Data Fusion Results Research Articles

Fusion Method and Application of Several Source Vibration Fault Signal Spatio-Temporal Multi-Correlation

Combined with other signal processing methods, related algorithms are widely used in the diagnosis and identification of rotor faults. In order to solve the problem that the vibration signal of a single sensor is too single, a new multi-source vibration signal fusion method is proposed. This method explores the correlation between vibration sensors at different locations by using multiple cross-correlations of spatial locations. First, wavelet noise reduction and linear normalization are used to process the original data. Then, the signal energy correlation function between the sensors is established, and the adaptive weight is obtained. Finally, the data fusion result is obtained. Taking rotor bearing and gear failures at different speeds as an example, the data of three vibration sensors at different positions are fused using the spatio-temporal multiple correlation fusion method (STMF). Through the intelligent fault diagnosis method stacked auto encoder (SAE), compared with single sensor data, average weighted fusion data and neural network fusion data, STMF method can reach a diagnosis accuracy of more than 94% at 700 rpm, 900 rpm and 1100 rpm. It is concluded that the result of the STMF method is more effective and superior.

SHARIDEAS: a smart collaborative assembly platform based on augmented reality supporting assembly intention recognition

With the development of augmented reality supporting manual assembly collaboration, it is particularly important to explore the transformation from traditional “human-machine” cooperation mode to smart “human-machine” cooperation mode. Early studies have shown that user cues (i.e., head, gesture, eye) and scene cues (i.e., objects, tools, space) are intuitive and highly expressive for traditional AR collaborative mode. However, how to integrate these cues into an assembly system, reasonably infer an operator’s work intention, and then give an appropriate rendering scheme is one of the problems in collaborative assembly. This paper describes a AR collaborative assembly platform: SHARIDEAS. It uses a generalized grey correlation method to integrate user cues and scene cues. The results of data fusion can provide appropriate and intuitive assembly guidance for local workers. A formal user study is to explore the usability and feasibility of SHAREDEAS in a manual assembly task. The experimental data show that SHAREDEAS is more conducive than traditional one to improve the efficiency of human-machine cooperation. Finally, some conclusions of SHARIDEAS are given and the future research direction has prospected.

Flatfish Measurement Performance Improvement Based on Multi-sensor Data Fusion

In this study, a multi-sensor data fusion system using a load cell and vision sensor was considered in the development of a flatfish classifier for systematic fish management in aquaculture. In the single-sensor measurement method, each sensor has disadvantages. A load cell shows high performance in the measurement of adult fish, but the measurement of fry is affected significantly due to water weight (water weight disturbance). A vision sensor shows high performance in the measurement of fry, but the movement of fish (movement disturbance) affects the accurate measurement of adult fish. Therefore, in this study, these disturbances were compensated for using a datafusion algorithm, of which the performance was evaluated by a comparison between single sensor measurements and multi-sensor data fusion results.

PCA applied to Data Fusion for Subsurface Target Imaging of Full-polarimetric GPR

Full-polarimetric ground penetrating radar (GPR) can obtain more comprehensive polarization data (called VV, HH, VH) for the same target than traditional commercial radar (only VV). We need to use data fusion technology to combine the polarization information of the three different polarization modes. However, the full-polarimetric GPR data fusion method has one weighted average fusion, which will mask the advantages of full polarization. Principal component analysis (PCA) is a technology of data dimensionality reduction and compression which can use VV, HH and VH as a three-dimensional data to conduct data dimensionality reduction and find the best data fusion results. In order to check the reliability, we obtained the full-polarimetric GPR data of three typical targets in the laboratory for analysis. Then we compare PCA with the weighted average fusion method by using the instantaneous amplitude and conclude that PCA can fuse full-polarimetric GPR data better than weighted average fusion.

EDCRA-IoT: Edge-based Data Conflict Resolution Approach for Internet of Things

In Internet of Things (IoT), data are collected from various sources (e.g., sensors and databases) to describe a phenomenon under-observation. Due to the lack of knowledge about the measurement environment and the limited accuracy of data sources, the IoT data inevitably appears uncertain, imperfect, and conflicting. This would lead to high data conflict among different data sources, and the final result of data fusion is degraded. This paper presents an edge-based data conflict resolution approach for IoT to tackle these issues while maintaining an accurate and reliable result. In the proposed approach, data conflict is significantly alleviated using two phases. The first is Faulty Data Detection and Correction (FDDC), which runs based on the confidence interval and estimated data. The second is Data Conflict Measure and Fusion (DCMF), which is developed to compute the degree of conflict among different data sources based on Fuzzy measures and calculate each data source’s credibility degree. Then, data fusion is carried out based on the Minimum Mean Square Error (MMSE) criterion accordingly. The simulation results reveal the remarkable effectiveness and efficiency of the proposed approach for solving IoT conflicting data.

