the predicted values with the observations from IRST. Thirdly, a set of decisions are presented. Lastly, simulation is performed on the given scenario to test the validity of the method. 展开更多
关键词 IRST RADAR data fusion multi sensor electromagnetic covertness POLYNOMIAL synergy decision approximation
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Sensor Registration in Asynchronous Data Fusion 被引量:3
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作者 胡士强 张天桥 《Journal of Beijing Institute of Technology》 EI CAS 2001年第3期285-290,共6页
To find an effective method to estimate and remove the registration error in asynchronous multisensor system, Kalman filtering technique and least squares approach have been proposed to estimate and remove sensor bia... To find an effective method to estimate and remove the registration error in asynchronous multisensor system, Kalman filtering technique and least squares approach have been proposed to estimate and remove sensor bias and sensor frame tilt errors in multisensor systems with asynchronous data. Simulation results is presented to demonstrate the performance of these approaches. The least squares approach can compress measurements to any time. The Kalman filter algorithm can detect registration errors and use the information to converge tracks from independent sensors. This is particularly important if the data from the sensors are to be fused. 展开更多
关键词 data fusion multisensor system REGISTRATION Kalman filter
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Asynchronous Data Fusion of Two Different Sensors 被引量:2
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作者 戴亚平 王军政 《Journal of Beijing Institute of Technology》 EI CAS 2001年第4期402-405,共4页
An algorithm is presented for fusion of tracks created by radar and IR sensor which have different dimensional measurement data. It’s assumed that these sensors are asynchronous and the measurement data are transmitt... An algorithm is presented for fusion of tracks created by radar and IR sensor which have different dimensional measurement data. It’s assumed that these sensors are asynchronous and the measurement data are transmitted to a central station at different rates. By means of the technique of time matching, two sets of asynchronous data are fused and then the filter is updated according to the fused information. The results show that the accuracy of the filter effect has been improved. 展开更多
关键词 target tracking multi sensor data fusion
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RELIABILITY EVALUATION MODEL BASED ON DATA FUSION FOR AIRCRAFT ENGINES 被引量:2
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作者 王华伟 吴海桥 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2012年第4期318-324,共7页
Reliability evaluation for aircraft engines is difficult because of the scarcity of failure data. But aircraft engine data are available from a variety of sources. Data fusion has the function of maximizing the amount... Reliability evaluation for aircraft engines is difficult because of the scarcity of failure data. But aircraft engine data are available from a variety of sources. Data fusion has the function of maximizing the amount of valu- able information extracted from disparate data sources to obtain the comprehensive reliability knowledge. Consid- ering the degradation failure and the catastrophic failure simultaneously, which are competing risks and can affect the reliability, a reliability evaluation model based on data fusion for aircraft engines is developed, Above the characteristics of the proposed model, reliability evaluation is more feasible than that by only utilizing failure data alone, and is also more accurate than that by only considering single failure mode. Example shows the effective- ness of the proposed model. 展开更多
关键词 aircraft engine reliability evaluation data fusion competing failure condition monitoring
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Distributed Computation Models for Data Fusion System Simulation
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作者 张岩 曾涛 +1 位作者 龙腾 崔智社 《Journal of Beijing Institute of Technology》 EI CAS 2001年第3期291-297,共7页
An attempt has been made to develop a distributed software infrastructure model for onboard data fusion system simulation, which is also applied to netted radar systems, onboard distributed detection systems and advan... An attempt has been made to develop a distributed software infrastructure model for onboard data fusion system simulation, which is also applied to netted radar systems, onboard distributed detection systems and advanced C3I systems. Two architectures are provided and verified: one is based on pure TCP/IP protocol and C/S model, and implemented with Winsock, the other is based on CORBA (common object request broker architecture). The performance of data fusion simulation system, i.e. reliability, flexibility and scalability, is improved and enhanced by two models. The study of them makes valuable explore on incorporating the distributed computation concepts into radar system simulation techniques. 展开更多
关键词 radar system computer network data fusion SIMULATION distributed computation
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Research on Kalman-filter based multisensor data fusion 被引量:11
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作者 Chen Yukun Si Xicai Li Zhigang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2007年第3期497-502,共6页
Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc. Various multisensor data fusion methods have been extensively investigat... Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc. Various multisensor data fusion methods have been extensively investigated by researchers, of which Klaman filtering is one of the most important. Kalman filtering is the best-known recursive least mean-square algorithm to optimally estimate the unknown states of a dynamic system, which has found widespread application in many areas. The scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman Filter methods, then a new method of state fusion is proposed. Finally the simulation results demonstrate the effectiveness of the introduced method. 展开更多
关键词 MULTISENSOR data fusion Kalman filter.