Dynamic GPS Spoofing Attack Detection, Localization, and Measurement Correction Exploiting PMU and SCADA

GPS receiver is the source of synchronization in phasor measurement unit (PMU) networks. While it guarantees time precision, it is vulnerable to cyberattacks, which may modify the time offset and results in mismatches in time. This modification (GPS spoofing attack) changes the phase angle of signals measured by PMU. This article deals with three important challenges in GPS spoofing attack detection; first, multiattack detection while there are probable attacks on different PMUs, second, attack detection considering the dynamic model of power systems, and third, measurement correction. For providing solution to the abovementioned problems, an anti-GPS spoofing attack mechanism is suggested, in which, a dynamic filter with the help of PMU and supervisory control and data acquisition (SCADA) systems measurements estimate the phase shifts caused by spoofing attacks. The results of data fusion of PMUs and SCADA in the detection method are shown. Numerical results demonstrate that this approach can detect dynamic spoofing attacks in a reasonable time and as soon as multiple attacks are detected, corrected measurements are provided.

A mid-level data fusion approach for evaluating the internal and external changes determined by FT-NIR, electronic nose and colorimeter in Curcumae Rhizoma processing

The aim of this study was to establish a robust and rapid method for identification of the changes of Curcumae Rhizoma (CR) in its physical properties and the components after processing. Color and odor used as the indexes for physical property evaluation in this study were quantified by non-targeted bionic detectors colorimeter and e-nose according to Chinese pharmacopoeia, and all chemical changes of the samples were observed by NIR. The technique of mid-level data fusion was adopted to simulate the recognition mode of human, and the prediction accuracy was 100%. A modified NIR band extraction method was used for feature selection after removing the redundant data which otherwise may compromise the accuracy of analysis, with a result showing bisdemethoxycurcumin and curcumol as the key factors most correlated with the changes during processing. The fused matrix was analyzed to evaluate the weight of different sensors and precisely describe the external changes of the samples, followed by a pairwise correlation analysis to investigate the role of colorimeter, e-nose and NIR in identifying the changes caused by processing as well as their correlation with each other. The results of data fusion revealed that aromatic derivatives produced during processing were closely associated with the changes in external characteristics, i.e., color and smell of the samples, while the changes in proteins did not cause significant differences. Correlation analysis demonstrated that bionic sensors could be classified into two groups: a*, WW and WS sensors, which were related to NIR band at about 6500−6700 cm−1 formed by NH vibration of amide and protein degradation, were sensitive to the processed CR; while L*, b* and WC sensors were found to be correlated to NIR band around 8000 cm−1 which was caused by first overtone of C–H combination of aromatic derivatives, thus could be employed as the detectors for raw CR.

A novel evaluation method for pavement distress based on impact of ride comfort

ABSTRACT Assessment of the pavement condition plays a significant role in pavement maintenance and driving comfort enhancement. Current evaluation methods primarily employ manual weights according to the geometric appearance of the distress, which makes it difficult to assess its depth or impact on passengers’ experience. This paper proposes a data fusion-based method for pavement distress evaluation, which comprehensively considers the joint effect of distress physical appearance and the corresponding impact on riding comfort. A deep convolutional neural network was employed to automatically detect and locate the pavement distress using image data. A wavelet transform was applied to extract the acceleration effectuated by the defects in the frequency domain using vibration data. Finally, a comfort evaluation index was constructed based on the results of image and vibration data fusion. Furthermore, a mobile vehicle-mounted collective system was designed for rapid evaluation of the pavement distress, which integrated multiple distributed accelerometers, an industrial camera, and a graphics processing unit. The results demonstrated the stability and efficiency of the proposed approach, making it a potential tool to comprehensively evaluate the condition of pavement distress.

Multiple Microphone Speaker Recognition System for Second Language Based on Biomimetic Pattern Recognition with Big Data Fusion

The speaker recognition problem for second language based on biomimetic pattern recognition with big data fusion by multiple microphone has been addressed in this paper. Biomimetic pattern recognition is a new machine learning algorithm which can be used to study the geometric characteristics of a large number of sample points in high-dimensional space. Machine learning is an important way for computer to realize intelligence. The development of artificial intelligence can not be separated from the support of machine learning. Big data Unifield architecture of Hadoop system integrates machine learning and big data processing. Several speaker feature extraction methods of big data are described. Cepstrum and Δcepstrum can offer a significant computational advantage in the eigenvalue problem in result of enhancing correct rate. The big data fusion framework of neural network multiple microphone verification system is presented; in particular, a neural network constructed by multiple value neural network algorithm of big data fusion is also proposed for adjusting the parameters. The experimental results of big data fusion using multiple microphone speaker recognition system for second language illustrate the effectiveness of the proposed biomimetic pattern recognition method.

An Improved Method to Manage Conflict Data Using Elementary Belief Assignment Function in the Evidence Theory

Dempster-Shafer evidence theory plays an important role in many applications such as multi-sensor data fusion and pattern recognition. However, if there are conflicts among evidences, the results of data fusion using Dempster combination rule may lead to counter-intuitive results. In this paper, we propose a new method named elementary belief assignment function for conflict data fusion. The proposed method aims at getting a more rational data fusion result by preprocessing the mass function before implementing data fusion with Dempster’s combination rule. The elementary belief assignment function takes into consideration not only the number of focal elements in the current body of evidence but also the proposition in the power set space. By assigning the mass value of potential conflict focal element to other related propositions in the power set space, we can reduce the conflict level among different bodies of evidences effectively. We verify the rationality and efficiency of the proposed method according to several experiment examples.

Probabilistic Health Index-Based Apparent Age Estimation for Power Transformers

This paper proposes a probabilistic health index-based method for estimating the apparent age of power transformer. Compared with the conventional weighted-score-sum based health index, the probabilistic health index is calculated as a data fusion result of kinds of transformer condition monitoring data through a constructed Bayesian belief network. The regression result of such a probabilistic health index is then applied to estimate the apparent age of the transformer through a few steps listed in the paper. The apparent age not only embodies an overall health status a transformer but also helpful for sorting a transformer fleet based on the estimated apparent age or even make it easy to make comparisons between transformer fleets. The estimated apparent age can be taken as a reference for power utilities to prioritize transformers and pay attention to the unit who owns the maximum apparent age among a fleet, thus helps to schedule replacement plans. Case studies with different transformers verify the usability and prove the advantages of the proposed method.

Incidence of upper extremity deep venous thrombosis after traumatic fracture in China: a Meta-analysis

Objective To evaluate the incidence of upper extremity deep venous thrombosis (UEDVT) after traumatic fracture using Meta-analysis. Methods Chinese Biomedical Science Articles Database (CBMdisc) , CNKI, Wanfang Medical Network Database were searched to collect studies about UEDVT after traumatic fracture from January 1st, 1990 to December 31st, 2018. After inclusion of exclusion criteria, literature was screened according to PICO principle, and literature quality was evaluated according to STROBE declaration. Qualified literature was used to extract data on the incidence of upper limb thrombosis, and data fusion was carried out using Meta-analysis method of single-arm study. Results Finally, 9 articles of high quality were included in the study, including 14 425 patients. Data fusion result showed that the incidence of UEDVT after upper limb fracture was 0.83% (95%CI: 0.69% - 0.98%) . Axillary vein thrombosis accounted for 19.99%, brachial vein thrombosis 46.66%, subclavian vein thrombosis 6.58%, subclavian vein-brachial vein thrombosis 14.84%, axillary vein-brachial vein thrombosis 11.93% in UEDVT after upper limb fracture. The incidence of UEDVT was 1.83% after humeral surgical neck fracture and 1.76% after humeral shaft fracture. Conclusions The overall incidence of UEDVT after upper limb fracture was 0.83%. Brachial vein and axillary vein are the main sites, which should be paid attention to in postoperative nursing. The incidence of UEDVT in patients with proximal humeral fracture and peri-shoulder fracture is higher than that in patients with other upper limb fractures, which should be paid more attention. Key words: Meta-analysis; Fractures; Deep vein thrombosis; Complications; Upper limb; Traumatic fracture

Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis.

Origin traceability is important for controlling the effect of Chinese medicinal materials and Chinese patent medicines. Paris polyphylla var. yunnanensis is widely distributed and well-known all over the world. In our study, two spectroscopic techniques (Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR)) were applied for the geographical origin traceability of 196 wild P. yunnanensis samples combined with low-, mid-, and high-level data fusion strategies. Partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to establish classification models. Feature variables extraction (principal component analysis—PCA) and important variables selection models (recursive feature elimination and Boruta) were applied for geographical origin traceability, while the classification ability of models with the former model is better than with the latter. FT-MIR spectra are considered to contribute more than NIR spectra. Besides, the result of high-level data fusion based on principal components (PCs) feature variables extraction is satisfactory with an accuracy of 100%. Hence, data fusion of FT-MIR and NIR signals can effectively identify the geographical origin of wild P. yunnanensis.