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Multisensor Data Fusion for High Quality Data Analysis and Processing in Measurement and Instrumentation 被引量:13
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作者 Yan-bo Huang Yu-bin Lan +1 位作者 W. C. Hoffmann R. E. Lacey 《Journal of Bionic Engineering》 SCIE EI CSCD 2007年第1期53-62,共10页
Multisensor data fusion (MDF) is an emerging technology to fuse data from multiple sensors in order to make a more accurate estimation of the environment through measurement and detection. Applications of MDF cross ... Multisensor data fusion (MDF) is an emerging technology to fuse data from multiple sensors in order to make a more accurate estimation of the environment through measurement and detection. Applications of MDF cross a wide spectrum in military and civilian areas. With the rapid evolution of computers and the proliferation of micro-mechanical/electrical systems sensors, the utilization of MDF is being popularized in research and applications. This paper focuses on application of MDF for high quality data analysis and processing in measurement and instrumentation. A practical, general data fusion scheme was established on the basis of feature extraction and merge of data from multiple sensors. This scheme integrates artificial neural networks for high performance pattern recognition. A number of successful applications in areas of NDI (Non-Destructive Inspection) corrosion detection, food quality and safety characterization, and precision agriculture are described and discussed in order to motivate new applications in these or other areas. This paper gives an overall picture of using the MDF method to increase the accuracy of data analysis and processing in measurement and instrumentation in different areas of applications. 展开更多
关键词 multisensor data fusion artificial neural networks NDI food quality and safety characterization precision agriculture
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Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function 被引量:7
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作者 BIN Guangfu JIANG Zhinong +1 位作者 LI Xuejun DHILLON B S 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2011年第5期899-904,共6页
As the differences of sensor's precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery... As the differences of sensor's precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn't require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor's own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement. 展开更多
关键词 vibration signal MULTI-SENSOR data level fusion correlation function weighted value
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STUDY ON THE COAL-ROCK INTERFACE RECOGNITION METHOD BASED ON MULTI-SENSOR DATA FUSION TECHNIQUE 被引量:7
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作者 Ren FangYang ZhaojianXiong ShiboResearch Institute of Mechano-Electronic Engineering,Taiyuan University of Technology,Taiyuan 030024, China 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2003年第3期321-324,共4页
The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data... The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones. 展开更多
关键词 Coal-rock interface recognition (CIR) data fusion (DF) MULTI-SENSOR
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4D DATA FUSION TECHNIQUE IN URBAN WATERLOG-DRAINING DECISION SUPPORT SYSTEM 被引量:3
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作者 Li Jun Bian Fuling 《Geo-Spatial Information Science》 2000年第3期42-46,共5页
This paper studies urban waterlog_draining decision support system based on the 4D data fusion technique.4D data includes DEM,DOQ,DLG and DRG.It supplies entire databases for waterlog forecast and analysis together wi... This paper studies urban waterlog_draining decision support system based on the 4D data fusion technique.4D data includes DEM,DOQ,DLG and DRG.It supplies entire databases for waterlog forecast and analysis together with non_spatial fundamental database.Data composition and reasoning are two key steps of 4D data fusion.Finally,this paper gives a real case: Ezhou Waterlog_Draining Decision Support System (EWDSS) with two application models,i.e.,DEM application model,water generating and draining model. 展开更多
关键词 4D data fusion RASTER VECTOR
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Data Fusion in Distributed Multi-sensor System 被引量:7
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作者 GUOHang YUMin 《Geo-Spatial Information Science》 2004年第3期214-217,234,共5页
This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a ... This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a coefficient matrices calculation method of the information sharing principle is derived. Finally, the federated Kalman filter is used to combine these independent, parallel, real\|time data. A pseudolite (PL) simulation example is given. 展开更多
关键词 PSEUDOLITE distributed multi-sensor system data fusion federated Kalman filtering
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