Multi-sensor detection of forest-cover change across 45 years in Mato Grosso, Brazil

The ongoing march toward freely available, highly pre-processed satellite imagery has given both researchers and the public unprecedented access to a vast and varied data stream teeming with potential. Among many sources, the multi-decade Landsat archive is certainly the best known, but legacy and current data from other sensors is available as well through the USGS data portals: these include CBERS, ASTER, and more. Though the particular band combinations or non-global missions have made their integration into analyses more challenging, these data, in conjunction with the entire Landsat record, are available to contribute to multi-decade surveys of land-cover change.With the goal of tracing forest change through time near the Roosevelt River in the state of Mato Grosso, Brazil, we used BULC and Google Earth Engine to fuse information from 13 space-borne imagers capturing 140 images spanning 45 years. With high accuracy, the resulting time series of classifications shows the timing and location of land-use/land-cover change—both deforestation and regrowth—at sub-annual time scales. Accuracy estimates showed that the synthesized BULC classification time series was better than nearly all of the single-day image classifications, covering the entire study area at sub-annual frequency while reducing the impact of clouds and most unwanted noise as it fused information derived from a wide array of imaging platforms. The time series improved and gradually sharpened as the density of observations increased in recent decades, when there were three or more clear, higher-resolution views of a pixel annually from any sensor combination. In addition to detailing the methodology and results of multi-source data fusion with the BULC approach, this study raises timely points about integrating information from early satellite data sources and from sensors with footprints smaller than Landsat's. There are decades of research deriving sensor-specific techniques for classifying land use and land cover from a single image in a variety of settings. The BULC approach leverages the many successes of single-sensor research and can be used as a straightforward, complementary tool for blending many good-quality mapped classifications from disparate sources into a coherent, high-quality time series.

Additive Aggregate Function-based Data Privacy Protection Algorithm

Data privacy protection is a key problem in the research and application of the internet of things that in wireless sensor networks has broad application prospects, such as agriculture environmental monitoring, healthcare, etc. For the core part of the internet of things, wireless sensor network, it is a very challenging task to provide effective privacy protection in the process of data transmission. In this paper, a new type of data privacy protection strategy based on the additive aggregation function is proposed (hereafter referred to as DPPA). On the one hand, applying the superposition property of the node data, the paper introduces the class aggregation protocol. On the other hand, taking advantage of the algebraic properties of the polynomials and the correlation properties of the superposed data, it can reduce the data of the header files of the data packages, and reduce the data size, while improving the privacy preservation. Simulation results show that DPPA can effectively protect data privacy, and get accurate data fusion results, while reducing the amount of data traffic.

Additive aggregate function-based data privacy protection algorithm

Data privacy protection is a key problem in the research and application of the internet of things that in wireless sensor networks has broad application prospects, such as agriculture environmental monitoring, healthcare, etc. For the core part of the internet of things, wireless sensor network, it is a very challenging task to provide effective privacy protection in the process of data transmission. In this paper, a new type of data privacy protection strategy based on the additive aggregation function is proposed (hereafter referred to as DPPA). On the one hand, applying the superposition property of the node data, the paper introduces the class aggregation protocol. On the other hand, taking advantage of the algebraic properties of the polynomials and the correlation properties of the superposed data, it can reduce the data of the header files of the data packages, and reduce the data size, while improving the privacy preservation. Simulation results show that DPPA can effectively protect data privacy, and get accurate data fusion results, while reducing the amount of data traffic.

RETRACTED ARTICLE: An effective stadium monitoring control algorithm based on big data in emerging mobile networks

In the process of monitoring the gymnasium by the traditional radio frequency technology, the parallel computing problem of the large data environment in the gymnasium monitoring cannot be handled effectively. It cannot be identified independently and accurately, and the gymnasium monitoring algorithm based on large data is proposed. In the process of Map-Reduce parallel monitoring based on AE, off-line training of monitoring image recognition model based on AE and neural network is carried out. Through the weighted fusion algorithm of trajectory correction, the best data fusion result is obtained, and the offline training recognition model is used to identify the image information. In parallel monitoring, if there is a correlation between the monitoring events, the Map function is used to read the test sample data, and the mapping of the corresponding key values is obtained. The mapping records generated by the Map function are performed by the Reduce function to obtain the monitoring and identification results of the gymnasium. The experimental results show that the proposed algorithm can accurately and efficiently identify the monitoring images of the gymnasium.

Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs

Spatial and temporal data fusion approaches have been developed to fuse reflectance imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), which have complementary spatial and temporal sampling characteristics. The approach relies on using Landsat and MODIS image pairs that are acquired on the same day to estimate Landsat-scale reflectance on other MODIS dates. Previous studies have revealed that the accuracy of data fusion results partially depends on the input image pair used. The selection of the optimal image pair to achieve better prediction of surface reflectance has not been fully evaluated. This paper assesses the impacts of Landsat-MODIS image pair selection on the accuracy of the predicted land surface reflectance using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) over different landscapes. MODIS images from the Aqua and Terra platforms were paired with images from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) to make different pair image combinations. The accuracy of the predicted surface reflectance at 30 m resolution was evaluated using the observed Landsat data in terms of mean absolute difference, root mean square error and correlation coefficient. Results show that the MODIS pair images with smaller view zenith angles produce better predictions. As expected, the image pair closer to the prediction date during a short prediction period produce better prediction results. For prediction dates distant from the pair date, the predictability depends on the temporal and spatial variability of land cover type and phenology. The prediction accuracy for forests is higher than for crops in our study areas. The Normalized Difference Vegetation Index (NDVI) for crops is overestimated during the non-growing season when using an input image pair from the growing season, while NDVI is slightly underestimated during the growing season when using an image pair from the non-growing season. Two automatic pair selection strategies are evaluated. Results show that the strategy of selecting the MODIS pair date image that most highly correlates with the MODIS image on the prediction date produces more accurate predictions than the nearest date strategy. This study demonstrates that data fusion results can be improved if appropriate image pairs are used.

A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks.

Wireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and cause errors when making a final decision. Data fusion is well suited to reduce the influence of actuator-based noise and improve automation accuracy. A key step is to identify the sensor nodes disturbed by actuator noise and reduce their degree of participation in the data fusion results. A smoothing value is introduced and a searching method based on Prim’s algorithm is designed to help obtain stable sensing data. A voting mechanism with dynamic weights is then proposed to obtain the data fusion result. The dynamic weighting process can sharply reduce the influence of actuator noise in data fusion and gradually condition the data to normal levels over time. To shorten the data fusion time in large networks, an acceleration method with prediction is also presented to reduce the data collection time. A real-time system is implemented on STMicroelectronics STM32F103 and NORDIC nRF24L01 platforms and the experimental results verify the improvement provided by these new algorithms.

Fusion of Multispectral Imagery and Spectrometer Data in UAV Remote Sensing

Abstract: High spatial resolution hyperspectral data often used in precision farming applications are not available from current satellite sensors, and difficult or expensive to acquire from standard aircraft. Alternatively, in precision farming, unmanned aerial vehicles (UAVs) are emerging as lower cost and more flexible means to acquire very high resolution imagery. Miniaturized hyperspectral sensors have been developed for UAVs, but the sensors, associated hardware, and data processing software are still cost prohibitive for use by individual farmers or small remote sensing firms. This study simulated hyperspectral image data by fusing multispectral camera imagery and spectrometer data. We mounted a multispectral camera and spectrometer, both being low cost and low weight, on a standard UAV and developed procedures for their precise data alignment, followed by fusion of the spectrometer data with the image data to produce estimated spectra for all the multispectral camera image pixels. To align the data collected from the two sensors in both the time and space domains, a post-acquisition correlation-based global optimization method was used. Data fusion, to estimate hyperspectral reflectance, was implemented using several methods for comparison. Flight data from two crop sites, one being tomatoes, and the other corn and soybeans, were used to evaluate the alignment procedure and the data fusion results. The data alignment procedure resulted in a peak R2 between the spectrometer and camera data of 0.95 and 0.72, respectively, for the two test sites. The corresponding multispectral camera data for these space and time offsets were taken as the best match to a given spectrometer reading, and used in modelling to estimate hyperspectral imagery from the multispectral camera pixel data. Of the fusion approaches evaluated, principal component analysis (PCA) based models and Bayesian imputation reached a similar accuracy, and outperformed simple spline interpolation. Mean absolute error (MAE) between predicted and observed spectra was 17% relative to the mean of the observed spectra, and root mean squared error (RMSE) was 0.028. This approach to deriving estimated hyperspectral image data can be applied in a simple fashion at very low cost for crop assessment and monitoring within individual fields.